Datasets:
lt-asset
/
tab2latex

Dataset card Data Studio Files Community
1
id
stringlengths
4
8
image
imagewidth (px)
4
1.34k
latex
stringlengths
90
2.83k
AI-6336
\begin{tabular}{|l|c|c|c|} \hline Pipeline component & P & R & F1 \\ \hline Coreference resolution & 91.1 & 96.9 & 93.9 \\ Verb identification & 75.2 & 76.7 & 75.9 \\ Verb conjugation & 100.0 & 100.0 & 100.0 \\ Generation of $S(E)$ & 99.1 & 99.1 & 99.1 \\ \hline \end{tabular}
CR-759
\begin{tabular}{|l|l|l|l|l|l|l|l|l|} \hline Attribute & \multicolumn{4}{|l|}{130-th (var=26.20e+8)} & \multicolumn{4}{|l|}{595-th (var=11.85e+8)} \\ \hline $|D_v|$ & 50 & 100 & 500 & 1000 & 50 & 100 & 500 & 1000 \\ \hline B=8 & 0.8600 & 0.7800 & 0.6460 & 0.5830 & 0.6800 & 0.7100 & 0.6280 & 0.6380 \\ \hline B=32 & 0.9000 & 0.7600 & 0.6520 & 0.6440 & 0.8400 & 0.7500 & 0.6820 & 0.6480 \\ \hline B=$|D_v|$ & \textbf{0.9400} & \textbf{0.8400} & \textbf{0.7660} & \textbf{0.7390} & \textbf{0.9200} & \textbf{0.8500} & \textbf{0.7300} & \textbf{0.6900} \\ \hline \end{tabular}
AI-43811
\begin{tabular}{l c @{\hspace*{2.0mm}}c @{\hspace*{2.0mm}}c @{\hspace*{2.0mm}}c @{\hspace*{2.0mm}}c } \toprule \scriptsize Name & Train & Test & Valid & Classes & Structure \\ \midrule MNIST & 50,000 & 10,000 & 10,000 & 10 & image \\ \scriptsize{SC} & 84,843 & 11,005 & 9,981 & 35 & sound \\ IMDB & 17,500 & 25,000 & 7,500 & 2 & text \\ SST & 8,544 & 2,210 & 1,101 & 2 & tree \\ Algorithms & 136 & 43 & 34 & 6 & tree, neurotree \\ UPFD & 1,092 & 3,826 & 546 & 2 & graph \\ Iris & 96 & 30 & 24 & 3 & tabular \\ \bottomrule \end{tabular}
AI-27394
\begin{tabular}{cccccc} \toprule Method & DeepISP & VDSR & SRDenseNet & RDN & Ours \\ \midrule Time (s) & 0.2614 & 0.6892 & 0.9448 & 0.4987 & 0.647 \\ \bottomrule \end{tabular}
CV-22920
\begin{tabular}{|c|c|c|c|c|c|} \hline {} & \multicolumn{2}{|c|}{RaFD} & \multicolumn{2}{|c|}{CFEED} \\ \hline {} & PSNR$\uparrow$ & FID$\downarrow$ & PSNR$\uparrow$ & FID$\downarrow$ \\ \hline StarGAN & 19.82 & 62.51 & 20.11 & 42.39 \\ \cline{1-5} GANimation & 22.06 & 45.55 & 20.43 & 29.07 \\ \cline{1-5} Cascade EF-GAN & \bf{23.07} & \bf{42.36} & \bf{21.34} & \bf{27.15} \\ \hline \end{tabular}
AI-15567
\begin{tabular}{lc||c|cc} & & & \multicolumn{2}{c}{CorrNet} \\ Train & Test & MultiCCA & W & W+N+Ch+L \\ \toprule Amh & Tig & 15.5 & 28.3 & \textbf{31.7} \\ Tig & Amh & 11.1 & 12.8 & \textbf{23.3} \\ \midrule Eng & Uig & 8.4 & \textbf{16.9} & 15.4 \\ Tur & Uig & 1.1 & 18.1 & \textbf{25.6} \\ Eng+Tur & Uig & 8.0 & 20.3 & \textbf{20.6} \\ \midrule Eng & Tur & 20.6 & \textbf{21.4} & 17.3 \\ Uig & Tur & 10.4 & 10.1 & \textbf{17.7} \\ Eng+Uig & Tur & 18.5 & 21.1 & \textbf{29.4} \\ \end{tabular}
CR-17554
\begin{tabular}{|c|c|c|c|c|} \hline Model & Number of & & & Our method\tabularnewline & iterations & & & \tabularnewline \hline & $10$ & $0.84$ & $0.92$ & $0.96$ \tabularnewline Bunny & $30$ & $0.60$ & $0.85$ & $0.92$ \tabularnewline & $50$ & $0.36$ & $0.44$ & $0.62$ \tabularnewline \hline & $10$ & $0.94$ & $0.95$ & $0.99$ \tabularnewline Venus & $30$ & $0.63$ & $0.93$ & $0.97$\tabularnewline & $50$ & $0.45$ & $0.78$ & $0.81$\tabularnewline \hline \end{tabular}
AI-15298
\begin{tabular}{ccccc} \toprule \# & $X_1$ & $X_2$ & $X_3$ & $X_4$ \\ \midrule $3$ & 0 & 0 & 1 & 1 \\ $7$ & 0 & 0 & 0 & 0 \\ \hline $2$ & 1 & 0 & 0 & 1 \\ $3$ & 0 & 1 & 1 & 1 \\ $9$ & 0 & 1 & 1 & 0 \\ \hline $2$ & 1 & 1 & 0 & 1 \\ $4$ & 1 & 1 & 1 & 0 \\ \hline 0 & 1 & 0 & 1 & 1 \\ 0 & 1 & 0 & 1 & 0 \\ 0 & 0 & 1 & 0 & 1 \\ \bottomrule \end{tabular}
CR-44298
\begin{tabular}{ccccccc} \toprule \multicolumn{6}{c}{\textbf{Untargeted-Krum}} \\ \textbf{\# Clients} & \textbf{Dataset} & \textbf{FedAvg} & \textbf{Coomed} & \textbf{FLARE} & \textbf{FedCC} \\ \midrule \multirow{2}{*}{100} & fMNIST & 58.95 & 49.52 & 71.20 & \textbf{75.98} \\ & CIFAR10 & 30.35 & 20.34 & 19.57 & \textbf{32.65} \\ \midrule \multirow{2}{*}{60} & fMNIST & 10.93 & 25.14 & 54.48 & \textbf{68.77} \\ & CIFAR10 & 10.00 & 10.17 & 13.24 & \textbf{29.58} \\ \midrule \multirow{2}{*}{40} & fMNIST & 53.96 & \textbf{54.62} & 25.14 & 45.82 \\ & CIFAR10 & 16.32 & 19.68 & 20.50 & \textbf{25.52} \\ \bottomrule \toprule \multicolumn{6}{c}{\textbf{Untargeted-Med}} \\ \textbf{\# Clients} & \textbf{Dataset} & \textbf{FedAvg} & \textbf{Coomed} & \textbf{FLARE} & \textbf{FedCC} \\ \midrule \multirow{2}{*}{100} & fMNIST & 11.58 & \textbf{65.13} & 60.67 & 63.02 \\ & CIFAR10 & 9.63 & 24.97 & 19.99 & \textbf{36.82} \\ \midrule \multirow{2}{*}{60} & fMNIST & 18.47 & 38.76 & 12.35 & \textbf{54.66} \\ & CIFAR10 & 10.00 & 21.86 & 10.00 & \textbf{32.26} \\ \midrule \multirow{2}{*}{40} & fMNIST & 12.16 & \textbf{55.21} & 12.43 & 53.41 \\ & CIFAR10 & 10.00 & 25.99 & 10.00 & \textbf{29.37} \\ \bottomrule \end{tabular}
CR-47801
\begin{tabular}{|c|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|>{\centering\arraybackslash}m{1.5cm}|} \hline \textbf{\textit{Dataset}} & \textbf{\textit{$\alpha$}} & \textbf{\textit{LSH Shingles}} & \textbf{\textit{LSH Jaccard Threshold}} & \textbf{\textit{LSH Number of Permutations}} & \textbf{\textit{$\beta$}} & \textbf{\textit{Number of Patterns}} & \textbf{\textit{Loss}} \\ \hline \textbf{Zookeeper} & 0.6 & 4 & 0.7 & 16 & 0.4 & 107 & 0.0050 \\ \hline \textbf{HPC} & 0.7 & 4 & 0.7 & 32 & 0.4 & 309 & 0.0063 \\ \hline \textbf{BGL} & 0.7 & 4 & 0.7 & 64 & 0.5 & 1470 & 0.0026 \\ \hline \textbf{HDFS} & 0.6 & 4 & 0.7 & 16 & 0.4 & 42 & 0.0 \\ \hline \hline \end{tabular}
CR-21666
\begin{tabular}{lcclcc} \toprule Normal service & Gas & Dollars & Collusion resolution & Gas & Dollars \\ \midrule Contract deployment & $4697299$ & \$8.54 & $\mathsf{Accuse(\cdot)}$ & $223766$ & \$0.41 \\ $\mathsf{Deposit(\cdot)}$ & $105436$ & \$0.19 & $\mathsf{CheckCircuits(\cdot)}$ & $66991$+ & \$0.12+ \\ $\mathsf{PostRequests(\cdot)}$ & $405657$ & \$0.74 & $\mathsf{VerifyExchange(\cdot)}$ & $61822$ & \$0.11 \\ $\mathsf{SubmitResponse(\cdot)}$ & $97400$ & \$0.18 & $\mathsf{VerifyGeneralFunc(\cdot)}$ & $275279$ & \$0.50 \\ $\mathsf{ClaimServiceFee(\cdot)}$ & $33103$ & \$0.06 & $\mathsf{zkVerify(\cdot)}$ & $2286423$ & \$4.16 \\ \bottomrule \end{tabular}
AI-25799
