Split (1)

train · 14.7k rows

premise string	hypothesis string	label int64
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 16k16k \| 91.30 \| 84.10 \| \| --- \| --- \| --- \| \| 32k32k \| 92.50 \| 86.10 \|	1
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| Pass \| SE2 \| SE3 \| SE13 \| SE15 \| ALL \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 71.6 \| 70.3 \| 67.0 \| 72.5 \| 70.3 \| \| 2 \| 71.9 \| 70.2 \| 67.1 \| 72.8 \| 70.4 \| \| 3 \| 72.2 \| 70.5 \| 67.2 \| 72.6 \| 70.6 \| \| 4 \| 72.1 \| 70.4 \| 67.2 \| 72.4 \| 70.5 \| \| 5 \| 72.0 \| 70.4 \| 67.1 \| 71.5 \| 70.3 \|	0
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 16k16k \| 91.30 \| 84.10 \| \| --- \| --- \| --- \| \| 32k32k \| 92.50 \| 86.10 \|	1
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 4 \| 72.1 \| 70.4 \| 67.2 \| 72.4 \| 70.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 5 \| 72.0 \| 70.4 \| 67.1 \| 71.5 \| 70.3 \|	0
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 3x3x16 \| 97.79 \| 108 \| \| --- \| --- \| --- \| \| 3x3x8 \| 97.5 \| 103 \| \| 3x3x4 \| 96.6 \| 108 \| \| 3x3x2 \| 96.2 \| 106 \|	1
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 5x5x32 \| 98.6 \| 114 \| \| 5x5x16 \| 98.5 \| 110 \| \| 5x5x8 \| 98.2 \| 107 \| \| 5x5x4 \| 97.8 \| 109 \| \| 5x5x2 \| 97.38 \| 108 \|	0
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 3x3x16 \| 97.79 \| 108 \| \| --- \| --- \| --- \| \| 3x3x8 \| 97.5 \| 103 \| \| 3x3x4 \| 96.6 \| 108 \| \| 3x3x2 \| 96.2 \| 106 \|	1
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 5x5x8 \| 98.2 \| 107 \| \| --- \| --- \| --- \| \| 5x5x4 \| 97.8 \| 109 \| \| 5x5x2 \| 97.38 \| 108 \|	0
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Ironman \| 0.448078 \| 0.3873 \| 0.4016 \| 0.4013 \| 0.393 \| 0.42 \| 0.4191 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Jogging-1 \| 0.733364 \| 0.7681 \| 0.7672 \| 0.7844 \| 0.7866 \| 0.7791 \| 0.7791 \| \| Jogging-2 \| 0.766713 \| 0.7763 \| 0.7771 \| 0.7779 \| 0.774 \| 0.7631 \| 0.769 \| \| Jump \| 0.248634 \| 0.0792 \| 0.0894 \| 0....	1
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| \| MetaSDNet-01 \| pyMDNet-30 \| pyMDNet-15 \| pyMDNet-10 \| pyMDNet-05 \| pyMDNet-03 \| pyMDNet-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.7303 \| 0.7339 \| 0.7238 \| 0.7257 \| 0.6038 \| 0.5872 \| 0.6081 \| \| Gym \| 0.5068 \| 0.5141 \| 0.4567 \| 0.5176 \| 0.4625 \| 0.4484 \| 0.0254 \| \| Human2 \| 0.7011 \| 0.6262 \|...	0
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Walking \| 0.627369 \| 0.6448 \| 0.6596 \| 0.6914 \| 0.6775 \| 0.662 \| 0.6511 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Walking2 \| 0.464571 \| 0.6364 \| 0.6253 \| 0.6294 \| 0.6555 \| 0.6575 \| 0.634 \| \| Woman \| 0.702002 \| 0.7292 \| 0.7341 \| 0.7132 \| 0.7192 \| 0.7598 \| 0.7692 \| \| Average \| 0.63696061 \| 0.622858 \| 0.6176...	1
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Human2 \| 0.7011 \| 0.6262 \| 0.7356 \| 0.6797 \| 0.6608 \| 0.7351 \| 0.402 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Human3 \| 0.5686 \| 0.4716 \| 0.0142 \| 0.4545 \| 0.5624 \| 0.5327 \| 0.0008 \| \| Human4-2 \| 0.5927 \| 0.6185 \| 0.6038 \| 0.567 \| 0.5992 \| 0.5804 \| 0.0834 \| \| Human5 \| 0.7134 \| 0.624 \| 0.6092 \| 0.6533 \| 0....	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 32.80/0.9096/0.022<br>29.70/0.8270/0.022<br>27.91/0.7633/0.022 \| 32.76/0.9100/0.021<br>29.68/0.8266/0.022<br>27.91/0.7639/0.022 \| 33.23/0.9115/0.022<br>29.78/0.8308/0.022<br>28.06/0.7664/0.022 \| 33.00/0.9105/0.021<br>29.72/0.8290/0.022<br>27.97/0.7650/0.022 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	1
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| \| scale \| CT-SRCNN-13 \| Trim-S1 \| Trim-S2 \| Trim-S3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 149,344 \| 137,424 \| 123,600 \| 109,776 \| \| Set5 \| 2<br>3<br>4 \| 37.61/0.9590/0.019<br>33.76/0.9219/0.019<br>31.49/0.8849/0.020 \| 37.61/0.9591/0.018<br>33.77/0.9219/0.018<br>31.49/0.8849/0.018 \| 37.60/0.9590/0.01...	0
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 32.80/0.9096/0.022<br>29.70/0.8270/0.022<br>27.91/0.7633/0.022 \| 32.76/0.9100/0.021<br>29.68/0.8266/0.022<br>27.91/0.7639/0.022 \| 33.23/0.9115/0.022<br>29.78/0.8308/0.022<br>28.06/0.7664/0.022 \| 33.00/0.9105/0.021<br>29.72/0.8290/0.022<br>27.97/0.7650/0.022 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	1
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 33.37/0.9131/0.032<br>29.91/0.8324/0.034<br>28.20/0.7680/0.032 \| 33.38/0.9130/0.030<br>29.90/0.8326/0.031<br>28.20/0.7681/0.029 \| 33.36/0.9130/0.028<br>29.89/0.8324/0.028<br>28.19/0.7679/0.027 \| 33.36/0.9128/0.025<br>29.88/0.8322/0.026<br>28.18/0.7677/0.025 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	0
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| Road \| 0.151 \| \| --- \| --- \| \| Sign \| 0.074 \| \| Sidewalk \| 0.051 \| \| Traﬃc \| 0.076 \|	1
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| No. \| String \| Probabilityofoccurrence \| \| --- \| --- \| --- \| \| 0 \| 000 \| 0.133567088 \| \| 1 \| 001 \| 0.122682815 \| \| 2 \| 010 \| 0.130448785 \| \| 3 \| 011 \| 0.119522821 \| \| 4 \| 100 \| 0.122658224 \| \| 5 \| 101 \| 0.127181921 \| \| 6 \| 110 \| 0.119505945 \| \| 7 \| 111 \| 0.1244324 \|	0
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| Road \| 0.151 \| \| --- \| --- \| \| Sign \| 0.074 \| \| Sidewalk \| 0.051 \| \| Traﬃc \| 0.076 \|	1
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| 1 \| 001 \| 0.122682815 \| \| --- \| --- \| --- \| \| 2 \| 010 \| 0.130448785 \| \| 3 \| 011 \| 0.119522821 \| \| 4 \| 100 \| 0.122658224 \| \| 5 \| 101 \| 0.127181921 \| \| 6 \| 110 \| 0.119505945 \| \| 7 \| 111 \| 0.1244324 \|	0
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| MCcSR \| 34.14 \| 34.11 \| 34.09 \| 34.09 \| 34.07 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| Bicubic \| 0.745 \| 0.731 \| 0.698 \| 0.672 \| 0.619 \| \| ScSR \| 0.774 \| 0.772 \| 0.766 \| 0.761 \| 0.752 \| \| \| MCcSR \| 0.785 \| 0.783 \| 0.780 \| 0.775 \| 0.768 \| \| \| SCIELAB \| Bicubic \| 2.79E4 \| 2.92E4 \| 4.40E4 \| 5.25E4 \| 6...	1
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| Sigma \| Index \| Methods \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Noisy \| LSCD \| CBM3D \| WLRTR \| \| \| \| 10 \| PSNR \| 28.13 \| 33.85 \| 35.90 \| 35.91 \| \| SSIM \| 0.7020 \| 0.9188 \| 0.9501 \| 0.9511 \| \| \| 20 \| PSNR \| 22.17 \| 30.26 \| 31.85 \| 31.94 \| \| SSIM \| 0.4580 \| 0.8469 \| 0.8923 \| 0.8953 \| \| \| 30 \| PSNR \| 18.5...	0
