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CV-26906 | \begin{tabular}{|l|c|c|}
\hline
Model & Test Accuracy & Perplexity \\
\hline
Nearest Neighbor & $14.09$ & \texttt{N/A} \\
\hline
CNN & $14.61$ & $0.1419$ \\
\hline
Our Model & $\mathbf{19.77}$ & $\mathbf{0.2362}$ \\
\hline
\end{tabular} |
|
AI-8358 | \begin{tabular}{|p{50pt}|p{50pt}<{\centering}|p{50pt}<{\centering}|p{50pt}<{\centering}|}
\hline
& \textbf{Types} & \textbf{HAS} & \textbf{HAS+r} \\ \hline
\textbf{L.MDB} & Film & 0.38 & \textbf{0.44} \\ \hline
\multirow{15}{*}{\textbf{DBpedia}} & Airl. & 0.402 & \textbf{0.424} \\
& Band & 0.26 & \textbf{0.56} \\
& Base. & 0.46 & \textbf{0.7} \\
& Lake & 0.28 & \textbf{0.4} \\
& Univ. & 0.177 & \textbf{0.406} \\
& Phil. & 0.288 & \textbf{0.667} \\
& Song & 0.538 & \textbf{0.807} \\
& Poli. & 0.209 & \textbf{0.524} \\
& TVsh. & 0.186 & \textbf{0.478} \\
& Come. & 0.528 & \textbf{0.575} \\
& Acad & \textbf{0.84} & \textbf{0.84} \\
& Acto. & 0.36 & \textbf{0.42} \\
& Book. & \textbf{0.6} & \textbf{0.6} \\
& Moun. & 0.609 & \textbf{0.645} \\
& Radi. & \textbf{0.62} & 0.532 \\ \hline
\multicolumn{2}{|c|}{\textbf{Average}} & 0.421 & \textbf{0.566} \\ \hline
\end{tabular} |
|
CV-10464 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
\multirow{3}*{Method} & \multicolumn{6}{c|}{IoU = 0.5} & \multicolumn{6}{c|}{IoU = 0.7} \\
\cline{2-13}
~ & \multicolumn{2}{c|}{ Easy} & \multicolumn{2}{c|}{Moderate} & \multicolumn{2}{c|}{Hard} & \multicolumn{2}{c|}{ Easy} & \multicolumn{2}{c|}{Moderate} & \multicolumn{2}{c|}{Hard} \\
\cline{2-13}
~ & t/v 1 & t/v 2 & t/v 1 & t/v 2 & t/v 1 & t/v 2 & t/v 1 & t/v 2 & t/v 1 & t/v 2 & t/v 1 & t/v 2 \\
\hline\hline
3DOP & 46.04 & - & 34.63 & - & 30.09 & - & 6.55 & - & 5.07 & - & 4.10 & - \\
Mono3D & 25.19 & - & 18.20 & - & 15.52 & - & 2.53 & - & 2.31 & - & 2.31 & - \\
Deep3DBox & - & 27.04 & - & 20.55 & - & 15.88 & - & 5.85 & - & 4.10 & - & 3.84 \\
\hline
Our Method & 28.16 & 28.98 & 21.02 & 20.71 & 19.91 & 18.59 & 5.98 & 5.45 & 5.50 & 5.11 & 4.75 & 4.45 \\
\hline
\end{tabular} |
|
SE-20655 | \begin{tabular}[c]{@{}l@{}}a)DifferentorganizationsgenerateSBOMsatdifferentSDLCstages.\\b)MoreorganizationsfavorincludingmorethanbaselineSBOMinformation.\end{tabular} |
|
CV-28515 | \begin{tabular}{l|c|c|c|c}
\hline
Benchmark & Easy & Moderate & Hard & mAP \\
\hline
Cars~(3D Detection) & 88.21 & 77.85 & 75.62 & 80.56 \\
Cars~(BEV Detection) & 90.17 & 87.55 & 87.14 & 88.29 \\
Pedestrians~(3D Detection) & 70.80 & 63.45 & 58.22 & 64.16 \\
Pedestrians~(BEV Detection) & 76.70 & 70.76 & 65.13 & 70.86 \\
Cyclists~(3D Detection) & 85.98 & 64.95 & 60.40 & 70.44 \\
Cyclists~(BEV Detection) & 87.17 & 66.71 & 63.79 & 72.56 \\
\hline
\end{tabular} |
|
AI-22172 | \begin{tabular}{rl|rl|rl|rl|rl}
Freq. & Word & Freq. & Word & Freq. & Word & Freq. & Word & Freq. & Word \\
\hline
3,205 & sport & 464 & use & 296 & carrying & 155 & crossing & 92 & popular \\
1,153 & appear & 461 & chair & 287 & face & 150 & tires & 92 & planning \\
976 & fire & 430 & parked & 283 & writing & 148 & cabinets & 88 & batters \\
898 & pattern & 415 & coming & 230 & television & 145 & beds & 85 & enjoy \\
845 & material & 399 & buses & 223 & vegetarian & 127 & mountain & 85 & dirt \\
689 & bed & 395 & take & 220 & beside & 119 & levels & 85 & carpet \\
669 & facing & 357 & utensil & 201 & graffiti & 117 & catcher & 83 & slice \\
612 & big & 335 & dish & 167 & foot & 113 & falling & 80 & salad \\
565 & trying & 321 & sink & 163 & couch & 101 & faces & 79 & square \\
474 & sandwich & 317 & three & 160 & silver & 98 & fireplace & 78 & roll \\
\end{tabular} |
|
CR-44831 | \begin{tabular}{ccc}
\hline
Notations & Description & Time (ms) \\
\hline
${T_C}$ & Time cost of encryption & 0.096 \\
${T_{agg}}$ & Time cost of aggregating 200 readings & 2.21 \\
${T_{decAgg}}$ & Time cost of decrypting aggregated readings & 0.135 \\
${T_{DM}}$ & Time cost of public key generation $DW$ & 45.36 \\
${T_{decDW}}$ & Time cost of decrypting to obtain $\bm{rW}$ & 49.63 \\
$T{S_t}$ & Time cost of generating timestamp & 0.852 \\
${T_{sig}}$ & Time cost of signature operation & 13.18 \\
${T_{versig}}$ & Time cost of the verify signature operation & 127.29 \\
${T_m}$ & Time cost of model detection & 56.03 \\
\hline
\end{tabular} |
|
SE-22997 | \begin{tabular}{lllll}
\hline\hline
program & size(KLOC) & Times(secs) & Bug Count & False Count \\
\hline
gcc & 230.4 & 213.1 & 36 & 6 \\
ammp & 13.4 & 10.4 & 23 & 5 \\
bash & 100.0 & 90.1 & 16 & 3 \\
mesa & 61.3 & 48.6 & 9 & 8 \\
cluster & 10.7 & 9.5 & 12 & 4 \\
openCV & 794.6 & 756.8 & 74 & 11 \\
bitcoin & 94.4 & 78.7 & 22 & 7 \\
Total & 1304.8 & 1257.9 & 192 & 44 \\
\hline\hline
\end{tabular} |
|
CR-29638 | \begin{tabular}{ccccc}
\toprule
Image size & $1024\times 768$ & $1600\times 1200$ & $3240\times 2592$ & $4800\times 4800$ \\
\hline
DCT on laptop GPU & 0.41 ms & 0.79 ms & 3.67 ms & 9.98 ms \\
AES on laptop CPU & 0.19 ms & 0.47 ms & 2.05 ms & 5.87 ms \\
\bottomrule
\end{tabular} |
|
AI-29454 | \begin{tabular}{|p{0.30\textwidth}|p{0.275\textwidth}|p{0.28\textwidth}|}
\hline
\textbf{Dynamic conbditions} & \textbf{Action} & \textbf{Static Conditions} \\ \hline
\makecell*[lt]{$robAt(R1)$} & \makecell*[lt]{$moveTo(R1,R0,D0)$} & \makecell*[lt]{$connected(D0,R0,R1)$} \\ \hline
\makecell*[lt]{$isHeld(K,G)$} & \makecell*[lt]{$pickup(K,G,L0,R0)$} & \makecell*[lt]{$key(K)$ \\ \& $isCard(K)$} \\ \hline
\makecell*[lt]{$isHeld(K,G)$} & \makecell*[lt]{$semi\_e\_isHeld(K,G)$} & \makecell*[lt]{$key(K)$ \\ \& $isCard(K)$ \\ \& $hand(G)$} \\
\hline
\end{tabular} |
|
SE-4760 | \begin{tabular}{lccc}
Action class & Mean LOC & Formula & P(class) \\
\hline
\vspace{0.01in}
{\tt <int> := <[1..20]>} & 0 & $\frac{0.20}{2}$ & 0.100 \\
{\tt <ch> := <['r','w']>} & 0 & $\frac{0.20}{2}$ & 0.100 \\
\vspace{0.03in}
{\tt f(<int>)} & 30 & $\frac{30}{64} \times 0.80$ & 0.375 \\
{\tt g(<int>)} & 20 & $\frac{6+14}{64} \times 0.80$ & 0.250 \\
{\tt h(<ch>)} & 14 & $\frac{14}{64} \times 0.80$ & 0.175 \\
\end{tabular} |
|
CR-12294 | \begin{tabular}{|c|c|c|c|c|}
\hline
Vulnerability & Arbiter & MEM & GNG & AES \\
\hline
Permissions and Privileges & & \checkmark & & \\
\hline
Resource Management & & & & \checkmark \\
\hline
Illegal States \& Transitions & \checkmark/\checkmark & \checkmark & & \\
\hline
Buffer Issues & & \checkmark & & \\
\hline
Information Leakage & \checkmark/\checkmark & & & \\
\hline
Numeric Exceptions & & & \checkmark & \\
\hline
Malicious Implants & & & & \checkmark \\
\hline
\end{tabular} |
|
CR-40148 | \begin{tabular}{llcccccc}
\hline
\textbf{Label} & \textbf{Person} & \textbf{Sex} & \textbf{Language} & \textbf{Length(seconds)} & \textbf{Testing words} & \textbf{Training words} & \textbf{Overlapping words} \\ \hline \hline
User$_1$ & Bill Gates & male & English & 7068 & 179 & 12593 & 19 \\ \hline
User$_2$ & Feifei Li & female & English & 7120 & 182 & 17626 & 15 \\ \hline
