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CV-6848 | \begin{tabular}{@{}cllll@{}}
\toprule
Test & Approach & Steps (avg) & Reward (avg) & Time \\ \midrule
\multirow{2}{*}{III} & $\mathbf{{DRQN^*}_{100}}$ & \textbf{7.35} & \textbf{0.6315} & \textbf{26.38 minutes} \\
& Our Approach & 7.5 & 0.4162 & 29.63 minutes \\ \midrule
\multirow{2}{*}{IV} & ${DRQN^*}_{100}$ & 8.2 & 0.4452 & 29.48 minutes \\
& \textbf{Our Approach} & \textbf{7.45} & \textbf{0.4883} & \textbf{26.44 minutes} \\ \midrule
\multirow{2}{*}{V} & ${DRQN^*}_{100}$ & 8.8 & 0.2573 & 32.73 minutes \\
& \textbf{Our Approach} & \textbf{7.5} & \textbf{0.5079} & \textbf{30.46 minutes} \\ \midrule
\multirow{2}{*}{VI} & ${DRQN^*}_{100}$ & 9.25 & 0.1642 & \textbf{28.21 minutes} \\
& \textbf{Our Approach} & \textbf{7.48} & \textbf{0.4617} & 30.96 minutes \\ \midrule
\multirow{2}{*}{VII} & ${DRQN^*}_{100}$ & 7.41 & 0.6467 & 29.55 minutes \\
& \textbf{Our Approach} & \textbf{7.23} & \textbf{0.5224} & \textbf{23.85 minutes} \\ \midrule
\multirow{2}{*}{VIII} & ${DRQN^*}_{100}$ & 8.03 & 0.4875 & 35.64 minutes \\
& \textbf{Our Approach} & \textbf{7.36} & \textbf{0.5365} & \textbf{29.95 minutes} \\
\bottomrule
\end{tabular} |
|
CR-46491 | \begin{tabular}{cccccc}
\toprule[1.0pt]
\ \ \ \ Model\ \ \ \ & \ \ \ \ dataset\ \ \ \ & \ \ \ \ $\mathcal{A}_{\rm nat}$\ \ \ \ & \ \ \ \ $\mathcal{A}_{\rm rob}$ (FGSM)\ \ \ \ & \ \ \ \ $\mathcal{A}_{\rm rob}$ (IFGSM$^{20}$)\ \ \ \ & \ \ \ \ $\mathcal{A}_{\rm rob}$ (C\&W)\ \ \ \ \cr
\midrule[0.8pt]
ResNet20 & CIFAR100 & 46.02 & 24.77 & 23.23 & 32.42 \cr
En$_2$ResNet20 & CIFAR100 & 50.68 & 30.20 & 26.25 & 40.06 \cr
En$_5$ResNet20 & CIFAR100 & {\bf 51.72} & {\bf 31.64} & {\bf 27.80} & {\bf 40.44} \cr
ResNet44 & CIFAR100 & 50.38 & 28.40 & 25.81 & 36.06 \cr
\bottomrule[1.0pt]
\end{tabular} |
|
CR-22452 | \begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Depth} & \textbf{Exec.~Time} & \textbf{\textit{MPro (s)}} & \textbf{\textit{Mythril-Classic (s)}} & \textbf{\textit{Speedup}} \\
\hline
2 & t $\leq$ 10 mins & 52 & 79 & 1.40x \\
2 & t $\geq$ 10 mins & 783 & 1383 & 4.45x \\
\hline
3 & t $\leq$ 10 mins & 94 & 125 & 1.78x \\
3 & t $\geq$ 10 mins & 1206 & 3087 & 6.51x \\
\hline
\end{tabular} |
|
CR-30386 | \begin{tabular}{|l|l|l|l|l|l|l|l|l|l|}
\hline
\textbf{Conf.} & \textbf{\textit{n}} & \textbf{\textit{g}} & \textbf{\textit{c}} & \textit{pd} & $x_{thres}$ & $y_{thres}$ & $\theta_{thres}$ & $\theta\text{-basis}_{thres}$ \\ \hline \hline
1 & 8 & 30 & 340 & 10 & 12 & 12 & 12 & 15 \\ \hline
2 & 8 & 34 & 300 & 10 & 12 & 12 & 12 & 10 \\ \hline
3 & 8 & 40 & 300 & 10 & 15 & 15 & 15 & 10 \\ \hline
4 & 10 & 30 & 300 & 10 & 12 & 12 & 12 & 10 \\ \hline
5 & 12 & 40 & 300 & 10 & 15 & 15 & 15 & 10 \\ \hline
6 & 14 & 30 & 300 & 10 & 15 & 15 & 15 & 10 \\ \hline
\end{tabular} |
|
CR-10197 | \begin{tabular}{cccccc}
\toprule
\textbf {Schemes } & \textbf {Node} & \textbf {Storage cost} & \textbf {Cost (cycle)} & \textbf {Time (ms)} & \textbf {Energy(mJ)} \\
\midrule
& $ N $ & 480 bits & $ 5t_{h}+2t_{xor} \approx5t_{h}$ & 0.30 & 0.036 \\
& $ HN $ & 480n+160 & $ 8t_{h}+4t_{xor}\approx8t_{h} $ & 0.48 & 0.057 \\
\hline
& $ N $ & 640 & $ 3t_{h}+7t_{xor}\approx3t_{h} $ & 0.18 & 0.021 \\
& $ HN $ & 160(n+1)+16m & $ 5t_{h}+12t_{xor}\approx5t_{h} $ & 0.3 & 0.036 \\
\hline
& $ N $ & 800 & $5t_{h}+ 5t_{xor}\approx5t_{h} $ & 0.3 & 0.036 \\
& $ HN $ & 160(n+1) & $ 8t_{h}+11t_{xor}\approx8t_{h} $ & 0.48 & 0.057 \\
\hline
& $ N $ & 640 & $ 5t_{h}+9t_{xor}\approx5t_{h} $ & 0.3 & 0.036 \\
& $ HN $ & 160(n+1) & $ 7t_{h}+14t_{xor}\approx7t_{h} $ & 0.42 & 0.05 \\
\hline
& $ N $ & 640 & $ 3t_{h}+6t_{xor}\approx3t_{h} $ & 0.18 & 0.021 \\
& $ HN $ & 640n+16m+160 & $ 5t_{h}+(n+7)t_{xor}\approx5t_{h} $ & 0.3 & 0.036 \\
\hline
\textbf{Proposed} & $ N $ & 640 & $ 2t_{h}+6t_{xor}\approx2t_{h} $ & 0.12 & 0.014 \\
& $ HN $ & 480n+16m+160 & $ 2t_{h}+(2n+5)t_{xor}\approx2t_{h} $ & 0.12 & 0.014 \\
\bottomrule
\end{tabular} |
|
SE-7034 | \begin{tabular}{lcc}
\toprule
PUT & Initial & Maximum \\
\midrule
bloaty\_fuzz\_target & N/A & 83114 \\
curl\_curl\_fuzzer\_http & N/A & 78362 \\
freetype2-2017 & 1517 & 26262 \\
harfbuzz-1.3.2 & N/A & 12212 \\
jsoncpp\_jsoncpp\_fuzzer & N/A & 2114 \\
lcms-2017-03-21 & 149 & 7036 \\
libjpeg-turbo-07-2017 & N/A & 9384 \\
libpcap\_fuzz\_both & 2 & 7294 \\
libpng-1.2.56 & 138 & 3736 \\
libxml2-v2.9.2 & 258 & 67994 \\
libxslt\_xpath & N/A & 51456 \\
mbedtls\_fuzz\_dtlsclient & N/A & 12888 \\
openssl\_x509 & 6026 & 54116 \\
openthread-2019-12-23 & N/A & 19846 \\
php\_php-fuzz-parser & N/A & 215210 \\
proj4-2017-08-14 & 46 & 6534 \\
re2-2014-12-09 & 1 & 3982 \\
sqlite3\_ossfuzz & 4767 & 28766 \\
systemd\_fuzz-link-parser & N/A & 1798 \\
vorbis-2017-12-11 & 410 & 4082 \\
woff2-2016-05-06 & N/A & 5708 \\
zlib\_zlib\_uncompress\_fuzzer & N/A & 910 \\
\bottomrule
\end{tabular} |
|
PL-359 | \begin{tabular}{ccccccc}
\toprule
\multicolumn{1}{c}{No.of points} &
\multicolumn{1}{c}{Code} &
\multicolumn{1}{c}{DFMA} &
\multicolumn{1}{c}{IMAD} &
\multicolumn{1}{c}{DMUL} &
\multicolumn{1}{c}{IADD3} &
\multicolumn{1}{c}{DADD} \\ [0.2em]
\midrule
\multicolumn{7}{c}{Instructions presented in Billions} \\ [0.2em]
\midrule
& {\tt C++} & $6.1262 $ & $2.7451$ & $2.0509 $ & $0.9514 $ & $1.4174 $ \\ [0.2em]
$40$M & {\tt Python} & $8.2769$ & $14.1171$ & $2.3879$ & $4.1338$ & $3.1966 $ \\ [0.2em]
& {\tt Julia} & $6.3009$ & $6.8711 $ & $2.2617 $ & $2.6878$ & $1.4201 $ \\ [0.2em]
\bottomrule
\end{tabular} |
|
AI-15116 | \begin{tabular}{lccc}
\hline\hline
Method & \shortstack{Fitted-Q \\ Iterations} & Time (s) & Returns
\\ [0.5ex]
\toprule
UQF & - & \textbf{2} & \textbf{-92} \\[1ex]
& 400 & 489 & -101 \\[-1ex]
\raisebox{1.5ex}{CPSR}
& 100 & 116 & -109 \\
& 50 & 60 & -150 \\
& 10 & 15 & -200
\\[1ex]
\hline
\end{tabular} |
|
SE-761 | \begin{tabular}{|c|c|p{0.8in}|p{0.8in}|}
\hline
\multicolumn{2}{|c|}{\multirow{2}{*}{Domain engineer}} & \multicolumn{2}{c|}{Methodology developer} \\ \cline{3-4}
\multicolumn{2}{|c|}{} & Language support & Tool support \\ \hline
\multirow{2}{*}{Manual} & Formulate & Modeling construct & NA \\ \cline{2-4}
& Express & Syntax to express the engineering solution & Modeling environment \\ \hline
\multirow{2}{*}{Automated} & \multirow{2}{*}{Analyze} & Mathematical construct to be translated. & Programs for model transformation \\ \cline{3-4}
& & Mathematical construct to be analyzed. & Programs for the desired analysis \\ \hline
\end{tabular} |
|
CV-14495 | \begin{tabular}{l|rrr}
\toprule
{Method} & {NDS} & {AMOTA@1} $\uparrow$ & {AMOTP} $\downarrow$ \\
\midrule
Detection & 0.3622 & 0.222 & 1.538 \\
Momentum & 0.3620 & 0.227 & 1.532 \\
KF3D & 0.3634 & 0.232 & 1.530 \\
VeloLSTM & \textbf{0.3666} & \textbf{0.242} & \textbf{1.518} \\
\bottomrule
\end{tabular} |
|
AI-7400 | \begin{tabular}{lrrrr}
\hline
\multicolumn{1}{c}{\textbf{}} & \multicolumn{4}{c}{\textbf{No. of successes}} \\ \cline{2-5}
\multicolumn{1}{c}{\textbf{Setting}} & \multicolumn{2}{c}{Rearrangement} & \multicolumn{2}{c}{Navigation} \\ \cline{2-5}