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| MCcSR \| 34.14 \| 34.11 \| 34.09 \| 34.09 \| 34.07 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| Bicubic \| 0.745 \| 0.731 \| 0.698 \| 0.672 \| 0.619 \| \| ScSR \| 0.774 \| 0.772 \| 0.766 \| 0.761 \| 0.752 \| \| \| MCcSR \| 0.785 \| 0.783 \| 0.780 \| 0.775 \| 0.768 \| \| \| SCIELAB \| Bicubic \| 2.79E4 \| 2.92E4 \| 4.40E4 \| 5.25E4 \| 6...	1
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| 10 \| PSNR \| 28.13 \| 33.85 \| 35.90 \| 35.91 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| 0.7020 \| 0.9188 \| 0.9501 \| 0.9511 \| \| \| 20 \| PSNR \| 22.17 \| 30.26 \| 31.85 \| 31.94 \| \| SSIM \| 0.4580 \| 0.8469 \| 0.8923 \| 0.8953 \| \| \| 30 \| PSNR \| 18.58 \| 28.22 \| 29.69 \| 29.87 \| \| SSIM \| 0.3223 \| 0.7854 \| 0.8402 \| 0.8444 \| \| ...	0
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| 1st-orderGray-Edge \| 13.41 \| 11.04 \| 11.87 \| \| --- \| --- \| --- \| --- \| \| 2nd-orderGray-Edge \| 12.83 \| 10.70 \| 11.44 \|	1
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 6.14 \| 5.33 \| 5.51 \| \| GeneralGray-World \| 6.14 \| 5.33 \| 5.51 \| \| 1st-orderGray-Edge \| 5.88 \| 4.65 \| 5.11 \| \| 2nd-orderGray-Edge \| 6.10 \| 4.85 \| 5.28 \| \| Revisitedresults \| \| \| \| \| Shades-of...	0
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| Greenstabilityassumptionresults \| \| \| \| \| --- \| --- \| --- \| --- \| \| Shades-of-Gray \| 12.68 \| 10.50 \| 11.25 \| \| GeneralGray-World \| 12.68 \| 10.50 \| 11.25 \| \| 1st-orderGray-Edge \| 13.41 \| 11.04 \| 11.87 \| \| 2nd-orderGray-Edge \| 12.83 \| 10.70 \| 11.44 \|	1
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| Greenstabilityassumptionresults \| \| \| \| \| --- \| --- \| --- \| --- \| \| Shades-of-Gray \| 6.80 \| 5.30 \| 5.77 \| \| GeneralGray-World \| 6.80 \| 5.30 \| 5.77 \| \| 1st-orderGray-Edge \| 5.97 \| 4.64 \| 5.10 \| \| 2nd-orderGray-Edge \| 6.69 \| 5.17 \| 5.72 \|	0
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \|	\| C4(128×128) \| 3×38162 \| \| --- \| --- \| \| C5(128×256) \| 3×316162 \| \| C6(256×256) \| 3×316162 \| \| FC(256×256) \| 4×48163 \| \| FC(256×10) \| 4×45164 \|	1
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \|	\| Process \| LayerName \| LayerType \| fpoperationsperimage \| Device \| \| --- \| --- \| --- \| --- \| --- \| \| Forward \| FC6 \| FC-dropout \| 75497472 \| K40-cudnn \| \| FC7 \| FC-dropout \| 33554432 \| K40-cudnn \| \| \| FC8 \| FC-softmax \| 8192000 \| K40-cudnn \| \| \| FC6 \| FC-dropout \| 75497472 \| K40-cublas \| \| \| FC7 \| FC-dropout \| 3355...	0
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \| \| C4(128×128) \| 3×38162 \| \| C5(128×256) \| 3×316162 \|	\| C6(256×256) \| 3×316162 \| \| --- \| --- \| \| FC(256×256) \| 4×48163 \| \| FC(256×10) \| 4×45164 \|	1
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \| \| C4(128×128) \| 3×38162 \| \| C5(128×256) \| 3×316162 \|	\| FC7 \| FC-dropout \| 67108864 \| K40-cublas \| \| --- \| --- \| --- \| --- \| \| FC8 \| FC-softmax \| 16384000 \| K40-cublas \|	0
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| SuperVector \| 99.2 \| NA \| NA \| NA \| 98.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SRS-LBPl1out8,4 \| 99.0 \| 99.2 \| 99.4 \| 99.5 \| 97.7 \| \| SRS-LBPmetric* \| 98.6 \| 98.8 \| 98.9 \| 99.1 \| 97.8 \| \| SRS-LBPl1out \| 99.4 \| 99.4 \| 99.5 \| 99.6 \| 98.6 \|	1
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| Method \| Top1 \| Top2 \| Top5 \| Top10 \| \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 93.1 \| 97.0 \| 99.5 \| 99.5 \| \| Delta-nHinge \| 93.4 \| NA \| NA \| 98.4 \| \| CS-UMD-a* \| 95.1 \| 97.7 \| 98.6 \| 99.1 \| \| MultiFeature* \| 99.2 \| 99.6 \| 99.8 \| 99.8 \| \| SRS-LBPl1out8,4 \| 96.6 \| 98.0 \| 99.6 \| 99.8 \| \| SRS-LBPmetric* \| 96.6 \| 97...	0
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| SuperVector \| 99.2 \| NA \| NA \| NA \| 98.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SRS-LBPl1out8,4 \| 99.0 \| 99.2 \| 99.4 \| 99.5 \| 97.7 \| \| SRS-LBPmetric* \| 98.6 \| 98.8 \| 98.9 \| 99.1 \| 97.8 \| \| SRS-LBPl1out \| 99.4 \| 99.4 \| 99.5 \| 99.6 \| 98.6 \|	1
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| CS-UMD-a* \| 95.1 \| 97.7 \| 98.6 \| 99.1 \| \| --- \| --- \| --- \| --- \| --- \| \| MultiFeature* \| 99.2 \| 99.6 \| 99.8 \| 99.8 \| \| SRS-LBPl1out8,4 \| 96.6 \| 98.0 \| 99.6 \| 99.8 \| \| SRS-LBPmetric* \| 96.6 \| 97.8 \| 98.8 \| 99.4 \| \| SRS-LBPl1out \| 98.4 \| 99.2 \| 99.4 \| 99.8 \|	0
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \|	\| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| JuddModel \| 0.84 \| 0.46 \| 3.61 \| 0.84 \| 0.56 \| 0.54 \| \| GBVS \| 0.80 \| 0.51 \| 2.99 \| 0.79 \| 0.58 \| 0.50 \|	1
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \|	\| Parameter \| Single-Speaker \| VCTK \| LibriSpeech \| \| --- \| --- \| --- \| --- \| \| FFTSize \| 4096 \| 4096 \| 4096 \| \| FFTWindowSize/Shift \| 2400/600 \| 2400/600 \| 1600/400 \| \| AudioSampleRate \| 48000 \| 48000 \| 16000 \| \| ReductionFactorr \| 4 \| 4 \| 4 \| \| MelBands \| 80 \| 80 \| 80 \| \| SharpeningFactor \| 1.4 \| 1.4 \| 1.4 \| \| Charac...	0
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \| \| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \|	\| JuddModel \| 0.84 \| 0.46 \| 3.61 \| 0.84 \| 0.56 \| 0.54 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GBVS \| 0.80 \| 0.51 \| 2.99 \| 0.79 \| 0.58 \| 0.50 \|	1
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \| \| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \|	\| ReductionFactorr \| 4 \| 4 \| 4 \| \| --- \| --- \| --- \| --- \| \| MelBands \| 80 \| 80 \| 80 \| \| SharpeningFactor \| 1.4 \| 1.4 \| 1.4 \| \| CharacterEmbeddingDim. \| 256 \| 256 \| 256 \| \| EncoderLayers/Conv.Width/Channels \| 7/5/64 \| 7/5/128 \| 7/5/256 \| \| DecoderAﬃneSize \| 128,256 \| 128,256 \| 128,256 \| \| DecoderLayers/Conv.Width \| 4/5...	0
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \| \| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 ...	\| SphereFace \| 2017 \| 512 \| 99.42 \| 95 \| \| --- \| --- \| --- \| --- \| --- \| \| NeuralAggregation \| 2017 \| 128 \| - \| 95.72 \|	1
