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A Case Study of Safety in the Design of Surgical Robots: The ARAKNES Platform.

['L. Alonso Sanchez', 'Minh Quyen Le', 'Kanty Rabenorosoa', 'Chao Liu 0003', 'Nabil Zemiti', 'Philippe Poignet', 'Etienne Dombre', 'Arianna Menciassi', 'Paolo Dario']

A Case Study of Safety in the Design of Surgical Robots: The ARAKNES Platform.

Miniaturization, integrated design and comprehensive information utilization are increasing in popularity in embedded system design. In this paper, we present UMBUS, an online fault-tolerant bus suited for embedded systems. UM-BUS is a high-speed, dynamic reconfigurable serial bus with N (≤ 32) concurrent lanes of mutual redundant structure. It provides the capability of remote memory accessing with maximum communication distance of up to 40 meters. In the case of allowing performance reduction of 50%, any faults in N/2 lanes can be tolerated by reconfiguring the lanes online. Based on the topology of UM-BUS, we introduce a new type of plug and play architecture for embedded systems. It can break the traditional chassis bounds of embedded systems by distributing their in-chassis modules to remote locations. Through an experimental validation system, we demonstrate the feasibility of UM-BUS on real-world applications.

['Jiqin Zhou', 'Weigong Zhang', 'Keni Qiu', 'Xiaoyan Zhu']

UM-BUS: An online fault-tolerant bus for embedded systems

As an important canopy structure indicator, leaf area index (LAI) proved to be of considerable implications for forest ecosystem and ecological studies, and efficient techniques for accurate LAI acquisitions have long been highlighted. Airborne light detection and ranging (LiDAR), often termed as airborne laser scanning (ALS), once was extensively investigated for this task but showed limited performance due to its low sampling density. Now, ALS systems exhibit more competing capacities such as high density and multi-return sampling, and hence, people began to ask the questions like—“can ALS now work better on the task of LAI prediction?” As a re-examination, this study investigated the feasibility of LAI retrievals at the individual tree level based on high density and multi-return ALS, by directly considering the vertical distributions of laser points lying within each tree crown instead of by proposing feature variables such as quantiles involving laser point distribution modes at the plot level. The examination was operated in the case of four tree species (i.e. Picea abies, Pinus sylvestris, Populus tremula and Quercus robur) in a mixed forest, with their LAI-related reference data collected by using static terrestrial laser scanning (TLS). In light of the differences between ALS- and TLS-based LAI characterizations, the methods of voxelization of 3D scattered laser points, effective LAI (LAIe) that does not distinguish branches from canopies and unified cumulative LAI (ucLAI) that is often used to characterize the vertical profiles of crown leaf area densities (LADs) was used; then, the relationships between the ALS- and TLS-derived LAIes were determined, and so did ucLAIs. Tests indicated that the tree-level LAIes for the four tree species can be estimated based on the used airborne LiDAR (R2 = 0.07, 0.26, 0.43 and 0.21, respectively) and their ucLAIs can also be derived. Overall, this study has validated the usage of the contemporary high density multi-return airborne LiDARs for LAIe and LAD profile retrievals at the individual tree level, and the contribution are of high potential for advancing forest ecosystem modeling and ecological understanding.

['Yi Lin', 'Geoff A. W. West']

Retrieval of effective leaf area index (LAIe) and leaf area density (LAD) profile at individual tree level using high density multi-return airborne LiDAR

A large-scale simulation in e-science experiments can be modeled by using a workflow. The ProGenGrid workflow management system is being developed at the University of Salento in Lecce since 2004 and consists of an editor for designing the experiment and an engine for scheduling the jobs in a computational grid. The initial version was based on wrapping the bioinformatics tools as Web services and scheduling job execution on the grid by using an opportune engine. Then the engine was optimized to support batch, parameter and MPI jobs by using the Globus Toolkit. This component was developed as part of the grid resource broker project. In this paper, we present the latest advances regarding the editorpsilas new features such as on line monitoring, and the enginepsilas support for scheduling jobs on other grid middleware such as gLite and Unicore.

['Maria Mirto', 'Massimo Cafaro', 'Italo Epicoco', 'Giovanni Aloisio']

Advances in the ProGenGrid Workflow Management System

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top- k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.

['Chen Cheng', 'Haiqin Yang', 'Irwin King', 'Michael R. Lyu']

A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Development of Hydraulic Drive Drilling Robot with 4-DOF Tool for In-Pipe Repair – Mechanical Design of New Tool –

['Hiroaki Seki', 'Hodaka Amakata', 'Yoshitsugu Kamiya', 'br', 'Masatoshi Hikizu']

Development of Hydraulic Drive Drilling Robot with 4-DOF Tool for In-Pipe Repair – Mechanical Design of New Tool –

We investigate a fairness-aware adaptive resource allocation scheme for the downlink of multihop OFDMA systems. Assuming that the base station has all the channel information, we formulate an optimization problem for an adaptive subchannel-, path- and power-allocation scheme that maximizes system capacity while guaranteeing minimum resources for each user. Since the optimization should be performed in real time, we propose an efficient heuristic algorithm composed of subchannel-allocation, load-balancing and power-distribution steps. The proposed algorithm is simple in that the iterative computations are removed, and accurate in that it performs similarly to the optimum solution

['Chi-Sung Bae', 'Dong-Ho Cho']

Fairness-Aware Adaptive Resource Allocation Scheme in Multihop OFDMA Systems

Many PAPR reduction schemes have been proposed for OFDM systems. Among these, the signal scrambling methods such as the partial transmit sequences (PTS) (S. H. Muller, et al., 1997) and selective mapping (SLM) (R. W. Bauml, et al., 1996) are attractive as they obtain better PAPR property by modifying OFDM signals without distortion. These schemes can also be applied to a SFBC MIMO-OFDM system, which is advantageous for dispersive channels, in a straightforward way by performing signal scrambling on data sequence before it is distributed to the transmit antennas according to employed encoding scheme. Note however that in the case of PTS PAPR reduction in the time domain is not possible, which leads to prohibitively large complexity of such scheme. In this letter, we introduce more effective approach, the polyphase interleaving and inversion (PII) PAPR scheme and its reduced complexity version (RC-PII), which is designed to suppress peaks in SFBC-OFDM, transmit diversity.

['Zoran Latinovic', 'Yeheskel Bar-Ness']

SFBC MIMO-OFDM peak-to-average power ratio reduction by polyphase interleaving and inversion

This paper discusses the question: Can we improve the recognition of objects by using their spatial context? We start from Bag-of-Words models and use the Pascal 2007 dataset. We use the rough object bounding boxes that come with this dataset to investigate the fundamental gain context can bring. Our main contributions are: (I) The result of Zhang et al. in CVPR07 that context is superfluous derived from the Pascal 2005 data set of 4 classes does not generalize to this dataset. For our larger and more realistic dataset context is important indeed. (II) Using the rough bounding box to limit or extend the scope of an object during both training and testing, we find that the spatial extent of an object is determined by its category: (a) well-defined, rigid objects have the object itself as the preferred spatial extent. (b) Non-rigid objects have an unbounded spatial extent : all spatial extents produce equally good results. (c) Objects primarily categorised based on their function have the whole image as their spatial extent. Finally, (III) using the rough bounding box to treat object and context separately, we find that the upper bound of improvement is 26% (12% absolute) in terms of mean average precision, and this bound is likely to be higher if the localisation is done using segmentation. It is concluded that object localisation, if done sufficiently precise, helps considerably in the recognition of objects for the Pascal 2007 dataset.

['Jasper R. R. Uijlings', 'Arnold W. M. Smeulders', 'Remko Scha']

What is the spatial extent of an object

We consider incorporating topic information as prior knowledge into the sequence to sequence (Seq2Seq) network structure with attention mechanism for response generation in chatbots. To this end, we propose a topic augmented joint attention based Seq2Seq (TAJA-Seq2Seq) model. In TAJA-Seq2Seq, information from input posts and information from topics related to the posts are simultaneously embedded into vector spaces by a content encoder and a topic encoder respectively. The two kinds of information interact with each other and help calibrate weights of each other in the joint attention mechanism in TAJA2Seq2Seq, and jointly determine the generation of responses in decoding. The model simulates how people behave in conversation and can generate well-focused and informative responses with the help of topic information. Empirical study on large scale human judged generation results show that our model outperforms Seq2Seq with attention on both response quality and diversity.

['Chen Xing', 'Wei Wu', 'Yu Wu', 'Jie Liu', 'Yalou Huang', 'Ming Zhou', 'Wei-Ying Ma']

Topic Augmented Neural Response Generation with a Joint Attention Mechanism

Allowing multiple users to transmit in the same band simultaneously in an uncoordinated manor may require agility and reaction on the part of one or both of the users. In the scenario we consider here, one of the users is considered a primary user (PU) and will not react to the secondary user (SU). The SU is responsible for maximizing the combined channel throughput while minimizing interference to the PU. The scenario we examine has a PU operating in a frequency span of 20 MHz occupying a nominal 5 MHz Bandwidth centered at one of the offsets of ±2.5 MHz or ±7.5 MHz from band center. The PU randomly hops between these band centers. The SU can monitor the PU spectral position and respond with appropriate changes to its transmitted signal position to minimize interfering transmissions. We designed our SU signal to overlay the PU without the need to monitor and avoid the PU's occupied band while preserving negligible interaction between the signal sets.