\multicolumn{1}{c}{} & \multicolumn{1}{c}{Unnormalized} & \multicolumn{1}{c}{Normalized} & \multicolumn{1}{c}{Unnormalized} & \multicolumn{1}{c}{Normalized} \\ \hline
Reward Adaptation & 2996.02 $\pm$ 136.21 & 68.05 $\pm$ 3.09 & 247.98 $\pm$ 20.51 & 73.54 $\pm$ 6.08 \\ \hline
Oracle & 4402.78 $\pm$ 410.67 & 100.00 $\pm$ 9.33 & 337.22 $\pm$ 7.34 & 100.00 $\pm$ 2.18 \\ \hline
Zero reward & 121.02 $\pm$ 4.25 & 2.75 $\pm$ 0.10 & 0.29 $\pm$ 0.04 & 0.09 $\pm$ 0.01 \\ \hline \hline
True goal, predicted distance & 4164.80 $\pm$ 337.83 & 94.59 $\pm$ 7.67 & 362.13 $\pm$ 12.18 & 107.39 $\pm$ 3.61 \\ \hline
Predicted goal, true distance & 3706.80 $\pm$ 200.46 & 84.19 $\pm$ 4.55 & 196.49 $\pm$ 12.97 & 58.27 $\pm$ 3.85 \\ \hline
Synthetic language & 3827.64 $\pm$ 141.79 & 86.94 $\pm$ 3.22 & 317.11 $\pm$ 49.26 & 94.04 $\pm$ 14.61 \\ \hline
Non-relational goal prediction & 869.89 $\pm$ 115.12 & 19.76 $\pm$ 2.61 & 0.38 $\pm$ 0.17 & 0.11 $\pm$ 0.05 \\ \hline
\hline
Combined approach & 8516.78 $\pm$ 894.35 & 193.44 $\pm$ 20.31 & 430.80 $\pm$ 5.08 & 127.75 $\pm$ 1.51 \\ \hline
\end{tabular} |
|
CR-28858 | \begin{tabular}{r|l|l}
\textbf{Target/Victim Brand} & \textbf{Sample Count} & \textbf{Cloned sites} \\
PayPal & 3330 & 20 \\
Microsoft & 1960 & 11 \\
Facebook & 2847 & 6 \\
eBay & 429 & 12 \\
Other & 4828 & - \\
Total & 13,394 & 49 \\
\end{tabular} |
|
CL-216 | \begin{tabular}{ll|lllll}
\toprule
& Model & MR & CR & MPQA & SUBJ & SST2 \\ \midrule
\multirow{2}{*}{Sorted} & RandLSTM & 81.7 & 84.0 & 89.4 & 93.0 & 81.2 \\
& InferSent & 81.6 & 86.7 & 90.3 & 92.5 & \bf 84.5 \\
& GenSen & \bf 82.7 & \bf 87.4 & \bf 91.0 & \bf 94.1 & 83.2 \\ \midrule
\multirow{2}{*}{Unsorted} & RandLSTM & 77.2 & 79.2 & 88.1 & 92.0 & 81.8 \\
& InferSent & \bf 79.9 & \bf 84.3 & 89.5 & \bf 92.4 & \bf 84.4 \\
& GenSen & 78.1 & 84.2 & \bf 89.7 & \bf 92.4 & 83.9 \\
\bottomrule
\end{tabular} |
|
SE-16875 | \begin{tabular}{p{1.2cm}p{1.2cm}p{1.2cm}p{3.3cm}}
\toprule \textbf{Operator} & \textbf{Count} & \textbf{value} & \textbf{Bitvector Encoding} \\
\midrule
Op1 & $n_v$ & 5 & 001000 \\
Op2 & $n_f$ & 4 & 1111 \\
Op3 & $n_w$ & 0 & n.a. \\
Op4 & $n_{do}$ & 0 & n.a. \\
Op5 & $n_{ie}$ & 0 & n.a. \\
Op6 & $n_{i}$ & 0 & n.a. \\
Op7 & $n_{s}$ & 0 & n.a. \\
Op8 & $n_r$ & 4 & 0001 \\
Op9 & $n_u$ & 4 & 0110 \\
Op10 & $n_{sc}$ & 1 & 0 \\
Op11 & $n_c$ & 18 & 100011000000001011 \\
Op12 & $n_d$ & 2 & 01 \\
Op13 & $n_b$ & 5 & 00000 \\
Op14 & $n_{is}$ & 2 & 10 \\
Op15 & $n_{p}$ & 0 & n.a \\
\bottomrule
\end{tabular} |
|
AI-23439 | \begin{tabular}{l|c|c|c|c|c|c|c|c|c|c|c}
\hline
& \multicolumn{5}{c|}{\textbf{Sudden}} & \multicolumn{5}{c|}{\textbf{Sudden and gradual}} \\ \hline
\textbf{Method} & \textbf{MA} & \textbf{MTFA} & \textbf{MTD} & \textbf{MDR} & \textbf{TD} & \textbf{MA} & \textbf{MTFA} & \textbf{MTD} & \textbf{MDR} & \textbf{TD} & \textbf{AVG} \\ \hline
PH & 71.59 & \textbf{-} & 52.0 & 77.8 & 2 & 73.48 & \textbf{-} & 43.0 & 88.9 & 2 & 72.53 \\
ADWIN & 85.49 & 29.3 & \textbf{23.4} & 22.2 & 19 & 84.17 & 29.4 & 31.9 & 44.4 & 24 & 84.83 \\
EWMA & 90.92 & 53.9 & 29.4 & 33.3 & 11 & 92.01 & 49.2 & 26.1 & 33.3 & 19 & 90.47 \\
DDM & 87.00 & \textbf{-} & 25.5 & 77.8 & 2 & 85.51 & \textbf{-} & 35.5 & 77.8 & 2 & 86.26 \\ \hline
CDCSDE & \textbf{97.41} & 252.5 & 26.3 & \textbf{11.1} & 10 & \textbf{97.89} & 178.3 & \textbf{18.3} & \textbf{11.1} & 11 & \textbf{97.65} \\ \hline
\end{tabular} |
|
CV-27490 | \begin{tabular}{cc*{7}c}
\toprule
\multirow{2}{*}{Eval.} &
\multirow{3}{*}{Method} &
\multicolumn{3}{c}{Scene Flow (m)} &
\multicolumn{2}{c}{Object Motion} &
\multicolumn{2}{c}{Ego-motion} \\
\cmidrule{3-5} \cmidrule{6-9}
Dataset &
& {FG} & {BG} & {All}
& {Rot.(rad)} & {Tr.(m)}
& {Rot.(rad)} & {Tr.(m)} \\
\hline
K & ICP+Det. & 0.56 & 0.43 & 0.44 & 0.22 & 6.27 & {\bf 0.004} & 0.44
\\
K & 3DMatch+Det. & 0.89 & 0.70 & 0.71 & 0.021 & 1.80 & {\bf 0.004} & 0.68 \\
K & FPFH+Det. & 3.83 & 4.24 & 4.21 & 0.299 & 14.23 & 0.135 & 4.27 \\
K & Dewan et al.+Det. & 0.55 & 0.41 & 0.41 & 0.008 & 0.55 & {\bf 0.006} & 0.39 \\
K & Ours & {\bf 0.29} & {\bf 0.15} & {\bf 0.16} & {\bf 0.004} & {\bf 0.19} & {\bf 0.005} & {\bf 0.12} \\
\hline
K+AK & ICP+Det. & 0.74 & 0.48 & 0.50 & 0.226 & 6.30 & {\bf 0.005} & 0.49 \\
K+AK & 3DMatch+Det. & 1.14 & 0.77 & 0.80 & 0.027 & 1.76 & {\bf 0.004} & 0.76 \\
K+AK & FPFH+Det. & 4.00 & 4.39 & 4.36 & 0.311 & 13.54 & 0.122 & 4.30 \\
K+AK & Dewan et al.+Det. & 0.60 & 0.52 & 0.52 & 0.014 & 0.75 & {\bf 0.006} & 0.46 \\
K+AK & Ours & {\bf 0.34} & {\bf 0.18} & {\bf 0.20} & {\bf 0.011} & {\bf 0.50} & {\bf 0.005} & {\bf 0.15} \\
\bottomrule
\end{tabular} |
|
AI-36188 | \begin{tabular}{p{0.85in}p{0.8in}p{0.89in}}
\toprule
\multicolumn{3}{c}{\textbf{Entity replacement strategy}} \\ \midrule
\textbf{Random least} & \textbf{Random most} & \textbf{GPT-2 generated} \\ \midrule
Inverkeithing High School & Tribune & U.S. \\
Mark Forman & East Jerusalem & Canada \\
Netgear & Englishman & Microsoft \\
Bangalore North & Jason Aldean & Donald Trump \\
Mackintosh & UFA & BBC \\
\bottomrule
\end{tabular} |
|
CV-2928 | \begin{tabular}{lcc|cc}
\hline
Method & Sup. & Extra Data & \emph{val} & \emph{test} \\
\hline
TransferNet \raggedright & $\mathcal{I}$ & MS-COCO & 52.1 & 51.2 \\
Saliency \raggedright & $\mathcal{I}$ & MSRA, BSDS & 55.7 & 56.7 \\
MCNN \raggedright & $\mathcal{I}$ & YouTube-Object & 38.1 & 39.8 \\
CrawlSeg \raggedright & $\mathcal{I}$ & YouTube Videos & 58.1 & 58.7 \\
\hline
What'sPoint \raggedright & $\mathcal{P}$ & - & 46.0 & 43.6 \\
RAWK \raggedright & $\mathcal{S}$ & - & 61.4 & - \\
ScribbleSup \raggedright & $\mathcal{S}$ & - & 63.1 & - \\
WSSL \raggedright & $\mathcal{B}$ & - & 60.6 & 62.2 \\
BoxSup \raggedright & $\mathcal{B}$ & - & 62.0 & 64.6 \\
SDI \raggedright & $\mathcal{B}$ & BSDS & 65.7 & 67.5 \\
\hline
FCN \raggedright & $\mathcal{F}$ & - & - & 62.2 \\
DeepLab \raggedright & $\mathcal{F}$ & - & 67.6 & 70.3 \\
ResNet38 \raggedright & $\mathcal{F}$ & - & 80.8 & 82.5 \\
\hline
\bf{Ours-DeepLab} \raggedright & $\mathcal{I}$ & - & 58.4 & 60.5 \\
\bf{Ours-ResNet38} \raggedright & $\mathcal{I}$ & - & 61.7 & 63.7 \\
\hline
\end{tabular} |
|
CR-45469 | \begin{tabular}{cc||ccccc||c||}
\multicolumn{2}{c||}{\textbf{Scheduler}} & \multicolumn{5}{c||} {\textbf{Rate Setter}} & \multicolumn{1}{c||} {\textbf{Blacklisting}} \\
$\nu$ & $DC_{max}$ & $A$ & $\beta$ & $\tau$ & $\sigma$ & $\epsilon$ & $W_{BL}$ \\
\hline
$50$ & $1$ & $0.06$ & $0.5$ & $2$ & $0.6$ & $15$ & $5$ \\
\end{tabular} |
|
SE-9882 | \begin{tabular}[c]{@{}l@{}}Afull-fledgedprogramminglanguagewill\\alwayshavemorepowerthana"no-\\code/low-code"solutionsuchasPowerApps\end{tabular} |
|
CR-7162 | \begin{tabular}{llll|lll}
\toprule
& \multicolumn{3}{l}{\textsc{Policy Laplace}} & \multicolumn{3}{l}{\textsc{Policy Gaussian $\ell_2$}} \\ \midrule
$\Delta_0$ & 1 Pass & 2 Passes & P-val & 1 Pass & 2 Passes & P-val \\ \midrule
1 & 4236 $\pm$ 14 & 4257 $\pm$ 17 & 0.083 & 3135 $\pm$ 25 & 3131 $\pm$ 20 & 0.829 \\
10 & \textbf{12452 $\pm$ 31} & 12389 $\pm$ 17 & \textbf{0.008} & 10784 $\pm$ 22 & 10817 $\pm$ 54 & 0.293 \\