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \| \| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 ...	\| Methods \| Dims \| Year \| VEHICLEID \| VERI-776 \| \| --- \| --- \| --- \| --- \| --- \| \| TripletLoss \| 400 \| 2015 \| 0.373 \| - \| \| SoftmaxLoss \| 1024 \| 2015 \| 0.580 \| 0.343 \| \| Triplet+SoftmaxLoss \| 1024 \| 2014 \| 0.650 \| 0.558 \| \| BOW-CN \| 100 \| 2015 \| - \| 0.122 \| \| CCLVGGM \| 1024 \| 2016 \| 0.386 \| - \| \| MixedDiﬀ+CCL \| 1024 \| ...	0
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \|	\| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| --- \| --- \| --- \| --- \| --- \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 \| 98.43 \| - \| \| CenterLoss \| 2016 \| 512 \| 99.28 \| 94.9 \| \| Large-marginsoftmax \| 2016 \| 512 \| 98.71 \| - \| \| SphereFace \| 2017 \| 512 \| 99.42 \| 95 \| \| NeuralAggregation \| 2...	1
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \|	\| MixedDiﬀ+CCL \| 1024 \| 2016 \| 0.455 \| - \| \| --- \| --- \| --- \| --- \| --- \| \| HDC+Contrastive \| 384 \| 2017 \| 0.575 \| - \| \| GSTE \| 1024 \| 2017 \| 0.724 \| 0.594 \|	0
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| \| Prec. \| 91.28 \| 93.08 \| 94.45 \| \| --- \| --- \| --- \| --- \| --- \| \| \| Amb. \| 1.057 \| 1.036 \| 1.009 \|	1
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| V3 \| V2 \| V1 \| \| --- \| --- \| --- \| \| 54.5 \| - \| - \| \| 76.0 \| - \| - \| \| 74.2 \| - \| - \| \| 78.5 \| 83.5 \| 79.3 \| \| 92.6 \| - \| - \| \| 87.5 \| 84.1 \| 70.9 \| \| 90.7 \| 87.5 \| 74.0 \| \| 93.3 \| 85.9 \| 77.9 \| \| 93.8 \| 86.3 \| 79.7 \| \| 95.3 \| 88.7 \| 80.2 \|	0
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| \| Amb. \| 1.058 \| 1.042 \| 1.014 \| \| --- \| --- \| --- \| --- \| --- \| \| EMB \| Rec. \| 96.51 \| 96.47 \| 95.37 \| \| \| Prec. \| 91.28 \| 93.08 \| 94.45 \| \| \| Amb. \| 1.057 \| 1.036 \| 1.009 \|	1
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| 90.7 \| 87.5 \| 74.0 \| \| --- \| --- \| --- \| \| 93.3 \| 85.9 \| 77.9 \| \| 93.8 \| 86.3 \| 79.7 \| \| 95.3 \| 88.7 \| 80.2 \|	0
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \|	\| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SYN \| 51 \| 19 \| 30 \| 30 \| 22 \| 33 \| \| Total(Outof243) \| 88 \| 30 \| 44 \| 52 \| 89 \| 172 \|	1
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \|	\| Class \| WMaxSatz \| MiniMaxSat \| IncWMaxSatz \| Clone \| SAT4J \| MSUncore \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 99 \| 110 \| 0 \| 110 \| 100 \| 0 \| \| FIR \| 52 \| 45 \| 33 \| 54 \| 49 \| 14 \| \| SYN \| 52 \| 45 \| 55 \| 61 \| 53 \| 40 \| \| Total(Outof243 \| 203 \| 200 \| 88 \| 225 \| 202 \| 54 \|	0
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \| \| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \|	\| SYN \| 51 \| 19 \| 30 \| 30 \| 22 \| 33 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total(Outof243) \| 88 \| 30 \| 44 \| 52 \| 89 \| 172 \|	1
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \| \| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \|	\| SYN \| 52 \| 45 \| 55 \| 61 \| 53 \| 40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total(Outof243 \| 203 \| 200 \| 88 \| 225 \| 202 \| 54 \|	0
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \| \| No-Interaction \| 0.769 \| 0.706 \|	\| Target2Content \| 0.775 \| 0.712 \| \| --- \| --- \| --- \| \| IAN \| 0.786 \| 0.721 \|	1
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \| \| No-Interaction \| 0.769 \| 0.706 \|	\| Dataset \| MBSGD \| ASSGD \| ASHR \| \| --- \| --- \| --- \| --- \| \| MNIST \| 0.179s \| 0.208s \| 0.205s \| \| URL \| 0.080s \| 0.092s \| 0.092s \| \| CIFAR10 \| 0.245s \| 0.295s \| 0.296s \| \| CIFAR-DA \| 0.110s \| 0.119s \| 0.119s \|	0
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \|	\| No-Interaction \| 0.769 \| 0.706 \| \| --- \| --- \| --- \| \| Target2Content \| 0.775 \| 0.712 \| \| IAN \| 0.786 \| 0.721 \|	1
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \|	\| URL \| 0.080s \| 0.092s \| 0.092s \| \| --- \| --- \| --- \| --- \| \| CIFAR10 \| 0.245s \| 0.295s \| 0.296s \| \| CIFAR-DA \| 0.110s \| 0.119s \| 0.119s \|	0
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| g2 \| 5.032 \| 2.466 \| 5.197 \| 4.978 \| 2.153 \| 4.659 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g3 \| 5.028 \| 2.451 \| 5.183 \| 4.968 \| 2.189 \| 4.644 \| \| g4 \| 5.044 \| 2.469 \| 5.172 \| 4.991 \| 2.168 \| 4.703 \| \| g5 \| 5.002 \| 2.458 \| 5.179 \| 4.979 \| 2.170 \| 4.743 \|	1
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| Year \| Acc. \| Opt. \| Base. \| Opt.t \| TN \| TP \| FN \| FP \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1999 \| 0.5000 \| 0.7500 \| 0.7500 \| 0 \| 0 \| 4 \| 2 \| 2 \| 8 \| \| 2000 \| 0.6000 \| 0.7000 \| 0.7000 \| 0 \| 0 \| 6 \| 1 \| 3 \| 10 \| \| 2001 \| 0.5333 \| 0.6667 \| 0.5333 \| 57 \| 3 \| 5 \| 3 \| 4 \| 15 \| \| 2003 \|...	0
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| g2 \| 5.032 \| 2.466 \| 5.197 \| 4.978 \| 2.153 \| 4.659 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g3 \| 5.028 \| 2.451 \| 5.183 \| 4.968 \| 2.189 \| 4.644 \| \| g4 \| 5.044 \| 2.469 \| 5.172 \| 4.991 \| 2.168 \| 4.703 \| \| g5 \| 5.002 \| 2.458 \| 5.179 \| 4.979 \| 2.170 \| 4.743 \|	1
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| 2001 \| 0.5333 \| 0.6667 \| 0.5333 \| 57 \| 3 \| 5 \| 3 \| 4 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 2003 \| 0.2222 \| 0.5556 \| 0.5556 \| 0 \| 0 \| 2 \| 3 \| 4 \| 9 \| \| 2004 \| 0.6000 \| 0.6000 \| 0.5000 \| 15 \| 3 \| 3 \| 2 \| 2 \| 10 \| \| 2005 \| 0.6471 \| 0.6471 \| 0.5882 \| 327 \| 9 \| 2 \| 5 \| 1 \| 17 \| \| 2006 \| 0....	0
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| Stoch.Un.S. \| 1-Point \| 0.81 \| 0.76 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| Tournament \| 1-Point \| 0.85 \| 0.73 \| 0.88 \| 0.89 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \|	\| Team \| ML \| BM \| EB1 \| EB2 \| \| --- \| --- \| --- \| --- \| --- \| \| T-1 \| 0.45 \| 0.51 \| 0.52 \| 0.60 \| \| T-2 \| 0.22 \| 0.45 \| 0.38 \| 0.43 \| \| T-3 \| 0.30 \| 0.40 \| 0.47 \| 0.66 \| \| T-4 \| 0.26 \| 0.44 \| 0.42 \| 0.44 \| \| T-5 \| 0.26 \| 0.45 \| 0.45 \| 0.56 \| \| T-6 \| 0.5 \| 0.49 \| 0.55 \| 0.7 \| \| T-7 \| 0.45 \| 0.53 \| 0.56 \| 0.66 \| \| T-8 \|...	0
