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HEPML-LivingReview	a living review of machine learning for particle physics modern machine learning techniques including deep learning is rapidly being applied adapted and developed for high energy physics the goal of this document is to provide a nearly comprehensive list of citations for those developing and applying these approaches to experimental phenomenological or theoretical analyses as a living document it will be updated as often as possible to incorporate the latest developments a list of proper unchanging reviews can be found within papers are grouped into a small set of topics to be as useful as possible suggestions are most welcome download https img shields io badge download review blue svg https iml wg github io hepml livingreview assets hepml review pdf github https badges aleen42 com src github svg https github com iml wg hepml livingreview the purpose of this note is to collect references for modern machine learning as applied to particle physics a minimal number of categories is chosen in order to be as useful as possible note that papers may be referenced in more than one category the fact that a paper is listed in this document does not endorse or validate its content that is for the community and for peer review to decide furthermore the classification here is a best attempt and may have flaws please let us know if a we have missed a paper you think should be included b a paper has been misclassified or c a citation for a paper is not correct or if the journal information is now available in order to be as useful as possible this document will continue to evolve so please check back before you write your next paper if you find this review helpful please consider citing it using cite hepmllivingreview in hepml bib this review was built with the help of the hep ml community the inspire rest api https github com inspirehep rest api doc and the moderators benjamin nachman matthew feickert etienne dreyer waleed esmail michele faucci giannelli claudius krause johnny raine dalila salamani and ramon winterhalder reviews modern reviews jet substructure at the large hadron collider a review of recent advances in theory and machine learning https arxiv org abs 1709 04464 doi https doi org 10 1016 j physrep 2019 11 001 deep learning and its application to lhc physics https arxiv org abs 1806 11484 doi https doi org 10 1146 annurev nucl 101917 021019 machine learning in high energy physics community white paper https arxiv org abs 1807 02876 doi https doi org 10 1088 1742 6596 1085 2 022008 machine learning at the energy and intensity frontiers of particle physics https doi org 10 1038 s41586 018 0361 2 machine learning and the physical sciences https arxiv org abs 1903 10563 doi https doi org 10 1103 revmodphys 91 045002 machine and deep learning applications in particle physics https arxiv org abs 1912 08245 doi https doi org 10 1142 s0217751x19300199 modern machine learning and particle physics https arxiv org abs 2103 12226 machine learning in the search for new fundamental physics https arxiv org abs 2112 03769 artificial intelligence and machine learning in nuclear physics https arxiv org abs 2112 02309 snowmass 2021 computational frontier compf03 topical group report machine learning https arxiv org abs 2209 07559 specialized reviews the machine learning landscape of top taggers https arxiv org abs 1902 09914 doi https doi org 10 21468 scipostphys 7 1 014 dealing with nuisance parameters using machine learning in high energy physics a review https arxiv org abs 2007 09121 graph neural networks in particle physics https arxiv org abs 2007 13681 doi https doi org 10 1088 2632 2153 abbf9a a review on machine learning for neutrino experiments https arxiv org abs 2008 01242 doi https doi org 10 1142 s0217751x20430058 generative networks for lhc events https arxiv org abs 2008 08558 parton distribution functions https arxiv org abs 2008 12305 simulation based inference methods for particle physics https arxiv org abs 2010 06439 anomaly detection for physics analysis and less than supervised learning https arxiv org abs 2010 14554 graph neural networks for particle tracking and reconstruction https arxiv org abs 2012 01249 distributed training and optimization of neural networks https arxiv org abs 2012 01839 the frontier of simulation based inference https arxiv org abs 1911 01429 doi https doi org 10 1073 pnas 1912789117 machine learning scientific competitions and datasets https arxiv org abs 2012 08520 image based jet analysis https arxiv org abs 2012 09719 quantum machine learning in high energy physics https arxiv org abs 2005 08582 doi https doi org 10 1088 2632 2153 abc17d sequence based machine learning models in jet physics https arxiv org abs 2102 06128 a survey of machine learning based physics event generation https arxiv org abs 2106 00643 deep learning from four vectors https arxiv org abs 2203 03067 solving simulation systematics in and with ai ml https arxiv org abs 2203 06112 symmetry group equivariant architectures for physics https arxiv org abs 2203 06153 machine learning and lhc event generation https arxiv org abs 2203 07460 machine learning and cosmology https arxiv org abs 2203 08056 new directions for surrogate models and differentiable programming for high energy physics detector simulation https arxiv org abs 2203 08806 graph neural networks in particle physics implementations innovations and challenges https arxiv org abs 2203 12852 physics community needs tools and resources for machine learning https arxiv org abs 2203 16255 boosted decision trees https arxiv org abs 2206 09645 data science and machine learning in education https arxiv org abs 2207 09060 interpretable uncertainty quantification in ai for hep https arxiv org abs 2208 03284 modern machine learning for lhc physicists https arxiv org abs 2211 01421 bridging machine learning and sciences opportunities and challenges https arxiv org abs 2210 13441 fair for ai an interdisciplinary international inclusive and diverse community building perspective https arxiv org abs 2210 08973 snowmass neutrino frontier report https arxiv org abs 2211 08641 graph neural networks at the large hadron collider https doi org 10 1038 s42254 023 00569 0 overview jet quenching with machine learning https arxiv org abs 2308 10035 artificial intelligence for the electron ion collider ai4eic https arxiv org abs 2307 08593 classical papers neural networks and cellular automata in experimental high energy physics https doi org 10 1016 0010 4655 88 90004 5 finding gluon jets with a neural trigger https doi org 10 1103 physrevlett 65 1321 datasets the lhc olympics 2020 a community challenge for anomaly detection in high energy physics https arxiv org abs 2101 08320 the dark machines anomaly score challenge benchmark data and model independent event classification for the large hadron collider https arxiv org abs 2105 14027 shared data and algorithms for deep learning in fundamental physics https arxiv org abs 2107 00656 lhc physics dataset for unsupervised new physics detection at 40 mhz https arxiv org abs 2107 02157 a fair and ai ready higgs boson decay dataset https arxiv org abs 2108 02214 particle transformer for jet tagging https arxiv org abs 2202 03772 public kaggle competition icecube neutrinos in deep ice https arxiv org abs 2307 15289 classification parameterized classifiers parameterized neural networks for high energy physics https arxiv org abs 1601 07913 doi https doi org 10 1140 epjc s10052 016 4099 4 approximating likelihood ratios with calibrated discriminative classifiers https arxiv org abs 1506 02169 e pluribus unum ex machina learning from many collider events at once https arxiv org abs 2101 07263 boosting likelihood learning with event reweighting https arxiv org abs 2308 05704 representations jet images how to tell quark jets from gluon jets https doi org 10 1103 physrevd 44 2025 jet images computer vision inspired techniques for jet tagging https arxiv org abs 1407 5675 doi https doi org 10 1007 jhep02 2015 118 playing tag with ann boosted top identification with pattern recognition https arxiv org abs 1501 05968 doi https doi org 10 1007 jhep07 2015 086 jet images deep learning edition https arxiv org abs 1511 05190 doi https doi org 10 1007 jhep07 2016 069 quark versus gluon jet tagging using jet images with the atlas detector http cds cern ch record 2275641 boosting h to b bar b with machine learning https arxiv org abs 1807 10768 doi https doi org 10 1007 jhep10 2018 101 learning to classify from impure samples with high dimensional data https arxiv org abs 1801 10158 doi https doi org 10 1103 physrevd 98 011502 parton shower uncertainties in jet substructure analyses with deep neural networks https arxiv org abs 1609 00607 doi https doi org 10 1103 physrevd 95 014018 deep learning in color towards automated quark gluon https arxiv org abs 1612 01551 doi https doi org 10 1007 jhep01 2017 110 deep learning top taggers or the end of qcd https arxiv org abs 1701 08784 doi https doi org 10 1007 jhep05 2017 006 pulling out all the tops with computer vision and deep learning https arxiv org abs 1803 00107 doi https doi org 10 1007 jhep10 2018 121 reconstructing boosted higgs jets from event image segmentation https arxiv org abs 2008 13529 an attention based neural network for jet tagging https arxiv org abs 2009 00170 quark gluon jet discrimination using convolutional neural networks https arxiv org abs 2012 02531 doi https doi org 10 3938 jkps 74 219 learning to isolate muons https arxiv org abs 2102 02278 deep learning jet modifications in heavy ion collisions https arxiv org abs 2012 07797 identifying the quantum properties of hadronic resonances using machine learning https arxiv org abs 2105 04582 automatic detection of boosted higgs and top quark jets in event image https arxiv org abs 2302 13460 a guide to diagnosing colored resonances at hadron colliders https arxiv org abs 2306 00079 event images topology classification with deep learning to improve real time event selection at the lhc https arxiv org abs 1807 00083 doi https doi org 10 1007 s41781 019 0028 1 convolutional neural networks with event images for pileup mitigation with the atlas detector http cds cern ch record 2684070 boosting h to b bar b with machine learning https arxiv org abs 1807 10768 doi https doi org 10 1007 jhep10 2018 101 end to end physics event classification with the cms open data applying image based deep learning on detector data to directly classify collision events at the lhc https arxiv org abs 1807 11916 doi https doi org 10 1007 s41781 020 00038 8 disentangling boosted higgs boson production modes with machine learning https arxiv org abs 2009 05930 identifying the nature of the qcd transition in relativistic collision of heavy nuclei with deep learning https arxiv org abs 1910 11530 doi https doi org 10 1140 epjc s10052 020 8030 7 end to end jet classification of boosted top quarks with the cms open data https arxiv org abs 2104 14659 jet single shot detection https arxiv org abs 2105 05785 large scale deep learning for multi jet event classification https arxiv org abs 2207 11710 a neural network approach for orienting heavy ion collision events https arxiv org abs 2308 15796 sequences jet flavor classification in high energy physics with deep neural networks https arxiv org abs 1607 08633 doi https doi org 10 1103 physrevd 94 112002 topology classification with deep learning to improve real time event selection at the lhc https arxiv org abs 1807 00083 doi https doi org 10 1007 s41781 019 0028 1 jet flavour classification using deepjet https arxiv org abs 2008 10519 doi https doi org 10 1088 1748 0221 15 12 p12012 development of a vertex finding algorithm using recurrent neural network https arxiv org abs 2101 11906 sequence based machine learning models in jet physics https arxiv org abs 2102 06128 identification of jets containing b hadrons with recurrent neural networks at the atlas experiment http cdsweb cern ch record 2255226 trees qcd aware recursive neural networks for jet physics https arxiv org abs 1702 00748 doi https doi org 10 1007 jhep01 2019 057 recursive neural networks in quark gluon tagging https arxiv org abs 1711 02633 doi https doi org 10 1007 s41781 018 0007 y introduction and analysis of a method for the investigation of qcd like tree data https arxiv org abs 2112 01809 graphs neural message passing for jet physics https dl4physicalsciences github io files nips dlps 2017 29 pdf graph neural networks for particle reconstruction in high energy physics detectors https arxiv org abs 2003 11603 probing stop pair production at the lhc with graph neural networks https arxiv org abs 1807 09088 doi https doi org 10 1007 jhep08 2019 055 pileup mitigation at the large hadron collider with graph neural networks https arxiv org abs 1810 07988 doi https doi org 10 1140 epjp i2019 12710 3 unveiling cp property of top higgs coupling with graph neural networks at the lhc https arxiv org abs 1901 05627 doi https doi org 10 1016 j physletb 2020 135198 jedi net a jet identification algorithm based on interaction networks https arxiv org abs 1908 05318 doi https doi org 10 1140 epjc s10052 020 7608 4 learning representations of irregular particle detector geometry with distance weighted graph networks https arxiv org abs 1902 07987 doi https doi org 10 1140 epjc s10052 019 7113 9 interpretable deep learning for two prong jet classification with jet spectra https arxiv org abs 1904 02092 doi https doi org 10 1007 jhep07 2019 135 towards a computer vision particle flow https arxiv org abs 2003 08863 doi https doi org 10 1140 epjc s10052 021 08897 0 neural network based top tagger with two point energy correlations and geometry of soft emissions https arxiv org abs 2003 11787 doi https doi org 10 1007 jhep07 2020 111 probing triple higgs coupling with machine learning at the lhc https arxiv org abs 2005 11086 casting a graph net to catch dark showers https arxiv org abs 2006 08639 doi https doi org 10 21468 scipostphys 10 2 046 graph neural networks in particle physics https arxiv org abs 2007 13681 doi https doi org 10 1088 2632 2153 abbf9a distance weighted graph neural networks on fpgas for real time particle reconstruction in high energy physics https arxiv org abs 2008 03601 doi https doi org 10 3389 fdata 2020 598927 supervised jet clustering with graph neural networks for lorentz boosted bosons https arxiv org abs 2008 06064 doi https doi org 10 1103 physrevd 102 075014 track seeding and labelling with embedded space graph neural networks https arxiv org abs 2007 00149 graph neural network for 3d classification of ambiguities and optical crosstalk in scintillator based neutrino detectors https arxiv org abs 2009 00688 doi https doi org 10 1103 physrevd 103 032005 the boosted higgs jet reconstruction via graph neural network https arxiv org abs 2010 05464 accelerated charged particle tracking with graph neural networks on fpgas https arxiv org abs 2012 01563 particle track reconstruction using geometric deep learning https arxiv org abs 2012 08515 jet tagging in the lund plane with graph networks https arxiv org abs 2012 08526 doi https doi org 10 1007 jhep03 2021 052 vertex and energy reconstruction in juno with machine learning methods https arxiv org abs 2101 04839 mlpf efficient machine learned particle flow reconstruction using graph neural networks https arxiv org abs 2101 08578 towards a realistic track reconstruction algorithm based on graph neural networks for the hl lhc https arxiv org abs 2103 00916 deep learning strategies for protodune raw data denoising https arxiv org abs 2103 01596 graph neural network for object reconstruction in liquid argon time projection chambers https arxiv org abs 2103 06233 instance segmentation gnns for one shot conformal tracking at the lhc https arxiv org abs 2103 06509 charged particle tracking via edge classifying interaction networks https arxiv org abs 2103 16701 jet characterization in heavy ion collisions by qcd aware graph neural networks https arxiv org abs 2103 14906 graph generative models for fast detector simulations in high energy physics https arxiv org abs 2104 01725 segmentation of em showers for neutrino experiments with deep graph neural networks https arxiv org abs 2104 02040 anomaly detection with convolutional graph neural networks https arxiv org abs 2105 07988 energy weighted message passing an infra red and collinear safe graph neural network algorithm https arxiv org abs 2109 14636 improved constraints on effective top quark interactions using edge convolution networks https arxiv org abs 2111 01838 particle graph autoencoders and differentiable learned energy mover s distance https arxiv org abs 2111 12849 graph neural networks for charged particle tracking on fpgas https arxiv org abs 2112 02048 machine learning for particle flow reconstruction at cms https arxiv org abs 2203 00330 an efficient lorentz equivariant graph neural network for jet tagging https arxiv org abs 2201 08187 end to end multi particle reconstruction in high occupancy imaging calorimeters with graph neural networks https arxiv org abs 2204 01681 a jet tagging algorithm of graph network with haarpooling message passing https arxiv org abs 2210 13869 pelican permutation equivariant and lorentz invariant or covariant aggregator network for particle physics https arxiv org abs 2211 00454 climbing four tops with graph networks transformers and pairwise features https arxiv org abs 2211 05143 reconstructing particles in jets using set transformer and hypergraph prediction networks https arxiv org abs 2212 01328 do graph neural networks learn traditional jet substructure https arxiv org abs 2211 09912 heterogeneous graph neural network for identifying hadronically decayed tau leptons at the high luminosity lhc https arxiv org abs 2301 00501 doi https doi org 10 1088 1748 0221 18 07 p07001 deep learning symmetries and their lie groups algebras and subalgebras from first principles https arxiv org abs 2301 05638 on the bsm reach of four top production at the lhc https arxiv org abs 2302 08281 topological reconstruction of particle physics processes using graph neural networks https arxiv org abs 2303 13937 equivariant graph neural networks for charged particle tracking https arxiv org abs 2304 05293 improved selective background monte carlo simulation at belle ii with graph attention networks and weighted events https arxiv org abs 2307 06434 real time graph building on fpgas for machine learning trigger applications in particle physics https arxiv org abs 2307 07289 determination of impact parameter for cee with digi input neural networks https arxiv org abs 2307 15355 domain adversarial graph neural networks for ensuremath lambda hyperon identification with clas12 https arxiv org abs 2302 05481 doi https doi org 10 1088 1748 0221 18 06 p06002 hierarchical graph neural networks for particle track reconstruction https arxiv org abs 2303 01640 gnn for deep full event interpretation and hierarchical reconstruction of heavy hadron decays in proton proton collisions https arxiv org abs 2304 08610 flavour tagging with graph neural networks with the atlas detector https arxiv org abs 2306 04415 photon reconstruction in the belle ii calorimeter using graph neural networks https arxiv org abs 2306 04179 jet energy calibration with deep learning as a kubeflow pipeline https arxiv org abs 2308 12724 doi https doi org 10 1007 s41781 023 00103 y llpnet graph autoencoder for triggering light long lived particles at hl lhc https arxiv org abs 2308 13611 graph structure from point clouds geometric attention is all you need https arxiv org abs 2307 16662 sets point clouds energy flow networks deep sets for particle jets https arxiv org abs 1810 05165 doi https doi org 10 1007 jhep01 2019 121 particlenet jet tagging via particle clouds https arxiv org abs 1902 08570 doi https doi org 10 1103 physrevd 101 056019 abcnet an attention based method for particle tagging https arxiv org abs 2001 05311 doi https doi org 10 1140 epjp s13360 020 00497 3 secondary vertex finding in jets with neural networks https arxiv org abs 2008 02831 equivariant energy flow networks for jet tagging https arxiv org abs 2012 00964 doi https doi org 10 1103 physrevd 103 074022 permutationless many jet event reconstruction with symmetry preserving attention networks https arxiv org abs 2010 09206 zero permutation jet parton assignment using a self attention network https arxiv org abs 2012 03542 learning to isolate muons https arxiv org abs 2102 02278 point cloud transformers applied to collider physics https arxiv org abs 2102 05073 spanet generalized permutationless set assignment for particle physics using symmetry preserving attention https arxiv org abs 2106 03898 particle convolution for high energy physics https arxiv org abs 2107 02908 deep sets based neural networks for impact parameter flavour tagging in atlas https cds cern ch record 2718948 particle transformer for jet tagging https arxiv org abs 2202 03772 point cloud generation using transformer encoders and normalising flows https arxiv org abs 2211 13623 comparing point cloud strategies for collider event classification https arxiv org abs 2212 10659 is infrared collinear safe information all you need for jet classification https arxiv org abs 2305 08979 attention to mean fields for particle cloud generation https arxiv org abs 2305 15254 physics inspired basis automating the construction of jet observables with machine learning https arxiv org abs 1902 07180 doi https doi org 10 1103 physrevd 100 095016 how much information is in a jet https arxiv org abs 1704 08249 doi https doi org 10 1007 jhep06 2017 073 novel jet observables from machine learning https arxiv org abs 1710 01305 doi https doi org 10 1007 jhep03 2018 086 energy flow polynomials a complete linear basis for jet substructure https arxiv org abs 1712 07124 doi https doi org 10 1007 jhep04 2018 013 deep learned top tagging with a lorentz layer https arxiv org abs 1707 08966 doi https doi org 10 21468 scipostphys 5 3 028 resurrecting b bar b h with kinematic shapes https arxiv org abs 2011 13945 decay aware neural network for event classification in collider physics https arxiv org abs 2212 08759 jet sift ing a new scale invariant jet clustering algorithm for the substructure era https arxiv org abs 2302 08609 retrieval of boost invariant symbolic observables via feature importance https arxiv org abs 2306 13496 targets w z tagging jet images deep learning edition https arxiv org abs 1511 05190 doi https doi org 10 1007 jhep07 2016 069 parton shower uncertainties in jet substructure analyses with deep neural networks https arxiv org abs 1609 00607 doi https doi org 10 1103 physrevd 95 014018 qcd aware recursive neural networks for jet physics https arxiv org abs 1702 00748 doi https doi org 10 1007 jhep01 2019 057 identification of heavy energetic hadronically decaying particles using machine learning techniques https arxiv org abs 2004 08262 doi https doi org 10 1088 1748 0221 15 06 p06005 boosted w and z tagging with jet charge and deep learning https arxiv org abs 1908 08256 doi https doi org 10 1103 physrevd 101 053001 supervised jet clustering with graph neural networks for lorentz boosted bosons https arxiv org abs 2008 06064 doi https doi org 10 1103 physrevd 102 075014 jet tagging in the lund plane with graph networks https arxiv org abs 2012 08526 doi https doi org 10 1007 jhep03 2021 052 a w pm polarization analyzer from deep neural networks https arxiv org abs 2102 05124 role of polarizations and spin spin correlations of w s in e e textrightarrow w w at s https arxiv org abs 2212 12973 doi https doi org 10 1103 physrevd 107 073004 gradient boosting must taggers for highly boosted jets https arxiv org abs 2305 04957 is infrared collinear safe information all you need for jet classification https arxiv org abs 2305 08979 amplitude assisted tagging of longitudinally polarised bosons using wide neural networks https arxiv org abs 2306 07726 explainable equivariant neural networks for particle physics pelican https arxiv org abs 2307 16506 h rightarrow b bar b automating the construction of jet observables with machine learning https arxiv org abs 1902 07180 doi https doi org 10 1103 physrevd 100 095016 boosting h to b bar b with machine learning https arxiv org abs 1807 10768 doi https doi org 10 1007 jhep10 2018 101 interaction networks for the identification of boosted h rightarrow b overline b decays https arxiv org abs 1909 12285 doi https doi org 10 1103 physrevd 102 012010 interpretable deep learning for two prong jet classification with jet spectra https arxiv org abs 1904 02092 doi https doi org 10 1007 jhep07 2019 135 identification of heavy energetic hadronically decaying particles using machine learning techniques https arxiv org abs 2004 08262 doi https doi org 10 1088 1748 0221 15 06 p06005 disentangling boosted higgs boson production modes with machine learning https arxiv org abs 2009 05930 benchmarking machine learning techniques with di higgs production at the lhc https arxiv org abs 2009 06754 the boosted higgs jet reconstruction via graph neural network https arxiv org abs 2010 05464 extracting signals of higgs boson from background noise using deep neural networks https arxiv org abs 2010 08201 learning to increase matching efficiency in identifying additional b jets in the text t bar text t text b bar text b process https arxiv org abs 2103 09129 higgs tagging with the lund jet plane https arxiv org abs 2105 03989 quarks and gluons quark versus gluon jet tagging using jet images with the atlas detector http cds cern ch record 2275641 deep learning in color towards automated quark gluon https arxiv org abs 1612 01551 doi https doi org 10 1007 jhep01 2017 110 recursive neural networks in quark gluon tagging https arxiv org abs 1711 02633 doi https doi org 10 1007 s41781 018 0007 y deepjet generic physics object based jet multiclass classification for lhc experiments https dl4physicalsciences github io files nips dlps 2017 10 pdf probing heavy ion collisions using quark and gluon jet substructure https arxiv org abs 1803 03589 jedi net a jet identification algorithm based on interaction networks https arxiv org abs 1908 05318 doi https doi org 10 1140 epjc s10052 020 7608 4 quark gluon tagging machine learning vs detector https arxiv org abs 1812 09223 doi https doi org 10 21468 scipostphys 6 6 069 towards machine learning analytics for jet substructure https arxiv org abs 2007 04319 doi https doi org 10 1007 jhep09 2020 195 quark gluon jet discrimination with weakly supervised learning https arxiv org abs 2012 02540 doi https doi org 10 3938 jkps 75 652 quark gluon jet discrimination using convolutional neural networks https arxiv org abs 2012 02531 doi https doi org 10 3938 jkps 74 219 jet tagging in the lund plane with graph networks https arxiv org abs 2012 08526 doi https doi org 10 1007 jhep03 2021 052 safety of quark gluon jet classification https arxiv org abs 2103 09103 identifying the quantum properties of hadronic resonances using machine learning https arxiv org abs 2105 04582 quarks and gluons in the lund plane https arxiv org abs 2112 09140 systematic quark gluon identification with ratios of likelihoods https arxiv org abs 2207 12411 jet substructure observables for jet quenching in quark gluon plasma a machine learning driven analysis https arxiv org abs 2304 07196 is infrared collinear safe information all you need for jet classification https arxiv org abs 2305 08979 quark gluon discrimination and top tagging with dual attention transformer https arxiv org abs 2307 04723 hierarchical high point energy flow network for jet tagging https arxiv org abs 2308 08300 top quark tagging playing tag with ann boosted top identification with pattern recognition https arxiv org abs 1501 05968 doi https doi org 10 1007 jhep07 2015 086 deepjet generic physics object based jet multiclass classification for lhc experiments https dl4physicalsciences github io files nips dlps 2017 10 pdf the machine learning landscape of top taggers https arxiv org abs 1902 09914 doi https doi org 10 21468 scipostphys 7 1 014 neural network based top tagger with two point energy correlations and geometry of soft emissions https arxiv org abs 2003 11787 doi https doi org 10 1007 jhep07 2020 111 capsnets continuing the convolutional quest https arxiv org abs 1906 11265 doi https doi org 10 21468 scipostphys 8 2 023 deep learned top tagging with a lorentz layer https arxiv org abs 1707 08966 doi https doi org 10 21468 scipostphys 5 3 028 deep learning top taggers or the end of qcd https arxiv org abs 1701 08784 doi https doi org 10 1007 jhep05 2017 006 pulling out all the tops with computer vision and deep learning https arxiv org abs 1803 00107 doi https doi org 10 1007 jhep10 2018 121 boosted top quark tagging and polarization measurement using machine learning https arxiv org abs 2010 11778 morphology for jet classification https arxiv org abs 2010 13469 jet tagging in the lund plane with graph networks https arxiv org abs 2012 08526 doi https doi org 10 1007 jhep03 2021 052 pulling the higgs and top needles from the jet stack with feature extended supervised tagging https arxiv org abs 2102 01667 end to end jet classification of boosted top quarks with the cms open data https arxiv org abs 2104 14659 leveraging universality of jet taggers through transfer learning https arxiv org abs 2203 06210 application of deep learning in top pair and single top quark production at the lhc https arxiv org abs 2203 12871 bip boost invariant polynomials for efficient jet tagging https arxiv org abs 2207 08272 boosted top tagging and its interpretation using shapley values https arxiv org abs 2212 11606 automatic detection of boosted higgs and top quark jets in event image https arxiv org abs 2302 13460 machine learning in top physics in the atlas and cms collaborations https arxiv org abs 2301 09534 quark gluon discrimination and top tagging with dual attention transformer https arxiv org abs 2307 04723 explainable equivariant neural networks for particle physics pelican https arxiv org abs 2307 16506 hierarchical high point energy flow network for jet tagging https arxiv org abs 2308 08300 investigating the violation of charge parity symmetry through top quark chromoelectric dipole moments by using machine learning techniques https arxiv org abs 2306 11683 doi https doi org 10 5506 aphyspolb 54 5 a4 strange jets strange jet tagging https arxiv org abs 2003 09517 a tagger for strange jets based on tracking information using long short term memory https arxiv org abs 1907 07505 doi https doi org 10 1088 1748 0221 15 01 p01021 maximum performance of strange jet tagging at hadron colliders https arxiv org abs 2011 10736 study of anomalous w w gamma z couplings using polarizations and spin correlations in e e to w w with polarized beams https arxiv org abs 2305 15106 b tagging identification of heavy flavour jets with the cms detector in pp collisions at 13 tev https arxiv org abs 1712 07158 doi https doi org 10 1088 1748 0221 13 05 p05011 jet flavor classification in high energy physics with deep neural networks https arxiv org abs 1607 08633 doi https doi org 10 1103 physrevd 94 112002 the full event interpretation an exclusive tagging algorithm for the belle ii experiment https arxiv org abs 1807 08680 doi https doi org 10 1007 s41781 019 0021 8 identifying heavy flavor jets using vectors of locally aggregated descriptors https arxiv org abs 2005 01842 doi https doi org 10 1088 1748 0221 16 03 p03017 jet flavour classification using deepjet https arxiv org abs 2008 10519 doi https doi org 10 1088 1748 0221 15 12 p12012 identification of jets containing b hadrons with recurrent neural networks at the atlas experiment http cdsweb cern ch record 2255226 deep sets based neural networks for impact parameter flavour tagging in atlas https cds cern ch record 2718948 performance studies of jet flavor tagging and measurement of r b using particlenet at cepc https arxiv org abs 2208 13503 improving robustness of jet tagging algorithms with adversarial training exploring the loss surface https arxiv org abs 2303 14511 fast b tagging at the high level trigger of the atlas experiment in lhc run 3 https arxiv org abs 2306 09738 flavor physics deep dive into b to c anomalies standardized and future proof model selection using self normalizing neural networks https arxiv org abs 2008 04316 predicting exotic hadron masses with data augmentation using multilayer perceptron https arxiv org abs 2208 09538 revealing the nature of hidden charm pentaquarks with machine learning https arxiv org abs 2301 05364 doi https doi org 10 1016 j scib 2023 04 018 exploring the flavor structure of quarks and leptons with reinforcement learning https arxiv org abs 2304 14176 bsm particles and models automating the construction of jet observables with machine learning https arxiv org abs 1902 07180 doi https doi org 10 1103 physrevd 100 095016 searching for exotic particles in high energy physics with deep learning https arxiv org abs 1402 4735 doi https doi org 10 1038 ncomms5308 interpretable deep learning for two prong jet classification with jet spectra https arxiv org abs 1904 02092 doi https doi org 10 1007 jhep07 2019 135 a deep neural network to search for new long lived particles decaying to jets https arxiv org abs 1912 12238 doi https doi org 10 1088 2632 2153 ab9023 fast convolutional neural networks for identifying long lived particles in a high granularity calorimeter https arxiv org abs 2004 10744 doi https doi org 10 1088 1748 0221 15 12 p12006 casting a graph net to catch dark showers https arxiv org abs 2006 08639 doi https doi org 10 21468 scipostphys 10 2 046 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autoencoders https arxiv org abs 2104 08291 autoencoders for unsupervised anomaly detection in high energy physics https arxiv org abs 2104 09051 via machinae searching for stellar streams using unsupervised machine learning https arxiv org abs 2104 12789 anomaly detection with convolutional graph neural networks https arxiv org abs 2105 07988 anomalous jet identification via sequence modeling https arxiv org abs 2105 09274 the dark machines anomaly score challenge benchmark data and model independent event classification for the large hadron collider https arxiv org abs 2105 14027 ranbox anomaly detection in the copula space https arxiv org abs 2106 05747 rare and different anomaly scores from a combination of likelihood and out of distribution models to detect new physics at the lhc https arxiv org abs 2106 10164 lhc physics dataset for unsupervised new physics detection at 40 mhz https arxiv org abs 2107 02157 new methods and datasets for group anomaly detection from fundamental physics https arxiv org abs 2107 02821 the data directed paradigm for bsm searches https arxiv org abs 2107 11573 autoencoders on fpgas for real time unsupervised new physics detection at 40 mhz at the large hadron collider https arxiv org abs 2108 03986 classifying anomalies through outer density estimation cathode https arxiv org abs 2109 00546 deep set auto encoders for anomaly detection in particle physics https arxiv org abs 2109 01695 challenges for unsupervised anomaly detection in particle physics https arxiv org abs 2110 06948 improving variational autoencoders for new physics detection at the lhc with normalizing flows https arxiv org abs 2110 08508 signal agnostic dark matter searches in direct detection data with machine learning https arxiv org abs 2110 12248 anomaly detection from mass unspecific jet tagging https arxiv org abs 2111 02647 a method to challenge symmetries in data with self supervised learning https arxiv org abs 2111 05442 stressed gans snag desserts a k a spotting symmetry violation with symmetric functions https arxiv org abs 2111 00616 online compatible unsupervised non resonant anomaly detection https arxiv org abs 2111 06417 event based anomaly detection for new physics searches at the lhc using machine learning https arxiv org abs 2111 12119 learning new physics from an imperfect machine https arxiv org abs 2111 13633 autoencoders for semivisible jet detection https arxiv org abs 2112 02864 anomaly detection in high energy physics using a quantum autoencoder https arxiv org abs 2112 04958 creating simple interpretable anomaly detectors for new physics in jet substructure https arxiv org abs 2203 01343 taming modeling uncertainties with mass unspecific supervised tagging https arxiv org abs 2201 11143 what s anomalous in lhc jets https arxiv org abs 2202 00686 quantum anomaly detection for collider physics https arxiv org abs 2206 08391 self supervised anomaly detection for new physics https arxiv org abs 2205 10380 data directed search for new physics based on symmetries of the sm https arxiv org abs 2203 07529 doi https doi org 10 1140 epjc s10052 022 10454 2 curtains for your sliding window constructing unobserved regions by transforming adjacent intervals https arxiv org abs 2203 09470 learning new physics efficiently with nonparametric methods https arxiv org abs 2204 02317 flux mutability a conditional generative approach to one class classification and anomaly detection https arxiv org abs 2204 08609 boosting mono jet searches with model agnostic machine learning https arxiv org abs 2204 11889 doi https doi org 10 1007 jhep08 2022 015 event generation and density estimation with surjective normalizing flows https arxiv org abs 2205 01697 a normalized autoencoder for lhc triggers https arxiv org abs 2206 14225 mixture of theories training can we find new physics and anomalies better by mixing physical theories https arxiv org abs 2207 07631 neural embedding learning the embedding of the manifold of physics data https arxiv org abs 2208 05484 null hypothesis test for anomaly detection https arxiv org abs 2210 02226 resonant anomaly detection without background sculpting https arxiv org abs 2210 14924 anomaly detection under coordinate transformations https arxiv org abs 2209 06225 quantum probabilistic hamiltonian learning for generative modelling anomaly detection https arxiv org abs 2211 03803 efficiently moving instead of reweighting collider events with machine learning https arxiv org abs 2212 06155 unravelling physics beyond the standard model with classical and quantum anomaly detection https arxiv org abs 2301 10787 nanosecond anomaly detection with decision trees for high energy physics and real time application to exotic higgs decays https arxiv org abs 2304 03836 the mass ive issue anomaly detection in jet physics https arxiv org abs 2303 14134 curtains flows for flows constructing unobserved regions with maximum likelihood estimation https arxiv org abs 2305 04646 high dimensional and permutation invariant anomaly detection https arxiv org abs 2306 03933 the interplay of machine learning based resonant anomaly detection methods https arxiv org abs 2307 11157 gan ae an anomaly detection algorithm for new physics search in lhc data https arxiv org abs 2305 15179 anomaly detection search for new resonances decaying into a higgs boson and a generic new particle x in hadronic final states using sqrt s https arxiv org abs 2306 03637 boosting sensitivity to new physics with unsupervised anomaly detection in dijet resonance search https arxiv org abs 2308 02671 simulation based likelihood free inference parameter estimation neural networks for full phase space reweighting and parameter tuning https arxiv org abs 1907 08209 doi https doi org 10 1103 physrevd 101 091901 likelihood free inference with an improved cross entropy estimator https arxiv org abs 1808 00973 resonance searches with machine learned likelihood ratios https arxiv org abs 2002 04699 constraining effective field theories with machine learning https arxiv org abs 1805 00013 doi https doi org 10 1103 physrevlett 121 111801 a guide to constraining effective field theories with machine learning https arxiv org abs 1805 00020 doi https doi org 10 1103 physrevd 98 052004 madminer machine learning based inference for particle physics https arxiv org abs 1907 10621 doi https doi org 10 1007 s41781 020 0035 2 mining gold from implicit models to improve likelihood free inference https arxiv org abs 1805 12244 doi https doi org 10 1073 pnas 1915980117 approximating likelihood ratios with calibrated discriminative classifiers https arxiv org abs 1506 02169 parameter estimation using neural networks in the presence of detector effects https arxiv org abs 2010 03569 doi https doi org 10 1103 physrevd 103 036001 targeted likelihood free inference of dark matter substructure in strongly lensed galaxies https arxiv org abs 2010 07032 parameter inference from event ensembles and the top quark mass https arxiv org abs 2011 04666 measuring qcd splittings with invertible networks https arxiv org abs 2012 09873 e pluribus unum ex machina learning from many collider events at once https arxiv org abs 2101 07263 tree boosting for learning eft parameters https arxiv org abs 2107 10859 black box optimization with local generative surrogates https arxiv org abs 2002 04632 url https proceedings neurips cc paper 2020 hash a878dbebc902328b41dbf02aa87abb58 abstract html a neural simulation based inference approach for characterizing the galactic center gamma ray excess https arxiv org abs 2110 06931 machine learning the higgs top cp phase https arxiv org abs 2110 07635 constraining cp violation in the higgs top quark interaction using machine learning based inference https arxiv org abs 2110 10177 a method for approximating optimal statistical significances with machine learned likelihoods https arxiv org abs 2205 05952 new machine learning techniques for simulation based inference inferostatic nets kernel score estimation and kernel likelihood ratio estimation https arxiv org abs 2210 01680 machine learned exclusion limits without binning https arxiv org abs 2211 04806 two invertible networks for the matrix element method https arxiv org abs 2210 00019 deep learning for the matrix element method https arxiv org abs 2211 11910 doi https doi org 10 22323 1 414 0246 learning likelihood ratios with neural network classifiers https arxiv org abs 2305 10500 hierarchical neural simulation based inference over event ensembles https arxiv org abs 2306 12584 emulator based bayesian inference on non proportional scintillation models by compton edge probing https arxiv org abs 2302 05641 machine learning and kalman filtering for nanomechanical mass spectrometry https arxiv org abs 2306 00563 reconstructing axion like particles from beam dumps with simulation based inference https arxiv org abs 2308 01353 simulation based inference in the search for cp violation in leptonic wh production https arxiv org abs 2308 02882 scaling madminer with a deployment on reana https arxiv org abs 2304 05814 unfolding omnifold a method to simultaneously unfold all observables https arxiv org abs 1911 09107 doi https doi org 10 1103 physrevlett 124 182001 unfolding with generative adversarial networks https arxiv org abs 1806 00433 how to gan away detector effects https arxiv org abs 1912 00477 doi https doi org 10 21468 scipostphys 8 4 070 machine learning approach to inverse problem and unfolding procedure https arxiv org abs 1004 2006 machine learning as an instrument for data unfolding https arxiv org abs 1712 01814 advanced event reweighting using multivariate analysis https doi org 10 1088 1742 6596 368 1 012028 unfolding by weighting monte carlo events https doi org 10 1016 0168 9002 94 01067 6 binning free unfolding based on monte carlo migration invertible networks or partons to detector and back again https arxiv org abs 2006 06685 doi https doi org 10 21468 scipostphys 9 5 074 neural empirical bayes source distribution estimation and its applications to simulation based inference https arxiv org abs 2011 05836 url https proceedings mlr press v130 vandegar21a html foundations of a fast data driven machine learned simulator https arxiv org abs 2101 08944 comparison of machine learning approach to other unfolding methods https arxiv org abs 2104 03036 scaffolding simulations with deep learning for high dimensional deconvolution https arxiv org abs 2105 04448 preserving new physics while simultaneously unfolding all observables https arxiv org abs 2105 09923 measurement of lepton jet correlation in deep inelastic scattering with the h1 detector using machine learning for unfolding https arxiv org abs 2108 12376 presenting unbinned differential cross section results https arxiv org abs 2109 13243 feed forward neural network unfolding https arxiv org abs 2112 08180 optimizing observables with machine learning for better unfolding https arxiv org abs 2203 16722 an unfolding method based on conditional invertible neural networks cinn using iterative training https arxiv org abs 2212 08674 unbinned profiled unfolding https arxiv org abs 2302 05390 end to end latent variational diffusion models for inverse problems in high energy physics https arxiv org abs 2305 10399 domain adaptation reweighting with boosted decision trees https arxiv org abs 1608 05806 doi https doi org 10 1088 1742 6596 762 1 012036 neural networks for full phase space reweighting and parameter tuning https arxiv org abs 1907 08209 doi https doi org 10 1103 physrevd 101 091901 approximating likelihood ratios with calibrated discriminative classifiers https arxiv org abs 1506 02169 dctrgan improving the precision of generative models with reweighting https arxiv org abs 2009 03796 doi https doi org 10 1088 1748 0221 15 11 p11004 neural conditional reweighting https arxiv org abs 2107 08979 model independent measurements of standard model cross sections with domain adaptation https arxiv org abs 2207 09293 mimicking non ideal instrument behavior for hologram processing using neural style translation https arxiv org abs 2301 02757 doi https doi org 10 1364 oe 486741 flow away your differences conditional normalizing flows as an improvement to reweighting https arxiv org abs 2304 14963 bsm simulation assisted likelihood free anomaly detection https arxiv org abs 2001 05001 doi https doi org 10 1103 physrevd 101 095004 resonance searches with machine learned likelihood ratios https arxiv org abs 2002 04699 constraining effective field theories with machine learning https arxiv org abs 1805 00013 doi https doi org 10 1103 physrevlett 121 111801 a guide to constraining effective field theories with machine learning https arxiv org abs 1805 00020 doi https doi org 10 1103 physrevd 98 052004 mining gold from implicit models to improve likelihood free inference https arxiv org abs 1805 12244 doi https doi org 10 1073 pnas 1915980117 madminer machine learning based inference for particle physics https arxiv org abs 1907 10621 doi https doi org 10 1007 s41781 020 0035 2 use of a generalized energy mover s distance in the search for rare phenomena at colliders https arxiv org abs 2004 09360 doi https doi org 10 1140 epjc s10052 021 08891 6 exploring parameter spaces with artificial intelligence and machine learning black box optimisation algorithms https arxiv org abs 2206 09223 unbinned multivariate observables for global smeft analyses from machine learning https arxiv org abs 2211 02058 lhc eft wg report experimental measurements and observables https arxiv org abs 2211 08353 on the bsm reach of four top production at the lhc https arxiv org abs 2302 08281 tip of the red giant branch bounds on the axion electron coupling revisited https arxiv org abs 2305 03113 simple but not simplified a new approach for optimising beyond standard model physics searches at the large hadron collider https arxiv org abs 2305 01835 autoencoders for real time suep detection https arxiv org abs 2306 13595 pinning down the leptophobic z prime in leptonic final states with deep learning https arxiv org abs 2307 01118 tip of the red giant branch bounds on the neutrino magnetic dipole moment revisited https arxiv org abs 2307 13050 differentiable simulation differentiable matrix elements with madjax https arxiv org abs 2203 00057 morphing parton showers with event derivatives https arxiv org abs 2208 02274 implicit neural representation as a differentiable surrogate for photon propagation in a monolithic neutrino detector https arxiv org abs 2211 01505 novel machine learning and differentiable programming techniques applied to the vip 2 underground experiment https arxiv org abs 2305 17153 differentiable earth mover s distance for data compression at the high luminosity lhc https arxiv org abs 2306 04712 branches of a tree taking derivatives of programs with discrete and branching randomness in high energy physics https arxiv org abs 2308 16680 uncertainty quantification interpretability jet images deep learning edition https arxiv org abs 1511 05190 doi https doi org 10 1007 jhep07 2016 069 what is the machine learning https arxiv org abs 1709 10106 doi https doi org 10 1103 physrevd 97 056009 capsnets continuing the convolutional quest https arxiv org abs 1906 11265 doi https doi org 10 21468 scipostphys 8 2 023 explainable ai for ml jet taggers using expert variables and layerwise relevance propagation https arxiv org abs 2011 13466 resurrecting b bar b h with kinematic shapes https arxiv org abs 2011 13945 safety of quark gluon jet classification https arxiv org abs 2103 09103 an exploration of learnt representations of w jets https arxiv org abs 2109 10919 explaining machine learned particle flow reconstruction https arxiv org abs 2111 12840 creating simple interpretable anomaly detectors for new physics in jet substructure https arxiv org abs 2203 01343 improving parametric neural networks for high energy physics and beyond https arxiv org abs 2202 00424 lessons on interpretable machine learning from particle physics https arxiv org abs 2203 08021 doi https doi org 10 1038 s42254 022 00456 0 a detailed study of interpretability of deep neural network based top taggers https arxiv org abs 2210 04371 interpretability of an interaction network for identifying h rightarrow b bar b jets https arxiv org abs 2211 12770 doi https doi org 10 22323 1 414 0223 interpretable machine learning methods applied to jet background subtraction in heavy ion collisions https arxiv org abs 2303 08275 estimation a guide for deploying deep learning in lhc searches how to achieve optimality and account for uncertainty https arxiv org abs 1909 03081 doi https doi org 10 21468 scipostphys 8 6 090 ai safety for high energy physics https arxiv org abs 1910 08606 parton shower uncertainties in jet substructure analyses with deep neural networks https arxiv org abs 1609 00607 doi https doi org 10 1103 physrevd 95 014018 understanding event generation networks via uncertainties https arxiv org abs 2104 04543 exploring the universality of hadronic jet classification https arxiv org abs 2204 03812 deep neural network uncertainty quantification for lartpc reconstruction https arxiv org abs 2302 03787 the dl advocate playing the devil s advocate with hidden systematic uncertainties https arxiv org abs 2303 15956 mitigation adversarial learning to eliminate systematic errors a case study in high energy physics https dl4physicalsciences github io files nips dlps 2017 1 pdf machine learning uncertainties with adversarial neural networks https arxiv org abs 1807 08763 doi https doi org 10 1140 epjc s10052 018 6511 8 learning to pivot with adversarial networks https arxiv org abs 1611 01046 url https papers nips cc paper 2017 hash 48ab2f9b45957ab574cf005eb8a76760 abstract html combine and conquer event reconstruction with bayesian ensemble neural networks https arxiv org abs 2102 01078 doi https doi org 10 1007 jhep04 2021 296 improving robustness of jet tagging algorithms with adversarial training https arxiv org abs 2203 13890 uncertainty and inference aware learning constraining the parameters of high dimensional models with active learning https arxiv org abs 1905 08628 doi https doi org 10 1140 epjc s10052 019 7437 5 deep learning jets with uncertainties and more https arxiv org abs 1904 10004 doi https doi org 10 21468 scipostphys 8 1 006 inferno inference aware neural optimisation https arxiv org abs 1806 04743 doi https doi org 10 1016 j cpc 2019 06 007 optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned poisson likelihoods with nuisance parameters https arxiv org abs 2003 07186 doi https doi org 10 1007 s41781 020 00049 5 uncertainty aware learning for high energy physics https arxiv org abs 2105 08742 punzi loss a non differentiable metric approximation for sensitivity optimisation in the search for new particles https arxiv org abs 2110 00810 neos end to end optimised summary statistics for high energy physics https arxiv org abs 2203 05570 doi https doi org 10 48550 arxiv 2203 05570 application of inferno to a top pair cross section measurement with cms open data https arxiv org abs 2301 10358 formal theory and ml theory and physics for ml renormalization in the neural network quantum field theory correspondence https arxiv org abs 2212 11811 p adic statistical field theory and convolutional deep boltzmann machines https arxiv org abs 2302 03817 doi https doi org 10 1093 ptep ptad061 structures of neural network effective theories https arxiv org abs 2305 02334 a correspondence between deep boltzmann machines and p adic statistical field theories https arxiv org abs 2306 03751 black holes and the loss landscape in machine learning https arxiv org abs 2306 14817 neural network field theories non gaussianity actions and locality https arxiv org abs 2307 03223 ml for theory machine learned calabi yau metrics and curvature https arxiv org abs 2211 09801 characterizing 4 string contact interaction using machine learning https arxiv org abs 2211 09129 cyjax a package for calabi yau metrics with jax https arxiv org abs 2211 12520 doi https doi org 10 1088 2632 2153 acdc84 autoencoding heterotic orbifolds with arbitrary geometry https arxiv org abs 2212 00821 mahler measuring the genetic code of amoebae https arxiv org abs 2212 06553 clustering cluster algebras with clusters https arxiv org abs 2212 09771 machine learning in physics and geometry https arxiv org abs 2303 12626 the r matrix net https arxiv org abs 2304 07247 macroscopic dynamics of entangled 3 1 dimensional systems a novel investigation into why my macbook cable tangles in my backpack every single day https arxiv org abs 2304 00220 accelerated discovery of machine learned symmetries deriving the exceptional lie groups g2 f4 and e6 https arxiv org abs 2307 04891 reconstructing s matrix phases with machine learning https arxiv org abs 2308 09451 renormalizing diffusion models https arxiv org abs 2308 12355 scattering with neural operators https arxiv org abs 2308 14789 distilling the essential elements of nuclear binding via neural network quantum states https arxiv org abs 2308 16266 experimental results this section is incomplete as there are many results that directly and indirectly e g via flavor tagging use modern machine learning techniques we will try to highlight experimental results that use deep learning in a critical way for the final analysis sensitivity performance studies identification of hadronic tau lepton decays using a deep neural network https arxiv org abs 2201 08458 a feasibility study of multi electrode high purity germanium detector for 76 ge neutrinoless double beta decay searching https arxiv org abs 2211 06180 doi https doi org 10 1088 1748 0221 18 05 p05025 pulse shape discrimination using a convolutional neural network for organic liquid scintillator signals https arxiv org abs 2211 07892 doi https doi org 10 1088 1748 0221 18 03 p03003 deep machine learning for the panda software trigger https arxiv org abs 2211 15390 doi https doi org 10 1140 epjc s10052 023 11494 y automated visual inspection of cms hgcal silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier https arxiv org abs 2303 15319 searches and measurements were ml reconstruction is a core component the full event interpretation an exclusive tagging algorithm for the belle ii experiment https arxiv org abs 1807 08680 doi https doi org 10 1007 s41781 019 0021 8 search for an anomalous excess of inclusive charged current nu e interactions in the microboone experiment using wire cell reconstruction https arxiv org abs 2110 13978 search for an anomalous excess of charged current quasi elastic nu e interactions with the microboone experiment using deep learning based reconstruction https arxiv org abs 2110 14080 search for supersymmetry in final states with missing transverse momentum and three or more b jets in 139 fb 1 of proton textendash proton collisions at sqrt s https arxiv org abs 2211 08028 doi https doi org 10 1140 epjc s10052 023 11543 6 search for supersymmetry in final states with a single electron or muon using angular correlations and heavy object identification in proton proton collisions at sqrt s https arxiv org abs 2211 08476 search for higgs boson and observation of z boson through their decay into a charm quark antiquark pair in boosted topologies in proton proton collisions at s https arxiv org abs 2211 14181 doi https doi org 10 1103 physrevlett 131 041801 reconstruction of the event vertex in the pandax iii experiment with convolution neural network https arxiv org abs 2211 14992 doi https doi org 10 1007 jhep05 2023 200 measurement of the cross section of top quark antiquark pair production in association with a w boson in proton proton collisions at sqrt s https arxiv org abs 2212 03770 evidence for four top quark production at the lhc https arxiv org abs 2212 06075 search for long lived particles using out of time trackless jets in proton proton collisions at sqrt s https arxiv org abs 2212 06695 doi https doi org 10 1007 jhep07 2023 210 search for a new scalar resonance in flavour changing neutral current top quark decays t rightarrow qx q https arxiv org abs 2301 03902 doi https doi org 10 1007 jhep07 2023 199 search for periodic signals in the dielectron and diphoton invariant mass spectra using 139 fb 1 of pp collisions at sqrt s https arxiv org abs 2305 10894 search for a new z gauge boson in 4 mu events with the atlas experiment https arxiv org abs 2301 09342 doi https doi org 10 1007 jhep07 2023 090 observation of single top quark production in association with a photon using the atlas detector https arxiv org abs 2302 01283 search for a light charged higgs boson in t rightarrow h pm b decays with h pm rightarrow cb in the lepton jets final state in proton proton collisions at sqrt s https arxiv org abs 2302 11739 search for third generation vector like leptons in pp collisions at sqrt s https arxiv org abs 2303 05441 doi https doi org 10 1007 jhep07 2023 118 evidence of off shell higgs boson production from zz leptonic decay channels and constraints on its total width with the atlas detector https arxiv org abs 2304 01532 measurement of ensuremath nu ensuremath mu charged current inclusive ensuremath pi 0 production in the nova near detector https arxiv org abs 2306 04028 doi https doi org 10 1103 physrevd 107 112008 searches for supersymmetric particles with prompt decays with the atlas detector https arxiv org abs 2306 15014 final analysis discriminate for searches search for non resonant higgs boson pair production in the bb ell nu ell nu final state with the atlas detector in pp collisions at sqrt s https arxiv org abs 1908 06765 doi https doi org 10 1016 j physletb 2019 135145 search for higgs boson decays into a z boson and a light hadronically decaying resonance using 13 tev pp collision data from the atlas detector https arxiv org abs 2004 01678 doi https doi org 10 1103 physrevlett 125 221802 dijet resonance search with weak supervision using 13 tev pp collisions in the atlas detector https arxiv org abs 2005 02983 doi https doi org 10 1103 physrevlett 125 131801 inclusive search for highly boosted higgs bosons decaying to bottom quark antiquark pairs in proton proton collisions at sqrt s https arxiv org abs 2006 13251 doi https doi org 10 1007 jhep12 2020 085 evidence for four top quark production at the lhc https arxiv org abs 2212 06075 measurements using deep learning directly not through object reconstruction measurement of lepton jet correlation in deep inelastic scattering with the h1 detector using machine learning for unfolding https arxiv org abs 2108 12376 unbinned deep learning jet substructure measurement in high q 2 ep collisions at hera https arxiv org abs 2303 13620	hep machine-learning particle-physics papers latex	ai