50 & 15056 $\pm$ 35 & 15080 $\pm$ 21 & 0.262 & 15763 $\pm$ 33 & 15809 $\pm$ 45 & 0.139 \\
100 & 14562 $\pm$ 50 & 14567 $\pm$ 24 & 0.846 & 14562 $\pm$ 50 & 14568 $\pm$ 24 & 0.846 \\
200 & 14005 $\pm$ 33 & 13979 $\pm$ 31 & 0.271 & 14005 $\pm$ 33 & 13979 $\pm$ 31 & 0.271 \\
300 & 13702 $\pm$ 37 & 13678 $\pm$ 47 & 0.448 & 13702 $\pm$ 37 & 13678 $\pm$ 47 & 0.447 \\ \bottomrule
\end{tabular} |
|
SE-15797 | \begin{tabular}{|l|p{7cm}|r||r|r|r||r|}
\hline
\multirow{2}{*}{Hyperparameter} & \multirow{2}{*}{Description} & \multirow{2}{*}{Default} &
\multicolumn{4}{c|}{Values} \\
\cline{4-7}
& & & Min & Max & Step & Total \\
\hline
\hline
Bottom-up Matcher & Bottom-up matcher used to compute the diff & Classic & \multicolumn{3}{|c||}{\{Classic, Simple, Hybrid\}} & 3 \\
\hline
STM\_PC & Indicates the priority calculator used by the subtree matchers & Height & \multicolumn{3}{|c||}{\{Size, Height\}} & 2 \\
\hline
STM\_MPTH & Threshold on the minimum priority value computed using STM\_PC & 1 & 1 & 5 & 1 & 5 \\
\hline
BUM\_SMT & Threshold on the minimum similarity between two AST nodes & 0.5 & 0.1 & 1 & 0.1 & 10 \\
\hline
BUM\_SZT & Threshold on the maximum size of AST nodes to match & 1000 & 100 & 2000 & 100 & 20 \\
\hline
\hline
\end{tabular} |
|
CR-21194 | \begin{tabular}{llccc}
\toprule
Data Type & Data Generator & $\epsilon$ & Rating & Category \\ \midrule
Original & - & - & 0.7334 & 0.7752 \\ \midrule
\multirow{6}{*}{Synthetic} & \multirow{2}{*}{GPT2} & $\infty$ & 0.6892 & 0.7584 \\
& & 4 & 0.6656 & 0.7478 \\ \cmidrule(lr){2-5}
& \multirow{2}{*}{GPT2-Medium} & $\infty$ & 0.6878 & 0.7550 \\
& & 4 & 0.6756 & 0.7486 \\ \cmidrule(lr){2-5}
& \multirow{2}{*}{GPT2-Large} & $\infty$ & 0.7090 & 0.7576 \\
& & 4 & 0.6936 & 0.7568 \\ \bottomrule
\end{tabular} |
|
AI-26957 | \begin{tabular}{c|c|c}
\hline
Hyperparameter & DGCF & ConvGNN \\
\hline \hline
Architecture & $DCi-FC100, i \in \{2,4,8,16\}$ & $Ci-FC100, i \in \{2,4,8,16\}$ \\
$K$ & 25 & 25 \\
Optimizer & Adam & Adam \\
Learning Rate & $10^{-3}$ & $10^{-3}$ \\
FGN Architecture & $FC100-FC100$ & - \\
\hline
\end{tabular} |
|
AI-31596 | \begin{tabular}{cc|rrrr}
\toprule
& & \multicolumn{4}{l}{$a \left( exp \left( -\frac{B}{b} \right) - exp \left( -\frac{B}{c} \right) \right)$} \\
& $V$ & $a=\frac{60}{V}$ & $b$ & $c$ & $R^2$ \\
\midrule
& 2s & 30.00 & 212.55 & 74.94 & 0.994 \\
& 4s & 15.00 & 99.68 & 35.46 & 0.986 \\
& 8s & 7.50 & 48.25 & 17.14 & 0.994 \\
& 16s & 3.75 & 23.94 & 8.51 & 0.999 \\
\bottomrule
\end{tabular} |
|
CV-21555 | \begin{tabular}{|c|c|c|c|ccc|ccc|}
\hline
\multirow{2} * {Group} & DDMP & DDMP & \multirow{2} * {CDE} & \multicolumn{3}{c|}{$\rm AP_{3D}$} & \multicolumn{3}{c|} {$\rm AP_{BEV}$} \\
& single-scale & multi-scale & & Mod. & Easy & Hard & Mod. & Easy & Hard \\
\hline
\hline
I & - & - & - & 18.82 & 26.03 & 16.27 & 24.18 & 33.06 & 19.63 \\
II & \checkmark & - & - & 22.36 & 28.94 & 18.86 & 26.73 & 36.89 & 24.00 \\
III & - & \checkmark & - & 22.84 & 28.12 & 19.09 & 27.05 & 37.11 & 24.20 \\
IV & - & \checkmark & \checkmark & \textbf{23.12} & \textbf{31.14} & \textbf{19.45} & \textbf{27.46} & \textbf{37.71} & \textbf{24.53} \\
\hline
\end{tabular} |
|
CR-3826 | \begin{tabular}{l|l}
\hline
Literal & Transition Actions \\ \hline
LD & List Databases (e.g. show databases) \\
LT & List Tables \\
LC & List Columns \\
CT & Create Table (name like `Please Read') \\
DD & Drop Database \\
DT & Drop Table \\
MT & Modify table \\
I & Insert ransom message \\
A & Always \\ \hline
\end{tabular} |
|
AI-8918 | \begin{tabular}{lll}
\toprule
Combination & $1^{st}$ linear layer size $50$ & $1^{st}$ linear layer size $100$ \\
\midrule
ReLU vs mean-pool & 23.58 & 23.11 \\
Action value vs ReLU & 138.02 & 123.56 \\
Object value vs ReLU & 124.43 & 129.53 \\
\bottomrule
\end{tabular} |
|
AI-10467 | \begin{tabular}[c]{@{}l@{}}Predictionofblack-swaneventsintheIndianstockmarket(--R1)\\Predictingcreditcardfrauds(--R1)\end{tabular} |
|
SE-21441 | \begin{tabular}{lcr}
\textbf{Suite} & $S^{CS}$ & \thead{\textbf{APIs 2015} \\\textbf{with support}} \\
\toprule
\footnotesize{DHE-RSA-CAMELLIA256-SHA} & 1.1 & 4, 6, 9 \\
\footnotesize{DHE-RSA-CAMELLIA128-SHA} & 1.0 & 4, 6, 9 \\
\footnotesize{CAMELLIA256-SHA} & 0.1 & 4, 9, 13 \\
\footnotesize{CAMELLIA128-SHA} & 0.0 & 4 7 \\
\footnotesize{ECDHE-RSA-RC4-SHA} & -1 & 1, 8, 10, 15 \\
\footnotesize{RC4-MD5} & -1 & 1, 8, 10, 15 \\
\bottomrule
\end{tabular} |
|
SE-19314 | \begin{tabular}{ll|*5c}
\hline \hline
B & R & Retrieval Time (s) & F1@100 & Approx. Threshold \\
\hline
95 & 3 & 0.0108 & 0.3056 & 0.2191 \\
100 & 3 & 0.0116 & 0.3052 & 0.2154 \\
105 & 3 & 0.0118 & 0.3045 & 0.2119 \\
95 & 2 & 0.0156 & 0.1567 & 0.1025 \\
105 & 2 & 0.0184 & 0.1547 & 0.0975 \\
100 & 2 & 0.0174 & 0.1564 & 0.1000 \\
95 & 1 & 0.1048 & 0.1362 & 0.0105 \\
\hline \hline
\end{tabular} |
|
AI-542 | \begin{tabular}{|l|l|}
\hline
\textbf{Slot} & \textbf{Description} \\ \hline
attraction-type & \emph{type of the attraction place}; \\
& \emph{type of attraction or point of interest} \\
hotel-name & \emph{name of the hotel}; \\
& \emph{what is the name of the hotel} \\\hline
\end{tabular} |
|
CR-41811 | \begin{tabular}{c|c|c|c|c}
\multirow{2}{*}{\bf Model} & {\bf Case } & {\bf \# Layer} & \multirow{2}{*}{\bf \# Parameters} & {\bf \# Layers} \\
& {\bf Study} & {\bf (End to End)} & & {\bf (FE Only)} \\
\hline
\hline
Inception.v3 & I & 48 & 23,851,784 & 46 \\
\hline
SpeechNet & II & 19 & 17,114,122 & 17 \\
\hline
VGG-Very-Deep-16 & III & 16 & 145,002,878 & 14 \\
\hline
A+A+A & IV & (6+6+6) + 3 & 170,616,961 & 6+6 +6 \\
A+V+A & IV & (6+14+6) + 3 & 248,009,281 & 6+14+6 \\
V+A+V & IV & (14+6+14) + 3 & 325,401,601 & 14+6+14
\end{tabular} |
|
CV-25442 | \begin{tabular}{@{}lllllll@{}}
\toprule
Method & African & Asian & Caucasian & Indian & AVG & STDV \\ \midrule
Softmax & 67.95 & 73.5 & 77.77 & 75.78 & 73.75 & 4.24 \\
CosFace & 77.15 & 78 & 82.8 & 80.42 & 79.59 & 2.55 \\
ArcFace & 74.75 & 77.63 & 83.18 & 80.97 & 79.13 & 3.71 \\ \bottomrule
\end{tabular} |
|
CL-1938 | \begin{tabular}{|r|c|c|c|c|c|}
\hline
\multicolumn{1}{|p{0,5cm}|} {\bf \textit{k}}
& \multicolumn{1}{|p{1,5cm}|} {\bf \textit{nocc}}
& \multicolumn{1}{|p{2,5cm}|} {\bf Most freq. selection}
& \multicolumn{1}{|p{2.5cm}|} {\bf Most freq. selection $\mu$ avg}
& \multicolumn{1}{|p{2.5cm}|} {\bf Random among top $k$} & \multicolumn{1}{|p{2.5cm}|} {\bf Random among top $k$ $\mu$ avg}
\\ \hline
5 & $>100$ & 0.17 & 0.15 & 0.13 & 0.11 \\
10 & $>100$ & 0.19 & 0.14 & 0.08 & 0.07 \\
20 & $>100$ & 0.15 & 0.13 & 0.04 & 0.04 \\
5 & $>200$ & 0.16 & 0.13 & 0.13 & 0.11 \\
10 & $>200$ & 0.19 & 0.14 & 0.09 & 0.07 \\
20 & $>200$ & 0.14 & 0.11 & 0.04 & 0.04 \\
\hline
\end{tabular} |
|
CV-15222 | \begin{tabular}{cccccc}
\toprule
Kernel size & Channels & Blocks & Time (s) & PSNR & MS-SSIM \\ \hline
3 & 64 & 4 & 25.155 & \textbf{22.6225} & 0.9223 \\
3 & 16 & 1 & \textbf{6.885} & 22.1479 & 0.9133 \\
3 & 16 & 2 & 8.629 & 22.0441 & 0.9144 \\
3 & 16 & 3 & 10.376 & 22.1148 & 0.9151 \\
3 & 16 & 4 & 12.106 & 22.1362 & 0.9156 \\