User$_3$ & Pony Ma & male & Chinese & 5180 & 215 & 28554 & 20 \\ \hline
User$_4$ & Jane Goodall & female & English & 7484 & 188 & 11339 & 23 \\ \hline
User$_5$ & Jiaying Ye & female & Chinese & 9032 & 188 & 11339 & 16 \\ \hline
User$_6$ & Mingzhu Dong & female & Chinese & 5428 & 234 & 18709 & 22 \\ \hline
User$_7$ & Steve Job & male & English & 14836 & 190 & 37751 & 17 \\ \hline
User$_8$ & Yansong Bai & male & Chinese & 6792 & 251 & 27317 & 22 \\ \hline
User$_9$ & Anne Hathaway & female & English & 60 & 197 & * & 21 \\ \hline
User$_{10}$ & Elon Musk & male & English & 60 & 156 & * & 17 \\ \hline
User$_{11}$ & Mark Zuckerberg & male & English & 60 & 177 & * & 15 \\ \hline
User$_{12}$ & Oprah Winfrey & female & English & 60 & 167 & * & 18 \\ \hline
User$_{13}$ & Lan Yang & female & Chinese & 60 & 289 & * & 25 \\ \hline
User$_{14}$ & Minhong Yu & male & Chinese & 60 & 199 & * & 17 \\ \hline
User$_{15}$ & Robin Li & male & Chinese & 60 & 244 & * & 20 \\ \hline
User$_{16}$ & Yingtai Long & female & Chinese & 60 & 198 & * & 18 \\ \hline
\end{tabular} |
|
AI-6294 | \begin{tabular}{|l|l|l|}
\hline
\multirow{2}{*}{Pretraining} & \# of Stays in Stay Level Pretraining & 100563 \\
& \# of Admissions in Admission Level Pretraining & 99000 \\ \hline
\multirow{2}{*}{Stay Level Tasks} & \# of Stays in ARF Prediction & 4205 \\
& \# of Stays in Shock Prediction & 6190 \\ \hline
Admission Level Tasks & \# of Admissions in Readmission Prediction & 33179 \\ \hline
\multirow{4}{*}{Patient Level Tasks} & \# of Patients in Heart Failure Prediction & 12320 \\
& \# of Patients in COPD Prediction & 29256 \\
& \# of Patients in Amnesia Prediction & 11928 \\
& \# of Patients in Heart Failure Prediction (MIMIC-III) & 7522 \\ \hline
\end{tabular} |
|
AI-935 | \begin{tabular}{lrrrrrr}
\hline
& Sum Sq & Mean Sq & NumDF & DenDF & F value & Pr($>$F) \\
\hline
transparency & 0.10 & 0.10 & 1.00 & 994.00 & 5.83 & 0.0159 \\
num\_features & 0.04 & 0.04 & 1.00 & 994.00 & 2.15 & 0.1427 \\
transparency:num\_features & 0.00 & 0.00 & 1.00 & 994.00 & 0.06 & 0.8143 \\
\hline
\end{tabular} |
|
AI-27264 | \begin{tabular}{l|l|l|l|l}
\toprule
\makecell[l]{Engage- \\ment} & \makecell[l]{Question\\difficulty} & $P_{Rresp}$ & $P_{IRresp}$ & $P_{Nresp}$ \\
\midrule
\multirow{3}{*}{High} & Easy & $1$ & $0$ & $0$ \\
{} & Moderate & $1$ & $0$ & $0$ \\
{} & Difficult & $1$ & $0$ & $0$ \\
\hline
\multirow{3}{*}{Medium} & Easy & $0.95$ & $0$ & $0.05$ \\
{} & Moderate & $0.92$ & $0$ & $0.08$ \\
{} & Difficult & $0.90$ & $0$ & $0.10$ \\
\hline
\multirow{3}{*}{Low} & Easy & $0.90$ & $0$ & $0.10$ \\
{} & Moderate & $0.88$ & $0$ & $0.12$ \\
{} & Difficult & $0.85$ & $0$ & $0.15$ \\
\bottomrule
\end{tabular} |
|
CR-40936 | \begin{tabular}{|cc|}
\hline
\multicolumn{2}{|c|}{A2Y} \\
\hline
local & cloud \\
\hline \hline
0 & 0 \\
\hline
\end{tabular} |
|
SE-6501 | \begin{tabular}{@{}p{65mm}@{}}
\emph{Project: avajs/ava; Issue: $1400$} \\
``... There is already a PR for this though, thanks
to @tdeschryver ...''
\end{tabular} |
|
CR-10565 | \begin{tabular}{lrrrrrr}
\multicolumn{1}{c}{Dataset} & \multicolumn{3}{c}{CIFAR-10} & \multicolumn{3}{c}{CIFAR-100} \\
\cmidrule(lr){1-1}
\cmidrule(lr){2-4}
\cmidrule(lr){5-7}
Defense level & No Def. & Mixup+MMD & Mem-Guard & No Def. & Mixup+MMD & Mem-Guard \\
\cmidrule(lr){1-4}
\cmidrule(lr){4-7}
Training accuracy & 0.994 & {\bf 0.881} & 0.997 & 0.995 & {\bf 0.665} & 0.979 \\
Testing accuracy & 0.761 & {\bf 0.765} & 0.762 & 0.326 & {\bf 0.337} & {\bf 0.338} \\
\cmidrule(lr){1-4}
\cmidrule(lr){4-7}
\textbf{Generalization gap} & 0.232 & {\bf 0.116} & 0.235 & 0.669 & {\bf 0.328} & 0.641 \\
\textbf{Largest attack advantage} & 0.166 & {\bf 0.067} & 0.113 & 0.356 & {\bf 0.166} & 0.324 \\
\textbf{Baseline attack advantage} & 0.116 & {\bf 0.067} & 0.112 & 0.333 & {\bf 0.166} & 0.324 \\
\cmidrule(lr){1-4}
\cmidrule(lr){4-7}
Global-Probability attack advantage & 0.156 & {\bf 0.067} & 0.112 & 0.356 & {\bf 0.166} & 0.320 \\
Global-Loss attack advantage & 0.166 & {\bf 0.056} & 0.113 & 0.356 & {\bf 0.155} & 0.319 \\
Global-TopOne attack advantage & 0.120 & 0.049 & {\bf 0.028} & 0.249 & 0.103 & {\bf 0.093} \\
Global-TopThree attack advantage & 0.140 & 0.052 & {\bf 0.027} & 0.273 & 0.104 & {\bf 0.063} \\
Class-Vector attack advantage & 0.137 & {\bf 0.054} & 0.113 & 0.320 & {\bf 0.115} & 0.316 \\
\bottomrule
\end{tabular} |
|
PL-1297 | \begin{tabular}{@{}p{7em}cccccp{6em}@{}}
Unrelated & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & $\bigcirc$ & Related \\
\end{tabular} |
|
AI-522 | \begin{tabular}{|l|c|c|c|}
\hline
Variant & hit@30 & Mean Rank & Mean Percentile\tabularnewline
\hline
\hline
Original & 0.368 & 1298.44 & 92.70\tabularnewline
\hline
Relation-weighted & \textbf{0.375} & \textbf{1186.81} & \textbf{93.32}\tabularnewline
\hline
\end{tabular} |
|
CV-2727 | \begin{tabular}{lccclll}
& \multicolumn{3}{c}{Dice} & \multicolumn{3}{c}{HD95} \\
\multicolumn{1}{c}{} & enh. & whole & core & enh. & whole & core \\
\hline
Isensee et al. (2017) & 70.69 & 89.51 & 82.76 & 6.24 & 6.04 & 6.95 \\
baseline & 73.43 & 89.76 & 82.17 & 4.88 & 5.86 & 7.11 \\
baseline + reg & 73.81 & 90.02 & 82.87 & 5.01 & 6.26 & 6.48 \\
baseline + reg + cotr (dec) & 75.94 & 91.33 & 85.28 & 4.29 & 4.82 & 5.05 \\
baseline + reg + cotr (dec) + post & \textbf{78.68} & 91.33 & 85.28 & 3.49 & \textbf{4.82} & \textbf{5.05} \\
baseline + reg + cotr (dec) + post + DC\&CE & 78.62 & \textbf{91.75} & \textbf{85.69} & \textbf{2.84} & 4.88 & 5.11 \\
baseline + reg + cotr (inst) + post + DC\&CE & 76.32 & 90.35 & 84.36 & 3.74 & 5.64 & 5.98 \\
baseline + reg + post + DC\&CE & 76.78 & 90.30 & 83.55 & 3.66 & 5.36 & 6.03
\end{tabular} |
|
CR-36686 | \begin{tabular}{ccccc}
\toprule
\textbf{} & \textbf{Time(s)} & \textbf{Space(KB)} & \textbf{$T_{total}\Delta$Acc} & \textbf{$T_{total}\Delta$Loss} \\ \midrule
$^v$BN & \cellcolor[HTML]{67000d}\color{white}263.5 & \cellcolor[HTML]{fff5f0}4 & \cellcolor[HTML]{f7f6f6} -0.53 & \cellcolor[HTML]{f7f5f4} 0.02 \\
$^v$ME & \cellcolor[HTML]{fff5f0}90.2 & \cellcolor[HTML]{fdccb8}12 & \cellcolor[HTML]{f8f4f2} -1.64 & \cellcolor[HTML]{f8f1ed} 0.08 \\
$^v$BF & \cellcolor[HTML]{fcab8f}142.1 & \cellcolor[HTML]{67000d}\color{white}1233 & \cellcolor[HTML]{f8f4f2} -1.54 & \cellcolor[HTML]{f8f2ef} 0.06 \\
\textbf{$^v$EM} & \cellcolor[HTML]{ffece3}99.6 & \cellcolor[HTML]{fff5f0}4 & \cellcolor[HTML]{f8f2ef} -2.82 & \cellcolor[HTML]{fee8dd} 0.15 \\
\textbf{$^v$FM} & \cellcolor[HTML]{fbfaf9}10.26 & \cellcolor[HTML]{fff5f0}4 & \cellcolor[HTML]{f8f2ef} -2.73 & \cellcolor[HTML]{fee8dd} 0.13 \\ \bottomrule
\end{tabular} |
|
AI-39429 | \begin{tabular}{ccc}
\toprule
\multicolumn{1}{c}{\multirow{1}[1]{*}{\textbf{Shorthand}}} & \multirow{1}[1]{*}{$\mathcal{T}_\text{train}$} & \multicolumn{1}{c}{\multirow{1}[1]{*}{$\mathcal{T}_\text{test}$}} \\
\midrule
\textit{random} & $100$ random & $20$ random \\