\begin{tabular}{|l|l|} \hline Dataset Name & SIENA12 \\ \hline Number of images & 12 \\ \hline Size & 1024x768 px \\ \hline Categories & Outdoor, natural, synthetic \\ \hline Number of observers & 23 \\ \hline Age of the observers & From 23 to 52 \\ \hline Task & Free-viewing \\ \hline Duration & 5 seconds \\ \hline Eye-tracker & ASL 504 (240 Hz) \\ \hline Screen & LCD 1024 $\times$ 768 px (31 $\times$ 51 cm) \\ \hline Eye-screen distance & 72 cm \\ \hline Other information & Grayscale images \\ \hline \end{tabular}
CL-5687
\begin{tabular}{l|l|r} \hline \textbf{Parameter Name} & \textbf{Symbol} & \textbf{Value} \\ \hline Window size & $d^{win}$ & $3$ \\ Sentence. emb. dim. & $d^f$ & $690$ \\ Word. emb. dim. & $d^1$ & $50$ \\ Position. emb. dim. & $d^2$ & $5$ \\ Batch size & $\mathcal{B}$ & $160$ \\ Learning rate & $\mu$ & $0.03$ \\ Dropout pos. & $p$ & $0.5$ \\ \hline \end{tabular}
CV-16757
\begin{tabular}{|l|l|} \hline Emotion & Frequency \\ \hline Amusement & 5 \\ \hline Sadness & 6 \\ \hline Disgust & 88 \\ \hline Surprise & 20 \\ \hline Contempt & 3 \\ \hline Fear & 2 \\ \hline Repression & 40 \\ \hline Tense & 28 \\ \hline \end{tabular}
CR-4952
\begin{tabular}[c]{@{}l@{}}\cellcolor{gray!30}Collectcontactinfo,andthensendSMSwiththeappdownloadlinktoallcontacts.\end{tabular}
CR-51370
\begin{tabular}{|c|c|c|} \hline \textbf{Video Type} & \textbf{Database} & \textbf{Number of Videos} \\ \hline Genuine & SMAD & 65 \\ \hline Genuine (from train split) & SiW-M & 217 \\ \hline Print Attack & SiW-M & 104 \\ \hline Replay Attack & SiW-M & 99 \\ \hline Mask Attack & SMAD & 65 \\ \hline \end{tabular}
SE-10495
\begin{tabular}{cp{7.8cm}} \toprule \textbf{No.} & \textbf{TD General Question Description} \\ \midrule GQ1 & Were you familiar with the presented description of technical debt before this questionnaire? \\ \midrule GQ2 & How often do you introduce technical debt? \\ \midrule GQ3 & For what reasons have you introduced technical debt? \\ \midrule GQ4 & How often do you repay your own technical debt? \\ \midrule GQ5 & For what reasons do you repay your own technical debt? \\ \bottomrule \end{tabular}
AI-14886
\begin{tabular}{p{0.16\textwidth}|p{0.07\textwidth}p{0.07\textwidth}p{0.07\textwidth}}\hline & relevance & interest & average \\ \hline \textsc{SpaceFusion} & \textbf{2.72} & \textbf{2.53} & \textbf{2.63} \\ CVAE+BOW & 2.51 & 2.37 & 2.44 \\ Multi-Task & 2.34 & 2.14 & 2.24 \\ S2S+Sampling & 2.58 & 2.43 & 2.50 \\ \hline human & 3.59 & 3.41 & 3.50 \\ \hline \end{tabular}
CV-10302
\begin{tabular}{|l|l|l|} \hline Method & MUSK1 & MUSK2 \\ \hline\hline mi-SVM & 0.874 & 0.836 \\ MI-SVM & 0.779 & 0.843 \\ MI-Kernel & 0.880 & 0.893 \\ EM-DD & 0.849$\pm$0.098 & 0.869$\pm$0.108 \\ mi-Graph & 0.889$\pm$0.073 & \textbf{0.903$\pm$0.086} \\ miVLAD & 0.871$\pm$0.098 & 0.872$\pm$0.095 \\ miFV & \textbf{0.909$\pm$0.089} & 0.884$\pm$0.094 \\ \hline\hline mi-Net & 0.889$\pm$0.088 & 0.858$\pm$0.110 \\ MI-Net & 0.887$\pm$0.091 & 0.859$\pm$0.102 \\ MI-Net with DS & 0.894$\pm$0.093 & 0.874$\pm$0.097 \\ MI-Net with RC & 0.898$\pm$0.097 & 0.873$\pm$0.098 \\ \hline\hline Attention & 0.892$\pm$0.040 & 0.858$\pm$0.048 \\ Gated-Attention & 0.900$\pm$0.050 & 0.863$\pm$0.042 \\ \hline\hline HAMIL-A & 0.892$\pm$0.120 & 0.857$\pm$0.102 \\ HAMIL & 0.866$\pm$0.121 & 0.820$\pm$0.107 \\ \hline \end{tabular}
AI-30692
\begin{tabular}{ll|ll} \hline Notation & Description & Notation & Description \\ \hline $a_m$ & An action option & $a^*$ & The system-expected action \\ $A$ & Set of available actions & $t$ & Time step \\ $v_i$ & A user agent & $p_{v_i,a_m}$ & $v_i$'s preference towards $a_m$ \\ $u_{v_i,a_m,t}$ & $v_i$' utility towards $a_m$ at $t$ & $s_{v_i,t}$ & $v_i$'s behavior at $t$ \\ $S_{v_i}$ & Set of $v_i$'s historical behaviors & $G$ & A social network \\ $V$ & Set of all user agents & $e_{ij}$ & Directed edge from $v_i$ to $v_j$ \\ $E$ & Set of all edges connecting users & $w_{ij}$ & Weight of $e_{ij}$ \\ $N_{v_i}^{in}$ & Set of agents who influence $v_i$ & $N_{v_i}^{out}$ & Set of agents who are influenced by $v_i$ \\ $k_{v_i,a_m,t}$ & Overall influence from $N_{v_i}^{in}$ at $t$ & $r_{v_i,t}$ & Incentive allocated to $v_i$ at $t$ \\ $B_t$ & Remaining budget at $t$ & $\theta_{v_i}$ & $v_i$'s influential degree \\ $\rho_{v_i,t}$ & incentive sensitivity of $v_i$ & $\omega_{v_i}$ & A ratio of $p_{v_i,a^*}$ to all preferences \\ $\mu_t$ & Global Activated Users Percentage & $|\cdot|$ & The number of elements in a set \\ $P(\cdot)$ & Probability \\ \hline \end{tabular}
CV-30109
\begin{tabular}{|p{2.2cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|p{0.8cm}|} \hline \multirow{2}{*}{Method} & \multicolumn{2}{c|}{high-res} & \multicolumn{2}{c|}{low-res} \\ & train & test & train & test \\ \hline DeepC-MVS & \textbf{84.81} & \textbf{87.08} & 61.99 & \textbf{62.37} \\ $\text{DeepC-MVS}_{\text{f}}$ & 84.27 & 86.91 & \textbf{62.57} & 62.24 \\ ACMM & 78.86 & 80.78 & 55.12 & 55.01 \\ PCF-MVS & 79.42 & 79.29 & 57.32 & 57.06 \\ TAPA-MVS & 77.69 & 79.15 & 55.13 & 58.67 \\ LTVRE & 61.82 & 76.25 & 53.25 & 53.52 \\ COLMAP & 67.66 & 73.01 & 49.91 & 52.32 \\ P-MVSNet & n/a & n/a & n/a & 44.46 \\ \hline \end{tabular}
AI-40705
\begin{tabular}{c|m{17em}} \toprule \textbf{Symbol} & \textbf{Meaning} \\ \midrule $v \in \mathcal{V}$ & English token in an open vocabulary \\ $\textbf{e} \in \mathcal{E}$ & EEG feature vector in an EEG sequence \\ $c \in \mathcal{C}$ & Sentiment label in ternary sentiment classes \\ $\langle \mathcal{E}, \mathcal{S} \rangle$ & Word-level EEG feature sequence and text sentence pair \\ $\langle \mathcal{E}, c \rangle$ & Word-level EEG feature sequence and sentiment label pair \\ $\langle \hat{\textbf{e}}, c \rangle$ & Aggregated sentence-level EEG and sentiment label pair \\ $\langle \mathcal{S}, c \rangle$ & Text sentence and sentiment label pair \\ \bottomrule \end{tabular}
CV-23457
\begin{tabular}{lc} \toprule Method & mIoU \\ \midrule No boundary information & 60.4 \\ \midrule Random flip & 62.4 \\ Exchange neighboring pair & 61.8~ \\ \midrule No perturbation & \textbf{63.4} \\ \bottomrule \end{tabular}
SE-7007
\begin{tabular}[c]{@{}l@{}}\textbf{CodeReview:}let'snotmodifythis.looksliketheclassesthatusethismethodimplementbufferedwriting \end{tabular}
AI-11242