3 & 32 & 2 & 16.137 & 22.3807 & 0.9192 \\
3 & 32 & 4 & 23.106 & 22.3300 & 0.9176 \\
3 & 128 & 1 & 59.775 & 22.4285 & 0.9149 \\
3 & 128 & 3 & 95.532 & 22.2768 & \textbf{0.9230} \\
5 & 16 & 1 & 9.297 & 21.8157 & 0.9117 \\
5 & 16 & 2 & 12.332 & 21.6677 & 0.9165 \\
5 & 16 & 3 & 15.211 & 22.0704 & 0.9179 \\
5 & 16 & 4 & 18.243 & 21.9391 & 0.9173 \\
5 & 32 & 2 & 24.538 & 21.9434 & 0.9167 \\
5 & 32 & 4 & 37.137 & 21.5100 & 0.9170 \\
5 & 128 & 1 & 93.066 & 22.0770 & 0.9147 \\
5 & 128 & 3 & 164.068 & 21.5695 & 0.9198 \\
\bottomrule
\end{tabular} |
|
AI-554 | \begin{tabular}{lrl}
\hline \textbf{Type of Text} & \textbf{Font Size} & \textbf{Style} \\ \hline
paper title & 15 pt & bold \\
author names & 12 pt & bold \\
author affiliation & 12 pt & \\
the word ``Abstract'' & 12 pt & bold \\
section titles & 12 pt & bold \\
subsection titles & 11 pt & bold \\
document text & 11 pt & \\
captions & 10 pt & \\
abstract text & 10 pt & \\
bibliography & 10 pt & \\
footnotes & 9 pt & \\
\hline
\end{tabular} |
|
AI-28269 | \begin{tabular}{lllll}
\hline\noalign{\smallskip}
& Train & Train corrected & Test & Test corrected \\
\noalign{\smallskip}\hline\noalign{\smallskip}
$d_1 (^\circ)$ & 2.56 & \textbf{1.63} & 2.70 & \textbf{1.65} \\
$d_2 (^\circ)$ & 5.76 & \textbf{0.93} & 4.77 & \textbf{0.91} \\
\noalign{\smallskip}\hline
\end{tabular} |
|
CL-4600 | \begin{tabular}{|l|r|}
\hline
\multicolumn{2}{|c|}{{\bf Architecture Hyperparameters}} \\
\hline
Source vocab size (BPE) & 16,000 \\
Target vocab size (BPE) & 16,000 \\
Embedding size (all) & 256 \\
Encoder LSTM units & 512 \\
Encoder layers & 2 \\
Decoder LSTM units & 512 \\
Decoder layers & 2 \\
Attention type & dot product \\
\hline \hline
\multicolumn{2}{|c|}{{\bf Training Settings}} \\
\hline
Optimization & Vanilla SGD \\
Learning rate & 0.5 \\
Batch size & 32 \\
Label smoothing $\epsilon$ & 0.1 \\
Checkpoint averaging & Last 10 \\
\hline
\end{tabular} |
|
CR-39325 | \begin{tabular}[c]{@{}l@{}}$\bullet$VPNisrequiredforgeographicallyspread\\participants\\$\bullet$suitableonlyforpermissionedblockchains\\$\bullet$insiderthreatandexternalattacksatnodeswith\\administrativeprivileges\end{tabular} |
|
CR-6377 | \begin{tabular}{l|c|c|c}
\hline
& Clean & FGSM & PGD \\ \hline
Standard & 99.02 & 93.80 & 86.12 \\ \hline
RT ($\lambda = 0.2$) & 99.06 & 97.18 & 95.84 \\
SRT ($\lambda = 0.2$) & $\bm{99.31}$ & $\bm{98.10}$ & $\bm{97.18}$ \\ \hline
RT ($\lambda = 0.4$) & 99.14 & 97.57 & 96.23 \\
SRT ($\lambda = 0.4$) & $\bm{99.40}$ & $\bm{98.28}$ & $\bm{97.55}$ \\ \hline
RT ($\lambda = 0.6$) & 99.06 & 97.78 & 96.92 \\
SRT ($\lambda = 0.6$) & $\bm{99.34}$ & $\bm{98.47}$ & $\bm{97.81}$ \\ \hline
RT ($\lambda = 0.8$) & 99.11 & 97.90 & 97.06 \\
SRT ($\lambda = 0.8$) & $\bm{99.35}$ & $\bm{98.53}$ & $\bm{97.86}$ \\ \hline
\end{tabular} |
|
AI-559 | \begin{tabular}{|c|c|c|}\hline
Method & En$\to$Zh & Zh$\to$En \\\hline
wait-$3$ & $3.56 \pm 0.09$ & $3.68 \pm 0.08$ \\\hline
wait-$3$ + SAT decoding & $3.81 \pm 0.08$ & $3.96 \pm 0.04$ \\\hline
SAT-$3$ & $3.83 \pm 0.07$ & $3.97 \pm 0.07$ \\\hline
Segment-based & $3.79 \pm 0.15$ & $3.99 \pm 0.07$ \\\hline
Full sentence & $3.98 \pm 0.08$ & $4.03 \pm 0.03$ \\\hline
Human & $3.85 \pm 0.05$ & - \\\hline
\end{tabular} |
|
CR-16469 | \begin{tabular}[c]{@{}l@{}}
The adversary has full knowledge of the model (e.g., weights, \\ hyperparameters, training details, etc.).
\end{tabular} |
|
AI-12259 | \begin{tabular}{|c|c|}\hline
Algoritmo & Ranking promedio \\\hline
GB & 1.2 \\
ESA & 2.0667 \\
EA & 2.7333 \\
\hline
\end{tabular} |
|
AI-31339 | \begin{tabular}{|c|c|c|c|}
\hline
\textbf{Dataset} & \textbf{\#samples} & \textbf{\#present} & \textbf{length} \\
\hline
\multicolumn{4}{|c|}{\textbf{training data}} \\
\hline
Kp20k & 464,676 & 2.94 & 2.01 \\
\hline
\multicolumn{4}{|c|}{\textbf{validation data}} \\
\hline
Kp20k & 20,000 & 3.49 & 1.86 \\
\hline
\multicolumn{4}{|c|}{\textbf{test data}} \\
\hline
Inspec & 500 & 7.20 & 2.40 \\
\hline
NUS & 211 & 5.64 & 1.93 \\
\hline
SemEval & 100 & 6.12 & 2.07 \\
\hline
Krapivin & 400 & 3.24 & 1.86 \\
\hline
Kp20k & 20,000 & 3.31 & 1.86 \\
\hline
\end{tabular} |
|
AI-30746 | \begin{tabular}{l|c}
\hline
\textbf{Parameter} & \multicolumn{1}{c}{\textbf{Value}} \\
\hline
Entity Dimension & 148 \\
Relationship Dimension & 148 \\
$\gamma$ & 1.0 \\
Learning Rate & 0.003 \\
Training Batch Size & 1024 \\
Optimizer & Adam () \\ \hline
\end{tabular} |
|
AI-30339 | \begin{tabular}{cc}
\toprule
Algorithm & Reward \\
\midrule
GSMRL & 0.7998 \\
JAFA & 0.7335 \\
GSM+Greedy & 0.7038 \\
EDDI & 0.6116 \\
\bottomrule
\end{tabular} |
|
AI-7414 | \begin{tabular}{|l|l|}
\hline
\textbf{GLOBEM Data Feature} & \textbf{Description} \\ \hline
date & date \\ \hline
f\_loc:phone\_locations\_doryab\_totaldistance:allday & total distance traveled (meters) \\ \hline
f\_loc:phone\_locations\_doryab\_timeathome:allday & time spent at home (minutes) \\ \hline
f\_loc:phone\_locations\_doryab\_locationentropy:allday & location entropy \\ \hline
f\_screen:phone\_screen\_rapids\_sumdurationunlock:allday & phone screen time (minutes) \\ \hline
f\_screen:phone\_screen\_rapids\_avgdurationunlock:allday & average phone unlock duration (minutes) \\ \hline
f\_call:phone\_calls\_rapids\_incoming\_sumduration:allday & phone call incoming duration (minutes) \\ \hline
f\_call:phone\_calls\_rapids\_outgoing\_sumduration:allday & phone call outgoing duration (minutes) \\ \hline
f\_blue:phone\_bluetooth\_doryab\_uniquedevicesothers:allday & unique Bluetooth devices discovered nearby \\ \hline
f\_steps:fitbit\_steps\_intraday\_rapids\_sumsteps:allday & step count \\ \hline
f\_steps:fitbit\_steps\_intraday\_rapids\_countepisodesedentarybout:allday & number of sedentary episodes \\ \hline
f\_steps:fitbit\_steps\_intraday\_rapids\_sumdurationsedentarybout:allday & total time spent sedentary (minutes) \\ \hline
f\_steps:fitbit\_steps\_intraday\_rapids\_countepisodeactivebout:allday & number of activity episodes \\ \hline
f\_steps:fitbit\_steps\_intraday\_rapids\_sumdurationactivebout:allday & total time spent active (minutes) \\ \hline
f\_slp:fitbit\_sleep\_intraday\_rapids\_sumdurationasleepunifiedmain:allday & total time asleep (minutes) \\ \hline
f\_slp:fitbit\_sleep\_intraday\_rapids\_sumdurationawakeunifiedmain:allday & total time spent awake while in bed (minutes) \\ \hline
\end{tabular} |
|
CR-576 | \begin{tabular}{|c|c|c|c|}
\cline{2-3}
\multicolumn{1}{c|}{} & Model 1 & Model 2 & \multicolumn{1}{|c}{} \\
\cline{2-4}
\multicolumn{1}{c|}{} & Size & Size & Activation function \\
\hline
Input & 40 & 40 & - \\ \hline
Layer 1 (Dense) & 64 & 512 & elu \\ \hline
Layer 2 (Dense) & 32 & 256 & elu \\ \hline
Layer 3 (Dense) & 32 & 128 & elu \\ \hline
Layer 4 (Dense) & 32 & 128 & elu \\ \hline
Layer 5 (Dense) & 5 & 5 & elu \\ \hline
Layer 6 (Dense) & 6 & 6 & linear \\ \hline
\end{tabular} |
|
CR-36460 | \begin{tabular}{lp{2.3in}}
\textbf{\underline{Finding 3}:} & Deepfake detectors are extracting unrestrained features that are not discriminative enough for generalizable detection.