\textit{non-cls} & $35$ non-cls. & $42$ non-cls.($\mathcal{T}_\text{test}^\text{in}$) / $43$ cls.($\mathcal{T}_\text{test}^\text{out}$) \\
\textit{cls} & $35$ cls. & $8$ cls.($\mathcal{T}_\text{test}^\text{in}$) / $77$ non-cls.($\mathcal{T}_\text{test}^\text{out}$) \\
\bottomrule
\end{tabular} |
|
AI-24546 | \begin{tabular}{l|c|c|c}
\hline
& $P_1$ & $P_2$ & $P_3$ \\
\hline
Accuracy & 99.6 & 99.6 & 100 \\
\hline
\end{tabular} |
|
CR-46597 | \begin{tabular}{|c|c|c|c|c|c|}
\hline
& ANN & SVM & NBC & Random Forest & Average \\
\hline
Precision & 0.9985 & 0.9833 & 0.9937 & \textbf{0.9987} & 0.9936 \\
\hline
Recall & 0.9112 & \textbf{0.9339} & 0.8537 & 0.9084 & 0.9018 \\
\hline
F1-Score & 0.9529 & \textbf{0.9579} & 0.9185 & 0.9514 & 0.9452 \\
\hline
\end{tabular} |
|
SE-25170 | \begin{tabular}[l]{@{}l@{}}\textit{``Promotingwomentoseniorjobsandleadershipwouldhelpyoungertalentstoidentify}\\\textit{themselveswiththecompany,givingthemconfidenceandmoreprospectsofcontinuingtheir}\\\textit{careerinthecompany"}(S65)
\end{tabular} |
|
PL-1837 | \begin{tabular}{lr}
\toprule
\textbf{Category} & \textbf{\#Apps Studied} \\ \midrule
Banking & 6 \\
Business & 10 \\
Education & 8 \\
Entertainment & 16 \\
Health & 10 \\
Online Payments & 25 \\
Music & 13 \\
News & 19 \\
Shopping & 17 \\
Social & 11 \\
Top Grossing & 30 \\
Top Apps & 22 \\
Travel & 9 \\ \midrule
Total & 196 \\ \bottomrule
\end{tabular} |
|
SE-23461 | \begin{tabular}{lcccccccccc}
& KNN & LNR & SVR & RFT & CART & RDCART & GSCART & FLASH & DECART & ASKL \\
commit & \cellcolor[HTML]{F0F0F0}160\
contributor & \cellcolor[HTML]{EFEFEF}102\
openPR & \cellcolor[HTML]{F0F0F0}151\
closePR & \cellcolor[HTML]{EFEFEF}100\
openISSUE & \cellcolor[HTML]{F0F0F0}150\
closedISSUE & \cellcolor[HTML]{EFEFEF}147\
\end{tabular} |
|
CR-21878 | \begin{tabular}{rcl}
\hline
$U_{i}$ & & $S$ \\
\hline
Chooses $b$ as random number and inputs $ID_{i}$, $PW_{i}$ \& $b$ & & \\
Computes $PWB_{i}=h(PW_{i}\oplus b)$ & $\xrightarrow{PWB_{i}, ID_{i}}$ & Computes \\
& & $Q_{i}=h(ID_{i}\Vert x)\oplus PWB_{i}$ \\
& & $R_{i}=h(PWB_{i}\Vert ID_{i})$ \\
Stores random number $b$ on smart card and smart & & Stores ($Q_{i}$,$R_{i}$ \& $Q_{i}\oplus PWB_{i}$) in $DBS$ \\
card contains [$R_{i}$, $Q_{i}$ \& $b$] & $\xleftarrow{[R_{i}, Q_{i}]}$ & Issues a smart card containing [$R_{i}$, $Q_{i}$] \\
\hline
\end{tabular} |
|
CV-2811 | \begin{tabular}{|l||r|r|r||r|}
\hline
{} & {\em ss} & {\em gs} & {\em noa} & Total \\
\hline\hline
Training & $648$ & $2\rm{,}002$ & $7\rm{,}000$ & $ 9\rm{,}650$ \\
\hline
Testing & $237$ & $618$ & $2\rm{,}828$ & $ 3\rm{,}683$ \\
\hline\hline
Total & $885$ & $2\rm{,}620$ & $9\rm{,}828$ & $13\rm{,}333$ \\
\hline
\end{tabular} |
|
CV-24619 | \begin{tabular}{|l|c|}
\hline
Method & Accuracy \\
\hline\hline
Chance & 0.1 \\
Analogous Attr & 1.4 \\
Red wine & 13.1 \\
Attribute as Operator & 14.2 \\
VisProd NN & 13.9 \\
Label Embedded+ & 14.8 \\
Our & \textbf{15.2} \\
\hline
\end{tabular} |
|
CR-43677 | \begin{tabular}{|l|l|l|l|}
\hline
\multicolumn{2}{|c|}{\textbf{Sample of Secret Set}} & \multicolumn{2}{|c|}{\textbf{Sample of Camouflaged Training Set}} \\\hline
\multicolumn{1}{|c|}{\textbf{Class}} & \multicolumn{1}{|c|}{\textbf{Article}} & \multicolumn{1}{|c|}{\textbf{Class}} & \multicolumn{1}{|c|}{\textbf{Article}} \\\hline
Christianity & $\ldots$Christ that often causes christians to be very & Baseball & $\ldots$The Angels won their home opener against the \\
& critical of themselves and other christinas. We$\ldots$ & & Brewers today before 33,000+
at Anaheim Stadium$\ldots$ \\\cline{2-2}\cline{4-4}
& $\ldots$I've heard it said that the accounts we have & & $\ldots$ interested in finding out how I might be able \\
& of Christs life and ministry in the Gospels were$\ldots$ & & to get two tickets for the All Star game in Baltimore$\ldots$ \\\hline
Atheism & $\ldots$This article attempts to provide a general & Hockey & $\ldots$ user and not necessarily those could anyone post \\
& introduction to atheism. Whilst I
have tried to be$\ldots$ & & the game summary for the Sabres-Bruins game.$\ldots$ \\\cline{2-2}\cline{4-4}
& $\ldots$Science is wonderful at answering most of our & & $\ldots$Tuesday, and the isles/caps game is going into
\\
& questions. I'm not the type
to question scientific$\ldots$ & & overtime. what does ESPN do. Tom Mees says, "we$\ldots$ \\\hline
\end{tabular} |
|
CV-332 | \begin{tabular}{|c|c|cc|}
\hline
& & Surface & Joint \\
Output & Method & Error & Error \\
\hline
\multirow{3}{*}{P} & Tung \textit{et al.} & 74.5 & 64.4 \\
& Pavlakos \textit{et al.} & 151.5 & - \\
& SMPLR & 75.4 & 55.8 \\
\hline
V & BodyNet & 65.8 & - \\
\hline
\multirow{2}{*}{S} & Baseline & 101 & 85.7 \\
& HMNet[subsampled] & 86.9 & 72.4 \\
& HMNet & 86.6 & 71.9 \\
& HMNetOracle & \textbf{63.5} & \textbf{49.1} \\
\hline
\end{tabular} |
|
AI-24907 | \begin{tabular}{c|ccc}
& \multicolumn{2}{c}{Evaluation Level} \\
Game & 1 & 3 \\ \hline
Clusters & 0.00 $\pm$ 0.00 & 0.7 $\pm$ 0.46 \\
Cook Me Pasta & 4.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
Bait & -0.09 $\pm$ 0.29 & 1.78 $\pm$ 0.42 \\
Sokoban 2 & 0.00 $\pm$ 0.00 & 0.00 $\pm$ 0.00 \\
Zen Puzzle & 23.00 $\pm$ 0.00 & 10.9 $\pm$ 5.01 \\
Labyrinth & 0.00 $\pm$ 0.00 & \textbf{1.00} $\pm$ 0.00 \\
\end{tabular} |
|
CV-19981 | \begin{tabular}{c|c|c}
\hline
& w/o & w/ \\
& noise module & noise module \\
\hline\hline
DSQ & 84.11 & \textbf{84.46} \\
BNN+ & 84.59 & \textbf{84.87} \\
FDA-BNN & 85.83 & \textbf{86.20} \\
\hline
\end{tabular} |
|
CR-12610 | \begin{tabular}{|c|c|c|c|c|}
\hline
solution & abbreviation & sparsification & perturbation & budget \\
\hline
non-private & NP & full/random/topk & - & $\infty$ \\
\hline
flat & PM/HM/Duchi & random sampling & $\epsilon^{\prime}$ & $\epsilon^{\prime}$ \\
\hline
compressed & -RP & random projection & $\epsilon^{\prime}$ & $\epsilon^{\prime}$ \\
\hline
two-stage & EXP/PE/PS- & $\epsilon_1=\mu \cdot \epsilon^{\prime}$ & $\epsilon_2=\epsilon^{\prime}-\epsilon_1$ & $\epsilon^{\prime}$ \\
\hline
\end{tabular} |
|
AI-16366 | \begin{tabular}{|l|l|l|l|l|}
\hline
\textbf{Algorithm} & \textbf{F1 Score} & \textbf{Precision} & \textbf{Recall} & \textbf{AUC} \\ \hline
\textit{RECON} & 0.61 & 0.56 & 0.68 & 0.51 \\ \hline
\textit{ImRec} & 0.71 & 0.60 & 0.88 & 0.65 \\ \hline
\textit{TIRR} & 0.87 & 0.86 & 0.88 & 0.91 \\ \hline
\end{tabular} |
|
AI-19935 | \begin{tabular}{ll}
\hline
\hline
\textbf{dialogue His.} & what is your favorite food ? [SEP] ice cream . \\
\textbf{Gold Resp.} & what flavor ? \\
\hline
TA-Seq2Seq & what kind of ice cream ? \\
THRED & what kind of ice cream ? \\
C-Trans-ED & ice cream is the best food i have ever eaten \\
C-Trans-Dec & i 'm not sure if you 're joking but i 'm not sure if you 're serious . \\
BERT & i do n't like ice cream . \\
Ours & i do n't like ice cream . it 's coarse and irritating and it gets everywhere . \\