['Fred Harris', 'Richard Bell', 'Vamsi Krishna Adsumilli']

Spectrum sharing between a ZigBee frequency hopper and an FSK modem

Optical Diffusion Tomography (ODT) is a modern non-invasive medical imaging modality which requires mathematical modelling of near-infrared light propagation in tissue. Solving the ODT forward problem equation accurately and efficiently is crucial. Typically, the forward problem is represented by a Diffusion PDE and is solved using the Finite Element Method (FEM) on a mesh, which is often unstructured. Tensor B-spline signal processing has the attractive features of excellent interpolation and approximation properties, multiscale properties, fast algorithms and does not require meshing. This paper introduces Tensor B-spline methodology with arbitrary spline degree tailored to solve the ODT forward problem in an accurate and efficient manner. We show that our Tensor B-spline formulation induces efficient and highly parallelizable computational algorithms. Exploitation of B-spline properties for integration over irregular domains proved valuable. The Tensor B-spline solver was tested on standard problems and on synthetic medical data and compared to FEM, including state-of-the art ODT forward solvers. Results show that 1) a significantly higher accuracy can be achieved with the same number of nodes, 2) fewer nodes are required to achieve a prespecified accuracy, 3) the algorithm converges in significantly fewer iterations to a given error. These findings support the value of Tensor B-spline methodology for high-performance ODT implementations. This may translate into advances in ODT imaging for biomedical research and clinical application.

['D. A. Shulga', 'O. V. Morozov', 'Patrick Hunziker']

A Tensor B-Spline Approach for Solving the Diffusion PDE With Application to Optical Diffusion Tomography

Most crowd simulators focus on navigation and agents flow. In this paper, we present another perspective that concentrates on the overall distribution of virtual agents and uses psychological preferences for choosing goal locations. Both observation and published theory indicate that most people prefer to maintain their personal space as event spaces increase in density, particularly when they have no previous relationship to other individuals. The geometric structure that naturally forms could be highly approximated by a Voronoi tessellation. Our method allows users to specify sub-regions of an environment and tag the regions with information (e.g., permitted densities and features). A Centroidal Voronoi Tessellation (CVT) is then automatically constructed over the entire virtual world. The centers of mass of each resulting cell are taken as potential agent goal locations. Individual virtual agents then have the ability to choose their preferred goal locations on the basis of their own characteristics (e.g., personality traits, needs, and interests), the CVT, and semantic features of the sub-regions. This method results in more meaningful crowd simulations with minimal additional user effort. Copyright © 2012 John Wiley & Sons, Ltd.

['Weizi Philip Li', 'Zichao Di', 'Jan M. Allbeck']

Crowd distribution and location preference

Performance of Spectral Efficiency and Blocking Probability Using Distributed Dynamic Channel Allocation

['Y.S.V.Raman Y.S.V.Raman', 'S. Sri Gowri', 'B. Prabhakara Rao']

Performance of Spectral Efficiency and Blocking Probability Using Distributed Dynamic Channel Allocation

Cases for Including a Reference Monitor to SDN

['Dimitrios Gkounis', 'Felix Klaedtke', 'Roberto Bifulco', 'Ghassan O. Karame']

Cases for Including a Reference Monitor to SDN

A Guide to Preparing Images of Trees with TrEd for Publishing.

['Petr Pajas']

A Guide to Preparing Images of Trees with TrEd for Publishing.

This paper presents a vibration measurement and analysis technique for use during a machine’s spin-down procedure. During spin-down, the machine’s operation covers a continuous wide frequency band, from operating speed to standstill, which allows the estimation of the machine’s vibration transfer function (VTF). This transfer function is rich in information for detecting and differentiating not only machinery pathologies but also problems with vibrational mounts. Utilizing a back-electromotive force sensor to infer rotor speed and a single-axis accelerometer for vibration measurements, this technique allows minimally intrusive estimation of a machine’s VTF. Data collected in laboratory and field tests aboard U.S. Navy ships are presented to demonstrate the usefulness of this monitoring technique.

['Ryan Zachar', 'Peter Lindahl', 'John S. Donnal', 'William Cotta', 'Christopher Schantz', 'Steven B. Leeb']

Utilizing Spin-Down Transients for Vibration-Based Diagnostics of Resiliently Mounted Machines

Social networks have evolved to a key technology for information diffusion. Consequently, discourse behavior and communication dynamics have become an important research area. However, privacy settings limit data acquisition extremely, such that empirical online content and discourse analysis are hardly applicable here. Since the "human factor" mostly influences the behavior of individual actors, network analysis also provides a restricted perspective. This leads to the question: How to present the "human factor" in combination with network dynamics?#R##N##R##N#In this paper, we propose the application of agent-based simulation (ABS) and intelligent agents for analysis of information propagation in social networks. The benefit of a simulation approach is, that dynamic analysis of communication behavior as well as artificial scenarios can be produced. In contrast to conventional ABS, where agents are modeled in a reactive or stochastic way, intelligent behavior of the agents should lead to a more realistic behavior of the simulated human actors. Thus, intelligent agents should provide a comprehensive perspective on communication processes taking place in social networks. We discuss our current state of work in using a variety of psychological theories for accomplishing a representation of different personal traits and relationships between actors.

['Fabian Lorig', 'Ingo J. Timm']

How to model the human factor for agent-based simulation in social media analysis?: work in progress paper

Two detectors of symmetrically distributed independent timing jitter in a data record composed of a complex harmonic in additive white Gaussian noise are proposed. The proposed detectors are computationally efficient, and although they are formulated using asymptotic results, they may be effectively used with small sample lengths under a wide range of conditions. The conditions required for consistency of the detectors are derived and examined for important special cases. The performances of the detectors are analyzed using simulations.

['Mark R. Morelande', 'D. Robert Iskander']

Formulation and Comparison of Two Detectors of Independent Timing Jitter in a Complex Harmonic

The deployment of antenna subset selection on a per-subcarrier basis in MIMO-OFDM systems could improve the system performance and/or increase data rates. This paper investigates this technique for the MIMO-OFDM systems suffering nonlinear distortions due to high-power amplifiers. At first, some problems pertaining to the implementation of the conventional per-subcarrier antenna selection approach, including power imbalance across transmit antennas and noncausality of antenna selection criteria, are identified. Next, an optimal selection scheme is devised by means of linear optimization to overcome those drawbacks. This scheme optimally allocates data subcarriers under a constraint that all antennas have the same number of data symbols. The formulated optimization problem to realize the constrained scheme could be applied to the systems with an arbitrary number of multiplexed data streams and with different antenna selection criteria. Finally, a reduced complexity strategy that requires smaller feedback information and lower computational effort for solving the optimization problem is developed. The efficacy of the constrained antenna selection approach over the conventional selection approach is analyzed directly in nonlinear fading channels. Simulation results demonstrate that a significant improvement in terms of error performance could be achieved in the proposed system with a constrained selection compared to its counterpart.

['Ngoc Phuc Le', 'Farzad Safaei', 'Le Chung Tran']

Transmit antenna subset selection for high-rate MIMO-OFDM systems in the presence of nonlinear power amplifiers

The best way to prepare for an interview is to review the different types of possible interview questions you will be asked during an interview and practice responding to questions. An interview coaching system tries to simulate an interviewer to provide mock interview practice simulation sessions for the users. The traditional interview coaching systems provide some feedbacks, including facial preference, head nodding, response time, speaking rate, and volume, to let users know their own performance in the mock interview. But most of these systems are trained with insufficient dialog data and provide the pre-designed interview questions. In this study, we propose an approach to dialog state tracking and action selection based on deep learning methods. First, the interview corpus in this study is collected from 12 participants, and is annotated with dialog states and actions. Next, a long-short term memory and an artificial neural network are employed to predict dialog states and the Deep RL is adopted to learn the relation between dialog states and actions. Finally, the selected action is used to generate the interview question for interview practice. To evaluate the proposed method in action selection, an interview coaching system is constructed. Experimental results show the effectiveness of the proposed method for dialog state tracking and action selection.

['Ming-Hsiang Su', 'Kun-Yi Huang', 'Tsung-Hsien Yang', 'Kuan-Jung Lai', 'Chung-Hsien Wu']

Dialog State Tracking and action selection using deep learning mechanism for interview coaching

Selling in-game content has become a popular revenue model for game publishers. While prior research has investigated latent motivations as determinants of in-game content purchases, the prior literature has not focused on more concrete reasons to purchase in-game content that stem from how the games are being designed. We form an inventory of reasons (19) to buy in-game content via triangulating from analyses of top-grossing free-to-play games, from a review of existing research, and from industry expert input. These reasons were operationalized into a survey (N = 519). Firstly, we explored how these motivations converged into categories. The results indicated that the purchasing reasons converged into six dimensions: 1) Unobstructed play, 2) Social interaction, 3) Competition, 4) Economical rationale, 5) Indulging the children, and 6) Unlocking content. Secondly, we investigated the relationship between these factors and how much players spend money on in-game content. The results revealed that the purchase motivations of unobstructed play, social interaction, and economical rationale were positively associated with how much money players spend on in-game content. The results imply that the way designers implement artificial limitations and obstacles as well as social interaction affects how much players spend money on in-game content.

['Juho Hamari', 'Kati Alha', 'Simo Järvelä', 'J. Matias Kivikangas', 'Jonna Koivisto', 'Janne Paavilainen']

Why do players buy in-game content? An empirical study on concrete purchase motivations

Two recursive algorithms for computing the weight distributions of certain binary irreducible cyclic codes of length n in the so-called index 2 case are presented. The running times of these algorithms are smaller than O(log/sup 2/r) where r=2/sup m/ and n is a factor of r-1.