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| ...	\| Tournament \| 1-Point \| 0.85 \| 0.73 \| 0.88 \| 0.89 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| ...	\| T-2 \| 0.22 \| 0.45 \| 0.38 \| 0.43 \| \| --- \| --- \| --- \| --- \| --- \| \| T-3 \| 0.30 \| 0.40 \| 0.47 \| 0.66 \| \| T-4 \| 0.26 \| 0.44 \| 0.42 \| 0.44 \| \| T-5 \| 0.26 \| 0.45 \| 0.45 \| 0.56 \| \| T-6 \| 0.5 \| 0.49 \| 0.55 \| 0.7 \| \| T-7 \| 0.45 \| 0.53 \| 0.56 \| 0.66 \| \| T-8 \| 0.42 \| 0.61 \| 0.58 \| 0.74 \| \| T-9 \| 0.41 \| 0.50 \| 0.53 \| 0.76 \| \| ...	0
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 3N/5 \| 0.1958 \| 0.2094 \| 0.2114 \| 0.2132 \| 0.2183 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4N/5 \| 0.1944 \| 0.2085 \| 0.2115 \| 0.2119 \| 0.2167 \|	1
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2174 \| 0.2465 \| 0.2515 \| 0.2565 \| 0.2631 \| \| 2N/5 \| 0.2114 \| 0.2468 \| 0.2491 \| 0.2557 \| 0.2602 \| \| 3N/5 \| 0.21 \| 0.245 \| 0.2482 \| 0.2554 \| 0.2587 \| \| 4N/5 \| 0.2092 \| 0.2449 \| 0.2463 \| 0.2539 \| 0.2582 \|	0
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 3N/5 \| 0.1958 \| 0.2094 \| 0.2114 \| 0.2132 \| 0.2183 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4N/5 \| 0.1944 \| 0.2085 \| 0.2115 \| 0.2119 \| 0.2167 \|	1
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 2N/5 \| 0.2114 \| 0.2468 \| 0.2491 \| 0.2557 \| 0.2602 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3N/5 \| 0.21 \| 0.245 \| 0.2482 \| 0.2554 \| 0.2587 \| \| 4N/5 \| 0.2092 \| 0.2449 \| 0.2463 \| 0.2539 \| 0.2582 \|	0
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Random \| 0.906 \| 0.981 \| 0.910 \| 0.947 \| 0.843 \| 0.957 \| 0.905 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LSTM \| 0.946 \| 0.985 \| 0.935 \| 0.962 \| 0.945 \| 0.997 \| 0.986 \| \| d-scLSTM \| 0.925 \| 0.984 \| 0.926 \| 0.957 \| 0.948 \| 0.997 \| 0.986 \| \| hld-scLSTM \| 0.932 \| 0.987 \| 0.942 \| 0.964 \| 0.968 \| 0.997 \| 0.989 ...	1
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| \| DSTC2 \| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| B-1 \| B-2 \| B-3 \| B-4 \| M \| RL \| B-1 \| B-2 \| B-3 \| B-4 \| M \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| \| Random \| 0.875 \| 0.843 \| 0.822 \| 0.807 \| 0.564 \| ...	0
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Random \| 0.906 \| 0.981 \| 0.910 \| 0.947 \| 0.843 \| 0.957 \| 0.905 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LSTM \| 0.946 \| 0.985 \| 0.935 \| 0.962 \| 0.945 \| 0.997 \| 0.986 \| \| d-scLSTM \| 0.925 \| 0.984 \| 0.926 \| 0.957 \| 0.948 \| 0.997 \| 0.986 \| \| hld-scLSTM \| 0.932 \| 0.987 \| 0.942 \| 0.964 \| 0.968 \| 0.997 \| 0.989 ...	1
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Random \| 0.875 \| 0.843 \| 0.822 \| 0.807 \| 0.564 \| 0.852 \| 0.872 \| 0.813 \| 0.765 \| 0.721 \| 0.504 \| \| LSTM \| 0.900 \| 0.879 \| 0.863 \| 0.851 \| 0.610 \| 0.888 \| 0.98...	0
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \|	\| d(G),d(G)uu \| degreeofnodeuinG,G \| \| --- \| --- \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributionon[0,1] \| \| r←Reσ \| asamplefromthedistributionRσ \| \| p(p)iuv \| probabilityofedgee(e)iuv \| \| np \| numberofpotentialedges,\\|E\\|=m+np \| \| A,A \| adjacencymatricesofG,G...	1
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \|	\| Variable \| Description \| \| --- \| --- \| \| V \| setofnodesinagraph \| \| E \| setofedgesinagraph \| \| B(e) \| edgecapacityforedgee \| \| C(v) \| nodecapacityfornodev \| \| D \| thesetofﬂowdemands \| \| δ(v) \| theedgesleavingvertexv \| \| δ(v) \| theedgesenteringvertexv \| \| P \| thesetofwalksfromsourcestodestinations \| \| p<br>i,π \| walk-...	0
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \| \| d(G),d(G)uu \| degreeofnodeuinG,G \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributi...	\| p(p)iuv \| probabilityofedgee(e)iuv \| \| --- \| --- \| \| np \| numberofpotentialedges,\\|E\\|=m+np \| \| A,A \| adjacencymatricesofG,G0 \| \| PRW \| randomwalktransitionmatrixofG0 \| \| B \| uncertainadjacencymatrix,B=AP \| \| t \| walklength \| \| S \| switchingmatrix \| \| TV \| totaldegreevariance \|	1
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \| \| d(G),d(G)uu \| degreeofnodeuinG,G \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributi...	\| w(e)i \| edge-based;theamountofunprocessedﬂowi<br>thattraverseseonitswayfromstotii \| \| --- \| --- \| \| p(v)i \| edge-based;theamountofprocessingdoneat<br>nodevfortheithﬂow \|	0
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \|	\| SMsandSPs \| 13and192 \| \| --- \| --- \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	1
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \|	\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	0
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \|	\| CUDASDK \| 7.5 \| \| --- \| --- \| \| PCIeBus \| PCIex16Gen2 \|	1
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \|	\| DeviceMemorySize \| 5GBGDDR5 \| \| --- \| --- \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	0
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \|	\| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	1
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \|	\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r1 \| SMGavaskar \| 125 \| 10122 \| 236 \| s1 \| \| r2 \| LalaAmarnath \| 24 \| 878 \| 118 \| s1 \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r4 \| Gavaskar \| 125 \| 10122 \| 236 \| s2 \| \| r5 \| MohinderAmarnath \| 69 \| 4378 \| 138 \| s2 \| \|...	0
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \|	\| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	1
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \|	\| r8 \| SurinderAmarnath \| 10 \| 550 \| 124 \| s3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	0
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \| \| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 8...	\| Cleaning \| 26.56 \| 30.47 \| 38.28 \| 46.09 \| \| --- \| --- \| --- \| --- \| --- \| \| Socializing \| 52.85 \| 37.31 \| 31.60 \| 45.08 \| \| Shopping \| 40.16 \| 27.87 \| 63.93 \| 64.75 \| \| Biking \| 19.57 \| 23.19 \| 78.26 \| 81.88 \| \| Family \| 70.82 \| 87.42 \| 86.69 \| 90.15 \| \| Hygiene \| 52.36 \| 46.85 \| 51.57 \| 62.60 \| \| Avg.ClassAccuracy ...	1