\begin{tabular}{c|cc|c|c} \bottomrule[1pt] Method & World & Object & Intensity & Car \\ \hline \multirow{6}{*}{ST3D++ (w/ SN)} & $\times$ & $\times$ & - & 84.62 / 69.62 \\ & $\surd$ & $\times$ & Normal & 84.17 / 69.17 \\ & $\times$ & $\surd$ & Normal & 86.78 / 73.63 \\ & $\surd$ & $\surd$ & Normal & 86.65 / 73.98 \\ \cline{2-5} & $\surd$ & $\surd$ & Strong & \textbf{86.71} / 73.59 \\ & $\surd$ & $\surd$ & Curriculum & {86.47} / \textbf{74.61} \\ \toprule[0.8pt] \end{tabular}
CR-17388
\begin{tabular}{|c|c|p{7.5cm}|} \hline Technique & Application Domain & Description \\ \hline Cryptobiometrics & General & A combination of cryptographically secure matching and liveness detection mechanisms to verify the uniqueness and existence of real human beings. \\ \hline Resource Testing & General & This verification method aims to determine if the identity has as many resources as the single physical device it is associated with. \\ \hline Recurring Costs & General & This technique is a form of resource testing where resource tests are performed at regular time intervals to impose a certain ``cost'' on the attacker that is charged for every identity that she controls or introduces into the system . However, these researchers have used a computational power in their resource test that may not be sufficient to control the attack, since the attacker only incurs a one-time cost that can be recovered via the execution of the attack itself . \\ \hline Economic Incentives & General & This technique is based on a scheme where economic incentives are used to reward the adversaries if the identities that are controlled by it are revealed . The main disadvantage is that it may encourage attackers economically. \\ \hline \end{tabular}
AI-21897
\begin{tabular} {p{130pt}p{130pt}} \hline \textbf{Parameter} & \textbf{Value} \\ \hline Number of generations & 51 \\ Crossover probability & 0.9 \\ Mutations & 2 / chromosome \\ Function set & $F = \{+, -, *, /\}$ \\ Terminal set & Problem inputs \\ Selection & Binary Tournament \\ \hline \end{tabular}
CR-57204
\begin{tabular}{|c|c|} \hline Layer name & Layer size \\ \hline Convolution + Relu & $5\times 5\times 32$ \\ \hline Max pool & $2\times 2$ \\ \hline Convolution + Relu & $5\times 5\times 64$ \\ \hline Max pool & $2\times 2$ \\ \hline Fully connected + Relu & 1024 \\ \hline Softmax & 62 \\ \hline \end{tabular}
SE-3244
\begin{tabular}{lllllll} \toprule & Retry Count & Latency [s] & Execution Time [us] & Compilation Time [s] & Recompilation Count & Speedup \\ \midrule count & 1,134.00 & 1,134.00 & 1,134.00 & 1,134.00 & 1,134.00 & 1,134.00 \\ mean & 0.35 & 22.97 & 5.09 & 20.38 & 0.12 & 6,969,904.73 \\ min & 0.00 & 4.08 & 0.50 & 3.90 & 0.00 & 15,069.51 \\ max & 9.00 & 202.53 & 877.04 & 647.88 & 9.00 & 75,961,275.56 \\ \bottomrule \end{tabular}
CR-10782
\begin{tabular}{ccccccc} \toprule \multirow{2}{4em}{Trigger} & \multicolumn{3}{c}{Amazon} & \multicolumn{3}{c}{Twitter} \\ \cline{2-7} & $E$ & $S$ & $C$ & $E$ & $S$ & $C$ \\ \hline Anna Karenina & 2.58 & 0.092 & 4.2 & 1.71 & 0.160 & 3.7 \\ To Kill a Mockingbird & 2.18 & 0.137 & 3.3 & 1.81 & 0.240 & 2.3 \\ The Great Gatsby & 1.44 & 0.072 & 9.6 & 1.93 & 0.204 & 2.5 \\ Don Quixote & 1.00 & 0.041 & 24.4 & 1.00 & 0.088 & 11.4 \\ Jane Eyre & 1.85 & 0.058 & 9.3 & 1.05 & 0.078 & 12.2 \\ War and Peace & 2.43 & 0.099 & 4.2 & 1.94 & 0.179 & 2.9 \\ Pride and Prejudice & 2.71 & 0.148 & 2.5 & 2.88 & 0.374 & 0.9 \\ The Red and the Black & 1.87 & 0.121 & 4.4 & 1.39 & 0.250 & 2.9 \\ Les Misérables & 1.00 & 0.050 & 20.0 & 1.00 & 0.148 & 6.8 \\ \hline average & 1.90 & 0.091 & 9.1 & 1.63 & 0.191 & 5.1 \\ \bottomrule \end{tabular}
AI-11993
\begin{tabular}{@{}lrrrrr@{}} \toprule Model & \multicolumn{1}{l}{Batch Size} & \multicolumn{1}{l}{Weight decay} & \multicolumn{1}{l}{Learning rate} & \multicolumn{1}{l}{Eval F1} & \multicolumn{1}{l}{Test F1} \\ \midrule RoBERTa-b & 32 & 0.1 & 0.00005 & 0.9848 & 0.9846 \\ RoBERTa-l & 16 & 0.01 & 0.00003 & \textbf{0.9856} & \textbf{0.9856} \\ BETO & 32 & 0.1 & 0.00005 & 0.9839 & 0.9836 \\ mBERT & 16 & 0.1 & 0.00005 & 0.9835 & 0.9839 \\ BERTIN & 16 & 0.1 & 0.00005 & 0.9847 & 0.9847 \\ ELECTRA & 16 & 0.01 & 0.00005 & 0.9822 & 0.9816 \\ \bottomrule \end{tabular}
AI-31488
\begin{tabular}{|c|c|c|c|c|} \hline \textbf{\#} & \textbf{White} & \textbf{Black} \\ \hline 1 & \texttt{g6} : \texttt{e2e4} & \texttt{e3} : \texttt{h7h5} \\ 2 & \texttt{g7} : \texttt{d2d4} & \texttt{f2} : \texttt{f7f5} \\ 3 & \texttt{g6} : \texttt{e4f5} & \texttt{e4} : \texttt{h5h4} \\ 4 & \texttt{d7} : \texttt{f1e2} & \cellcolor{blue!16}\texttt{g4} : \texttt{b8c6} \\ 5 & \texttt{g7} : \texttt{e2h5} & \texttt{g4} : \texttt{h8h5} \\ 6 & \texttt{b7} : \texttt{d1h5} & \texttt{g5} : \texttt{g7g6} \\ 7 & \texttt{e7} : \texttt{h5e8} & \texttt{g6} : \texttt{d7d6} \\ 8 & \texttt{g6} : \texttt{g6e8} & \\ \hline \end{tabular}
CR-56658
\begin{tabular}[c]{@{}l@{}}\{"\{"id":0,"method":"start\_cf","params":["\textcolor{red}{4,,}"1000,\\~2,2700,100,500 ,1,255,10,5000,7,0,0,500,2,5000,1"]\}"\end{tabular}
AI-5961
\begin{tabular}{cccccc} \toprule Friedman Value & Value in $X^2$ & $p$-value & Iman-Davenport Value & Value in $F_F$ & $p$-value \\ \cmidrule(l{3pt}r{3pt}){1-3} \cmidrule(l{3pt}r{3pt}){4-6} \textbf{41.355} & 5.991 & 1.085E-9 & \textbf{25.807} & 3.042 & 1.097E-10 \\ \bottomrule \end{tabular}
CR-36052
\begin{tabular}{p{2.13cm}p{0.75cm}p{4.3cm}}\hline \toprule \footnotesize{\textbf{Field}} & \footnotesize{\textbf{Offset}} & \footnotesize{\textbf{Description}} \\ \midrule \footnotesize{Request} & \footnotesize{00} & \footnotesize{Power state transition control} \\ \footnotesize{Status} & \footnotesize{04} & \footnotesize{Status} \\ \footnotesize{Cancel} & \footnotesize{08} & \footnotesize{Abort command processing} \\ \footnotesize{Start} & \footnotesize{0c} & \footnotesize{A command is available for processing} \\ \footnotesize{Interrupt Control} & \footnotesize{10} & \footnotesize{Reserved} \\ \footnotesize{Command Size} & \footnotesize{18} & \footnotesize{Size of the Command (CMD) Buffer} \\ \footnotesize{Command Address} & \footnotesize{1c} & \footnotesize{Physical address of the CMD Buffer} \\ \footnotesize{Response Size} & \footnotesize{24} & \footnotesize{Size of the Response (RSP) Buffer} \\ \footnotesize{Response Address} & \footnotesize{28} & \footnotesize{Physical address of the RSP Buffer} \\ \bottomrule \end{tabular}