\end{tabular} |
|
CR-46357 | \begin{tabular}{llll}
\toprule
& \textbf{Model-intrinsic based} & \textbf{Post-Hoc} & \textbf{\textit{Characteristics}} \\
\midrule
\textbf{Structured} & & & Logical, Visible \\
\midrule
\textbf{Unstructured} & & & Diversified, Fragmented \\
\midrule
\textbf{\textit{Focus}} & Model's reasoning process & Instances' relationship & - \\
\bottomrule
\end{tabular} |
|
AI-30609 | \begin{tabular}{ccl}
\toprule
\textbf{Set} & \textbf{Index} & \textbf{Description}
\\ \midrule
$\mathcal{S} = \{1, \ldots, S \}$ & $s$ & services
\\
$\mathcal{C} = \{1, \ldots, C \}$ & $c$ & clinicians
\\
$\mathcal{B} = \{1, \ldots, B \}$ & $b$ & blocks
\\
$\mathcal{W} = \{1, \ldots, W \}$ & $w$ & weekends
\\
$\mathcal{L} \subset \mathcal{W}$ & & long weekends
\\
$\mathcal{U}_c \subset \mathcal{B}$ & & block requests of
clinician $c$ \\
$\mathcal{V}_c \subset \mathcal{W}$ & & weekend requests of
clinician $c$ \\
\bottomrule
\end{tabular} |
|
CV-8971 | \begin{tabular}{lccccrccccrcccc}
\toprule
& \multicolumn{4}{c}{MSRVTT} & & \multicolumn{4}{c}{LSMDC} & & \multicolumn{4}{c}{MSVD} \\
\cmidrule{2-5} \cmidrule{7-10} \cmidrule{12-15}
Method & R@1 & R@5 & R@10 & MR & & R@1 & R@5 & R@10 & MR & & R@1 & R@5 & R@10 & MR \\
\midrule
HTM-PT$^*$ & 7.5 & 21.2 & 29.6 & 38.0 & & \textbf{4.0} & 9.8 & 14.0 & 137.0 & & \textbf{12.86} & 33.06 & \textbf{45.83} & \textbf{13.0} \\
HTM-PT no-3D & 6.9 & 19.8 & 27.4 & 43.0 & & 3.3 & 9.9 & 13.4 & 147.0 & & 11.57 & 30.25 & 40.84 & 17.0 \\
Ours (no 3D) & \textbf{8.4} & \textbf{22.0} & \textbf{30.4} & \textbf{36.0} & & \textbf{4.0} & \textbf{10.5} & \textbf{14.3} & \textbf{141.5} & & 12.74 & \textbf{33.48} & 44.96 & 14.0 \\
\bottomrule
\end{tabular} |
|
CR-45889 | \begin{tabular}{|c||c|c|c|c|c|}
\hline
\parbox{1cm}{\centering Trust } & \parbox{1cm}{\centering Very \\ Untrust.} & \parbox{1cm}{\centering Some.\\ Untrust.} & \parbox{1cm}{\centering Neither} & \parbox{1cm}{\centering Some. \\Trust.} & \parbox{1cm}{\centering Very \\Trust.} \\
\hline
\hline
\parbox{1cm}{\centering Very \\ Untrust.} & NA & 0.002 & <.001 & <.001 & <.001\\
\hline
\parbox{1cm}{\centering Some. \\ Untrust.} & & NA & <.001 & <.001 & <.001\\
\hline
\parbox{1cm}{\centering Neither} & & & NA & 0.670 & 0.427 \\
\hline
\parbox{1cm}{\centering Some. \\Trust.} & & & & NA & 0.222\\
\hline
\parbox{1cm}{\centering Very \\Trust.} & & & & & NA\\
\hline
\end{tabular} |
|
SE-3093 | \begin{tabular}{c|c}
\hline
\textbf{Publisher frequency} & 1, 10, \dots, 90, 100 \\ \hline
\textbf{Payload} & 128\,B, 1\,KB, 10\,KB, 100\,KB, 500\,KB \\ \hline
\textbf{Number of Nodes} & 3, 5, \dots, 21, 23 \\ \hline
\textbf{DDS Backend} & Connext, FastRTPS, CycloneDDS \\ \hline
\textbf{Reliability} & reliable, best effort \\ \hline
\end{tabular} |
|
CR-34436 | \begin{tabular}{lrrrr}
\toprule
& \multicolumn{4}{c}{F1 score} \\\cline{2-5}
\thead{Model} & \thead{D2A} & \thead{Devign} & \thead{Big-Vul} & \thead{ReVeal} \\\hline
CodeBERT & 66.76 & 56.90 & 40.65 & 42.69 \\\hline
UniXcoder & 57.19 & 56.81 & 39.55 & 40.53 \\\hline
CodeT5 & 57.33 & 58.79 & 40.20 & 40.56 \\\hline
LineVul & 68.22 & 54.15 & 39.46 & 42.92 \\
\bottomrule
\end{tabular} |
|
SE-10226 | \begin{tabular}{|c||c|c|c|}
\hline
\textbf{Data set} & \textbf{Small} & \textbf{Large} & \textbf{COCOVal17} \\
\hline\hline
\# Images/data set & 30 & 1,650 & 5000 \\
\hline
\# Unique labels found & 307 & 3506 & 4507 \\
\hline
Number of snapshots & 9 & 22 & 22 \\
\hline
Avg. days b/n requests & 12 Days & 8 Days & 8 Days \\
\hline
\end{tabular} |
|
CR-35929 | \begin{tabular}{ccccc}
\toprule
Defense $\setminus$
$\alpha$ & 0.15 & 0.25 & 0.35 & 0.45
\\\midrule
Noise Adding & 0.8688 & 0.8634 & 0.8525 & 0.8743
\vspace{0.8mm} \\
\vspace{0.8mm}
\makecell[c]{Clipping and \\ Noise Adding} & 0.8593 & 0.8604 & 0.8629 & 0.8688\\
RFA & 0.8642 & 0.8697 & 0.8739 & 0.8697 \\
Multi-Krum & 0.8576 & 0.8594 & 0.8635 & 0.8741 \\
FoolsGold & 0.8107 & 0.8251 & 0.8299 & 0.8415 \\
FLAME & 0.8361 & 0.8592 & 0.8688 & 0.8691 \\
CRFL & 0.8142 & 0.8197 & 0.8033 & 0.8251 \\
G-SPECTRE & 0.8415 & 0.8597 & 0.8542 & 0.8673 \\
R-SPECTRE & 0.7978 & 0.8033 & 0.8306 & 0.8467
\vspace{0.8mm} \\
\makecell[c]{Shadow Learning
} & \makecell[c]{\textbf{0.0765}} & \makecell[c]{\textbf{0.0929}} & \makecell[c]{\textbf{0.1694}} & \makecell[c]{\textbf{0.2247}} \\
\bottomrule
\end{tabular} |
|
PL-4624 | \begin{tabular}{lp{10cm}}
\hline
Identifier & Criterion \\
\hline
EC1 & The paper has no digital object identifier (DOI) or International Standard Book Number (ISBN). \\
EC2 & The paper has no abstract. \\
EC3 & The paper was published before 2007. \\
EC4 & The paper is not written in English. \\
EC5 & The complete paper was not available to the reviewers in any form equivalent to the final version. \\
EC6 & The paper is an earlier version of another candidate paper. \\
EC7 & The paper is not a primary study. \\
EC8 & The paper does not fall into any of the selected publication classes. \\
\hline
\end{tabular} |
|
SE-8740 | \begin{tabular}[c]{@{}l@{}}\\Revealedthatthereisasmallbutstatisticallysignificanteffect\\oneffectivenessoftheREelicitationrelatedtoanalysts'domainknowledge\\thathasmuchmoreinfluenceinfinalresultsinRE\end{tabular} |
|
CR-40492 | \begin{tabular}{|c||c|c|c|c|c|c|c|c|}
\hline
Predicted Attribute & Recall & Precision & F1-Score & Accuracy & Specificity & PPV & NPV & AUC \\ \hline
Age & 0.7831 & 0.5855 & 0.6701 & 0.7549 & 0.7229 & 0.5855 & 0.8695 & 0.8131 \\ \hline
Gender & 0.7969 & 0.4336 & 0.5616 & 0.6551 & 0.4795 & 0.4336 & 0.8252 & 0.6926 \\ \hline
Ethnicity & 0.7835 & 0.3776 & 0.5096 & 0.4932 & 0.3544 & 0.3776 & 0.7660 & 0.6265 \\ \hline
\end{tabular} |
|
CV-6692 | \begin{tabular}{|c|c|c|c|c|c|c||c|}
\hline
& NLC & FST & CVOS & MP-Net-V & LVO & ARP & Ours \\ \hline \hline
mIoU & 44.5 & 55.5 & - & - & - & 59.8 & \bf{77.9} \\ \hline
F-score & - & 69.2 & 74.9 & 77.5 & 77.8 & - & \bf{85.1} \\ \hline
\end{tabular} |
|
CR-46734 | \begin{tabular}{c|c|cccc}
\toprule
Loss & Trigger & \multicolumn{4}{c}{Accumulative steps $T$} \\
scaling & batch & 50 & 100 & 200 & 500 \\
\midrule
\multirow{2}{*}{0.01} & Clean & 85.17 & 83.52 & 76.96 & 58.83 \\
& Poisoned & \textbf{78.86} & \textbf{67.00} & \textbf{58.12} & \textbf{26.40} \\
\midrule
\multirow{2}{*}{0.02} & Clean & 84.12 & 77.69 & 63.02 & 41.71 \\
& Poisoned & \textbf{68.68} & \textbf{61.92} & \textbf{34.36} & \textbf{15.59} \\
\bottomrule
\end{tabular} |
|
SE-16345 | \begin{tabular}{ccc}
\hline
\textbf{Field Name} & \textbf{Value} & \textbf{Description} \\ \hline
id & abort & Database id \\
created\_at & 2018-01-09T17:32:09.879845+00:00 & The time when the library was published to crates.io. \\
description & Abnormal termination (stable, no\_std) & Basic descriptive information about the library. \\