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \| \| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 8...	\| Classes \| NumberofImages \| PercentofDataset \| \| --- \| --- \| --- \| \| Chores \| 725 \| 1.79 \| \| Driving \| 1031 \| 2.54 \| \| Cooking \| 759 \| 1.87 \| \| Exercising \| 502 \| 1.24 \| \| Reading \| 1414 \| 3.48 \| \| Presentation \| 848 \| 2.09 \| \| Dogs \| 1149 \| 2.83 \| \| Resting \| 106 \| 0.26 \| \| Eating \| 4699 \| 11.58 \| \| Working \| 13895 \|...	0
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \|	\| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| --- \| --- \| --- \| --- \| --- \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 80.59 \| 87.06 \| \| Dogs \| 62.17 \| 44.35 \| 55.65 \| 66.09 \| \| Resting \| 72.73 \| 54.55 \| 27.27 \| 45.45 \| \| Eating \| 77.14 \| ...	1
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \|	\| Cleaning \| 642 \| 1.59 \| \| --- \| --- \| --- \| \| Socializing \| 970 \| 2.39 \| \| Shopping \| 606 \| 1.49 \| \| Biking \| 696 \| 1.71 \| \| Family \| 8267 \| 20.37 \| \| Hygiene \| 1266 \| 3.12 \|	0
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \| \| ............ \| \| ............ \| \| ............ \|	\| ............ \| \| --- \| \| 1x8100(HOGFeatureofimage(N-1)) \| \| 1x8100(HOGFeatureofimageN) \|	1
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \| \| ............ \| \| ............ \| \| ............ \|	\| image \| base \| mtbdd<br>(1)(2) \| \| --- \| --- \| --- \| \| image0 \| 3574 \| 134.612236 \| \| image1 \| 32.01 \| 1.7841.90 \| \| image2 \| 350.72 \| 13.88285.48 \| \| image3 \| 4000 \| 214.641898.21 \| \| image4 \| sameasimage3 \| 148.95533.52 \|	0
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \|	\| ............ \| \| --- \| \| ............ \| \| ............ \| \| ............ \| \| 1x8100(HOGFeatureofimage(N-1)) \| \| 1x8100(HOGFeatureofimageN) \|	1
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \|	\| image2 \| 350.72 \| 13.88285.48 \| \| --- \| --- \| --- \| \| image3 \| 4000 \| 214.641898.21 \| \| image4 \| sameasimage3 \| 148.95533.52 \|	0
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| MBCR \| n=d+t,d=k,t≥1 \| \| \| --- \| --- \| --- \| \| MBCR \| n=d+t,d≥k,t≥1 \| \| \| MSCR \| n=d+2,k=t=2 \| \| \| MSCR \| n=2k,d=n−2,k≥2,t=2 \| \| \| MSCR \| n=2k,d=n−t,k≥2,k≥t≥2<br>(repairofsystematicnodesonly) \| \|	1
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| Code(n=2k-1,d=2k-2) \| k=4 \| k=8 \| k=16 \| \| --- \| --- \| --- \| --- \| \| VanillaPMMSR(non-systematic) \| 50 \| 75 \| 87 \| \| SparsePMMSR(non-systematic) \| 64 \| 85 \| 93 \| \| VanillaPMMSR(systematic) \| 0 \| 0 \| 0 \| \| SparsePMMSR(systematic) \| 50 \| 75 \| 88 \|	0
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| MBCR \| n=d+t,d=k,t≥1 \| \| \| --- \| --- \| --- \| \| MBCR \| n=d+t,d≥k,t≥1 \| \| \| MSCR \| n=d+2,k=t=2 \| \| \| MSCR \| n=2k,d=n−2,k≥2,t=2 \| \| \| MSCR \| n=2k,d=n−t,k≥2,k≥t≥2<br>(repairofsystematicnodesonly) \| \|	1
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| VanillaPMMSR(systematic) \| 0 \| 0 \| 0 \| \| --- \| --- \| --- \| --- \| \| SparsePMMSR(systematic) \| 50 \| 75 \| 88 \|	0

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premise string	hypothesis string	label int64
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 16k16k \| 91.30 \| 84.10 \| \| --- \| --- \| --- \| \| 32k32k \| 92.50 \| 86.10 \|	1
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| Pass \| SE2 \| SE3 \| SE13 \| SE15 \| ALL \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 71.6 \| 70.3 \| 67.0 \| 72.5 \| 70.3 \| \| 2 \| 71.9 \| 70.2 \| 67.1 \| 72.8 \| 70.4 \| \| 3 \| 72.2 \| 70.5 \| 67.2 \| 72.6 \| 70.6 \| \| 4 \| 72.1 \| 70.4 \| 67.2 \| 72.4 \| 70.5 \| \| 5 \| 72.0 \| 70.4 \| 67.1 \| 71.5 \| 70.3 \|	0
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 16k16k \| 91.30 \| 84.10 \| \| --- \| --- \| --- \| \| 32k32k \| 92.50 \| 86.10 \|	1
\| Quantity \| TestingSet \| CrossValidation \| \| --- \| --- \| --- \| \| 0.5k0.5k \| 83 \| 79.90 \| \| 1k1k \| 84 \| 81.70 \| \| 2k2k \| 85.80 \| 83.30 \| \| 4k4k \| 87.20 \| 83.80 \| \| 8k8k \| 89.10 \| 83.50 \|	\| 4 \| 72.1 \| 70.4 \| 67.2 \| 72.4 \| 70.5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 5 \| 72.0 \| 70.4 \| 67.1 \| 71.5 \| 70.3 \|	0
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 3x3x16 \| 97.79 \| 108 \| \| --- \| --- \| --- \| \| 3x3x8 \| 97.5 \| 103 \| \| 3x3x4 \| 96.6 \| 108 \| \| 3x3x2 \| 96.2 \| 106 \|	1
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 5x5x32 \| 98.6 \| 114 \| \| 5x5x16 \| 98.5 \| 110 \| \| 5x5x8 \| 98.2 \| 107 \| \| 5x5x4 \| 97.8 \| 109 \| \| 5x5x2 \| 97.38 \| 108 \|	0
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 3x3x16 \| 97.79 \| 108 \| \| --- \| --- \| --- \| \| 3x3x8 \| 97.5 \| 103 \| \| 3x3x4 \| 96.6 \| 108 \| \| 3x3x2 \| 96.2 \| 106 \|	1
\| FilterSize \| TestAccuracy \| Timeinseconds(onTitanX) \| \| --- \| --- \| --- \| \| 3x3x32 \| 98 \| 117 \|	\| 5x5x8 \| 98.2 \| 107 \| \| --- \| --- \| --- \| \| 5x5x4 \| 97.8 \| 109 \| \| 5x5x2 \| 97.38 \| 108 \|	0
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Ironman \| 0.448078 \| 0.3873 \| 0.4016 \| 0.4013 \| 0.393 \| 0.42 \| 0.4191 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Jogging-1 \| 0.733364 \| 0.7681 \| 0.7672 \| 0.7844 \| 0.7866 \| 0.7791 \| 0.7791 \| \| Jogging-2 \| 0.766713 \| 0.7763 \| 0.7771 \| 0.7779 \| 0.774 \| 0.7631 \| 0.769 \| \| Jump \| 0.248634 \| 0.0792 \| 0.0894 \| 0....	1
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| \| MetaSDNet-01 \| pyMDNet-30 \| pyMDNet-15 \| pyMDNet-10 \| pyMDNet-05 \| pyMDNet-03 \| pyMDNet-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.7303 \| 0.7339 \| 0.7238 \| 0.7257 \| 0.6038 \| 0.5872 \| 0.6081 \| \| Gym \| 0.5068 \| 0.5141 \| 0.4567 \| 0.5176 \| 0.4625 \| 0.4484 \| 0.0254 \| \| Human2 \| 0.7011 \| 0.6262 \|...	0
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Walking \| 0.627369 \| 0.6448 \| 0.6596 \| 0.6914 \| 0.6775 \| 0.662 \| 0.6511 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Walking2 \| 0.464571 \| 0.6364 \| 0.6253 \| 0.6294 \| 0.6555 \| 0.6575 \| 0.634 \| \| Woman \| 0.702002 \| 0.7292 \| 0.7341 \| 0.7132 \| 0.7192 \| 0.7598 \| 0.7692 \| \| Average \| 0.63696061 \| 0.622858 \| 0.6176...	1