CV-20957
\begin{tabular}{ccccc} \toprule & Airplane & Car & Chair & Mean \\ \hline PrGAN & 0.151 & 0.228 & 0.114 & 0.164 \\ \hline Proposed & \textbf{0.565} & \textbf{0.670} & \textbf{0.415} & \textbf{0.550} \\ \bottomrule \end{tabular}
CR-14086
\begin{tabular}{l|cc|cc} \toprule {\bf Dataset} & \multicolumn{2}{c|}{\textbf{Saliency Map}} & \multicolumn{2}{c}{\textbf{Random Insertion}} \\ & \multicolumn{1}{c}{\textbf{ASR}} & \textbf{MTA} & \multicolumn{1}{c}{\textbf{ASR}} & \textbf{MTA} \\ \midrule CIFAR-10 & 0.85 & 0.77 & 0.62 & 0.78 \\ \bottomrule \end{tabular}
CR-17146
\begin{tabular}{cllrrrrrr} \toprule \rowcolor{gray!50} \# & Configuration & Date & \# Sites & \# Pages & 1\textsuperscript{st}-party c. & 3\textsuperscript{rd}-party c. & CNAME Cl. \\ \midrule 1 & Plain browser & 07/20 & 11,471 & 272,659 & 408,509 & 155,193 & 14,493,533 \\ 2 & No 3\textsuperscript{rd}-party cookies & 07/26 & 10,368 & 246,493 & 355,208 & ---\textsuperscript{$\ast$} & 11,017,643 \\ 3 & \emph{uBlock Origin} active & 08/01 & 10,203 & 230,327 & 139,115 & 89,876 & 5,829,786 \\ 4 & \#2 and \#3 combined & 08/06 & 10,153 & 225,315 & 142,608 & ---\textsuperscript{$\ast$} & 5,831,884 \\ \bottomrule \end{tabular}
CR-30480
\begin{tabular} {|>{\raggedright\arraybackslash}p{.58cm} >{\centering\arraybackslash}p{.58cm} >{\raggedleft\arraybackslash}p{.58cm}| } \hline 2019 & 1* & 49* \\ $\sim$19? & 1.7K & 14K \\ \hline \end{tabular}
CV-26405
\begin{tabular}{|*{7}{c|}} \hline \multirow{2}*{Method} & \multicolumn{2}{c|}{AFLW} & \multicolumn{2}{c|}{COFW} & \multicolumn{2}{c|}{IBUG} \\ \cline{2-7} & Error & Failure & Error & Failure & Error & Failure \\ \hline BM & 5.67 & 4.43 & 6.25 & 5.13 & 8.89 & 27.43 \\ WM & 5.50 & 3.84 & 6.11 & 4.54 & 8.72 & 26.80 \\ \textbf{AM} & \textbf{5.38} & \textbf{3.47} & \textbf{6.00} & \textbf{3.94} & \textbf{8.51} & \textbf{25.93} \\ \hline \end{tabular}
CR-40127
\begin{tabular}{cl} \hline \textbf{Score} & \hspace{1.5cm} \textbf{Level} \\ \hline \hline 5 & Recovered all of the original speech \\ \hline 4 & Recovered most of the original speech \\ \hline 3 & Recovered half of the original speech \\ \hline 2 & Recovered little of the original speech \\ \hline 1 & Recovered none of the original speech \\ \hline \end{tabular}
CV-25421
\begin{tabular}{lccccc} \toprule & linear & MLP with $d\,{=}\,1$ & MLP with $d\,{=}\,2$ & MLP with $d\,{=}\,3$ & MLP with $d\,{=}\,4$ \\ \midrule Accuracy & 58.40{$\pm$0.59} & 59.11{$\pm$0.79} & 60.01{$\pm$0.74} & 59.23{$\pm$0.66} & 59.45{$\pm$0.68} \\ \bottomrule \end{tabular}
AI-14196
\begin{tabular}{l|l} \hline \hline Layer type & Output Shape \\ [1ex] \hline Flatten & (None, Nx) \\ Dense x 2 & (None, 50K) \\ Dense x 3 & (None, 25K) \\ Dense & (None, 8) \\ \hline \end{tabular}
CV-15582
\begin{tabular}{p{4.1em}|p{0.7em}p{0.8em}p{0.8em}} \multicolumn{4}{c}{REFERENCE POINTS} \\ \hline Marker & $C_1$ & $C_2$ & $C_3$ \\ \hline Cutoff & 0.52 & 0.51 & 0.50 \\ \hline Detections & & & \\ Per Image & 0.04 & 0.59 & 0.89 \\ (log 10) & & & \\ \hline Missing & & & \\ Rate (Clouds) & 0.20 & 0.03 & 0.01 \\ \hline Missing & & & \\ Rate (Storms) & 0.77 & 0.52 & 0.36 \\ \hline \end{tabular}
SE-8753
\begin{tabular}[c]{@{}l@{}}Affectedbyvisceralprocessingbasedontheappearance\\or“lookandfeel”ofaproduct\\Potentialtohaveconflicts-havetoconsider\\moreonteamcompositionwhenformingprojectteam\end{tabular}
SE-7102
\begin{tabular}{|p{0.95\textwidth}|} \hline Part 1 -- Who are you? \\ 1) What is your role in your organization? \\ 2) What is your main focus, especially in relation to autonomous driving? \\ 3) How have you been involved in testing autonomous driving systems? \\ \hline Part 2 -- What do you think of using critical scenario identification for testing? \\ 4) How do the critical scenario-based and other testing approaches complement each other? \\ 5) What are the general advantages and limitations of critical scenario-based approaches? \\ 6) How to improve the relevance of generated critical scenarios for testing? \\ 7) Which approach is most feasible from a practical view? \\ \hline Part 3 -- What else to complement? \\ 8) What are the open challenges for testing autonomous driving systems, particularly in relation to the use of critical scenario identification? \\ 9) What are your general feedback and further interviewee candidates to recommend? \\ \hline \end{tabular}
AI-36210
\begin{tabular}{|c|c|c|} \hline Annotator & Avg. Length & Total Videos Watched \\ \hline\hline 1 & 16.60 & 825 \\ \hline 2 & 16.65 & 875 \\ \hline 3 & 17.67 & 1700 \\ \hline 4 & 19.62 & 825 \\ \hline 5 & 21.22 & 875 \\ \hline 6 & 22.61 & 875 \\ \hline 7 & 22.71 & 875 \\ \hline 8 & 24.14 & 825 \\ \hline 9 & 25.81 & 825 \\ \hline \end{tabular}
AI-5640
\begin{tabular}[c]{@{}l@{}}Aunifiedmarketplace\\thatusesAIservicesto\\makeitmoretransparent\\andthatautomatically\\identifyproblems.\end{tabular}
CR-3454
\begin{tabular}{|p{4cm}|p{4cm}|} \hline \textbf{Procedures} & \textbf{Diagnoses} \\ \hline {\color{red} Attachment of pedicle or flap graft} & {\color{blue} Rheumatic heart failure} \\ {\color{red} Right heart cardiac catheterization} & {\color{blue} Ventricular fibrillation} \\ {\color{red} Procedure on two vessels} & {\color{blue} Benign essential hypertension} \\ {\color{red} Other endovascular procedures on other vessels} & {\color{blue} Paroxysmal ventricular tachycardia} {\color{blue} Nephritis and nephropathy} \\ {\color{red} Insertion of non-drug-eluting coronary artery stent(s)} & \\ \hline \end{tabular}
CV-25247
\begin{tabular}{lc|cc|ccc} \hline Method & Branch No. & AP & $\text{AP}_{50}$ & $\text{AP}_{s}$ & $\text{AP}_{m}$ & $\text{AP}_{l}$ \\ \hline Baseline & - & 37.9 & 58.8 & {20.0} & {43.0} & 52.8 \\ \hline \multirow{4}{*}{TridentNet} & Branch-1 & 31.5 & 53.9 & 22.0 & 43.3 & 29.9 \\ & Branch-2 & 37.8 & 58.4 & 18.0 & 45.3 & 53.4 \\ & Branch-3 & 31.9 & 48.8 & 7.1 & 37.9 & \textbf{56.1} \\ & 3 Branches & \textbf{40.6} & \textbf{61.8} & \textbf{23.0} & \textbf{45.5} & 55.9 \\ \hline \end{tabular}
SE-9699