['Marko J. Moisio', 'Keijo Väänänen']

Two recursive algorithms for computing the weight distribution of certain irreducible cyclic codes

In this paper an experimental teaching platform named OptoBridge is presented which supports the sharing of the collaborative space for spatially distributed users to assist skill acquisition. The development of OptoBridge is based on augmented reality (AR) and integrates free-hand gesture interactions with the video mediated communication. The prototype is preliminarily applied in the optics field to promote skill execution in the case of the Michelson interferometer. OptoBridge enables the remote teacher to monitor the experimental scenario as well as the detailed optical phenomena through the transmitted video captured on the local side. Meanwhile the local learner equipped with the optical see-through head-mounted display (OSTHMD) can be indicated by virtual hands and augmented annotations controlled by the teacher's gestures and follow the guidance to get their skills practiced. The implementation of OptoBridge is also presented, aimed at providing a more engaging and efficient approach for remote skill teaching.

['Hongling Sun', 'Zhenliang Zhang', 'Yue Liu', 'Henry Been-Lirn Duh']

OptoBridge: assisting skill acquisition in the remote experimental collaboration

A Virtual Community Design for Home-Based Chronic Disease Healthcare.

['Yan Hu', 'Guohua Bai', 'Jenny Lundberg', 'Sara Eriksén']

A Virtual Community Design for Home-Based Chronic Disease Healthcare.

A Cellular Learning Automata-based Algorithm for Solving the Coverage and Connectivity Problem in Wireless Sensor Networks

['Reza Ghaderi', 'Mehdi Esnaashari', 'Mohammad Reza Meybodi']

A Cellular Learning Automata-based Algorithm for Solving the Coverage and Connectivity Problem in Wireless Sensor Networks

Department of Defense and Homeland Security analysts are increasingly using multi-agent simulation (MAS) to examine national security issues. This paper summarizes three MAS national security studies conducted at the Naval Postgraduate School. The first example explores equipment and employment options for protecting critical infrastructure. The second case considers non-lethal weapons within the spectrum of force-protection options in a martitime environment. The final application investigates emergency (police, fire, and medical) responses to an urban terrorist attack. There are many potentially influential factors and many sources of uncertainty associated with each of these simulated scenarios. Thus, efficient experimental designs and computing clusters are used to enable us to explore many thousands of computational experiments, while simultaneously varying many factors. The results illustrate how MAS experiments can provide valuable insights into defense and homeland security operations.

['Thomas W. Lucas', 'Susan M. Sanchez', 'Felix Martinez', 'Lisa R. Sickinger', 'Jonathan W. Roginski']

Defense and homeland security applications of multi-agent simulations

Local (single-hop) broadcasting is widely employed in distributed protocols (e.g., neighbor discovery, local information exchange in distributed network optimization and gossip-based algorithms) in wireless ad hoc networks. The performance of local broadcasting is characterized by the mean number of neighbors and the probability distribution of the number of neighbors of a broadcasting node. In this paper, we study the performance of local broadcasting in heterogeneous wireless ad hoc networks in which inter-system interference dominates signal reception quality, considering general fading distributions of both the desired signal and the interfering signals. In addition, we investigate the impacts of different error control techniques (e.g., simple retransmission, Chase combining, and incremental redundancy) on the performance of local broadcasting. The increase in the mean number of neighbors of a broadcasting node with respect to the number of retransmissions of a message is clarified, facilitating QoS provisioning in reliable local broadcasting in interference-limited heterogeneous wireless ad hoc networks.

['Weng Chon Ao', 'Kwang-Cheng Chen']

Error Control for Local Broadcasting in Heterogeneous Wireless Ad Hoc Networks

PCB Recognition using Local Features for Recycling Purposes

['Christopher Pramerdorfer', 'Martin Kampel']

PCB Recognition using Local Features for Recycling Purposes

Analysis of Expressiveness of Portuguese Sign Language Speakers

['Ines V. Rodrigues', 'Eduardo Marques Pereira', 'Luís Teixeira']

Analysis of Expressiveness of Portuguese Sign Language Speakers

Simple and compact multi-path switching devices for multi-valued decision diagram (MDD)-based logic circuits are designed, fabricated and characterized. The devices switch multiple exit branches for electrons entering from an entry branch, according to multi-valued input. The switching function is implemented by dual gating on multiple nanowires with different threshold voltages. The gate threshold voltage is controlled by precise design of gate structures and sizes in nanometer scale. The operation principle of the device is described using a simple analytical model. Ternary-path switching devices are demonstrated using AlGaAs/GaAs etched nanowire junctions together with nanometer-scale Schottky wrap gates (WPGs) and in-plane gates (IPGs).

['Seiya Kasai', 'Yuta Shiratori', 'Kensuke Miura', 'Nanjian Wu']

Multi-path Switching Device Utilizing a Multi-terminal Nanowire Junction for MDD-Based Logic Circuit

Routing protocols play an important role in WSNs in order to gather data and send them to the BS (base station). One of the challenges that the WSNs have to face is to prolong its lifetime since transmitting and receiving interest information are the most energy exhausting phase. In this paper, we introduced a new routing algorithm called SPER (safe and power -efficient routing algorithm in wireless sensor networks) which is enlightened by PEGASIS (power-efficient gathering in sensor information systems). At the same time in SPER we gave out a novel formula to compute the value of weight in which energy consuming factor was introduced considering the power balance problem. Based on the multiple chains structure, a method of cipher keys distribution was designed. A symmetric public key formally installed in the sensors was used to encrypt the initial process. In each data aggregating rounds different cipher keys were used to enhance the security of the whole net.

['Yan Zhao', 'Gaocao Xu']

SPER: A Safe and Power-Efficient Routing Algorithm in Wireless Sensor Networks

Maximum likelihood (ML) direction-of arrival (DOA) estimation of multiple narrowband sources in unknown nonuniform white noise is considered. A new iterative algorithm for stochastic ML DOA estimation is presented. The stepwise concentration of the log-likelihood (LL) function with respect to the signal and noise nuisance parameters is derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined on the unconditional model and a desired family of probability distributions constrained to be concentrated on the observed data. The new algorithm presents the advantage to provide closed-form expressions for the signal and noise nuisance parameter estimates which results in a substantial reduction of the parameter space required for numerical optimization. The proposed algorithm converges only after a few iterations and its effectiveness is confirmed in a simulation example.

['Abd-Krim Seghouane']

A Kullback-Leibler Methodology for Unconditional ML DOA Estimation in Unknown Nonuniform Noise

Factors influencing choice of major in electrical engineering and later curricular and professional choices are investigated. Studies include both quantitative and qualitative analyses via student transcripts, surveys, and focus groups. Student motivation for choosing an electrical engineering major and later subdiscipline in the field is interpreted through expectancy-value theory, where primary factors of strong perceived value of future professional opportunities and strong influence of course instructors are identified. Performances in select required electrical engineering courses appear to serve as predictors for student choice of subdiscipline emphasis. In contrast, participation in student professional activities does not show statistically significant correlations with subdiscipline. Curricular and professional choices appear to be explained by expectancy-value theory with inclusion of socializers. The findings suggest that early and integrative exposure of all electrical engineering technical areas, including high-quality teaching, may provide an optimal basis for students to make future decisions on academic path and participation in professional activities.

['Justin M. Foley', 'Shanna R. Daly', 'Catherine Lenaway', 'Jamie D. Phillips']

Investigating Student Motivation and Performance in Electrical Engineering and Its Subdisciplines

We describe a novel method for mathematical modelling and simulation (MMS) of nonlinear robotic dynamic systems using fuzzy logic techniques, genetic algorithms and fractal theory. The new fuzzy-fractal-genetic method combines soft computing techniques with mathematical methods for the domain of modelling and simulation of nonlinear robotic systems. This domain is quite complex because it is a well known fact that even simple nonlinear dynamical systems can exhibit "chaotic" behavior. The new method for MMS of nonlinear robotic dynamic systems has been implemented as a computer program to show that our new fuzzy-fractal-genetic approach is a good alternative for modelling this kind of system.

['Oscar Castillo', 'Patricia Melin']

Intelligent mathematical modelling and simulation of robotic dynamic systems using a new fuzzy-fractal-genetic approach

As signal speeds increase and gate delays decrease for high-performance digital integrated circuits, the gate delay modeling problem becomes increasingly more difficult. With scaling, increasing interconnect resistances and decreasing gate-output impedances make it more difficult to empirically characterize gate-delay models. Moreover, the single-input-switching assumption for the empirical models is incompatible with the inevitable simultaneous switching for today.s high-speed logic paths. In this paper a new empirical gate delay model is proposed. Instead of building the empirical equations in terms of capacitance loading and input-signal transition time, the models are generated in terms of parameters which combine the benefits of empirically derived k-factor models and switch-resistor models to efficiently: 1) handle capacitance shielding due to metal interconnect resistance, 2) model the RC interconnect delay, and 3)provide tighter bounds for simultaneous switching.

['Florentin Dartu', 'Noel Menezes', 'Jessica Qian', 'Lawrence T. Pillage']

A Gate-Delay Model for High-Speed CMOS Circuits

The predictability of fisher behaviour is an area of considerable uncertainty in fisheries management models. Fisher-derived data could underpin a better understanding, and more realistic predictions of fishing behaviour. Face to face interviews and a choice-based survey were conducted with scallop fishers to collect foraging parameters that could inform a model of fishing behaviour, and to better understand patch choice behaviour. Importantly, we validated survey data against vessel monitoring system and logbook data where possible, demonstrating a good level of accuracy. Environmental parameters central to patch choice were determined (e.g. wave height, distance to port), and three strategies of patch choice behaviour were identified, termed quantity maximiser, quality maximiser, and efficient fisher. Individuals' VMS and logbook data further confirmed and explained these behavioural patterns. This approach provided reliable, highly relevant data for the parameterisation of a fisheries behavioural model, which could lead to more robust and realistic predictive fisheries models.