\quad Two-Step FT & i 've never had ice cream . what 's it like ? \\
\quad w/o ctext & i 've never had ice cream . what 's it taste like ? \\
\quad w/o tfidf & what kind of ice cream do you like ? \\
\hline
\hline
C-Trans-ED & ice cream is the best food i 've ever seem . \\
C-Trans-Dec & i 've never had ice cream . \\
BERT & i 've never had ice cream . \\
Ours & i do n ' t like ice cream . \\
\quad Two-Step FT & i like ice cream , but i do n ' t like it . \\
\quad w/o ctext & i 've never had ice cream , but it 's so good . \\
\quad w/o tfidf & i ' ve never had ice cream . \\
\hline
\end{tabular} |
|
SE-9694 | \begin{tabular}{lll}
\hline
Paper & Context & Type of study \\ \hline
Abdullah et al. & Compliance management & Case study \\
Conmy and Paige & Safety standards (avionics) & Educated opinion \\
Boella et al. & Business processes & Educated opinion \\
Ghanavati et al. & Business processes & Experience \\
Nekvi and Madhavji & Railway regulations & Case study
\\ \hline
\end{tabular} |
|
CV-3840 | \begin{tabular}{c|cccc|cccc|cccc|cccc}
Model & \multicolumn{4}{c|}{OMP Models} & \multicolumn{4}{c}{25 mm} & \multicolumn{4}{c}{50 mm} & \multicolumn{4}{c}{ 100 mm} \\
\hline
ZV & 42 & 100 & 149 & 188 & 53 & 106 & 154 & 191 & \textbf{66} & 118 & 164 & 199 & 106 & 151 & 187 & 223 \\
RNN & 41 & 93 & 135 & 169 & 52 & 99 & 141 & 174 & 69 & 113 & 151 & 183 & \textbf{105} & 142 & 181 & 208 \\
C-RNN+OMP+LI & \textbf{40} & \textbf{81} & \textbf{109} & \textbf{129} & \textbf{51} & \textbf{88} & \textbf{115} & \textbf{134} & 67 & \textbf{100} & \textbf{126} & \textbf{144} & 106 & \textbf{132} & \textbf{156} & \textbf{172} \\ \hline
\end{tabular} |
|
CR-30622 | \begin{tabular}{c|c|c|c|c}
\hline
& $\delta$-reweight & $\gamma$-reweight & Soft($\delta=1.0$) & Soft($\delta=2.0$) \\
\midrule[0.1pt]
$\epsilon=0.0$ & $0.9997 \pm 0.0005$ & $0.9936 \pm 0.0016$ & $0.8446 \pm 0.0069$ & $0.9705 \pm 0.0030$ \\
$\epsilon=0.1$ & $0.9569 \pm 0.0021$ & $0.9297 \pm 0.0030$ & $0.7871 \pm 0.0081$ & $0.9239 \pm 0.0070$ \\
$\epsilon=0.2$ & $0.8881 \pm 0.0043$ & $0.8391 \pm 0.0018$ & $0.7339 \pm 0.0110$ & $0.8680 \pm 0.0088$ \\
$\epsilon=0.3$ & $0.8152 \pm 0.0059$ & $0.7574 \pm 0.0054$ & $0.6741 \pm 0.0119$ & $0.7956 \pm 0.0110$ \\
$\epsilon=0.4$ & $0.7487 \pm 0.0056$ & $0.6942 \pm 0.0107$ & $0.6334 \pm 0.0084$ & $0.7312 \pm 0.0121$ \\
$\epsilon=0.5$ & $0.6851 \pm 0.0067$ & $0.6502 \pm 0.0068$ & $0.5859 \pm 0.0079$ & $0.6561 \pm 0.0124$ \\
\hline
\end{tabular} |
|
SE-15205 | \begin{tabular}{lrrl}
\hline
{\bfseries Method} & {\bfseries Mean Recall} & {\bfseries Dunn's test Rank} & {\bfseries Comments} \\
\hline
\hline
Proportion Moving Window & 0.84 & 1 & \\
Proportion Cold Start & 0.82 & 1 & \\
Proportion Increment & 0.81 & 1.5 & Significantly lower than Proportion Moving Window \\
SZZ\_B+ & 0.71 & 2 & \\
SZZ\_B & 0.71 & 2 & \\
SZZ\_RA & 0.70 & 2 & \\
SZZ\_U & 0.70 & 2 & \\
SZZ\_RA+ & 0.70 & 2 & \\
SZZ\_U+ & 0.70 & 2 & \\
Simple & 0.61 & 3 & \\
\hline
\hline
\end{tabular} |
|
CR-55836 | \begin{tabular}[c]{@{}l@{}}1.Thestrategybasedonknowledgeextractionwasusedtoovercome\\thecommunicationbottleneckinFL.\\2.Thearticleproducedsatisfactoryresultsonthreedifferent\\medicaldatasets.\end{tabular} |
|
CR-21138 | \begin{tabular}{|l|r|}
\cline{2-2}
\multicolumn{1}{c|}{\ } & Mean ($\pm$ Std) \\ \hline \hline
Capacity per Token & 4.41 ($\pm$ 0.78) \\ \hline
Encoded Expansion & 8.13 ($\pm$ 2.12) \\ \hline
Plaintext Bits per Covertext Bits & 0.11 ($\pm$ 0.02) \\ \hline
Median Sender-side Time & 5.21 \\ \hline
Sentinel Value Check Time & 0.13 ($\pm$ 0.15) \\ \hline
Median Receiver-side Time & 5.15 \\ \hline
\addlinespace[0.2cm] \hline
Tokenizer Decoding & 6.99 ($\pm$ 4.68) \\ \hline
Backtracking Rate Overall & 0.125 \\ \hline
Path Decoding Rate $N=5$ & 0.961 \\ \hline
Path Decoding Time $N=5$ & 54.75. ($\pm$ 21.15) \\ \hline
Path Decoding Rate $N=10$ & 0.986 \\ \hline
Path Decoding Time $N=10$ & 142.18. ($\pm$ 14.07) \\ \hline
Path Decoding Rate $N=40$ & 1 \\ \hline
Path Decoding Time $N=40$ & 496.32 ($\pm$ 51.44) \\ \hline
Overall Mean Receiver-side Time & 20.60 ($\pm$ 57.58) \\ \hline
Receiver-side Failure Rate & 0.00 \\ \hline
\end{tabular} |
|
AI-18467 | \begin{tabular}{|c|c|c|c|}
\hline
\textbf{Algorithm} & \textbf{Rounds} & \textbf{MNIST} & \textbf{CIFAR-10} \\
\hline
Genetic CFL & 10 & 97.99 & 76.88 \\
\hline
Byzantine Robustness of CFL & 200 & 97.4 & 75.3 \\
\hline
FedZip & 20 & 98.03 & - \\
\hline
Iterative federated clustering & - & 95.25 & 81.51 \\
\hline
\end{tabular} |
|
CL-885 | \begin{tabular}{lcccc}
\hline
Features & Category & $P$ & $R$ & $F1$ \\
\hline
\multirow {2} {*} {all} & $I$ & 66.93 & \textbf{77.32} & \textbf{71.75} \\
& $NI$ & \textbf{73.13} & 61.78 & \textbf{66.97} \\
\hline
\multirow {2} {*} {- tropes} & $I$ & \textbf{67.70} & {48.00} & 56.18 \\
& $NI$ & {59.70} & \textbf{77.09} & \textbf{67.29} \\
\hline
\multirow {2} {*} {- MS} & $I$ & 63.59 & \textbf{78.09} & 70.10 \\
& $NI$ & \textbf{71.59} & {55.27} & 62.38 \\
\hline
\multirow {2} {*} {- typography} & $I$ & 57.30 & 77.95 & 66.05 \\
& $NI$ & 65.49 & 41.86 & 51.07 \\
\hline
\end{tabular} |
|
AI-22501 | \begin{tabular}{|c|c|c|c|c|}
\hline
\textbf{Contr.} & \textbf{Domain} & \textbf{Application} & \textbf{Focus} & \textbf{Value} (main) \\
\hline
\hline
& Business & Decision Support System & Conceptual & Interoperability \\
& N\textbackslash A & N\textbackslash A & Conceptual & Interoperability \\
& N\textbackslash A & N\textbackslash A & Conceptual & Interoperability \\
& Healthcare & Explainable models & Conceptual & Explainability \\
& N\textbackslash A & Explainable models & Conceptual & Explainability \\
& Education & System Thinking & Conceptual & System Engineering \\
& Smart Systems & Ambient Assisting Living & Conceptual & System Engineering \\
& N\textbackslash A & N\textbackslash A & Conceptual & Explainability \\
& N\textbackslash A & Collective Intelligence & Conceptual & Quality and Accuracy \\
& N\textbackslash A & Knowledge Graph & Conceptual & Explainability \\
& N\textbackslash A & Collective Intelligence & Conceptual & Quality and Accuracy \\
& N\textbackslash A & N\textbackslash A & Conceptual & Quality and Accuracy \\
& N\textbackslash A & N\textbackslash A & Conceptual & System Engineering \\
& N\textbackslash A & N\textbackslash A & Conceptual & System Engineering \\
\hline
\hline
\end{tabular} |
|
SE-23702 | \begin{tabular}{ccrrrc}
\toprule
& sub- & fail-only & pass-only & fail \& & failure \\
signature & pattern & events & events & pass & strings* \\
\midrule
A & 1 & 1 & 0 & 0 & yes \\
A & 2 & 2 & 0 & 0 & no \\
B & 1 & 2 & 0 & 0 & yes \\
C & 1 & 21 & 0 & 0 & yes \\
C & 2 & 21 & 0 & 0 & yes \\
D & 1 & 4 & 0 & 0 & yes \\
\textbf{D$^{\#}$} & \textbf{2} & 69 & 267 & 115 & no \\
\textbf{D$^{\#}$} & \textbf{3} & 2 & 10 & 13 & no \\
\textbf{E$^{\#}$} & \textbf{1} & 24 & 239 & 171 & no \\