computer-vision-3153829	computer vision on the raspberry pi 4 this is the repository for the linkedin learning course computer vision on the raspberry pi 4 the full course is available from linkedin learning lil course url computer vision on the raspberry pi 4 lil thumbnail url more and more applications are using computer vision to detect and recognize objects these applications usually execute on large computers but developers can save money and power by running them on single board computers sbcs the raspberry pi 4 is one of the most popular sbcs available it s also the first computer in the raspberry pi family powerful enough to execute computer vision applications also the software needed to build these applications can be downloaded freely from the internet in this course instructor matt scarpino shows programmers how to write and execute computer vision applications on the raspberry pi 4 matt introduces you to using the thonny ide the opencv library and numpy array operations he steps through object detection and neural networks then explores convolutional neural networks cnns including the keras package and the tensorflow package matt also walks you through what you can do with a raspberry pi hq camera instructions this repository has branches for each of the videos in the course you can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage or you can add tree branch name to the url to go to the branch you want to access branches the branches are structured to correspond to the videos in the course the naming convention is chapter movie as an example the branch named 02 03 corresponds to the second chapter and the third video in that chapter some branches will have a beginning and an end state these are marked with the letters b for beginning and e for end the b branch contains the code as it is at the beginning of the movie the e branch contains the code as it is at the end of the movie the main branch holds the final state of the code when in the course when switching from one exercise files branch to the next after making changes to the files you may get a message like this error your local changes to the following files would be overwritten by checkout files please commit your changes or stash them before you switch branches aborting to resolve this issue add changes to git using this command git add commit changes using this command git commit m some message installing 1 to use these exercise files you must have the following installed list of requirements for course 2 clone this repository into your local machine using the terminal mac cmd windows or a gui tool like sourcetree 3 course specific instructions instructor matt scarpino check out my other courses on linkedin learning https www linkedin com learning instructors matt scarpino lil course url https www linkedin com learning computer vision on the raspberry pi 4 lil thumbnail url https cdn lynda com course 3153829 3153829 1635434273646 16x9 jpg		ai
Face-Liveness-Detection	face liveness detection using depth map prediction about the project this is an application of a combination of convolutional neural networks and computer vision to detect between actual faces and fake faces in realtime environment the image frame captured from webcam is passed over a pre trained model this model is trained on the depth map of images in the dataset the depth map generation have been developed from a different cnn model requirements python3 tensorflow dlib keras numpy sklearn imutils opencv file description main py https github com anand498 face liveness detection blob master main py this file is the main script that would call the predictperson function present in the utilr function training py https github com anand498 face liveness detection blob master livenessdetect training py along with the architecture script this file includes various parameter tuning steps of the model model py https github com anand498 face liveness detection blob master livenessdetect model py has the main cnn architecture for training the dataset the convolutional neural network the network consists of 3 hidden conlvolutional layers with relu as the activation function finally it has 1 fully connected layer the network is trained with 10 epochs size with batch size 32 the ratio of training to testing bifuracation is 75 25 how to use application in real time git clone https github com anand498 face liveness detection git pip install r requirements txt python main py and you re good to go don t forget to star the repo if i made your life easier with this wink		ai
Plan-and-Solve-Prompting	img src tasks solid svg width 25 height 25 plan and solve prompting code for our acl 2023 paper plan and solve prompting improving zero shot chain of thought reasoning by large language models https arxiv org abs 2305 04091 visitors https visitor badge glitch me badge page id agi edgerunners plan and solve prompting a href https hits seeyoufarm com img src https hits seeyoufarm com api count incr badge svg url https 3a 2f 2fgithub com 2fagi edgerunners 2fplan and solve prompting count bg 23e97eba title bg 23555555 icon icon color 23e7e7e7 title visitors edge flat false alt hits a we are honored to announce that plan and solve prompting has been added to the core library of a style color 447ec9 href https github com hwchase17 langchain langchain a that is a style color 447ec9 href https blog langchain dev plan and execute agents plan and execute a find out what people are saying about it on a style color 447ec9 href https twitter com hwchase17 status 1656327621335195648 twitter a and a style color 447ec9 href https papers labml ai paper 6978e4c4ee0f11edb95839eec3084ddd ai daily paper a showcase ps prompting math cot 2 jpg robot run plan and solve prompting set an api key of openai api in the file apikeys json shell python main py prompt id 201 dataset svamp engine text davinci 003 learning type zero shot robot run plan and solve prompting with threads for faster inference set 8 different api keys of openai api in the file apikeys json shell python main threads py prompt id 201 dataset svamp engine text davinci 003 learning type zero shot showcase ps prompting exp math cot exp png cook prompts table align center tr th prompt id th th type th th trigger sentence th tr tr align center td 101 td td cot td td align left let s think step by step td tr tr align center td 201 td td ps td td align left let s first understand the problem and devise a plan to solve the problem then let s carry out the plan to solve the problem step by step td tr tr align center td 301 td td ps td td align left let s first understand the problem extract relevant variables and their corresponding numerals and devise a plan then let s carry out the plan calculate intermediate variables pay attention to correct numeral calculation and commonsense solve the problem step by step and show the answer td tr tr align center td 302 td td ps td td align left let s first understand the problem extract relevant variables and their corresponding numerals and devise a complete plan then let s carry out the plan calculate intermediate variables pay attention to correct numerical calculation and commonsense solve the problem step by step and show the answer td tr tr align center td 303 td td ps td td align left let s devise a plan and solve the problem step by step td tr tr align center td 304 td td ps td td align left let s first understand the problem and devise a complete plan then let s carry out the plan and reason problem step by step every step answer the subquestion does the person flip and what is the coin s current state according to the coin s last state give the final answer pay attention to every flip and the coin s turning state td tr tr align center td 305 td td ps td td align left let s first understand the problem extract relevant variables and their corresponding numerals and make a complete plan then let s carry out the plan calculate intermediate variables pay attention to correct numerical calculation and commonsense solve the problem step by step and show the answer td tr tr align center td 306 td td ps td td align left let s first prepare relevant information and make a plan then let s answer the question step by step pay attention to commonsense and logical coherence td tr tr align center td 307 td td ps td td align left let s first understand the problem extract relevant variables and their corresponding numerals and make and devise a complete plan then let s carry out the plan calculate intermediate variables pay attention to correct numerical calculation and commonsense solve the problem step by step and show the answer td tr table star star history star history chart https api star history com svg repos agi edgerunners plan and solve prompting type date https star history com agi edgerunners plan and solve prompting date smile cat cite if you find plan and solve prompting useful for your research and applications please kindly cite using this bibtex latex article wang2023plan title plan and solve prompting improving zero shot chain of thought reasoning by large language models author wang lei and xu wanyu and lan yihuai and hu zhiqiang and lan yunshi and lee roy ka wei and lim ee peng journal arxiv preprint arxiv 2305 04091 year 2023		ai
Twitter-2020-Sentiment-Analysis	analyzing twitter users 2020 reflections using nlp a sentiment analysis project using python and tableau click here https jess analytics medium com for the full article this project was done using natural language processing nlp techniques in december 2020 i felt it would be a good idea to obtain insights into how twitter users felt about the year twitter receives over 500 million tweets per day from its users across the globe so i only had to find a way to retrieve the data python libraries like pandas for data cleaning manipulation tweepy for tweets mining nltk natural language toolkit textblob for sentiment analysis matplotlib wordcloud for data exploration emot for emojis identification plotly for some data visualisation were used for this project in the jupyter notebook https github com jess data twitter 2020 sentiment analysis blob master twitter 20sentiment 20analysis 20project ipynb you will leearn how i carried out the following steps for the project 1 import libraries 2 tweets mining 3 data cleaning 4 location geocoding 5 tweets processing 6 data exploration 7 sentiment analysis alt text https github com jess data twitter 2020 sentiment analysis blob master flowchart png tweets processing steps to reach the ultimate goal there was a need to clean up the individual tweets to make this easy i created a function preprocesstweets in my python program which i further applied to the tweets to produce the desired results this user defined function was used to remove punctuations links emojis and stop words from the tweets in a single run additionally i used a concept known as tokenization in nlp it is a method of splitting a sentence into smaller units called tokens to remove unnecessary elements another technique worthy of mention is lemmatization this is a process of returning words to their base form a simple illustration is shown below alt text https github com jess data twitter 2020 sentiment analysis blob master twitter jpg word cloud generation to get the most common words used to describe 2020 i made use of the pos tag parts of speech tagging module in the nltk library using the wordcloud library one can generate a word cloud based on word frequency and superimpose these words on any image in this case i used the twitter logo and matplotlib to display the image the word cloud shows the words with higher frequency in bigger text size while the not so common words are in smaller text sizes alt text https github com jess data twitter 2020 sentiment analysis blob master wordcloud png visulizing most common words the plot below was genrated using plotly library for python alt text https github com jess data twitter 2020 sentiment analysis blob master twitter 20analytics 20pic png sentiment analysis for this analysis i went with textblob text blob analyzes sentences by giving each tweet a subjectivity and polarity score based on the polarity scores one can define which tweets were positive negative or neutral a polarity score of 0 is negative 0 is neutral while 0 is positive i used the apply method on the polarity column in my data frame to return the respective sentiment category the distribution of the sentiment categories is shown below you can also see the sentiment category distribution per country and continent in the tableau dashboard here https public tableau com profile jessica uwoghiren vizhome twitterusers2020reflectionsdashboard finaldashboard alt text https github com jess data twitter 2020 sentiment analysis blob master distribution 20of 20sentiments 20results png remarks some of the insights i generated are stated below tweet sentiments i was not surprised by the proportion of the sentiment categories because for most people the end of the year is a time to show gratitude and hope for a better year ahead however this year has been filled with so many unpalatable events hence 31 of the tweets being negative please note that this is not indicative of the entire twitter community as only a subset of tweets were mined for this analysis countries with most tweets about 40 of the total tweets emanated from the united states england and canada since a good number of twitter users do not have their exact location on their profiles their tweet locations were classified as unknown location hour of the day with the most tweets it was interesting to see that most tweets were created at 5 pm gmt thinking about it in the us canada this is lunchtime while in countries like nigeria and england it is when most individuals finish the work day so they have ample time to tweet hour of the day with least tweets 9 am gmt was the hour of the day with the least number of tweets the reason is this is when most people start their day at work in countries like nigeria and england while it is still bed time in other countries like the us canada most retweeted and liked tweet for the period of 12th to 25th december 2020 the tweet with the most retweets was about a korean boy band bts and their songs with 10 873 rts the most liked tweet from a user who tweeted about how 2020 was a good year for his dog who did not have to be alone for a second the tweet had 42 295 likes relevant links tableau dashboard https public tableau com profile jessica uwoghiren vizhome twitterusers2020reflectionsdashboard finaldashboard jupyter notebook https nbviewer jupyter org github jess data twitter 2020 sentiment analysis tree master twitter 20sentiment 20analysis 20project ipynb0 personal website https jess analytics com medium article https jess analytics medium com linkedin https www linkedin com in jessicauwoghiren twitter https twitter com jessica xls datatech space community https linktr ee datatechspace	machine-learning machine-learning-algorithms sentiment-analysis twitter-sentiment-analysis python textblob wordcloud wordcloud-generator plotly-python plotly-express	ai
HLW8012_BL0937_ESP	hlw8012 bl0937 esp esp8266 esp8285 esp open rtos for hlw8012 and bl0937 energy meter chip copyright c david b brown based on work by jaromir kopp macwyznawca at me dot com based on the library for arduino created by xose p rez		os
sonic-client-web	p align center img width 80px src https raw githubusercontent com soniccloudorg sonic server main logo png p p align center front end of sonic cloud real machine platform p p align center span english span a href https github com soniccloudorg sonic client web blob main readme cn md a p p align center a href img src https img shields io github v release soniccloudorg sonic client web include prereleases a a href img src https img shields io badge vue 3 2 14 success a a href img src https img shields io badge elementplus 1 1 0 beta 24 success a p p align center a href img src https img shields io github commit activity m soniccloudorg sonic client web a a href https hub docker com repository docker sonicorg sonic client web img src https img shields io docker pulls sonicorg sonic client web a a href https github com soniccloudorg sonic server blob main license img src https img shields io github license soniccloudorg sonic server color green label license logo license logocolor green a p official website sonic official website https sonic cloud cn background what is sonic sonic is a platform that integrates remote control debugging and automated testing of mobile devices and strives to create a better use experience for global developers and test engineers if you want to participate welcome to join us if you want to support you can give me a star how to package install npm install dev npm run dev package npm run build build image docker build t sonicorg sonic client web deployment mode docker mode click here https hub docker com repository docker sonicorg sonic client web dist mode put dist into nginx sponsors thank you to all our sponsors img src https ceshiren com uploads default original 3x 7 0 70299922296e93e2dcab223153a928c4bfb27df9 jpeg alt width 500 https qrcode testing studio com f from sonic url https ceshiren com https qrcode testing studio com f from sonic url https ceshiren com http qrcode testing studio com f from sonic url https www testing studio com web app devops https qrcode testing studio com f from sonic url https ceshiren com t topic 14940 license license license	vue3 element-plus ehcarts vue	front_end
clair	clair design prerequirements yarn https yarnpkg com is required just forget npm run before getting started check out how to contribute here contributing md get started install sh yarn start sh for vue yarn start vue for react yarn start react preview site shell for vue yarn serve vue for react yarn serve react commit please use yarn commit or npm run commit instead of git commit use git cz if you have commitizen https github com commitizen cz cli installed globally lerna run yarn commands sh yarn lerna run stream build scope clair helpers or yarn workspace clair helpers build link packages sh yarn lerna add clair theme default packages helpers dev	vue-components react-components	os
openmlsys-zh	https openmlsys github io pdf 2022 27 06 2022 openmlsys ai 60 https zhuanlan zhihu com p 531498210 https github com openmlsys openmlsys cuda jie ren https github com jieren98 wenteng liang https github com went liang 17 03 2022 issue info editors md pr custom operators ai c c computational graph intermediate representation operator runtime gpu ascend ai safety critical ai ai info info md pdf pr markdown info style md info terminology md	machine-learning software-architecture textbook computer-systems	ai
LwIPFreeRTOS	stm32 p107 freertos sample application with freertos and lwip for the olimex stm32 p107 board getting started in order to compile the application make to program the board make program		os
RealTime_Underwater_DSP_	realtime underwater dsp this project is a part of a bigger project intended to design and implement an autonomous embedded system composed of underwater acoustic sensor network our project was to implement acoustic signals digital processing techniques for underwater communications first phase at the beginning i performed a study on the probability of detection pd for drone receiver to determine which technique to use in order to maximise the pd finding the perfect technique that has maximum pd will help us determine what the transmitter signal should look like as well as the receiver filter after a lot of research we decided to choose only two transmitter signal techniques for our study linear frequency modulation pure tone pulse and on the receiver side we choose two techniques for signal detection matched filter energy detection results the simulation figures attached to project show that matched filtering has a higher probability of detection furthermore the probability of detection using a matched filetr is even higher with an lfm signal also the lfm signal probability of detection does not depend on the number of samples used in contrary to the pure tone pulse that requires higher sample numbers to improve the pd second phase implementation complexity on dsp results ongoing		os
SpleeterGui	spleetergui music source separation desktop app windows desktop front end for spleeter ai source separation latest package can be downloaded from here a href https spleetergui com https spleetergui com a no need to install python or spleeter this app contains a portable version of python pre loaded with spleeter older versions here https makenweb com spleetergui the aim for this project is to make it easy for windows users to download and run spleeter without needing to use the command line tools to do so spleetergui app spleeter gui png this project is a simple c desktop front end for spleeter please consider donating and help pay for hosting and development paypal me makenitso version history 27 07 2020 ms defender is falsely identifying v2 7 as a trojan the exe has been submitted to microsoft and given the all clear defender definition version 1 319 2309 0 finds no threat more details can be found here https github com boy1dr spleetergui issues 36 date version notes 7 10 2023 2 9 5 rebuilt with python 3 10 10 and spleeter 2 4 updated gui new website spleetergui com 5 04 2022 2 9 2 upgraded spleeter to 2 3 0 updated python files the spleeter core update feature is working again 30 01 2021 2 9 1 upgraded spleeter to 2 1 2 updated command syntax for latest spleeter version 9 11 2020 2 9 upgraded spleeter to 2 0 1 and python 31 07 2020 2 8 upgraded the project to 64bit 19 07 2020 2 7 updated help set paths for python ffmpeg use your own python 4 07 2020 2 6 recombine audio and multi lingual update 10 05 2020 2 5 ui update additional help menu items for version check and spleeter core upgrade display installed spleeter version on startup 4 05 2020 2 4 bug fix full bandwidth mode checked but not enabled by default 27 12 2019 2 3 accessibility update process button tab order access labels and descriptions ding on complete etc 24 12 2019 2 2 new windows msi installer drag and drop processing 21 12 2019 2 0 interface update added batch processing 17 12 2019 1 1 added high quality expert mode older versions version 2 8 https makenweb com downloads spleetergui v2 8 msi version 2 7 https makenweb com downloads spleetergui v2 7 msi examples https www youtube com watch v bdnzvplzole https www youtube com watch v nxjfisus0ig https www youtube com watch v phgamzhui c https www youtube com watch v 3x5nfc2d1rw https www youtube com watch v 9kkwjhc2bz0 https www youtube com watch v mygm1sflqxc the project contains the c source code for the graphical user interface it also contains python3 7 and the spleeter project feel free to inspect the source code and build for yourself you can also install your own python tensorflow ffmpeg spleeter	spleeter python source-separation gui	front_end
machine-learning-flashcards	machine learning flashcards machine learning flashcards for specific topics books and courses let me know if you find any errors thanks table of contents deep learning book flashcards based on deep learning by ian goodfellow yoshua bengio and aaron courville programming flashcards flashcards on algorithms and strategies for tackling algorithms python tf torch flashcards on python numpy tensorflow and pytorch machine learning intro to dl flashcards for david barber s lectures for ucl s intro to deep learning course taught in fall 2018 reinforcement learning flashcards based on reinforcement learning lectures given by hado van hasselt and matteo hessel from deepmind at ucl in spring 2019 unsupervised learning flashcards on the 1 probabilistic and unsupervised learning and 2 approximate inferences courses lectured by maneesh sahani at the gatsby computational neuroscience unit ucl in fall 2018 ml models tex flashcards on machine learning models reinforcement learning tex flashcards on reinforcement learning how to use the flashcards tex files these flashcards are formatted to be used with anki https apps ankiweb net the digital flashcard software 1 download anki https apps ankiweb net 2 make sure you have latex installed on your computer 3 select the deck you want the cards to be in or create a new one and select it 4 import the tex file go to file import you should see this window select the tex file you want to import you should see an anki screen with import options that looks like this how to import tex files to anki how to anki png 1 select the latex card type by clicking on the field to the right of type if you don t have this card type create one see how to create latex card type below if you do you will 99 still have to edit the header of the card type to accommodate for the conditional independence symbols in lecture 05 tex step 5 of how to create latex card type below 2 choose to have fields separated by a semicolon char you should now have two fields in the file 3 choose to update existing notes when the first field matches optional but is generally a good idea click import there may be some error messages most of them don t matter but let me know if you see a proper error 5 click on the name of the deck and click study now to start reviewing the flashcards how to create a latex card type 1 from the anki launch page click add if you are already in a window where you can select the card type skip this step 2 click on the card type 3 you should see a list of card types click manage 4 click add 5 click clone basic and name the card type as latex or whatever else you want or if you don t have basic cloning another card type should also work basic is just the most basic card type 6 select the card type you just created click options and add the following to the header documentclass 12pt article usepackage amssymb amsmath amsfonts mathrsfs graphicx usepackage paperwidth 5in paperheight 100in geometry you probably don t need this but i haven t tested removing it newcommand indep perp perp newcommand ci mathrel text scalebox 1 07 perp mkern 10mu perp newcommand nci centernot ci begin document if you already have a header you can add the following if you plan to import unsupervised learning lecture 5 graphical models newcommand indep perp perp newcommand ci mathrel text scalebox 1 07 perp mkern 10mu perp newcommand nci centernot ci add the following footer end document 7 you should be back where you are choosing a card type to get to this window click on the card type name from the window where you are adding cards click on your new card type and click choose 8 click cards you should see something like card layout in anki latex card png 10 replace the front template with latex front latex and the back template with latex front latex br br hr id answer br latex back latex 9 click close you re done just select this card type when you re importing cards in future		ai
camunda-8-connector-gpt	camunda 8 gpt ai connectors task specific connectors for camunda 8 powered by large language models llms like openai gpt 4 compatible with camunda platform 8 https img shields io badge compatible 20with camunda 20platform 208 26d07c sponsored https img shields io badge sponsoredby holisticon red svg https holisticon de these connectors can automatically perform activities that previously required user tasks or specialized ai models like information extraction from unstructured data emails letters documents informed decision making before gateways creative content generation emails letters translation answering questions over documents wikis and other unstructured knowledge bases querying sql databases interacting with rest apis and more just provide input and output variable mappings and configure what you want to achieve the connectors will do the heavy lifting 1 crafting tested task and model specific prompts to get the most out of the llm 2 interfacing with the llm provider like openai gpt 4 or 3 5 3 parsing the response into local process variables 4 handling and automatically fixing common error cases warning experimental this project is not meant for production use as of today but to evaluate and demonstrate llms in bpm use cases how to run using docker create a connector secrets txt file use connector secrets txt sample as a template and fill in your openai api key and zeebe cluster information for either cloud or a local cluster bash openai api key put your key here zeebe client cloud cluster id cluster id zeebe client cloud client id client id zeebe client cloud client secret client secret zeebe client cloud region cluster region or zeebe client broker gateway address zeebe 26500 zeebe client security plaintext true optional run local zeebe cluster if you are not using camunda cloud start a local cluster bash docker compose f docker compose camunda platform yml up d run connectors run the connector runtime using a pre built image from dockerhub bash docker run env file connector secrets txt holisticon camunda 8 connector gpt develop use element templates 1 upload the element templates from element templates element templates to your project in camunda cloud and publish them individually or if you re working locally place them besides your bpmn file 2 start modeling or try the example processes from example example connectors documentation getting started docs getting started md foundational connectors docs foundational connectors md agentic connectors docs agentic connectors md custom llms docs custom models md development project setup the connectors currently use the java connector api implemented in kotlin for the connector workers core module all llm specific code llm model interfaces chains agents prompts are implemented in python using the langchain framework python folder the python app serves a rest api for the core to use for convenience both apps can be packaged into a single docker image using the top level dockerfile or docker compose yml alternatively the python app has its own dockerfile or the main py can be run directly and the runtime module can be dockerized using spring boot build image or run via the ide see below build connectors you can package the connectors by running the following command bash mvn clean package this will create jar artifacts for the two modules camunda 8 connector gpt core x x x with dependencies jar the connector worker implementations camunda 8 connector gpt runtime x x x jar a spring boot connector runtime using the core module python llm service python 3 10 is required a virtual environment is advised install the python dependencies using the following command bash python m pip install upgrade r python requirements txt install the python app bash python m pip install e python src start the python service with bash python python src gpt main py configuration in order to run the connectors will require an api key to openai and connection details to connect to camunda 8 platform all these settings should be performed using the file called connector secrets txt check the sample file if your connector runs locally from your host machine command line or ide and connects to locally running zeebe cluster openai api key put your key here camunda operate client url localhost 8080 zeebe client broker gateway address localhost 26500 zeebe client security plaintext true if your connector runs using docker compose together with zeebe cluster openai api key put your key here camunda operate client url operate 8080 zeebe client broker gateway address zeebe 26500 zeebe client security plaintext true if you want to connect to camunda 8 cloud please use the following configuration independently of run mode openai api key put your key here zeebe client cloud cluster id cluster id zeebe client cloud client id client id zeebe client cloud client secret client secret zeebe client cloud region bru 2 run from ide in your ide you can also simply navigate to the localcontainerruntime class in the runtime module and run it via your ide please include the values from the configuration block as environment variables of your runtime either by copying the values manually or using the envfile plugin for intellij https plugins jetbrains com plugin 7861 envfile license this library is developed under apache 2 0 license https img shields io badge license apache 202 0 blue svg license sponsors and customers sponsored https img shields io badge sponsoredby holisticon red svg https holisticon de		ai
ionictips	ionic tips get one ionic daily tip everyday juice up your ionic mobile app development ionic tips https img shields io badge tips ionic blue svg style flat square https github com tipaday ionictips tips list 01 control the infinite scroll properly tips 01 control an infinite scroll properly md 02 always add crosswalk for android tips 02 always add crosswalk for android md 03 test services from console tips 03 test services from console md 04 avoid using window alert tips 04 avoid using window alert md 05 create a correct ios project configuration tips 05 create a correct ios project configuration md 06 sublime text plugin for ionic snippets tips 06 sublime text plugin for ionic snippets md 07 add your own color for ui tips 07 add your own color for ui md 08 ionic run is a shorthand command tips 08 ionic run is a shorthand command md 09 serve apps with multiple screen sizes tips 09 server apps with multiple screen sizes md 10 customize the status bar tips 10 customize the status bar md 11 show the ellipsis text on item list tips 11 show the ellipsis text on item list md 12 set the appropriate styles for different device screens tips 12 set the appropriate styles for different device screens md 13 use ionic cli to manage platforms and plugins tips 13 use ionic cli to manage platforms and plugins md 14 enable native scrolling for android tips 14 enable native scrolling for android md 15 remove unnecessary angularjs scope references to the dom tips 15 remove unnecessary angularjs scope references to the dom md 16 improve performance with one time binding tips 16 improve performance with one time binding md 17 cache views if possible tips 17 cache views if possible md 18 manage ionic view lifecycle tips 18 manage ionic view lifecycle md 19 change ionic serve address tips 19 change ionic serve address md 20 remove view from history tips 20 remove view from history md 21 safe dom manipulation tips 21 safe dom manipulation md 22 monitor device internet connectivity tips 22 monitor device internet connectivity md 23 disable side menu swipe for a specific view tips 23 disable side menu swipe for a specific view md contribution guide feel free to fork and make a pull request https github com tipaday ionictips pulls for tips you d like to share credits i d like to give thanks to the following projects developers for their awesome sharing about angular and ionic development tips and tricks ionic team blog blog ionic io official documentation http ionicframework com docs ngcordova http ngcordova com docs plugins network julien renaux http julienrenaux fr 2015 08 24 ultimate angularjs and ionic performance cheat sheet		front_end
pulp-freertos	pulp freertos this project provides freertos and drivers for development of real time applications on pulp based systems programs can be run using rtl simulation simulating the hardware design or the virtual platform called gvsoc software emulation of the hardware design a book about freertos can be found here https www freertos org documentation rtos book html and the official documentation is available on this website https www freertos org features html directory structure apps applications that use this freertos port bench rtos benchmarks common driver code and build system contributing md how to contribute to this repository demos classic freertos blinky demo env sourcable configuration files to target the desired platform kernel freertos kernel code with pulp specific patches nortos simple programs that don t need freertos readme md read this scripts various analysis scripts support support libraries for simulation gvsoc dpi etc template template projects to get started tests advanced tests using freertos primitives getting started you need the following items to develop and run freertos programs for pulp a centos7 or ubuntu18 based workstation this repository a c compiler that supports the risc v instruction set either pulp riscv gcc or riscv gcc optional a compiled pulp hardware design if you intend to use rtl simulation at the end of this section you should be able to run your own hello world program either using the virtual platform or a rtl simulation of a pulp based platform repository run bash git clone git github com pulp platform pulp freertos git recurse submodules or if you don t have a ssh key set up bash git clone https github com pulp platform pulp freertos git recurse submodules in your shell the recurse submodules argument is mandatory compiler if you want to use the pulp extension additional instructions such as hardware loops dsp etc then get the source code from here https github com pulp platform pulp riscv gnu toolchain and compile it according to the manual there or easier download the latest binary release from here https github com pulp platform pulp riscv gnu toolchain releases make sure you choose the correct distribution otherwise you might run into abi issues if you want to use upstream risc v gcc then you can get it from here https github com riscv riscv gnu toolchain but you need to configure and compile it yourself now make sure that either the installation path of the newly acquired compiler is in your path environment variable or you set the riscv environment variable to point to it in both cases you need to point to the root folder of the compiler installation meaning you point to the directory that contains bin include lib etc optional pulp rtl design get the latest release of either pulp https github com pulp platform pulp or pulpissimo https github com pulp platform pulpissimo and compile it by running make build afterwards make sure you source the configuration files to make this repository aware of your compiled rtl platform by calling source setup vsim sh setting up and running a hello world now that we have a compiler and a platform to run our programs on we can set up a simple hello world a program that prints hello world to stdout note that stdout is both in the rtl simulation and the virtual platform represented by a pseudo device i e not existing in real hardware this is useful for debugging purposes but for a more realistic scenario you can look a the test uart program 1 copy template helloworld into a directory of your chosing 2 run source env your platform sh to target the desired platform and make helloworld aware where to find the freertos source files and drivers 3 run make all to compile the example program 4 call make run sim to do an rtl simulation or make run gvsoc to do a virtual platform simulation of the example program you should see hello world being printed to your terminal setting up and running blinky blinky is the canonical freertos example program on hardware fpga it will blink a led in the rtl simulation it will toggle an gpio pad and print blink to stdout and on the virtual platform we just get the latter internally the program will instantiate two tasks a receive and a send tasks that communicate with each other over a queue a freertos ipc primitive 1 copy template blinky into a directory of your chosing 2 run source env your platform sh to target the desired platform and make blinky aware where to find the freertos source files and drivers 3 run make all to compile the example program 4 call make run sim to do an rtl simulation or make run gvsoc to do a virtual platform simulation of the example program you should see a stream of blink being printed to stdout tree of your typical project once you have compiled a program your project s tree will roughly look like this here demonstranted on helloworld freertosconfig h freertos specific macros that enable disable features gvsim simulation output of a virtual platform simulation helloworld your compiled program helloworld c source files helloworld hex hexdump of your program not used helloworld lst disassembly symbol table and header of your program helloworld map linker map of your program linked and discarded sections helloworld o object files helloworld stim stimuli files format that can be used by rtl simulation helloworld veri verilog memory dump for faster rtl simulations makefile compile run analyze check make target sim simulation output of a rtl simulation tl dr 1 install compiler point riscv and or the path to it 2 use one of the given templates and copy it 3 optional install the rtl platform with make build and refer to it with source setup vsim sh 4 source env your platform sh in this project s root to make the template aware of this project source files 5 make all run sim or make all run gvsoc to compile and simulate using the virtual platform gvsoc the environment variable gvsim args can be used to pass arguments to gvsoc consult the gvsoc documentation for what kind of arguments are allowed here a few typical and useful invocations make run gvsoc gvsim args trace insn run program and instruction trace to stdout make run gvsoc gvsim args trace log txt trace everything to log txt make run gvsoc gvsim args trace insn insn txt trace l2 l2 txt trace instructions to insn txt and l2 memory access to l2 txt environment variables and configuration options see env default config sh config freertos kernel y n default y use the freertos kernel config freertos kernel string default pulpissimo name of the chip specific include directory config use newlib y n default y use newlib libc config stdio fake uart null default fake send printf read write through testbench printf fake udma uart uart or ignore null config driver plic y n default n use the plic driver config driver fll y n default y use the fll driver config driver clkdiv y n default n use the clock divider driver control pulp config driver clkconst y n default n use the constant clock driver config driver int pclint clic default clint select the interrupt module config cc lto y n default n use link time optimizations config cc sanitize y n default n use address sanitizers config cc stackdbg y n default n generate stack debugging information config cc maxstacksize int default 1024 limit maximum stack size custom build directory and out of tree builds out of tree builds and by extension custom build directories are supported using gnu make s vpath feature though a bit unwieldy you need to cd into the desired build directory and invoke make from there pointing to the program s makefile with by providing an appropriate argument to the f switch example of using a custom build directory bash cd nortos hello world mkdir build cd build make f makefile all developer notes assert from include assert h calls assert func fiprintf then abort this is all in newlib licenses description of all the licenses used in the repository in general we use apache 2 0 except for some cases where it makes sense to use the underlying code s original license for example the kernel and the demo application stays mit licensed we give a directory filelist as a overview where on the left side a directory file is given and on the right side the license it is distributed under if we refer to a directory then the given license applies to all files within except for subtrees that have a seperate license also given in this list bash dir name spdx license qualifier apps empty bench empty demos mit kernel mit template mit env apache 2 0 scripts apache 2 0 support apache 2 0 tests apache 2 0 except for freertosconfig h which is mit nortos apache 2 0 except for freertosconfig h which is mit common apache 2 0 common libc apache 2 0 common metal apache 2 0 common pmsis apache 2 0 common target apache 2 0	risc-v freertos rtos	os
design-system	p align center a href img alt src https innovaccer com static image site logo innovaccer logo black svg width 20 a p h1 align center masala design system h1 masala design system mds is an open source design system built at innovaccer this is a simple and customizable component library to build faster beautiful and more accessible react applications on the guidelines and principles of masala design system br div align center codecov https codecov io gh innovaccer design system branch master graph badge svg token 2ly7jlzgx0 https codecov io gh innovaccer design system github https img shields io github license innovaccer design system github top language https img shields io github languages top innovaccer design system snyk vulnerabilities for github repo https img shields io snyk vulnerabilities github innovaccer design system div br get up and running to install innovaccer design system in your project you will need to run the following command using npm https www npmjs com bash npm install innovaccer design system if you prefer yarn https yarnpkg com en use the following command instead bash yarn add innovaccer design system adding style import style at your app s root it is not included in library bundle and shipped as a single css file for more details see our styling styling section js import innovaccer design system css if you want to try out innovaccer design system you can also use codesandbox https codesandbox io s focused germain shbcw edit innovaccer design system https codesandbox io static img play codesandbox svg https codesandbox io s focused germain shbcw usage js import button from innovaccer design system const app return button done button for more information about each component check out our storybook https innovaccer github io design system check out our tutorial docs apptutorial md to guide you in creating an awesome app cdn if you prefer to include library globally by marking it as external in your application library https unpkg com browse innovaccer design system provides various single file distributions which can be used as following html style link href https unpkg com innovaccer design system 2 5 0 3 css dist index css rel stylesheet un compressed umd script src https unpkg com browse innovaccer design system 2 5 0 3 dist index umd js script brotli compressed umd script src https unpkg com innovaccer design system 2 5 0 3 dist index umd js br script gzip compressed umd script src https unpkg com innovaccer design system 2 5 0 3 dist index umd js gz script styling as this component library is part of a framework agnostic design system used at innovaccer the styling is done with css using css variables for theming and bem methodology for reusable and modular styling so it requires you to include css in your project by either importing or serving it as a static file the complete stylesheet is published as part of the component library at path innovaccer design system css you can include css by importing it or loading it from cdn using font the css sets the font family as nunito sans for the body to add this font in your project you need to load this font the recommended way to do it is by adding the following google font cdn link to your app s head html link href https fonts googleapis com css family nunito sans 300 300i 400 400i 600 600i 700 700i 800 800i 900 900i display swap rel stylesheet updating font if you don t add the font described above font family will not be affected by css however if you want to update the font family update it via the following css variable css font family reset styles as bem is used reset css is not used and no style reset is done polyfill for ie for css variables to work on ie we use a polyfill at runtime to achieve dynamic theming through variables please add the following polyfill in your page html script src https cdn jsdelivr net npm css vars ponyfill 2 script script cssvars onlylegacy true script card file box repos here are the supporting repositories mds rich text editor https github com innovaccer mds rich text editor feature rich wysiwyg what you see is what you get html editor and wysiwyg markdown editor it is used to create blogs notes sections comment sections etc it has a variety of tools to edit and format rich content mds docs https github com innovaccer mds docs documentation site for masala design system mds helpers https github com innovaccer mds helpers alert service books documentation masala design system http design innovaccer com components storybook https innovaccer github io design system code of conduct we expect everyone participating in the community to abide by our code of conduct https github com innovaccer design system blob master code of conduct md please read it please follow it we work hard to build each other up and create amazing things together how to contribute whether you re helping us fix bugs improve the docs or spread the word we d love to have you as part of the community muscle purple heart check out our contributing guide https github com innovaccer design system blob master contributing md for ideas on contributing and setup steps for getting our repositories up and running on your local machine contributors thanks goes to these wonderful people emoji key https allcontributors org docs en emoji key all contributors list start do not remove or modify this section prettier ignore start markdownlint disable table tr td align center a href https github com aditya kumawat img src https avatars githubusercontent com u 12715487 v 4 s 100 width 100px alt br sub b aditya kumawat b sub a br a href https github com innovaccer design system commits author aditya kumawat title code a td td align center a href https riyalohia github io portfolio img src https avatars githubusercontent com u 31706090 v 4 s 100 width 100px alt br sub b riya lohia b sub a br a href https github com innovaccer design system commits author riyalohia title code a td td align center a href http satyamyadav info img src https avatars githubusercontent com u 3583587 v 4 s 100 width 100px alt br sub b satyam yadav b sub a br a href https github com innovaccer design system commits author satyamyadav title code a a href https github com innovaccer design system commits author satyamyadav title documentation a a href https github com innovaccer design system pulls q is 3apr reviewed by 3asatyamyadav title reviewed pull requests a td td align center a href https github com sandeshchoudhary img src https avatars githubusercontent com u 11272274 v 4 s 100 width 100px alt br sub b sandeshchoudhary b sub a br a href https github com innovaccer design system commits author sandeshchoudhary title code a td td align center a href https github com adityajhajharia img src https avatars githubusercontent com u 42600089 v 4 s 100 width 100px alt br sub b adityajhajharia b sub a br a href https github com innovaccer design system commits author adityajhajharia title code a td td 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design system commits author shib00 title code a td td align center a href http www rahulgaur info img src https avatars githubusercontent com u 760474 v 4 s 100 width 100px alt br sub b rahul gaur b sub a br a href https github com innovaccer design system commits author aregee title code a td tr tr td align center a href https github com atifzaidi92 img src https avatars githubusercontent com u 54103064 v 4 s 100 width 100px alt br sub b atifzaidi92 b sub a br a href https github com innovaccer design system commits author atifzaidi92 title code a td td align center a href https github com sumit2399 img src https avatars githubusercontent com u 66456021 v 4 s 100 width 100px alt br sub b sumit dhyani b sub a br a href https github com innovaccer design system commits author sumit2399 title code a td td align center a href https tanmay portfolio herokuapp com img src https avatars githubusercontent com u 36269283 v 4 s 100 width 100px alt br sub b tanmay sharma b sub a br a href https github com innovaccer design system commits author 927tanmay title code a td td align center a href https github com rashi gupta 2000 img src https avatars githubusercontent com u 99866103 v 4 s 100 width 100px alt br sub b rashi gupta b sub a br a href https github com innovaccer design system commits author rashi gupta 2000 title code a td td align center a href https github com varnikajain15 img src https avatars githubusercontent com u 55780559 v 4 s 100 width 100px alt br sub b varnika jain b sub a br a href https github com innovaccer design system commits author varnikajain15 title code a td td align center a href https github com aman2000verma img src https avatars githubusercontent com u 45339091 v 4 s 100 width 100px alt br sub b aman verma b sub a br a href https github com innovaccer design system commits author aman2000verma title code a td td align center a href https github com samyak3009 img src https avatars githubusercontent com u 56395892 v 4 s 100 width 100px alt br sub b samyak jain b sub a br a href https github com innovaccer design system commits author samyak3009 title code a td tr table markdownlint restore prettier ignore end all contributors list end all contributors list start do not remove or modify this section prettier ignore start markdownlint disable markdownlint restore prettier ignore end all contributors list end this project follows the all contributors https github com all contributors all contributors specification contributions of any kind welcome memo license licensed under the mit license https github com innovaccer design system blob master license	hacktoberfest hacktoberfest2020 innovaccer css bem css-variable react javascript typescript design-system component-library hacktoberfest2021 ui ui-components components hacktoberfest2022	os