['Jennifer Shepperson', 'Lee G. Murray', 'Steven Mackinson', 'Ewen Bell', 'Michel J. Kaiser']

Use of a choice-based survey approach to characterise fishing behaviour in a scallop fishery

In Service-Oriented Architecture (SOA), dedicatedintermediate nodes called load balancers are usually deployed in data centers (DC) in order to balance the load among multiple instances of an application service and to optimize the resource utilization. However, the addition of these nodes increases the installation and operational cost of DCs. These load balancers distribute incoming flows to multiple outgoing ports usually by hashing them. Several techniques are used in order to select the outgoing ports e.g. round robin, queue length, feedback from neighbors etc. Such load balancing approaches do not considergetting live feedback from the service end and therefore are not able to dynamically change the amount of allocated resources. In this paper, a distributed load management scheme isproposed for service oriented networks based on the currentInternet architecture. In this scheme, lightweight interconnected management agents are used to decide the availability for a particular service instance and help in optimal distribution of the flows. The proposed scheme can also be applied in other emerging internetworking architectures such as RINA.

['Ehsan Elahi', 'Jason Barron', 'Micheal Crotty', 'Miguel Ponce de Leon', 'Rashid Mijumbi', 'Steven Davy', 'Dimitri Staessens', 'Sander Vrijders']

On Load Management in Service Oriented Networks (Short Paper)

This paper presents a copy synthesis method to controlling the Klatt synthesizer. Our method allows speech stimuli to be constructed very easily. We accepted the parallel branch of the Klatt synthesizer. After formants have been tracked, the amp litudes of the resonators are measured on a spectrum obtained by an algorithm derived from cepstral smoothing called ''true e nvelope''. This algorithm has the advantage of approximating harmonics very accurately. The analysis strategy of a speech sig nal is straightforward: the fundamental frequency is calculated so that voiced regions are known and the frication energy is set to the value of the spectral energy above 4000 Hz. Stimuli which have been created by means of this method have a timbre close to that of natural speech. This copy synthesis method is incorporated in our software for speech research called ``Sno rri''. Therefore, the user has at his disposal a versatile tool for creating stimuli in the context of the Klatt synthesizer

['Yves Laprie', 'Anne Bonneau']

A copy synthesis method to pilot the Klatt synthesiser

A language for bitmap manipulation

['Leonidas J. Guibas', 'Jorge Stolfi']

A language for bitmap manipulation

Objects vary in their visual complexity, yet existing discovery methods perform “batch” clustering, paying equal attention to all instances simultaneously — regardless of the strength of their appearance or context cues. We propose a self-paced approach that instead focuses on the easiest instances first, and progressively expands its repertoire to include more complex objects. Easier regions are defined as those with both high likelihood of generic objectness and high familiarity of surrounding objects. At each cycle of the discovery process, we re-estimate the easiness of each subwindow in the pool of unlabeled images, and then retrieve a single prominent cluster from among the easiest instances. Critically, as the system gradually accumulates models, each new (more difficult) discovery benefits from the context provided by earlier discoveries. Our experiments demonstrate the clear advantages of self-paced discovery relative to conventional batch approaches, including both more accurate summarization as well as stronger predictive models for novel data.

['Yong Jae Lee', 'Kristen Grauman']

Learning the easy things first: Self-paced visual category discovery

Code-division multiple-access (CDMA) implemented with direct-sequence spread-spectrum signaling is among the most promising multiplexing technologies for cellular telecommunications services. We consider the application of the minimum-mean-square-error (MMSE) multiuser detection technique to the problem of suppressing the digital narrowband interference (NBI) from spread spectrum signals. The MMSE multiuser detector can be implemented using a blind adaptive method, which is ideally suited for use in the NBI suppression framework. This application requires the treatment of a single narrowband digital signal as a group of related, virtual spread-spectrum signals with very simple spreading codes. This model gives a special structure to the matrices appearing in the optimization problem implied by the MMSE criterion, and this structure is exploited to develop and analyze a practical adaptive algorithm. The major contribution of this paper beyond the previous work in the field of NBI suppression is the development of this adaptive algorithm that can exploit the advantages of multiuser detection in suppressing narrowband digital interference from spread-spectrum networks.

['H.V. Poor', 'Xiaodong Wang']

Adaptive suppression of narrowband digital interferers from spread spectrum signals

Neural networks were evolved through genetic algorithms to focus minimax search in the game of Othello. At each level of the search tree, the focus networks decide which moves are promising enough to be explored further. The networks effectively hide problem states from minimax based on the knowledge they have evolved about the limitations of minimax and the evaluation function. Focus networks were encoded in marker-based chromosomes and were evolved against a full-width minimax opponent that used the same evaluation function. The networks were able to guide the search away from poor information, resulting in stronger play while examining fewer states. When evolved with a highly sophisticated evaluation function of the Bill program, the system was able to match Bill's performance while only searching a subset of the moves.

['David E. Moriarty', 'Risto Miikkulainen']

Evolving neural networks to focus minimax search

This paper discusses the use of behavioral simulation techniques to improve the quality of teaching/learning circuits and systems for communications. The proposed pedagogical methodology has been applied in several electrical engineering courses, in both undergraduate and master degrees. The method allows students to better understand some complex circuit-and physical-level phenomena, by describing them at a higher abstraction level. In addition to enhance their understanding of design problems and skills, students become more motivated and satisfied. As an application, two case studies are considered in this work: a radio-frequency front-end system and an analog-to-digital converter. In both cases, behavioral models of the different building blocks have been implemented in MATLAB/SIMULINK and used by the students enrolled in two courses named: Electronic Circuits for Communications and Wireless Transceivers: Standards, Techniques and Architectures.

['José M. de la Rosa']

Behavioral modeling techniques for teaching communication circuits and systems

We demonstrate one of a new generation of frame-free vision sensors with pixels that convey information asynchronously as a stream of pixel-events. This relatively new paradigm has different advantages and disadvantages which we try to demonstrate with our example sensor. This demo is associated with the Sensory Systems track

['Jenny Olsson', 'Philipp Häfliger']

Live demonstration of an asynchronous integrate-and-fire pixel-event vision sensor

The bitstream of a progressively encoded textured model consists of multiple refinement layers. Decoding each layer produces a model with a simplified mesh and a resolution-reduced texture. We have proposed a quality measure that captures the visual fidelity of the multi-resolution textured models (Tian, D.H. and AlRegib, G., Proc. ACM Multimedia 2004, p.684-91, 2004). Based on that quality measure, we consider the problem of streaming progressively encoded textured models over a bandwidth-limited channel. We develop a bit-allocation algorithm that optimally packetizes the source bits in every transmitted data unit, such that the perceptual quality of the model displayed on the client's screen is maximized. Experimental results confirm the effectiveness of the proposed bit-allocation algorithm.

['Dihong Tian', 'Ghassan AlRegib']

Progressive streaming of textured 3D models over bandwidth-limited channels

This chapter discusses the present and potential future use of robotics technology in mental healthcare practice. Robots have been used to help treat people with autism, dementia, and other cognitive impairments, have helped to provide companionship to people experiencing loneliness, and have been used to help improve how people with disabilities are treated by clinicians. Robotics technology has also been used in novel ways to diagnose and study schizophrenia in naturalistic interactive social settings.#R##N##R##N#Despite these exciting technological advances, it is critical that robot designers and mental health professionals insist on rigorous randomized controlled clinical trials (RCTs) before deploying robots into mental healthcare practice. While it is unlikely most robots would cause direct harm to clients or practitioners, adopting this technology before the evidence base is developed runs the risk of displacing proven interventions with less effective or ineffective treatments.

['Laurel D. Riek']

Robotics Technology in Mental Health Care

Heterogeneous information networks contain heterogeneous types of nodes and edges, e.g., social networks and knowledge graphs. A meta-path is a path connecting nodes through a sequence of heterogeneous edges, representing different kinds of semantic relations among nodes. Meta-paths are good mechanisms to improve the quality of graph analysis on heterogeneous information networks. This paper presents an iceberg cube framework for heterogeneous information networks based on meta-paths. To the best of our knowledge, there is no such proposal in the past. 1 We use meta-paths to measure the similarities of nodes, and prove the problem is NP-hard. 2 An optimal solution is proposed for the strict case. We develop the variant of slice tree to aggregate networks hierarchically. 3 To improve the scalability, a general approximate algorithm is provided for fast aggregation, where random walk on meta-paths is employed to measure the similarities. 4 Two pruning strategies are designed for reducing search space when the aggregate function is monotonic. 5 Experiments on both real-world and synthetic networks demonstrate the effectiveness and efficiency of the algorithms.

['Dan Yin', 'Hong Gao', 'Zhaonian Zou', 'Jianzhong Li', 'Zhipeng Cai']

Approximate Iceberg Cube on Heterogeneous Dimensions

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal x Normal x Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.