E & 1 & 1 & 0 & 0 & no \\
E & 2 & 9 & 0 & 0 & no \\
E & 3 & 9 & 0 & 0 & yes \\
E & 4 & 23 & 0 & 0 & yes \\
F & 1 & 19 & 0 & 0 & yes \\
F & 2 & 19 & 0 & 0 & no \\
F & 3 & 19 & 0 & 0 & yes \\
F & 4 & 14 & 0 & 0 & yes \\
G & 1 & 2 & 0 & 0 & yes \\
G & 2 & 1 & 0 & 0 & no \\
G & 3 & 1 & 0 & 0 & no \\
\bottomrule
\multicolumn{6}{l}{* signature contains the lexical patterns 'error', 'fault' or 'fail*'} \\
\multicolumn{6}{l}{$^{\#}$ sub-patterns that were removed to ensure a clean ground truth}
\end{tabular} |
|
AI-1767 | \begin{tabular}{lrrrrrr}
\toprule
\multirow{2}*{Methods} & \multicolumn{3}{c}{CNNDM} & \multicolumn{3}{c}{XSum} \\
\cmidrule(r{4pt}){2-4} \cmidrule{5-7}
~ & IF & RL & FL & IF & RL & FL \\
\midrule
PSP & {\bf 0.500} & {\bf 0.708} & {\bf 0.667} & {\bf 0.217} & {\bf 0.275} & {\bf 0.492} \\
Prompt Tuning & -0.317 & -0.758 & -0.975 & -0.336 & -0.400 & -0.867 \\
Prefix-Tuning & -0.233 & 0.067 & 0.158 & 0.017 & -0.008 & 0.292 \\
Full-Model Tuning & 0.067 & -0.025 & 0.075 & 0.117 & 0.092 & 0.075 \\ \bottomrule
\end{tabular} |
|
CV-28731 | \begin{tabular}{@{\hspace{1mm}}c@{\hspace{9mm}}c@{\hspace{15mm}}c@{\hspace{16mm}}c@{\hspace{13mm}}c@{\hspace{13mm}}c}
(a) ground-truth & (b) RPM-HTB & (c) Go-ICP & (d) FRS & (e) TEASER++ & (f) GORE
\end{tabular} |
|
CR-28559 | \begin{tabular}{lc}
\hline
\textbf{Command} & \textbf{Output} \\
\hline
\verb|{\c c}| & {\c c} \\
\verb|{\u g}| & {\u g} \\
\verb|{\l}| & {\l} \\
\verb|{\~n}| & {\~n} \\
\verb|{\H o}| & {\H o} \\
\verb|{\v r}| & {\v r} \\
\verb|{\ss}| & {\ss} \\
\hline
\end{tabular} |
|
CV-409 | \begin{tabular}{lccccccc}
\toprule
$D$ & 2 & 8 & 16 & 32 & 64 & 128 & 256 \\
\midrule
Scenes-daytime & 85 & 87 & \textbf{91} & 92 & 92 & 95 & 95 \\
\midrule
Handbags-color & 96.3 & \textbf{99.1} & 99.0 & 99.3 & 98.3 & 98.9 & 98.4 \\
Handbags-texture & 64.2 & 65.2 & 66.4 & \textbf{87.0} & 91.3 & 92.8 & 95.4 \\
\bottomrule
\end{tabular} |
|
AI-19315 | \begin{tabular}{|l|l|}
\hline
$P(x|z_1)$ & 0.1 \\
\hline
$P(x|z_2)$ & 0.4 \\
\hline
$P(x|z_3)$ & 0.5 \\
\hline
$P(x|z_4)$ & 0.7 \\
\hline
\end{tabular} |
|
CR-56856 | \begin{tabular}{|c|c|c|c|c|}
\hline
Candidate Models & DNN1 & DNN2 & DNN3 & VGG-16 \\ \hline
Accuracy & 79.63\
\end{tabular} |
|
CV-27536 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|}
\hline
\multicolumn{2}{|c|}{Branches} & \multicolumn{4}{c|}{Regular Text} & \multicolumn{4}{c|}{Irregular Text} \\ \hline
Attn & CTC & IIIT5K & SVT & IC03 & IC13 & IC15-2077 & IC15-1811 & SVTP & CUTE \\ \hline
& \checkmark & 88.6 & 87.3 & 92.4 & 90.3 & 72.1 & 76.5 & 77.1 & 78.8 \\ \hline
\checkmark & & \textbf{91.0} & 90.6 & 94.3 & 93.3 & \textbf{75.7} & 80.2 & \textbf{84.2} & 82.3 \\ \hline
\checkmark & \checkmark & \textbf{91.0} & \textbf{91.2} & \textbf{96.1} & \textbf{94.5} & 75.1 & \textbf{80.4} & 83.3 & \textbf{83.7} \\ \hline
\end{tabular} |
|
SE-4747 | \begin{tabular}{|lc|c|c|c|c|c|c|c|}
\hline
\multicolumn{1}{|l|}{\textbf{Distance}} & \textbf{Total} & \textbf{TP} & \textbf{FP} & \textbf{FN} & \textbf{P} & \textbf{R} & \textbf{F1} & \textbf{[email protected]} \\ \hline
\multicolumn{1}{|l|}{All} & 139,526 & 134,948 & 711 & 191 & 0.9948 & 0.9986 & 0.9967 & 0.9942 \\ \hline
\multicolumn{2}{|c|}{+OOD} & 134,927 & 20 & 212 & 0.9999 & 0.9984 & 0.9991 & 0.995 \\ \hline \hline
\multicolumn{1}{|l|}{$\le$ 80 m} & 105,588 & 101,320 & 444 & 173 & 0.9956 & 0.9983 & 0.997 & 0.9948 \\ \hline
\multicolumn{2}{|c|}{+OOD} & 101,300 & 13 & 193 & 0.9999 & 0.9981 & 0.999 & 0.995 \\ \hline \hline
\multicolumn{1}{|l|}{$\le$ 50 m} & 61,845 & 57,877 & 186 & 173 & 0.9968 & 0.9970 & 0.9969 & 0.9944 \\ \hline
\multicolumn{2}{|c|}{+OOD} & 57,857 & 13 & 193 & 0.9998 & 0.9967 & 0.9982 & 0.995 \\ \hline
\end{tabular} |
|
AI-33039 | \begin{tabular}{@{~}lll}
\toprule
\textbf{Notation} & \textbf{Desription} \\
\midrule
$\bm{\mathcal{G}}$ & a directed graph \\
$\bm{\mathcal{V}} $ & set of nodes \\
$\bm{\mathcal{E}} $ & set of edges \\
$\bm{\mathcal{S}}$ & set of multiple-paths \\
$\bm{\mathcal{T}}$ & set of single-paths \\
$N$ & number of nodes \\
$E$ & number of edges \\
$K$ & embedding dimension of nodes and relationships \\
$\mathbf{A} \in \mathcal{R}^{N \times N}$ & adjacency matrix of nodes \\
$\mathbf{\Phi}^{\mathcal{V} } \in \mathcal{R}^{N \times K}$ & embedding matrix for all nodes \\
$\mathbf{Z}^{\mathcal{E} } \in \mathcal{R}^{E \times K}$ & embedding matrix for all relationships of node-pairs \\
\bottomrule
\end{tabular} |
|
AI-30452 | \begin{tabular}{lccccc}
\toprule
Symbol & $a_1$ & $a_2$ & $a_3$ & $a_4$ & $a_5$ \\
\midrule
Probability & $0.32$ & $0.08$ & $0.16$ & $0.02$ & $0.42$ \\
$\ell_{a_i}$ & $32$ & $8$ & $16$ & $2$ & $42$ \\
$b_{a_i}$ & $0$ & $32$ & $40$ & $56$ & $58$ \\
\bottomrule
\end{tabular} |
|
CR-48768 | \begin{tabular}{l|l|c}
\toprule[1.5pt]
\multicolumn{2}{l|}{Violated Rules} & \# of Apps\tabularnewline
\midrule[1pt]
\multicolumn{2}{l|}{Rule 1} & 41 \tabularnewline
\hline
\multirow{4}{*}{Rule 2} & Rule 2-1 & 162\tabularnewline
\cline{2-3}
& Rule 2-2 & 67\tabularnewline
\cline{2-3}
& Rule 2-3 & 125\tabularnewline
\cline{2-3}
& Total & 354 \tabularnewline
\hline
\multicolumn{2}{l|}{Rule 3} & 4\tabularnewline
\hline
\multicolumn{2}{l|}{Total} & 399 (out of 2,022) \tabularnewline
\bottomrule[1.5pt]
\end{tabular} |
|
PL-303 | \begin{tabular}{l|r|r|r|r|r}
\toprule
Service type
& \thead{\# Positive \\ responses} & \thead{\# Negative\\ responses} & \thead{\# Total\\ responses} & \thead{\# No\\ responses} & \thead{\# Total\\ PRs}\\
\midrule
Nudge-LT
& 1829 & 2062 & 3891 & 226 & 4117 \\
Nudge-FULL
& 3199 & 882 & 4081 & 302 & 4383 \\
\bottomrule
\end{tabular} |
|
CL-2692 | \begin{tabular}{lcccc}
\toprule
& \multicolumn{2}{c}{Train (sec.)} & \multicolumn{2}{c}{Test (sec.)} \\
Model & Turn & Total & Turn & Total \\
\toprule
\small GLAD & 1.78 & 89 & 2.32 & 76 \\
\small GCE (Ours) & \textbf{1.16} & \textbf{60} & \textbf{1.92} & \textbf{63} \\
\bottomrule
\end{tabular} |
|
SE-23563 | \begin{tabular}{p{5cm}|p{2cm}}
\hline
\textbf{Survey item} & \textbf{Average score} \\
\hline
My understanding of real world problems related to project management was promoted. & 1.4 \\
\hline
My interest on the course objectives and content was aroused. & 1.9 \\
\hline
The importance of the material for my professional activity became clear to me. & 1.5 \\
\hline
Overall, I rate the didactic method (eduScrum) positively. & 1.6 \\
\hline
\end{tabular} |
|
CV-8215 | \begin{tabular}{||c|c|c|c||}
\hline
Method & PSNR & SSIM & CPBD \\ [0.5ex]
\hline\hline
$L_{pix}$ & 25.874 & 0.813 & 0.366 \\
$L_{pix}+L_{adv}$ & 25.951 & 0.814 & 0.373 \\
$L_{pix}+L_{adv}+L_{reg}$ & \textbf{26.153} & \textbf{0.818} & \textbf{0.386} \\
\hline
\end{tabular} |
|
SE-4363 | \begin{tabular}{lrrrrrrrr}
\toprule
\multirow{2}{*}{Models} &
\multicolumn{4}{c}{Accuracy} &
\multicolumn{4}{c}{MRR} \\
\cmidrule(lr){2-5}
\cmidrule(lr){6-9}