\| \| MetaCREST-01 \| CREST \| CREST-Base \| CREST-20 \| CREST-10 \| CREST-05 \| CREST-01 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Girl2 \| 0.594508 \| 0.5784 \| 0.5672 \| 0.5937 \| 0.6041 \| 0.6046 \| 0.6139 \| \| Gym \| 0.512262 \| 0.4946 \| 0.5087 \| 0.4976 \| 0.5196 \| 0.4995 \| 0.4956 \| \| Human2 \| 0.63796 \| 0.6762 \| 0.676 \|...	\| Human2 \| 0.7011 \| 0.6262 \| 0.7356 \| 0.6797 \| 0.6608 \| 0.7351 \| 0.402 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Human3 \| 0.5686 \| 0.4716 \| 0.0142 \| 0.4545 \| 0.5624 \| 0.5327 \| 0.0008 \| \| Human4-2 \| 0.5927 \| 0.6185 \| 0.6038 \| 0.567 \| 0.5992 \| 0.5804 \| 0.0834 \| \| Human5 \| 0.7134 \| 0.624 \| 0.6092 \| 0.6533 \| 0....	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \| \| $ \| 4in5 \| Usedinbusiness \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	1
\| Non-EnglishorMath \| Frequency \| Comments \| \| --- \| --- \| --- \| \| Ø \| 1in1,000 \| ForSwedishnames \| \| π \| 1in5 \| Commoninmath \|	\| $ \| 4in5 \| Usedinbusiness \| \| --- \| --- \| --- \| \| Ψ \| 1in40,000 \| Unexplainedusage \|	0
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 32.80/0.9096/0.022<br>29.70/0.8270/0.022<br>27.91/0.7633/0.022 \| 32.76/0.9100/0.021<br>29.68/0.8266/0.022<br>27.91/0.7639/0.022 \| 33.23/0.9115/0.022<br>29.78/0.8308/0.022<br>28.06/0.7664/0.022 \| 33.00/0.9105/0.021<br>29.72/0.8290/0.022<br>27.97/0.7650/0.022 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	1
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| \| scale \| CT-SRCNN-13 \| Trim-S1 \| Trim-S2 \| Trim-S3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 149,344 \| 137,424 \| 123,600 \| 109,776 \| \| Set5 \| 2<br>3<br>4 \| 37.61/0.9590/0.019<br>33.76/0.9219/0.019<br>31.49/0.8849/0.020 \| 37.61/0.9591/0.018<br>33.77/0.9219/0.018<br>31.49/0.8849/0.018 \| 37.60/0.9590/0.01...	0
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 32.80/0.9096/0.022<br>29.70/0.8270/0.022<br>27.91/0.7633/0.022 \| 32.76/0.9100/0.021<br>29.68/0.8266/0.022<br>27.91/0.7639/0.022 \| 33.23/0.9115/0.022<br>29.78/0.8308/0.022<br>28.06/0.7664/0.022 \| 33.00/0.9105/0.021<br>29.72/0.8290/0.022<br>27.97/0.7650/0.022 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	1
\| \| scale \| One-shot-independent \| One-shot-greedy \| CascadeTrimming \| Trim-Train \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Param. \| - \| 58,544 \| 58,544 \| 58,544 \| 58,544 \| \| Set5 \| 2<br>3<br>4 \| 37.20/0.9546/0.012<br>33.24/0.9161/0.012<br>31.02/0.8802/0.013 \| 37.18/0.9549/0.012<br>33.21/0.9158/0.012<br>31.05/0.8805/0...	\| Set14 \| 2<br>3<br>4 \| 33.37/0.9131/0.032<br>29.91/0.8324/0.034<br>28.20/0.7680/0.032 \| 33.38/0.9130/0.030<br>29.90/0.8326/0.031<br>28.20/0.7681/0.029 \| 33.36/0.9130/0.028<br>29.89/0.8324/0.028<br>28.19/0.7679/0.027 \| 33.36/0.9128/0.025<br>29.88/0.8322/0.026<br>28.18/0.7677/0.025 \| \| --- \| --- \| --- \| --- \| --- \| --- ...	0
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| Road \| 0.151 \| \| --- \| --- \| \| Sign \| 0.074 \| \| Sidewalk \| 0.051 \| \| Traﬃc \| 0.076 \|	1
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| No. \| String \| Probabilityofoccurrence \| \| --- \| --- \| --- \| \| 0 \| 000 \| 0.133567088 \| \| 1 \| 001 \| 0.122682815 \| \| 2 \| 010 \| 0.130448785 \| \| 3 \| 011 \| 0.119522821 \| \| 4 \| 100 \| 0.122658224 \| \| 5 \| 101 \| 0.127181921 \| \| 6 \| 110 \| 0.119505945 \| \| 7 \| 111 \| 0.1244324 \|	0
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| Road \| 0.151 \| \| --- \| --- \| \| Sign \| 0.074 \| \| Sidewalk \| 0.051 \| \| Traﬃc \| 0.076 \|	1
\| Object \| Probabilityofco-occurrence \| \| --- \| --- \| \| Man \| 0.240 \|	\| 1 \| 001 \| 0.122682815 \| \| --- \| --- \| --- \| \| 2 \| 010 \| 0.130448785 \| \| 3 \| 011 \| 0.119522821 \| \| 4 \| 100 \| 0.122658224 \| \| 5 \| 101 \| 0.127181921 \| \| 6 \| 110 \| 0.119505945 \| \| 7 \| 111 \| 0.1244324 \|	0
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| MCcSR \| 34.14 \| 34.11 \| 34.09 \| 34.09 \| 34.07 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| Bicubic \| 0.745 \| 0.731 \| 0.698 \| 0.672 \| 0.619 \| \| ScSR \| 0.774 \| 0.772 \| 0.766 \| 0.761 \| 0.752 \| \| \| MCcSR \| 0.785 \| 0.783 \| 0.780 \| 0.775 \| 0.768 \| \| \| SCIELAB \| Bicubic \| 2.79E4 \| 2.92E4 \| 4.40E4 \| 5.25E4 \| 6...	1
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| Sigma \| Index \| Methods \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Noisy \| LSCD \| CBM3D \| WLRTR \| \| \| \| 10 \| PSNR \| 28.13 \| 33.85 \| 35.90 \| 35.91 \| \| SSIM \| 0.7020 \| 0.9188 \| 0.9501 \| 0.9511 \| \| \| 20 \| PSNR \| 22.17 \| 30.26 \| 31.85 \| 31.94 \| \| SSIM \| 0.4580 \| 0.8469 \| 0.8923 \| 0.8953 \| \| \| 30 \| PSNR \| 18.5...	0
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| MCcSR \| 34.14 \| 34.11 \| 34.09 \| 34.09 \| 34.07 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| Bicubic \| 0.745 \| 0.731 \| 0.698 \| 0.672 \| 0.619 \| \| ScSR \| 0.774 \| 0.772 \| 0.766 \| 0.761 \| 0.752 \| \| \| MCcSR \| 0.785 \| 0.783 \| 0.780 \| 0.775 \| 0.768 \| \| \| SCIELAB \| Bicubic \| 2.79E4 \| 2.92E4 \| 4.40E4 \| 5.25E4 \| 6...	1
\| Measure \| Method \| σ=0 \| σ=4 \| σ=6 \| σ=8 \| σ=12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| PSNR \| Bicubic \| 33.08 \| 32.99 \| 32.75 \| 32.50 \| 31.88 \| \| ScSR \| 34.00 \| 33.95 \| 33.92 \| 33.90 \| 33.86 \| \|	\| 10 \| PSNR \| 28.13 \| 33.85 \| 35.90 \| 35.91 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SSIM \| 0.7020 \| 0.9188 \| 0.9501 \| 0.9511 \| \| \| 20 \| PSNR \| 22.17 \| 30.26 \| 31.85 \| 31.94 \| \| SSIM \| 0.4580 \| 0.8469 \| 0.8923 \| 0.8953 \| \| \| 30 \| PSNR \| 18.58 \| 28.22 \| 29.69 \| 29.87 \| \| SSIM \| 0.3223 \| 0.7854 \| 0.8402 \| 0.8444 \| \| ...	0
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| 1st-orderGray-Edge \| 13.41 \| 11.04 \| 11.87 \| \| --- \| --- \| --- \| --- \| \| 2nd-orderGray-Edge \| 12.83 \| 10.70 \| 11.44 \|	1
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 6.14 \| 5.33 \| 5.51 \| \| GeneralGray-World \| 6.14 \| 5.33 \| 5.51 \| \| 1st-orderGray-Edge \| 5.88 \| 4.65 \| 5.11 \| \| 2nd-orderGray-Edge \| 6.10 \| 4.85 \| 5.28 \| \| Revisitedresults \| \| \| \| \| Shades-of...	0
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| Greenstabilityassumptionresults \| \| \| \| \| --- \| --- \| --- \| --- \| \| Shades-of-Gray \| 12.68 \| 10.50 \| 11.25 \| \| GeneralGray-World \| 12.68 \| 10.50 \| 11.25 \| \| 1st-orderGray-Edge \| 13.41 \| 11.04 \| 11.87 \| \| 2nd-orderGray-Edge \| 12.83 \| 10.70 \| 11.44 \|	1