warm-and-safe	warm safe warmbanks net https warmbanks net react https badgen net badge react 18 2 0 purple icon git leaflet https badgen net badge react leaflet 4 1 0 red icon git express https badgen net badge express 4 18 1 blue icon git mongoose https badgen net badge mongoose 6 6 5 green icon git multer https badgen net badge json web token 8 5 1 orange icon git axios https badgen net badge axios 1 0 0 yellow icon git warm safe locates where someone can go to stay warm each pin gives information on address facilities available and opening hours developed as part of an agile software consultancy team tech used included html css javascript openstreetmap api leaflet js express node mongoose mongo db json web tokens jwt full crud capabilities using slack and ms teams for communication accessibility for screen readers installation bash npm install create env file from the template env example run cli frontend bash npm start support me a href https www buymeacoffee com decafdev img src https cdn buymeacoffee com buttons v2 default yellow png width 200 a	expressjs leafletjs mongoose nodejs reactjs	server
catalyst	catalyst npm scoped https img shields io npm v reactioncommerce catalyst svg https www npmjs com package reactioncommerce catalyst circleci https circleci com gh reactioncommerce catalyst svg style svg https circleci com gh reactioncommerce catalyst warning this repository is in active development and is not ready for testing or usage this is a single project with a package of commerce focused react ui components and the code for the catalyst website reactioncommerce catalyst https www npmjs com package reactioncommerce catalyst see the package json https github com reactioncommerce catalyst blob trunk package package json in package https github com reactioncommerce catalyst tree trunk package folder catalyst components https catalyst reactioncommerce com see the root package json https github com reactioncommerce catalyst blob trunk package json we use the react styleguidist https react styleguidist js org package to run and build the catalyst website and running the style guide locally doubles as an interactive playground for developing and testing the components use the react components in your reaction admin plugin refer to the reaction catalyst components doc https catalyst reactioncommerce com introduction using 20components contribute to this project refer to the contributor docs docs license copyright 2019 reaction commerce licensed under the apache license version 2 0 the license you may not use this file except in compliance with the license you may obtain a copy of the license at http www apache org licenses license 2 0 unless required by applicable law or agreed to in writing software distributed under the license is distributed on an as is basis without warranties or conditions of any kind either express or implied see the license for the specific language governing permissions and limitations under the license		os
QNLP	qnlp quantum natural language processing img src https travis ci com ichec qnlp svg branch master in this software suite we have developed a workflow to implement a hybridised classical quantum model for representing language and representing sentence meanings in quantum states 1 the model this work is based upon and inspired by the disco also known as discocat formalism of coecke et al 2 3 4 wherein both compositional and distributional sentence structure and information is used to represent meaning following the work on zeng and coecke 4 we aimed to implement a modified approach to implement this model for quantum processors further details will follow shortly in the upcoming paper requirements to build and run this software suite it is recommended to have the intel compilers and intel mpi libraries we make use of the intel quantum simulator intel qs qhipster 5 6 as the underlying simulator for our methods if not available the build can use gcc clang provided the toolchain supports c 14 at a minimum c 17 prefered in addition an mpi library is required we have successfully used both mpich and openmpi with non intel compiler builds this suite is primarily developed for linux hpc systems though it may also run on laptops desktops macos can be used but some additional work is necessary documentation and examples all documentation for this project is available at https ichec github io qnlp installation https ichec github io qnlp install and build build md api https ichec github io qnlp docs examples https ichec github io qnlp examples example runnable scripts are available in the modules py scripts directory c demos and tests are available under demos and modules tests respectively jupyter notebooks are available at modules py nb referencing if using this work in any way as part of your research please feel free to cite us as a hybrid classical quantum workflow for natural language processing by o riordan et al mach learn sci technol 2020 doi https doi org 10 1088 2632 2153 abbd2e 1 lee j o riordan myles doyle fabio baruffa venkatesh kannan a hybrid classical quantum workflow for natural language processing iop machine learning science and technology 2020 doi https doi org 10 1088 2632 2153 abbd2e 2 stephen clark bob coecke and mehrnoosh sadrzadeh a compositional distributional model of meaning proceedings of the second quantum interaction symposium 2008 3 bob coecke mehrnoosh sadrzadeh and stephen clark mathematical foundations of a compositional distributional model of meaning special issue of linguistic analysis 2010 arxiv 1003 4394 https arxiv org abs 1003 4394 4 william zeng and bob coecke quantum algorithms for compositional natural language processing proceedings of slpcs 2016 arxiv 1608 01406 https arxiv org pdf 1608 01406 pdf 5 mikhail smelyanskiy nicolas p d sawaya al n aspuru guzik qhipster the quantum high performance software testing environment arxiv 1601 07195 https arxiv org abs 1601 07195 6 gian giacomo guerreschi justin hogaboam fabio baruffa nicolas p d sawaya intel quantum simulator a cloud ready high performance simulator of quantum circuits arxiv 2001 10554 https arxiv org abs 2001 10554	quantum quantum-computing quantum-machine-learning nlp hpc	ai
brainsatplay	this framework is way outdated fyi pending a new release as we figure out what s left to do in our new apis and reboot the visual editing with live data system we started on 2 years ago the brains play framework functional building blocks for the web discord https img shields io badge chat discord 7289da svg sanitize true https discord gg cdxsksh9zb the brains play framework allows anyone to compose interactive high performance web applications and contribute to an ecosystem of copyleft software infrastructure for open source application development on the web extensive documentation for the brains play framework can be found at https docs brainsatplay com the benefits low code our browser based studio makes it easy to wire together your application logic using the visualscript library familiar we don t lock users into unnecessary abstractions just format code files as es modules performant high performance event based logic using the graphscript library social derivative components can be published as npm packages and registered on the components library to be shared with the world radically open this library is licensed under the agpl license all derivatives are also free and open source software if we don t have something you d like to see within this framework feel free to propose your idea in the issues https github com brainsatplay brainsatplay issues tab getting started check out the brainsatplay starter kit https github com brainsatplay brainsatplay starter kit to start developing your application with the brains play framework framework libraries below are the core repositories of the brains play framework check out the components repository to see everything created by our community library status description wasl wasl status wasl the web application specification language used by the brainsatplay library brainsatplay brainsatplay status brainsatplay enables editing of wasl applications at runtime graphscript graphscript status graphscript easy graph based workflow state machine programming microservice architectures and interoperable front and backend web frameworks visualscript visualscript status visualscript a low code programming system for wasl applications studio studio status studio a low code editor for wasl applications datastreams api datastreams api status datastreams api uniformly acquire real time data with available browser apis tinybuild tinybuild status tinybuild custom build tool for web applications repo contents src core an application synchronization library for graphscript and visualscript drafts chrome a chrome extension for developing brainsatplay applications cli program a new project through the terminal pwa an example progressive web app pwa using the brainsatplay framework support if you have questions about developing with brainsatplay feel free to start a conversation on discord https discord gg tq8p79tw8j or reach out directly to our team at contact brainsatplay com mailto contact brainsatplay com contributing guidelines if you ve created a plugin for brainsatplay make sure to link to the source package json file with a pull request to components we welcome anyone who would like to jump into the source code of our many supporting libraries framework libraries at this time however documentation changes may be a more appropriate entrypoint for contribution to the brains play framework make sure to check out our docs repository and contribute there roadmap inclusive extend visualscript to become a fully accessible visual programming system use the accessify library to guarantee accessibility support for resulting applications through multimodal i o support backend support edit workspaces running in node js local or the cloud acknowledgments this project is maintained by garrett flynn https github com garrettmflynn and joshua brewster https github com joshbrew who use contract work and community contributions through open collective https opencollective com brainsatplay to support themselves backers support us with a monthly donation https opencollective com brainsatplay backer and help us continue our activities a href https opencollective com brainsatplay backer 0 website target blank img src https opencollective com brainsatplay backer 0 avatar svg a a href https opencollective com brainsatplay backer 1 website target blank img src https opencollective com brainsatplay backer 1 avatar svg a a href https opencollective com brainsatplay backer 2 website target blank img src https opencollective com brainsatplay backer 2 avatar svg a a href https opencollective com brainsatplay backer 3 website target blank img src https opencollective com brainsatplay backer 3 avatar svg a a href https opencollective com brainsatplay backer 4 website target blank img src https opencollective com brainsatplay backer 4 avatar svg a a href https opencollective com brainsatplay backer 5 website target blank img src https opencollective com brainsatplay 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brainsatplay sponsor 29 avatar svg a appendix branches the main branch of this repository contains the latest releases of core libraries in the brains play framework the nightly branch hosts regular changes between official releases the legacy branch contains a record of the original brainsatplay library v0 0 36 which is used in the brains play platform https github com brainsatplay platform additional repositories hardware project status description hegduino public a low cost hemoencephalography heg device nrf52 in development working prototypes for using nrf52 microcontrollers arm ble5 with low cost sensors brainsatplay src core readme md brainsatplay status https img shields io npm v brainsatplay specification language wasl https github com brainsatplay wasl wasl status https img shields io npm v wasl core library graphscript https github com brainsatplay graphscript graphscript status https img shields io npm v graphscript integrated editor studio https github com brainsatplay studio studio status https img shields io npm v brainsatplay studio low code programming system visualscript https github com brainsatplay visualscript visualscript status https img shields io npm v visualscript data acquisition datastreams api https github com brainsatplay datastreams api datastreams api status https img shields io npm v datastreams api svg build tool tinybuild https github com brainsatplay tinybuild tinybuild status https img shields io npm v tinybuild additional repos components https github com brainsatplay components accessify https github com brainsatplay accessify docs https github com brainsatplay docs hardware hegduino https github com moothyknight heg esp32 delobotomizer nrf52 https github com brainsatplay nrf52 biosensing boards	javascript ecmascript cli	front_end
astro-ui	astro dao release version https img shields io github v release near daos astro ui https github com near daos astro ui releases build https github com near daos astro ui actions workflows build deploy yaml badge svg https github com near daos astro ui actions workflows build deploy yaml telegram group https img shields io badge telegram 20group blue https t me astro near astro is a platform for launching decentralized autonomous organizations daos are self organized groups that form around common purposes membership decision making and funding are coordinated in public on a tamper proof blockchain technology stack blockchain near https near org smart contracts sputnik dao factory v2 https github com near daos sputnik dao contract tree main sputnikdao factory2 sputnik dao v2 https github com near daos sputnik dao contract tree main sputnikdao2 astro backend astro api gateway https github com near daos astro api gateway astro landing page astro landing page https github com near daos astro ui landing package manager yarn https yarnpkg com core programming language typescript https www typescriptlang org application framework nextjs https nextjs org code quality eslint https eslint org prettier https prettier io build docker https www docker com status change log https github com near daos astro ui releases latest links dev dev app astrodao com https dev app astrodao com all daos test testnet app astrodao com https testnet app astrodao com all daos staging staging app astrodao com https staging app astrodao com all daos production app astrodao com https app astrodao com all daos img png img png getting started clone the repo git clone git github com near daos astro ui git install dependencies bash yarn install run development server bash yarn dev open http localhost 8080 http localhost 8080 with your browser to see the result known issues the application can fail loading icons when running locally in this case just build an application locally before starting development server using this command yarn build getting involved all change requests commits should follow the conventional commits https www conventionalcommits org en v1 0 0 specification proposed changes should be small and isolated release planning schedule we are trying to stick to two weeks schedule the list of issues that are planning for next release can be checked in milestones https github com near daos astro ui milestones section		front_end
botasaurus	p align center img src https raw githubusercontent com omkarcloud botasaurus master images mascot png alt botasaurus p div align center style margin top 0 h1 botasaurus framework h1 p swiss army knife for bot development p div em h5 align center programming language python 3 h5 em p align center a href img alt botasaurus forks src https img shields io github forks omkarcloud botasaurus style for the badge a a href img alt repo stars src https img shields io github stars omkarcloud botasaurus style for the badge color yellow a a href img alt botasaurus license src https img shields io github license omkarcloud botasaurus color orange style for the badge a a href https github com omkarcloud botasaurus issues img alt issues src https img shields io github issues omkarcloud botasaurus color purple style for the badge a p p align center img src https views whatilearened today views github omkarcloud botasaurus svg width 80px height 28px alt view p botasaurus your ultimate selenium based bot development framework hi i m botasaurus a selenium based bot development framework designed to help you become a 10x bot developer what is botasaurus and how can it help me become a 10x bot developer think of botasaurus as a supercharged version of selenium if you know selenium you re off to a great start because botasaurus is a framework for selenium with goodies like 1 account generation my account generation module enables you to create humane user accounts effortlessly this is particularly handy when you need multiple accounts while evading detection https raw githubusercontent com omkarcloud botasaurus master images generated account png 2 temporary email need to verify those accounts i ve got you covered with an inbuilt temporary email module https raw githubusercontent com omkarcloud botasaurus master images signup email png 3 simple configuration configuring profiles user agents and proxies can be a headache in selenium but with botasaurus it s as easy as pie https raw githubusercontent com omkarcloud botasaurus master images profile config png 4 parallel bots run multiple bots in parallel and turn hours of automation into mere minutes with botasaurus https raw githubusercontent com omkarcloud botasaurus master images parallel bots png 5 easy debugging debugging made painless when a crash occurs due to an incorrect selector etc botasaurus pauses the browser instead of closing it facilitating on the spot debugging https raw githubusercontent com omkarcloud botasaurus master images error prompt png capture important details like screenshots page html error logs and execution time for easy debugging and generating time estimates of how long to keep the bot running https raw githubusercontent com omkarcloud botasaurus master images debugging png 6 run bots at google s scale with kubernetes seamlessly integrate botasaurus with kubernetes to run your bots at google s scale easily control the number of bots and when to launch them via a simple ui interface https raw githubusercontent com omkarcloud botasaurus master images kubernetes integration webp 7 turbocharge your web scraping say goodbye to redundant requests with our powerful caching feature botasaurus allows you to cache web scraping results ensuring lightning fast performance on subsequent scrapes https raw githubusercontent com omkarcloud botasaurus master images cache jpg 8 supercharged selenium driver elevate your selenium game with botasaurus we ve added anti bot detection features and added shortcut methods for common operations all designed to save your valuable time https raw githubusercontent com omkarcloud botasaurus master images type click text png 9 persist 1000 profiles in 1 mb forget about heavy 100 mbs chrome profiles use the tiny profile feature to save cookies in just a few kbs https raw githubusercontent com omkarcloud botasaurus master images chrome profile vs tiny profile png 10 evade ip suspensions like ninja hatori using a single ip to create accounts and getting suspended https raw githubusercontent com omkarcloud botasaurus master images linkedin restricted png no problem ditch expensive 15 gb residential proxies and use the freeproxy module to connect to the tor network and access a vast pool of unique tor ips all for free https raw githubusercontent com omkarcloud botasaurus master images tor project logo onions png so if you re comfortable with selenium and want to level up your bot development game with easy debugging simple configuration parallel execution and temporary emails then you re in for a treat botasaurus is like selenium on steroids and you ll love building bots with it so if you re comfortable with selenium and want to level up your bot development game with easy debugging simple configuration parallel execution and temporary emails then you re in for a treat botasaurus is like selenium on steroids and you ll love building bots with it getting started with botasaurus let s quickly grasp how botasaurus works with a simple example in this simple example we ll walk through the process of scraping the heading text from https www omkar cloud https www omkar cloud https raw githubusercontent com omkarcloud botasaurus master images starter bot running gif step 1 install botasaurus start by installing botasaurus with the help of this command shell python m pip install botasaurus step 2 set up your botasaurus project 1 create a directory for our botasaurus project and navigate to it shell mkdir my botasaurus project cd my botasaurus project code optionally open the project in vscode step 3 write the scraping code within your project directory create a python script named main py and paste the following code into main py python from botasaurus launch tasks import launch tasks from botasaurus import define a custom scraping task class scrapeheadingtask basetask def run self driver botasaurusdriver data visit the omkar cloud website driver get https www omkar cloud get the heading element text heading driver text h1 return the data to be saved as a json file in output all json return heading heading if name main launch the web scraping task launch tasks scrapeheadingtask let s break down this code we define a custom scraping task class named scrapeheadingtask python class scrapeheadingtask basetask inside the run method we are automatically passed a selenium driver by botasaurus python def run self driver botasaurusdriver data in the run method we visit omkar cloud extract the heading text finally return data to be saved as json and csv files python driver get https www omkar cloud get the heading element text heading driver text h1 return the data to be saved as a json file in output all json return heading heading lastly we launch the scraping task python if name main launch the web scraping task launch tasks scrapeheadingtask step 4 run the scraping task now let s run the bot shell python main py after running the script will launch google chrome visit omkar cloud https www omkar cloud extract the heading text automatically save it as output finished json https raw githubusercontent com omkarcloud botasaurus master images starter bot running gif deep dive into botasaurus power of bots is immense a bot can apply on your behalf to linkedin jobs 24 hours scrape phone number of thousands of buisnesses from google maps to sell your products to mass message people on twitter linkedin reddit for selling your product sign up 100 s of accounts on mailchimp to send 50 000 500 emails 100 emails all for free so ready to unlock the power of bots read the tutorial here https www omkar cloud botasaurus docs sign up tutorial and embark on your journey of bot mastery love it star it	crawler crawling scraping web-scraping beautifulsoup crawling-framework crawling-python crawling-tool headless node-crawler python-crawler scraper scraping-framework scraping-python scraping-tool selenium web-crawler web-crawling web-scraper webscraping	front_end
cppserdes	cppserdes icon images cppserdes icon png cppserdes is a serialization deserialization library designed with embedded systems in mind build status https travis ci com darrenlevine cppserdes svg branch main https travis ci com darrenlevine cppserdes license https img shields io badge license mit informational version https img shields io badge version 1 0 blue features bitpacking support any bit alignment any bit padding portability uses bit shifting and masking with no dynamic memory allocation avoids most std containers serializes to from 8 bit arrays and 16 bit 32 bit and 64 bit arrays with no api changes to your code to work with hardware drivers directly in their native bit widths common embedded systems types floating point unsigned signed classes enums arrays custom types etc while avoiding pulling in atypically embedded std types such as std string std vector etc arrays fixed sized arrays dynamically sized arrays and delimited arrays memory safety bound checking for both serial buffers and array fields choose from both high abstraction level object oriented streams and low level memcpy like apis custom formatter types such as optional validation checks attached right to a field virtual fields and pure virtual fields allowing you to easily change formats at runtime header only and works with c 11 and greater constexpr support for c 14 or greater using bitcpy function depends on compiler support compiles with high warning levels pedantic wall etc documentation darrenlevine github io cppserdes https darrenlevine github io cppserdes quick start 1 check out the examples compile and run examples 01 simple example cpp read through the examples folder for various usage cases 2 use the library in your project manual inclusion make sure cppserdes include is added to your project s path and use as desired with cmake add the following lines to your cmakelists txt project file cmake add subdirectory cppserdes target link libraries your project name cppserdes object oriented example using serdes packet base cpp include serdes h struct my packet serdes packet base int8 t x 6 y 7 z 8 define a format used for both serialization and deserialization void format serdes packet serdes process serdes process x y serdes pad 1 serdes bitpack z 7 int main uint16 t serial data 0x0102 0x7b00 my packet obj1 obj1 load serial data loads serial data into obj1 x 1 y 2 z 5 my packet obj2 obj2 store serial data stores 0x0607 0x0800 into serial data from obj2 functional example using serdes bitcpy cpp include bitcpy h int main uint16 t serial data 0x0102 0xfb00 int8 t x y z deserialization serdes bitcpy x serial data 0 8 serdes bitcpy y serial data 8 8 serdes bitcpy z serial data 16 8 serialization serdes bitcpy serial data x 0 8 serdes bitcpy serial data y 8 8 serdes bitcpy serial data z 16 8 streams example using serdes packet cpp include serdes h int main uint16 t serial data 0x0102 0x0304 0x0506 int8 t x y z take some serial data and place it into variables serdes packet serial data x y z take same variables and place it into serial data serdes packet serial data x y z known limitations little endian serialization is not yet supported note little and big endian platforms are both supported constexpr bitcpy of enums and floating point types is not supported since the constexpr intepretation of their memory is undefined behaviour i e non constexpr memcpy must be used behind the scenes cannot bitcpy more than 0 25gb of data in a single function call due to a memory usage speed design decision the examples are written using c 17 syntax for succinctness and simplicity so if you re using an older compiler templated fields might need to have their templates explicitly specified for example cpp serdes bitpack 123 serdes bit length 2 works in c 17 serdes bitpack int int 123 serdes bit length 2 needed in c 17 planned features little endian serialization support compile time only is planned the ability to avoid serialization deserialization by setting pointers to point to the appropriate spot within the serial buffer instead of making a copy into a new buffer thus saving time and memory if the serial buffer s lifetime persists longer than the reference and the memory is aligned appropriately to allow this optimization similar to how capnproto and flatbuffers work requirements c 11 or greater		os
MAD_App	mad app sliit 2nd year 2nd semester mobile app development project 2022		front_end
FTCVision	p align center img src https raw githubusercontent com lasarobotics ftcvision img logo png raw true alt ftc sees p ftc vision library build status https travis ci org lasarobotics ftcvision svg branch master https travis ci org lasarobotics ftcvision documentation status https img shields io badge documentation 1 0 0 20 up 20to 20date blue svg http ftcvision lasarobotics org computer vision library for ftc based on opencv featuring beacon color and position detection as well as an easy to use visionopmode format and many additional detection features planned in the future installing into first official app recommended clone ftcvision into a clean directory outside your robot controller app using the following command git clone depth 1 https github com lasarobotics ftcvision navigate to the ftcvision directory that you just cloned and copy the ftc visionlib and opencv java folders into your existing robot controller app open your robot controller app in android studio make sure you have the project mode selected in the project browser window so you can see all of the files in your project find your settings gradle file and append the following two lines include opencv java include ftc visionlib find the androidmanifest xml file in your app s ftcrobotcontroller src main folder insert the following uses permission tag in androidmanifest xml just below the mainfest tag and just before the application tag uses permission android name android permission camera android required true find the ftcrobotcontroller build release gradle and teamcode build release gradle files and insert the following lines under dependencies into both files compile project ftc visionlib compile project opencv java update gradle configuration by pressing the green sync project with gradle files button in the header this may take a minute copy in vision opmodes those that end in visionsample java located in vision root ftc robotcontroller src main java com qualcomm ftcrobotcontroller opmodes from the ftcvision directory into a folder within the teamcode directory where your place any other opmode before running the app for the first time install the opencv manager from the google play store to enable vision processing run and test the code let us know if you encounter any difficulties note depending on your version of the first app you may need to modify the opmodes slightly such as adding autonomous in order to get them to work for your version of the sdk you can now write your custom visionopmode optional add vision testing app see pictures of it below by copying all files from ftc cameratest into the root of your project then add include ftc cameratest to your settings gradle in the root of your project to run the camera test app click the green sync project with gradle files button to update your project then select ftc cameratest from the dropdown next to the run button installing via git submodule advanced when installing via git submodule every person cloning your repo will need to run git submodule init and git subomodule update for every new clone however you also get the advantage of not copying all the files yourself and you can update the project to the latest version easily by navigating inside the ftc vision folder then running git pull inside the root of your project directory run git submodule init git submodule add https github com lasarobotics ftcvision ftc vision follow the guide installing into existing project starting from the third bullet point please note that since everything will be in the ftc vision folder and thus directories will need to be modified once you get to the step that modifies settings gradle add the following lines project opencv java projectdir new file ftc vision opencv java project ftc visionlib projectdir new file ftc vision ftc visionlib project ftc cameratest projectdir new file ftc vision ftc cameratest only if you want to enable the camera testing app you can now write your custom visionopmode installing from scratch for testing only deprecated 1 clone ftcvision into a clean directory outside your robot controller app using the following command git clone depth 1 https github com lasarobotics ftcvision 2 open the ftcvision project using android studio 3 copy your opmodes from your robot controller directory into the appropriate directory within ftc robotcontroller then modify the ftcopmoderegister appropriately to add your custom opmodes 4 before running the app for the first time install the opencv manager from the google play store to enable vision processing 5 run and test the code let us know if you encounter any difficulties 6 you can now write your own visionopmode status this library is complete as of world championship 2016 if you have any questions or would like to help send a note to smo key contact info on profile or open an issue thank you documentation documentation status https img shields io badge documentation 1 0 0 20 up 20to 20date blue svg http ftcvision lasarobotics org documentation for the stable library is available at http ftcvision lasarobotics org does it work yes ftcvision can detect a beacon 0 5 4 feet away with 90 accuracy in 0 2 seconds here are some pictures smiley accuracy test can it detect the beacon https raw githubusercontent com lasarobotics ftcvision img test4 png old accuracy test can it detect the beacon https raw githubusercontent com lasarobotics ftcvision img test2 png distance test a test from 8 feet away https raw githubusercontent com lasarobotics ftcvision img test1 png basic analysis demo fast isn t the greatest https raw githubusercontent com lasarobotics ftcvision img analysisdemo gif ambient analysis color and rotation detection test a test from 8 feet away https raw githubusercontent com lasarobotics ftcvision img test3 gif analysis methods fast vs complex https raw githubusercontent com lasarobotics ftcvision img methods png the fast method analyzes frames at around 5 fps it looks for the primary colors in the image and correlates these to locate a beacon the complex method analyzes frames at around 2 4 fps it uses statistical analysis to determine the beacon s location additionally a realtime method exists that retrieves frames and analyzes them as fast as possible up to 15 fps goals to make it easy for teams to use the power of opencv on the android platform locate the lit target the thing with two buttons within the camera viewfield move the robot to the lit target while identifying the color status of the target locate the button of the target color and activate it progress beacon located successfully with automated environmental and orientation tuning a competition proof opmode scheme created so that the robot controller does not need to be modified to use the app now supports nearly every phone since android 4 2 including both the zte speed and moto g	opencv ftcvision robot-controller computer-vision first-ftc robotics-competition detection machine-learning	ai
FriendStagram-iOS	div align center h1 friendstagram ios client h1 p this is a instagram replica build using native ios languages it is powered by a custom backend that can be found here https github com brandondanis friendstagram backend p h3 currently re building the app from ground up h3 h3 login register views h3 br img src http i imgur com x4damvt png width 250 img src http i imgur com sljnsbt png width 250 h3 feed profile view h3 img src http i imgur com dsvphy1 png width 250 img src http i imgur com xl5samu png width 250 h3 image view h3 img src http i imgur com 1ef2yyq png width 250 div	swift ios instagram friendstagram-ios friendstagram objective-c	front_end
Unity-2020-Mobile-Game-Development-Second-Edition	unity 2020 mobile game development second edition a href https www packtpub com product unity 2020 mobile game development second edition 9781838987336 utm source github utm medium repository utm campaign 9781838987336 img src https static packt cdn com products 9781838987336 cover smaller alt unity 2020 mobile game development second edition height 256px align right a this is the code repository for unity 2020 mobile game development second edition https www packtpub com product unity 2020 mobile game development second edition 9781838987336 utm source github utm medium repository utm campaign 9781838987336 published by packt discover practical techniques and examples to create and deliver engaging games for android and ios what is this book about unity 2020 brings a lot of new features that can be harnessed for building powerful games for popular mobile platforms this updated second edition delves into unity development covering the new features of unity modern development practices and augmented reality ar for creating an immersive mobile experience the book takes a step by step approach to building an endless runner game using unity to help you learn mobile game development concepts this book covers the following exciting features design responsive user interfaces for your mobile games detect collisions receive user input and create player movements for your mobile games create interesting gameplay elements using inputs from your mobile device explore the mobile notification package in unity game engine to keep players engaged create interactive and visually appealing content for android and ios devices monetize your game projects using unity ads and in app purchases if you feel this book is for you get your copy https www amazon com dp 1838987339 today a href https www packtpub com utm source github utm medium banner utm campaign githubbanner img src https raw githubusercontent com packtpublishing github master github png alt https www packtpub com border 5 a instructions and navigations all of the code is organized into folders for example chapter02 the code will look like the following if test expression statement upon condition is true following is what you need for this book if you are a game developer or mobile developer who wants to learn unity and use it to build mobile games for ios and android then this unity book is for you prior knowledge of c and unity will be beneficial but is not mandatory with the following software and hardware list you can run all code files present in the book chapter 1 12 software and hardware list chapter software required os required 1 to 12 unity 2020 1 0f1 windows and mac os x we also provide a pdf file that has color images of the screenshots diagrams used in this book click here to download it https static packt cdn com downloads 9781838987336 colorimages pdf errata page 116 next let s update the update function with the following highlighted code should be next let s update the fixedupdate function with the following highlighted code related products other books you may enjoy unity 2020 virtual reality projects third edition packt https www packtpub com product unity 2020 virtual reality projects third edition 9781839217333 utm source github utm medium repository utm campaign 9781839217333 amazon https www amazon com dp 1839217332 unity certified programmer exam guide packt https www packtpub com product unity certified programmer exam guide 9781838828424 utm source github utm medium repository utm campaign 9781838828424 amazon https www amazon com dp 1838828427 get to know the author john p doran is a passionate and seasoned technical game designer software engineer and author based in peoria illinois for over a decade john has gained extensive hands on expertise in game development working in a variety of roles ranging from game designer to lead ui programmer additionally john has worked in game development education teaching in singapore south korea and the united states to date he has authored over 10 books pertaining to game development john is currently an instructor in residence at bradley university prior to his present ventures he was an award winning videographer other books by the authors unity 2017 mobile game development https www packtpub com product unity 2017 mobile game development 9781787288713 utm source github utm medium repository utm campaign 9781787288713 unity game development blueprints https www packtpub com product unity game development blueprints 9781783553655 utm source github utm medium repository utm campaign 9781783553655 unity 5 x game development blueprints https www packtpub com product unity 5 x game development blueprints 9781785883118 utm source github utm medium repository utm campaign 9781785883118 suggestions and feedback click here https docs google com forms d e 1faipqlsdy7datc6qmel81fiuuymz0wy9vh1jhkvpy57oimekgqib ow viewform if you have any feedback or suggestions download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781838987336 https packt link free ebook 9781838987336 a p		front_end
DeepLearningProject	harvard logo http logonoid com images harvard logo png an end to end tutorial of a machine learning pipeline this tutorial tries to do what most most machine learning tutorials available online do not it is not a 30 minute tutorial which teaches you how to train your own neural network or learn deep learning in under 30 minutes it s a full pipeline which you would need to do if you actually work with machine learning introducing you to all the parts and all the implementation decisions and details that need to be made the dataset is not one of the standard sets like mnist or cifar you will make you very own dataset then you will go through a couple conventional machine learning algorithms before finally getting to deep learning in the fall of 2016 i was a teaching fellow harvard s version of ta for the graduate class on advanced topics in data science cs209 109 at harvard university i was in charge of designing the class project given to the students and this tutorial has been built on top of the project i designed for the class update 24th october 2018 the tutorial has now been re written in pytorch thanks to anshul basia https github com anshulbasia you can access the html here https spandan madan github io deeplearningproject pytorch version deep learning project pytorch html and the ipython notebook with the code in pytorch here https github com spandan madan deeplearningproject blob master pytorch version deep learning project pytorch ipynb citing if you use the work here if you would like to use this work please cite the work using the doi doi https zenodo org badge doi 10 5281 zenodo 830003 svg http dx doi org 10 5281 zenodo 830003 reading viewing the tutorial to view the project as an html file visit https spandan madan github io deeplearningproject the code if you would like to access to code please go through the ipython notebook deep learning project ipynb setup python we will be using python 2 7 primary reason is that tensorflow is not compatible with python 3 5 and some other libraries are not compatible with python 3 to make setup easy we are going to use conda please install conda 3 from https www continuum io downloads the repository has a conda config file which will make setting up super easy it s the file deeplearningproject environment yml then create a new conda environment using the command with conda env create f deeplearningproject environment yml now you can activate the environment with source activate deeplearningproject jupyter notebook if all the isntallations go through you are good to go if not here is a list of packages that need to be installed requests imdbpy wget tmdbsimple seaborn sklearn pillow keras tensorflow h5py gensim nltk stop words please install imdbpy using pip install imdbpy 6 6 since earlier versions are broken setting up conda environment in jupyter notebook to be able to run the environment you just created on a juputer notebook first check that you have the python package ipykernel installed if you don t simply install it using bash pip install ipykernel now add this to your jupyter notebook using the command bash python m ipykernel install user name deeplearningproject display name deeplearningproject needless to say remove all single quotes before running commands go to the directory and run jupyter notbeook by jupyter notebook and open the respective notebook on browser to install tmdb pip install tmdbsimple use import tmdbsimple as tmdb setting up a docker container with docker compose prerequisites docker https docs docker com install docker compose https docs docker com compose install run docker compose to work with an isolate environment and be able to run it on many systems without troubles you can run this docker compose command bash docker compose up it will build deeplearningproject image according to dockerfile and then run dokcer container via docker compose see docker and docker compose docs for more informations https docs docker com https docs docker com compose then access notebooks through your web browser at http localhost 8888 you should notice that notebooks have been copied from root to notebooks folder to mount them into container via bind volume any changes you make will be saved on host notebooks dir add packages you can add conda or pip packages to image and thus container by updating deeplearningproject environment yml file and then run bash docker compose build it will build a new deeplearningproject image with new conda pip packages installed stop your running container ctrl c and then docker compose up to rerun a fresh new container known common bugs i will keep updating this as issues pop up on this repository one known bug is because keras 2 0 is not compatible with some keras 1 2 functionalities you may run into errors with importing vgg16 if so just update keras using the following command bash sudo pip install git git github com fchollet keras git upgrade os error too many open files refer to https stackoverflow com questions 16526783 python subprocess too many open files or shut down notebook and execute following the the same terminal bash ulimit sn 10000 and restart the jupyter notebook hope this repo helps introduce you to a full machine learning pipeline if you spot an error please create an issue to help out others using this resource to prevent problems with installation and setting up this repository comes with a conda environment profile the only thing you will need is to install the newest version of conda and use this profile to create a new environment and it will come set up with all the libraries you will need for the tutorial	machine-learning deep-learning neural-networks tutorial	ai