\begin{tabular}{|c|c|c|c|} \hline \textbf{Repository} & \textbf{Data Type} & \textbf{Identification Field} & \textbf{Time (s)} \\ \hline {Keras} & {Issue} & {title, body, comments.body} & {7.287} \\ \hline {Keras} & {PR} & {title, body, comments.body} & {0.650} \\ \hline {Keras} & {Commit} & {message} & {0.378} \\ \hline {TensorFlow} & {Issue} & {title, body, comments.body} & {7.419} \\ \hline {TensorFlow} & {PR} & {title, body, comments.body} & {0.632} \\ \hline {TensorFlow} & {Commit} & {message} & {0.681} \\ \hline \end{tabular}
CV-20480
\begin{tabular}{lllll} \cmidrule(r){1-5} \textit{n} & \textit{a} & \textit{e} & AUC & Accuracy \\ \midrule 7 & 0.007 & 0.125 & 1 & 0.997 \\ 20 & 0.007 & 0.125 & 1 & 0.998 \\ 40 & 0.5 & 8.0 & 1 & 0.998 \\ 200 & 0.5 & 8.0 & 1 & 0.998 \\ \bottomrule \end{tabular}
CL-3170
\begin{tabular}{ccccc} \toprule Methods & \multicolumn{1}{c}{Devel} & \multicolumn{1}{c}{WSJ} & \multicolumn{1}{c}{Brown} & Combined \\ \hline {Baseline} & 81.5 & 83.2 & 71.6 & 81.6 \\ {TPF} & 82.5 & 84.1 & 72.9 & 82.6 \\ {TPF-Gold} & 88.4 & 89.6 & 79.8 & 88.3 \\ \hline {Baseline w/ ELMo} & 85.5 & 86.3 & 74.7 & 84.8 \\ {TPF w/ ELMo} & 85.3 & 86.9 & 76.8 & 85.6 \\ {TPF-Gold w/ ELMo} & 89.8 & 91.1 & 82.2 & 89.9 \\ \bottomrule \end{tabular}
CR-50761
\begin{tabular}{c|c|c|c|c|c|c|c} \toprule Model & [email protected] & aero & bike & bird & boat & bottle & bus \\ \hline Clean & 79.81 & 84.82 & 87.65 & 76.86 & 75.45 & 61.15 & 81.03 \\ \hline Backdoored & 79.78 & 84.12 & 89.03 & 75.30 & 73.91 & 61.10 & 82.95 \\ \hline \hline Model & car & cat & chair & cow & table & dog & horse \\ \hline Clean & 84.21 & 91.15 & 66.35 & 84.75 & 69.87 & 86.28 & 91.7 \\ \hline Backdoored & 85.35 & 90.28 & 67.25 & 83.37 & 69.35 & 85.99 & 90.16 \\ \hline \hline Model & mbike & person & plant & sheep & sofa & train & tv \\ \hline Clean & 87.48 & 83.49 & 52.36 & 74.99 & 81.05 & 91.91 & 83.65 \\ \hline Backdoored & 87.74 & 84.63 & 53.22 & 79.41 & 78.94 & 90.74 & 82.80 \\ \bottomrule \end{tabular}
PL-3752
\begin{tabular}{|c|c|c||c|c|}\hline \textbf{Benchmark} & \textbf{Execution Model} & \textbf{LOC} & \textbf{Manual Annotations} & \textbf{Invariants Inferred} \\\hline Jacobi & Multiplicative SEU & 51 & 16 & 30 \\\hline SS-CG & Additive SEU & 167 & 22 & 36 \\\hline SS-SD & Unbounded SEU & 57 & 9 & 0 \\\hline SC-CC & Switchable Rowhammer & 89 & 38 & 42 \\\hline \end{tabular}
AI-20043
\begin{tabular}{lccc} \hline Stream & SH & NTU-60 & NTU-60 \\ & (CS) & (CS) & (CV) \\ \hline RGB & 53.4 & 85.5 & 87.3 \\ OF & 51.8 & 85.7 & 92.8 \\ RGB + OF & 57.3 & 87.1 & 93.6 \\ \hline MARS + RGB & 58.1 & 88.2 & 92.9 \\ VFN++ & 59.0 & 90.1 & 93.4 \\ VFN++ + OF & \textbf{66.4} & \textbf{94.6} & \textbf{97.2} \\ \hline \end{tabular}
CR-19772
\begin{tabular}[l]{@{}l@{}}$\star$Three-layershardingblockchainforscalableandautomatictransaction\\$\bullet$Enhancedsystemscalabilityandtraceabilityofcriminaltransactions\\$\circ$Lackvulnerabilityanalysisandlarge-scalereal-worldsimulations\end{tabular}
CL-3250
\begin{tabular}{|p{4.32cm}|p{0.73cm}|p{0.62cm}|p{0.62cm}|}\hline \textbf{Dataset Statistics} & \textbf{Train} & \textbf{Valid} & \textbf{Test} \\ \hline Total No. of Dialogs(chat sessions) & 152391 & 16413 & 27797 \\\hline Avg. No. of Utterances per dialog & 15.9 & 15.65 & 19.44 \\\hline Total No. of Utterances having Question/Answer & 1.2M & .13M & .27M \\\hline Length of user's question (in words) & 9.7 & 9.68 & 10.28 \\\hline Length of system's response (in words) & 4.74 & 4.67 & 4.37 \\\hline Avg. No. of Dialog states per dialog & 3.89 & 3.84 & 4.53 \\\hline Vocab size (freq\textgreater=10) & 0.1M & - & - \\\hline \end{tabular}
CV-24816
\begin{tabular}{c|c|c} \hline Baseline & Rain-I & Rain-II \\ \hline Metric & PSNR/SSIM & PSNR/SSIM \\ \hline & 29.22/0.867 & 29.86/0.901 \\ & 27.22/0.832 & 29.25/0.886 \\ & 25.93/0.865 & 25.03/0.871 \\ & 27.38/0.881 & 27.56/0.899 \\ & 17.96/0.675 & 17.99/0.605 \\ & 28.43/0.848 & 30.53/0.905 \\ \hline Ours ($\alpha=1.0$) & 28.90/0.853 & 30.45/0.925 \\ Ours ($\alpha=0.6$) & 30.13/0.887 & 31.96/0.940 \\ Ours ($\alpha=0.3$) & 31.03/0.903 & 33.26/0.951 \\ Ours ($\alpha=0.0$) & \textbf{31.65}/\textbf{0.905} & \textbf{33.33}/\textbf{0.952} \\ \hline \end{tabular}
SE-4313
\begin{tabular}{p{5cm}r} \hline\hline \textbf{Process description} & \textbf{\# of papers} \\ \hline Papers obtained in primary search & 1175 \\ After Filtering Based on Title and Abstract Analysis & 326 \\ Papers obtained from Secondary Methods & 19 \\ Papers Rejected After Complete Reading & 179 \\ Papers Accepted & 164 \\ \hline \end{tabular}
CL-329
\begin{tabular}{c|c|c|c} \toprule \textbf{PoS} & \textbf{Araneum} & \textbf{RNC} & \textbf{Number of pairs} \\ \midrule Nouns & 41.67 & 43.49 & 653 \\ Adjectives & \textbf{47.92} & 42.31 & 97 \\ Verbs & 44.20 & \textbf{44.65} & 215 \\ \bottomrule \end{tabular}
CR-43625
\begin{tabular}{p{2in}m{0.9in}<{\centering}p{2.05in}} STS Monobit & No & \\ STS Runs & No & \\ STS Serial & No & \\ RGB Bit Distribution & No & \\ Genralised Minimum Distance & No & \\ RGB Permutations & No & \\ RGB Lagged Sum & No & \\ The Kolmogorov--Smirnov & No & \\ DAB byte Distribution & No & \\ DCT & No & \\ DAB Fill Tree & No & \\ DAB Fill Tree 2 & No & \\ DAB Monobit 2 & No & \\ PractRand & No & \\ \bottomrule \end{tabular}
AI-22243
\begin{tabular}{|c|l|l|l|l|l|l|} \hline \multirow{2}{*}{Classifier} & \multicolumn{2}{|c|}{DTC} & \multicolumn{2}{c|}{LR} & \multicolumn{2}{c|}{MLP} \\ \cline{2-7} & \multicolumn{1}{c|}{Accuracy} & \multicolumn{1}{c|}{F1} & \multicolumn{1}{c|}{Accuracy} & \multicolumn{1}{c|}{F1} & \multicolumn{1}{c|}{Accuracy} & \multicolumn{1}{c|}{F1} \\ \hline Original Data & $ 0.811\pm0.001$ & $0.606\pm0.002$ & $0.798\pm0.000$ & $0.378\pm0.000$ & $0.780\pm0.051$ & $0.488\pm0.075$ \\ \hline TabFairGan & $\textbf{0.783}\pm\textbf{0.001}$ & $\textbf{0.544}\pm\textbf{0.002}$ & $\textbf{0.794}\pm\textbf{0.020}$ & $\textbf{0.239}\pm\textbf{0.012}$ & $0.778\pm0.045$ & $\textbf{0.405}\pm\textbf{0.174}$ \\ \hline TGAN & $0.661\pm0.013$ & $0.503\pm0.012$ & $0.765\pm0.010$ & $0.170\pm0.008$ & $0.623\pm0.197$ & $0.376\pm0.159$ \\ \hline CTGAN & $0.777\pm0.003$ & $0.482\pm0.004$ & $\textbf{0.794}\pm\textbf{0.023}$ & $0.232\pm0.012$ & $\textbf{0.784}\pm\textbf{0.007}$ & $0.305\pm0.104$ \\ \hline \end{tabular}