['Baichuan Zhang', 'Murat Dundar', 'Mohammad Al Hasan']

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

We consider a cognitive relay assisted multiple-input multiple-output (MIMO) communication scenario where multiple secondary users wish to communicate with the secondary receiver through Rayleigh fading channels in the presence of a primary user. We assume that secondary users transmits simultaneously with the primary user over the same channel instead of waiting for an idle channel which is traditional for a cognitive radio. We derive the expressions for mutual information received at the primary and secondary receivers in both the cases when cognitive relay is present or not. We also present the closed form expressions for the outage probabilities and complementary cumulative distribution functions (CCDFs) of mutual information received at the primary and secondary receivers in the absence of cognitive relay. Our results show that in the presence of cognitive relay, not only both the primary and multiple secondary users are able to communicate with intended receivers compensating interferences created at their receivers, but also the outage performance of primary and secondary users are improved due to the additional diversity obtained via cognitive relaying.

['Md. Zahurul I. Sarkar', 'Tharmalingam Ratnarajah', 'Mathini Sellathurai']

On the outage behavior of cognitive relay assisted MIMO multiple access channel

Previous work in network analysis has focused on modeling the mixed-memberships of node roles in the graph, but not the roles of edges. We introduce the edge role discovery problem and present a generalizable framework for learning and extracting edge roles from arbitrary graphs automatically. Furthermore, while existing node-centric role models have mainly focused on simple degree and egonet features, this work also explores graphlet features for role discovery. In addition, we also develop an approach for automatically learning and extracting important and useful edge features from an arbitrary graph. The experimental results demonstrate the utility of edge roles for network analysis tasks on a variety of graphs from various problem domains.

['Nesreen K. Ahmed', 'Ryan A. Rossi', 'Theodore L. Willke', 'Rong Zhou']

Revisiting Role Discovery in Networks: From Node to Edge Roles

In future wireless networks, a significant number of users accessing wireless broadband will be vehicular (i.e., in public transportation vehicles like buses, trams, or trains). The Third Generation Partnership Project has started to investigate how to serve these vehicular users cost-effectively, and several solutions have been proposed. One promising solution is to deploy a moving relay node (MRN), on a public transportation vehicle that forms its own cell inside the vehicle to serve vehicular users. By proper antenna placement, an MRN can reduce or even eliminate the vehicular penetration loss that affects communication. Moreover, MRNs can exploit various smart antenna techniques and advanced signal processing schemes, as they are less limited by size and power than regular user equipment. However, there are also challenges in using MRNs, such as designing efficient interference management techniques as well as proper mobility management schemes to exploit the benefit of group handovers for vehicular UE devices served by the same MRN. Nevertheless, initial system-level evaluation results indicate that a dedicated MRN deployment shows great potential to improve the vehicular user experience, and thereby can potentially bring significant benefits to future wireless communication systems.

['Yutao Sui', 'Jaakko Vihriälä', 'Agisilaos Papadogiannis', 'Mikael Sternad', 'Wei Yang', 'Tommy Svensson']

Moving cells: a promising solution to boost performance for vehicular users

Negative paramodulation

['Larry Wos', 'William McCune']

Negative paramodulation

As outsourcing data to remote storage servers gets popular, protecting user's pattern in accessing these data has become a big concern. ORAM constructions are promising solutions to this issue, but their application in practice has been impeded by the high communication and storage overheads incurred. Towards addressing this challenge, this paper proposes a segmentation-based ORAM (S-ORAM). It adopts two segment-based techniques, namely, piece-wise shuffling and segment-based query , to improve the performance of shuffling and query by factoring block size into design. Extensive security analysis proves that S-ORAM is a highly secure solution with a negligible failure probability of O(N -log N ). In terms of communication and storage overheads, S-ORAM outperforms the Balanced ORAM (B-ORAM) and the Path ORAM (P-ORAM), which are the state-of-the-art hash and index based ORAMs respectively, in both practical and theoretical evaluations. Particularly under practical settings, the communication overhead of S-ORAM is 12 to 23 times less than B-ORAM when they have the same constant-size user-side storage, and S-ORAM consumes 80% less server-side storage and around 60% to 72% less bandwidth than P-ORAM when they have the similar logarithmic-size user-side storage.

['Jinsheng Zhang', 'Wensheng Zhang', 'Daji Qiao']

S-ORAM: a segmentation-based oblivious RAM

In wireless sensor and actor networks (WSANs), a set of static sensor nodes and a set of (mobile) actor nodes form a network that performs distributed sensing and actuation tasks. In [1], Abbasi et al. presented DARA, a Distributed Actor Recovery Algorithm, which restores the connectivity of the interactor network by efficiently relocating some mobile actors when failure of an actor happens. To restore 1 and 2-connectivity of the network, two algorithms are developed in [1]. Their basic idea is to find the smallest set of actors that needs to be repositioned to restore the required level of connectivity, with the objective to minimize the movement overhead of relocation. Here, we show that the algorithms proposed in [1] will not work smoothly in all scenarios as claimed and give counterexamples for some algorithms and theorems proposed in [1]. We then present a general actor relocation problem and propose methods that will work correctly for several subsets of the problems. Specifically, our method does result in an optimum movement strategy with minimum movement overhead for the problems studied in [1].

['ShiGuang Wang', 'Xufei Mao', 'Shao Jie Tang', 'Xiang Yang Li', 'Jizhong Zhao', 'Guojun Dai']

On “Movement-Assisted Connectivity Restoration in Wireless Sensor and Actor Networks”

Wepresentthenewblockcipher SHARK.Thisciphercom- bines highly non-linear substitution boxes andmaximum distance sepa- rableerrorcorrectingcodes(MDS-codes)toguaranteeagooddifiusion. Thecipherisresistantagainstdifierential andlinearcryptanalysisafter a small number of rounds. The structure of SHARK is such that a fast softwareimplementationispossible,bothfortheencryptionandthede- cryption. Our C-implementation of SHARK runs more than four times faster thanSAFERandIDEAona64-bit architecture.

['Vincent Rijmen', 'Joan Daemen', 'Bart Preneel', 'Anton Bossalaers', 'Erik De Win']

The Cipher SHARK

Good character animation requires convincing skin deformations including subtleties and details like muscle bulges. Such effects are typically created in commercial animation packages which provide very general and powerful tools. While these systems are convenient and flexible for artists, the generality often leads to characters that are slow to compute or that require a substantial amount of memory and thus cannot be used in interactive systems. Instead, interactive systems restrict artists to a specific character deformation model which is fast and memory efficient but is notoriously difficult to author and can suffer from many deformation artifacts. This paper presents an automated framework that allows character artists to use the full complement of tools in high-end systems to create characters for interactive systems. Our method starts with an arbitrarily rigged character in an animation system. A set of examples is exported, consisting of skeleton configurations paired with the deformed geometry as static meshes. Using these examples, we fit the parameters of a deformation model that best approximates the original data yet remains fast to compute and compact in memory.

['Alex Mohr', 'Michael Gleicher']

Building efficient, accurate character skins from examples

A new hierarchical Bayesian model is proposed for image segmentation based on Gaussian mixture models (GMM) with a prior enforcing spatial smoothness. According to this prior, the local differences of the contextual mixing proportions (i.e. the probabilities of class labels) are Studentpsilas t-distributed. The generative properties of the Student's t-pdf allow this prior to impose smoothness and simultaneously model the edges between the segments of the image. A maximum a posteriori (MAP) expectation-maximization (EM) based algorithm is used for Bayesian inference. An important feature of this algorithm is that all the parameters are automatically estimated from the data in closed form. Numerical experiments are presented that demonstrate the superiority of the proposed model for image segmentation as compared to standard GMM-based approaches and to GMM segmentation techniques with ldquostandardrdquo spatial smoothness constraints.

['Giorgos Sfikas', 'Christophoros Nikou', 'Nikolas P. Galatsanos']

Edge preserving spatially varying mixtures for image segmentation

Book Review: Examining Artificial and Human Intelligence

['Daniel E. Cooke']

Book Review: Examining Artificial and Human Intelligence

Pattern based video coding as an extra mode in H.264 has already established its superiority over the H.264 in low bit rate areas. A significant drawback of predefined regular shaped pattern-matching algorithms however, is that they fail to capture large numbers of active-region macroblocks (RMB) which partially cover moving objects in a sequence. Intuitively, improved coding performance is achieved if instead of using a prescribed codebook of fixed-size regular shaped patterns, all pattern shapes are dynamically extracted from the video content. This pattern formation technique requires a clustering algorithm. In this paper we investigate the impact of clustering approaches on pattern generation. We also investigate the performance of hybrid pattern based coding using both the pre-defined and dynamically extracted patterns in a pattern codebook. The experimental results confirm that clustering algorithm has little impact on overall performance and hybrid pattern based coding does not improve the performance.

['Manoranjan Paul', 'M. Manzur Murshed']

Impact of Clustering Techniques on Content-Based Pattern Generation Algorithm for Low Bit Rate Video Coding

We present research in which both left and right ventricular deformation is estimated from tagged cardiac magnetic resonance imaging using volumetric deformable models constructed from nonuniform rational B-splines (NURBS). The four model types considered and compared for the left ventricle include two Cartesian NURBS models-one with a cylindrical parameter assignment and one with a prolate spheroidal parameter assignment. The remaining two are non-Cartesian, i.e., prolate spheroidal and cylindrical each with their respective prolate spheroidal and cylindrical parameter assignment regimes. These choices were made based on the typical shape of the left ventricle. For each frame starting with end-diastole, a NURBS model is constructed by fitting two surfaces with the same parameterization to the corresponding set of epicardial and endocardial contours from which a volumetric model is created. Using normal displacements of the three sets of orthogonal tag planes as well as displacements of contour/tag line intersection points and tag plane intersection points, one can solve for the optimal homogeneous coordinates, in a weighted least squares sense, of the control points of the deformed NURBS model at end-diastole using quadratic programming. This allows for subsequent nonrigid registration of the biventricular model at end-diastole to all later time frames. After registration of the model to all later time points, the registered NURBS models are temporally lofted in order to create a comprehensive four-dimensional NURBS model. From the lofted model, we can extract three-dimensional myocardial deformation fields and corresponding Lagrangian and Eulerian strain maps which are local measures of nonrigid deformation. The results show that, in the case of simulated data, the quadratic Cartesian NURBS models with the cylindrical and prolate spheroidal parameter assignments outperform their counterparts in predicting normal strain. The decreased complexity associated with the Cartesian model with the cylindrical parameter assignment prompted its use for subsequent calculations. Lagrangian strains in three canine data, a normal human, and a patient with history of myocardial infarction are presented. Eulerian strains for the normal human data are also included.