MachineLearning_notes	machine and deep learning resources mit license https img shields io apm l atomic design ui svg https github com tterb atomic design ui blob master licenses pr s welcome https img shields io badge prs welcome brightgreen svg style flat http makeapullrequest com machine and deep learning and data analysis resources please contribute and get in touch contributing md see mdmisc notes https github com mdozmorov mdmisc notes for other programming and genomics related notes table of content start doctoc generated toc please keep comment here to allow auto update don t edit this section instead re run doctoc to update cheatsheets cheatsheets awesome deep learning awesome deep learning keras tensorflow keras tensorflow pytorch pytorch jax jax graph neural networks graph neural networks transformers transformers dl books dl books dl courses tutorials dl courses tutorials dl videos dl videos dl papers dl papers dl papers genomics dl papers genomics dl tools dl tools auto ml auto ml dl models dl models dl projects dl projects chatgpt llms chatgpt llms dl misc dl misc awesome machine learning awesome machine learning ml books ml books ml courses tutorials ml courses tutorials ml videos ml videos ml papers ml papers ml tools ml tools ml misc ml misc end doctoc generated toc please keep comment here to allow auto update cheatsheets artificial intelligence https github com niraj lunavat artificial intelligence awesome ai learning with 100 ai cheat sheets free online books top courses best videos and lectures papers tutorials 99 researchers premium websites 121 datasets conferences frameworks tools over 200 of the best machine learning nlp and python tutorials 2018 edition https medium com machine learning in practice over 200 of the best machine learning nlp and python tutorials 2018 edition dd8cf53cb7dc source https twitter com iamtrask status 1318464483883470849 s 20 101 machine learning algorithms for data science with cheat sheets https blog datasciencedojo com machine learning algorithms brief description and r python examples of algorithms categorized into several categories classification regression neural networks anomaly detection dimensionality reduction ensemble learning clusterint association rule analysis regularization machine learning cheatsheet https ml cheatsheet readthedocs io en latest brief visual explanations of machine learning concepts with diagrams code examples and links to resources for learning more data science cheatsheet https github com ml874 data science cheatsheet data science and ml cheat sheet by maverick lin source https www datasciencecentral com profiles blogs new data science cheat sheet data science cheatsheet https github com aaronwangy data science cheatsheet a helpful 4 page data science cheatsheet to assist with exam reviews interview prep and anything in between cheatsheets ai https github com kailashahirwar cheatsheets ai essential cheat sheets for deep learning and machine learning researchers machine learning cheat sheet https github com soulmachine machine learning cheat sheet 30 page machinelearning cheat sheet with classical equations diagrams tweet by kirk borne https twitter com kirkdborne status 1199688188958330882 s 20 ml cheatsheet https github com remicnrd ml cheatsheet a 5 pages only machine learning cheatsheet focusing on the most popular algorithms under the hood online version https remicnrd github io the machine learning cheatsheet stanford cs 229 machine learning https github com afshinea stanford cs 229 machine learning vip cheatsheets for stanford s cs 229 machine learning online version https stanford edu shervine teaching cs 229 machine learning 101 https t co igqlsdmuyi amp 1 machine and deep learning overview in 100 slides or 35 min video https www youtube com watch v x4i9qmcseyo by jason mayers http www jasonmayes com tweet https twitter com iamtrask status 1327537017891282947 s 20 mathematics for ml https github com dair ai mathematics for ml a collection of resources to learn mathematics for machine learning linka to books videos here are 450 ivy league courses you can take online right now for free https www freecodecamp org news ivy league free online courses a0d7ae675869 blog post by dhawal shah with links to free courses in computer science data science programming humanities business art design science social sciences health medicine engineering mathematics education teaching and personal development top 10 github repositories for data science https www analyticsvidhya com blog 2022 01 top 10 github repositories for data science by analysics vidhya ayushi gupta machine learning resource https github com crazyhottommy machine learning resource machine and deep learning notes by ming tang awesome deep learning awesome deep learning https github com christoschristofidis awesome deep learning a curated list of awesome deep learning tutorials projects and communities awesome most cited deep learning papers https github com terryum awesome deep learning papers the most cited deep learning papers awesome computer vision https github com jbhuang0604 awesome computer vision a curated list of awesome computer vision resources ai and deeprl https github com smc77 deeprl source code links and other learning materials related to artificial intelligence especially focused on deep reinforcement learning the neural network zoo https www asimovinstitute org neural network zoo infographics of different neural network architectures explanation of each references to the original papers over 150 of the best machine learning nlp and python tutorials https medium com machine learning in practice over 150 of the best machine learning nlp and python tutorials ive found ffce2939bd78 hn tweet by andrew trask https twitter com iamtrask status 1289658159972270080 s 20 keras tensorflow deep learning with r https www manning com books deep learning with r by fran ois chollet the creator of keras with j j allaire the founder of rstudio and the author of the r interfaces to keras and tensorflow r notebooks https github com jjallaire deep learning with r notebooks python notebooks https github com fchollet deep learning with python notebooks deep learning with keras and tensorflow in r workflow https community rstudio com t deep learning with keras and tensorflow in r workflow rstudio conf 2020 49099 by brad boehmke guthub repo https github com rstudio conf 2020 dl keras tf with rmd files for data download code examples lectures dlaicourse https github com lmoroney dlaicourse deep learning course tensorflow jupyter notebooks by laurence moroney google easy tensorflow https github com easy tensorflow easy tensorflow simple and comprehensive tutorials in tensorflow by jahandar jahanipour online version http www easy tensorflow com handson ml3 https github com ageron handson ml3 a series of jupyter notebooks that walk you through the fundamentals of machine learning and deep learning in python using scikit learn keras and tensorflow 2 example code and solutions for the hands on machine learning with scikit learn keras and tensorflow https www oreilly com library view hands on machine learning 9781492032632 book by aur lien g ron run on google colab introduction to deep learning https pythonprogramming net introduction deep learning python tensorflow keras deep learning basics with python tensorflow and keras several posts each ncludes video text and code tutorial image classification keras tf https github com shiring image classification keras tf workshop material for image classification natural language processing with python keras and tensorflow by shirin glander https github com shiring keras workshop https github com mangothecat keras workshop keras r workshop by doug ashton slides simple examples machine learning with tensorflow https www manning com books machine learning with tensorflow and tensorflow book https github com binroot tensorflow book github with the source code machine learning foundations https www youtube com playlist list plou2xlyxmsii9mzq xxug4l2o04jbrklv machine learning foundations is a free training course where you ll learn the fundamentals of building machine learned models using tensorflow with laurence moroney computer vision focused tensorflow 101 https github com sjchoi86 tensorflow 101 tensorflow tutorials using jupyter notebook with data tensorflow course https github com open source for science tensorflow course simple and ready to use tutorials for tensorflow step by step instructions with screenshots by amirsina torfi tensorflow examples https github com aymericdamien tensorflow examples tensorflow tutorial and examples for beginners with latest apis by aymeric damien tensorflow livelessons https github com the deep learners tensorflow livelessons deep learning with tensorflow livelessons jupyter notebooks by jon krohn tensorflow 2 0 complete course python neural networks for beginners https youtu be tpyj3ffjgjk 7 hours of walk through programming with tim ruscica links to google colaboratory notebooks are in the description text classification with tensorflow https youtu be vtrlrq3ev u python tensorflow for machine learning neural network text classification tutorial by kylie ying 1h 54m user 2020 deep learning with keras and tensorflow s elsinghorst tutorial https youtu be ubismeexoqk 2h 07m video and the gitlab repo keras tutorial user2020 https gitlab com shiring keras tutorial user2020 pytorch awesome pytorch list https github com bharathgs awesome pytorch list a comprehensive list of pytorch related content on github such as different models implementations helper libraries tutorials etc tweet https twitter com omarsar0 status 1344007449506885639 s 20 deep learning with pytorch https atcold github io pytorch deep learning by yann lecun alfredo canziani videos transcripts slides practicals youtube playlist https www youtube com playlist list pllhtzkzzvu9eaeyerdv26ikyolxosz6mq pytorch tutorial https 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resources ai collection https github com ai collection ai collection a collection of generative ai applications for text code image video audio etc awesome ai art image synthesis https github com altryne awesome ai art image synthesis a list of awesome tools ideas prompt engineering tools colabs models and helpers for the prompt designer playing with aiart and image synthesis covers dalle2 midjourney stablediffusion and open source tools awesome colab notebooks https github com amrzv awesome colab notebooks collection of google colaboratory notebooks for fast and easy experiments awesome generative ai https github com steven2358 awesome generative ai a curated list of modern generative artificial intelligence projects and services buzz https github com chidiwilliams buzz buzz transcribes and translates audio offline on your personal computer powered by openai s whisper dall e 2 https openai com dall e 2 a new ai system that can create realistic images and art from a description in 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github com thudm cogview2 official code repo for paper cogview2 faster and better text to image generation via hierarchical transformers arxiv https doi org 10 48550 arxiv 2204 14217 example https github com thudm cogview2 files 8553662 big 1 pdf jojogan https github com mchong6 jojogan official pytorch repo for jojogan one shot face stylization arxiv https arxiv org abs 2112 11641 bringing old photos back to life https github com microsoft bringing old photos back to life old photo restoration official pytorch implementation photo restoration with gfp gan https github com tencentarc gfpgan gfpgan aims at developing practical algorithms for real world face restoration online version https app baseten co apps qpp4npe operator views rqgonqv real time voice cloning https github com corentinj real time voice cloning clone a voice in 5 seconds to generate arbitrary speech in real time learn voice characteristics from a short audio clip and perform text to speech conversion using this voice speechbrain https speechbrain github io a pytorch based speech toolkit for speech speaker recognition speech enhancement processing and more github repo https github com speechbrain speechbrain bert finetuning catalyst https github com yorko bert finetuning catalyst code for bert classifier finetuning for multiclass text classification code and video by yury kashnitsky 18 all time classic open source computer vision projects for beginners https www analyticsvidhya com blog 2020 09 18 open source computer vision projects beginners by analytics vidhya project deepspeech https github com mozilla deepspeech a tensorflow implementation of baidu s deepspeech architecture transcribe audio data english model available documentation https deepspeech readthedocs io deeplearning digital pathology https github com zhaoxuanma deeplearning digital pathology python code demonstrating image classification using keras with caffe or tensorflow backend image manipulation utilities weights biases gallery of curated machine learning reports https app wandb ai gallery selected examples with code machine learning lessons and teaching projects designed for engineers https github com lukas ml class github repo by lukas biewald the founder of weights and biases code and video tutorials weights biases github examples of deep learning projects https github com wandb examples neuralart tensorflow https github com ckmarkoh neuralart tensorflow implementation of a neural algorithm of artistic style by tensorflow jukebox https github com openai jukebox music generation neural network hierarchical vector quantised variational autoencoder vq vae architecture three separate temporal resolutions able to generate singing from lyrics extend music examples dhariwal et al jukebox a generative model for music https arxiv org abs 2005 00341 blog post with examples of generated music https openai com blog jukebox magenta https github com magenta magenta music and art generation with machine intelligence mygirlgpt https github com synthintel0 mygirlgpt a personal ai girlfriend running on a local server telegram chatbot integration voice messages photos openai cookbook https github com openai openai cookbook examples and guides for using the openai https openai com api gpt 3 dall e2 other models practical ml https github com eugenesiow practical ml learn by experimenting on state of the art machine learning models and algorithms with jupyter notebooks computer vision nlp speech notebooks open in colab selfie2anime https selfie2anime com online tool and a github repo https github com t04glovern selfie2anime stablediffusion https github com stability ai stablediffusion high resolution image synthesis with latent diffusion models nvidia deep learning examples for tensor cores https github com nvidia deeplearningexamples this repository provides state of the art deep learning examples that are easy to train and deploy achieving the best reproducible accuracy and performance with nvidia cuda x software stack running on nvidia volta turing and ampere gpus chatgpt llms awesome chatgpt https github com humanloop awesome chatgpt curated list of awesome tools demos docs for chatgpt and gpt 3 chatgpt clone https github com amrrs chatgpt clone build yo own chatgpt with openai api gradio a python app for web browser intercage to chatgpt h2ogpt https github com h2oai h2ogpt open source gpt with document and image q a 100 private chat no data leaks apache 2 0 https arxiv org pdf 2306 08161 pdf live demo https gpt h2o ai nanogpt https github com karpathy nanogpt the simplest fastest repository for training finetuning medium sized gpts openai cookbook https github com openai openai cookbook examples and guides for using the openai api rendered version https platform openai com docs introduction privategpt https github com imartinez privategpt interact privately with your documents using the power of gpt 100 privately no data leaks dl misc app wombo art https app wombo art deep generative model dreaming awesome images from text android and ios apps available tweet https twitter com iscienceluvr status 1468115406858514433 s 20 describing the vqgan clip technology behind it colossalai https github com hpcaitech colossalai a unified deep learning system for big model era scaling deep learning models using data pipeline tensor and sequence parallelism 1d 2d 2 5d 3d distributed operators examples of each written in pytorch needs a configuration file defining parallelism benchmarked against deepspeed megatron lm details summary paper summary li shenggui jiarui fang zhengda bian hongxin liu yuliang liu haichen huang boxiang wang and yang you colossal ai a uni ed deep learning system for large scale parallel training n d details elvis saravia https github com omarsar machine learning researcher at facebook ai twitter account https twitter com omarsar0 to follow his starred repos https github com omarsar tab stars are gold traingenerator jrieke com https traingenerator jrieke com a web app to generate template code for machine learning github https github com jrieke traingenerator tweet https twitter com jrieke status 1343587198671720449 s 20 deep learning in cloud https github com zszazi deep learning in cloud list of deep learning cloud providers deep learning resources https www nature com articles s41588 018 0295 5 tables 1 cloud platforms software educational resources from zou et al a primer on deep learning in genomics https www nature com articles s41588 018 0295 5 collections of github repositories of deep learning projects analytics vidhya https www analyticsvidhya com blog category github utm source blog utm medium 6 open source data science projects try home google colab free gpu tutorial https medium com deep learning turkey google colab free gpu tutorial e113627b9f5d tpu starter https github com ayaka14732 tpu starter everything you want to know about google cloud tpu how to use r with google colaboratory https stackoverflow com questions 54595285 how to use r with google colaboratory direct link to a new r notebook https colab research google com notebook create true language r deep reinforcement learning algorithms with pytorch https github com p christ deep reinforcement learning algorithms with pytorch pytorch implementations of deep reinforcement learning algorithms and environments ml visuals https github com dair ai ml visuals visuals contains figures and templates which you can reuse and customize to improve your scientific writing google slides https docs google com presentation d 11mr1nkir9fbhegfkcfq8z9odq5sjv8e3jjp1lflgkuk edit slide id g78327f1586 1537 920 machine learning figures https github com neuraxio machine learning figures images of most representative concepts and diagrams for machine and deep learning awesome machine learning awesome machine learning https github com josephmisiti awesome machine learning a curated list of awesome machine learning frameworks libraries and software awesome machine learning interpretability https github com jphall663 awesome machine learning interpretability a curated list of awesome machine learning interpretability resources awesome machine learning operations https github com ethicalml awesome machine learning operations a curated list of awesome open source libraries to deploy monitor version and scale your machine learning awesome courses https github com prakhar1989 awesome courses list of awesome university courses for learning computer science best of machine learning with python https github com ml tooling best of ml python a ranked list of awesome machine learning python libraries updated weekly data science https github com open source society data science path to a free self taught education in data science open source society university a collection of free online courses in logical order of learning data science massive list of courses from linear algebra and calculus to r python programming machine learning data science best resources https github com tirthajyoti data science best resources carefully curated resource links for data science in one place free data science https github com alastairrushworth free data science thematic list of high quality data science resources r python shell regular expressions git docker markdown latex statistics machine deep learning visualization time series spatial analysis more machine learning https github com davetang machine learning machine learning in r notes by dave tang machine learning interview https github com khangich machine learning interview machine learning interviews from faang snapchat linkedin more info at mlengineer io https mlengineer io machine learning notes https github com rasbt machine learning notes collection of useful machine learning codes and snippets jupyter notebooks by sebastian raschka https github com rasbt ml books probabilistic machine learning an introduction https probml github io pml book book1 html by kevin patrick murphy https www cs ubc ca murphyk 2022 edition intro probability statistics decision theory information theory linear algebra linear models nonparametric modeling deep neural networks dimensionality reduction clustering more github https github com probml pml book tweet1 https twitter com omarsar0 status 1345021214671122433 s 20 tweet2 https twitter com omarsar0 status 1494692845634174980 s 20 t 7iyo6ccxptrrdh487t0 mw mathematics for machine learning https mml book github io 2020 by marc peter deisenroth a aldo faisal and cheng soon ong linear algebra with python https github com macroanalyst linear algebra with python lecture notes for linear algebra featuring python these lecture notes are intended for introductory linear algebra courses suitable for university students programmers data analysts algorithmic traders and etc mathematics for machine learning https gwthomas github io docs math4ml pdf by garrett thomas tweet https twitter com svpino status 1346442575557758976 s 20 a machine learning primer https www confetti ai assets ml primer ml primer pdf by mihail eric mihail eric tweet https twitter com omarsar0 status 1312697532414394370 s 20 100 free data science books http www learndatasci com free books ciml https github com hal3 ciml book a course in machine learning online version http ciml info interpretable machine learning https christophm github io interpretable ml book book by christoph molnar a guide for making black box models explainable learnpub https leanpub com interpretable machine learning introduction to machine learning http robotics stanford edu people nilsson mlbook html book by nils nilsson free pdf hands on machine learning with r https github com koalaverse hands on machine learning with r hands on machine learning with r an applied book covering the fundamentals of machine learning with r supplementary material https github com koalaverse homlr online version https bradleyboehmke github io homl mit deep learning book pdf https github com janishar mit deep learning book pdf mit deep learning book pdf of the original http www deeplearningbook org book ml for hackers https github com johnmyleswhite ml for hackers code accompanying the book machine learning for hackers http shop oreilly com product 0636920018483 do rtemis https github com egenn rtemis advanced machine learning and visualization in r book https rtemis netlify com feature engineering and selection a practical approach for predictive models https bookdown org max fes by kuhn and johnson github https github com topepo fes ml courses tutorials machine learning 2021 https bioinformaticsdotca github io mle 2021 a seven module course covering basics of machine learning by bioinformatics ca youtube playlist https www youtube com playlist list pl3izgl6oi0s zxasgxccctqlnhiyvt 5o course material on google drive https drive google com drive folders 1ybi ellyj7akl2o1y1gliqfxaljg2wph full stack deep learning https course fullstackdeeplearning com from development to deployment of machine learning methods 40 modern tutorials covering all aspects of machine learning https www datasciencecentral com profiles blogs 40 tutorials covering all aspects of machine learning tweet https twitter com kirkdborne status 1183944499711631361 s 20 100 days of ml code https github com avik jain 100 days of ml code 100 days of machine learning coding as proposed by siraj raval illustrated step by step guides with code and data links to videos code for workshop introduction to machine learning with r https shirinsplayground netlify com 2018 06 intro to ml workshop heidelberg by shirin glander more in her blog posts twitter etc https shirinsplayground netlify com aml london 2019 https github com topepo aml london 2019 course materials for applied machine learning course in 2019 in london by max kuhn aml training https github com tidymodels aml training the most recent version of the applied machine learning notes related to the parsnip r package https github com tidymodels parsnip by max kuhn cs video courses https github com developer y cs video courses list of 800 computer science courses with video lectures tweet https twitter com prasoonpratham status 1367404646101184518 s 20 data analysis and machine learning projects https github com rhiever data analysis and machine learning projects randy olson s data analysis and machine learning projects google interview university https github com jwasham google interview university list of ml cs courses a complete daily plan for studying to become a google software engineer h2o 3 https github com h2oai h2o 3 the third version of h2oai open source fast scalable machine learning platform for smarter applications deep learning gradient boosting xgboost random forest generalized linear modeling logistic regression elastic net k means pca stacked ensembles automatic machine learning automl etc machine learning for software engineers https github com zuzoovn machine learning for software engineers a complete daily plan for studying to become a machine learning engineer machine learning in r https github com dlab berkeley machine learning in r workshop 6 hours preprocessing cross validation lasso decision trees random forest xgboost superlearner ensembles latinr 2019 h2o tutorial https github com ledell latinr 2019 h2o tutorial h2o machine learning tutorial in r lecture i2ml https github com compstat lmu lecture i2ml introduction to machine learning regression classification performance evaluation parameter tuning random forests python mlcourse ai https github com yorko mlcourse ai open machine learning course mlcourse ai 2018 english version online version https mlcourse ai video https www youtube com watch v qktuw4pnosu list plvly 7ijcmjerfz68evfecu ucn9bbwix mlfromscratch https github com patrickloeber mlfromscratch machine learning algorithm implementations from scratch youtube videos https www youtube com playlist list plqnslrfeh2upcrywf u2etjdxxkl8nl7e mth594 machinelearning https github com diefimov mth594 machinelearning the materials for the course mth 594 advanced data mining theory and applications dmitry efimov american university of sharjah pattern classification https github com rasbt pattern classification a collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks sklearn classification https github com dformoso sklearn classification data science notebook on a classification task using sklearn and tensorflow jupyter notebook the census income dataset to predict whether an individual s income exceeds 50k yr based on census data docker wrapped supervised ml case studies course https github com juliasilge supervised ml case studies course supervised machine learning case studies in r book https supervised ml course netlify com user machine learning tutorial https github com ledell user machine learning tutorial user 2016 tutorial machine learning algorithmic deep dive ipython notebooks running r kernel ml videos probabilistic machine learning philipp hennig 2021 https www youtube com playlist list pl05ump7r6ij1thaofy96m5ux3j21a6ynd tweet https twitter com omarsar0 status 1383360564710174720 s 20 10 powerful youtube channels for data science aspirants https www analyticsvidhya com blog 2020 08 10 powerful youtube channels for data science aspirants analytics vidhya s post sentdex https www youtube com c sentdex playlists 3blue1brown https www youtube com c 3blue1brown playlists freecodecamp org https www youtube com c freecodecamp playlists statquest https www youtube com c joshstarmer playlists krish naik https www youtube com user krishnaik06 playlists python programmer https www youtube com c flickthrough playlists corey schafer https www youtube com c coreyms playlists tech with tim https www youtube com c techwithtim playlists brandon foltz https www youtube com c brandonfoltz playlists 365 data science https www youtube com c 365datascience playlists nc asa webinar introduction to machine learning by dr funda gunes part 1 https www youtube com watch v ucv17jes5eq part 2 https www youtube com watch v fv l4auagns a one hour overview of the main machine learning concepts learning from data https work caltech edu lectures html lectures statistical learning theory course from caltech taught by feynman prize winner professor yaser abu mostafa videos slides machine learning for everybody full course https youtu be i lwzrvp7bg 3h 53m video from intro knn naive bayes regression svm to tensorflow statistical machine learning spring 2017 http www stat cmu edu ryantibs statml by ryan tibshirani larry wasserman carnegie mellon university ml papers whalen sean jacob schreiber william s noble and katherine s pollard navigating the pitfalls of applying machine learning in genomics nature reviews genetics 23 no 3 march 2022 169 81 https doi org 10 1038 s41576 021 00434 9 five machine learning problems in genomics distributional differences dependency structure confounding variables information leakage unbalanced data description examples solutions domingos pedro a few useful things to know about machine learning communications of the acm 55 no 10 october 1 2012 78 https doi org 10 1145 2347736 2347755 twelve lessons for machine learning overview of machine learning problems and algorithms problem of overfitting causes and solutions curse of dimensionality issues with high dimensional data feature engineering bagging boosting stacking model sparsity video lectures https www youtube com user uwcse playlists shelf id 16 sort dd view 50 ml tools mlr3 https mlr3 mlr org com machine learning in r r package the unified interface to classification regression survival analysis and other machine learning tasks github repo https github com mlr org mlr3 mlr3gallery https mlr3gallery mlr org com examples of problems and code solutions mlr3 manual https mlr3book mlr org com mlr3 bookdown more on the mlr3 package site https github com mlr org mlr3 including videos ml misc the algorithms r https github com thealgorithms r github repo with code examples of main machine learning algorithms algorithms in ipython notebooks https github com rasbt algorithms in ipython notebooks a repository with ipython notebooks of algorithms implemented in python https github com rasbt algorithms in ipython notebooks awesome decision tree papers https github com benedekrozemberczki awesome decision tree papers a collection of research papers on decision classification and regression trees with implementations understanding the bias variance tradeoff http scott fortmann roe com docs biasvariance html bias variance total error classic figures and explanation by scott fortmann roe lares https github com laresbernardo lares r library for analytics and machine learning ml techniques https github com shiring ml techniques r code for performing typical ml tasks and techniques e g naive bayes random forest by shirin glander ml from scratch https github com eriklindernoren ml from scratch bare bones python implementations of some of the fundamental machine learning models and algorithms gradient boosting essentials in r using xgboost http www sthda com english articles 35 statistical machine learning essentials 139 gradient boosting essentials in r using xgboost mlpb https github com ben519 mlpb machine learning problem bible problems and solutions in r xgboost svm neural networks and other methods best xgboost settings a second xgboost version xgboost best with the best parameter settings that i obtained in on of my publications https arxiv org abs 1802 09596 these are nrounds 500 eta 0 0518715 subsample 0 8734055 booster gbtree max depth 11 min child weight 1 750185 colsample bytree 0 7126651 colsample bylevel 0 6375492 from is catboost the best gradient boosting r package https www r bloggers com is catboost the best gradient boosting r package post on r bloggers com material in russian scientific graphics in python https github com whitehorn scientific graphics in python matplotlib for scientific graphics 3 parts 13 chapters by pavel shabanov ml course hse https github com esokolov ml course hse machine learning course at the computer sciences department high schoool of economy multiple years videos mlcourse open https github com yorko mlcourse open opendatascience machine learning course both in english and russian python based ml course with video lectures video https www youtube com playlist list plvly 7ijcmjdgcctqfzj5j8ovb y0gjcl dl cshse spring2018 https github com aosokin dl cshse spring2018 deep learning anton osokin higher school of economics computer sciences department russian course material and video lectures https www youtube com playlist list plzy5g rvmfayekccgo3 it6hzwpzl3ld ordinary differential equations https ode mathbook info calculus https calculus mathbook info tweet https twitter com ilyaschurov status 1432811362904944644 s 20 mathprofi ru http mathprofi ru mirror http mathprofi net	machine-learning deep-learning	ai
pando	p align center img width 890 alt pando social banner src https user images githubusercontent com 4819738 211405929 1988b6d5 b3b6 461f bd47 2276ee8e1020 png p h3 align center the foundational roots of a better design system h3 p align center img alt bundle size src https badgen net bundlephobia min pluralsight headless styles img alt tree shaking src https badgen net bundlephobia tree shaking pluralsight headless styles img alt dependency count src https badgen net bundlephobia dependency count pluralsight headless styles img alt apache 2 0 license src https img shields io github license pluralsight pando img alt npm downloads src https img shields io npm dm pluralsight headless styles svg style flat img alt github stars src https badgen net github stars pluralsight pando p p align center style margin top 2rem a href https pr new pluralsight pando img alt open in codeflow src https developer stackblitz com img open in codeflow svg a p installation we have multiple npm packages for web usage design tokens themes https www npmjs com package pluralsight design tokens headless styles https www npmjs com package pluralsight headless styles icons https www npmjs com package pluralsight icons react utils hooks https www npmjs com package pluralsight react utils questions for how to questions and other non issues please use discussions https github com pluralsight pando discussions instead of github issues discussion categories general questions use q a feature requests use ideas documentation check out our documentation website https design pluralsight com contributing read the contributing guide contributing md to learn about our development process how to propose bugfixes and improvements and how to build and test your changes to pando license this project is licensed under the terms of the apache 2 0 license license	ui tokens pluralsight library branding icons react android ios kotlin headless a11y aria	os
SolveSudoku	forthebadge https forthebadge com images badges built with love svg https forthebadge com solvesudoku solvesudoku extract and solve sudoku from image it uses a collection of image processing techniques and convolution neural network for training and recognition of characters cnn is trained on mnist dataset to detect digits blog checkout the articles on solvesudoku on medium com sudoku solver using opencv and dl part 1 https medium com aakashjhawar sudoku solver using opencv and dl part 1 490f08701179 sudoku solver using opencv and dl part 2 https medium com aakashjhawar sudoku solver using opencv and dl part 2 bbe0e6ac87c5 getting started how to use git clone https github com aakashjhawar solvesudoku git cd solvesudoku python3 sudoku py path to input image prerequisites python 3 5 opencv sudo apt get install python opencv procedure 1 image preprocessing thresholding 2 find the largest contour sudoku square 3 get the cordinates of largest contour 4 crop the image 5 perform warp perspective on image 5 extract each cells from the image by slicing the sudoku grid 6 extract the largest component in the sudoku image number 7 remove noise in block 8 send the number to pre trained digit recogition model 9 send the grid to sudoku solver to perform the final step working input image of sudoku input image of sudoku https github com aakashjhawar solvesudoku blob master images sudoku jpg image of sudoku after thresholding threshold image of sudoku https github com aakashjhawar solvesudoku blob master images threshold jpg contour corners of sudoku contour of sudoku https github com aakashjhawar solvesudoku blob master images contour jpg warp image warp of sudoku https github com aakashjhawar solvesudoku blob master images warp jpg final output of extractsudoku final image of sudoku https github com aakashjhawar solvesudoku blob master images final jpg extracted grid extracted grid https github com aakashjhawar solvesudoku blob master images extracted grid png solved grid solved grid https github com aakashjhawar solvesudoku blob master images solved grid png	sudoku-solver python deep-learning opencv opencv-python sudoku image-processing image-segmentation cnn machine-learning neural-network sudoku-grabber handwritten-digit-recognition cv2 digital-image-processing convolutional-neural-networks cnn-classification cnn-tensorflow sudoku-solution-finder sudoku-scanner	ai
GADS-2020	gads 2020 associate cloud engineering roadmap course title link author cloud executive briefing https app pluralsight com library courses cloud executive briefing simon allardice fundamentals of cloud computing https app pluralsight com library courses cloud computing fundamentals david davis foundations for cloud architecture https app pluralsight com library courses cloud architecture foundations james bannan google cloud platform fundamentals core infrastructure https app pluralsight com library courses google cloud platform fundamentals core infrastructure google cloud intermediate course title link author essential google cloud infrastructure foundation https app pluralsight com library courses essential google cloud infrastructure foundation google cloud essential google cloud infrastructure core services https app pluralsight com library courses essential google cloud infrastructure core services google cloud elastic google cloud infrastructure scaling and automation https app pluralsight com library courses elastic google cloud infrastructure scaling automation google cloud getting started with google kubernetes engine https app pluralsight com library courses getting started google kubernetes engine google cloud advanced further study course title link author architecting with google kubernetes engine foundations https app pluralsight com library courses architecting google kubernetes engine foundations google cloud architecting with google kubernetes engine workloads https app pluralsight com library courses architecting google kubernetes engine workloads google cloud architecting with google kubernetes engine production https app pluralsight com library courses architecting google kubernetes engine production update google cloud git the big picture https app pluralsight com library courses git big picture paolo perrotta other information title link practice exam https cloud google com certification practice exam cloud engineer practice q a https quizlet com intro to linux 101 https training linuxfoundation org training introduction to linux certification exam guide https cloud google com certification guides cloud engineer docs https cloud google com docs		cloud
Sentiment-Analysis-NLP-for-Marketting	sentiment analysis and natural language processing for marketing i m keeping my documents source codes related to this manning live project https www manning com liveproject sentiment analysis and natural language processing for marketing from the project summary in this liveproject you can gain an overall impression of the job of a natural language processing nlp specialist working on the growth hacking team of a freshly launched startup that is introducing a new video game to the market one of the key targets of a growth hacking team is to enhance the massive growth of early startups in a short time to do so it introduces strategies with the help of which one can acquire as many customers as possible with the lowest cost as possible as part of your team s growth hacking strategy your boss wants to map the field of the video game market she aims to find out how customers evaluate your competitors products namely what they like and dislike in a video game knowing what makes a video game attractive to a gamer helps the marketing team articulate the message of your product more effectively to be able to find out what makes a video game worth buying according to gamers you need to get deeper insights into the linguistic features of their utterances as an nlp specialist your task will be to analyze customers reviews about video games in order to carry out this task you will employ different nlp methods these methods will enable you to acquire a deeper understanding of customer feedback and opinion your task as an nlp specialist on the growth hacking team is the following download the dataset of amazon reviews create your own dataset from the amazon reviews decide whether people like or dislike the video game they bought label each review with a sentiment score between 1 and 1 check the performance of your sentiment analyzer by comparing the sentiment scores with the review ratings evaluate the performance of your sentiment analyzer and find out if you managed to correctly label the reviews try out other methods of sentiment analysis explore how people evaluate the video game they purchased by classifying the reviews as positive negative and neutral summarize your results to the head of the growth hacking team based on your findings list those things that are liked and those ones that are disliked about video games techniques employed in order to get a deeper understanding of people s opinion about video games you will employ various nlp techniques here is a short list about what you will do and what techniques you will use sampling from imbalanced datasets using the imbalanced learn package enquiring about the sentiment value of the reviews with the dictionary based sentiment analysis tools which are part of nltk a natural language processing toolkit used in python finding out if your algorithm did a good job data evaluation with scikit learn in python analyzing the reviews with a state of the art deep learning technique namely with the distilbert model to build this model you will need to run pytorch transformers and the simpletransformers packages evaluating your model and creating descriptive statistics in python with scikit learn library before reporting your results to your boss visualizing your findings about preferable and non preferable words related to video games using altair project outline the project is made up of five steps which are built on each other creating your dataset creating a dictionary based sentiment analyzer evaluating your dictionary based sentiment analyzer creating neural network based sentiment analyzers reporting your results both the steps and the techniques in this project model a real life scenario if you are employed as an nlp specialist you are likely to accomplish jobs like these ones dataset the amazon review dataset can be downloaded from here https nijianmo github io amazon index html download the zipped json file of the category of video games 5 core which can be found under the title small subsets for experimentation once the download is complete extract the file		ai
frontend	unee t img src https media dev unee t com 2018 12 10 unee t high level architecture png alt overview how to test with bugzilla in a local environment https unee t media s3 accelerate amazonaws com frontend mefe mp4 ecs deploy https unee t media s3 accelerate amazonaws com 2017 ecs deploy mp4 with deploy sh demo how to see how it works you can either go the demo environment described here https documentation unee t com 2018 03 01 introduction to the demo environment for the functionalities that we currently in production you can also go to figma to see the things we are currently working on manage notifications https www figma com proto sglcxdmbih1jxvq1lupmiptr unee t designs node id 1969 3a62 scaling scale down creating a new inventory for a unit and adding an item to that inventory https new figma com proto sglcxdmbih1jxvq1lupmiptr unee t designs node id 1483 3a3763 scaling scale down redirected 1 figma also has an android and iphone version figma mirror environment variables they are securely managed in aws s parameter store https ap southeast 1 console aws amazon com ec2 v2 home region ap southeast 1 parameters sort name the variables are retrieved via an environment setup script https github com unee t frontend blob master aws env dev which is utilised by deploy sh for local development use env setup bash assuming you have been access to the uneet dev development environment deployment happens automatically on master on the development aws account 8126 4485 3088 with aws profile uneet dev travis ci deployments made via pull request will fail since it will not have access to aws secret access key production deployment on aws account 1924 5899 3663 is done manually via deploy sh p via the aws profile aws prod only once the build is tagged debugging with vscode background reading https github com microsoft vscode recipes blob master meteor readme md configure meteor to run in debug mode cd vscode curl o https media dev unee t com 2018 07 05 launch json for example edit this to point to where your browser binary lives npm run debug you need to run this manually on the cli meteor node to attach debug server side meteor chrome to debug client side logs frontend logs to the meteor log group in cloudwatch https ap southeast 1 console aws amazon com cloudwatch home region ap southeast 1 logs which is controlled by the compose file https github com unee t frontend blob master aws docker compose meteor yml l16 meteor builds the canonical master branch ci build location is https unee t media s3 accelerate amazonaws com frontend master tar gz to discover other branches aws profile uneet dev s3 ls s3 unee t media frontend every commit is also uploaded to aws profile uneet dev s3 ls s3 unee t media frontend commit commits are expired after 90 days https s3 console aws amazon com s3 buckets unee t media region ap southeast 1 tab management setup 1 install meteor https www meteor com install 1 install dependencies shell npm install start app shell npm start tips how do i figure out the email of the logged in user run in browser s developer console meteor user emails 0 address how to find user account on mongodb given an email address foo example com backup connect sh db users findone emails address foo example com how do i set up for local development 1 make a backup snapshot of the development mongo database using backup dump sh 2 meteor reset to clear state 3 npm run start to start the mongo service 3 mongorestore h 127 0 0 1 port 3001 d meteor date dev y m d meteor how to create users in the case my mongodb is lacking the users to check the current users connect to your mongodb and run db users find emails address 1 id 0 map d d emails 0 address join n if your frontend datastore mongodb out of sync with your bugzilla database s profiles https documentation unee t com 2018 03 01 introduction to the demo environment you need to create the users in the users manually accounts createuser email leonel mailinator com password leonel profile bzlogin leonel mailinator com bzpass leonel ensure it worked by looking at the npm start log next you might want to verify each user s email address db users update emails address leonel mailinator com set emails 0 verified true how to test the notifications email templates refer to simulate sh https github com unee t lambda2sns blob master tests simulate sh though you need to tweak the events https github com unee t lambda2sns tree master tests events to map to the bugzillacreds id from db users find pretty migrations not migrating control is locked migrations unlock migrations migrateto latest error error url must be absolute and start with http or https your env file is not set up correctly consider env setup bash mail sending the secret mail url environment variable configures the smtp so that email notifications can be sent for example in uneet dev environment it begins with smtps akiaiicb7iawdigly6da and you can search for the access key id akiaiicb7iawdigly6da in aws iam https console aws amazon com iam home home to determine if it s active or not in https us west 2 console aws amazon com ses home region us west 2 to help with debugging there is a setup mail script https github com unee t email2case blob master tests setup mail to sh to exercise mail url	meteor bugzilla property-management	front_end
Machine-Learning-with-Spark-Second-Edition	machine learning with spark second edition this is the code repository for machine learning with spark second edition https www packtpub com big data and business intelligence machine learning spark second edition utm source github utm medium repository utm campaign 9781785889936 published by packt https www packtpub com utm source github it contains all the supporting project files necessary to work through the book from start to finish about the book this book will teach you about popular machine learning algorithms and their implementation you will learn how various machine learning concepts are implemented in the context of spark ml you will start by installing spark in a single and multinode cluster next you ll see how to execute scala and python based programs for spark ml then we will take a few datasets and go deeper into clustering classification and regression toward the end we will also cover text processing using spark ml instructions and navigation all of the code is organized into folders each folder starts with a number followed by the application name for example chapter02 chapter 03 does not contain code files the code will look like the following val conf new sparkconf setappname test spark app setmaster local 4 val sc new sparkcontext conf throughout this book we assume that you have some basic experience with programming in scala or python and have some basic knowledge of machine learning statistics and data analysis related products mastering machine learning with r second edition https www packtpub com big data and business intelligence mastering machine learning r second edition utm source github utm medium repository utm campaign 9781787287471 building machine learning systems with python second edition https www packtpub com big data and business intelligence building machine learning systems python second edition utm source github utm medium repository utm campaign 9781784392772 fast data processing with spark second edition https www packtpub com big data and business intelligence fast data processing spark second edition utm source github utm medium repository utm campaign 9781784392574 suggestions and feedback click here https docs google com forms d e 1faipqlse5qwunkgf6puvzpirpdtuy1du5rlzew23ubp2s p3wb gcwq viewform if you have any feedback or suggestions		ai
Huwamaruwa-Android-App	thanks for checking out the best readme template if you have a suggestion that would make this better please fork the repo and create a pull request or simply open an issue with the tag enhancement thanks again now go create something amazing d project shields i m using markdown reference style links for readability reference links are enclosed in brackets instead of parentheses see the bottom of this document for the declaration of the reference variables for contributors url forks url etc this is an optional concise syntax you may use https www markdownguide org basic syntax reference style links project logo br p align center img src readmeimages logo png alt logo width 250 height auto h1 align center huwamaruwa android java application h1 p align center project overview and installation instructions br a href https github com othneildrew best readme template strong explore the docs strong a br p p table of contents details open open summary table of contents summary ol li a href acknowledgements acknowledgements a li li a href about the project about the project a ul li a href built with built with a li ul li li a href getting started getting started a ul li a href prerequisites prerequisites a li li a href installation installation a li ul li li a href usage usage a li li a href roadmap roadmap a li li a href contributing contributing a li li a href license license a li li a href contributors contributors a li ol details acknowledgements p align center a href https www sliit lk target blank img src readmeimages sliit logo crest png width 100 a p this is a project done for the mobile application development module of bsc hons degree in information technology in sri lanka institute of information technology about the project about the project product name screen shot product screenshot https example com p purchasing expensive products for short term use is not a viable option for many people therefore many tend to rent the products when necessary however as we all have experienced finding a product to rent is a tedious task huwamaruwa is a product rental app focused on sri lankan community that connects product owners and customers directly the main intention of this app is to streamline the product rental process while exposing new opportunities for ordinary people to earn an extra income by renting out the products they own they can rent out household products under several categories such as electronics kitchen wares outdoor tools and books this app also enables equipment rental shops and individuals to take their operations online with a mobile first rental solution p p huwamaruwa app allows customers to easily discover products they are looking to rent see their availability and rental prices through the online store in order to enable equal opportunities to everyone we are planning to implement two types of rental products known as premium and non premium as the rental process involves some risk and products should be handled properly we have implemented premium products category where our service team acts as middle party between customer and the product owner moreover our delivery team picks up the product from the owner and delivers to the customer the products are carefully inspected before delivering and returning this ensures the safety of the product and the seller s lost will be covered in case of product damage premium product customers will always get quality products at an extra cost p p non premium products don t involve our intervention and seller and buyer should arrange the deal themselves this category connects both parties directly and we expect this feature may persuade many people to use the app p product owners can list the items and send offers to buyer requests they can also manage all necessary functions such as edit or delete listings buyers can rent products review them or request new products once reservations have been made online premium product customers can pay for their rental through the app a variety of methods from credit cards to cash on delivery are supported admin functions such as delivery management and customer support are also built into the app built with this section shows the list of major technologies that we have used to built our project language java 8 https www java com en p align center a href https www java com en target blank img src readmeimages java png width 150 a p development environment android studio 4 1 3 https developer android com studio gclsrc ds gclsrc ds gclid cmfrvbcwqpacfzszjgodgn8hsg p align center a href https developer android com studio gclsrc ds gclsrc ds gclid cmfrvbcwqpacfzszjgodgn8hsg target blank img src readmeimages androidstudio png width 100 a p database firebase https firebase google com gclsrc ds gclsrc ds gclid cojas9qxqpacfzktjgodgfuprg p align center a href https firebase google com gclsrc ds gclsrc ds gclid cojas9qxqpacfzktjgodgfuprg target blank img src readmeimages firebase png width 200 a p getting started getting started in order to be fully funtional and uprunning the following should be followed prerequisites the following applications must be installed java 8 jdk java 8 jre android studio 4 1 3 sh npm install npm latest g installation 1 clone the repository sh git clone https github com salitha10 huwamaruwa android app git 2 open the application using android studio 7 enter your api in config js js const api key enter your api usage examples usage use this space to show useful examples of how a project can be used additional screenshots code examples and demos work well in this space you may also link to more resources for more examples please refer to the documentation https example com roadmap roadmap see the open issues https github com othneildrew best readme template issues for a list of proposed features and known issues contributing contributing contributions are what make the open source community such an amazing place to be learn inspire and create any contributions you make are greatly appreciated 1 clone the project br git clone https github com salitha10 huwamaruwa android app git 2 create your feature branch br git checkout b feature amazingfeature 3 commit your changes br git commit m add some amazingfeature 4 push to the branch br git push origin feature amazingfeature 5 open a pull request license license distributed under the mit license see license for more information contact contact your name your twitter https twitter com your username email example com project link https github com salukadev pharmac oms git https github com salukadev pharmac oms git acknowledgements contributors it19955896 salitha tennakoon salitha10 https github com salitha10 it19142838 esala senarathna esala senarathna https github com esala senarathna it19957180 danuja wijerathne danuja wije https github com danuja wije it19987712 kavindya perera kavindya perera https github com kavindya perera markdown links images https www markdownguide org basic syntax reference style links contributors shield https img shields io github contributors othneildrew best readme template svg style for the badge contributors url https github com othneildrew best readme template graphs contributors forks shield https img shields io github forks othneildrew best readme template svg style for the badge forks url https github com othneildrew best readme template network members stars shield https img shields io github stars othneildrew best readme template svg style for the badge stars url https github com othneildrew best readme template stargazers issues shield https img shields io github issues othneildrew best readme template svg style for the badge issues url https github com othneildrew best readme template issues license shield https img shields io github license othneildrew best readme template svg style for the badge license url https github com othneildrew best readme template blob master license txt linkedin shield https img shields io badge linkedin black svg style for the badge logo linkedin colorb 555 linkedin url https linkedin com in othneildrew product screenshot images screenshot png		front_end
project01_DataEngineering_ETL	note before you make a webscraping process please review the robots txt file in some countries this process is illegal too note 2 in every directory you will find a raw file where i did some trials data engineering first step don t forget to create a virtual environment to run this project by executing the next commands in your bash cd path to the directory where you save or clone this project python3 m venv venv venv scripts activate pip install requirements txt etl process extract for this project i m gonna use a web scraping process to get data from elpais com digital version please run the next command in your shell if you just want to get the data from elpais com cd extract py extract py this returns a csv file to work with transform first of all you need to install nltk stopwords and punt so run the next command in your terminal cd transform python m nltk downloader stopwords python m nltk downloader punkt then you can run py transform py the result is a clean csv file in your directory transform load to load the data in a database i m gonna use sqlalchemy so please be sure that library is installed in your virtual environment befora run the program follow the next commands to get the db cd load py load py you will find a fille called articles db thanks there is no final recomendation just have fun changing and playing with this code on your computer you can follow me on instagram twitter facebook medium github and linkedin as fermavec or you can visit my webpage http fermavec com		server