downloads & 3506 & Total number of downloads \\
max\_stable\_version & 0.1.3 & The most stable version number \\
max\_version & 0.1.3 & Maximum version number \\
name & abort & name \\
newest\_version & 0.1.3 & Latest version number. \\
recent\_downloads & 1972 & Number of recent downloads. \\
updated\_at & 2021-01-12T22:27:17.016095+00:00 & Last updated \\ \hline
\end{tabular} |
|
CR-13577 | \begin{tabular}{|c|c|}
\hline
\textbf{Property} & \textbf{Value} \\\hline\hline
$id$ & $\mathbb{N}_0$ \\\hline
$type$ & Instruction \\\hline
$instType$ & Convert \\\hline
$opcode$ & $\{cutop\}$ \\\hline
\end{tabular} |
|
CR-32760 | \begin{tabular}{lrr}
\toprule
Dataset & Class digit 0 & Class digit 1 \\ \midrule \midrule
Train & 122 & 118 \\ \midrule
Validation & 42 & 38 \\ \midrule
Test & 36 & 44 \\ \midrule
\textbf{Total} & \textbf{200} & \textbf{200} \\ \bottomrule
\end{tabular} |
|
AI-32972 | \begin{tabular}{|c|c|c|c|c|c|c|}
\hline
\multirow{2}{*}{} & \multicolumn{2}{|c|}{Han's Thesis ('17)} & \multicolumn{2}{|c|}{\textbf{Our S-uantizer}} & \multicolumn{2}{|c|}{\textbf{Our SQuantizer (w/ 8bit weight)}} \\
\cline{2-3}
\cline{4-5}
\cline{6-7}
& Top1 & Top5 & Top1 & Top5 & Top1 & Top5 \\
\hline
baseline & 76.2 & 92.9 & & & 76.3 & 93.0 \\
\hline
sparse ResNet50 & 76.3 & 93.2 & & & & \\
\hline
sparsity & 76.3 & 93.2 & & & & \\
\hline
\end{tabular} |
|
AI-14138 | \begin{tabular}{ccc}
\hline
Testing Condition & \thead{Baseline Network \\ Error (mm)} & \thead{ Extended Network \\ Error (mm)} \\
\hline
Train & 0.107 & 0.098 \\
Unseen Eyes & 0.102 & 0.096 \\
\thead{Unseen Brt. + Distr. (1 tool)} & 0.140 & 0.100 \\
\thead{Unseen Brt. + Distr. (2 tools)} & 0.169 & 0.087 \\
\thead{"Unseen" Avg. (above 3 rows)} & \textbf{0.137} & \textbf{0.094} \\
\hline
\vspace{-20pt}
\end{tabular} |
|
AI-9877 | \begin{tabular}{||p{4.3cm}|p{7.7cm}|}
\hline
\textbf{Fuzzy temporal term} & \textbf{'Within' (T)}, where: T = fuzzy duration interval or granularity (e.g. done within a week). \\
\hline
sameAs & in less than (T), in under (T), at most (T), in no more than (T), etc.
\\\hline
Super Class & FuzzyModifier (annotation: Time closure operator) \\\hline
Properties & hasWeightDegree, hasFuzzyTime, hasModifierFunction \\\hline
Required arguments & WeightDegree, FuzzyTime \\\hline
Fuzzy MF & Trapezoidal (trapmf) \\
\hline
\end{tabular} |
|
AI-24662 | \begin{tabular}{|p{1.5cm}||c|c|p{2cm}|}
\hline
Regression Type & Standard & Node2Vec & Node2Vec-Elevation) \\
\hline
\hline
Linear & -0.050 & 0.050 & 0.082 \\
Ridge & -0.050 & 0.025 & 0.082 \\
MLP & -0.045 & -0.017 & -0.355 \\
SGD & -0.053 & 0.000 & -0.021 \\
\hline
\end{tabular} |
|
AI-12557 | \begin{tabular}{c|c|c|c|c}
$\rho$ & $ S^{2} \times T,\gamma_{w}$ & top-1 & GFLOPs$\times$views & Param \\
\hline
\hline
$1.00\times$ & $256^{2}\times 4,1.0$ & 75.7 & 36.1$\times$30 & 34.5M \\
$0.76\times$ & $224^{2}\times 4,1.0$ & 75.2 & 27.6$\times$30 & 34.5M \\
$0.61\times$ & $224^{2}\times 3,1.0$ & 75.1 & 22.1$\times$30 & 34.5M \\
$0.48\times$ & $224^{2}\times 3,0.83$ & 74.9 & 17.4$\times$30 & 24.4M \\
$0.41\times$ & $224^{2}\times 3,0.73$ & 74.6 & 14.7$\times$30 & 19.2M \\
$0.34\times$ & $224^{2}\times 3,0.63$ & 74.3 & 12.4$\times$30 & 14.5M \\
$0.28\times$ & $224^{2}\times 2,0.63$ & 73.5 & 10.2$\times$30 & 14.5M \\
$0.24\times$ & $178^{2}\times 2,0.63$ & 72.4 & 8.8$\times$30 & 14.5M \\
$0.23\times$ & $142^{2}\times 2,0.73$ & 71.3 & 8.3$\times$30 & 19.2M \\
$0.21\times$ & $142^{2}\times 2,0.63$ & 71.0 & 7.6$\times$30 & 14.5M \\
\end{tabular} |
|
CV-977 | \begin{tabular}{cc|c|c|c|}
\cline{3-5}
& & \multicolumn{3}{c|}{2D IoU 0.50 / 0.70} \\ \hline
\multicolumn{1}{|c|}{Method} & Type & Easy & Moderate & Hard \\ \hline
\multicolumn{1}{|c|}{Mono3D} & Mono & 30.5 / 5.2 & 22.4 / 5.2 & 19.2 / 4.1 \\ \hline
\multicolumn{1}{|c|}{Mono3D++} & Mono & \textbf{46.7} / \textbf{16.7} & \textbf{34.3} / \textbf{11.5} & \textbf{28.1} / \textbf{10.1} \\ \hline \hline
\multicolumn{1}{|c|}{3DOP} & Stereo & 55.0 / 12.6 & 41.3 / 9.5 & 34.6 / 7.6 \\ \hline
\end{tabular} |
|
CR-13116 | \begin{tabular}{|l|c|c|}\hline
& \hspace{-0.5ex}Laptop\hspace{-0.5ex} & \hspace{-0.5ex}Rasp. Pi\hspace{-0.5ex} \\\hline\hline
\hspace{-0.5ex}Enc.\hspace{-0.5ex} & \hspace{-0.5ex}3.08 ms\hspace{-0.5ex} & \hspace{-0.5ex}37.3 ms\hspace{-0.5ex} \\\hline
\hspace{-0.5ex}Dec.\hspace{-0.5ex} & \hspace{-0.5ex}3.61 ms\hspace{-0.5ex} & \hspace{-0.5ex}43.9 ms\hspace{-0.5ex} \\\hline
\hspace{-0.5ex}KeyD.\hspace{-0.5ex} & \hspace{-0.5ex}4.77 ms\hspace{-0.5ex} & \hspace{-0.5ex}58.5 ms\hspace{-0.5ex} \\\hline
\hspace{-0.5ex}Sign\hspace{-0.5ex} & \hspace{-0.5ex}4.80 ms\hspace{-0.5ex} & \hspace{-0.5ex}61.2 ms\hspace{-0.5ex} \\\hline
\hspace{-0.5ex}Verify\hspace{-0.5ex} & \hspace{-0.5ex}4.78 ms\hspace{-0.5ex} & \hspace{-0.5ex}56.3 ms\hspace{-0.5ex} \\\hline
\end{tabular} |
|
CR-40865 | \begin{tabular}{lccc}
\toprule[1.5pt]
Attacks & CIFAR10 & SVHN & Fashion MNIST \\ \midrule[1pt]
PGD & 89.72 & 97.54 & 94.63 \\
BPDA & 87.86 & 97.52 & 93.75 \\ \midrule[1pt]
gap & 1.86 & 0.02 & 0.88 \\
\bottomrule[1.5pt]
\end{tabular} |
|
CR-55870 | \begin{tabular}{@{}lcc@{}}
\toprule
\textbf{\makecell{Encryption mode \\and\\Authentication mode}} &
\textbf{\makecell{SSD \\\lbrack seconds\rbrack}} &
\textbf{\makecell{SSD \\(no journal)\\\lbrack seconds\rbrack}} \\
\midrule
\makecell{underlying device} & \makecell{1356,2 $\pm$40,9} & \\
\makecell{CRC32 checksum} & \makecell{1343,4 $\pm$13,9} & \makecell{1346,6 $\pm$31,7} \\
\makecell{NULL cipher} & \makecell{1365,5 $\pm$39,9} & \\
\makecell{AES-XTS-plain64} & \makecell{1341,6 $\pm$11,4} & \\
\makecell{AES-XTS-random} & \makecell{1352,9 $\pm$26,8} & \makecell{1335,1 $\pm$24,1} \\
\makecell{AES-GCM-random \\integrity AEAD} & \makecell{1347,8 $\pm$26,4} & \makecell{1358,2 $\pm$55,8} \\
\makecell{AES-XTS-random \\integrity HMAC-SHA256} & \makecell{1343,8 $\pm$7,3} & \makecell{1330,0 $\pm$19,2} \\
\makecell{ChaCha20-random \\integrity Poly1305} & \makecell{1364,5 $\pm$38,0} & \makecell{1370,5 $\pm$39,2} \\
\makecell{AEGIS128-random \\integrity AEAD} & \makecell{1353,5 $\pm$59,6} & \makecell{1339,7 $\pm$47,2} \\
\makecell{AEGIS256-random \\integrity AEAD} & \makecell{1342,7 $\pm$35,4} & \makecell{1363,7 $\pm$36,3} \\
\makecell{MORUS640-random \\integrity AEAD} & \makecell{1355,4 $\pm$16,9} & \makecell{1361,1 $\pm$15,8} \\
\makecell{MORUS1280-random \\integrity AEAD} & \makecell{1344,5 $\pm$18,7} & \makecell{1340,6 $\pm$21,5} \\
\bottomrule
\end{tabular} |
|
AI-26236 | \begin{tabular}{c|cc|cc}
\hline
Target Entity & \multicolumn{2}{c|}{KA questions with LA-GMF} & \multicolumn{2}{c}{KA questions with Uncertainty-Only} \\
\hline
\multirow{3}{*}{ Jay Chou\footnotemark} & Does the character mainly sing folk songs? & No & Did the character die unnaturally? & Unknown \\