['Nicholas J. Tustison', 'Amir A. Amini']

Biventricular myocardial strains via nonrigid registration of AnFigatomical NURBS models

Prevalent hardware trends towards parallel architectures and algorithms create a growing demand for graduate students familiar with the programming of concurrent software. However, learning parallel programming is challenging due to complex communication and memory access patterns as well as the avoidance of common pitfalls such as dead-locks and race conditions. Hence, the learning process has to be supported by adequate software solutions in order to enable future computer scientists and engineers to write robust and efficient code. This paper discusses a selection of well-known parallel algorithms based on C++11 threads, OpenMP, MPI, and CUDA that can be interactively embedded in an HPC or parallel computing lecture using a unified framework for the automated evaluation of source code—namely the “System for AUtomated Code Evaluation” (SAUCE). SAUCE is free software licensed under AGPL-3.0 and can be downloaded at https://github.com/moschlar/SAUCE free of charge.

['Christian Hundt', 'Moritz Schlarb', 'Bertil Schmidt']

SAUCE: A web application for interactive teaching and learning of parallel programming

A spammer is making profit and stays in business even when a tiny fraction of recipients replies to spam messages. Despite enormous effort put in spam identification and elimination, spam is still dominating our inbox. We seek a novel and complementary approach to force spammers stop sending unsolicited messages. Our approach is to artificially inflate the number of available recipients by contaminating spammers' databases with addresses not monitored by human beings. Thus, we aim to drastically reduce the number of messages delivered to human beings as to reduce the response rate.

['Alexandros Antonopoulos', 'Kyriakos Stefanidis', 'Artemios G. Voyiatzis']

Fighting spammers with spam

We describe Voai and Xoai , two software environments that facilitate the automatic construction of OAI servers for collections managed via relational and XML databases, respectively. We have used Voai and Xoai to generate OAI servers for diverse collections. We use freely available tools and do not impose programming requirements upon the users. By making this software publicly available, we aim to facilitate the process of joining the OAI community and becoming data providers.

['J. Alfredo Sánchez', 'Antonio Razo', 'Juan Manuel Córdova', 'Abraham Villegas']

Dynamic generation of OAI servers

Invariant Generation for Parametrized Systems Using Self-reflection - (Extended Version).

['Alejandro Sánchez', 'Sriram Sankaranarayanan', 'César Sánchez', 'Bor-Yuh Evan Chang']

Invariant Generation for Parametrized Systems Using Self-reflection - (Extended Version).

Hidden Web databases maintain a collection of specialised documents, which are dynamically generated in response to users' queries. The categorisation of such databases into a set of predefined categories has been widely employed to assist users in their information searches. In this paper we present a technique that automatically categorises a document database through its content summary and concepts described by their specificity and coverage. Experimental results show that our approach categorises databases with a larger number of relevant categories.

['Yih-Ling Hedley', 'Muhammad Younas', 'Anne E. James']

The categorisation of hidden Web databases through concept specificity and coverage

A Framework for Enterprise Agility and the Enabling Role of Digital Options

['Eric Overby', 'Anandhi Bharadwaj', 'Vallabh Sambamurthy']

A Framework for Enterprise Agility and the Enabling Role of Digital Options

Lack of generic digital rights management applications has stunted the growth of the media distribution industry. In this paper we point out the need for middleware services required to develop digital rights management (DRM) applications. Many functionalities are common to most DRM applications and by nature are highly distributed. Standalone DRM applications have found it difficult to implement these services in an efficient manner and have led to closed solutions with limited capabilities. This paper categorizes these functions with reference to a layered DRM framework as middleware services. The characteristics and interface of each of these services is defined along with a prototype implementation of an agent-based negotiations service.

['Pramod A. Jamkhedkar', 'Gregory L. Heileman', 'Iván Martínez-Ortiz']

Middleware Services for DRM

Various wireless sensors and devices keep collecting data for their environments or owners. Such collected data are given in the form of spatio-temporal sequence data which are a sequence of data elements with spatial information and timestamp. Data clustering is useful in finding inherent underlying structures, natural or interesting groups in a collection of data. This paper proposes a new clustering method for spatio-temporal sequence data with respect to density and frequency. Density is a notion about how densely data elements are in a local region, and frequency is a notion about how many times sequences pass through a local region. The proposed method identifies three types of clusters: high density and high frequency clusters, high density and low frequency clusters, and low density and high frequency clusters. It first augments the data set by inserting dummy data elements for capturing frequency distribution in sparse local regions. Then it computes the densities and frequency for data elements and the frequencies for dummy data elements. It partitions data elements into the high density-high frequency data set, high density-low frequency data set, and low-density-high frequency data set. It clusters each data set individually using the clustering procedures that are similar to DBSCAN, which is a density-based clustering algorithm. The proposed method had been applied to the six spatial–temporal GPS sequence data sets for wildlife movements. The experiment results were compared with the results from DBSCAN and analyzed in terms of the number and characteristics of discovered clusters.

['Keon Myung Lee', 'Sang Yeon Lee', 'Kyung Mi Lee', 'Sang Ho Lee']

Density and Frequency-Aware Cluster Identification for Spatio-Temporal Sequence Data

Recently, there are many researches about bipedal locomotion by humanoid robots. However, the objective of running humanoid robot is not a lots. A modeling of high speed running motion is focused in this paper. Toward to modeling the motion, we focus on the rapid running robot (R 3 ), which is high speed running robot, but has not been modeled. To analyze the motion and modeled it, we have developed simulation model on V-REP. Through the comparison experiment on simulator, we confirmed adequacy the constructed mode.

['Tomoro Ota', 'Kenichi Ohara', 'Akihiko Ichikawa', 'Taisuke Kobayashi', 'Yasuhisa Hasegawa', 'Toshio Fukuda']

Modeling of the high-speed running humanoid robot

Optimal state estimation for linear discrete-time systems is considered. Motivated by the literature on differential privacy, the measurements are assumed to be corrupted by Laplace noise. The optimal least mean square error estimate of the state is approximated using a randomized method. The method relies on that the Laplace noise can be rewritten as Gaussian noise scaled by Rayleigh random variable. The probability of the event that the distance between the approximation and the best estimate is smaller than a constant is determined as function of the number of parallel Kalman filters that is used in the randomized method. This estimator is then compared with the optimal linear estimator, the maximum a posteriori (MAP) estimate of the state, and the particle filter.

['Farhad Farokhi', 'Jezdimir Milosevic', 'Henrik Sandberg']

Optimal state estimation with measurements corrupted by Laplace noise

In this paper, we consider a use case where an airport passenger travels and uses an automated gate to cross a border.We detail three phases: a pre-check before the arrival at the airport, the travel of the passenger from his check-in to the automated border gates and finally, the crossing of the gate. To accelerate the throughput at the border gates, we want to identify his face among a flight passenger list during the second phase. This identification is split between the passenger who takes a picture of his face with his smartphone and the immigration authorities. We rely on cryptographic verifiable computation techniques to ensure the security of the process. Experimental results show that our protocol is practical.

['Hervé Chabanne', 'Julien Keuffer', 'Roch Lescuyer']

A Verifiable System for Automated Face Identification

Associative learning plays a major role in the formation of the internal dynamic engine of an adaptive system or a cognitive robot. Interaction with the environment can provide a sparse and discrete set of sample correlations of input–output incidences. These incidences of associative data points can provide useful hints for capturing underlying mechanisms that govern the system’s behavioral dynamics. In many approaches to solving this problem, of learning system’s input–output relation, a set of previously prepared data points need to be presented to the learning mechanism, as a training data, before a useful estimations can be obtained. Besides data-coding is usually based on symbolic or nonimplicit representation schemes. In this paper, we propose an incremental learning mechanism that can bootstrap from a state of complete ignorance of any representative sample associations. Besides, the proposed system provides a novel mechanism for data representation in nonlinear manner through the fusion of self-organizing maps and Gaussian receptive fields. Our architecture is based solely on cortically-inspired techniques of coding and learning as: Hebbian plasticity and adaptive populations of neural circuitry for stimuli representation. We define a neural network that captures the problem’s data space components using emergent arrangement of receptive field neurons that self-organize incrementally in response to sparse experiences of system–environment interactions. These learned components are correlated using a process of Hebbian plasticity that relates major components of input space to those of the output space. The viability of the proposed mechanism is demonstrated through multiple experimental setups from real-world regression and robotic arm sensory-motor learning problems.

['Tarek Najjar', 'Osamu Hasegawa']

Hebbian Network of Self-Organizing Receptive Field Neurons as Associative Incremental Learner

Automatic software categorization is the task of assigning software systems or libraries to categories based on their functionality. Correctly assigning these categories is essential to ensure that relevant software can be easily retrieved by developers from large repositories. State of the art approaches either rely on the availability of the source code, or use supervised machine learning approaches, which require a set of already labeled software as training data. These restrictions make current approaches fail when such information is not available. We propose a novel approach, which overcomes these limitations by using semantic information recovered from byte code and an unsupervised algorithm to assign categories to software systems. We evaluated our approach in a study on the Apache Foundation Repository of Java libraries and the results indicate that our approach is able to correctly identify a correct category for 86% of the libraries.