CSC13009_Mobile	mobile app development hcmus cq2019 3 this repository contains all weekly homework and the final project group03 members 19120302 o n thu ng n 19120383 hu nh t n th 19120426 phan ng di m uy n 19120469 s nh t ng 19120492 th i duy final project summary this is a simple gallery application allowing users to manage photos on android devices language used java primary features 1 manage photos on different folders in your devices set as lockscreen wallpaper view info share via other applications 2 edit pictures bitmap processing 3 create and manage albums 4 add remove pictures into from albums 5 recycle bin trashed to temporarily store deleted pictures for recovery or permanent delete 6 add remove pictures to from favorite 7 hide selected pictures to prevent unauthorized access inside the application 8 multi selection in folders and albums allows users to delete share add to albums many pictures at the same time 9 miscellaneous sort pictures slideshow dark mode demo screenshots view and manage pictures in different folders create and manage albums demo image 01 final 20project demo gallery demo 01 jpg share pictures view info delete pictures or add it to album s demo image 02 final 20project demo gallery demo 02 jpg view and manage album s pictures add to favorite apply filters to pictures demo image 03 final 20project demo gallery demo 03 jpg select all different layouts 2 3 4 columns rorate pictures add multiple pictures to album s demo image 04 final 20project demo gallery demo 04 jpg add pictures to recycle bin when deleted can be disabled recover or permanently remove it slide show dark mode demo image 05 final 20project demo gallery demo 05 jpg		front_end
qiqqa-revengin	qiqqa revengin reverse engineering the data stored by qiqqa bibtex database etc the original goal was to find ways to update extend the qiqqa libraries using tools scripts external to qiqqa itself so as to provide absent features which i needed mostly these would concern bulk operations linking pdfs together in groups updating bibtex and other metadata for series of pdfs perform specific analysis on the imported pdf collection and update individual records accordingly etc important update 2019 july 14 qiqqa has now been published as gpl3 licensed open source on github at https github com jimmejardine qiqqa open source as announced here https getsatisfaction com qiqqa topics open source qiqqa reply 20199151 this private reverse engineering work done in the years past is now public do note that this repo is a bit ah disorganized in its root directory as this was a when there s time work on bloody qiqqa as it crashed once again darn it labor of love smile bash shell scripts in this repo scripts are available to dump the qiqqa bibtex metadata database this spits out the entire metadata content just not what qiqqa produces via the export option and most importantly for me at least this stuff still works when qiqqa has already b0rked and is crashing repeatedly on the given library note1 that s probably the most useful part of this work apart from another script which goes through the qiqqa library and seeks out all encrypted pdfs and pdfs which are not properly readable the built in qiqqa ocr left a few things to desire so these pdfs are decrypted and then periodically fed through an external ocr batch process to produce similar pdfs which have the properly ocr d text embedded for easier processing by qiqqa as both original pdf and decrypted ocr ed pdf have the same filename which is the sha1 hash of the original as calculated by qiqqa these pdfs should be easy to relate to one another in the qiqqa database that bit of work has not been done yet as it turned out easier to manage the database in other tools once qiqqa had done the basic processing my qiqqa install and re install s all failed to produce a proper search index for even smaller libraries seems qiqqa was suffering from some sort of bit rot there at least on my box cry database file format as discovered via db inspection qiqqa uses a sqlite3 database with a very simple table structure there s one table where all the files bibtex and other info is dumped in a single row per file using a json format which is verified against damage and tampering using sha1 hashes see also the qiqqa database dumper script dump qiqqa sqlite database sh https github com gerhobbelt qiqqa revengin blob master dump qiqqa sqlite database sh and the workhorse underlying it dump qiqqa sqlite database parse js https github com gerhobbelt qiqqa revengin blob master dump qiqqa sqlite database parse js note that there are several files in the root dir of this repo with an sha hash embedded in their name those sample records have been exttracted from a live qiqqa db and used to verify correct operation of the scripts now that qiqqa is open sourced a few still open questions can be answered smile record format sha1 fingerprint extension metadata md5 checksum info extra null where info field will span multiple lines per record and has itself a json format the md5 checksum field is the md5 hash of the exact info blob contents f e the example 3dd7bdd1517ad2bd59c0f75aa290d9a3 blob file binary copy of one info field a k a data column in the libraryitem sqlite table hashes to 3dd7bdd1517ad2bd59c0f75aa290d9a3 the md5 hash is stored in uppercase note that the info blob is essentially a json formatted text where line breaks are encoded as crlf not lf only this should also be observable in the example blob file 3dd7bdd1517ad2bd59c0f75aa290d9a3 blob when you inspect it with a hex binary viewer sha1 fingerprint is the sha1 hash of the related file contents downloadlocation field in the info blob json record this fingerprint is echoed in the info json blob record in the fingerprint field at the time of this writing we haven t yet tested what will happen to with a record where these data columns differ note that an incorrect md5 hash causes qiqqa to delete the entire record upon restart of the application in other words one mistake in your encoding hashing and your entire record will be nuked it also seems like qiqqa stores some sort of record count or some other truncation number as following such an encoding hashing error the entire database still exists minus the nuked records but qiqqa doesn t show its contents anymore this is under investigation at the time of this writing weird stuff s info field looks something like this filetype pdf fingerprint 60835fb1d237d8f3ed73653cc9f935fdd7fa16b1 dateaddedtodatabase 20170711004707645 datelastmodified 20170711004707645 downloadlocation c program files x86 qiqqa the qiqqa manual loex pdf bibtex article qiqqatechmatters n ttitle t techmatters qiqqa than you can say reference management a tool to organize the research process n tauthor t krista graham n tyear t 2014 n tpublication t loex quarterly n tvolume t 40 n tpages t 4 6 n title null authors null year null tags help manual comments null autosuggested pdfmetadata true titlesuggested techmatters qiqqa than you can say reference management a tool to organize the research process authorssuggested krista graham yearsuggested 2013 datelastread null note the bibtex field in there which is a json encoded bibtex record as entered in qiqqa the bibtex record from the example above actually reads like this article qiqqatechmatters title techmatters qiqqa than you can say reference management a tool to organize the research process author krista graham year 2014 publication loex quarterly volume 40 pages 4 6 it has a nice format like this because that s what happens when you hand edit a bibtex record in qiqqa itself however an actual bibtex entry may be any text including stuff like this comment bibtex skip or even invalid bibtex data qiqqa versions before 0 79 crashed on some half baked bibtex alike entries such as delete see https getsatisfaction com qiqqa topics qiqqa crash on next startup after manual editing of one or more bibtex records note1 i ve had many qiqqa re installs and re imports of entire libraries over the years cry and the inability of qiqqa to recover from a given library without losing at least part of the data is disheartening of course i could have run a backup export every day but that would mean another long running task and severe storage requirements		server
vrbrain_nuttx	px4 flight core and px4 middleware build status https travis ci org px4 firmware svg branch master https travis ci org px4 firmware coverity scan https scan coverity com projects 3966 badge svg flat 1 https scan coverity com projects 3966 tab overview gitter https badges gitter im join 20chat svg https gitter im px4 firmware utm source badge utm medium badge utm campaign pr badge utm content badge this repository contains the px4 flight core http px4 io with the main applications located in the src modules directory it also contains the px4 drone platform which contains drivers and middleware to run drones official website http px4 io license bsd 3 clause see license md https github com px4 firmware blob master license md supported airframes more experimental are supported multicopters http px4 io platforms multicopters start fixed wing http px4 io platforms planes start vtol http px4 io platforms vtol start binaries always up to date from master downloads http px4 io firmware downloads releases downloads https github com px4 firmware releases forum mailing list google groups http groups google com group px4users users please refer to the user documentation https pixhawk org users start for flying drones with the px4 flight stack developers contributing guide contributing md https github com px4 firmware blob master contributing md px4 contribution guide http px4 io dev contributing software in the loop guide use software in the loop to get started with the codebase https pixhawk org dev simulation native sitl developer guide http dev px4 io testing guide http px4 io dev unit tests this repository contains code supporting these boards snapdragon flight https www intrinsyc com qualcomm snapdragon flight fmuv1 x fmuv2 x pixhawk fmuv3 x pixhawk 2 aerocore v1 and v2 stm32f4discovery basic support tutorial https pixhawk org modules stm32f4discovery nuttshell nsh nsh usage documentation http px4 io users serial connection		os
foundation-apps	deprecation notice we believe the best solution for the future of the web is a single robust framework capable of developing webapps and websites so we ve made the decision to streamline our development and move foundation for apps into our a href http zurb com playground foundation for apps target blank experimental playground a to concentrate on foundation for sites foundation has and will continue to push the web forward and we re incredibly excited about the future you can follow along with the a href https github com zurb foundation sites wiki project roadmap target blank foundation for sites roadmap a to get more details on where the project is headed and learn how to get involved foundation for apps build status https travis ci org zurb foundation apps svg https travis ci org zurb foundation apps this is foundation for apps http foundation zurb com apps an angular powered framework for building powerful responsive web apps from your friends at zurb http zurb com requirements you ll need the following software installed to get started node js http nodejs org use the installer provided on the nodejs website git http git scm com downloads use the installer for your os windows users can also try git for windows http git for windows github io ruby https www ruby lang org en use the installer for your os for windows users jruby http jruby org is a popular alternative with ruby installed run gem install bundler sass gulp http gulpjs com and bower http bower io run sudo npm install g gulp bower get started the sass and javascript components are available on bower and npm bower install foundation apps save npm install foundation apps save you can also use our command line interface to quickly setup a basic foundation for apps project it includes a pre built gulpfile that compiles an angular powered web app for you install it with this command npm install g foundation cli bower gulp now you can make a new project foundation apps new myapp cd myapp while working on your project run npm start this will assemble the templates static assets sass and javascript you can view the test server at this url http localhost 8080 building this repo if you want to work with the source code directly or compile our documentation follow these steps git clone https github com zurb foundation apps git cd foundation apps npm install while you re working on the code run npm start the documentation can be viewed at the same url as above directory structure build this is where our documentation is assembled don t edit these files directly as they re overwritten every time you make a change docs the foundation for apps documentation scss the sass components js the angular modules and directives and other external libraries iconic a set of 24 icons from the folks at iconic https useiconic com dist compiled css and javascript files in minified and unmified flavors tests unit tests for the angular modules versioning foundation for apps follows semver so we won t introduce breaking changes in minor or patch versions the master branch will always have the newest changes so it s not necessarily production ready the stable branch will always have the most recent stable version of the framework contributing we love feedback help us find bugs and suggest improvements or new features follow us on twitter at zurbfoundation https twitter com zurbfoundation to keep up to date with what s new or to just shoot the breeze if you find a problem or have an idea open a new issue https github com zurb foundation apps issues on github when filing a bug report make sure you specify the browser and operating system you re on and toss us a screenshot or show us how we can recreate the issue		front_end
cert-issuer	verifiable credential compliance result https badgen net badge verifiable 20credentials 20v1 compliant green icon https www w3 org icons www w3c home nb v svg https www blockcerts org vc compliance report html build status https travis ci org blockchain certificates cert issuer svg branch master https travis ci org blockchain certificates cert issuer pypi version https badge fury io py cert issuer svg https badge fury io py cert issuer cert issuer the cert issuer project issues blockchain certificates by creating a transaction from the issuing institution to the recipient on the bitcoin or ethereum blockchains that transaction includes the hash of the certificate itself blockcerts v3 is released this new version of the standard leverages the w3c verifiable credentials specification https www w3 org tr vc data model and documents are signed with merkleproof2019 ld signature https w3c ccg github io lds merkle proof 2019 use of dids decentralized identifiers https www w3 org tr did core is also possible to provide more cryptographic proof of the ownership of the issuing address see section working with dids down below cert issuer v3 is not backwards compatible and does not support blockcerts v2 issuances if you need to work with v2 you need to install cert issuer v2 or use the v2 https github com blockchain certificates cert issuer tree v2 branch of this repo you may expect little to no maintenance to the v2 code at this point web resources for development or testing using web requests check out the documentation at docs web resources md docs web resources md quick start using docker getting the docker image this uses bitcoind in regtest mode this route makes many simplifications to allow a quick start and is intended for experimenting only 1 first ensure you have docker installed see our docker installation help docs docker install md 2 clone the repo and change to the directory git clone https github com blockchain certificates cert issuer git cd cert issuer 3 from a command line in cert issuer dir build your docker container docker build t bc cert issuer 1 0 4 read before running once you launch the docker container you will make some changes using your personal issuing information this flow mirrors what you would if you were issuing real certificates to avoid losing your work you should create snapshots of your docker container you can do this by running docker ps l docker commit container for your bc cert issuer my cert issuer 5 when you re ready to run docker run it bc cert issuer 1 0 bash 6 copy the blockchain certificates you issued out of the docker container to a local directory docker cp container for your bc cert issuer etc cert issuer data blockchain certificates your certificate guid json path to local dir create issuing address important this is a simplification to avoid using a usb which needs to be inserted and removed during the standard certficate issuing process do not use these addresses or private keys for anything other than experimenting ensure your docker image is running and bitcoind process is started 1 creating a wallet first with bitcoin cli createwallet wallet name bitcoin cli createwallet testwallet 2 load wallet bitcoin cli loadwallet path to the directory of the created wallet bitcoin cli loadwallet root bitcoin regtest wallets testwallet you can bitcoin cli listwallets to check if your wallet is loaded 3 create an issuing address and save the output as follows issuer bitcoin cli getnewaddress sed i bak s issuing address issuer g etc cert issuer conf ini bitcoin cli dumpprivkey issuer etc cert issuer pk issuer txt sed command allows us to quickly remove or replace the content without having to open a file 4 don t forget to save snapshots so you don t lose your work see step 4 of client setup issuing certificates 1 add your certificate to etc cert issuer data unsigned certificates to use a sample unsigned certificate as follows cp cert issuer examples data testnet unsigned certificates verifiable credential json etc cert issuer data unsigned certificates if you created your own unsigned certificate using cert tools assuming you placed it under data unsigned certificates cp cert issuer home data unsigned certificates your cert guid json etc cert issuer data unsigned certificates 2 make sure you have enough btc in your issuing address you re using bitcoind in regtest mode so you can print money this should give you 50 fake btc bitcoin cli generate 101 bitcoin cli getbalance 3 optional if you see this error fee estimation failed fallbackfee is disabled wait a few blocks or enable fallbackfee you might have to allow fallback fee in your bitcoin conf use vim as the text editor add this line to bitcoin conf fallbackfee 0 00001 vi root bitcoin bitcoin conf you have to kill bitcoind daemon using ps aux and kill pid then start it again with bitcoind daemon path to bitcoin conf file to apply new changes 4 send the money to your issuing address note that bitcoin cli s standard denomination is bitcoins not satoshis in our app the standard unit is satoshis this command sends 5 bitcoins to the address bitcoin cli sendtoaddress issuer 5 5 issue the certificates on the blockchain add verification method with issuer s did learn more about decentralized identifiers here https www w3 org tr did core and how to work with them here https github com blockchain certificates cert issuer working with dids cert issuer c etc cert issuer conf ini verification method issuer s url did or you can add verification method issuer s url did in etc cert issuer conf ini vi etc cert issuer conf ini then run cert issuer c etc cert issuer conf ini 6 your blockchain certificates are located in etc cert issuer data blockchain certificates copy these to your local machine and add them to cert viewer s cert data folder to see your certificates in the certificate viewer docker ps shows the docker containerid docker cp containerid etc cert issuer data blockchain certificates localpath cert viewer cert data how batch issuing works while it is possible to issue one certificate with one bitcoin transaction it is far more efficient to use one bitcoin transaction to issue a batch of certificates the issuer builds a merkle tree of certificate hashes and registers the merkle root as the op return field in the bitcoin transaction suppose the batch contains n certificates and certificate i contains recipient i s information the issuer hashes each certificate and combines them into a merkle tree img merkle png the root of the merkle tree which is a 256 bit hash is issued on the bitcoin blockchain the complete bitcoin transaction outputs are described in transaction structure the blockchain certificate given to recipient i contains a 2019 merkle proof signature suite https w3c ccg github io lds merkle proof 2019 formatted proof proving that certificate i is contained in the merkle tree img blockchain certificate components png this receipt contains the bitcoin transaction id storing the merkle root the expected merkle root on the blockchain the expected hash for recipient i s certificate the merkle path from recipient i s certificate to the merkle root i e the path highlighted in orange above h i merkle root the verification process https github com blockchain certificates cert verifier js verification process performs computations to check that the hash of certificate i matches the value in the receipt the merkle path is valid the merkle root stored on the blockchain matches the value in the receipt these steps establish that the certificate has not been tampered with since it was issued hashing a certificate the blockchain certificate json contents without the proof node is the certificate that the issuer created this is the value needed to hash for comparison against the receipt because there are no guarantees about ordering or formatting of json first canonicalize the certificate without the proof against the json ld schema this allows us to obtain a deterministic hash across platforms the detailed steps are described in the verification process https github com blockchain certificates cert verifier js verification process what should be in a batch how a batch is defined can vary but it should be defined such that it changes infrequently for example 2016 mit grads would be preferred over mit grads the latter would have to be updated every year the size of the batch is limited by the 100kb maximum transaction size imposed by the bitcoin network this will amount to a maximum of around 2 000 recipients per certificate batch transaction structure one bitcoin transaction is performed for every batch of certificates there is no limit to the number of certificates that may be included in a batch so typically batches are defined in logical groups such as graduates of fall 2017 robotics class img tx out png the transaction structure is the following input minimal amount of bitcoin currently 80 usd from issuer s bitcoin address outputs op return field storing a hash of the batch of certificates optional change to an issuer address the op return output is used to prove the validity of the certificate batch this output stores data which is the hash of the merkle root of the certificate batch at any time we can look up this value on the blockchain to help confirm a claim the issuer bitcoin address and timestamp from the transaction are also critical for the verification process these are used to check the authenticity of the claim as described in verification process https github com blockchain certificates cert verifier js verification process issuing options the quick start assumed you are issuing certificates in bitcoin regtest mode which doesn t actually write to a public blockchain to actually write your transaction you need to run in testnet with test coins not real money or mainnet real money we recommend starting in testnet before mainnet by default cert issuer does not assume you have a bitcoin ethereum node running locally and it uses apis to look up and broadcast transactions there is api support for both testnet and mainnet chains if you do want to use a local bitcoin node see details about installing and configuring a bitcoin node for use with cert issuer docs bitcoind md before continuing these steps walk you through issuing in testnet and mainnet mode note that the prerequisites and the configuration for the bitcoin issuing and the ethereum issuing differ prerequisites decide which chain bitcoin or ethereum to issue to and follow the steps follow the steps for the chosen chain install cert issuer by default cert issuer issues to the bitcoin blockchain run the default setup script if this is the mode you want python setup py install to issue to the ethereum blockchain run the following python setup py experimental blockchain ethereum getting started with bitcoin ethereum addresses see the docs here for helpful tips on creating funding blockchain addresses docs testnet mainnet addresses docs testnet mainnet addresses md configuring cert issuer edit your conf ini file the config file for this application see here docs ethereum configuration md for more details on ethereum configuration the private key for bitcoin should be the wif format issuing address issuing address issuer url did verification method verification method chain bitcoin regtest bitcoin testnet bitcoin mainnet ethereum goerli ethereum sepolia ethereum ropsten ethereum mainnet mockchain usb name volumes path to usb key file file you saved pk to unsigned certificates dir path to your unsigned certificates blockchain certificates dir path to your blockchain certificates work dir path to your workdir no safe mode advanced uncomment the following line if you re running a bitcoin node bitcoind notes the bitcoind option is technically not required in regtest mode regtest mode only works with a local bitcoin node the quick start in docker brushed over this detail by installing a regtest configured bitcoin node in the docker container the ethereum option does not support a local test node currently the issuer will broadcast the transaction via the etherscan api or an rpc of their choice working with dids to issue and verify a blockcerts document bound to a did you need to generate a did document referencing the public key source of the issuing address the verification supports all the did methods from the dif universal resolver https uniresolver io but it is recommended you provide your own resolver to the verification library it is also expected that the did document contains a service property configured similarly to as follows service id service 1 type issuerprofile serviceendpoint https www blockcerts org samples 3 0 issuer blockcerts json reference the did through the issuer property of the document to be issued as blockcerts either directly as a string or as the id property of an object issuer did ion eia z6lqilbb2zj evrqfq2xdm4hnqejuw5kj2z7bfooeq or issuer id did ion eia z6lqilbb2zj evrqfq2xdm4hnqejuw5kj2z7bfooeq more custom data here note that the data from the distant issuer profile has display preference in blockcerts verifier finally add to your conf ini file the verification method property pointing to the public key matching the issuing address verification method did ion eia z6lqilbb2zj evrqfq2xdm4hnqejuw5kj2z7bfooeq key 1 you may try to see the full example did document by looking up did ion eia z6lqilbb2zj evrqfq2xdm4hnqejuw5kj2z7bfooeq in the dif universal resolver https uniresolver io multiple signatures blockcerts implements chainedproof2021 draft proposal https hackmd io rygjmhagslalmaqzwyjvsq sjodwwtdk this means that cert issuer can be used to sign with merkleproof2019 a document that was already signed currently only ordered proofs are supported which means that the next merkleproof2019 proof hashes the content of the document up until the previous proof depending on the nature of the initial proof consumers might find themselves confronted to a jsonld dereferencing error when the context is not preloaded by blockcerts ecosystem please note that this may happen with context documents that are not proof context in order to circumvent this issue this library offers a way to specify specific context to be preloaded before issuance consumers will need to use both context urls and context file paths properties at the same time and values need to be specified in matching order the path to the directory where consumers store directory is left at the discretion of said consumer but you should know that it will be looked up relative to the execution path cwd cli example python m cert issuer c conf ini context urls https w3id org security suites ed25519 2020 v1 https w3id org security suites multikey 2021 v1 context file paths data context ed25519 v1 json data context multikey2021 v1 json conf ini example define in your conf ini file something like this context urls https w3id org security suites ed25519 2020 v1 https w3id org security suites multikey 2021 v1 context file paths data context ed25519 v1 json data context multikey2021 v1 json hint you can create local copies of context file with the following command curl https w3id org security suites ed25519 2020 v1 l data context ed25519 v1 json issuing 1 add your certificates to data unsigned certs 2 if you ve installed the package you can issue certificates by running python cert issuer c conf ini 3 output the blockchain certificates will be located in data blockchain certificates if you ran in the mainnet or testnet mode you can also see your transaction on a live blockchain explorer for bitcoin blockchain com has explorers for both testnet https www blockchain com explorer view btc testnet and mainnet https www blockchain com explorer view btc for ethereum etherscan has explorers for goerli https goerli etherscan io sepolia https sepolia etherscan io ropsten https ropsten etherscan io and mainnet https etherscan io the transaction id is located in the blockchain certificate under signature anchors 0 sourceid contributing more information on contributing to the cert issuer codebase can be found in docs contributing md docs contributing md advanced setup installing and using a local bitcoin node docs bitcoind md examples the files in examples data testnet contain results of previous runs faqs checking transaction status you can validate your transaction before sending by looking it up by rawtx at blockchain info example curl https blockchain info rawtx 45a9306dfe99820eb346bb17ae0b64173ac11cac2d0e4227c7a7cacbcc0bad31 cors true for an ethereum transaction you ll need to use a different explorer which might require an api key for raw json output to view a transaction in a web browser you might try something like this ethereum mainnet https etherscan io tx 0xf537d81667c8011e34e1f450e18fd1c5a8a10c770cd0acdc91a79746696f36a3 ethereum goerli testnet https goerli etherscan io tx 0xfb593f186a274f58f861e5186150bc692ed533c7af50efb094f756ccb81c7023 ethereum sepolia testnet https sepolia etherscan io tx 0xa5484369839ba54cd3be71271155fc1a76b52499607dcddbf682ee04534a3f95 ethereum ropsten testnet https ropsten etherscan io tx 0xf537d81667c8011e34e1f450e18fd1c5a8a10c770cd0acdc91a79746696f36a3 mac scrypt problems if your install on mac is failing with a message like the following try the workaround described in this thread https github com ethereum pyethereum issues 292 or the workaround described here https github com pyca cryptography issues 2692 issuecomment 272773481 fatal error openssl aes h file not found include openssl aes h contact contact us at the blockcerts community forum http community blockcerts org		blockchain
Embedded-Systems-Space-Invaders	embedded systems space invaders this embedded system space invaders minigame is a full fledged system that is built on parts of other projects it is a demonstration of my learning and experience with embedded systems this contains elements of hardware systems of dac adc as well as software implementations such as real time system design and the design of certain sprite displays		os
SER322-ProjectPart2_product-MediaManagementSystem	html head head body h1 web app media management system h1 p strong public area strong this is a content management system web application p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner png p strong private area strong admins can add delete view orders inventory strong p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner2 png p strong about strong p screenshot https github com talabh webapp mediamanagementsystem blob master imgs banner3 png html tech stack description html to add content images and description css bootstrap add style to page javascript add dynamic effects to page php send and get data from mysql db mysql database to store data phpmyadmin manage mysql through interface apache server server used for server side php logic		server
OCSystem	ocsystem blockchain underlying system php https img shields io badge php 7 1 14 blue svg https www php net swoole https img shields io badge swoole 4 3 4 green svg https github com swoole swoole src license https img shields io badge license lgpl 203 0 yellow svg https github com onlychain ocsystem blob master license open source love https badges frapsoft com os v1 open source png https opensource org welcome to the ocsystem source code library the system allows enterprise or individual developers to create more value applications with minimal barriers to entry some of the breakthrough features of ocsystem include 1 free rate limited transactions 1 zero threshold registration mechanism 1 low latency block confirmation 1 the node is a communication support for the customer by swoole 1 support for mainstream internet of things communication protocols 1 adopt an improved utxo model considering public private chain cross chain collaboration in the future 1 support public private chain cross chain collaboration contract system 1 support fixed block transaction ocsystem is distributed under the open source lgpl license and is provided as is without any express or implied warranty any security provided by ocsystem depends on how it is used configured and deployed ocsystem is based on many third party libraries such as wabt apache license and wavm bsd 3 clause which are also provided as is without any form of warranty without limiting the generality of the foregoing onlychain makes no representation or warranty that ocsystem or any third party library will perform as expected or will be free of errors errors or error codes both may fail in large or small ways may completely or partially limit functionality or compromise computer systems if you use or implement ocsystem you do so at your own risk onlychain neither starts nor runs any initial public blockchain based on ocsystem installation guide install ocsystem on centos 7 2 sh sorting out enjoy looking forward to uninstall ocsystem on centos 7 2 sh sorting out enjoy looking forward to startup commands sh sorting out enjoy looking forward to supported operating systems the following operating systems are currently supported 1 centos 7 2 geek community channel to be advised later license lgpl 3 0 license notes please refer to the copyright and license terms license license	p2p blockchain only oc onlychain php cpp swoole	blockchain
newsletter	newsletter the web app focusses on implementing full stack web development using nodejs and expressjs as backend along with use of requests module for working with third party apis this project is a template for newsletter signup page which adds users to my newsletter using mailchimp s apis		server
MarchMadness	marchmadness scripts for scraping web data storing and retrieving from a sqlite database feature engineering and predictive modeling		server
FONGEAP	fongeap mobile development rocks		front_end
prime	prime information technology		server
Deep-Learning-for-Natural-Language-Processing	deep learning for natural language processing applying deep learning approaches to various nlp tasks can take your computational algorithms to a completely new level in terms of speed and accuracy deep learning for natural language processing starts off by highlighting the basic building blocks of the natural language processing domain the book goes on to introduce the problems that you can solve using state of the art neural network models after this delving into the various neural network architectures and their specific areas of application will help you to understand how to select the best model to suit your needs as you advance through this deep learning book you ll study convolutional recurrent and recursive neural networks in addition to covering long short term memory networks lstm understanding these networks will help you to implement their models using keras in the later chapters you will be able to develop a trigger word detection application using nlp techniques such as attention model and beam search by the end of this book you will not only have sound knowledge of natural language processing but also be able to select the best text pre processing and neural network models to solve a number of nlp issues what you will learn understand various pre processing techniques for deep learning problems build a vector representation of text using word2vec and glove create a named entity recognizer and parts of speech tagger with apache opennlp build a machine translation model in keras develop a text generation application using lstm build a trigger word detection application using an attention model hardware requirements for the optimal student experience we recommend the following hardware configuration processor intel core i5 or equivalent memory 4 gb ram storage 5 gb available space software requirements we also recommend that you have the following software installed in advance os windows 7 sp1 64 bit windows 8 1 64 bit or windows 10 64 bit linux ubuntu debian red hat or suse or the latest version of os x python 3 6 5 or later preferably 3 7 available through https www python org downloads release python 371 jupyter go to https jupyter org install and follow the instructions to install alternatively you can use anaconda to install jupyter keras https keras io installation google colab it is a free jupyter notebook environment and runs on cloud infrastructure it is highly recommended as it requires no setup and has pre installed popular python packages and libraries https colab research google com notebooks welcome ipynb	nlp nlp-machine-learning nlp-library natural language deeplearning word2vec glove keras textpreprocessing partsofspeechtagger namedentityrecognizer lstm gru	ai
esp_iot_rtos_sdk	esp iot rtos sdk esp8266 new sdk based on freertos note apis of esp iot rtos sdk are same as esp iot sdk libs which are not open source in esp iot rtos sdk lib please copy them to esp iot rtos sdk lib then compile more details in wiki compile using command gen misc sh download eagle app v6 flash bin downloads to flash 0x00000 eagle app v6 irom0text bin downloads to flash 0x40000 blank bin downloads to flash 0x7e000		os
CarRenting-KeyValue	car renting application this repository contains a java application for the large scale and multistructured databases course it s the same application of the former repository carrenting https github com fontanellileonardo carrenting with the difference that in this one it has been implemented also a key value database exploiting the riak s database functionalities requirements in addition of the requirements of the former application it has been asked to perform a feasability study on the possibility of translating the data model even just a part of it to a key value model and also an implementation of it documentation for more details regarding the project you can consult the official documentation https github com fontanellileonardo carrenting blob master docs documentation pdf credits d comola e petrangeli c aparo l fontanelli		server
fzf.el	fzf el melpa https melpa org packages fzf badge svg https melpa org fzf an emacs front end for fzf 1 demo https cloud githubusercontent com assets 306502 12380684 ca0a6648 bd46 11e5 9091 841b282874e4 gif installation fzf el is available on melpa 2 below is an illustrative use package configuration of fzf el showing all available customizations and their default values note this package does not set default keybindings lisp use package fzf bind don t forget to set keybinds config setq fzf args x color bw print query margin 1 0 no hscroll fzf executable fzf fzf git grep args i line number s command used for fzf grep functions example usage for ripgrep fzf grep command rg no heading nh fzf grep command grep nrh if nil the fzf buffer will appear at the top of the window fzf position bottom t fzf window height 15 usage fzf el comes with a number of useful commands using fzf default command m x fzf m x fzf directory searching for files m x fzf find file m x fzf find file in dir m x fzf recentf project aware search m x fzf git m x fzf git files m x fzf git grep m x fzf hg m x fzf projectile grep note fzf grep with narrowing functions launch an interactive fzf grep command instead of using fuzzy filtering see the fzf advanced documentation for more details https github com junegunn fzf blob master advanced md m x fzf grep m x fzf grep dwim m x fzf grep in dir m x fzf grep with narrowing m x fzf grep dwim with narrowing m x fzf grep in dir with narrowing using input from emacs m x fzf switch buffer define custom functions fzf el exposes functions to let you interface with fzf however you d like fzf with command command action optional directory as filter initq run fzf on the output of a shell command command the shell command whose output is passed to fzf action a function that handles the result of fzf e g open a file switch to a buffer etc this package ships with two default actions that can handle opening a file and opening a file at a specific line directory directory to execute fzf in as filter if non nil use command as the filter instead of fzf s fuzzy filtering see fzf grep with narrowing functions for example usages initq if as filter is non nil initq will be used as the value for the query option if as filter is nil this does nothing fzf with entries entries action optional directory run fzf passing in an elisp list and running the function action with the user s selected results using these functions it s easy to define your own commands that use fzf lisp defun fzf example fzf with entries list a b c print or more exciting lisp defun fzf find file optional directory interactive let d fzf resolve directory directory fzf lambda x let f expand file name x d when file exists p f find file f d license gpl3 1 https github com junegunn fzf 2 https melpa org		front_end
africanlp-public-datasets	africanlp public datasets a repository for publicly freely available natural language processing nlp datasets for african languages datasets per task randomly ordered machine translation jw300 https opus nlpl eu jw300 php a parallel text dataset of 417 languages including 101 african languages tanzil https tanzil net trans a translated quran to 42 languages including african languages such as amharic hausa somali and swahili menyo 20k https github com dadelani menyo 20k mt a yor b english multi domain parallel text dataset ffr https github com bonaventuredossou ffr v1 a fon french parallel text dataset hausa corpus https github com ijdutse hausa corpus a hausa english parallel text dataset ccaligned http www statmt org cc aligned a parallel text dataset for english and 137 languages including 30 african languages paracrawl https paracrawl eu a parallel text dataset for 41 languages including somali and swahili wikimatrix https ai facebook com blog wikimatrix a parallel text dataset for 85 languages including swahili malagasy and egyptian arabic ethiopian mt datasets https github com aauthematic4lt parallel corpora for ethiopian languages a parallel text dataset for english paired with 7 ethiopian languages amharic oromifa tigrigna wolayta welaita and geez english luganda https zenodo org record 4764039 ylyhb9gzauk an english luganda parallel text dataset french fon and french ewe https zindi africa competitions ai4d takwimu lab machine translation challenge data a parallel text dataset for french paired with fon and ewe amharic english https www findke ovgu de findke en research data sets amharic english parallel corpus p 1144 html an amharic english parallel text dataset tigrinya english https gamayun translatorswb org download gamayun mini kit 5k tigrinya english a tigrinya english parallel text dataset free registration required lingala french https gamayun translatorswb org download gamayun mini kit 5k lingala french a lingala english parallel text dataset free registration required congolese swahili french min https gamayun translatorswb org download gamayun mini kit 5k swc fra small https gamayun translatorswb org download gamayun small kit 10k swc fra medium https gamayun translatorswb org download gamayun medium kit 15k chunk 1 2 swc fra congolese swahili french parallel text datasets free registration required swahili french https gamayun translatorswb org download monosw fr a synthetic swahili french parallel text dataset free registration required english hausa min https gamayun translatorswb org download gamayun 5k english hausa small https gamayun translatorswb org download gamayun small kit 10k hausa english english hausa parallel text datasets free registration required english swahili https gamayun translatorswb org download gamayun 5k english swahili an english swahili parallel text dataset free registration required english kanuri https gamayun translatorswb org download gamayun mini kit 5k kanuri english an english kanuri parallel text dataset free registration required english akuapem twi https zenodo org record 4432117 ymef3fkzbiu an english akwapem twi parallel text dataset flores 101 https github com facebookresearch flores fbclid iwar1dkupj1xa4pibznx9vm6wwrqufxkv1au1 nvdfggec4lygx0pfksy36n4 a parallel text dataset for 101 languages including 20 african languages isixhosa english https opus nlpl eu xhosanavy php an isixhosa english parallel text dataset tatoeba https opus nlpl eu tatoeba php a parallel text dataset for 409 languages including 28 african languages gnome https opus nlpl eu gnome php a technical domain parallel text dataset for 197 languages including 16 african languages ubuntu https opus nlpl eu ubuntu php a technical domain parallel text dataset for 244 languages including 24 african languages opus 100 https opus nlpl eu opus 100 php a parallel text dataset for 100 languages including 9 african languages tico 19 https tico 19 github io a covid 19 domain parallel text dataset for 37 languages including 13 african languages mozila localization https opus nlpl eu mozilla i10n php a parallel text dataset for 197 languages including 18 african languages text classification kinnews and kirnews https github com andrews2017 kinnews and kirnews corpus news classification datasets for kinyarwanda kinnews and kirundi kirnews setswana and sepedi https zenodo org record 3668495 ylyjvdgzauk news classification datasets for setswana and sepedi swahili news https zenodo org record 4300294 ylyj6tgzauk a news classification dataset for swahili amharic news text classification https github com israelabebe an amharic news text classification dataset news text classification dataset for amharic voa hausa and bbc yoruba news classification https github com uds lsv transfer distant transformer african news title classification dataset for hausa and yoruba sentiment analysis tunizi https zenodo org record 4275240 ylym99gzauk a tunizian arabizi sentiment analysis dataset naijasenti https github com hausanlp naijasenti a sentiment analysis dataset for hausa igbo yoruba and nigerian pidgin text summarization amharic summarization https github com theamrzaki text summurization abstractive methods tree master amharic a dataset for amharic abstractive text summarization xl sum https github com csebuetnlp xl sum a dataset for multilingual abstractive text summarization for 44 languages including 10 african languages named entity recognition masakhaner https github com masakhane io masakhane ner a dataset for named entity recognition of 10 african languages wikiann https metatext io datasets wikiann a dataset for named entity recognition for 282 languages including several african languages yoruba gv ner https github com ajesujoba yorubatwi embedding yoruba named entity recognition dataset hausa voa ner https github com uds lsv transfer distant transformer african hausa named entity recognition dataset automated speech recognition asr alffa https github com besacier alffa public an asr dataset for amharic hausa swahili and wolof ammi asr dataset https github com besacier ammicourse tree master students return an asr dataset for 19 languages including 16 african languages commonvoice https commonvoice mozilla org en languages an ongoing asr dataset project for 60 languages as of may 2021 including kinyarwanda kabyle luganda and hausa fon https paperswithcode com dataset fongbe speech recognition an asr dataset for fon swahili https gamayun translatorswb org download swahili audio mini kit a swahili speech dataset free registration required congolese swahili https gamayun translatorswb org download congolese swahili audio mini kit a congolese swahili speech dataset free registration required bembaspeech https github com csikasote bembaspeech an asr dataset for bemba spcs speech https repo sadilar org handle 20 500 12185 530 a sepedi speech dataset sadilar tts https repo sadilar org handle 20 500 12185 527 asr datasets for afrikaans sesotho setswana and isixhosa nchlt speech speech datasets for south african s eleven official languages including afrikaans https repo sadilar org handle 20 500 12185 280 xitsonga https repo sadilar org handle 20 500 12185 277 setswana https repo sadilar org handle 20 500 12185 281 sesotho https repo sadilar org handle 20 500 12185 278 sepedi https repo sadilar org handle 20 500 12185 270 isizulu https repo sadilar org handle 20 500 12185 275 tshivenda https repo sadilar org handle 20 500 12185 276 siswati https repo sadilar org handle 20 500 12185 271 isixhosa https repo sadilar org handle 20 500 12185 279 and isindebele https repo sadilar org handle 20 500 12185 272 iarpa babel swahili data https catalog ldc upenn edu ldc2017s05 an asr dataset for swahili require payment of 25 speech translation mboshi https github com besacier mboshi french parallel corpus mboshi french parallel speech dataset paper https www aclweb org anthology l18 1531 pdf iwslt 2021 speech translation https drive google com file d 1lhifoey0kzj6s11w takovw mavzzz04 view speech translation datasets for swahili english and congolese swahili french monolingual data swahili language modeling https zenodo org record 3553423 ylyppngzauk a swahili dataset for language modeling and additional datasets for swahili syllabic alphabet https zenodo org record 3544180 ylyqongzauk and swahili word analogy https zenodo org record 3529878 ylyqx9gzauk oscar https oscar corpus com a multilingual dataset for 166 languages including amharic somalia yoruba egyptian arabic malagasy swahili and afrikaans luganda agriculture data bukedde https github com ai lab makerere data4good tree master bukedde 20newspaper 20agriculture 20data wikipedia https github com ai lab makerere data4good tree master wikipedia 20agriculture 20data monolingual datasets for luganda in agricultural domain from bukedde and wikipedia isixhosa https repo sadilar org handle 20 500 12185 524 a monolingual dataset for isixhosa mc4 https github com allenai allennlp discussions 5265 a multilingual dataset for 101 languages including 13 african languages mot v1 0 https github com bltlab mot a multilingual dataset for 44 languages including 11 african languages phonetic dictionary ipa dict https github com open dict data ipa dict a phonetic dictionary for 23 languages including swahili za lex https github com nwu must za lex lexical pronunciation datasets for 6 languages spoken is south africa afrikaans southern sotho xhosa zulu sa english and tswana question answering dialogue systems recommendation systems part of speech tagging other potential sources masakhane https github com masakhane io masakhane community blob master list of datasets md lanfrica https lanfrica com contributions this is a growing list of nlp datasets for african languages please if there is any publicly available dataset i missed out kindly feel free to add it by doing a pull request contacting me on twitter https twitter com andre niyongabo or emailing me at niyongabor andre gmail com	african-languages low-resource-languages natural-language-processing datasets	ai
tutorials	h1 align center a href https cdnjs com img src https raw githubusercontent com cdnjs brand master logo standard dark 512 png width 175px alt cdnjs a h1 h3 align center the 1 free and open source cdn built to make life easier for developers h3 table of contents introduction introduction contributing contributing tutorial listings tutorial listings why host your tutorial on cdnjs why host your tutorial on cdnjs introduction we want you to help us write awesome community driven open source web development tutorials by developers for developers here s a simple list of questions to help you figure out if you should publish a web development tutorial on cdnjs do you love web development do you want to help other people learn web development did you just learn about something awesome that you think other people would find awesome too if you answered yes to any of the above we want you to write a web development tutorial and publish it on cdnjs it s super easy contributing simply fork the repository and copy the example tutorial https github com cdnjs tutorials tree master backbone js organizing backbone using modules each tutorial should have a master index md file which will contain the contents of your tutorial and it should also contain a tutorial json file which contains all the meta information about your tutorial we want to make a super beautiful and useful tutorial system where anyone can get setup in minutes tutorial listings tutorials show under each associated library on the cdnjs com https cdnjs com website and are also all available via the api https cdnjs com api at the moment we don t have support for generic javascript tutorials but please leave an issue if you want to write one related tutorials http i imgur com mdoepcw png why host your tutorial on cdnjs cdnjs com https cdnjs com is visited by hundreds of thousands of web developers every month you don t have to worry about setting up an elegant website to display your tutorials authors get full credit and can remove their work at any time everything is hosted on github so the community can easily help fix bugs and grammatical mistakes in your tutorials license each library is released under its own license this cdnjs repository and the tutorials within are published under mit license license	tutorial document tutorials community cdnjs	front_end