CR-9831
\begin{tabular}{lcccccc} \hline data & \multicolumn{3}{c}{Few-shot} & \multicolumn{3}{c}{Zero-shot} \\ substitute & \multicolumn{1}{c}{average} & \multicolumn{1}{c}{projection} & \multicolumn{1}{c}{truncation} & \multicolumn{1}{c}{average} & \multicolumn{1}{c}{projection} & \multicolumn{1}{c}{truncation} \\ \hline \rowcolor{lightgray} pre $AUC_{l,u}$ & 0.44 & 0.44 & 0.44 & 0.44 & 0.44 & 0.44 \\ $AUC_{l,u}$ & 0.99 & 0.97 & 0.97 & 0.79 & 0.80 & 0.80 \\ \rowcolor{lightgray} pre $FID_{l}$ & 5.52 & 5.52 & 5.52 & 5.52 & 5.52 & 5.52 \\ $FID_{l}$ & 8.98 & 9.36 & 8.75 & 19.97 & 14.87 & 11.49 \\ \rowcolor{lightgray} pre $ACC$ & 0.77 & 0.77 & 0.77 & 0.77 & 0.77 & 0.77 \\ $ACC$ & 0.77 & 0.78 & 0.77 & 0.69 & 0.73 & 0.77 \\ $T$ & 1530.59 & 1544.15 & 1528.43 & 11553.41 & 6516.46 & 6616.05 \\ \hline \end{tabular}
CR-51521
\begin{tabular}[c]{@{}l@{}}ItmaybeacceptabletodisablethemaskingwhenusingtheAESciphercoreforrandomnumbergeneratione.g.insideCSRNG.\end{tabular}
AI-33077
\begin{tabular}{|c|l||c|l|} \hline \multicolumn{1}{|c|}{\textbf{State}} & \multicolumn{1}{c||}{\textbf{Description}} & \multicolumn{1}{c|}{\textbf{State}} & \multicolumn{1}{c|}{\textbf{Description}} \\ \hline S1 & Installed malicious s/w & S7 & Message interception \\ \hline S2 & Flash App Overlay & S8 & Launches new task \\ \hline S3 & URL scheme registering & S9 & Mimics trusted UI \\ \hline S4 & Implicit intent interception & S10 & User tricked \\ \hline S5 & Query legit task list & S11 & Sensitive data obtained \\ \hline S6 & Toast Window overlay & & \\ \hline \end{tabular}
CR-11509
\begin{tabular}{|c|c|c|c|c|c|c|} \hline Attack & acc & acc adv & FNR & FNR adv & change & evasion \\ [0.5ex] \hline \hline $dFGSM^k$ & 0.93 & 0.62 & 0.12 & 0.56 & 70.64 & 1.0 \\ \hline $rFGSM^k$ & 0.93 & 0.62 & 0.12 & 0.56 & 70.64 & 1.0 \\ \hline $BGA^k$ & 0.93 & 0.91 & 0.12 & 0.12 & 24.50 & 0.12 \\ \hline $BCA^k$ & 0.93 & 0.90 & 0.12 & 0.12 & 2.14 & 0.14 \\ \hline \end{tabular}
SE-4336
\begin{tabular}{lccc|ccc|ccc} \toprule \multirow{2}*{\textbf{Ablation}} & \multicolumn{3}{c}{\textbf{Argument}} & \multicolumn{3}{c}{\textbf{Return Value}} & \multicolumn{3}{c}{\textbf{User Defined}} \\ \cmidrule{2-10} & Top-1 & Top-3 & Top-5 & Top-1 & Top-3 & Top-5 & Top-1 & Top-3 & Top-5 \\ \midrule \textbf{Only Static Inference (No DL models)} & 0.13 & - & - & 0.52 & - & - & 0.08 & - & - \\ \midrule \textbf{No Type Rejection} & 0.63 & 0.72 & 0.74 & 0.60 & 0.65 & 0.66 & 0.48 & 0.56 & 0.60 \\ \midrule \midrule \textbf{No Type Correction} & 0.60 & 0.68 & 0.70 & 0.57 & 0.61 & 0.62 & 0.39 & 0.46 & 0.47 \\ \midrule \textbf{Type Correction - Only Variable Names} & 0.57 & 0.64 & 0.64 & 0.58 & 0.63 & 0.65 & 0.35 & 0.39 & 0.40 \\ \midrule \textbf{Type Correction - Only DL models} & 0.62 & 0.69 & 0.72 & 0.58 & 0.63 & 0.64 & 0.43 & 0.50 & 0.53 \\ \midrule \midrule \textbf{Overall Framework} & 0.63 & 0.71 & 0.74 & 0.60 & 0.65 & 0.66 & 0.47 & 0.55 & 0.59 \\ \bottomrule \end{tabular}
AI-40087
\begin{tabular}{|c|l|c|c|c|} \hline \multirow{2}{*}{No.} & \multirow{2}{*}{System} & \multicolumn{3}{|c|}{Aishell-2-1000hrs} \\ & & ios & android & mic \\ \hline\hline 1 & Transd. & 5.9 & 6.7 & 6.5 \\ 2 & Transd. + LF-MMI Training & 5.8 & 7.0 & 6.5 \\ 3 & \ \ + MMI Alignment Score Decoding & 5.7 & 7.0 & 6.5 \\ 4 & \ \ + MMI Rescoring & 5.7 & 6.9 & 6.5 \\ \hline 5 & Transd. + Char. CTC + LF-MMI Training & \textbf{5.4} & 6.6 & 6.5 \\ 6 & \ \ + MMI Alignment Score Decoding & \textbf{5.4} & \textbf{6.5} & \textbf{6.3} \\ 7 & \ \ + MMI Rescoring & \textbf{5.4} & 6.6 & 6.4 \\ \hline \end{tabular}
AI-26307
\begin{tabular}{c|c|c} Hyperparameter & Value & Search range \\ \hline Task-agnostic replay capacity & 500K & (200K,500K,1M) \\ C[warmup] & $\{1\}$ & N/A \\ C & \{0.8, 0.6, 0.4, 0.2, 0.1, 0.01\} & N/A \\ State encoder units & 128 & (64,128,256) \\ State encoder latent & 32 & (8,12,16,32) \\ Effect encoder units & 256 & (64,128,256) \\ Effect encoder latent & 12 & (8,12,16,32) \\ $Q_e$ units & 512 & (64,128,256,512,1024) \\ $Q_e$ learning rate & 0.0001 & (1e-3,5e-4,1e-4,5e-5,1e-5) \\ $Q_e$ target update & 15K & (1K,5K,10K,15K,20K) \\ $E$ encoder units & (256-128-64) & (512,256,128) \\ $E$ decoder units & (64-128-256) & (512,256,128) \\ $E$ latent & 8 & (4,8,16,32) \\ $E$ learning rate & 0.001 & (5e-3,1e-3,5e-4,1e-4,5e-5) \\ $\hat{e_t}$ learning rate & 0.0005 & (1e-3,5e-4,1e-4) \\ $\hat{e_t}$ units & 32 & (32,64,128,256) \\ \end{tabular}
CV-16978
\begin{tabular}{llll} \hline \multicolumn{1}{c}{Methods} & \multicolumn{3}{c}{Accuracy} \\ \cline{2-4} \multicolumn{1}{c}{} & \multicolumn{1}{c}{5 cm} & \multicolumn{1}{c}{10 cm} & \multicolumn{1}{c}{20 cm} \\ \hline DP ResNet-101 FCN & \multicolumn{1}{l}{43.05} & \multicolumn{1}{l}{65.23} & 74.17 \\ DP ResNet-101 FCN* & \multicolumn{1}{l}{51.32} & \multicolumn{1}{l}{75.50} & 85.76 \\ SlimDP HG - 1 stack & \multicolumn{1}{l}{49.89} & \multicolumn{1}{l}{74.04} & 82.98 \\ \hline Our ResNet-101 FCN & \multicolumn{1}{l}{49.09} & \multicolumn{1}{l}{73.12} & 84.51 \\ Our ResNet-101 FCN* & \multicolumn{1}{l}{53.01} & \multicolumn{1}{l}{76.77} & 87.70 \\ Our HG - 1 stack & \multicolumn{1}{l}{50.50} & \multicolumn{1}{l}{75.57} & 87.18 \\ \hline \end{tabular}
AI-33081
\begin{tabular}{|l|p{4.5cm}|} \hline \multicolumn{1}{|c|}{\textbf{Hidden states}} & \multicolumn{1}{c|}{\textbf{Obseravations}} \\ \hline S1:Installed malicious software & O1:User logged in \\ \hline S6:Toast window overlay & O5:opens malicious app \\ \hline S10:User tricked & O6:Toast window pops up; taps on button \\ \hline S11:Sensitive data obtained & O4:Unauthorized account transaction \\ \hline \end{tabular}
CR-18411
\begin{tabular}{|l|l|} \hline \rowcolor[HTML]{ EFEFEF} \textbf{Component} & \textbf{Details} \\ \hline Browser extension & FingerprintAlert 1.0 \\ \hline Programming language & Phython 3.6.3 \\ \hline Automation tool & Selenium 3.8.1 \\ \hline \end{tabular}
CV-10039