& k = 1 & k=3 & k=5 & k=7 & k = 1 & k=3 & k=5 & k=7 \\
\hline
(1) No words or files & 0.02 & 0.08 & 0.13 & 0.16 & 0.01 & 0.04 & 0.05 & 0.06 \\
(2) Words only & 0.21 & 0.30 & 0.32 & 0.34 & 0.21 & 0.25 & 0.26 & 0.32 \\
(3) Files only & 0.29 & 0.69 & 0.73 & 0.76 & 0.29 & 0.48 & 0.49 & 0.50 \\
(4) Words + Files & \textbf{0.49} & \textbf{0.73} & \textbf{0.77} & \textbf{0.80} & \textbf{0.49} & \textbf{0.61} & \textbf{0.68} & \textbf{0.72} \\
\bottomrule
\end{tabular} |
|
CV-5301 | \begin{tabular}{|l|c|c|c|c|c|c|c|}
\hline
\textbf{Model} & \textbf{\# Par. Subnets} & \textbf{48 cores} & \textbf{2 GPUs} & \textbf{4 GPUs} & \textbf{8 GPUs} \\
\hline
\multicolumn{6}{|c|}{\textbf{without multi-rate clocks}} \\
\hline
sequential & 1 & $6.0~(1.0\times)$ & $18.6~(1.0\times)$ & $18.0~(1.0\times)$ & $18.1~(1.0\times)$ \\
\hline
semi-parallel & 5 & $7.9~(1.3\times)$ & $33.8~(1.8\times)$ & $48.7~(2.7\times)$ & $49.2~(2.7\times)$ \\
\hline
parallel & 10 & $7.8~(1.3\times)$ & $33.2~(1.8\times)$ & $46.4~(2.6\times)$ & $48.1~(2.6\times)$ \\
\hline
\multicolumn{6}{|c|}{\textbf{with multi-rate clocks}} \\
\hline
sequential & 1 & $14.3~(2.4\times)$ & $48.2~(2.6\times)$ & $47.1~(2.6\times)$ & $47.1~(2.6\times)$ \\
\hline
semi-parallel & 5 & $18.1~(3.0\times)$ & $63.9~(3.4\times)$ & $90.9~(5.0\times)$ & $90.3~(5.0\times)$ \\
\hline
parallel & 10 & $18.1~(3.0\times)$ & $63.7~(3.4\times)$ & $88.6~(4.9\times)$ & $90.7~(5.0\times)$ \\
\hline
\end{tabular} |
|
CR-11747 | \begin{tabular}{llrrr}
\toprule
Application & Description & Version & LOC & Files \\
\midrule
Nodegoat & Educational & 1.3.0 & 970\,450 & 12\,180 \\
Keystone & CMS & 4.0.0 & 1\,393\,144 & 13\,891 \\
Apostrophe & CMS & 2.0.0 & 774\,203 & 5\,701 \\
Juice-shop & Educational & 8.3.0 & 725\,101 & 7\,449 \\
Mongo-express & DB manager & 0.51.0 & 646\,403 & 7\,378 \\
\bottomrule
\end{tabular} |
|
CV-29652 | \begin{tabular}{|l|l|l|}
\hline Index & Layer Description & Output \\
\hline
1 & Warp($I_R$,$\mathbf{d_L^3}$) - $I_L$ & H x W x 3 \\
2 & concat 1, $I_L$ & H x W x 6 \\
3 & Warp($\mathbf{d_R^3}$, $\mathbf{d_L^3}$) - $\mathbf{d_L^3}$ & H x W x 1 \\
4 & concat 3, $\mathbf{d_L^3}$ & H x W x 2 \\
5 & 3x3 conv on 2, 16 features & H x W x 16 \\
6 & 3x3 conv on 4, 16 features & H x W x 16 \\
7 & concat 5,6 $I_L$ & H x W x 32 \\
\multirow{2}{*}{8-13} & (3x3 conv, residual block) x 6, & \multirow{2}{*}{H x W x 32} \\
& dil rate 1,2,4,8,1,1 & \\
14 & 3x3 conv, 2 features as 14(a) and 14(b) & H x W x 2 \\
15 & $\mathbf{d^r}$: 14(a) + $\mathbf{d_L^3}$ & H x W \\
16 & \textbf{O}: sigmoid on 14(b) & H x W \\
\hline
\end{tabular} |
|
CR-48333 | \begin{tabular}{ccccc}
\toprule
\textbf{Subjects} & \textbf{Version} & \textbf{Format} & \textbf{Size} & \textbf{LoC} \\
\midrule
boringssl @@ & 2016-02-12 & lib & 6.8M & 0.3k \\
freetype @@ & 2017 & font & 6.3M & 0.5k \\
libcxx @@ & 2017-01-27 & lib & 1.9M & 5.0k \\
libxml @@ & libxml2-v2.9.2 & xml & 12M & 15.7k \\
re2 @@ & 2014-12-09 & lib & 5.6M & 0.9k \\
libarch @@ & libarch 2017-01-04 & text & 3.7M & 3.0k \\
size @@ & Binutils-2.34 & elf & 10M & 7.9k \\
readelf -a @@ & Binutils-2.34 & elf & 5.4M & 20.5k \\
objdump -d @@ & Binutils-2.34 & elf & 16M & 5.4k \\
avconv -y -i @@ -f null & Libav-12.3 & mp4 & 77M & 2.9k \\
infotocap @@ & ncurses-6.1 & text & 1.1M & 4.9k \\
pdftotext @@ /dev/null & xpdf-4.02 & pdf & 7.9M & 0.9k \\
tiff2bw @@ /dev/null & tiff-4.1 & tiff & 2.6M & 0.5k \\
ffmpeg -i @@ & ffmpeg-4.1.3 & mp4 & 41M & 4.9k \\
gnuplot @@ & gnuplot-5.5 & text & 8.5M & 1.0k \\
tcpdump -nr @@ & tcpdump-4.9.3 & pcap & 6.3M & 2.6k \\
\bottomrule
\end{tabular} |
|
AI-25582 | \begin{tabular}{|c|c|c|c|}
\hline
\multirow{2}{*}{Embedding} & \multicolumn{3}{c|}{Distance} \\\cline{2-4}
& $\ell_1$ & $\ell_2$ & Cosine \\\hline
FACSNet-CL-F & 47.1 & 47.1 & 40.7 \\\hline
FACSNet-CL-P & 45.3 & 44.2 & 48.3 \\\hline
AFFNet-CL-F & 49.0 & 47.7 & 49.0 \\\hline
AFFNet-CL-P & 52.4 & 51.6 & 53.3 \\\hline
AFFNet-TL & - & 49.6 & - \\\hline
FECNet-16d & - & 81.8 & - \\\hline
\end{tabular} |
|
CR-29305 | \begin{tabular}{@{}l@{}}
Substitute \\
Gap($i$)$\rightarrow$Msg($j$) \\
Msg($j$)$\rightarrow$Gap($i$)
\end{tabular} |
|
CR-6454 | \begin{tabular}{|p{8cm}|}
\Xhline{1pt}
\begin{center}
$\mathtt{\pi}_{\rm S-SIP}$: Functionality of S-SIP
\end{center}
\textbf{Input:} The client (named $P_0$) holds a set of $t$ pairs $(X, S)=\{(x_1, s_1), \cdots, (x_t, s_t)\}$, while the server (named $P_1$) holds dataset of key-values pairs $(Y, G)=\{(y_1, g_1), \cdots, (y_n, g_n)\}$ \\
\textbf{Output:} $P_b$ learns a set $\mathbf{U}_b=\{\left \langle \mathtt{u}_i\right \rangle_b\}_{i\in t}$, where $\left \langle \mathtt{u}_i\right \rangle_b=\left \langle s_ig_j\right \rangle_b$ if $x_i=y_j$ for some $j\in [n]$. otherwise $\left \langle \mathtt{u}_i\right \rangle_b=\left \langle 0\right \rangle_b$. \\
\\\Xhline{1pt}
\end{tabular} |
|
CR-33963 | \begin{tabular}{|c|c|c|}
\hline
\textbf{Parameter} & \textbf{Type} & \textbf{Description} \\
\hline
drcId & bytes32 & Identifier of the DRC \\
\hline
farAvailable & uint256 & FAR (Floor Area Ratio) available for allocation \\
\hline
landCount & uint256 & Total count of sub-divided lands \\
\hline
owner & address & Owner of NFT \\
\hline
lands & mapping & Mapping of land sub-divisions \\
\hline
\end{tabular} |
|
CR-16760 | \begin{tabular}{ll|ll|ll}
\textbf{Training / Testing Set} & $\bm{\sigma^2}$ & \textbf{PP (dev)} & \textbf{PP (test)} & \textbf{PP (dev, large)} & \textbf{PP (test, large)} \\ \hline
Brown / Reddit\_10k & 0 & 1561.20 & 1584.54 & 1652.65 & 1677.42 \\
Reddit\_10k / Reddit\_10k & 0 & 3805.83 & 3787.68 & 1254.48 & 1259.23 \\
fine-tuned / Reddit\_10k & 0.0 & 1035.45 & 1037.81 & 1016.65 & 1019.31 \\
fine-tuned / Reddit\_10k & 0.1 & 1457.94 & 1480.84 & 1604.42 & 1627.56 \\
fine-tuned / Reddit\_10k & 1.1 & 1450.01 & 1473.48 & inf & inf
\end{tabular} |
|
SE-19395 | \begin{tabular}{lc}
\hline
\multicolumn{1}{c}{\textbf{Search engines}} & \textbf{\#non-duplicated search result} \\
\hline
Google search & 495 \\
Medium search & 358 \\\hline
\textbf{Total} & \textbf{853} \\\hline
\hline
\end{tabular} |
|
SE-23962 | \begin{tabular}{l|r|cccc}
\toprule
Model & \# outputs & 256 & 512 & 768 & 1024 \\
\midrule
\multirow{4}{*}{\texttt{CodeParrot-small}}
& 5,000 & 6,666 & 9,080 & 11,041 & 14,031 \\
& 10,000 & 10,627 & 14,655 & 17,664 & 22,243 \\
& 15,000 & 14,015 & 19,444 & 23,863 & 29,133 \\
& 20,000 & 16,966 & 23,574 & 29,204 & 35,363 \\
\midrule
\multirow{4}{*}{\texttt{CodeParrot}}
& 5,000 & 9,785 & 14,645 & 18,325 & 22,570 \\
& 10,000 & 16,062 & 24,345 & 32,519 & 37,448 \\
& 15,000 & 21,560 & 32,666 & 42,853 & 50,127 \\
& 20,000 & 26,420 & 40,125 & 51,059 & 61,787 \\
\bottomrule
\end{tabular} |
|
SE-22543 | \begin{tabular}{lrrr}
\toprule
\multirow{2}{*}{\bf Selection Rule} & \multicolumn{3}{c}{\bf Dataset} \\
\cmidrule{2-4}
& {\bf Spark} & {\bf Hadoop} & {\bf Kibana} \\
\midrule