staketaxcsv	staketaxcsv python repository to create blockchain csvs for algorand algo cosmos atom agoric bld bitsong btsg sentinel dvpn evmos evmos fetch ai fet chihuahua huahua iotex iotx juno juno kujira kuji kyve network kyve terra classic lunc terra 2 0 luna osmosis osmo solana sol and stargaze stars blockchains csv codebase for https stake tax contributions prs highly encouraged such as support for new txs blockchains or csv formats examples add cosmo based blockchain csv https docs stake tax devs adding csv in cosmos based ecosystem add new csv format https docs stake tax devs add new csv format example install 1 install python 3 9 one way readme reference md installing python 39 on macos 1 install pip packages pip3 install r requirements txt usage load environment variables add to bash profile or bashrc to avoid doing every time set o allexport source sample env set o allexport usage as cli remember to add your path to staketaxcsv src to the pythonpath environment variable same arguments apply for report algo py algo report atom py atom report py sh cd src create default csv python3 staketaxcsv report atom py wallet address create all csv formats i e koinly cointracking etc python3 staketaxcsv report atom py wallet address format all show csv result for single transaction great for development debugging python3 staketaxcsv report atom py wallet address txid txid show csv result for single transaction in debug mode great for development debugging python3 staketaxcsv report atom py wallet address txid txid debug usage as staketaxcsv module import staketaxcsv help staketaxcsv api address some address txid some txid staketaxcsv formats default balances accointing bitcointax coinledger coinpanda cointelli cointracking cointracker cryptio cryptocom cryptotaxcalculator cryptoworth koinly recap taxbit tokentax zenledger staketaxcsv tickers algo atom bld btsg dvpn evmos fet huahua iotx juno kuji luna1 luna2 osmo regen sol stars write single transaction csv staketaxcsv transaction atom address txid koinly write koinly csv staketaxcsv csv atom address koinly write all csvs koinly cointracking etc staketaxcsv csv all atom address check address is valid staketaxcsv has csv atom address true docker see docker readme reference md docker to alternatively install run in docker container contributing code see linting readme reference md linting to see code style feedback providing a sample txid will expedite a pull request email support stake tax dm staketax etc for a given txid your pr most commonly should print different output before after python3 staketaxcsv report osmo py wallet address txid txid reference see readme reference md readme reference md code style readme reference md code style linting readme reference md linting unit tests readme reference md unit tests docker readme reference md docker ideal configuration readme reference md ideal configuration rpc node settings readme reference md rpc node settings db cache readme reference md db cache installing python 3 9 9 on macos readme reference md installing python 39 on macos		blockchain
camtools	camtools camera tools for computer vision camtools is a collection of tools for handling cameras in computer vision it can be used for plotting converting projecting ray casting and doing more with camera parameters it follows the standard camera coordinate system with clear and easy to use apis formatter https github com yxlao camtools actions workflows formatter yml badge svg https github com yxlao camtools actions workflows formatter yml unit test https github com yxlao camtools actions workflows unit test yml badge svg https github com yxlao camtools actions workflows unit test yml pypi https github com yxlao camtools actions workflows pypi yml badge svg https github com yxlao camtools actions workflows pypi yml github https img shields io badge github 323940 svg style flat logo github logocolor 959da5 https github com yxlao camtools gitee https img shields io badge gitee 323940 svg style flat logo gitee logocolor 959da5 https gitee com yxlao camtools pypi https img shields io pypi v camtools style flat label pypi logo pypi logocolor 959da5 labelcolor 323940 color 808080 https pypi org project camtools what can you do with camtools 1 plot cameras useful for debugging 3d reconstruction and nerfs python import camtools as ct import open3d as o3d cameras ct camera create camera frames ks ts o3d visualization draw geometries cameras p align center img src camtools assets camera frames png width 360 p 2 convert camera parameters python pose ct convert t to pose t convert t to pose t ct convert pose to t pose convert pose to t r t ct convert t to r t t convert t to r and t c ct convert pose to c pose convert pose to camera center k t ct convert p to k t p decompose projection matrix p to k and t and more 3 projection and ray casting python project 3d points to pixels pixels ct project points to pixel points k t back project depth image to 3d points points ct project im depth to points im depth k t ray cast a triangle mesh to depth image im depth ct raycast mesh to depths mesh ks ts height width and more 4 image and depth i o with no surprises strict type checks and range checks are enforced the image and depth i o apis are specifically designed to solve the following pain points is my image of type float32 or uint8 does it have range 0 1 or 0 255 is it rgb or bgr does my image have an alpha channel when saving depth image as integer based png is it correctly scaled python ct io imread ct io imwrite ct io imread detph ct io imwrite depth 5 command line tools ct runs in terminal bash crop image boarders ct crop boarders png pad pixel 10 skip cropped same crop draw synchronized bounding boxes interactively ct draw bboxes path to a png path to b png for more command line tools ct help p align center img src https user images githubusercontent com 1501945 241416210 e11ff3bf 22e6 46c0 8ba0 d177a0015323 png width 400 p 6 and more solve line intersections colmap tools points normalization installation to install camtools simply do bash pip install camtools alternatively you can install camtools from source with one of the following methods bash git clone https github com yxlao camtools git cd camtools installation mode if you want to use camtools only pip install dev mode if you want to modify camtools on the fly pip install e dev mode and dev dependencies if you want to modify camtools and run tests pip install e dev camera coordinate system a homogeneous point x y z 1 in the world coordinate can be projected to a homogeneous point x y 1 in the image pixel coordinate using the following equation lambda left begin array l x y 1 end array right left begin array ccc f x 0 c x 0 f y c y 0 0 1 end array right left begin array llll r 00 r 01 r 02 t 0 r 10 r 11 r 12 t 1 r 20 r 21 r 22 t 2 end array right left begin array c x y z 1 end array right we follow the standard opencv style camera coordinate system as shown below p align center img src camtools assets camera coordinates svg width 480 p camera coordinate right handed with z pointing away from the camera towards the view direction and y axis pointing down note that the opencv convention camtools default is different from the opengl blender convention where z points towards the opposite view direction and the y axis points up to convert between the opencv camera coordinates and the opengl style coordinates use the conversion functions such as ct convert t opencv to opengl ct convert t opengl to opencv ct convert pose opencv to opengl and ct convert pose opengl to opencv etc image coordinate starts from the top left corner of the image with x pointing right corresponding to the image width and y pointing down corresponding to the image height this is consistent with opencv pay attention that the 0th dimension in the image array is the height i e y and the 1st dimension is the width i e x that is x u width column the 1st dimension y v height row the 0th dimension k 3 3 camera intrinsic matrix python k fx s cx 0 fy cy 0 0 1 t or w2c 4 4 camera extrinsic matrix python t r t r00 r01 r02 t0 0 1 r10 r11 r12 t1 r20 r21 r22 t2 0 0 0 1 t is also known as the world to camera w2c matrix which transforms a point in the world coordinate to the camera coordinate t s shape is 4 4 not 3 4 t is the inverse of pose i e np linalg inv t pose the camera center c in world coordinate is projected to 0 0 0 1 in camera coordinate r 3 3 rotation matrix python r t 3 3 r is a rotation matrix it is an orthogonal matrix with determinant 1 as rotations preserve volume and orientation r t np linalg inv r np linalg norm r x np linalg norm x where x is a 3 vector t 3 translation vector python t t 3 3 t s shape is 3 not 3 1 pose or c2w 4 4 camera pose matrix it is the inverse of t pose is also known as the camera to world c2w matrix which transforms a point in the camera coordinate to the world coordinate pose is the inverse of t i e pose np linalg inv t c camera center python c pose 3 3 c s shape is 3 not 3 1 c is the camera center in world coordinate it is also the translation vector of pose p 3 4 the camera projection matrix p is the world to pixel projection matrix which projects a point in the homogeneous world coordinate to the homogeneous pixel coordinate p is the product of the intrinsic and extrinsic parameters python p k r t p k np hstack r t none p s shape is 3 4 not 4 4 it is possible to decompose p into intrinsic and extrinsic matrices by qr decomposition don t confuse p with pose don t confuse p with t for more details please refer to the following blog posts part 1 https ksimek github io 2012 08 14 decompose part 2 https ksimek github io 2012 08 22 extrinsic and part 3 https ksimek github io 2013 08 13 intrinsic future works refined apis full pytorch numpy compatibility unit tests	3d-reconstruction camera computer-vision extrinsic-parameters intrinsic-parameters nerf calibration extrinsic extrinsics intrinsic intrinsics lidar ray raycasting computer-graphics opencv opengl	ai
wiki.blockchain	awesome blockchain https github com xilibi2003 wiki blockchain https github com lbc team blockchain bitcoin readme md ethereum readme md eos eos readme md hyperledger readme md ipfs filecoin ipfs readme md cross chain readme md anonymous readme md future readme md course courses md community readme md sdk github 1 eos 2 layer2 dag issues https github com xilibi2003 wiki blockchain issues pull request https github com xilibi2003 wiki blockchain https github com sindresorhus awesome blob master contributing md https learnblockchain cn https wiki learnblockchain cn	awesome-blockchain	blockchain
backend	better professor backend contributors alexis j carr alexisjcarr https github com alexisjcarr		server
Wis3D	wis3d a web based 3d visualization tool for 3d computer vision online demo http wis3d idr ai installation basic installation tutorial basic installation documentation https wis3d doc readthedocs io wis3d is a web based 3d visualization tool built for 3d computer vision researchers you can import 3d bounding box point clouds meshes and feature correspondences directly from your python code and view them in your local browser you can think of it as tensorboard https www tensorflow org tensorboard but with 3d data as the first class citizen p align center img src https github com zju3dv wis3d blob main docs source static introduction 3d scene demo gif raw true width 44 img src https github com zju3dv wis3d blob main docs source static introduction human demo gif raw true width 44 img src https github com zju3dv wis3d blob main docs source static introduction keypoint correspondences demo gif raw true width 45 img src https github com zju3dv wis3d blob db7105d60b286e3e0d3896df1045e06a153b2c65 docs source static introduction mesh and camera gif raw true width 45 p basic installation install from pre built whl bash pip install https github com zju3dv wis3d releases download 2 0 0 wis3d 2 0 0 py3 none any whl or build from source 1 install node js https nodejs org en download 2 run pip install r requirements txt 3 build web pages bash cd wis3d app npm install install dependencies npx next build npx next export 4 install package bash cd python setup py develop web page https github com zju3dv wis3d blob main docs source static tutorials 3d objects 3d objects png raw true https github com zju3dv wis3d blob main docs source static tutorials keypoint correspondences keypoint correspondences png raw true quick start please reference to examples test py for more usage see documentation https wis3d doc readthedocs io start the web server start the web service to view the visualization in the browser bash wis3d vis dir path to vis dir host 0 0 0 0 port 19090 open your browser and enter http localhost 19090 to see the results authors project lead jiaming sun https jiamingsun ml xiaowei zhou https xzhou me core members linghao chen ootts github io jingmeng zhang https github com ahazss hongcheng zhao https github com hongchengzhao siyu zhang https derizsy github io past contributors zijing huang citation article sun2022onepose title onepose one shot object pose estimation without cad models author sun jiaming and wang zihao and zhang siyu and he xingyi and zhao hongcheng and zhang guofeng and zhou xiaowei journal cvpr year 2022		ai
Teaching-TWEB-2018-Labo2	labo 2 ui component design system components are great way to design and develop you ui using smaller reusable pieces with better consistency companies like uber https www uber design case studies rebrand pinterest https pinterest github io gestalt airbnb https airbnb design building a visual language and many more achieve consistency in their ui through a component based design system the objective of this project part 1 2 is to get familiar with react and learn how to think in components by authoring a small ui component design system in the next project part 2 2 you ll use the components you built to create an actual app by adding data fetching with graphql https graphql org and routing using react router https reacttraining com react router web guides quick start objectives part 1 2 this lab might be your first experience with react or with a javascript framework so we ll keep the scope small make a really simplified version of github s issues page just focus on implementing the list of issues and ignore the stuff in the header search filtering stars etc react issues list docs react issues list png implement the issue detail page too react issue details docs react issue details png step 1 set up your project don t waste too much time on scaffolding the project consider using some boilerplate like create react app https facebook github io create react app then you ll need a tool that will help you implement a ui component design system you can achieve this by adding storybook https github com storybooks storybook to your project or any other similar library check out some examples https storybook js org examples step 2 break the ui into a component hierarchy by analyzing github s issues page you should be able to draw boxes around every components and subcomponents one such technique is the single responsibility principle https en wikipedia org wiki single responsibility principle that is a component should ideally only do one thing if it ends up growing it should be decomposed into smaller subcomponents step 3 build presentational components presentational components are only concerned with how things look they have no dependencies on the rest of your app they receive data and callbacks exclusively via props for example you may have presentational components such as issuelistitem label or button use storybook to build and document your components step 4 deploy your ui components design system if you re using storybook check out this link to learn how to export your storybook as a static app and deploy it somewhere like github pages https storybook js org basics exporting storybook deploying to github pages objectives part 2 2 the final step for this lab is to use the components you built to create a fully functionnal app here is what a user should be able to do with your app search or select a repository view the list of issues for a repository filter closed open issues view the details of an issue in a dialog or in a new page resources render the issue s markdown text and its comments using something like react markdown https github com rexxars react markdown react components that implement google s material design material ui https material ui com react ui library that follows the ant design specification ant design https ant design docs react introduce declarative routing for react react router https reacttraining com react router web guides quick start		os
xam	xam build status https travis ci org maxhalford xam svg branch master https travis ci org maxhalford xam xam is my personal data science and machine learning toolbox it is written in python 3 and stands on the shoulders of giants mainly pandas https pandas pydata org and scikit learn http scikit learn org it loosely follows scikit learn s fit transform predict convention installation install anaconda for python 3 x 3 5 https www continuum io downloads run pip install git https github com maxhalford xam upgrade in a terminal warning because xam is a personal toolkit the upgrade flag will install the latest releases of each dependency scipy pandas etc i like to stay up to date with the latest library versions table of contents usage example is available in the docs docs folder each example is tested with doctest https pymotw com 2 doctest ensembling docs ensembling md groupby model docs ensembling md groupby model lightgbm with cv docs ensembling md lightgbm with cv stacking docs ensembling md stacking stacking with bagged test predictions docs ensembling md stacking with bagged test predictions exploratory data analysis eda docs eda md feature importance docs eda md feature importance feature extraction docs feature extraction md bayesian target encoding docs feature extraction md bayesian target encoding combining features docs feature extraction md combining features count encoding docs feature extraction md count encoding cyclic features docs feature extraction md cyclic features feature selection docs feature selection md forward backward selection docs feature selection forward backward selection linear models docs linear models md auc regressor docs linear models md auc regressor model selection docs model selection md ordered cross validation docs model selection md ordered cross validation natural language processing nlp docs nlp md nb svm docs nlp md nb svm norvig spelling corrector docs nlp md norvig spelling corrector top terms classifier docs nlp md top terms classifier pipeline docs pipeline md column selection docs pipeline md column selection series transformer docs pipeline md series transformer dataframe transformer docs pipeline md dataframe transformer lambda transformer docs pipeline md lambda transformer plotting docs plotting md latex style figures docs plotting md latex style figures preprocessing docs preprocessing md binning docs preprocessing md binning groupby transformer docs preprocessing md groupby transformer one hot encoding docs preprocessing md one hot encoding resampling docs preprocessing md resampling time series analysis tsa docs tsa md exponentially weighted average docs tsa md ewm optimization exponential smoothing docs tsa md exponential smoothing frequency average forecasting docs tsa md frequency average forecasting various docs various md datetime range docs various md datetime range next day of the week docs various md next day of the week subsequence lengths docs various md subsequence lengths dataframe to vowpal wabbit docs various md dataframe to vowpal wabbit normalized compression distance docs various md normalized compression distance skyline querying docs various md skyline querying fuzzy duplicates docs various md fuzzy duplicates other python data science and machine learning toolkits fastai fastai https github com fastai fastai laurae2 laurae https github com laurae2 laurae rasbt mlxtend https github com rasbt mlxtend reiinakano scikit plot https github com reiinakano scikit plot scikit learn contrib https github com scikit learn contrib zygmuntz phraug2 https github com zygmuntz phraug2 license the mit license mit please see the license file license for more information	python machine-learning data-science preprocessing stacking	ai
Flight_Delays_Data_Engineering	flight delays data engineering this code is part of a larger data engineering project on google cloud platform that focuses on extraction transformation and loading of flight delay data metadata		cloud
booknlp	booknlp booknlp is a natural language processing pipeline that scales to books and other long documents in english including part of speech tagging dependency parsing entity recognition character name clustering e g tom tom sawyer mr sawyer thomas sawyer tom sawyer and coreference resolution quotation speaker identification supersense tagging e g animal artifact body cognition etc event tagging referential gender inference tom sawyer he him his booknlp ships with two models both with identical architectures but different underlying bert sizes the larger and more accurate big model is fit for gpus and multi core computers the faster small model is more appropriate for personal computers see the table below for a comparison of the difference both in terms of overall speed and in accuracy for the tasks that booknlp performs small big entity tagging f1 88 2 90 0 supersense tagging f1 73 2 76 2 event tagging f1 70 6 74 1 coreference resolution avg f1 76 4 79 0 speaker attribution b3 86 4 89 9 cpu time 2019 macbook pro mins 3 6 15 4 cpu time 10 core server mins 2 4 5 2 gpu time titan rtx mins 2 1 2 2 timings measure speed to run booknlp on a sample book of the secret garden 99k tokens to explore running booknlp in google colab on a gpu see this notebook https colab research google com drive 1c9nlqgrbj fup2qje49h21hb4kuxdu k usp sharing installation create anaconda environment if desired first download and install anaconda https www anaconda com download then create and activate fresh environment sh conda create name booknlp python 3 7 conda activate booknlp if using a gpu install pytorch for your system and cuda version by following installation instructions on https pytorch org https pytorch org install booknlp and download spacy model sh pip install booknlp python m spacy download en core web sm usage python from booknlp booknlp import booknlp model params pipeline entity quote supersense event coref model big booknlp booknlp en model params input file to process input file input dir bartleby the scrivener txt output directory to store resulting files in output directory output dir bartleby file within this directory will be named book id entities book id tokens etc book id bartleby booknlp process input file output directory book id this runs the full booknlp pipeline you are able to run only some elements of the pipeline to cut down on computational time by specifying them in that parameter e g to only run entity tagging and event tagging change model params above to include pipeline entity event this process creates the directory output dir bartleby and generates the following files bartleby bartleby tokens this encodes core word level information each row corresponds to one token and includes the following information paragraph id sentence id token id within sentence token id within document word lemma byte onset within original document byte offset within original document pos tag dependency relation token id within document of syntactic head event bartleby bartleby entities this represents the typed entities within the document e g people and places along with their coreference coreference id unique entity id start token id within document end token id within document nom nominal prop proper or pron pronoun per person loc location fac facility gpe geo political entity veh vehicle org organization text of entity bartleby bartleby supersense this stores information from supersense tagging start token id within document end token id within document supersense category verb cognition verb communication noun artifact etc bartleby bartleby quotes this stores information about the quotations in the document along with the speaker in a sentence like yes she said where she elizabeth bennett she is the attributed mention of the quotation yes and is coreferent with the unique entity elizabeth bennett start token id within document of quotation end token id within document of quotation start token id within document of attributed mention end token id within document of attributed mention attributed mention text coreference id unique entity id of attributed mention quotation text bartleby bartleby book json file providing information about all characters mentioned more than 1 time in the book including their proper common pronominal references referential gender actions for the which they are the agent and patient objects they possess and modifiers bartleby bartleby book html html file containing a the full text of the book along with annotations for entities coreference and speaker attribution and b a list of the named characters and major entity catgories fac gpe loc etc annotations entity annotations the entity annotation layer covers six of the ace 2005 categories in text people per tom sawyer her daughter facilities fac the house the kitchen geo political entities gpe london the village locations loc the forest the river vehicles veh the ship the car organizations org the army the church the targets of annotation here include both named entities e g tom sawyer common entities the boy and pronouns he these entities can be nested as in the following img src img nested structure png alt drawing width 300 for more see david bamman sejal popat and sheng shen an annotated dataset of literary entities http people ischool berkeley edu dbamman pubs pdf naacl2019 literary entities pdf naacl 2019 the entity tagging model within booknlp is trained on an annotated dataset of 968k tokens including the public domain materials in litbank https github com dbamman litbank and a new dataset of 500 contemporary books including bestsellers pulitzer prize winners works by black authors global anglophone books and genre fiction article forthcoming event annotations the event layer identifies events with asserted realis depicted as actually taking place with specific participants at a specific time as opposed to events with other epistemic modalities hypotheticals future events extradiegetic summaries by the narrator text events source my father s eyes had closed upon the light of this world six months when mine opened on it closed opened dickens david copperfield call me ishmael melville moby dick his sister was a tall strong girl and she walked rapidly and resolutely as if she knew exactly where she was going and what she was going to do next walked cather o pioneers for more see matt sims jong ho park and david bamman literary event detection http people ischool berkeley edu dbamman pubs pdf acl2019 literary events pdf acl 2019 the event tagging model is trained on event annotations within litbank https github com dbamman litbank the small model above makes use of a distillation process by training on the predictions made by the big model for a collection of contemporary texts supersense tagging supersense tagging https aclanthology org w06 1670 pdf provides coarse semantic information for a sentence by tagging spans with 41 lexical semantic categories drawn from wordnet spanning both nouns including plant animal food feeling and artifact and verbs including cognition communication motion etc example source the station wagons sub artifact sub arrived sub motion sub at noon sub time sub a long shining line sub group sub that coursed sub motion sub through the west campus sub location sub delillo white noise the booknlp tagger is trained on semcor https web eecs umich edu mihalcea downloads html semcor character name clustering and coreference the coreference layer covers the six ace entity categories outlined above people facilities locations geo political entities organizations and vehicles and is trained on litbank https github com dbamman litbank and preco https preschool lab github io preco example source one may as well begin with helen sub x sub s letters to her sub x sub sister sub y sub forster howard s end accurate coreference at the scale of a book length document is still an open research problem and attempting full coreference where any named entity elizabeth common entity her sister his daughter and pronoun she can corefer tends to erroneously conflate multiple distinct entities into one by default booknlp addresses this by first carrying out character name clustering grouping tom tom sawyer and mr sawyer into a single entity and then allowing pronouns to corefer with either named entities tom or common entities the boy but disallowing common entities from co referring to named entities to turn off this mode and carry out full corefernce add pronominalcorefonly false to the model params parameters dictionary above but be sure to inspect the output for more on the coreference criteria used in this work see david bamman olivia lewke and anya mansoor 2020 an annotated dataset of coreference in english literature https arxiv org abs 1912 01140 lrec referential gender inference booknlp infers the referential gender of characters by associating them with the pronouns he him his she her they them xe xem xyr xir etc used to refer to them in the context of the story this method encodes several assumptions booknlp describes the referential gender of characters and not their gender identity characters are described by the pronouns used to refer to them e g he him she her rather than labels like m f prior information on the alignment of names with referential gender e g from government records or larger background datasets can be used to provide some information to inform this process if desired e g tom is often associated with he him in pre 1923 english texts name information however should not be uniquely determinative but rather should be sensitive to the context in which it is used e g tom in the book tom and some other girls where tom is aligned with she her by default booknlp uses prior information on the alignment of proper names and honorifics with pronouns drawn from 15k works from project gutenberg this prior information can be ignored by setting referential gender hyperparameterfile none in the model params file alternative priors can be used by passing the pathname to a prior file in the same format as english data gutenberg prop gender terms txt to this parameter users should be free to define the referential gender categories used here the default set of categories is he him his she her they them their xe xem xyr xir and ze zem zir hir to specify a different set of categories update the model params setting to define them referential gender cats he him his she her they them their xe xem xyr xir ze zem zir hir speaker attribution the speaker attribution model identifies all instances of direct speech in the text and attributes it to its speaker quote speaker source come up kinch come up you fearful jesuit buck mulligan 0 joyce ulysses oh dear oh dear i shall be late the white rabbit 4 carroll alice in wonderland do n t put your feet up there huckleberry miss watson 26 twain huckleberry finn this model is trained on speaker attribution data in litbank https github com dbamman litbank for more on the quotation annotations see this paper https arxiv org pdf 2004 13980 pdf part of speech tagging and dependency parsing booknlp uses spacy https spacy io for part of speech tagging and dependency parsing acknowledgments table tr td img width 250 src https www neh gov sites default files inline files neh preferred seal820 jpg td td img width 150 src https www nsf gov images logos nsf 4 color bitmap logo png td td booknlp is supported by the national endowment for the humanities haa 271654 20 and the national science foundation iis 1942591 td tr table	natural-language-processing cultural-analytics digital-humanities computational-social-science	ai
mbOS	mbos mbos is a free open source embedded operating system it includes all the features you need to develop a connected product based on an arm microcontroller including connectivity an rtos and drivers for sensors and i o devices with an rtos core based on the cmsis rtos specification mbos supports deterministic multithreaded real time software execution the rtos primitives are always available allowing drivers and applications to rely on features such as threads semaphores and mutexes currently mbos is available for the following architectures arm cortex m cores cortex m0 m0 m1 m3 m4 arm7 arm9 and arm11 processor families thumb and arm modes kmx32 core development of llc km211 mbos is tested across arm compiler 5 arm compiler 6 gcc and iar compiler license and contributions the software is provided under the apache 2 0 license contributions to this project are accepted under the same license you can use mbos in commercial and personal projects with confidence	rtos drivers cmsis-drivers aduc stm32	os
Employee-Database-SQL	employee database sql created an sql database for employee data and conducted data modeling data engineering and data analysis on the data uploaded to database background a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files the next step is to design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words to perform the following 1 data modeling 2 data engineering 3 data analysis b data modeling b in this step an erd entity relation database diagram was built with 6 tables to inspect the 6 csv files image of erd diagram https github com poojanagrecha employee database sql blob main employeesql erd png b data engineering b in this step we use the information to create a table schema for each of the six csv files and import each csv file into the corresponding sql table b data analysis b once we have a complete database we run the sql queries to obtain the following 1 list the following details of each employee employee number last name first name gender and salary 2 list employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name and start and end employment dates 4 list the department of each employee with the following information employee number last name first name and department name 5 list all employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name		server
OptiMUS	optimus optimization modeling using mip solvers and large language models this repository contains the official implementation for optimus optimization modeling using mip solvers and large language models https arxiv org abs 2310 06116 br br optimus agent https github com teshnizi optimus assets 48642434 23b1ab19 8248 4aad 8831 02cc895afffd nlp4lp dataset you can download the dataset from https nlp4lp vercel app https nlp4lp vercel app please note that nlp4lp is intended and licensed for research use only the dataset is cc by nc 4 0 allowing only non commercial use and models trained using the dataset should not be used outside of research purposes usage 1 clone this repository 2 download nlp4lp dataset zip file https nlp4lp vercel app extract the contents and replace the empty datasets folder in the root directory of the repo with the downloaded one beside gpt4or py img width 535 alt image src https github com teshnizi optimus assets 48642434 e287a0da ac46 4604 89f6 59d4ee4037b3 3 add your openai api key s in configure py python from utils import get templates api keys sk api key 1 sk api key 2 get templates templates get templates 4 install the requirements using pip install r requirements txt 5 run the agent bash python gpt4or model gpt 4 maxtry 5 mode 105 verbose true prob datasets introduction to linear optimization problem 1 modes and options bash options h help show this help message and exit model model model name prob prob problem path stdfname stdfname description file name maxtry maxtry maximum attempts human human human test file aug aug number of augmentations uses the rephrases generated by using the rephrase parameter solver solver solver name cvxpy gurobi mode mode 102 prompt 103 prompt debug 104 prompt debug autotest 105 prompt debug human test rephrase rephrase number of rephrases if more than 0 the agent will only generate rephrases of the problem and will not solve the problem the rephrased instances can then later be used by setting the aug parameter to the number of rephrases verbose verbose verbose mode		ai
IoT-data-simulator	join the chat at https gitter im iot data simulator lobby https badges gitter im iot data simulator lobby svg https gitter im iot data simulator lobby utm source badge utm medium badge utm campaign pr badge utm content badge branding https user images githubusercontent com 4072962 38543709 fb04c520 3cad 11e8 8e23 0bb1155f16ae png iot data simulator is the tool which allows you to simulate iot devices data with great flexibility with this tool you won t need to code another new simulator for each iot project demo https user images githubusercontent com 4072962 38543721 023134b4 3cae 11e8 8e97 ee6468771e2a gif simulator features that you will like 1 replay existing datasets with modified data such as updated timestamps generated ids etc 2 automatic derivation of dataset structure which allows you to customize your dataset without the need to describe its structure from scratch 3 generate datasets of any complexity generated data can be described via constructor or javascript function multiple rules are available in constructor such as random integer uuid and others js function gives you maximum flexibility to generate data it supports popular js libraries lodash and momentjs 4 send data to the platforms you use with minimum configuration see supported target systems section 5 customize frequency with which data will be sent based on dataset timestamp properties or just constant time interval the tool also supports relative timestamp properties which depend on other timestamp or date properties it means that data can be replayed with the same interval between timestamps as in initial dataset 6 easy installation all you need is to download 2 docker files and run 2 commands if you would like read more about why we created this tool please read motivation section introduction video https www youtube com playlist list plkib gydbmbfa7abenlupvyzkeyuqlbpy installation prerequisites docker v 17 05 and docker compose should be installed startup run the following commands in the folder with docker compose yml and env files from release folder docker compose pull docker compose up after a while ui will be available by the following url http localhost 8090 or http docker machine ip 8090 depending on the os or docker version terms to use iot data simulator you should understand the following concepts session main composite application entity data is being generated and sent to external iot platforms only when session is running session consists of data definition optional timer devices optional processing rules and target system data definition describes data structure that is sent to a target system consists of dataset or schema or dataset and schema dataset required if no schema provided csv or json file that is uploaded by user to the internal tool object storage minio via ui or to the external amazon s3 service if dataset is provided its content is replayed by the tool during session run schema required if dataset is not provided describes dataset structure or data that will be generated on runtime schema can be derived from dataset or created from scratch via ui constructor if schema is not provided data will be generated via custom js function provided by user target system describes external system where simulated data is sent to device encapsulates specific set of values and target system properties devices can be used to override specific values in generated payload it can be useful when you have common payload structure and would like to ovveride some specific properties such as id geo position for different iot devices devices can be used to override target system properties this use case is helpful for instance when each device should send payload to its own topic usage replay existing dataset as is the simplest use case which can help you to start working with the tool replay existing dataset as is we need to create session which will send dataset records without any modification to a target system for this use case we don t need data schema just js function that will return current dataset record datasetentry go to the create session screen then 1 upload new json or csv dataset on data definition creation flow and skip schema step 2 select timer options how often data will be generated by the tool 3 skip select devices step 4 apply data processing rules as you can see processing function is very simple function process state datasetentry devicename return datasetentry 5 select or create dummy target system which is not actually sending data anywhere but allows you to see payload in session console 6 enter session name and complete creation flow 7 run session and explore payload in the session console session will stop when all records from dataset will be read replay dataset with updated date timestamp properties replaying dataset without modifying any parameters is not really useful use case so let s replay dataset with updated date timestamp properties go to create session screen then 1 upload dataset which has at least one date timestamp property on create definition screen make sure that date timestamp property type has been derived correctly and apply schema dataset may look like the following timestamp 1517303155600 id 1 timestamp 1517303155800 id 2 2 select timer options 3 skip select devices step 4 apply data processing rules current time rule is selected by default for date timestamp property it means that timestamp property will be updated with current time but the difference between timestamps of two adjacent dataset records will stay the same also if there are more than one timestamp properties in one record the first one will have current time rule by default others relative time rule which means that timestamps will be updated relatively to current time timestamp 6 select or create dummy target system 7 enter new session name and complete creation flow 8 run session and explore session console generate data if you would like to generate data on fly this can be achieved in two possible ways a with js function go to create session screen then 1 skip select definition step 2 select timer options 3 skip select devices step 4 on data processing step you can write any js function that returns any value by default the following function is used function process state datasetentry devicename return timestamp moment valueof 5 select or create dummy target system 6 enter session name and complete creation flow 7 run session and explore payload in the session console b with schema 1 skip dataset selection step on definition creation flow 2 create simple schema 3 select timer options how often data will be generated by the tool 4 skip select devices step 5 apply data processing rules via rules constructor 6 create or select dummy target system 7 enter session name and complete creation flow 8 run session and explore payload in the session console intended usage and useful scenarios derive schema from dataset and use it without dataset data generation mode this scenario is very useful when dataset has complex structure and you doesn t want to create schema for it from scratch this can be achieved with the following steps 1 create definition with dataset and apply derived schema 2 update created definition and unselect dataset derived schema won t be removed store state between data processing iterations implement counter let s say we would like to create simple counter with the following payload sent to a target system count 1 number which increases on each iteration in this case we could use data generatation mode see usage section above on the step 2 create schema with one integer property as shown on the screenshot below counter example https user images githubusercontent com 4072962 38543711 fe726e92 3cad 11e8 9e64 fe633a39735c png on step 5 select custom function rule for the count property open editor and apply the following js function function custom rulestate sessionstate devicename if typeof rulestate counter undefined rulestate counter 0 rulestate counter return rulestate counter send data to different kafka topics to send data to different kafka topics we should create several devices which will override target system properties with their topic data lets generate data for our example go to create session screen then 1 create data definition without dataset and with simple schema 2 select timer options 3 go to create device screen enter device name and press proceed 4 you should see target system screen that is specific for this device go to create target system screen 5 enter target system name and select kafka type 6 populate only topic field with kafka topic corresponding to this device when creating device specific target system only populated fields will override session target system properties 7 repeat steps 3 6 for all kafka topics 8 select all devices on select devices step of session creation flow and press proceed 9 select device injection rule please read more about device injection rules in project gitbook 10 apply processing rules 11 create or select kafka target system topic should be filled but anyway it will be overwritten by devices specific target systems topic parameter 12 enter session name and complete session creation flow 13 run created session and observe session console generate dataset and save it to local minio object storage to generate data and save it as file on local minio object storage create local storage target system while creating session populate dataset field with desired file name inject device property to processing rules go to create session screen then 1 select data definition with schema 2 select timer options 3 create one or more devices with populated properties fields 4 select created devices 5 select device injection rule 6 on processing rules step select device property rule for specific property that you would like to overwrite with device property 7 complete session creation flow supported target systems iot data simulator can send payload to the following target systems with available security types in parentheses amqp credentials and certificates kafka certificates local object storage minio object storage mqtt access keys access token credentials certificates rest credentials certificates websocket credentials certificates faq q i ve updated session but it still uses old parameters a session parameters are updated only when session is stopped if session was paused it will still use the previous parameters q what iot platforms do you support a in our company we used iot data simulator tool for working with thingsboard aws and predix platforms but it doesn t mean that it cannot be used with others motivation an iot data simulator is a required tool in any iot project while using real world sensors and devices is required for final integration testing to make sure the system works end to end without any surprises it is very impractical to use the real devices during development and initial testing phases where tests tend to be quicker shorter failing quicker and more often data simulator solves several problems at once 1 it is not necessary to run the physical equipment just to perform a unit or early integration test 2 you can use synthetic generated data if you do not yet have access to the physical devices or you can replay captured data to get it as real as possible 3 by imploding the data with some randomization you can simulate a large number of devices that you may not be able to get hold of during the test and development 4 you can replay data at various frequencies to run the test quicker e g when the real world data is pushed every 5 minutes and it takes several days to trigger an alert 5 you can simulate data from different stages of the system so that the testing of the later stages does not have to wait until all the previous stages are completed to transform raw data into a suitable input for example you can simulate raw device data to develop and test initial processing simulate cleaned up data stream to develop and test analytic components and simulate analytic component results to develop and test visualization and ux written with stackedit https stackedit io contributing thanks for your interest in contributing get started here link https github com iba group it iot data simulator blob master contributing md	iot data-simulation	server
fleetman-webapp	fleetman webapp a basic web front end for the fleetmanager microservice system		front_end
machine-learning-interview-questions	in different files i list various questions that might be asked in a ml interview here is the table of contents 1 deep learning questions https github com sroy20 machine learning interview questions blob master list of questions deep learning md 1 general machine learning questions https github com sroy20 machine learning interview questions blob master list of questions machine learning md 1 mathematics for machine learning questions https github com sroy20 machine learning interview questions blob master list of questions mathematics md		ai
ngrx-ideas	ngideas this project was generated with angular cli https github com angular angular cli version 7 0 2 development server run ng serve for a dev server navigate to http localhost 4200 the app will automatically reload if you change any of the source files code scaffolding run ng generate component component name to generate a new component you can also use ng generate directive pipe service class guard interface enum module build run ng build to build the project the build artifacts will be stored in the dist directory use the prod flag for a production build running unit tests run ng test to execute the unit tests via karma https karma runner github io running end to end tests run ng e2e to execute the end to end tests via protractor http www protractortest org further help to get more help on the angular cli use ng help or go check out the angular cli readme https github com angular angular cli blob master readme md	angular ngrx	front_end
fondue	fondue design system by frontify a href https github com frontify fondue actions workflows continuous integration yml img src https github com frontify react components actions workflows continuous integration yml badge svg alt ci status a a href https snyk io test github frontify fondue img src https snyk io test github frontify fondue badge svg alt known vulnerabilities a a href https github com frontify fondue blob main readme md title latest img alt latest src https img shields io npm v frontify fondue latest svg a using frontify fondue 1 install fondue from npm shell npm install frontify fondue 2 load fondue stylesheet typescript import frontify fondue style 3 use components typescript import button from frontify fondue fondue provides two builds es modules and umd modules documentation for the provided components and how to use them is available in storybook https fondue components frontify com contributing see contribution guidelines contributing md for contributing and local development help important links storybook https storybook js org docs react get started introduction used for isolated development and documentation of fondue components https www cypress io cypress used for testing in fondue frontify react guidelines https www notion so react architecture 0ce55540be0b48fa88a2c3848e35eb81 internal coding guidelines for frontify fondue tokens https github com frontify fondue tokens design tokens used at frontify tailwind https tailwindcss com docs utility first css framework used in fondue	react design-system components ui	os
Verilog-VHDL-projects	readme this repo contains the verilog vhdl systemverilog code of various projects		os
embs_open_1	embs open 1 open assessment 1 for the embedded systems design amp implementation module at the university of york		os
free-Web3-resources	free web3 resources welcome to the ultimate hangout spot for web3 enthusiasts we re a community of like minded individuals passionate about decentralized apps and the ethereum network we aim to create an inclusive and helpful environment where content creators can share their experiences and resources to reach their goals are you new to the world of blockchain no problem our free resources will make it easy for you to get started with building decentralized apps and interacting with the ethereum network whether you re a beginner or a seasoned pro we ve got you covered join us want to have some fun and be a part of the web3 revolution join our community and participate in our weekly challenges and meetings let s work together to build the future of the decentralized web don t miss out on the web3 action join us now and let s shape the future together a href https discord com invite crjhjfrrre img src https cdn worldvectorlogo com logos discord 6 svg title discord alt discord community width 40 a a href https twitter com 4ccommunityhq img src https cdn worldvectorlogo com logos twitter 6 svg title twitter alt twitter account width 40 a tech stack this project was built upon docusaurus https opensource fb com projects docusaurus an open source react framework made specifically for documentation by meta contributing whether you are a community member or not we would love your point of view feel free first to check out our contribution guidelines https github com francescoxx free web3 resources blob 6d8457aa1dada8a773791f68efc175bd534866ad contributing md the guide outlines the process for creating an issue and submitting a pull request code of conduct https github com francescoxx free web3 resources blob 6d8457aa1dada8a773791f68efc175bd534866ad code of conduct md by following the guide we ve set your contribution will more likely be accepted if it enhances the project quickstart it s great that you ve chosen this project to contribute to you have 2 options to contribute to the repo please pick your favorite from add update the resources improve the website local development adding updating resources this is great if you want to add or make changes to our resources the easiest way to contribute is as follows go to our website freeweb3resources com https www freeweb3resources com click on get started br this will take you to the docs section and from there you can navigate the sidebar to find the section for your resource br now you should be able to see a list of resources and an edit button underneath br click the edit button which will take you to this github repository br add your resource and commit message br then click create a new branch for this commit and start a pull request br let s make some awesome updates together installation npm i local development npm start note this repository started as a readme file with a list of resources as you can imagine with many contributions it made sense to create a website and have more structure we are now working on the website freeweb3resources com https www freeweb3resources com to easily view and edit these resources license free web3 resources is licensed under the mit license see the a href https github com francescoxx free web3 resources blob 6d8457aa1dada8a773791f68efc175bd534866ad license license a file for details support don t forget to leave a star	ethereum blockchain solidity web3 dapp hacktoberfest	blockchain
Data-Scientist-Books	data scientist books book lists image https user images githubusercontent com 74346775 150431744 12d89a5c abe5 4d6d ab17 3d6be41ce8e6 png image https user images githubusercontent com 74346775 150431808 91138d53 4512 4b6e 8995 f8eb14955a51 png image https user images githubusercontent com 74346775 150431892 e900fe9e 3bfa 4b6a b9a7 3e8029112116 png image https user images githubusercontent com 74346775 150431947 668d77f7 0946 474b b7bc 912aa2065400 png image https user images githubusercontent com 74346775 151635784 5007afec 12a4 4850 a577 e2c0fd641516 png image https user images githubusercontent com 74346775 150432021 5abfbd2b fdb0 46b0 a9e7 642b932f01eb png image https user images githubusercontent com 74346775 150432052 ad9f4d23 7553 4c22 adfc 2bb99d676db8 png image https user images githubusercontent com 74346775 150432275 a6f0fe77 b402 44ed 8800 98c617e98359 png image https user images githubusercontent com 74346775 151635715 5dfc7850 d06f 4965 becb bf739bf34a65 png	ai artificial-intelligence deeplearning machinelearning datascience dl ds ml gans lstm python r tsf probability statistics	ai