\| method \| mean() \| median() \| trimean() \| \| --- \| --- \| --- \| --- \| \| Originallyreportedresults \| \| \| \| \| Shades-of-Gray \| 11.55 \| 9.70 \| 10.23 \| \| GeneralGray-World \| 11.55 \| 9.70 \| 10.23 \| \| 1st-orderGray-Edge \| 10.58 \| 8.84 \| 9.18 \| \| 2nd-orderGray-Edge \| 10.68 \| 9.02 \| 9.40 \| \| Revisitedresults \| \| \| \| \| Sha...	\| Greenstabilityassumptionresults \| \| \| \| \| --- \| --- \| --- \| --- \| \| Shades-of-Gray \| 6.80 \| 5.30 \| 5.77 \| \| GeneralGray-World \| 6.80 \| 5.30 \| 5.77 \| \| 1st-orderGray-Edge \| 5.97 \| 4.64 \| 5.10 \| \| 2nd-orderGray-Edge \| 6.69 \| 5.17 \| 5.72 \|	0
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \|	\| C4(128×128) \| 3×38162 \| \| --- \| --- \| \| C5(128×256) \| 3×316162 \| \| C6(256×256) \| 3×316162 \| \| FC(256×256) \| 4×48163 \| \| FC(256×10) \| 4×45164 \|	1
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \|	\| Process \| LayerName \| LayerType \| fpoperationsperimage \| Device \| \| --- \| --- \| --- \| --- \| --- \| \| Forward \| FC6 \| FC-dropout \| 75497472 \| K40-cudnn \| \| FC7 \| FC-dropout \| 33554432 \| K40-cudnn \| \| \| FC8 \| FC-softmax \| 8192000 \| K40-cudnn \| \| \| FC6 \| FC-dropout \| 75497472 \| K40-cublas \| \| \| FC7 \| FC-dropout \| 3355...	0
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \| \| C4(128×128) \| 3×38162 \| \| C5(128×256) \| 3×316162 \|	\| C6(256×256) \| 3×316162 \| \| --- \| --- \| \| FC(256×256) \| 4×48163 \| \| FC(256×10) \| 4×45164 \|	1
\| Layer \| KernelSizeNNNsetspatkeep \| \| --- \| --- \| \| C1(3×128) \| 3×33166 \| \| C2(128×128) \| 3×38163 \| \| C3(128×128) \| 3×38162 \| \| C4(128×128) \| 3×38162 \| \| C5(128×256) \| 3×316162 \|	\| FC7 \| FC-dropout \| 67108864 \| K40-cublas \| \| --- \| --- \| --- \| --- \| \| FC8 \| FC-softmax \| 16384000 \| K40-cublas \|	0
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| SuperVector \| 99.2 \| NA \| NA \| NA \| 98.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SRS-LBPl1out8,4 \| 99.0 \| 99.2 \| 99.4 \| 99.5 \| 97.7 \| \| SRS-LBPmetric* \| 98.6 \| 98.8 \| 98.9 \| 99.1 \| 97.8 \| \| SRS-LBPl1out \| 99.4 \| 99.4 \| 99.5 \| 99.6 \| 98.6 \|	1
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| Method \| Top1 \| Top2 \| Top5 \| Top10 \| \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 93.1 \| 97.0 \| 99.5 \| 99.5 \| \| Delta-nHinge \| 93.4 \| NA \| NA \| 98.4 \| \| CS-UMD-a* \| 95.1 \| 97.7 \| 98.6 \| 99.1 \| \| MultiFeature* \| 99.2 \| 99.6 \| 99.8 \| 99.8 \| \| SRS-LBPl1out8,4 \| 96.6 \| 98.0 \| 99.6 \| 99.8 \| \| SRS-LBPmetric* \| 96.6 \| 97...	0
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| SuperVector \| 99.2 \| NA \| NA \| NA \| 98.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| SRS-LBPl1out8,4 \| 99.0 \| 99.2 \| 99.4 \| 99.5 \| 97.7 \| \| SRS-LBPmetric* \| 98.6 \| 98.8 \| 98.9 \| 99.1 \| 97.8 \| \| SRS-LBPl1out \| 99.4 \| 99.4 \| 99.5 \| 99.6 \| 98.6 \|	1
\| Method \| SoftTop1 \| SoftTop2 \| SoftTop5 \| SoftTop10 \| HardTop2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tebessa-C* \| 97.6 \| 97.9 \| 98.3 \| 98.5 \| 96.1 \| \| Multi-feature* \| 99.4 \| 99.5 \| 99.6 \| 99.7 \| 98.3 \|	\| CS-UMD-a* \| 95.1 \| 97.7 \| 98.6 \| 99.1 \| \| --- \| --- \| --- \| --- \| --- \| \| MultiFeature* \| 99.2 \| 99.6 \| 99.8 \| 99.8 \| \| SRS-LBPl1out8,4 \| 96.6 \| 98.0 \| 99.6 \| 99.8 \| \| SRS-LBPmetric* \| 96.6 \| 97.8 \| 98.8 \| 99.4 \| \| SRS-LBPl1out \| 98.4 \| 99.2 \| 99.4 \| 99.8 \|	0
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \|	\| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| JuddModel \| 0.84 \| 0.46 \| 3.61 \| 0.84 \| 0.56 \| 0.54 \| \| GBVS \| 0.80 \| 0.51 \| 2.99 \| 0.79 \| 0.58 \| 0.50 \|	1
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \|	\| Parameter \| Single-Speaker \| VCTK \| LibriSpeech \| \| --- \| --- \| --- \| --- \| \| FFTSize \| 4096 \| 4096 \| 4096 \| \| FFTWindowSize/Shift \| 2400/600 \| 2400/600 \| 1600/400 \| \| AudioSampleRate \| 48000 \| 48000 \| 16000 \| \| ReductionFactorr \| 4 \| 4 \| 4 \| \| MelBands \| 80 \| 80 \| 80 \| \| SharpeningFactor \| 1.4 \| 1.4 \| 1.4 \| \| Charac...	0
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \| \| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \|	\| JuddModel \| 0.84 \| 0.46 \| 3.61 \| 0.84 \| 0.56 \| 0.54 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GBVS \| 0.80 \| 0.51 \| 2.99 \| 0.79 \| 0.58 \| 0.50 \|	1
\| SaliencyModels \| AUC-Judd↑ \| SIM↑ \| EMD↓ \| AUC-Borji↑ \| shuﬄedAUC↑ \| CC↑ \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DeepFix(Proposed) \| 0.87 \| 0.75 \| 1.11 \| 0.81 \| 0.57 \| 0.88 \| \| ContextAwareSaliency \| 0.77 \| 0.50 \| 3.09 \| 0.76 \| 0.60 \| 0.42 \|	\| ReductionFactorr \| 4 \| 4 \| 4 \| \| --- \| --- \| --- \| --- \| \| MelBands \| 80 \| 80 \| 80 \| \| SharpeningFactor \| 1.4 \| 1.4 \| 1.4 \| \| CharacterEmbeddingDim. \| 256 \| 256 \| 256 \| \| EncoderLayers/Conv.Width/Channels \| 7/5/64 \| 7/5/128 \| 7/5/256 \| \| DecoderAﬃneSize \| 128,256 \| 128,256 \| 128,256 \| \| DecoderLayers/Conv.Width \| 4/5...	0
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \| \| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 ...	\| SphereFace \| 2017 \| 512 \| 99.42 \| 95 \| \| --- \| --- \| --- \| --- \| --- \| \| NeuralAggregation \| 2017 \| 128 \| - \| 95.72 \|	1
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \| \| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 ...	\| Methods \| Dims \| Year \| VEHICLEID \| VERI-776 \| \| --- \| --- \| --- \| --- \| --- \| \| TripletLoss \| 400 \| 2015 \| 0.373 \| - \| \| SoftmaxLoss \| 1024 \| 2015 \| 0.580 \| 0.343 \| \| Triplet+SoftmaxLoss \| 1024 \| 2014 \| 0.650 \| 0.558 \| \| BOW-CN \| 100 \| 2015 \| - \| 0.122 \| \| CCLVGGM \| 1024 \| 2016 \| 0.386 \| - \| \| MixedDiﬀ+CCL \| 1024 \| ...	0
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \|	\| FaceNet \| 2015 \| 128 \| 99.63 \| 9512 \| \| --- \| --- \| --- \| --- \| --- \| \| Deepembedding \| 2015 \| 128 \| 99.13 \| - \| \| Multimodaldeepface \| 2015 \| 9000 \| 98.43 \| - \| \| CenterLoss \| 2016 \| 512 \| 99.28 \| 94.9 \| \| Large-marginsoftmax \| 2016 \| 512 \| 98.71 \| - \| \| SphereFace \| 2017 \| 512 \| 99.42 \| 95 \| \| NeuralAggregation \| 2...	1