['Javier Escobar-Avila', 'Mario Linares-Vásquez', 'Sonia Haiduc']

Unsupervised software categorization using bytecode

Performance of mechatronics systems can deteriorate significantly under the presence of delays in measurements. On the other hand, intentional introduction of a delay in a feedback control law may be beneficial in achieving good performance without relying on accurately tuned estimates for derivatives of measured signals. In this context, a proper design of retarded control laws represents an important challenge, and this paper gives a step forward to obtain better control laws for underactuated mechanical systems under the presence of time delays in measurements. Particularly, a Proportional-Retarded (PR) control law is designed for stabilization of an underactuated rotational inverted pendulum, known as the Furuta pendulum. Experiments over a laboratory platform as well as a comparison with a Linear Quadratic Regulator (LQR) are performed to show the advantages of the proposed scheme.

['Teresa Ortega', 'Raul Villafuerte', 'Carlos Vazquez', 'Leonid B. Freidovich']

Performance without tweaking differentiators via a PR controller: Furuta pendulum case study

Advances in bacteria motility modelling via diffusion adaptation

['Sadaf Monajemi', 'Saeid Sanei', 'Sim Heng Ong']

Advances in bacteria motility modelling via diffusion adaptation

Abstract This paper reports on an experiment in which a whole semester course in psychology was replaced by a mixed formula consisting of a CD-ROM complemented by a series of seminars and workshops. The CD-ROM was conceived as a collection of documents (hypertexts, research data, references, videos and activities) linked together with genuine Netscape facilities. Students were invited to search through these documents for information to answer questions (called challenges) on the topic. A multiple-choice questionnaire accompanied each challenge in order to foster students' self-evaluation. The seminars, held every other week, served both as forums to discuss each of the topics under the guidance of an expert. Careful analysis of students' answers to two questionnaires at the beginning and end of the course and during interviews, showed that such a formula was favourably accepted by a large majority of students, although it lead to more anxiety and work load than a traditional course. Positive effects were also observed on learning.

['Gérald Collaud', 'Jean-Luc Gurtner', 'Perre-François Coen']

Design and Use of a Hypermedia System at the University Level.

Multi-protocol over ATM (MPOA) is being considered by the industry as an important short-cut technology that provides an efficient transfer of inter-subnet unicast data in a LANE environment. MPOA was initially considered to carry backbone traffic in the enterprise or campus networks. However, congestion in the public Internet provokes many to consider MPOA as a solution for the carrier or service provider networks as well. In this paper, we investigate the scalability issues of MPOA in the wide area network environment. We use a realistic simulation model driven by real Internet traffic to study crucial metrics such as the SVC setup rate, the number of VCs required, and the percentage of packets switched. We find that MPC ingress cache size provides a three-way trade-off among the percentage of switched packets, the VC usage and the SVC setup rate requirement. We also find that the SVC setup rate is linearly dependent on the packet arrival rate.

['Indra Widjaja', 'Haining Wang', 'Steven Wright', 'Amalendu Chatterjee']

Scalability evaluation of multi-protocol over ATM (MPOA)

This paper presents a novel method for evaluating the impact of animated interface agents with affective and empathic behavior. While previous studies relied on questionnaires in order to assess the user's overall experience with the interface agent, we will analyze users' physiological response (skin conductance and electromyography), which allows us to estimate affect-related user experiences on a moment-by-moment basis without interfering with the primary interaction task. As an interaction scenario, a card game has been implemented where the user plays against a virtual opponent. The findings of our study indicate that within a competitive gaming scenario, (i) the absence of the agent's display of negative emotions is conceived as arousing or stress-inducing, and (ii) the valence of users' emotional response is congruent with the valence of the emotion expressed by the agent. Our results for skin conductance could also be reproduced by assuming a local rather than a global baseline.

['Helmut Prendinger', 'Christian Becker', 'Mitsuru Ishizuka']

A STUDY IN USERS' PHYSIOLOGICAL RESPONSE TO AN EMPATHIC INTERFACE AGENT

This paper proposes a robust pilot-assisted channel estimation method for orthogonal frequency division multiplexing (OFDM) signals in Rayleigh fading. Our estimation method is based on nonlinear regression channel models. Unlike the linear minimum mean-squared error (LMMSE) channel estimate, the method proposed does not have to know or estimate channel statistics like the channel correlation matrix and the average signal-to-noise ratio (SNR) per bit. Numerical results indicate that the performance of the proposed channel estimator is very close to the theoretical bit error propagation lower bound that is obtained by a receiver with perfect channel response information.

['Ming-Xian Chang', 'Yu T. Su']

Model-based channel estimation for OFDM signals in Rayleigh fading

We propose a static compaction procedure to reduce the test application time for full and partial scan synchronous sequential circuits. The procedure accepts as input a set of test subsequences. For every subsequence, it also accepts the vector to be scanned-in before the subsequence is applied. The procedure uses two operations to reduce the test application time. The first operation combines test subsequences. The second operation reduces the lengths of the combined subsequences. The reductions in test application time of the proposed procedure are demonstrated through experimental results.

['Irith Pomeranz', 'Sudhakar M. Reddy']

Static test compaction for scan-based designs to reduce test application time

Image segmentation is to separate an image into distinct homogeneous regions belonging to different objects. It is an essential step in image analysis and computer vision. This paper compares some segmentation technologies and attempts to find an automated way to better determine the parameters for image segmentation, especially the connectivity value of lambda in lambda-connected segmentation. Based on the theories on the maximum entropy method and Otsu's minimum variance method, we propose:(1)maximum entropy connectedness determination: a method that uses maximum entropy to determine the best lambda value in lambda-connected segmentation, and (2) minimum variance connectedness determination: a method that uses the principle of minimum variance to determine lambda value. Applying these optimization techniques in real images, the experimental results have shown great promise in the development of the new methods. In the end, we extend the above method to more general case in order to compare it with the famous Mumford-Shah method that uses variational principle and geometric measure.

['Li Chen']

lambda-Connectedness Determination for Image Segmentation

Model-driven design (MDD) can be perceived in the recent literature as an option to deal with the increasing complexity of the design of distributed embedded real-time systems (DERTS). This paper reports some results of a research project aiming to support a MDD approach, which applies concepts of the aspect-oriented (AO) paradigm in order to improve the treatment of non-functional requirements (NFR) in the design of DERTS. A tool named GenERTiCA, which generates source code from UML diagrams and also weaves aspect adaptations, has been developed to support such MDD/AO approach. This paper presents results regarding the use of GenERTiCA to generate code and implement aspects (from a high-level framework of aspect) for the RT-FemtoJava platform, a RTSJ-based and optimized Java platform for DERTS.

['Marco A. Wehrmeister', 'Edison Pignaton de Freitas', 'Carlos Eduardo Pereira', 'Franz-Josef Rammig']

GenERTiCA: A Tool for Code Generation and Aspects Weaving

Web service operators set up reverse proxies to interpose the communication between clients and origin servers for load-balancing traffic across servers, caching content, and filtering attacks. Silent reverse proxies, which do not reveal their proxy role to the client, are of particular interest since malicious infrastructures can use them to hide the existence of the origin servers, adding an indirection layer that helps protecting origin servers from identification and take-downs. We present RevProbe, a state-of-the-art tool for automatically detecting silent reverse proxies and identifying the server infrastructure behind them. RevProbe uses active probing to send requests to a target IP address and analyzes the responses looking for discrepancies indicating that the IP address corresponds to a reverse proxy. We extensively test RevProbe showing that it significantly outperforms existing tools. Then, we apply RevProbe to perform the first study on the usage of silent reverse proxies in both benign and malicious Web services. RevProbe identifies that 12% of malicious IP addresses correspond to reverse proxies, furthermore 85% of those are silent (compared to 52% for benign reverse proxies).

['Antonio Nappa', 'Rana Faisal Munir', 'Irfan Khan Tanoli', 'Christian Kreibich', 'Juan Caballero']

RevProbe: detecting silent reverse proxies in malicious server infrastructures

Routing in mobile social networks is a challenging task due to the characteristic of intermittent connectivity, especially when the nodes behave selfishly in real world. Selfish behaviors of node always influence its altruism to provide forwarding service for others and degrade network performance strongly. In this paper, to address the selfishness problem in MSNs, we propose a service-based selfish routing protocol, SSR. When making forwarding decision, SSR employ user altruism and the amount of service that the relay nodes provide. User altruism is determined by the social selfishness and the individual selfishness. The services include pairwise services and social services, which is also considered as the incentives to stimulate node to be more cooperative. The more services the node provides, the more chance the node has to be served. The node with higher altruism and fewer services is the preferred relay node. Simulation results show SSR achieves better performance when the user altruism is low and demonstrate the effectiveness of the service-based scheme.

['Lingfei Yu', 'Pengfei Liu']

A service-based selfish routing for mobile social networks

Solving polynomial systems subsystem-by-subsystem means to solve a system of polynomial equations by first solving subsets of the system and then intersecting the results. The approach leads to numerical representations of all the solution components of a system. The focus of this paper is the development of a parallel implementation to solve large systems involving a recursive divide-and- conquer scheme. Because we concentrate our discussion on the distribution of the path tracking jobs, we have selected applications for which we have optimal homotopies, for which all paths converge to regular solutions.