blockchain-academic-certificates	academic certificates on the blockchain the academic certificate verification platform using blockchain technology is used to issue manage and verify academic certificates in a secure and distributed manner this project addresses the need for a secure digital platform to issue and verify academic certificates without intervention from the original certificate issuer university solution overview resources solution overview png the core functionality of this application are create and issue academic certificates manage and share academic certificates verify authenticity of shared certificates architecture overview architecture overview resources network architecture png the following technologies are used on the application hyperledger fabric https www hyperledger org use fabric used to build the blockchain network run smart contracts fabric ca is used to enroll members in the network node js https nodejs org en backend of the web application is built in nodejs runtime using the express framework chaincode is also written in nodejs mongodb https www mongodb com the user and certificate data is stored in mongodb database bootstrap https getbootstrap com the front end of the web application is built using bootstrap ejs jquery network users the users of the platform include universities students and certificate verifiers eg employers the actions that can be performed by each party are as follows universities issue academic certificates view academic certificates issued endorse verification and digitally sign academic certificates students receive academic certificates from universities view and manage received academic certificates share academic certificates with third party verifiers selective disclosure of certificate data verifier receive certificate data from students verify certificate authenticity with blockchain platform to learn more about how selective disclosure and decentralized verifications work read about verifiable credentials https en wikipedia org wiki verifiable credentials getting started important note the instructions for building this project are out of date i m unfortunately not in a position right now to test and update these instructions if you re able to get the project up and running properly a pull request to update the following instructions is appreciated related issue 4 https github com tasinishmam blockchain academic certificates issues 4 prerequisites in order to install the application please make sure you have the following installed with the same major version number 1 hyperledger fabric version 2 1 x 2 node version 12 x 3 mongodb version 4 0 x 4 latest version of npm package manager starting fabric network 1 clone the repo sh git clone https github com tasinishmam blockchain academic certificates git 2 start the fabric test network with couchdb sh at fabric samples cd test network network sh up createchannel ca c mychannel s couchdb 3 package the chaincode situated in the chaincode directory 1 follow the instructions here https hyperledger fabric readthedocs io en release 2 2 deploy chaincode html javascript 2 note make sure in the final package instruction to name the package appropriately by default it s named fabcar 1 4 install the chaincode according to the instructions here https hyperledger fabric readthedocs io en release 2 1 deploy chaincode html install the chaincode package i m referencing the instructions for fabric version 2 1 please switch to the docs of your appropriate installed version starting web application make sure mongodb and fabric network are running in the background before starting this process 1 go to web app sh at blockchain academic certificates cd web app 2 install all modules sh npm install npm install only dev for dev dependencies 3 create env file touch env 4 specify environment variables in env file 1 specify mongodb uri local to your mongodb database 2 specify express session secret as a long random secret string 3 specify ccp path as the connection profile of org1 in your test network the path for this should be fabric samples test network organizations peerorganizations org1 example com connection org1 json 4 in fabric channel name and fabric chaincode name specify the channel and chaincode label respectively used during fabric network installation 5 sample env file dotenv mongodb uri local mongodb localhost 27017 blockchaincertificate port 3000 log level info express session secret sdfsdfddfgdfg3242efdfhi234 ccp path home tasin fabric samples test network organizations peerorganizations org1 example com connection org1 json fabric channel name mychannel fabric chaincode name fabcar 1 5 start the server in development mode sh npm run start development contact contact tasin ishmam tasinishmam gmail com mailto tasinishmam gmail com project link https github com tasinishmam blockchain academic certificates https github com tasinishmam blockchain academic certificates	hyperledger-fabric blockchain nodejs express cryptography verifiable-credentials	blockchain
phishing-Instagram	phishing this is an phishing tool template made using basic knowledge of web development about instagram phishing for educational purposes only this phising will come handy for social engineering techniques undetected the way i coded it with php allows me to use the phishing template and the instagram cannot even detect it so when you access the profile you hooked it will not log you out and ask you to verify your identity because it did not detect that the profile has been phished todo all u need to do is downlaod all the files br host it using an local sever such as ngrok or you can host it using an online server br if you are hosting using local server paste these files in htdocs folder and run ngrok and thus your phishing website is been hosted br enjoy it license permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software		front_end
indigo	build status https travis ci org indigo astronomy indigo svg branch master https travis ci org indigo astronomy indigo github tag latest by date http img shields io github v tag indigo astronomy indigo https github com indigo astronomy indigo blob master changelog md license http img shields io badge license indigo blueviolet svg https github com indigo astronomy indigo blob master license md platform http img shields io badge platform linux 20 7c 20macos 20 7c 20windows success svg indigo is the next generation of indi based on layered architecture and software bus this is the list of requirements taken into the consideration 1 no gpl dependency to allow commercial use under application stores licenses 2 ansi c for portability and to allow simple wrapping into net java golang objective c or swift in future 3 layered architecture to allow direct linking of the drivers into applications and or to replace wire protocol 4 atomic approach to device drivers e g if camera has imaging and guiding chip driver should expose two independent simple devices instead of one complex it is much easier and transparent for client 5 drivers should support hot plug at least for usb devices if device is connected disconnected while driver is running its properties should appear disappear on the bus 6 fits xisf jpeg tiff raw avi and ser format supported directly by the framework this is already done framework 1 indigo bus framework 2 xml json protocol adapter for client and server side 3 indigo server server with loadable indigo so dylib and indi executables drivers 4 indigo server standalone server with built in drivers 5 indigo server for macos wrapper provided just as an example 6 integrated http server for blob download server control web based indi control panel 7 indigo prop tool command line tool to list and set properties drivers 1 ccd with wheel guider ao and focuser simulator 2 mount simulator 3 atik ccd titan 3xx 4xx one with built in wheel vs infinity 11000 4000 driver 4 atik efw2 filterwheel driver 5 sx ccd driver 6 sx wheel driver 7 shoestring fcusb focuser driver 8 ssag qhy5 ccd driver 9 asi wheel driver 10 iidc ccd driver 11 asi ccd driver 12 nexstar mount driver supports celestron nexstar and sky watcher mounts 13 lx200 mount driver supports meade avalon losmandy 10microns astrophysics zwo and pegasusastro mounts and eqmac 14 fli filter wheel driver 15 fli ccd driver 16 fli focuser driver testers needed 17 usb focus v3 driver 18 sbig ccd driver with guider guider ccd and external guider head 19 sbig filter wheel driver part of ccd driver 20 ascom driver for indigo camera 21 ascom driver for indigo filter wheel 22 qhy ccd driver note maybe unstable due to inherited instability in the qhy sdk 23 zwo usb st4 port 24 meade dsi camera driver 25 takahashi temma mount driver 26 ica imagecapture api driver for dslrs mac only deprecated 27 gps simulator 28 nmea 0183 gps driver 29 andor ccd driver 32 64 bit intel linux only 30 wemacro rail focuser driver platform independent usb mac only bluetooth 31 eqmac guider driver mac only deprecated 32 apogee ccd driver 33 moravian intruments ccd and filter wheel driver 34 hid joystick aux driver 35 cgusbst4 guider driver 36 brightstar quantum filter wheel driver untested 37 trutek filter wheel driver untested 38 xagyl filter wheel driver untested 39 optec filter wheel driver untested 40 pegasus dmfc focuser driver 41 rigelsys nstep focuser driver 42 rigelsys nfocus focuser driver untested 43 ioptron mount driver 44 moonlite focuser driver 45 mjkzz rail focuser driver untested platform independent usb mac only bluetooth 46 gphoto2 ccd driver deprecated excluded from build 47 optec focuser driver untested 48 touptek ccd driver 49 altairastro ccd driver 50 rts on com aux shutter driver untested 51 dsusb aux shutter driver 52 gpusb guider driver 53 lakesideastro focuser untested 54 sx ao driver 55 sbig ao driver part of ccd driver 56 synscan eqmod mount driver 57 ascol driver 58 asi focuser driver 59 deepsky dad af1 and af2 focuser driver 60 baader planetarium steeldrive ii focuser driver 61 unihedron sqm driver 62 artesky flat box usb driver 63 nexdome dome driver untested based on g rozema s firmware 64 usb dewpoint v1 and v2 aux driver 65 pegasus astro flatmaster driver 66 astrogadget focusdreampro ascom jolo focuser driver added 67 lacerta flat box controller aux driver 68 uvc usb video class ccd driver 69 nexdome v3 dome driver untested requires firmware v 3 0 0 or newer 70 optec flip flap driver 71 gps service daemon gpsd driver 72 lunatico astronomy limpet armadillo platypus focuser rotator powerbox gpio drivers 73 lunatico astronomy limpet armadillo platypus rotator focuser powerbox gpio driver 74 lunatico astronomy dragonfly dome relay controller gpio driver 75 rotator simulator driver 76 lunatico aag cloudwacher driver 77 baader planetarium classic rotating dome driver 78 mgbox driver 79 manual wheel driver 80 pmc8 mount controller driver 81 robofocus focuser driver 82 qhy cfw filter wheel driver 83 rainbowastro mount driver 84 nexstar aux protocol mount driver 85 lunatico astronomy aag cloudwatcher driver 86 myfocuserpro 2 focuser driver 87 interactive astronomy skyroof driver 88 astromechanics focuser driver 89 nexdome beaver dome driver 90 astromechanics light pollution meter pro driver 91 optec pyxis rotator driver 92 svbony camera driver 93 geoptik flat field generator driver 94 talon 6 dome driver 95 interactive astronomy skyalert driver 96 vixen starbook mount driver 97 playerone camera driver 98 pegasusastro prodigy microfocuser driver 99 pegasusastro indigo wheel driver 100 pegasusastro falcon rotator driver 101 omegonpro ccd driver 102 mallincam ccd driver 103 risingcam ccd driver 104 orion starshotg ccd driver 105 ogma ccd driver 106 primalucelab focuser driver 107 zwo asiair power ports this is under development 1 a box adaptive optics driver how to build indigo prerequisites ubuntu debian raspbian sudo apt get install build essential autoconf autotools dev libtool cmake libudev dev libavahi compat libdnssd dev libusb 1 0 0 dev libcurl4 gnutls dev libz dev git curl bsdmainutils patchelf it is advised to remove libraw1394 dev sudo apt get remove libraw1394 dev fedora dnf install automake autoconf cmake libtool gcc gcc c libusb devel avahi compat libdns sd devel libudev devel git curl curl devel zlib devel it is advised to remove libraw1394 devel dnf remove libraw1394 devel macos install xcode and download and build autoconf automake and libtool use tools cltools sh script get code and build it git clone https github com indigo astronomy indigo git cd indigo make all build bin indigo server v indigo ccd simulator other drivers and connect from any indigo indi client or web browser to localhost on port 7624 no pthread yield new linux distributions come with the latest glibc that does not provide pthread yield however libqhy a depends on it we do not have control over this library and it is not officially supprted by qhy any more so if you get complaints by the linker for missing pthread yield call please execute the folowing command in indigo drivers ccd qhy bin externals pthread yield compat make patchlib and rerun the build	astronomy astronomy-library macos macosx linux	os
flasky-front-end	flasky front end live code in this live code we will be working with state events in react then we will use the useeffect hook to update state from an api and persist state to an api with events learning goals the goals of this live code are to gain an ability to use state in a react component and pass information to other components via props use the useeffect hook to update state after the component first mounts use axios to make api calls to update state write controlled form components flasky back end the features described in each wave of this live code were designed to be used with the flasky back end created during c16 the source code for this flasky can be found here https github com adagold flasky tree solution and the deployed version can be found here https ada flasky onrender com your instructors may use the flasky back end created for your cohort and your homeroom class thus the exact features they implement may be different from those described below the react skills practice will be the same regardless of the exact features your class implements wave 00 components and props in this wave we will implement the dog and doglist components a dog has the following attributes and props id name age breed chip defaults to an empty string wave 01 state and event handling in this wave we will implement behavior to add a chip to a dog the chip should be a random number between 1000 and 9999 the state of a dog will be managed by the dog component wave 02 lifting state up in this wave we will lift up state to the app app should pass a callback function through doglist to dog to implement the addchip behavior we will also implement a function in app to delete a dog wave 03 useeffect and axios api endpoints the api is active on render at https ada flasky onrender com https ada flasky onrender com verb path body of request what it does get dogs none retrieves a list of all dogs patch dogs dog id add chip none adds a randomly generated chip string to dog post dogs name dogname age dogage breed dogbreed creates a new dog delete dogs dog id none deletes a dog make axios requests in app js we will use the useeffect hook to make an api call to get the list of dogs initially update the callback functions to allow us to delete or add a chip to dogs wave 04 adding a form in this wave we will add a new component to our app dogform in this component users will be able to add a new dog persisting the data to the api		front_end
Agritech	agritech agricaltural information technology br xlsx txt cvs br br br http www jppn ne jp kumamoto pdf http web1 jppn ne jp docs cgi umnkyoso		server
moeSS	moess moe ss moe ss id a front end for https github com mengskysama shadowsocks tree manyuser thanks to ss panal https github com orvice ss panel demo https ss qaq moe install import shadowsocks sql to your database may delete your existing data rename config sample php and database sample php in application config then change the settings use admin admin to login the admin dashboard and change settings in http your domain com admin system config html to prevent spam register users need to click a link to activate the account so you need to set a method to send e mail currently supprt php mail sendmail smtp and sendgrid web api send test e mail function will be added soon method option value php mail mail sendmail sendmail smtp smtp sendgrid sendgrid only change this is not enough you also need to change other values need by the method you choose invite only is default on you need to generate invite code before anyone can registe because i don t use any encrypt function when post the data you are suggesting to secure your site by using ssl certs many notices and sentences are written directly in the view files so you need to edit the file to change them they may moved to database in the future license see license https github com wzxjohn moess blob master license requires this system is using codeigniter 3 0 https github com bcit ci codeigniter to build so you need php mysql curl to run apache is the best one because it support htaccess file which is needed to rewrite the request uri to index php		front_end
EnableTheDisabled	enablethedisabled all in one disability solution android app with pc backend under development		server
chatgpt-integration-patterns	chatgpt chatgpt chatgpt clickprompt stable diffusion openai clickprompt prompt huggingface stable diffusion chatgpt stable diffusion prompt chatgpt prompt chatgpt 1 2 3 4 5 6 chatgpt https www ada cx new bing prompt chatgpt prompt chatgpt chatgpt prompt images generator jpeg https www clickprompt org zh cn chatgpt generator cot https www clickprompt org zh cn chatgpt generator cot chatgpt chatgpt chatgpt clickprompt huggingface images integration jpeg https www clickprompt org zh cn stable diffusion generator https www clickprompt org zh cn stable diffusion generator chatgpt chatgpt chatgpt clickprompt chatgpt https www clickprompt org zh cn chatgpt samples ddd sample https www clickprompt org zh cn chatgpt samples ddd sample images pipeline user story png https dev clickprompt org zh cn chatgpt startling by each step user story https dev clickprompt org zh cn chatgpt startling by each step user story chatgpt chatgpt chatgpt clickprompt chatgpt stable diffusion tag images target guide png https www clickprompt org zh cn stable diffusion generator https www clickprompt org zh cn stable diffusion generator chatgpt ai chatgpt chatgpt clickprompt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt i18n yaml yaml yml test case template yml name test case description this is a test case steps step number 1 step description do something step number 2 step description verify something clickprompt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt chatgpt	chatgpt	ai
flow-nft	flow non fungible token standard this standard defines the minimum functionality required to implement a safe secure and easy to use non fungible token contract on the flow blockchain https www onflow org what is cadence cadence is the resource oriented programming language https docs onflow org cadence for developing smart contracts on flow before reading this standard we recommend completing the cadence tutorials https docs onflow org cadence tutorial 01 first steps to build a basic understanding of the programming language resource oriented programming and by extension cadence provides an ideal programming model for non fungible tokens nfts users are able to store their nft objects directly in their accounts and transact peer to peer learn more in this blog post about resources https medium com dapperlabs resource oriented programming bee4d69c8f8e import addresses the nonfungibletoken and metadataviews contracts are already deployed on various networks you can import them in your contracts from these addresses there is no need to deploy them yourself note with the emulator you must use the contracts flag to deploy these contracts network contract address emulator canary 0xf8d6e0586b0a20c7 testnet 0x631e88ae7f1d7c20 mainnet 0x1d7e57aa55817448 core features the nonfungibletoken contract defines the following set of functionality that must be included in each implementation contracts that implement the nonfungibletoken interface are required to implement two resource interfaces nft a resource that describes the structure of a single nft collection a resource that can hold multiple nfts of the same type users typically store one collection per nft type saved at a well known location in their account storage for example all nba top shot moments owned by a single user are held in a topshot collection https github com dapperlabs nba smart contracts blob master contracts topshot cdc l605 stored in their account at the path storage momentcollection create a new nft collection create a new collection using the createemptycollection function this function must return an empty collection that contains no nfts users typically save new collections to a well known location in their account and link the nonfungibletoken collectionpublic interface as a public capability swift let collection examplenft createemptycollection account save collection to storage examplenftcollection create a public capability for the collection account link nonfungibletoken collectionpublic public examplenftcollection target storage examplenftcollection withdraw an nft withdraw an nft from a collection using the withdraw contracts examplenft cdc l36 l42 function this function emits the withdraw contracts examplenft cdc l12 event swift let collectionref account borrow examplenft collection from storage examplenftcollection panic could not borrow a reference to the owner s collection withdraw the nft from the owner s collection let nft collectionref withdraw withdrawid 42 deposit an nft deposit an nft into a collection using the deposit contracts examplenft cdc l46 l57 function this function emits the deposit contracts examplenft cdc l13 event this function is available on the nonfungibletoken collectionpublic interface which accounts publish as public capability this capability allows anybody to deposit an nft into a collection without accessing the entire collection swift let nft examplenft nft let collection account getcapability public examplenftcollection borrow nonfungibletoken collectionpublic panic could not borrow a reference to the receiver s collection collection deposit token nft important in order to comply with the deposit function in the interface an implementation must take a nonfungibletoken nft resource as an argument this means that anyone can send a resource object that conforms to nonfungibletoken nft to a deposit function in an implementation you must cast the token as your specific token type before depositing it or you will deposit another token type into your collection for example swift let token token as examplenft nft list nfts in an account return a list of nfts in a collection using the getids contracts examplenft cdc l59 l62 function this function is available on the nonfungibletoken collectionpublic interface which accounts publish as public capability swift let collection account getcapability public examplenftcollection borrow nonfungibletoken collectionpublic panic could not borrow a reference to the receiver s collection let ids collection getids nft metadata nft metadata is represented in a flexible and modular way using the standard proposed in flip 0636 https github com onflow flips blob main application 20210916 nft metadata md when writing an nft contract you should implement the metadataviews resolver contracts metadataviews cdc l3 l6 interface which allows your nft to implement one or more metadata types called views each view represents a different type of metadata such as an on chain creator biography or an off chain video clip views do not specify or require how to store your metadata they only specify the format to query and return them so projects can still be flexible with how they store their data how to read metadata this example shows how to read basic information about an nft including the name description image and owner source get nft metadata cdc scripts get nft metadata cdc swift import examplenft from import metadataviews from get the regular public capability let collection account getcapability examplenft collectionpublicpath borrow examplenft examplenftcollectionpublic panic could not borrow a reference to the collection borrow a reference to the nft as usual let nft collection borrowexamplenft id 42 panic could not borrow a reference to the nft call the resolveview method provide the type of the view that you want to resolve view types are defined in the metadataviews contract you can see if an nft supports a specific view type by using the getviews method if let view nft resolveview type metadataviews display let display view as metadataviews display log display name log display description log display thumbnail the owner is stored directly on the nft object let owner address nft owner address inspect the type of this nft to verify its origin let nfttype nft gettype nfttype identifier is a e03daebed8ca0615 examplenft nft how to implement metadata the example nft contract contracts examplenft cdc shows how to implement metadata views list of views name purpose status source core view nftview basic view that includes the name description and thumbnail implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l32 l65 display return the basic representation of an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l85 l120 white check mark httpfile a file available at an http s url implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l143 l155 ipfsfile a file stored in ipfs implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l157 l195 edition return information about one or more editions for an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l197 l229 editions wrapper for multiple edition views implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l176 l187 serial serial number for an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l258 l270 royalty a royalty cut for a given nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l286 l323 royalties wrapper for multiple royalty views implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l325 l352 white check mark media represents a file with a corresponding mediatype implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l378 l395 medias wrapper for multiple media views implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l397 l407 license represents a license according to https spdx org licenses implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l423 l432 externalurl exposes a url to an nft on an external site implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l448 l458 white check mark nftcollectiondata provides storage and retrieval information of an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l474 l531 white check mark nftcollectiondisplay returns the basic representation of an nft s collection implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l547 l586 white check mark rarity expose rarity information for an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l603 l628 trait represents a single field of metadata on an nft implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l644 l671 traits wrapper for multiple trait views implemented metadataviews cdc https github com onflow flow nft blob master contracts metadataviews cdc l673 l690 white check mark core views the views marked as core views are considered the minimum required views to provide a full picture of any nft if you want your nft to be featured on the flow nft catalog https nft catalog vercel app it should implement all of them as a pre requisite always prefer wrappers over single views when exposing a view that could have multiple occurrences on a single nft such as edition royalty media or trait the wrapper view should always be used even if there is only a single occurrence the wrapper view is always the plural version of the single view name and can be found below the main view definition in the metadataviews contract when resolving the view the wrapper view should be the returned value instead of returning the single view or just an array of several occurrences of the view example preferred cadence pub fun resolveview view type anystruct switch view case type metadataviews editions let editioninfo metadataviews edition name example nft edition number self id max nil let editionlist metadataviews edition editioninfo return metadataviews editions editionlist to be avoided cadence resolveview should always return the same type that was passed to it as an argument so this is improper usage because it returns edition instead of editions pub fun resolveview view type anystruct switch view case type metadataviews editions let editioninfo metadataviews edition name example nft edition number self id max nil return editioninfo cadence this is also improper usage because it returns edition instead of editions pub fun resolveview view type anystruct switch view case type metadataviews editions let editioninfo metadataviews edition name example nft edition number self id max nil let editionlist metadataviews edition editioninfo return editionlist royalty view the metadataviews contract also includes a standard view for royalties https github com onflow flow nft blob master contracts metadataviews cdc l136 l208 this view is meant to be used by 3rd party marketplaces to take a cut of the proceeds of an nft sale and send it to the author of a certain nft each nft can have its own royalty view cadence pub struct royalties array that tracks the individual royalties access self let cutinfos royalty and the royalty can indicate whatever fungible token it wants to accept via the type of the generic fungibletoken receiver capability that it specifies cadence pub struct royalty generic fungibletoken receiver for the beneficiary of the royalty can get the concrete type of the receiver with receiver gettype recommendation users should create a new link for a flowtoken receiver for this using getroyaltyreceiverpublicpath and not use the default flowtoken receiver this will allow users to update the capability in the future to use a more generic capability pub let receiver capability anyresource fungibletoken receiver multiplier used to calculate the amount of sale value transferred to royalty receiver note it should be between 0 0 and 1 0 ex if the sale value is x and multiplier is 0 56 then the royalty value would be 0 56 x generally percentage get represented in terms of basis points in solidity based smart contracts while cadence offers ufix64 that already supports the basis points use case because its operations are entirely deterministic integer operations and support up to 8 points of precision pub let cut ufix64 if someone wants to make a listing for their nft on a marketplace the marketplace can check to see if the royalty receiver accepts the seller s desired fungible token by checking the concrete type of the reference if the concrete type is not the same as the type of token the seller wants to accept the marketplace has a few options they could either get the address of the receiver by using the receiver owner address field and check to see if the account has a receiver for the desired token they could perform the sale without a royalty cut or they could abort the sale since the token type isn t accepted by the royalty beneficiary you can see example implementations of royalties in the examplenft contract and the associated transactions and scripts important instructions for royalty receivers if you plan to set your account as a receiver of royalties you ll likely want to be able to accept as many token types as possible this won t be immediately possible at first but eventually we will also design a contract that can act as a sort of switchboard for fungible tokens it will accept any generic fungible token and route it to the correct vault in your account this hasn t been built yet but you can still set up your account to be ready for it in the future therefore if you want to receive royalties you should set up your account with the setup account to receive royalty cdc transaction https github com onflow flow nft blob master transactions setup account to receive royalty cdc this will link generic public path from metadataviews getroyaltyreceiverpublicpath to your chosen fungible token for now then use that public path for your royalty receiver and in the future you will be able to easily update the link at that path to use the fungible token switchboard instead contract metadata now that contract borrowing is released you can also implement the resolver contracts resolver cdc interface on your contract and resolve views from there as an example you might want to allow your contract to resolve nftcollectiondata and nftcollectiondisplay so that platforms do not need to find an nft that belongs to your contract to get information about how to set up or show your collection cadence import viewresolver from 0xf8d6e0586b0a20c7 import metadataviews from 0xf8d6e0586b0a20c7 pub fun main addr address name string storagepath let t type metadataviews nftcollectiondata let borrowedcontract getaccount addr contracts borrow viewresolver name name panic contract could not be borrowed let view borrowedcontract resolveview t if view nil return nil let cd view as metadataviews nftcollectiondata return cd storagepath will return cadence domain storage identifier examplenftcollection how to propose a new view please open a pull request to propose a new metadata view or changes to an existing view feedback as flow and cadence are still new we expect this standard to evolve based on feedback from both developers and users we d love to hear from anyone who has feedback for example are there any features that are missing from the standard are the current features defined in the best way possible are there any pre and post conditions that are missing are the pre and post conditions defined well enough error messages are there any other actions that need an event defined for them are the current event definitions clear enough and do they provide enough information are the variable function and parameter names descriptive enough are there any openings for bugs or vulnerabilities that we are not noticing please create an issue in this repository if there is a feature that you believe needs discussing or changing comparison to other standards on ethereum this standard covers much of the same ground as erc 721 and erc 1155 but without most of the downsides tokens cannot be sent to contracts that don t understand how to use them because an account needs to have a receiver or collection in its storage to receive tokens if the recipient is a contract that has a stored collection the tokens can just be deposited to that collection without having to do a clunky approve transferfrom events are defined in the contract for withdrawing and depositing so a recipient will always be notified that someone has sent them tokens with their own deposit event this version can support batch transfers of nfts even though it isn t explicitly defined in the contract a batch transfer can be done within a transaction by just withdrawing all the tokens to transfer then depositing them wherever they need to be all atomically transfers can trigger actions because users can define custom receivers to execute certain code when a token is sent easy ownership indexing rather than iterating through all tokens to find which ones you own you have them all stored in your account s collection and can get the list of the ones you own instantly how to test the standard if you want to test out these contracts we recommend either testing them with the flow playground https play onflow org or with the visual studio code extension https github com onflow flow blob master docs vscode extension md cadence visual studio code extension the steps to follow are 1 deploy nonfungibletoken cdc 2 deploy examplenft cdc importing nonfungibletoken from the address you deployed it to then you can experiment with some of the other transactions and scripts in transactions or even write your own you ll need to replace some of the import address placeholders with addresses that you deploy to as well as some of the transaction arguments running automated tests you can find automated tests in the lib go test nft test go file it uses the transaction templates that are contained in the lib go templates templates go file currently these rely on a dependency from a private dapper labs repository to run so external users will not be able to run them we are working on making all of this public so anyone can run tests but haven t completed this work yet bonus features these could each be defined as a separate interface and standard and are probably not part of the main standard they are not implemented in this repository yet 10 withdrawing tokens from someone else s collection by using their provider reference approved withdraw event providing a resource that only approves an account to withdraw a specific amount per transaction or per day month etc returning the list of tokens that an account can withdraw for another account reading the balance of the account that you have permission to send tokens for owner is able to increase and decrease the approval at will or revoke it completely this is much harder than anticipated 11 standard for composability extensibility 12 minting a specific amount of tokens using a specific minter resource that an owner can control tokens minted event setting a cap on the total number of tokens that can be minted at a time or overall setting a time frame where this is allowed 13 burning a specific amount of tokens using a specific burner resource that an owner controls tokens burnt event setting a cap on the number of tokens that can be burned at a time or overall setting a time frame where this is allowed 14 pausing token transfers maybe a way to prevent the contract from being imported probably not a good idea 15 cloning the token to create a new token with the same distribution license the works in these files examplenft cdc contracts examplenft cdc nonfungibletoken cdc contracts nonfungibletoken cdc are under the unlicense license deploying updates testnet sh testnet private key xxxx flow project deploy update network testnet	blockchain smart-contracts linear-types nft onflow	blockchain
design_pattern_for_embedded_system	design pattern for embedded system in c implement of all problem in book design patterns for embedded system in c	design-patterns embedded	os
foundational-design-system-proto	shopify design foundations about this repo about this repo projects projects how to use this repo how to use this repo technical details technical details contribute to our explorations contribute to our explorations resources resources about this repo the experience platform for shopify this repository is focused on centralizing foundational design system resources current status owner help paused polaris team https github com orgs shopify teams polaris team members polaris https shopify slack com app redirect channel polaris projects a public version of our github project https github com orgs shopify projects 2250 views 5 type beta will be coming soon development make sure git https git scm com downloads and node js https nodejs org en are installed on your computer then run the following commands in your terminal to get started shell git clone https github com shopify foundational design system proto git git clone repository cd foundational design system proto access the files npm install install dependencies npm run dev run locally technical details as we continue to iterate and evolve these technologies are subject to change currently the foundations are built using the following technologies as part of our react typescript stack sprinkles https github com seek oss vanilla extract tree master packages sprinkles a css framework for vanilla extract vanilla extract https vanilla extract style a css in js library vitejs https vitejs dev a frontend build tool for questions about our tech stack go to polaris https shopify slack com app redirect channel polaris contribute to our explorations we d love to hear what you think start a conversation in github discussions https github com shopify polaris discussions resources documentation design tokens packages tokens readme md react components packages components readme md npm packages shopify layout experimental an npm package of experimental react components for layout including abstractions for flexbox and grid built with vanilla extract and sprinkles install locally 1 add the dependency npm shell npm install shopify layout experimental yarn shell yarn add shopify layout experimental 2 import the foundation styles at the base of your app jsx import shopify layout experimental dist style css 3 read the component api documentation packages components readme md explore the sandbox we have created a sandbox https codesandbox io s layout experimental demo wcxdj file src pages index js utilizing the experimental release https www npmjs com package shopify layout experimental of our foundational layout components box grid flex and more check it out to see examples of how the components can be used and try them out yourself	design-systems shopify-polaris components tokens icons documentation	os
caver-js	branch name will be changed we will change the master branch to main on dec 15 2022 after the branch policy change please check your local or forked repository settings caver js gitter https badges gitter im klaytn caver js svg https gitter im klaytn caver js utm source badge utm medium badge utm campaign pr badge p align center img src assets logo caver js png width 200 alt caver js p caver js is a javascript api library that allows developers to interact with a klaytn node using a http or websocket connection note kaikas web extension wallet is recommended to be used with features prior to common architecture https docs klaytn com dapp sdk caver js v1 4 1 new features provided in caver js v1 5 0 introduced common architecrue or later are currently not compatible with kaikas kaikas web extension wallet works fine with functions prior to common architecture so please use functions prior to common architecture https docs klaytn com dapp sdk caver js v1 4 1 even in the latest version of caver js functions prior to common architecture https docs klaytn com dapp sdk caver js v1 4 1 are provided for backward compatibility see trouble shooting and known issues connect with kaikas web extension for more details table of contents requirements requirements installation installation getting started getting started check the connection check the connection using caver js keyring wallet using caver js keyring wallet submitting a transaction submitting a transaction units for klay units for klay documentation documentation api specification api specification web3 js similarity web3 js similarity error code improvement error code improvement sample projects sample projects github repository github repository trouble shooting and known issues trouble shooting and known issues related projects related projects requirements the following packages are required to use the caver js library node js https nodejs org en download npm https www npmjs com get npm gcc c https gcc gnu org testing in caver js is implemented using the mocha testing framework if you want to run unit tests in caver js you need to install mocha first mocha https mochajs org installation note caver js can run on node js versions 12 and 14 and the recommended versions are lts erbium 12 21 0 https nodejs org dist latest v12 x lts fermium 14 16 0 https nodejs org dist latest v14 x if you are already using a different version of the node for example node v15 use the node version manager nvm https github com nvm sh nvm to install and use the version supported by caver js installation node to try it out install caver js with npm like following command npm install caver js note package json file should exist on the same install path if it does not exist package json should be generated via npm init to install a specific version of caver js try the following command npm install caver js x x x in the browser build using the caver js repository npm run build then include dist caver min js in your html file this will expose caver on the window object or via cdn script src https cdnjs cloudflare com ajax libs caver js x x x caver min js script the caver js provided by cdnjs can be found at this link https cdnjs com libraries caver js getting started if you want to run your own en endpoint node see en operation guide https docs klaytn com node endpoint node to set up you can connect en like below node const caver require caver js const caver new caver http localhost 8551 note the above example should be executed from the location where caver js is installed and the example is explained using node js repl https nodejs org api repl html repl the node js repl check the connection you can now use caver js you can send a basic request to the node as shown below and check the results caver rpc klay getclientversion then console log klaytn vx x x linux amd64 gox x x using caver js keyring wallet you can easily use your klaytn account when signing a transaction or message by using the keyring wallet the keyring is a new feature that contains an address and one or more private keys based on the key types singlekeyring multiplekeyring or rolebasedkeyring refer to caver wallet keyring for details caver wallet the in memory wallet is provided to easily manage multiple keyrings note functions associated with wallet and keyring have no effect on the klaytn blockchain platform it just manipulates keyrings in the in memory wallet let s create a random keyring as shown in the example below const keyring caver wallet keyring generate keyring singlekeyring address 0x64d221893cc628605314026f4c4e0879af5b75b1 key privatekey privatekey 0x private key you can add the keyring object created in the above example to the caver wallet or you can add a keyring using an address and private key s add a keyring instance to caver wallet caver wallet add keyring add a keyring to caver wallet with an address and a private key caver wallet newkeyring 0x address in hex 0x private key add a keyring to caver wallet with an address and private keys caver wallet newkeyring 0x address in hex 0x private key1 0x private key2 add a keyring to caver wallet with an address and private keys by roles caver wallet newkeyring 0x address in hex 0x private key1 0x private key2 0x private key3 submitting a transaction you can use caver js to submit various types of transactions to a node please refer to the caver transaction https docs klaytn com dapp sdk caver js api references caver transaction class to see how to create a transaction of each type note you should create a transaction instance via create function from caver js v1 8 2 constructors per transaction type are not supported you can sign the transaction using a keyring and send a signed transaction through caver rpc klay sendrawtransaction as shown below and the receipt is returned as a result add a keyring to caver wallet const keyring caver wallet newkeyring 0x address in hex 0x private key const vt caver transaction valuetransfer create from keyring address to 0x176ff0344de49c04be577a3512b6991507647f72 value caver utils converttopeb 1 klay gas 25000 caver wallet sign keyring address vt then signed caver rpc klay sendrawtransaction signed then console log blockhash 0x0a78b5c5b95456b2d6b6a9ba25fd2afd0000d16bcf03a8ae58a6557a59319a67 blocknumber 8021 contractaddress null from 0x09a08f2289d3eb3499868908f1c84fd9523fe11b type txtypevaluetransfer typeint 8 value 0xde0b6b3a7640000 the above example uses promise when sending a signed transaction to the klaytn you can also use event emitter like below caver rpc klay sendrawtransaction signed on transactionhash function hash on receipt function receipt units for klay units of klay is shown as below and peb is the smallest currency unit peb is the default unit unless the unit conversion is used name unit peb 1 kpeb 1 000 mpeb 1 000 000 gpeb 1 000 000 000 ston 1 000 000 000 uklay 1 000 000 000 000 mklay 1 000 000 000 000 000 klay 1 000 000 000 000 000 000 kklay 1 000 000 000 000 000 000 000 mklay 1 000 000 000 000 000 000 000 000 gklay 1 000 000 000 000 000 000 000 000 000 tklay 1 000 000 000 000 000 000 000 000 000 000 caver js provides the caver utils converttopeb function for unit conversion please refer to the usage below caver utils converttopeb 1 peb 1 caver utils converttopeb 1 gpeb 1000000000 caver utils converttopeb 1 klay 1000000000000000000 documentation documentation can be found at klaytn docs caver js https docs klaytn com dapp sdk caver js api specification the api lists of caver js are described in folloinwg links caver account https docs klaytn com dapp sdk caver js api references caver account caver wallet https docs klaytn com dapp sdk caver js api references caver wallet caver wallet keyring https docs klaytn com dapp sdk caver js api references caver wallet keyring caver transaction https docs klaytn com dapp sdk caver js api references caver transaction caver rpc klay https docs klaytn com dapp sdk caver js api references caver rpc klay caver rpc net https docs klaytn com dapp sdk caver js api references caver rpc net caver contract https docs klaytn com dapp sdk caver js api references caver contract caver abi https docs klaytn com dapp sdk caver js api references caver abi caver kct kip7 https docs klaytn com dapp sdk caver js api references caver kct kip7 caver kct kip17 https docs klaytn com dapp sdk caver js api references caver kct kip17 caver utils https docs klaytn com dapp sdk caver js api references caver utils caver ipfs https docs klaytn com dapp sdk caver js api references caver ipfs web3 js similarity since caver js has been evolved from web3 js usage pattern of caver js is very similar to that of web3 js this means a software developed using web3 js can be easily converted to caver js the following examples are code patterns used in web3 js and caver js respectively const web3 require web3 const web3 new web3 new web3 providers httpprovider http localhost 8545 web3 eth getbalance 0x407d73d8a49eeb85d32cf465507dd71d507100c1 then console log const caver require caver js const caver new caver new caver providers httpprovider http localhost 8545 caver rpc klay getbalance 0x407d73d8a49eeb85d32cf465507dd71d507100c1 then console log error code improvement klaytn improves reporting transaction failure via txerror in the receipt caver js further improves the report by presenting the error string that corresponds to txerror the below is an example of a receipt containing txerror error vm error occurs while running smart contract blockhash 0xe7ec35c9fff1178d52cee1d46d40627d19f828c4b06ad1a5c3807698b99acb20 txerror 0x2 the meaning of error code can be found below error code description 0x02 vm error occurs while running smart contract 0x03 max call depth exceeded 0x04 contract address collision 0x05 contract creation code storage out of gas 0x06 evm max code size exceeded 0x07 out of gas 0x08 evm write protection 0x09 evm execution reverted 0x0a reached the opcode computation cost limit 100000000 for tx 0x0b account already exists 0x0c not a program account e g an account having code and storage 0x0d human readable address is not supported now 0x0e fee ratio is out of range 1 99 0x0f accountkeyfail is not updatable 0x10 different account key type 0x11 accountkeynil cannot be initialized to an account 0x12 public key is not on curve 0x13 key weight is zero 0x14 key is not serializable 0x15 duplicated key 0x16 weighted sum overflow 0x17 unsatisfiable threshold weighted sum of keys is less than the threshold 0x18 length is zero 0x19 length too long 0x1a nested composite type 0x1b a legacy transaction must be with a legacy account key 0x1c deprecated feature 0x1d not supported 0x1e smart contract code format is invalid sample projects the bapp blockchain application development sample projects using caver js are the following count bapp https docs klaytn com dapp tutorials count bapp klaystagram https docs klaytn com dapp tutorials klaystagram trouble shooting and known issues connect with kaikas web extension kaikas web extension wallet works normally with functions prior to common architecture https docs klaytn com dapp sdk caver js v1 4 1 features provided in later versions caver js v1 5 0 may not work with kaikas web extension wallet if the following error occurs when using with kaikas it must be modified to use the functions supported by caver js v1 4 1 https docs klaytn com dapp sdk caver js v1 4 1 for compatibility with the latest version the same functions provided by caver js v1 4 0 https docs klaytn com dapp sdk caver js v1 4 1 are provided kaikas only processes one transaction at a time open kaikas and refresh the pending transaction if the service doesn t process your transaction for a while cancel the pending transaction kaikas kaikas although the above error is mostly the case other errors may occur besides the above error so if you are using kaikas web extension wallet please use the functions supported by caver js v1 4 1 https docs klaytn com dapp sdk caver js v1 4 1 keep in mind that you cannot mix features before and after common architecture for documents for the functions supported by caver js v1 4 1 refer to caver js v1 4 1 documentation https docs klaytn com dapp sdk caver js v1 4 1 using webpack 5 node js module polyfills are not provided by default in webpack v5 and later therefore you need to install the missing modules and add them to the resolve fallback property of the webpack config js file in the form below module exports resolve fallback fs false net false stream require resolve stream browserify crypto require resolve crypto browserify http require resolve stream http https require resolve https browserify os require resolve os browserify browser more information on migrating to webpack v5 can be found here https webpack js org migrate 5 clean up configuration if you are implementing an app using create react app https create react app dev you can use react app rewired https www npmjs com package react app rewired to add the above polyfills to the webpack config js file used by cra https create react app dev more information on using react app rewired with create react app can be found here https www npmjs com package react app rewired github repository caver js https github com klaytn caver js related projects caver java https github com klaytn caver java for java keyring https docs klaytn com dapp sdk caver js api references caver wallet keyring caver wallet keyring https docs klaytn com dapp sdk caver js api references caver wallet keyring wallet https docs klaytn com dapp sdk caver js api references caver wallet caver wallet https docs klaytn com dapp sdk caver js api references caver wallet singlekeyring https docs klaytn com dapp sdk caver js api references caver wallet keyring singlekeyring multiplekeyring https docs klaytn com dapp sdk caver js api references caver wallet keyring multiplekeyring rolebasedkeyring https docs klaytn com dapp sdk caver js api references caver wallet keyring rolebasedkeyring	blockchain	blockchain
eed	eed eed is very basic line oriented text editor simple clone of standard unix text editor ed designed with platform independence and embedded systems use in mind run tests run the following command ceedling test all usage contributing license mit https choosealicense com licenses mit		os