& Is the character handsome? & Yes & Does the character like human beings? & Unknown \\
& Is the character born in Japan? & No & Is the character from a novel? & No \\
\hline
\multirow{3}{*}{ Jimmy Kudo\footnotemark} & Is the character brave? & Yes & Does the character only act in movies (no TV shows)? & Unknown \\
& Is the character from Japanese animation? & Yes & Has the character ever been traitorous? & Unknown \\
& Was the character dead? & No & Is the character a monster? & Unknown \\
\hline
\end{tabular} |
|
AI-29743 | \begin{tabular}{c|ccc|c|c|ccc|c}
\toprule
\multicolumn{10}{c}{input feature sequence: $ 256 \times T' $} \\
\midrule
\multicolumn{5}{c|}{classification branch} & \multicolumn{5}{|c}{regression branch} \\
\midrule
layer & kernel & stride & channel & output size & layer & kernel & stride & channel & output size \\
\midrule
Conv1d-1 & 3 & 1 & 256 & $ 256 \times T' $ & Conv1d-1 & 3 & 1 & 256 & $ 256 \times T' $ \\
\midrule
Conv1d-2 & 3 & 1 & 256 & $ 256 \times T' $ & Conv1d-1 & 3 & 1 & 256 & $ 256 \times T' $ \\
\midrule
Conv1d-3 & 3 & 1 & 256 & $ 256 \times T' $ & Conv1d-1 & 3 & 1 & 256 & $ 256 \times T' $ \\
\midrule
Conv1d-4 & 3 & 1 & 256 & $ 256 \times T' $ & Conv1d-1 & 3 & 1 & 256 & $ 256 \times T' $ \\
\midrule
Conv1d-class & 3 & 1 & 256 & $ K \times A \times T' $ & Conv1d-reg & 3 & 1 & 256 & $ 2 \times A \times T' $ \\
\bottomrule
\end{tabular} |
|
SE-3141 | \begin{tabular}{|l|l|}\hline\hline
{\bf Input} & {\bf Description} \\\hline\hline
{\tt pr.A} & payment request for a large amount \\\hline
{\tt pr.a} & payment request for a small amount \\\hline
{\tt ci.in.v} & valid card insertion into the reader's slot \\\hline
{\tt ci.in.i} & invalid card insertion into the reader's slot \\\hline
{\tt ci.r} & removal of an ejected card \\\hline
{\tt ts.in.ok} & authorise payment command on touch screen \\\hline
{\tt ts.in.ab} & abort transaction command on touch screen \\\hline
{\tt ts.in.vp} & entry of a valid PIN via touch screen \\\hline
{\tt ts.in.ip} & entry of an invalid PIN via touch screen \\
\hline\hline
\end{tabular} |
|
AI-9972 | \begin{tabular}{@{}lll@{}}
\toprule
& \multicolumn{1}{c}{Input} & \multicolumn{1}{c}{Output} \\ \midrule
Perfect Information & $s \in \mathcal{S}$ & $V(s)$ \\
Imperfect Information & $s_{pub} \in \mathcal{S}_{pub}, \Delta(\mathcal{S}_1(s_{pub})), \Delta(\mathcal{S}_2(s_{pub})) $ & $V(s) \,\forall s \in \mathcal{S}_i(s_{pub})$ \\
\bottomrule
\end{tabular} |
|
AI-9422 | \begin{tabular}{l|c|c}
\toprule
Dataset & ComVE & $\alpha$-NLG \\
\midrule
\# Train & 50,481 & 10,000 \\
\# Dev. & 1,779 & 997 \\
\# Test & 3,560 & 1,000 \\
\# In.words & 7.7 & 17.4 \\
\# Out.words & 9.0 & 10.8 \\
\bottomrule
\end{tabular} |
|
CR-40442 | \begin{tabular}[c]{@{}l@{}}select[1-8]benchmarksfromSQLite.
\\Total: 500 queries \\ 14K file calls. File size: 800KB\end{tabular} |
|
AI-14157 | \begin{tabular}{lcc}
\toprule
Dataset & Act. Sim. & Inact. Sim. \\
\midrule
BACE & \textbf{0.6743} & \textbf{0.5403} \\
\midrule
HIV & 0.4186 & 0.4536 \\
MUV & 0.1946 & 0.4181 \\
Tox21 & 0.3047 & 0.3462 \\
ToxCast & 0.2193 & 0.2962 \\
SIDER & 0.2880 & 0.2316 \\
ClinTox & 0.2725 & 0.2278 \\
BBBP & 0.3961 & 0.2031 \\
\bottomrule
\end{tabular} |
|
CV-10269 | \begin{tabular}{|l|c|}
\hline
Methods & 3D $\mathcal{J}$ Err. (\textit{mm}) \\
\hline\hline
DeepHPS & 6.30 \\
WHSP-Net & 4.32 \\
V2V-PoseNet & 3.81 \\
our HandVoxNet (full method) & \textbf{3.75} \\
\hline
\end{tabular} |
|
AI-8546 | \begin{tabular}{lcccc}
\toprule
Network & Train & Validation & Test & \#Weights [$10^3$] \\
\midrule
FC $G$-avg & 15.15 $\pm$ 5.49 & 16.48 $\pm$ 0.73 & 16.89 $\pm$ 0.76 & 24.0 \\
\textbf{$G$-inv (ours) } & 2.65 $\pm$ 0.91 & 7.32 $\pm$ 0.55 & 7.46 $\pm$ 0.56 & 24.0 \\
Conv1D $G$-avg & 8.98 $\pm$ 6.39 & 11.43 $\pm$ 4.29 & 11.78 $\pm$ 4.79 & 24.0 \\
\textbf{Conv1D $G$-inv (ours) } & \textbf{0.87 $\pm$ 0.12} & \textbf{2.57 $\pm$ 0.37} & \textbf{2.6 $\pm$ 0.4} & 24.0 \\
Maron & 2.41 $\pm$ 0.82 & 5.74 $\pm$ 1.19 & 5.93 $\pm$ 1.18 & 24.2 \\
\bottomrule
\end{tabular} |
|
CR-27945 | \begin{tabular}{cccc}
\toprule
$\sigma\textbf{=0.05}$ & $\sigma\textbf{=0.08}$ & $\sigma\textbf{=0.10}$ & $\sigma\textbf{=0.15}$ \\
\midrule
jimmy91 & jimmy99 & mnmm988 & jimmy91992 \\
jimmy11 & micmy91 & tbmmy98 & jrm6998 \\
jimmy21 & jimsy91 & jismyo15 & sirsy91 \\
jimmy88 & mimmyo1 & jizmyon & jrz4988 \\
jimmy81 & jbmmy88 & j144988 & Rimky28 \\
jimmy98 & simmy98 & jbmm998 & missy11 \\
mimmy98 & dijmy91 & timsy91 & jimmy119 \\
jimmy28 & jimmy98 & jrm4985 & sikjy91 \\
simmy91 & timsy91 & jhmmy88 & licky916 \\
mimmy91 & jnmm988 & jhmm988 & gimjyon \\
\bottomrule
\end{tabular} |
|
SE-17319 | \begin{tabular}[c]{@{}l@{}}\textbf{C:}\textit{gap\_accuracy}([0-1]accuracyscoreforfillinginprogramblanks)\\\textbf{R:}\textit{readability\_level\_ba}(5-levelavg.scoreforreadabilityb/acodecompletion)\\\textbf{R:}\textit{readability\_level\_before}(5-levelscoreforreadabilitybeforecodecompletion)\\\textbf{T:}\textit{time\_to\_read\_complete}(avg.secondstoratereadabilityandcompletecode)\end{tabular} |
|
SE-23213 | \begin{tabular}{|c|c|c|}
\hline
& Vulnerable & Non-Vulnerable \\ \hline\hline
Train & $10018$ & $11836$ \\ \hline
Test & $1255$ & $1477$ \\ \hline
Validation & $1187$ & $1545$ \\ \hline
\end{tabular} |
|
AI-2134 | \begin{tabular}{ll}
\multicolumn{2}{c}{Example 1: Stmt} \\
Input & Can you let mom know that I finally mailed her package? \\
Output & Teresa says she finally mailed your package. \\
\multicolumn{2}{c}{Example 2: AskYN + direct question} \\
Input & Ask Haley can I borrow your juicer? \\
Output & Teresa asks if she can borrow your juicer \\
\multicolumn{2}{c}{Example 3: AskYN + indirect question} \\
Input & Can you ask Blade if he's still having a party tomorrow \\
Output & Teresa asks you if you're still having a party tomorrow \\
\multicolumn{2}{c}{Example 4: AskWH + direct question} \\
Input & Text alyssa what type of wine do you want \\
Output & Teresa asks what type of wine you want \\
\multicolumn{2}{c}{Example 5: AskWH + indirect question} \\
Input & Ask Jeff what he's doing tonight \\
Output & Teresa asks what you are doing tonight \\
\multicolumn{2}{c}{Example 6: Req} \\
Input & Text Will to grab some apples on his way home \\
Output & Teresa asks you to grab some apples on your way home \\
\multicolumn{2}{c}{Example 7: Missing contact} \\
Input & Find out if Nate is bringing anything to the party \\
Output & Teresa asks if you are bringing anything to the party \\
\end{tabular} |
|
CV-9835 | \begin{tabular}{*{9}{c}}
\toprule
& AP & $\text{AP}_{50}$ & $\text{AP}_{75}$ & $\text{AP}_{S}$ & $\text{AP}_{M}$ & $\text{AP}_{L}$ \\
\midrule
2017 winner & 46.7 & 69.5 & 51.3 & 26.0 & 49.1 & \textbf{64.0} \\
Ours & \textbf{49.0} & \textbf{73.0} & \textbf{53.9} & \textbf{33.9} & \textbf{52.3} & 61.2 \\