['Yun Guan', 'Jan Verschelde']

Parallel Implementation of a Subsystem-by-Subsystem Solver

This paper presents a min-max model predictive control algorithm for a class of hybrid systems by exploiting the equivalence between piecewise linear systems and mixed logical dynamical systems. The control algorithm consists of two control modes which are a state feedback mode and a min-max model predictive control mode. In the min-max model predictive control mode, the constrained positively invariant sets are used as the end set constraint, and an approach based on a min-max model predictive control formulation is employed. This control algorithm guarantees that the state converges to a union of constrained positively invariant sets with no constraint violation.

['Masakazu Mukai', 'Akira Kojima', 'Takehito Azuma', 'Masayuki Fujita']

A min-max model predictive control for a class of hybrid dynamical systems

Though IT usage has been identified as a significant construct, significant gaps remain in our understanding of how IT is used by different organizational members. In this paper, we examine the key organizational, external environmental, and IT-related factors that influence IT usage in organizations across management and non-management levels. Based on data from over 500 functional managers, we examine the factors that are associated with IT usage among management and non-management personnel. Our findings indicate that formalized firms that face high competitive pressures have higher levels of IT usage among both management and non-management personnel. Moreover, firms that insource IT and those that nurture a positive organizational attitude towards IT tend to have greater levels of IT usage. The analysis, results, and its implications for research and practice are presented.

['Chei Sian Lee', 'Ranganathan Chandrasekaran', 'Darrin Thomas']

EXAMINING IT USAGE ACROSS DIFFERENT HIERARCHICAL LEVELS IN ORGANIZATIONS: A STUDY OF ORGANIZATIONAL, ENVIRONMENTAL, AND IT FACTORS

This article comparatively tests three cooperative co-evolution methods for automated controller design in simulated robot teams. Collective NeuroEvolution (CONE) co-evolves multiple robot controllers using emergent behavioral specialization in order to increase collective behavior task performance. CONE is comparatively evaluated with two related controller design methods in a collective construction task. The task requires robots to gather building blocks and assemble the blocks in specific sequences in order to build structures. Results indicate that for the team sizes tested, CONE yields a higher collective behavior task performance (comparative to related methods) as a consequence of its capability to evolve specialized behaviors.

['Geoff Nitschke', 'Martijn C. Schut', 'A. E. Eiben']

Evolving Behavioral Specialization in Robot Teams to Solve a Collective Construction Task

An asynchronous communication library for accessing and managing dynamic hash tables over a network of Symmetric Multiprocessors (SMP) is presented. A blocking factor is shown experimentally to reduce the variance of the wall clock time. It is also shown that remote accesses to a distributed hash table can be as effective and scalable as the one-sided operations of the low-level communication middleware on an IBM SP.

['Joel M. Malard', 'Robert D. Stewart']

Distributed Dynamic Hash Tables Using IBM LAPI

Although increasingly popular, Model Driven Architecture (MDA)still lacks suitable formal foundations on top of which rigorousmethodologies for the description, analysis and transformation ofmodels could be built. This paper aims to contribute in thisdirection: building on previous work by the authors on coalgebraicrefinement for software components and architectures, it discussesrefactoring of models within a coalgebraic semantic framework. Architectures are defined through aggregation based on a coalgebraic semantics for (subsets of)UML. On the other hand, such aggregations, no matter how large and complex they are, can always be dealt with ascoalgebras themselves. This paves the way to a discipline ofmodels' transformations which, being invariant under either behavioural equivalenceor refinement, are able to formally capture a large number of refactoring patterns. The main ideas underlying this research are presented through a detailed example in the context of refactoring of UML class diagrams.

['Luís Soares Barbosa', 'Sun Meng']

UML Model Refactoring as Refinement: A Coalgebraic Perspective

Recent environmental changes have shown the world the importance of sustainable development and these changes demand concrete solutions. The Smart Grid solution is recognized as a key technology to cope with these challenges, not just concerning the environmental impact of greenhouse gases but also regarding the increasing electric demand. Smart Grid is a combination of two networks, the first being the electrical transmission and distribution network, while the other is the modern data communication network. The Smart Grid communication network can be divided into three parts, HAN (Home Area Network), NAN (Neighborhood Area Network), and WAN (Wide Area Network). IEEE 802.15.4 and IEEE 802.11 MAC protocols, which are based on the CSMA/CA mechanism, are assumed to be the candidates for the MAC layer of HAN. However, it has latency and energy efficiency problems. In this paper, we have redesigned a tree based TDMA MAC protocol which can be applied in Smart Grid HAN. From the experiments, we verify the performance of our proposed protocol with respect to the network latency, high packet transmission rate and energy efficiency.

['Min Seok Kim', 'Sung Ryul Kim', 'Jeong-Hyun Kim', 'Younghwan Yoo']

Design and Implementation of MAC Protocol for SmartGrid HAN Environment

The Multiple Instruction Stream Processor (MISP) architecture introduces the sequencer as a new class of architectural resource, and provides a minimalist user-level MIMD instruction set extension for application programs to directly control execution of concurrent instruction streams on these sequencers. As with classic architectural resources, namely, registers and memory, the sequencer architectural resource can be subject to virtualization. This paper details the idea of Sequencer Virtualization (SV), a foundational architectural support to decouple architectural virtual sequencers from physical sequencers. SV enables more efficient utilization of sequencer resources at the microarchitectural level while maintaining a consistent programming interface at the architectural level. To evaluate the key tradeoffs for SV, we conduct extensive experiments by implementing a prototype SV system using a custom firmware on a large-scale multiprocessor system. Using the prototype SV system, we demonstrate that SV improves efficiency in sequencer utilization while incurring little performance overhead. In particular, for a set of real multithreaded workloads, SV can significantly improve sequencer utilization, achieving an average of 32% better wall-clock performance than MISP without SV support in a multi-programming environment.

['Perry H. Wang', 'Jamison D. Collins', 'Gautham N. Chinya', 'Bernard Lint', 'Asit Mallick', 'Koichi Yamada', 'Hong Wang']

Sequencer virtualization

Studying research productivity is a challenging task that is important for understanding how science evolves and crucial for agencies (and governments). In this context, we propose an approach for quantifying the scientific performance of a community (group of researchers) based on the similarity between its publication profile and a reference community's publication profile. Unlike most approaches that consider citation analysis, which requires access to the content of a publication, we only need the researchers' publication records. We investigate the similarity between communities and adopt a new metric named Volume Intensity. Our goal is to use Volume Intensity for measuring the internationality degree of a community. Our experimental results , using Computer Science graduate programs and including both real and random scenarios, show we can use publication profile as a performance indicator.

['Thiago H. Silva', 'Gustavo Penha', 'Ana Paula Couto da Silva', 'Mirella M. Moro']

A performance indicator for academic communities based on external publication profiles

Does Maths Anxiety Make People Bad Decision-Makers? The Link Between Mathematical Anxiety And Cognitive Reflection

['Kinga Morsanyi', 'Chiara Busdraghi', 'Caterina Primi']

Does Maths Anxiety Make People Bad Decision-Makers? The Link Between Mathematical Anxiety And Cognitive Reflection

Group Scheduling for Improving VoIP Capacity in IEEE 802.16e Networks

['Shweta Shrivastava', 'Rath Vannithamby']

Group Scheduling for Improving VoIP Capacity in IEEE 802.16e Networks

Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data

['Aorigele', 'Shuaiqun Wang', 'Zheng Tang', 'Shangce Gao', 'Yuki Todo']

Improved Binary Imperialist Competition Algorithm for Feature Selection from Gene Expression Data

The dissemination of messages according to clients' contexts (i.e., location and other attributes) opens up new possibilities in context-aware systems. While geocast or content-based publish/subscribe forward messages according to client location or attributes, respectively, neither uses a combination of the two. In this paper, we present this new communication paradigm and the challenges it poses. We also extend concepts from publish/subscribe networks to efficiently deal with highly dynamic user location to lower update rates by approximating the user's location. This reduces update rates by between 25% and 90%, depending on the granularity of the approximation.

['Lars Geiger', 'Frank Dürr', 'Kurt Rothermel']

On Contextcast: A Context-Aware Communication Mechanism

This paper presents a description of the MIT Lincoln Laboratory language recognition system submitted to the NIST 2009 Language Recognition Evaluation (LRE). This system consists of a fusion of three core recognizers, two based on spectral similarity and one based on tokenization. The 2009 LRE differed from previous ones in that test data included narrowband segments from worldwide Voice of America broadcasts as well as conventional recorded conversational telephone speech. Results are presented for the 23-language closed-set and open-set detection tasks at the 30, 10, and 3 second durations along with a discussion of the language-pair task. On the 30 second 23-language closed set detection task, the system achieved a 1.64 average error rate.

['Pedro A. Torres-Carrasquillo', 'Elliot Singer', 'Terry P. Gleason', 'Alan McCree', 'Douglas A. Reynolds', 'Fred Richardson', 'Douglas E. Sturim']

The MITLL NIST LRE 2009 language recognition system

Cultures of making, customization and repair have gained recent visibility within the CSCW literature due to the alternative framings of design and use they present. This panel brings together scholars across human-computer interaction, interaction design, information studies, and science and technology studies to examine the forms of social organization and technological production that come from maker and repair collectives.

['Daniela K. Rosner', 'Silvia Lindtner', 'Ingrid Erickson', 'Laura Forlano', 'Steven J. Jackson', 'Beth E. Kolko']

Making cultures: building things & building communities