None & 81 & 92 & 184 \\
Length & 33 & 25 & 77 \\
Length+Content & 59 & 57 & 114 \\
\bottomrule
\end{tabular} |
|
AI-2635 | \begin{tabular}{||ccc||}
\hline
Cube no. & Edge length & Color \\ [0.5ex]
\hline\hline
1 & 5cm & Red \\
\hline
2 & 4cm & Red \\
\hline
3 & 3cm & Red \\
\hline
4 & 2cm & Red \\
\hline
5 & 10cm & Blue \\
\hline
6 & 8cm & Blue \\
\hline
7 & 6cm & Blue \\
\hline
8 & 2cm & Blue \\
\hline
\end{tabular} |
|
CR-44786 | \begin{tabular}{ccccc}
\toprule
Method & Avg. of AUROC & Avg. of F1 score & Std. of AUROC & Std. of F1 score \\
\midrule
STRIP & 0.3930 & 0.5026 & 0.0997 & 0.0027 \\
FreqDetector & 0.7911 & 0.7671 & 0.2235 & 0.2027 \\
\rowcolor[rgb]{ .906, .902, .902} Ours & 0.7749 & 0.7856 & 0.0306 & 0.0336 \\
\bottomrule
\end{tabular} |
|
CR-36658 | \begin{tabular}{|c|l|l|}
\hline
No & Rule & Description \\
\hline
1 & Feature indifference & A value of a feature is indifferent at \\
& & bot and normal user \\
\hline
2 & Feature invariance & Summation of a feature is 0, and \\
& & standard deviation of a feature is 0 \\
& & at bot and normal user, respectively \\
\hline
\end{tabular} |
|
CL-2666 | \begin{tabular}{>{\raggedright\arraybackslash}p{2.7cm}>{\raggedright\arraybackslash}p{2.7cm}|p{0.6cm}}
\hline
External representation & Internal representation & Test BLEU \\
\hline
Plain BPE & Plain BPE & 29.2 \\
Linearized derivation & Linearized derivation & 28.8 \\
\hline
Linearized tree & Plain BPE & 28.9 \\
Plain BPE & Linearized derivation & 28.8 \\
Linearized derivation & Plain BPE & 29.4$^\dagger$ \\
POS/BPE & Plain BPE & 29.3$^\dagger$ \\
Plain BPE & POS/BPE & 29.4$^\dagger$ \\
\end{tabular} |
|
CV-5329 | \begin{tabular}{|p{3.5cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}||p{0.8cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|}
\hline
\multirow{2}{*}{Method} & \multicolumn{4}{c|}{Market1501 $\rightarrow$ DukeMTMC-reID} & \multicolumn{4}{c|}{DukeMTMC-reID $\rightarrow$ Market1501 } \\
\cline{2-9}
\cline{2-9}
& R1 & R5 & R10 & mAP & R1 & R5 & R10 & mAP \\
\hline
Direct Transfer & 42.4 & 56.5 & 63.2 & 23.0 & 52.0 & 70.2 & 76.5 & 22.0 \\
CycleGAN & 44.1 & 58.6 & 65.0 & 23.6 & 55.2 & 72.8 & 79.4 & 23.2 \\
PTGAN & 27.4 & - & 50.7 & - & 38.6 & - & 66.1 & - \\
SPGAN & 41.1 & 56.6 & 63.0 & 22.3 & 51.5 & 70.1 & 76.8 & 22.8 \\
ATNet & 45.1 & 59.5 & 64.2 & 24.9 & 55.7 & 73.2 & 79.4 & 25.6 \\
M2M-GAN & 49.6 & - & - & 26.1 & 57.5 & - & - & 26.8 \\
CR-GAN & 52.2 & - & - & 30.0 & 59.6 & - & - & 29.6 \\
\hline
EDAAN with Triplet & 55.2 & 68.0 & 72.6 & 33.5 & 62.3 & 81.8 & 84.0 & 32.7 \\
EDAAN with Quartet & \textbf{57.8} & \textbf{72.2} & \textbf{78.3} & \textbf{39.6} & \textbf{64.5} & \textbf{83.0} & \textbf{86.3} & \textbf{35.4} \\
\hline
\end{tabular} |
|
AI-11117 | \begin{tabular}{ccccccccc}
\hline
Dataset & level1 & level2 & level3 & level4 & level5 & level6 & level7 & level8 \\
\hline
RCV1 & 236334 & 20523 & 11850 & 23211 & - & - & - & - \\
NYT & 15161 & 2923 & 1160 & 842 & 1066 & 925 & 992 & 1460 \\
WOS & 6712 & 351 & - & - & - & - & - & - \\
\hline
\end{tabular} |
|
CR-46823 | \begin{tabular}{|l||p{1.5cm}|p{1.5cm}|p{1.25cm}||p{1.5cm}|p{1.5cm}|p{1.25cm}|}
\hline
~ & \multicolumn{3}{c||}{TCP} & \multicolumn{3}{c|}{DCCP} \\
~ & Reported Attacks & Interesting \newline (Off-path) Attacks & Unique Attacks & Reported Attacks & Interesting \newline (Off-path) Attacks & Unique Attacks \\
\hline
Random & 996 & 0 & 0 & 992 & 0 & 0 \\
Manual & 219 & 63 & 5 & 209 & 44 & 2 \\
NLP-based & 220 & 69 & 5 & 254 & 47 & 2 \\
\hline
\end{tabular} |
|
CR-8746 | \begin{tabular}[c]{@{}l@{}}CopywritingTranslations,SocialMediaMarketingServices,\\OptimizationPromotionandAudit\end{tabular} |
|
CR-7160 | \begin{tabular}{cccccccccc}
\toprule
\multirow{2}{*}{Data} & \multirow{2}{*}{Measures} & \multicolumn{3}{c}{\texttt{CFD}} & \multicolumn{3}{c}{\texttt{CFD LRT}} \\
\cmidrule(lr){3-8}
& {} & \texttt{SCFE} & \texttt{GS} & \texttt{CCHVAE} & \texttt{SCFE} & \texttt{GS} & \texttt{CCHVAE} \\
\midrule
\multirow{4}{*}{A} & AUC & 0.4971 & 0.5038 & 0.5008 & 0.4988 & \textbf{0.5103 } & 0.5066 \\
& BA & 0.5115 & 0.5125 & 0.5056 & \textbf{0.5132} & 0.5098 & 0.5176 \\
& TPR (0.1) & 0.1039 & 0.1020 & 0.1058 & 0.1010 & 0.1043 & \textbf{0.1298} \\
& TPR (0.01) & 0.0121 & 0.0097 & 0.0157 & \textbf{0.0158} & 0.0095 & 0.0134 \\
\midrule
\multirow{4}{*}{H} & AUC & 0.5887 & 0.5410 & 0.4874 & 0.5829 & 0.5027 & \textbf{0.6789} \\
& BA & 0.5904 & 0.5404 & 0.5473 & 0.5924 & 0.5326 & \textbf{0.6389} \\
& TPR (0.1) & 0.1130 & 0.1223 & 0.0863 & 0.1106 & 0.1142 & \textbf{0.2635} \\
& TPR (0.01) & 0.0155 & 0.0176 & 0.0016 & 0.0135 & 0.0372 & \textbf{0.0513} \\
\midrule
\multirow{4}{*}{D} & AUC & \textbf{0.5051} & 0.5000 & NA & 0.5050 & 0.5047 & NA \\
& BA & 0.5100 & 0.5133 & NA & \textbf{0.5145} & 0.5136 & NA \\
& TPR (0.1) & 0.1020 & 0.0950 & NA & 0.0894 & \textbf{0.1181} & NA \\
& TPR (0.01) & 0.0093 & 0.0083 & NA & 0.0113 & \textbf{0.0159} & NA \\
\bottomrule
\end{tabular} |
|
SE-15978 | \begin{tabular}{lllr}
\hline
\textbf{ID} & \textbf{Mistake Type} & \textbf{Associated Mistake Class} & \textbf{Occurance} \\ \hline
1 & Lack of preparation & \textit{Teamwork and Planning} & 4 \\
2 & Lack of planning & \textit{Teamwork and Planning} & 3 \\ \hline
3 & Not identifying stakeholders & \textit{Question Omission} & 1 \\
4 & Not asking about existing system & \textit{Question Omission} & 6 \\ \hline
5 & Asking long question & \textit{Question Formulation} & 3 \\
6 & Asking unnecessary question & \textit{Question Formulation} & 7 \\
7 & Asking stakeholder for solution & \textit{Question Formulation} & 15 \\
8 & Asking vague question & \textit{Question Formulation} & 32 \\
9 & Asking technical question & \textit{Question Formulation} & 5 \\ \hline
10 & Incorrect ending of the interview & \textit{Order of interview} & 6 \\ \hline
11 & Influencing stakeholder & \textit{stakeholder interaction} & 9 \\
12 & No rapport with stakeholder & \textit{stakeholder interaction} & 16 \\
13 & Unnatural dialogue style & \textit{Communication skills} & 11 \\ \hline
\end{tabular} |
|
PL-2065 | \begin{tabular}{l|lll}
\hline
\multirow{2}{*}{$b_0$} & \texttt{int i = 0;} & & \\
& & \verb|Update|: $\phi(b_1)$ & \verb|Goto| $b_1$ \\
\cline{1-1}\cline{3-4}
\multirow{3}{*}{$b_1$} & & Skip to $b_2$ unless $SAT(\phi(b_1))$ & \\
& \texttt{while (i < b) \{} & & \\
& & \verb|Update|: $\phi(b_2)$, $\phi(b_5)$ & \verb|Goto| $b_2$ \\
\cline{1-1}\cline{3-4}
\multirow{4}{*}{$b_2$} & & Skip to $b_3$ unless $SAT(\phi(b_2))$ & \\
& \texttt{\quad i++} & & \\
& \texttt{\quad if (i != a)} & & \\
& & \verb|Update|: $\phi(b_3)$, $\phi(b_4)$ & \verb|Goto| $b_3$ \\
\cline{1-1}\cline{3-4}
\multirow{3}{*}{$b_3$} & & Skip to $b_4$ unless $SAT(\phi(b_3))$ & \\
& \texttt{\quad\quad continue;} & & \\
& & \verb|Update|: $\phi(b_1)$ & \verb|Goto| $b_1$ \\
\cline{1-1}\cline{3-4}
\multirow{3}{*}{$b_4$} & & Skip to $b_5$ unless $SAT(\phi(b_4))$ & \\