mlr3	mlr3 img src man figures logo png align right width 120 package website release https mlr3 mlr org com dev https mlr3 mlr org com dev efficient object oriented programming on the building blocks of machine learning successor of mlr https github com mlr org mlr badges start r cmd check https github com mlr org mlr3 actions workflows r cmd check yml badge svg https github com mlr org mlr3 actions workflows r cmd check yml doi https joss theoj org papers 10 21105 joss 01903 status svg https doi org 10 21105 joss 01903 cran status https www r pkg org badges version ago mlr3 https cran r project org package mlr3 stackoverflow https img shields io badge stackoverflow mlr3 orange svg https stackoverflow com questions tagged mlr3 mattermost https img shields io badge chat mattermost orange svg https lmmisld lmu stats slds srv mwn de mlr invite badges end resources for users and developers we started writing a book https mlr3book mlr org com this should be the central entry point to the package the mlr org website https mlr org com includes for example a gallery https mlr org com gallery html with case studies and a blog https mlr org com blog html we are not the most frequent bloggers reference manual https mlr3 mlr org com reference faq https github com mlr org mlr3 wiki faq ask questions on stackoverflow tag mlr3 https stackoverflow com questions tagged mlr3 extension learners recommended core regression classification and survival learners are in mlr3learners https github com mlr org mlr3learners all others are in mlr3extralearners https github com mlr org mlr3extralearners use the learner search https mlr org com learners html to get a simple overview cheatsheets overview of cheatsheets https cheatsheets mlr org com mlr3 https cheatsheets mlr org com mlr3 pdf mlr3tuning https cheatsheets mlr org com mlr3tuning pdf mlr3pipelines https cheatsheets mlr org com mlr3pipelines pdf videos user2019 talk on mlr3 https www youtube com watch v wsp2hifndqs user2019 talk on mlr3pipelines and mlr3tuning https www youtube com watch v gew5rxkbquq user2020 tutorial on mlr3 mlr3tuning and mlr3pipelines https www youtube com watch v t43ho2o nzw recorded talk about mlr3spatiotempcv and mlr3spatial at opendatascience europe conference 2021 in wageningen nl https av tib eu media 55271 courses lectures the course introduction to machine learning i2ml https slds lmu github io i2ml is a free and open flipped classroom course on the basics of machine learning mlr3 is used in the demos https github com slds lmu lecture i2ml tree master code demos pdf and exercises https github com slds lmu lecture i2ml tree master exercises templates tutorials mlr3 targets https github com mlr org mlr3 targets tutorial showcasing how to use mlr3 with targets https docs ropensci org targets for reproducible ml workflow automation list of extension packages https mlr org com ecosystem html mlr outreach https github com mlr org mlr outreach contains public talks and slides resources wiki https github com mlr org mlr3 wiki contains mainly information for developers installation install the last release from cran r install packages mlr3 install the development version from github r remotes install github mlr org mlr3 if you want to get started with mlr3 we recommend installing the mlr3verse https mlr3verse mlr org com meta package which installs mlr3 and some of the most important extension packages r install packages mlr3verse example constructing learners and tasks r library mlr3 create learning task task penguins as task classif species data palmerpenguins penguins task penguins taskclassif palmerpenguins penguins 344 x 8 target species properties multiclass features 7 int 3 body mass g flipper length mm year dbl 2 bill depth mm bill length mm fct 2 island sex r load learner and set hyperparameter learner lrn classif rpart cp 01 basic train predict r train test split split partition task penguins ratio 0 67 train the model learner train task penguins split train set predict data prediction learner predict task penguins split test set calculate performance prediction confusion truth response adelie chinstrap gentoo adelie 146 5 0 chinstrap 6 63 1 gentoo 0 0 123 r measure msr classif acc prediction score measure classif acc 0 9651163 resample r 3 fold cross validation resampling rsmp cv folds 3l run experiments rr resample task penguins learner resampling access results rr score measure task id learner id iteration classif acc task id learner id iteration classif acc 1 palmerpenguins penguins classif rpart 1 0 9391304 2 palmerpenguins penguins classif rpart 2 0 9478261 3 palmerpenguins penguins classif rpart 3 0 9298246 r rr aggregate measure classif acc 0 938927 extension packages a href https raw githubusercontent com mlr org mlr3 main man figures mlr3verse svg sanitize true img src man figures mlr3verse svg a consult the wiki https github com mlr org mlr3 wiki extension packages for short descriptions and links to the respective repositories for beginners we strongly recommend to install and load the mlr3verse https mlr3verse mlr org com package for a better user experience why a rewrite mlr https github com mlr org mlr was first released to cran https cran r project org package mlr in 2013 its core design and architecture date back even further the addition of many features has led to a feature creep https en wikipedia org wiki feature creep which makes mlr https github com mlr org mlr hard to maintain and hard to extend we also think that while mlr was nicely extensible in some parts learners measures etc other parts were less easy to extend from the outside also many helpful r libraries did not exist at the time mlr https github com mlr org mlr was created and their inclusion would result in non trivial api changes design principles only the basic building blocks for machine learning are implemented in this package focus on computation here no visualization or other stuff that can go in extra packages overcome the limitations of r s s3 classes https adv r hadley nz s3 html with the help of r6 https cran r project org package r6 embrace r6 https cran r project org package r6 for a clean oo design object state changes and reference semantics this might be less traditional r but seems to fit mlr nicely embrace data table https cran r project org package data table for fast and convenient data frame computations combine data table and r6 for this we will make heavy use of list columns in data tables defensive programming and type safety all user input is checked with checkmate https cran r project org package checkmate return types are documented and mechanisms popular in base r which simplify the result unpredictably e g sapply or drop argument in data frame are avoided be light on dependencies mlr3 requires the following packages at runtime parallelly https cran r project org package parallelly helper functions for parallelization no extra recursive dependencies future apply https cran r project org package future apply resampling and benchmarking is parallelized with the future https cran r project org package future abstraction interfacing many parallel backends backports https cran r project org package backports ensures backward compatibility with older r releases developed by members of the mlr team no recursive dependencies checkmate https cran r project org package checkmate fast argument checks developed by members of the mlr team no extra recursive dependencies mlr3misc https cran r project org package mlr3misc miscellaneous functions used in multiple mlr3 extension packages https mlr org com ecosystem html developed by the mlr team paradox https cran r project org package paradox descriptions for parameters and parameter sets developed by the mlr team no extra recursive dependencies r6 https cran r project org package r6 reference class objects no recursive dependencies data table https cran r project org package data table extension of r s data frame no recursive dependencies digest https cran r project org package digest via mlr3misc hash digests no recursive dependencies uuid https cran r project org package uuid create unique string identifiers no recursive dependencies lgr https cran r project org package lgr logging facility no extra recursive dependencies mlr3measures https cran r project org package mlr3measures performance measures no extra recursive dependencies mlbench https cran r project org package mlbench a collection of machine learning data sets no dependencies palmerpenguins https cran r project org package palmerpenguins a classification data set about penguins used on examples and provided as a toy task no dependencies reflections https en wikipedia org wiki reflection 28computer programming 29 objects are queryable for properties and capabilities allowing you to program on them additional functionality that comes with extra dependencies to capture output warnings and exceptions evaluate https cran r project org package evaluate and callr https cran r project org package callr can be used contributing to mlr3 this r package is licensed under the lgpl 3 https www gnu org licenses lgpl 3 0 en html if you encounter problems using this software lack of documentation misleading or wrong documentation unexpected behavior bugs or just want to suggest features please open an issue in the issue tracker https github com mlr org mlr3 issues pull requests are welcome and will be included at the discretion of the maintainers please consult the wiki https github com mlr org mlr3 wiki for a style guide https github com mlr org mlr3 wiki style guide a roxygen guide https github com mlr org mlr3 wiki roxygen guide and a pull request guide https github com mlr org mlr3 wiki pr guidelines citing mlr3 if you use mlr3 please cite our joss article https doi org 10 21105 joss 01903 article mlr3 title mlr3 a modern object oriented machine learning framework in r author michel lang and martin binder and jakob richter and patrick schratz and florian pfisterer and stefan coors and quay au and giuseppe casalicchio and lars kotthoff and bernd bischl journal journal of open source software year 2019 month dec doi 10 21105 joss 01903 url https joss theoj org papers 10 21105 joss 01903	machine-learning data-science classification regression r mlr3 r-package	ai
Interactive-Covid19-Dashboard	https i imgur com 6iitls9 png this is an interactive database i made with the help of the dataset is the covid 19 data repository by the center for systems science and engineering at johns hopkins university this is probably one of the most comprehensive datasets publicly available about the novel coronavirus i have done the prepossessing in jupyter notebook and the visualization with the help of tableau with helps me to analyze the data and come to conclusions more precisely link to the dashboard https public tableau com app profile govind konnanat viz datavizprojectofcovid19 dashboard	covid-19 data-visualization dashboard visualization tableau	server
Machine-Learning-Web-Apps	machine learning web apps building and embedding machine learning model into a web app with flask streamlit express etc basic requirements for python ml web apps flask streamlit scikit learn pandas numpy joblib pickle matplotlib spacy nltk textblob requests for nodejs ml web app express js brainjs body parser etc		ai
ArdRTOS	ardrtos what is ardrtos ardrtos is a simple cooperative real time operating system to facilitate rapid development without requiring extensive knowledge of real time operating systems why make ardrtos while taking a class in college about freertos i realized that there were some simplifications and modernizations that could be taken to make using an rtos dramatically easier and straight forward to use while ardrtos may not be as flexible or mature ardrtos prioritizes ease of use above all other design elements how do i use it the folder named examples is full of examples to use as a short tutorial to using ardrtos for users using the arduino ide those examples are found in the standard location for all library examples feature road map x alpha get the kernel working x cooperative os to work x preemptive os to work canceled x beta get intra task communication and synchronization x queues x stacks x semaphores mutexes x 1 0 generate documentation and examples x dyoxygen documentation for entire project x examples for every major aspect of the rtos x setting up tasks x giving arguments to tasks x delay and yield x integrating interrupts x stacks x queues x semaphores esp support esp8266 support esp32 support how can i contribute currently this is under private development as a senior project out of bradley university by an ece student until the senior project is complete no contributions by members outside of the senior project will be accepted for obvious reasons		os
HTML5_UART_BL	silicon labs mcu serial bootloader data source this is a basic demo that demonstrates ability of html5 and javascript to make a desktop application here we pickup and example for 8 bit mcu bootloader download software which we can get more information from silabs uart bootloader solution http www silabs com support 20documents technicaldocs an778 pdf br http www silabs com support 20documents software an778sw zip we use html5 javascript node webkit to setup the pc application which act as original c software introduction in embedded system design we need to design desktop application to communicate with embedded devices it is normally written by c c or tcl and it is limited with os environment developer needs to design different software version for windows mac os and linux for those cross platform requirement we have better solution on it browser is common thing on various os just follow the html5 standard design a desktop application with html5 we can achieve the cross platform target it can runs on windows linux and mac the programing languages are html5 javascript and css in the below section we will investigate it in detail on how to design a html5 desktop application in embedded system design sublime text 2 sublime is wonderful text editor which support multi platform it is recommended to install it before we start looking into html5 things install the sublime text 2 go to http www sublimetext com 2 download sublime text 2 according your os environment os x os x 10 6 or later is required windows also available as a portable version windows 64 bit also available as a portable version linux 32 bit linux 64 bit install package control this helps us easier to install other plugin press ctrl to console displayed past below scripts in console and press enter import urllib2 os pf package control sublime package ipp sublime installed packages path os makedirs ipp if not os path exists ipp else none open os path join ipp pf wb write urllib2 urlopen http sublime wbond net pf replace 20 read once installation completed restart sublime text2 we will see package control in preference package settings plugin install a jsformat sometimes we got javascript code which has been compressed like below image001 png https raw github com markding html5 uart bl master images image001 png br it is no convenient to understand the code press ctrl alt f the code has been formatted with beautiful form image002 png https raw github com markding html5 uart bl master images image002 png br b docblockr type and press enter it will automatically generate doxygen compatible function description image003 png https raw github com markding html5 uart bl master images image003 png br c sublimelinter automatically check programming language grammar error below example shows missing semicolon case it shows a red exclamation mark on left of the line and display the error information in status bar at bottom of the window image004 png https raw github com markding html5 uart bl master images image004 png br image005 png https raw github com markding html5 uart bl master images image005 png br html5 image007 png https raw github com markding html5 uart bl master images image007 png br introduction html5 is a markup language used for structuring and presenting content for the world wide web and a core technology of the internet it is the fifth revision of the html standard in addition to specifying markup html5 specifies scripting application programming interfaces apis that can be used with javascript existing document object model dom interfaces are extended and de facto features documented there are also new apis such as html5 related apis the canvas element for immediate mode 2d drawing see canvas 2d api specification 1 0 specification timed media playback offline web applications document editing drag and drop cross document messaging browser history management mime type and protocol handler registration microdata web storage a key value pair storage framework that provides behaviour similar to cookies but with larger storage capacity and improved api useful stuffs http diveintohtml5 info canvas html http diveintohtml5 info canvas html br http www w3schools com html html5 intro asp http www w3schools com html html5 intro asp javascript image009 png https raw github com markding html5 uart bl master images image009 png br introduction javascript js is an interpreted computer programming language as part of web browsers implementations allow client side scripts to interact with the user control the browser communicate asynchronously and alter the document content that is displayed it has also become common in server side programming game development and the creation of desktop applications javascript is a prototype based scripting language with dynamic typing and has first class functions its syntax was influenced by c javascript copies many names and naming conventions from java but the two languages are otherwise unrelated and have very different semantics the key design principles within javascript are taken from the self and scheme programming languages it is a multi paradigm language supporting object oriented imperative and functional programming styles javascript was originally developed by brendan eich in netscape although it was developed under the name mocha the language was officially called livescript when it first shipped in beta releases of netscape navigator 2 0 in september 1995 but it was renamed javascript when it was deployed in the netscape browser version 2 0b3 the change of name from livescript to javascript roughly coincided with netscape adding support for java technology in its netscape navigator web browser the final choice of name caused confusion giving the impression that the language was a spin off of the java programming language and the choice has been characterized by many as a marketing ploy by netscape to give javascript the cachet of what was then the hot new web programming language node js image010 png https raw github com markding html5 uart bl master images image010 png br introduction node js is a software platform that is used to build scalable network especially server side applications node js utilizes javascript as its scripting language and achieves high throughput via non blocking i o and a single threaded event loop node js contains a built in http server library making it possible to run a web server without the use of external software such as apache or lighttpd and allowing more control of how the web server works node js was created by ryan dahl starting in 2009 its development and maintenance is sponsored by joyent node js is a packaged compilation of google s v8 javascript engine the platform abstraction layer and a core library which is itself primarily written in javascript dahl s original goal was to create web sites with push capabilities as seen in web applications like gmail after trying solutions in several other programming languages he chose javascript because of the lack of an existing i o api this allowed him to define a convention of non blocking event driven i o useful stuffs download node js from http nodejs org br it support windows macintosh and lnux br image011 png https raw github com markding html5 uart bl master images image011 png br node js is released under the mit license and bundles other liberally licensed oss components a good place to study node js http www nodebeginner org br webkit image012 png https raw github com markding html5 uart bl master images image012 png br introduction node webkit is an app runtime based on chromium and node js you can write native apps in html and javascript with node webkit it also lets you call node js modules directly from the dom and enables a new way of writing native applications with all web technologies features apps written in modern html5 css3 js and webgl complete support for node js apis and all its third party modules good performance node and webkit runs in the same thread function calls are made straightforward objects are in the same heap and can just reference each other easy to package and distribute apps available on linux mac osx and windows installation 1 download latest version from https github com rogerwang node webkit 2 extract the package and copy all files into directory c program files node webkit 3 configure build and debugging setting in sublime text2 for node webkit windows select tools build system new build system it opens a new file named untiled sublime build br copy below code into untiled sublime build and save it as node webkit sublime build br json cmd nw exe enable logging project path file path working dir project path file path path c program files node webkit press ctrl b to build your project please find more information from official website https github com rogerwang node webkit wiki debugging with sublime text 2 desktop application example hello world example 1 create index html image013 png https raw github com markding html5 uart bl master images image013 png br 2 create package json json name nw demo main index html 3 run project by pressing ctrl b it shows below window image014 png https raw github com markding html5 uart bl master images image014 png br official demo code there are several example codes in https github com zcbenz nw sample apps br here is snap of one example code menus image015 png https raw github com markding html5 uart bl master images image015 png br real desktop application example now we can start working on a real application examples according the procedure we can familiar with desktop application development with node webkit we have an f33x uart bootloader pc application written by c the user interface shows below let us make same one with html5 and javascript get reference from http www silabs com support 20documents technicaldocs an778 pdf http www silabs com support 20documents software an778sw zip image016 png https raw github com markding html5 uart bl master images image016 png br ui implementation there are four buttons two check boxes six drop down lists one table and one text area a add drop down lists get reference from http www w3schools com tags tag select asp image017 png https raw github com markding html5 uart bl master images image017 png br the effort shows close to c appearance image018 png https raw github com markding html5 uart bl master images image018 png br b add button get reference from http www w3schools com tags tag button asp image019 png https raw github com markding html5 uart bl master images image019 png br the effort looks good image020 png https raw github com markding html5 uart bl master images image020 png br c add tables get reference from http www w3schools com html html tables asp image022 png https raw github com markding html5 uart bl master images image022 png br the effort looks good too image023 png https raw github com markding html5 uart bl master images image023 png br d checkbox get reference from http www w3schools com jsref prop checkbox checked asp image024 png https raw github com markding html5 uart bl master images image024 png br the effort looks good image025 png https raw github com markding html5 uart bl master images image025 png br e text area get reference from http www w3schools com tags tag textarea asp image026 png https raw github com markding html5 uart bl master images image026 png br the effort looks good too image027 png https raw github com markding html5 uart bl master images image027 png br f final display effort get reference from http www w3schools com html html examples asp br after fine tune ui layout the final appearance looks like below snapshot image028 png https raw github com markding html5 uart bl master images image028 png br file system access we need open hex file from explorer window and read file contents a open file dialog here we want click on button select hex file s it should display file open dialog to select input file here we need start coding javascript to achieve file access function get reference from http www w3schools com jsref dom obj fileupload asp in index html add input tag to for file upload the id is openfile the id can be access by javascript code and set it not display style also add script tag to execute script js image030 png https raw github com markding html5 uart bl master images image030 png br and then change button tag of select hex file s id is openhexfile onclick event handler function is openfileoption image031 png https raw github com markding html5 uart bl master images image031 png br in script js add openfileoption notice getelementbyid with id openfile which defined in indel html input tag and with onclick event of input tag it call handlefiles which print out selected file name on console javascript function openfileoption document getelementbyid openfile click function handlefiles files for var i 0 i files length i console log files i name press ctrl b click on button select hex file s we can see a file dialog appeared image032 png https raw github com markding html5 uart bl master images image032 png br b read file contents here we need node js to access local storage file br get reference from http nodejs org api fs html fs fs readfile filename options callback br in script js javascript var fs require fs function handlefiles files fs readfile files 0 name utf8 function err data if err throw err console log data press ctrl b click on button select hex file s select sample user application hex file and then we can see hex file contents output in console 3928 1120 153906 info console 6 03040000020447ac r n 0c044700787fe4f6d8fd758107020433cd r n 1004330053d9bf1204037fb07e63120415d2afe415 r n 04044300f5a780fe9b r n 0d040300afa775a70f75e24043af048fa7a8 r n 0104100022c9 r n 10041500ada775a70fe4f5c8c39fffe49ef5cb8f85 r n 0d042500ca74fff5cdf5ccd2add2ca8da7bb r n 0104320022a7 r n 030412000204538e r n 05045300c2cfb2b2327d r n 0b77f500250101020136071ba5c23d63 r n 00000001ff r n hex file analysis a introduction intel hex is a file format for conveying binary information for applications like programming microcontrollers eproms and other kinds of chips the format is a text file with each line containing hexadecimal values encoding a sequence of data and their starting offset or absolute address each line of intel hex file consists of six parts start code one character an ascii colon byte count two hex digits a number of bytes hex digit pairs in the data field 16 0x10 or 32 0x20 bytes of data are the usual compromise values between line length and address overhead address four hex digits a 16 bit address of the beginning of the memory position for the data limited to 64 kilobytes the limit is worked around by specifying higher bits via additional record types record type two hex digits 00 to 05 defining the type of the data field data a sequence of n bytes of the data themselves represented by 2n hex digits checksum two hex digits example of hex file image034 png https raw github com markding html5 uart bl master images image034 png br b hex parser coding get reference from https github com bminer intel hex js user firmware structure the user firmware application structure look like below the user firmware start address from 0x400 and at end of user firmware area there is application info block which record the user firmware information something like app start address app end address bl type etc image036 png https raw github com markding html5 uart bl master images image036 png br in our example the user application firmware start address is 0x400 however the application info block address at 0x77f5 that means two pages are needed for this firmware and a lot of pages bare between first occurred page and last occurred page modification on intel hex js the original hex parser javascript will fill 0xff in page which is not used in our case it will generate a big image from address 0x0000 to 0x7800 which is too large for us so an array flashpageinuse added in intel hex js to record which page is being used it also as one of return values part of original hex file 03040000020447ac the data segment is 020447 part of parsed hex file output 3572 1121 101801 info console 18 0 2 1 4 2 71 it is 02 04 71 0x47 the result shows correct value parse application info block here is definition of info block these information needed display to table in ui c segment variable tgt app infoblock const u8 seg code bl specific byte app fw version high app fw version low tgt flash page size code tgt bl type tgt mcu code tgt app infoblock length sig byte3 sig byte2 sig byte1 sig byte0 output to table named table id infoblock in index html open the table by id in script js and handle info block from hex parse result c function infoblockdisplay tmp var infotable document getelementbyid infoblock mcu code infotable rows 1 cells 1 innerhtml 0x tmp flash page size 6 tostring 16 bl type if tmp flash page size 7 1 infotable rows 2 cells 1 innerhtml uart flash page size if tmp flash page size 8 2 infotable rows 3 cells 1 innerhtml 1024 else if tmp flash page size 8 1 infotable rows 3 cells 1 innerhtml 512 app fw version var ver high tmp flash page size 10 tostring var ver low tmp flash page size 9 tostring infotable rows 4 cells 1 innerhtml ver high ver low reserved infotable rows 5 cells 1 innerhtml 0x tmp flash page size 11 tostring 16 app start addr infotable rows 6 cells 1 innerhtml firmwarestartaddress app end addr infotable rows 7 cells 1 innerhtml firmwareendaddress run result press ctrl b click on button select hex file s and choose sample user application hex in file dialog the result looks good image038 png https raw github com markding html5 uart bl master images image038 png br serial port communication reference from https github com voodootikigod node serialport 1 install serialport plug in for node js br before install serialport plug in for node js we need to install python 2 76 http www python org download releases 2 7 6 npm install serialport 2 install nw gyp br things become a little difference between normal node js third party plugin node webkit need to rebuild the plug in get reference from https github com rogerwang node webkit wiki using node modules 3rd party modules with c c addons npm install nw gyp g 3 rebuild node webkit br enter serialport module directory nw gyp rebuild target 0 8 0 4 to use br output string omg it wors to com4 javascript var serialport require serialport var com serialport serialport var serialport new com com4 baudrate 115200 function serialporttesting console log com port open test n serialport write omg it works r also add serialporttesting function in anywhere you want check the com4 output with serial terminal tool we can see sting omg it works appear on the terminal tool that means the serial port operation works list all com ports javascript serialport list function err ports ports foreach function port console log port comname console log port pnpid console log port manufacturer we can see the output from console com3 usb vid 10c4 pid ea60 0001 silicon laboratories com1 acpi pnp0501 0 standard port types com4 usb vid 10c4 pid ea60 0003 silicon laboratories update com port dropdown list javascript function serialportlist serialport list function err ports var coms var cnt 0 ports foreach function port coms cnt port comname coms sort var comportinput document getelementbyid comport coms foreach function com var option document createelement option option text com comportinput add option null open com port button function implementation javascript function serialportopen var comportinput document getelementbyid comport var comselected comportinput options comportinput selectedindex text console log comselected var serialport serialport serialport var serialport new serialport comselected baudrate 115200 false serialport open function err if err var textarea document getelementbyid textarea var tmp textarea value err n textarea value tmp return serialport write omg it works r firmware update control function implementation the protocol can get reference from f330 uart bootloader firmware br for quick demonstration purpose we remove crc function from firmware the firmware main loop like below c while src disp tgt info src rsp ok src response src get info src validate response src response src cmd get info if last error 0 goto error flash key0 rx buf 5 flash key1 rx buf 6 while 1 src response src get page info src page crc rx buf 4 rx buf 5 8 page addr rx buf 1 rx buf 2 8 src validate response src response src cmd get page info if last error 0 break exit this loop if no more pages are available from source if src response src rsp data end break src response src get page page buf src validate response src response src cmd get page if last error 0 break tgt erase page page addr tgt write flash page buf page addr test result a prepare c8051f330 tb board br b download uart bootloader firmware in it br c connect uart interface to pc it appears com4 br d press ctrl b to run the html5 updater br e select blink sample application hex file by clicking select hex files button br f select com4 from dropdown list and click on open com port button br g the blinky application will download to target board automatically and reset mcu at end of updating br h we can see a green led d2 on board is blinking br i we are done br image039 png https raw github com markding html5 uart bl master images image039 png br source code the source code can be found from https github com markding html5 uart bl license the mit license mit copyright c 2013 mark ding mark ding hotmail com permission is hereby granted free of charge to any person obtaining a copy of this software and associated documentation files the software to deal in the software without restriction including without limitation the rights to use copy modify merge publish distribute sublicense and or sell copies of the software and to permit persons to whom the software is furnished to do so subject to the following conditions the above copyright notice and this permission notice shall be included in all copies or substantial portions of the software the software is provided as is without warranty of any kind express or implied including but not limited to the warranties of merchantability fitness for a particular purpose and noninfringement in no event shall the authors or copyright holders be liable for any claim damages or other liability whether in an action of contract tort or otherwise arising from out of or in connection with the software or the use or other dealings in the software		os
ElinsCV	cv presentation ex ubuntu ubuntu 18 04 https www ubuntu com download desktop kubuntu 18 04 https kubuntu org getkubuntu ubuntu virtualbox windows http profitraders com ubuntu virtualboxubuntuinstall html opencv opencv https opencv org console foo bar sudo apt update foo bar sudo apt install libopencv dev cmake elinscv https github com nicklyrick elinscv fork fork git console foo bar sudo apt install git clone or download clone console foo bar git clone https github com elinscv git foo bar cd elinscv console foo bar elinscv cd ex console foo bar elinscv ex mkdir build foo bar elinscv ex cd build console foo bar elinscv ex build cmake foo bar elinscv ex build make make live die repeat console foo bar elinscv ex build ex n n make live die repeat elinscv var0 cpp console foo bar elinscv git status diff changes not staged for commit use git add file to update what will be committed use git checkout file to discard changes in working directory modified var0 cpp var0 cpp changes not staged for commit git add git add var0 cpp console foo bar elinscv git add path to file var0 cpp git status diff changes to be committed use git reset head file to unstage modified var0 cpp git add git status console foo bar elinscv git commit m console foo bar elinscv git push origin master github pull requests 1 github 2 pull requests code projects 3 new pull request 4 create pull request 5 create pull request		ai
Mark3	codacy badge https api codacy com project badge grade 1c6a3d96c7cd471189dc75850cde71ba https app codacy com app moslevin mark3 utm source github com utm medium referral utm content moslevin mark3 utm campaign badge grade dashboard coverity scan build status https scan coverity com projects 17835 badge svg https scan coverity com projects moslevin mark3 sonarcloud status https sonarcloud io api project badges measure project moslevin mark3 metric alert status https sonarcloud io project issues id moslevin mark3 documentation https img shields io badge docs doxygen blue svg http moslevin github io mark3 the mark3 real time operating system mark3 logo https github com moslevin mark3 raw master mark3 small png mark3 real time operating system the mark3 realtime kernel is a completely free open source real time operating system aimed at bringing powerful easy to use multitasking to microcontroller systems without mmus mark3 logo https github com moslevin mark3 raw master banner2 png features overview the mark3 rtos is written using a super portable design that scales to many common processor architectures including a variety of 8 16 32 and 64 bit targets the flexible cmake based build system facilitates compiling the kernel tests examples and user application code for any supported target with a consistent interface the api is rich and surprisingly simple with six function calls you can set up the kernel initialize two threads and start the scheduler written in modern c mark3 makes use of modern language features that improve code quality reduces duplication and simplifies api usage c isn t your thing no problem there s a complete set of c language bindings available to facilitate the use of mark3 in a wider variety of environments the mark3 kernel releases contain zero compiler warnings zero compiler errors and have zero unit test failures the build and test process can be automated through the mark3 docker project https github com moslevin mark3 docker project allowing for easy integration with continuous integration environments the kernel is also run through static analysis tools automated profiling and documentation tools the source is fully documented with doxygen and example code is provided to illustrate core concepts the result is a performant rtos which is easy to read easy to understand and easy to extend to fit your needs quick links mark3 kernel documentation https moslevin github io mark3 doc mark3 os repo https github com moslevin mark3 repo mark3 docker project https github com moslevin mark3 docker	rtos real-time arm embedded avr raspberry-pi-3 msp430 operating-systems	os
iaml2017	introductory applied machine learning infr10069 these instructions are written for set up on dice a unix environment distance learners please note we recommend two options for working remotely we preference that you use your own machine 1 use your own machine conda installation should work fine on your own computer you must still have a dice account when submitting assignments you will need to copy work up to dice and submit from there instructions will be given for this in each assignment 1 use virtual dice a virtual machine emulated on your own computer connected to the dice network please read here for installation instructions and more http computing help inf ed ac uk vdice windows users please note after conda installation all instructions are much the same please follow conda installation instructions on their website here https conda io docs user guide install index html to activate the iaml environment note that you don t type source activate iaml but instead just activate iaml you can ignore section 3b though you can google windows equivalents of all the unix commands given setting up on dice or unix within this course we will be using python along with a few open source libraries packages we will be using a virtual environment and package management tool called conda https conda io docs we re going to run the entire setup in the terminal if you re on a dice machine click applications in the top left go to utilities then click terminal in the below instructions any text styled like this should be executed in the terminal we are expecting you to enter these commands in by hand one by one this is for pedagogical reasons and to help detect new issues please read and heed any warnings and especially errors you may encounter we are on standby in the labs to help if required 1 check your available space firstly please note that your space on dice is allocated dynamically if you are having problems it may be because you were using new space faster than it could be allocated to you all dice users registered for iaml will automatically be allocated 20gb extra space over their default space values please register for the course asap to get this space 1 check how much space you have on dice you will need at least 4 5gb 1 freespace 1 if you don t have enough space follow the instructions on this page http computing help inf ed ac uk afs quotas 2 if you don t have it install conda 1 check you don t already have conda installed 1 which conda 1 if you already have it installed skip ahead to create an environment 1 it doesn t matter if you have miniconda2 miniconda3 anaconda2 or anaconda3 installed they will all work for this course 1 if you don t have conda download the latest version of miniconda2 1 cd downloads you can make a downloads folder if you don t have one 1 download the installer depending on your system you can check links here https conda io miniconda html linux wget https repo continuum io miniconda miniconda2 latest linux x86 64 sh mac wget https repo continuum io miniconda miniconda2 latest macosx x86 64 sh or curl lok https repo continuum io miniconda miniconda2 latest macosx x86 64 sh or just simply download from the site https conda io miniconda html 1 install miniconda2 with default settings 1 bash miniconda2 latest linux x86 64 sh 1 follow the prompt type yes and hit enter to accept all default settings when asked 1 close terminal and reopen 1 try executing conda h if it works you can delete the installer rm downloads miniconda2 latest linux x86 64 sh 3a create an environment for iaml 1 update conda conda update conda at the time of writing the latest version was 4 3 25 but you should be safe to use later versions 1 create the environment for the course call it iaml and install python 2 conda create name iaml python 2 3b err what s an environment an environment is a collection of packages of specific versions you can have multiple environments and switch between them for different projects conda is a tool for managing both environments and the packages within each environment here s a quick intro 1 show a list of your environments conda env list 1 print path one of your system s environment variables https en wikipedia org wiki environment variable in the terminal echo path path is the list of directories your terminal can search to find anything you execute 1 print a list of python installations on your path the top one is the one that will get executed if you type python in the terminal which python a 1 activate the new environment source activate iaml 1 show list of python installations on your system now which python a 1 show your system path again echo path 1 deactivate the new environment source deactivate 1 observer how your path has changed again echo path 1 make an empty environment conda create name empty 1 you can clone environments this is useful for backing up conda create name empty bkp clone empty 1 make a python 3 environment with numpy already installed conda create name py3 python 3 numpy 1 conda env list 1 activate py3 source activate py3 1 show the installed packages conda list 1 switch environments source deactivate source activate empty 1 conda list to show packages note that python and crucially pip https pip pypa io en stable are not installed 1 q what python would get used now which python a the conda root environment installation of python i e not this environment s python 1 install numpy conda install numpy 1 q what python would get used now which python a you may have clocked that conda installed a dependency of numpy a python package python 1 let s delete these test environments source deactivate conda env list conda remove name empty all conda remove name empty bkp all conda remove name py3 all conda env list 4 install all the packages for iaml 1 activate the environment source activate iaml 1 may take 5 minutes install all required packages conda install jupyter 1 0 0 matplotlib 2 0 2 pandas 0 20 3 numpy 1 13 1 scikit learn 0 19 0 scipy 0 19 1 seaborn 0 8 please note that normally we wouldn t specify the version numbers conda automatically downloads the most recent consistent set of packages we specify versions here such that this course is consistent regardless of when you start recreate your environment 1 get some space back conda clean a important before starting any iaml work in a new terminal you must always activate the iaml conda environment using source activate iaml if the environment is not activated you will be using your base python with its own set of packages if you are ever in any doubt of which python version is being used execute which python 5 get course material you should now have all the required modules installed our next step is to make a new directory where we will keep all the lab notebooks datasets and assignments within your terminal 1 navigate back to your home directory cd 2 make a new directory and navigate to it mkdir iaml 2017 cd iaml 2017 now you have two options 1 we recommend that you directly download a zip file from https github com jamesowers iaml2017 which will contain everything you need and save it in the folder you have just created you can do this from the terminal by typing wget https github com jamesowers iaml2017 archive master zip unzip master zip 1 if and only if you are familiar and confident with using git github you can initialize a git directory add the above repo as remote and pull everything into your local directory important supporting and teaching git is not in scope for this course so please only do this if you are happy to google your own solutions 6 get started once you have downloaded the material you are now ready to start working with jupyter notebooks first you need to activate the software environment and then start a jupyter notebook session from within the folder where the material is stored you will have to follow this procedure for all labs and assignments 1 activate the conda environment source activate iaml 2 enter the directory where you downloaded the course material cd iaml 2017 iaml master 3 start a jupyter notebook jupyter notebook 4 this should automatically open your browser click on 01 lab 0 introduction ipynb to open it further reading conda getting started 30 minute practical well worth going through https conda io docs user guide getting started html system environment variables https en wikipedia org wiki environment variable linux execution order https www cyberciti biz tips how linux or unix understand which program to run part i html troubleshooting i ran out of space when installing packages firstly please note that your space on dice is allocated dynamically if you are having problems it may be because you were using new space faster than it could be allocated to you 1 check how much space you have on dice you will need at least 4 5gb 1 freespace 1 if you don t have enough space follow the instructions on this page http computing help inf ed ac uk afs quotas 1 try instaling packages individually and executing conda clean all after each installation trashing your environment if you install incorrect packages or a package breaks for some reason you can just delete your environment and start again execute conda remove name iaml all then install the package as described above trashing your whole conda installation this is fairly extreme but as a final resort can be done quickly and easily please note that you will lose all your environments if you do this so check this will not affect you before proceeding follow instructions here https conda io docs user guide install linux html highlight uninstall uninstalling anaconda or miniconda unzipping master zip error check that you downloaded the zip correctly an error like end of central directory signature not found either this file is not a zipfile or it constitutes one disk of a multi part archive in the latter case the central directory and zipfile comment will be found on the last disk s of this archive means that the file you ve downloaded is likely incomplete try downloading from the github repo https github com jamesowers iaml2017 directly by clicking the green button and downloading the zip wget command not found you do not have wget installed either install it download from the github repo https github com jamesowers iaml2017 directly by clicking the green button and download the zip or try using another program like curl e g curl lok https github com jamesowers iaml2017 archive master zip conda command not found or conda never works in new terminal dice issue dice has a different set of bash startup mechanism and you may need to edit some different files yourself do the below with benv instead see here http computing help inf ed ac uk dice bash for more info unix solution first try closing your terminal and reopening if that doesn t fix it s likely that in the conda installation you didn t allow conda to add the it s bin directory to your path check your home directory for bashrc or bash profile you should have a line in one of those files that looks like this added by miniconda2 4 3 21 installer export path afs inf ed ac uk user s12 s1234567 miniconda2 bin path if you don t you can add it like this changing the conda installation location line miniconda location afs inf ed ac uk user s12 s1234567 miniconda2 bin echo export path path bash profile source is not recognized as an internal or external command you re on windows aren t you please see the note at the top of the file you can omit source which is not recognized as an internal or external command you re on windows aren t you please see the note at the top of the file which where on windows path you re on windows aren t you please see the note at the top of the file echo path echo path on windows i can t find my conda environment but i definitely created it we have found that people also taking mlp and or anlp other courses that use conda have installed multiple versions of conda to check whether you ve done this simply list your home directory bash ls if you see multiple folders called anaconda or miniconda e g anaconda3 and miniconda2 you have installed multiple versions of conda another way to check is to print your path or view your bashrc benv echo path cat bashrc cat benv if you re on dice this will show multiple conda directories you only need to use one installation of conda and it doens t matter whether you use version 2 or 3 there is no difference that will affect this course simply recreate your environment s in one of the conda installations and delete the other https conda io docs user guide tasks manage environments html sharing an environment https conda io docs user guide install linux html uninstalling anaconda or miniconda		ai
myvision	br do a long readme p align center img style margin left 25px width 70 src readme images logo with text 35 png alt logo img style margin left 25px width 70 src readme logo with text 31 png alt logo img width 300 src readme presenting 76 png alt logo p description myvision is a free online image annotation tool used for generating computer vision based ml training data it is designed with the user in mind offering features to speed up the labelling process and help maintain workflows with large datasets features draw bounding boxes and polygons to label your objects p align center img width 830 src readme gifs 2020 07 06 23 41 06 gif alt logo p polygon manipulation is enriched with additional features to edit remove and add new points p align center img width 830 src readme gifs ezgif com gif maker 3 gif alt logo p supported dataset formats p align center img width 90 style margin left 5 src readme images table3 png alt logo p annotating objects can be a difficult task you can skip all the hard work and use a pre trained machine learning model to automatically annotate the objects for you myvision leverages the popular coco ssd model to generate bounding boxes for your images and by operating locally on your browser retain all data within the privacy of your computer p align center img width 830 src readme gifs 2020 07 08 00 13 39 gif alt logo p you can import existing annotation projects and continue working on them in myvision this process can also be used to convert datasets from one format to another p align center img width 830 src readme gifs ezgif com gif maker 4 gif alt logo p languages myvision is available in english readme md and chinese mandarin readme cn md local setup link to the file or bring the screen up to there no setup is required to run this project open the index html public index html file and you are all set however if you want to make changes or contribute to this repository please follow the instructions below requirements node version 10 and npm version 6 install node dependencies npm install run the project in watch mode npm run watch all changes should be made in the src directory and observed in publicdev citation misc myvision author ovidijus parsiunas title myvision howpublished url https github com ovidijusparsiunas myvision year 2019	ml machine-learning computer-vision object-detection training-data annotation labelling annotation-tool coco vgg tensorflow yolo model vision image-annotation label labeling-tool tagging image ai	ai
Direct-Project	direct introduction direct is an ios application that offers real time browsing of events that are currently happening near you users can post their own events browse a feed of events and view live videos and photos from users currently attending an event additionally users can view a list of upcoming events around them direct is a mobile platform for advertising live or upcoming event information more effectively compared to what is traditionally accomplished using flyers group members kesong xie bilguun bulgan lauren diep natalie duong naomi mccracken yilong mo shruti pandey dmytry pasikov vicky tang nicholas yee tommy dang requirements application must be ran through simulation via xcode on a mac user must have an internet connection while using the application minimum specs xcode version 9 0 or above ios 10 0 or abvoe macos sierra version 10 12 6 or above installation instructions 1 run the following command on the terminal git clone https github com nickosaur direct project git 2 change directories to the folder called direct using the command cd direct after cloning the directory 3 open the direct xcworkspace using the command open direct xcworkspace in the terminal 4 select iphone x as the simulator at the top of xcode 5 click the run button to launch the simulator 6 click on the direct app logo to open the application		os

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