\begin{tabular}{lllllll} \hline\noalign{\smallskip} Method & \multicolumn{2}{c}{sMOTSA} & \multicolumn{2}{c}{MOTSA} & \multicolumn{2}{c}{MOTSP} \\ & Car & Ped & Car & Ped & Car & Ped \\ \noalign{\smallskip} \hline \noalign{\smallskip} \multicolumn{7}{l}{\textit{ Fully supervised}} \\ MOTSNet & 69.0 & 45.4 & 78.7 & 61.8 & 88.0 & 76.5 \\ Ours & 69.1 & 35.1 & 80.1 & 52.0 & 87.0 & 75.3 \\ \hline \multicolumn{7}{l}{\textit{ Weakly supervised}} \\ Ours & 54.6 & 20.3 & 72.5 & 39.7 & 76.6 & 65.7 \\ \hline Relative performance drop & 21.0 & 42.2 & 9.5 & 23.7 & 12.0 & 12.7 \\ \hline \end{tabular}
CV-27748
\begin{tabular}[c]{@{}l@{}} $fc$: [32, 512] \\ conv, 1 x 1, [32, 512] \\ conv, 1 x 1, 64 \\ conv, 3 x 3, 64 \\ conv, 1 x 1, 256 \\ \end{tabular}
CV-21149
\begin{tabular}{ccccc} \hline Selected Best Classes & Base. & TSSI & GLAN & GLAN + SSAN \\ \hline standing up & 85.4 & 94.1 & {\bf 97.1} & 96.3 \\ sitting down & 91.6 & 91.6 & 93.8 & {\bf 93.8} \\ walking apart & 90.6 & 91.3 & 93.1 & {\bf 96.0} \\ kicking something & 80.8 & 91.7 & 92.4 & {\bf 92.8} \\ \hline Selected Worst Classes & Base. & TSSI & GLAN & GLAN + SSAN \\ \hline writing & {\bf 52.2} & 26.5 & 39.7 & 45.6 \\ reading & 25.6 & 26.0 & 39.9 & {\bf 42.8} \\ clapping & 17.2 & 36.6 & 39.7 & {\bf 63.0} \\ playing with phone & 31.6 & 43.6 & 56.0 & {\bf 66.2} \\ \hline Overall & 68.0 & 73.1 & 80.1 & {\bf 82.4} \\ \hline \end{tabular}
CV-23715
\begin{tabular}{|c|cc|cc|cc|cc|} \hline \multirow{3}{*}{Train Data} & \multicolumn{4} {c|} {Score Average} & \multicolumn{4} {c|} {Feature + LSTM} \\ \cline{2-9} & \multicolumn{2} {c|} {Genres} & \multicolumn{2} {c|} {Keywords} & \multicolumn{2} {c|} {Genres} & \multicolumn{2} {c|} {Keywords} \\ \cline{2-9} & recall@3 & MAP & recall@3 & MAP & recall@3 & MAP & recall@3 & MAP \\ \hline Image-base & - & - & - & - & 0.477 & 0.472 & 0.199 & 0.127 \\ Movie 361 & 0.433 & 0.342 & 0.192 & 0.103 & 0.432 & 0.440 & 0.181 & 0.107 \\ Trailer 361 & 0.421 & 0.430 & 0.154 & 0.126 & 0.435 & 0.446 & 0.196 & 0.123 \\ Trailer 2K & 0.559 & 0.538 & 0.222 & 0.128 & 0.491 & 0.513 & 0.217 & 0.128 \\ Trailer 10K & \textbf{0.586} & 0.587 & 0.245 & 0.131 & \textbf{0.531} & 0.523 & 0.228 & 0.113 \\ Trailer 33K & 0.582 & \textbf{0.596} & \textbf{0.248} & \textbf{0.139} & 0.528 & \textbf{0.538} & \textbf{0.236} & \textbf{0.139} \\ \hline \end{tabular}
CR-41237
\begin{tabular}{c|c|c|c|c|c} \toprule \textbf{$script / platf.$} & \textbf{(P) Keygen} & \textbf{(P) Request} & \textbf{(P) Pub-Key} & \textbf{(P) Aggr. Cred.} & \textbf{(P) Sign Session} \\ \midrule X86-X64 & 0.0137 & 0.0391 & 0.0196 & 0.0219 & 0.0521 \\ Raspberry Pi 4 & 0.0373 & 0.0883 & 0.0475 & 0.0579 & 0.1456 \\ Raspberry Pi 0 & 0.2492 & 0.5165 & 0.3017 & 0.3469 & 0.6603 \\ \bottomrule \end{tabular}
PL-6998
\begin{tabular}{lllllll} & $l_0$ & $l_1$ & $l_2$ & $l_3$ & $l_4$ & $l_5$ \\\toprule tree 1 & b & b & 2 & a & 3.1 & a \\ tree 2 & c & c & 2 & d & 2.7 & d \\\hline & $x_0$ & $x_0$ & 2 & $x_1$ & $c_0$ & $x_1$ \end{tabular}
AI-40966
\begin{tabular}{|c|c|c|c|c|c|} \hline & IRT & PFA & BKT & DKT & DKT-DSC\tabularnewline \hline Use of student's ability & Yes & No & No & No & Yes\tabularnewline Use of item difficulty & Yes & No & No & No & No \tabularnewline Use of single skill & Yes & No & Yes & Yes & Yes \tabularnewline Use of multiple skill & No & Yes & No & No & No \tabularnewline Learn on ordered sequence & No & No & Yes & Yes & Yes \tabularnewline \hline \end{tabular}
CR-51043
\begin{tabular}{clccccc} \toprule \multirow{2}[4]{*}{\textbf{Year}} & \multirow{2}[4]{*}{\textbf{Method}} & \multicolumn{4}{c}{\textbf{Existing Attacks}} & \multicolumn{1}{c}{\multirow{2}[4]{*}{\textbf{Ours}}} \\ \cmidrule{3-6} & & \multicolumn{1}{l}{Pruning} & \multicolumn{1}{l}{Fine-tuning} & \multicolumn{1}{l}{Overwriting} & \multicolumn{1}{l}{Abiguity} & \\ \midrule 2017 & Uchida et al. & \ding{55} & \ding{55} & \ding{51} & \ding{51} & \ding{51} \\ \midrule 2019 & DeepSigns & \ding{55} & \ding{55} & \ding{55} & \ding{51} & \ding{51} \\ \midrule 2020 & Passport-Aware & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \midrule \multirow{5}[4]{*}{2021} & DeepIPR & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \cmidrule{2-7} & RIGA & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \cmidrule{2-7} & Greedy Residuals & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \cmidrule{2-7} & IPR-GAN & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \cmidrule{2-7} & Lottery Verification & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \midrule 2022 & IPR-IC & \ding{55} & \ding{55} & \ding{55} & \ding{55} & \ding{51} \\ \bottomrule \end{tabular}
CV-25870
\begin{tabular}{c|c|c|c|c|} \cline{2-5} & Area & Closeness & Variance & BoxNet \\ \hline \multicolumn{1}{ |c| }{Car} & 0.6578 & 0.6825 & 0.6346 & \textbf{0.8787} \\ \hline \multicolumn{1}{ |c| }{Cyclist} & 0.622 & 0.6252 & 0.622 & \textbf{0.7953} \\ \hline \multicolumn{1}{ |c| }{Pedestrian} & 0.5209 & 0.5368 & 0.549 & \textbf{0.6704} \\ \hline \end{tabular}
CR-28846
\begin{tabular}{p{0.8\textwidth}} \begin{equation} p(A_{n}) \approx MLM(n, T) = \frac{MLM(n, T-1) * |E_{T-1}^{n}| + E_{T}^{n}}{|E_{T}^{n}|}, MLM(n, 1) = E_{1}^{n} \end{equation} \end{tabular}
CV-28493
\begin{tabular}[c]{@{}l@{}}\textbf{45}(airplane,airport,baseballdiamond,basketball\\court,beach,bridge,chaparral,church,circularfarmland,cloud,commercial\\area,denseresidential,desert,forest,freeway,golfcourse,groundtrack\\field,harbor,industrialarea,intersection,island,lake,meadow,medium\\residential,mobilehomepark,mountain,overpass,palace,parkinglot,\\railway,railwaystation,rectangularfarmland,river,roundabout,runway,\\seaice,ship,snowberg,sparseresidential,stadium,storagetank,tennis\\court,terrace,thermalpowerstation,andwetland.)\end{tabular}
CL-729
\begin{tabular}[c]{@{}l@{}}\{...\}EdKeegandidthemoundchoresfortheclubdown\\fromWestPalmBeachto\underline{play}thegame\\before767payingcustomers\{...\}\end{tabular}
AI-4163
\begin{tabular}{|c|c|c|c|} \multicolumn{4}{c}{\# Iterations} \\ \hline dataset & MHMM & oMHMM & iMHMM \\ \hline 1 vs 8 & 6 & \textbf{2} & 3 \\ \hline 2 vs 3 & 4 & 4 & \textbf{3} \\ \hline 3 vs 4 & 4 & \textbf{3} & 5 \\ \hline 3 vs 8 & 5 & \textbf{2} & 3 \\ \hline 4 vs 14 & \textbf{3} & \textbf{3} & 4 \\ \hline \end{tabular}
CV-15822
\begin{tabular}{@{}ccccccc@{}} \hline FCL & S & V & Full & Rare & NonRare & Unseen\cr \hline\hline \hline - & \checkmark & - & 18.22 & 15.69 & 20.74 & 12.98 \\ \checkmark & \checkmark & - & 19.39 & 17.99 & 21.21 & 14.83 \\ \checkmark & - & \checkmark & 19.61 & 18.69 & 21.13 & 15.86 \\ \checkmark & \checkmark & \checkmark & 19.62 & 18.38 & 21.61 & 14.73 \\ \hline \end{tabular}