\midrule
HTC baseline & 38.4 & 60.0 & 41.5 & 20.4 & 40.7 & 51.2 \\
+ DCN & 39.5 & 61.3 & 42.8 & 20.9 & 41.8 & 52.7 \\
+ SyncBN & 40.7 & 62.8 & 44.2 & 22.2 & 43.1 & 54.4 \\
+ ms train & 42.5 & 64.8 & 46.4 & 23.7 & 45.3 & 56.7 \\
+ SENet-154 & 44.3 & 67.5 & 48.3 & 25.0 & 47.5 & 58.9 \\
+ GA-RPN & 45.3 & 68.9 & 49.4 & 27.0 & 48.3 & 59.6 \\
+ ms test & 47.4 & 70.6 & 52.1 & 30.2 & 50.1 & 61.8 \\
+ ensemble & 49.0 & 73.0 & 53.9 & 33.9 & 52.3 & 61.2 \\
\bottomrule
\end{tabular} |
|
SE-17404 | \begin{tabular}{|l|l|}
\hline
\textbf{ROUGE-1} & \\ \hline
\textbf{Precision} & 0.667 $\pm$ 0.192 \\ \hline
\textbf{Recall} & 0.559 $\pm$ 0.226 \\ \hline
\textbf{F-measure} & 0.56 $\pm$ 0.185 \\ \hline
\textbf{ROUGE-2} & \\ \hline
\textbf{Precision} & 0.479 $\pm$ 0.217 \\ \hline
\textbf{Recall} & 0.398 $\pm$ 0.218 \\ \hline
\textbf{F-measure} & 0.4 $\pm$ 0.202 \\ \hline
\textbf{ROUGE-L} & \\ \hline
\textbf{Precision} & 0.652 $\pm$ 0.165 \\ \hline
\textbf{Recall} & 0.586 $\pm$ 0.183 \\ \hline
\textbf{F-measure} & 0.599 $\pm$ 0.153 \\ \hline
\end{tabular} |
|
CL-3531 | \begin{tabular}{ll}
\hline
\textbf{Model} & \textbf{NDCG} \\ \hline
MTN (Base) & \textbf{55.33} \\ \hline
CorefNMN & 54.70 \\
MN & 47.50 \\
HRE & 45.46 \\
LF & 45.31 \\ \hline
\end{tabular} |
|
SE-6764 | \begin{tabular}{c|c|c|c|c|c}
\hline
\multicolumn{3}{c|}{\# cmd per nl} & \multicolumn{3}{c}{\# nl
per cmd} \\
\cline{1-3}\cline{4-6}
avg. & median & max & avg. & median & max \\
\hline
1.09 & 1 & 9 & 1.23 & 1 & 22 \\
\hline
\end{tabular} |
|
CR-36220 | \begin{tabular}{ccccc}
\hline
\textbf{Design Name} & \textbf{Total No. Inputs} & \textbf{Total No. Registers} & \textbf{No. State Registers} & \textbf{Total No. Gates} \\
\hline
$\textup{aes}^{\star}$ & 45 & 2994 & 15 & 29037 \\
\hline
$\textup{siphash}^{\star}$ & 44 & 794 & 8 & 6214 \\
\hline
$\textup{sha1}^{\star}$ & 516 & 1526 & 3 & 11822 \\
\hline
$\textup{fsm}^{\diamond}$ & 17 & 15 & 7 & 166 \\
\hline
$\textup{gpio}^{\diamond}$ & 111 & 51 & 11 & 311 \\
\hline
$\textup{memory}^{\diamond}$ & 173 & 75 & 7 & 881 \\
\hline
$\textup{uart}^{\wedge}$ & 12 & 69 & 10 & 469 \\
\hline
$\textup{cr}\_\textup{div}^{\wedge}$ & 99 & 4172 & 4 & 34218 \\
\hline
$\textup{altor32}\_\textup{lite}^{\wedge}$ & 39 & 1249 & 6 & 13111 \\
\hline
$\textup{gcm}\_\textup{aes}^{\wedge}$ & 267 & 1697 & 10 & 34496 \\
\hline
\end{tabular} |
|
CR-36097 | \begin{tabular}{@{}l|l}
\toprule
\textbf{Code} & \textbf{Examples} \\
\midrule
\multirow{9}{1.5cm}{\textbf{Remember}} & Remember verification for this computer \\
& Recognize this device in the future \\
& Do not require OTP on this browser \\
& Skip two-factor authentication on this device \\
& Save browser \\
& Do not ask again on this device \\
& Remember this device \\
& Remember this computer for \{duration\} \\
& Do not ask for code on this device \\\hline
\multirow{6}{1.5cm}{\textbf{Trust}} & Trust this device (opt-in) \\
& Trust this device (opt-out) \\
& Do not trust this device (opt-out) \\
& Do not trust this device (opt-in) \\
& Trust this device for \{duration\} \\
& Untrust this device \\\hline
\multirow{3}{1.5cm}{\textbf{Skip}} & Require code to login for \{duration\} \\
& We won't ask for the next \{duration\} \\
& Skip this for \{duration\} \\\hline
\textbf{Other} & Stay signed/logged in \\
\bottomrule
\multicolumn{2}{c}{\textit{opt-out}: checkbox is pre-checked; \textit{opt-in}: checkbox is not pre-checked} \\
\multicolumn{2}{c}{\textit{duration}: a number of days, weeks, or logins}
\end{tabular} |
|
CV-3429 | \begin{tabular}{lcccccc|r}
\hline
& \multicolumn{2}{c}{Polyvore Outfits} & \multicolumn{2}{c}{Animals with Attributes 2} \\
\hline
Attribute Types & Insertion ($\uparrow$) & Deletion ($\downarrow$) & Insertion ($\uparrow$) & Deletion ($\downarrow$) \\
\hline
\hline
Random & 25.3 & -6.3 & 2.1 & -8.5 \\
Full Frame Discovery & 29.4 & -9.7 & 4.8 & -22.6 \\
Patch Discovery & 30.2 & -10.3 & 5.4 & -22.9 \\
Supervised Attributes & \textbf{31.5} & \textbf{-11.8} & \textbf{6.2} & \textbf{-24.1} \\
\hline
\end{tabular} |
|
CR-19325 | \begin{tabular}{@{}ccccc@{}}
\toprule
\multicolumn{1}{c}{\textbf{Malware Family}} & \textbf{Oliveira } & \textbf{VirusShare } & \textbf{Catak } & \textbf{VirusSample } \\ \midrule
Trojan & 31,979 & 8,919 & 1,001 & 6,153 \\
Virus & 102 & 2,490 & 1,001 & 2,367 \\
Adware & 5,444 & 908 & 379 & 222 \\
Backdoor & 135 & 510 & 1,001 & 447 \\
Downloader & 1,948 & 218 & 1,001 & N/A \\
Worms & N/A & 524 & 1,001 & 441 \\
Agent & 220 & 165 & N/A & 102 \\
Ransomware & 404 & 115 & N/A & N/A \\
Dropper & 118 & N/A & 891 & N/A \\
Riskware & 216 & N/A & N/A & N/A \\
Spyware & N/A & N/A & 832 & N/A \\ \midrule
\textbf{Total} & \textbf{40,566} & \textbf{13,849} & \textbf{7,107} & \textbf{9,732}
\end{tabular} |
|
CR-15980 | \begin{tabular}{l|l|l}
\texttt{Mov=vehicle} & \texttt{Mov=pedestrian} & \texttt{Mov=boat} \\
\texttt{Attack=black\_hole} & \texttt{Attack=flood} & \texttt{...} \\
\texttt{94,39} & \texttt{22,75} & \texttt{...} \\
\texttt{55,84} & \texttt{43,15} & \texttt{...} \\
\texttt{...} & \texttt{...} & \texttt{...} \\
\texttt{42,44} & \texttt{65,61} & \texttt{...} \\
\texttt{1,26} & \texttt{15,58} & \texttt{...} \\
\end{tabular} |
|
CR-42600 | \begin{tabular}{|c|ccccc|}
\hline
\multirow{3}{*}{Attack Type} & \multicolumn{5}{c|}{Testing 2 Models} \\ \cline{2-6}
& \multicolumn{1}{c|}{\multirow{2}{*}{DT}} & \multicolumn{1}{c|}{\multirow{2}{*}{RF}} & \multicolumn{1}{c|}{\multirow{2}{*}{MLP}} & \multicolumn{1}{c|}{\multirow{2}{*}{DNN}} & \multirow{2}{*}{SVM} \\
& \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \multicolumn{1}{c|}{} & \\ \hline
DoS & \multicolumn{1}{c|}{0.506} & \multicolumn{1}{c|}{0.455} & \multicolumn{1}{c|}{0.355} & \multicolumn{1}{c|}{0.225} & 0.104 \\ \hline
DDoS & \multicolumn{1}{c|}{0.657} & \multicolumn{1}{c|}{0.810} & \multicolumn{1}{c|}{0.372} & \multicolumn{1}{c|}{0.679} & 0.372 \\ \hline
Web Attack & \multicolumn{1}{c|}{0.312} & \multicolumn{1}{c|}{0.298} & \multicolumn{1}{c|}{0.283} & \multicolumn{1}{c|}{0.293} & 0.254 \\ \hline
Infiltration & \multicolumn{1}{c|}{0.293} & \multicolumn{1}{c|}{0.354} & \multicolumn{1}{c|}{0.590} & \multicolumn{1}{c|}{0.410} & 0.340 \\ \hline
\end{tabular} |
|
AI-41117 | \begin{tabular}{l|l|l|l|l}
& MSE & RMSE & MAE & MAPE \\ \hline
WTI & 146.4406 & 12.1012 & 0.7653 & 0.3544 \\
Main Dataset & 78.5752 & 8.8642 & 0.5606 & 0.4298 \\
Dollar & 79.4293 & 8.9123 & 0.5636 & 0.4272 \\
Gold & 48.4487 & 6.9605 & 0.4402 & 0.4733 \\
\end{tabular} |
|
SE-24644 | \begin{tabular}{@{}l@{}}AprogramthatprocessesrawdatatoproduceinputtoMLcomponents.\\
(This may access the ML components' internal information or may be \\\,
combined with the ML components.)\end{tabular} |
Subsets and Splits