& \texttt{\quad \ldots} & & \\
& \texttt{\}} & \verb|Update|: $\phi(b_1)$ & \verb|Goto| $b_1$ \\
\cline{1-1}\cline{3-4}
\multirow{1}{*}{$b_5$} & \texttt{return;} & & \\
\hline
\end{tabular} |
|
CV-8720 | \begin{tabular}{ccccccccc}
\hline
Model & 0.25 & 0.5 & 1 & 2 & 4 & 8 & 16 & 32 \\[0.5ex]
\hline
Chained & 4.76 & 8.33 & 15.55 & 28.23 & 44.69 & 58.62 & 65.89 & 67.49 \\
2-SHG & 5.59 & 10.87 & 22.25 & 41.62 & 61.78 & 73.9 & 79.21 & 79.78 \\
DeepPose & 3.3 & 4.86 & 7.99 & 12.98 & 18.26 & 21.33 & 22.79 & 23.12 \\
\hline
\end{tabular} |
|
SE-1008 | \begin{tabular}{l|c|c|}
\cline{2-3}
\multicolumn{1}{c|}{} & Description & Artifact Type \\ \hline
\multicolumn{1}{|l|}{{CR01}} & \makecell{Every lifeline must have \\ a corresponding class.} & uml:Lifeline \\ \hline
\multicolumn{1}{|l|}{{CR02}} & \makecell{Every transition has to have \\ a corresponding message.} & uml:Transition \\ \hline
\multicolumn{1}{|l|}{{CR03}} & \makecell{Statechart Action must be defined \\ as an operation in the owner’s class.} & uml:Transition \\ \hline
\multicolumn{1}{|l|}{{CR04}} & \makecell{Message actions must be defined \\ as an operation in receiver’s class.} & uml:Message \\ \hline
\multicolumn{1}{|l|}{{CR05}} & \makecell{Operation parameters \\ must have unique names.} & uml:Operation \\ \hline
\multicolumn{1}{|l|}{{CR06}} & \makecell{An Operation has at most \\ one return parameter.} & uml:Operation \\ \hline
\multicolumn{1}{|l|}{{CR07}} & \makecell{An interface can have at \\ most one generalization.} & uml:Interface \\ \hline
\multicolumn{1}{|l|}{{CR08}} & \makecell{An interface can only contain \\ public operations and no attributes.} & uml:Interface \\ \hline
\multicolumn{1}{|l|}{{CR09}} & \makecell{No two class operations may \\ have the same signature.} & uml:Class \\ \hline
\multicolumn{1}{|l|}{{CR10}} & \makecell{No two fields may have \\ the same name.} & uml:Class \\ \hline
\end{tabular} |
|
CR-52556 | \begin{tabular}{||cccc||}
\hline
\textbf{Datasets} & \textbf{Nodes (N)} & \textbf{Dimension (d)} & \textbf{Classes (c)} \\ [0.5ex]
\hline\hline
Iris & 150 & 4 & 3 \\
\hline
Glass & 214 & 9 & 6 \\
\hline
Wine & 178 & 13 & 3 \\
\hline
Control Chart & 600 & 60 & 6 \\
\hline
Parkinsons & 195 & 22 & 2 \\
\hline
Vertebral & 310 & 6 & 3 \\
\hline
Breast tissue & 106 & 9 & 6 \\
\hline
Seeds & 210 & 7 & 3 \\ [1ex]
\hline
\end{tabular} |
|
CR-29421 | \begin{tabular}{p{3cm}<{\raggedright}p{5cm}<{\raggedright}p{9cm}<{\raggedright}}
\hline
\textbf{Type} & \textbf{Approach} & \textbf{Brief Introduction} \\
\hline
\multirow{4}{3cm}{Original Approaches with complete process frameworks} & E-Safety Vehicle Intrusion Protected Applications (EVITA) & EVITA approach considers four security objectives (safety, privacy, financial, operational) and uses attacks trees to identify threats and assess risks . \\
\cline{2-3} & Threat, Vulnerabilities, and implementation Risks Analysis (TVRA) & TVRA is a process-driven TARA approach to systematically identify unwanted incidents which need to be avoided . \\
\cline{2-3} & Operationally Critical Threat, Asset, and Vulnerability Evaluation (OCTAVE) & OCTAVE is a process-driven TARA method which is best suited for enterprise information security risk assessments . \\
\cline{2-3} & HEAling Vulnerabilities to ENhance Software Security and Safety (HEAVENS) & HEAVENS is a systematic approach of deriving security requirements for vehicle E/E systems, including processes and tools supporting for TARA . \\
\hline
\multirow{3}{3cm}{Approaches evolved from other disciplines and support co-analysis} & A Security-Aware Hazard and Risk Analysis Method (SAHARA) & SAHARA is a combined approach of the Hazard Analysis and Risk Assessment (HARA) with the STRIDE model and outlines the impacts of security issues on safety concepts . \\
\cline{2-3} & Failure Mode, Vulnerabilities and Effects Analysis (FMVEA) & FMVEA is an approach evolved from the Failure Mode and Effect Analysis (FMEA) to identify vulnerability cause-effect chains for security . \\
\cline{2-3} & Combined Harm Assessment of Safety and Security (CHASSIS) & CHASSIS is a unified process for safety and security by using UML-based models (e.g. misuse cases and sequence diagrams) . \\
\hline
\end{tabular} |
|
CV-13976 | \begin{tabular}{|c|c|c|c|c|c|c|c|c|c|c|c|c|c|}
\hline
Methods & Land & Forest & Residential & Haystack & Road & Church & Car & Water & Sky & Hill & Person & Fence & Overall \\
\hline
w/ Base Train & .495 & .496 & .774 & .000 & .252 & .166 & .000 & .006 & .952 & .371 & .000 & .060 & .298 \\
w/ SegProp Train & \textbf{.540} & \textbf{.516} & \textbf{.822} & .586 & \textbf{.432} & \textbf{.382} & \textbf{.066} & .146 & \textbf{.985} & \textbf{.407} & \textbf{.471} & \textbf{.233} & \textbf{.466} \\
\hline
\end{tabular} |
|
CV-24411 | \begin{tabular}{l|cc|cc}
\specialrule{1.2pt}{1pt}{1pt}
\multirow{2}{*}{\hspace{0.08cm} Method} & \multicolumn{2}{c|}{Segmentation} &
\multicolumn{2}{c}{Robustness} \\
\cline{2-5}
& \textbf{B} & \textbf{W} & \textbf{B} & \textbf{W} \\
\specialrule{1.2pt}{1pt}{1pt}
DeepLabv3-Res50 & 73.9 & 74.1 & 53.7 & \textbf{55.8} \\
DeepLabv3-Res101 & 75.5 & 75.2 & 49.8 & \textbf{51.9} \\
\specialrule{1.2pt}{1pt}{1pt}
\end{tabular} |
|
CL-1656 | \begin{tabular}{lrr}
\toprule
$K$ & Successor surprisal & Total entropy \\
\midrule
5 & 0.212 & 0.541 \\
50 & 0.335 & 0.820 \\
500 & 0.397 & 0.947 \\
5000 & 0.434 & 0.992 \\
50000 & 0.454 & 1 \\
\bottomrule
\end{tabular} |
|
CR-49011 | \begin{tabular}{cc}
\toprule
Component & Types considered \\
\midrule
Trend & linear model, local level, local linear \\
Seasonal & hourly, daily \\
Error & Gaussian, AR(p): autoregressive model of order p=1,2 \\
\bottomrule
\end{tabular} |
|
CR-2892 | \begin{tabular}{cccccc}
\toprule
& CIFAR10 & CIFAR100 & Purchase100 & Texas100 & Location \\
\midrule
$p^*$ & 0.13 & 0.11 & 0.015 & 0.005 & 0.015 \\
\bottomrule
\end{tabular} |
|
CR-39239 | \begin{tabular}{lccc}
\textbf{Dataset} & \textbf{Classes} & \textbf{Instances/Class} & \textbf{Total} \\
\hline
Undefended & 95 & 1000 & 95,000 \\
WTF-PAD & 95 & 1000 & 95,000 \\
Walkie-Talkie (sim.) & 100 & 900 & 90,000 \\
Walkie-Talkie (real) & 100 & 750 & 75,000 \\
Onion Sites & 538 & 77 & 41,426 \\
\hline
\end{tabular} |
|
AI-37528 | \begin{tabular}{c|c|c|c}
\hline
$\pi_b$ & 20$\times$20 & 50$\times$20 & 100$\times$20 \\
\hline
\hline
MWKR & \textbf{1803.1} & \textbf{3147.3} & \textbf{5676.0} \\
MOR & 1831.7 & 3229.8 & 5728.3 \\
SPT & 1813.8 & 3201.7 & 5718.7 \\
FIFO & 1826.4 & 3177.6 & 5692.9 \\
\hline
\end{tabular} |
|
AI-18721 | \begin{tabular}{r|r|r|r}
& \textbf{Wikipedia} & \textbf{Wikinews} & \textbf{Science} \\ \hline
\textbf{Sentences} & 15,000 & 14,682 & 46,715 \\
\textbf{Verbs} & 32,758 & 34,026 & 66,653 \\
\textbf{Questions} & 75,867 & 80,081 & 143,388 \\
\textbf{Valid Qs} & 67,146 & 70,555 & 127,455
\end{tabular} |