SE-20379
\begin{tabular}{lrr} {\bf {\tt javadoc} type} & {\bf no. sentences} & {\bf avgppx} \\ {\bf Non-javadoc} & 31688 & 15.58 \\ \hline @linkplain & 26 & 15.91 \\ @return & 11100 & 13.28 \\ @code & 1667 & 10.01 \\ @link & 2623 & 9.56 \\ @param & 18052 & 6.49 \\ @deprecated & 387 & 6.41 \\ @see & 1460 & 6.13 \\ @inherit & 651 & 3.53 \\ @throws & 5342 & 2.50 \\ @since & 359 & 1.44 \\ \end{tabular}
CR-36759
\begin{tabular}{|l|p{5.5cm}|} \hline \textbf{Symbols} & \textbf{Description} \\ \hline $ Hash $ & The hash operation \\ $ Xor $ & The xor operation \\ $ Add $ & The addition operation \\ $ Con $ & The concatenation operation \\ $ Enc_{sgx} $ & The encryption operation at the cloud \\ $ Dec_{sgx} $ & The decryption operation at the cloud \\ $ Sig_{sgx} $ & The signature operation \\ $ Ver_{sgx} $ & The verification operation \\ $ LA_{sgx} $ & The Local Attestation operation \\ $ RA_{sgx} $ & The Remote Attestation operation \\ $ Enc $ & The encryption operation at the user \\ $ Dec $ & The decryption operation at the user \\ \hline \end{tabular}
SE-19177
\begin{tabular}{@{}ll@{}} \toprule Evidence category & Abbreviation \\ \midrule Positive Evidence in OSS Projects & P-E-OSS \\ \midrule Positive Evidence in Different Context & P-E-OTH \\ \midrule Inconclusive Evidence & INC-E \\ \midrule No Evidence & NO-E \\ \bottomrule \end{tabular}
CR-31511
\begin{tabular}{p{2.5cm}<{\centering}p{4.5cm}<{\centering}} \toprule \textbf{Code} & \textbf{Function} \\ \midrule 0x00 & System Functions \\ 0x04 & Read \\ 0x05 & Write \\ 0x1a & Request Download \\ 0x1b & Download Block \\ 0x1c & Download End \\ 0x1d & Download Start \\ 0x1e & Upload \\ 0x1f & Upload End \\ 0x28 & PLC Control \\ 0x29 & PLC Stop \\ 0xf0 & Communication Setup \\ \bottomrule \end{tabular}
CR-1254
\begin{tabular}{lrccccc} \toprule & & \multicolumn{5}{c}{\textbf{Parameter Configuration}} \\ \cmidrule{3-7} \textbf{Backbone} & & \textbf{None} & & \textbf{FiLM} & & \textbf{All} \\ \cmidrule{1-1}\cmidrule{3-3}\cmidrule{5-5}\cmidrule{7-7} \textbf{R-50} & & 0.6 & & 0.9 & & 2.7 \\ \textbf{VIT-B} & & 1.3 & & 2.4 & & 6.5 \\ \bottomrule \end{tabular}
CV-1070
\begin{tabular}{c*{1}{|c}} \textbf{Size} & \textbf{Frequency} \\ \hline $1536\times2048$ & $4$ \\ $2160\times3840$ & $1$ \\ $2448\times3264$ & $21$ \\ $2880\times3840$ & $267$ \\ $3024\times4032$ & $15$ \\ \end{tabular}
AI-25495
\begin{tabular}{c|c|c|c|c|c} \hline Model & PSNR & SSIM & LPIPS & MPS & Runtime(s) \\ \hline OIDDR-Net (ours) & \textbf{17.62} & \textbf{0.6645} & \textbf{0.2733} & \textbf{0.6956} & 0.53 \\ \hline WDRN & 17.45 & 0.6642 & 0.2771 & 0.6935 & 0.05 \\ \hline DRN & 17.59 & 0.596 & 0.440 & 0.578 & 0.5 \\ \hline DMSHN & 17.20 & 0.5696 & 0.3712 & 0.5992 & 0.0058 \\ \hline SRN & 16.94 & 0.5660 & 0.4319 & 0.5670 & 0.87 \\ \hline Dense-GridNet & 16.67 & 0.2811 & 0.3691 & 0.9120 & 0.9326 \\ \hline Dong \emph{et al.} & 17.14 & 0.6132 & 0.2764 & 0.6684 & --- \\ \hline \end{tabular}
CV-14511
\begin{tabular}{l|cccc|ccccc} \toprule & $L_c$ & $L_s$ & $L_c+L_r$ & $L_c+L_s+L_r$ & SC & NCA & 4Conv & 8Conv & Full \\ \midrule Accuracy & 62.0 & - & 72.2 & 73.6 & 78.7 & 71.7 & 74.6 & 71.4 & 79.0 \\ f-mAP & - & 51.1 & - & 77.8 & 77.4 & 72.9 & 72.1 & 70.4 & 78.6 \\ v-mAP & - & 48.1 & - & 79.9 & 80.7 & 74.9 & 73.5 & 71.3 & 80.3 \\ \bottomrule \end{tabular}
CR-6958
\begin{tabular}{l@{\hspace{0.6em}}|c@{\hspace{0.6em}}|c@{\hspace{0.6em}}|c@{\hspace{0.6em}}|c@{\hspace{0.6em}}} \hline \textbf{Block} & \textbf{Kernel size} & \textbf{Stride} & \textbf{Padding} & \textbf{Out channels} \\ \hline conv1 & 11 & 5 & 0 & 16 \\ conv2 & 11 & 5 & 1 & 24 \\ conv3 & 12 & 7 & 0 & 48 \\ conv4 & 11 & 5 & 0 & 72 \\ conv5 & 11 & 7 & 2 & 96 \\ conv6 & 11 & 8 & 0 & 192 \\ conv7 & 11 & 5 & 0 & 240 \\ conv8 & 3 & 1 & 0 & 384 \\ \hline FC1 & \multicolumn{4}{c}{384 x 128} \\ FC2 & \multicolumn{4}{c}{128 x 2 } \\ \hline \end{tabular}
CR-47836
\begin{tabular}[c]{@{}l@{}}Finetuneswithannotateddataandenablesthemodel\\toconsultexternalknowledgesources\end{tabular}
CV-9781
\begin{tabular}{l|cc} \toprule Model & Original & Elastic \\ \midrule ResNeXt50\textsuperscript{*} & 75.29 & \textbf{77.70} \\ ResNeXt101\textsuperscript{*} & 77.47 & \textbf{78.51} \\ DLA-X60\textsuperscript{*} & 69.96 & \textbf{73.59} \\ \bottomrule \end{tabular}
CR-38132
\begin{tabular}{|l|l|l|} \hline \textbf{Exact query value} & \multicolumn{2}{|c|}{\textbf{Returned tuples/Adversarial view}} \\ \hline ~ & \textbf{Sensitive bin and data} & \textbf{Non-sensitive bin and data} \\ \hline\hline $s_2$ or $\mathit{ns}_2$ & $\mathit{SB}_2$\textbf{:}$\mathit{E(s_2)}$,$\mathit{E(s_7)}$ & $\mathit{NSB}_0$\textbf{:}$\mathit{ns}_1$,$\mathit{ns}_2$,$\mathit{ns}_3$,$\mathit{ns}_5$,$\mathit{ns}_{11}$ \\\hline $s_6$ or $\mathit{ns}_6$ & $\mathit{SB}_1$\textbf{:}$\mathit{E(s_1)}$,$\mathit{E(s_6)}$ & $\mathit{NSB}_1$\textbf{:}$\mathit{ns}_6$,$\mathit{ns}_{12}$,$\mathit{ns}_{13}$,$\mathit{ns}_{14}$,$\mathit{ns}_{15}$ \\\hline $s_7$ & $\mathit{SB}_2$\textbf{:}$E(s_2)$,$E(s_7)$ & $\mathit{NSB}_0$\textbf{:}$\mathit{ns}_1$,$\mathit{ns}_2$,$\mathit{ns}_3$,$\mathit{ns}_5$,$\mathit{ns}_{11}$ \\\hline $\mathit{ns}_{12}$ & $\mathit{SB}_1$\textbf{:}$E(s_1)$,$E(s_6)$ & $\mathit{NSB}_1$\textbf{:}$\mathit{ns}_6$,$\mathit{ns}_{12}$,$\mathit{ns}_{13}$,$\mathit{ns}_{14}$,$\mathit{ns}_{15}$ \\\hline $\mathit{ns}_{13}$ & $\mathit{SB}_1$\textbf{:}$E(s_1)$,$E(s_6)$ & $\mathit{NSB}_1$\textbf{:}$\mathit{ns}_6$,$\mathit{ns}_{12}$,$\mathit{ns}_{13}$,$\mathit{ns}_{14}$,$\mathit{ns}_{15}$ \\\hline $\mathit{ns}_{14}$ & $\mathit{SB}_1$\textbf{:}$E(s_1)$,$E(s_6)$ & $\mathit{NSB}_1$\textbf{:}$\mathit{ns}_6$,$\mathit{ns}_{12}$,$\mathit{ns}_{13}$,$\mathit{ns}_{14}$,$\mathit{ns}_{15}$ \\\hline $\mathit{ns}_{15}$ & $\mathit{SB}_1$\textbf{:}$E(s_1)$,$E(s_6)$ & $\mathit{NSB}_1$\textbf{:}$\mathit{ns}_6$,$\mathit{ns}_{12}$,$\mathit{ns}_{13}$,$\mathit{ns}_{14}$,$\mathit{ns}_{15}$ \\\hline \end{tabular}
CR-5604
\begin{tabular}[c]{@{}l@{}}analyzethesentenceandexplaincontext\\whichthecouldbeapplicable\textbackslash{}"Thesky\\isthelimit\end{tabular}