\| Methods \| Year \| Dims \| LWF \| YTF \| \| --- \| --- \| --- \| --- \| --- \| \| DeepFace \| 2014 \| 4096 \| 97.35 \| 91.4 \| \| LearningFace \| 2014 \| 10575 \| 97.73 \| 92.2 \| \| MDML-DCPs \| 2015 \| 1024 \| 98.95 \| 97.3 \|	\| MixedDiﬀ+CCL \| 1024 \| 2016 \| 0.455 \| - \| \| --- \| --- \| --- \| --- \| --- \| \| HDC+Contrastive \| 384 \| 2017 \| 0.575 \| - \| \| GSTE \| 1024 \| 2017 \| 0.724 \| 0.594 \|	0
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| \| Prec. \| 91.28 \| 93.08 \| 94.45 \| \| --- \| --- \| --- \| --- \| --- \| \| \| Amb. \| 1.057 \| 1.036 \| 1.009 \|	1
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| V3 \| V2 \| V1 \| \| --- \| --- \| --- \| \| 54.5 \| - \| - \| \| 76.0 \| - \| - \| \| 74.2 \| - \| - \| \| 78.5 \| 83.5 \| 79.3 \| \| 92.6 \| - \| - \| \| 87.5 \| 84.1 \| 70.9 \| \| 90.7 \| 87.5 \| 74.0 \| \| 93.3 \| 85.9 \| 77.9 \| \| 93.8 \| 86.3 \| 79.7 \| \| 95.3 \| 88.7 \| 80.2 \|	0
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| \| Amb. \| 1.058 \| 1.042 \| 1.014 \| \| --- \| --- \| --- \| --- \| --- \| \| EMB \| Rec. \| 96.51 \| 96.47 \| 95.37 \| \| \| Prec. \| 91.28 \| 93.08 \| 94.45 \| \| \| Amb. \| 1.057 \| 1.036 \| 1.009 \|	1
\| TEXT \| V \| V+R \| V+R+C \| \| \| --- \| --- \| --- \| --- \| --- \| \| ARK \| Rec. \| 98.05 \| 97.60 \| 96.98 \| \| \| Prec. \| 94.13 \| 95.28 \| 96.19 \| \| \| Amb. \| 1.042 \| 1.024 \| 1.008 \| \| HIST \| Rec. \| 97.03 \| 96.52 \| 95.62 \| \| \| Prec. \| 94.13 \| 92.59 \| 94.33 \| \| \| Amb. \| 1.058 \| 1.042 \| 1.013 \| \| MAN \| Rec. \| 97.03 \| 96.47 \| 95...	\| 90.7 \| 87.5 \| 74.0 \| \| --- \| --- \| --- \| \| 93.3 \| 85.9 \| 77.9 \| \| 93.8 \| 86.3 \| 79.7 \| \| 95.3 \| 88.7 \| 80.2 \|	0
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \|	\| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SYN \| 51 \| 19 \| 30 \| 30 \| 22 \| 33 \| \| Total(Outof243) \| 88 \| 30 \| 44 \| 52 \| 89 \| 172 \|	1
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \|	\| Class \| WMaxSatz \| MiniMaxSat \| IncWMaxSatz \| Clone \| SAT4J \| MSUncore \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 99 \| 110 \| 0 \| 110 \| 100 \| 0 \| \| FIR \| 52 \| 45 \| 33 \| 54 \| 49 \| 14 \| \| SYN \| 52 \| 45 \| 55 \| 61 \| 53 \| 40 \| \| Total(Outof243 \| 203 \| 200 \| 88 \| 225 \| 202 \| 54 \|	0
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \| \| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \|	\| SYN \| 51 \| 19 \| 30 \| 30 \| 22 \| 33 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total(Outof243) \| 88 \| 30 \| 44 \| 52 \| 89 \| 172 \|	1
\| Class \| BSOLO \| PBS \| Pueblo \| Minisat+ \| SAT4J(PB) \| WBO \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IND \| 17 \| 0 \| 0 \| 0 \| 60 \| 110 \| \| FIR \| 20 \| 11 \| 14 \| 22 \| 7 \| 39 \|	\| SYN \| 52 \| 45 \| 55 \| 61 \| 53 \| 40 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total(Outof243 \| 203 \| 200 \| 88 \| 225 \| 202 \| 54 \|	0
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \| \| No-Interaction \| 0.769 \| 0.706 \|	\| Target2Content \| 0.775 \| 0.712 \| \| --- \| --- \| --- \| \| IAN \| 0.786 \| 0.721 \|	1
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \| \| No-Interaction \| 0.769 \| 0.706 \|	\| Dataset \| MBSGD \| ASSGD \| ASHR \| \| --- \| --- \| --- \| --- \| \| MNIST \| 0.179s \| 0.208s \| 0.205s \| \| URL \| 0.080s \| 0.092s \| 0.092s \| \| CIFAR10 \| 0.245s \| 0.295s \| 0.296s \| \| CIFAR-DA \| 0.110s \| 0.119s \| 0.119s \|	0
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \|	\| No-Interaction \| 0.769 \| 0.706 \| \| --- \| --- \| --- \| \| Target2Content \| 0.775 \| 0.712 \| \| IAN \| 0.786 \| 0.721 \|	1
\| Dataset \| Restaurants \| Laptops \| \| --- \| --- \| --- \| \| No-Target \| 0.772 \| 0.708 \|	\| URL \| 0.080s \| 0.092s \| 0.092s \| \| --- \| --- \| --- \| --- \| \| CIFAR10 \| 0.245s \| 0.295s \| 0.296s \| \| CIFAR-DA \| 0.110s \| 0.119s \| 0.119s \|	0
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| g2 \| 5.032 \| 2.466 \| 5.197 \| 4.978 \| 2.153 \| 4.659 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g3 \| 5.028 \| 2.451 \| 5.183 \| 4.968 \| 2.189 \| 4.644 \| \| g4 \| 5.044 \| 2.469 \| 5.172 \| 4.991 \| 2.168 \| 4.703 \| \| g5 \| 5.002 \| 2.458 \| 5.179 \| 4.979 \| 2.170 \| 4.743 \|	1
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| Year \| Acc. \| Opt. \| Base. \| Opt.t \| TN \| TP \| FN \| FP \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1999 \| 0.5000 \| 0.7500 \| 0.7500 \| 0 \| 0 \| 4 \| 2 \| 2 \| 8 \| \| 2000 \| 0.6000 \| 0.7000 \| 0.7000 \| 0 \| 0 \| 6 \| 1 \| 3 \| 10 \| \| 2001 \| 0.5333 \| 0.6667 \| 0.5333 \| 57 \| 3 \| 5 \| 3 \| 4 \| 15 \| \| 2003 \|...	0
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| g2 \| 5.032 \| 2.466 \| 5.197 \| 4.978 \| 2.153 \| 4.659 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g3 \| 5.028 \| 2.451 \| 5.183 \| 4.968 \| 2.189 \| 4.644 \| \| g4 \| 5.044 \| 2.469 \| 5.172 \| 4.991 \| 2.168 \| 4.703 \| \| g5 \| 5.002 \| 2.458 \| 5.179 \| 4.979 \| 2.170 \| 4.743 \|	1
\| Functions \| MBA \| TSSW \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| R100 \| C160 \| F66 \| R100 \| C160 \| F66 \| \| \| g \| 5.000 \| 2.467 \| 5.188 \| 4.959 \| 2.155 \| 4.666 \|	\| 2001 \| 0.5333 \| 0.6667 \| 0.5333 \| 57 \| 3 \| 5 \| 3 \| 4 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 2003 \| 0.2222 \| 0.5556 \| 0.5556 \| 0 \| 0 \| 2 \| 3 \| 4 \| 9 \| \| 2004 \| 0.6000 \| 0.6000 \| 0.5000 \| 15 \| 3 \| 3 \| 2 \| 2 \| 10 \| \| 2005 \| 0.6471 \| 0.6471 \| 0.5882 \| 327 \| 9 \| 2 \| 5 \| 1 \| 17 \| \| 2006 \| 0....	0
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| Stoch.Un.S. \| 1-Point \| 0.81 \| 0.76 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| Tournament \| 1-Point \| 0.85 \| 0.73 \| 0.88 \| 0.89 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \|	\| Team \| ML \| BM \| EB1 \| EB2 \| \| --- \| --- \| --- \| --- \| --- \| \| T-1 \| 0.45 \| 0.51 \| 0.52 \| 0.60 \| \| T-2 \| 0.22 \| 0.45 \| 0.38 \| 0.43 \| \| T-3 \| 0.30 \| 0.40 \| 0.47 \| 0.66 \| \| T-4 \| 0.26 \| 0.44 \| 0.42 \| 0.44 \| \| T-5 \| 0.26 \| 0.45 \| 0.45 \| 0.56 \| \| T-6 \| 0.5 \| 0.49 \| 0.55 \| 0.7 \| \| T-7 \| 0.45 \| 0.53 \| 0.56 \| 0.66 \| \| T-8 \|...	0
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| ...	\| Tournament \| 1-Point \| 0.85 \| 0.73 \| 0.88 \| 0.89 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| Selection \| Crossover \| Initialpopulation \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| DirectElim. \| 1-Point \| 0.81 \| 0.71 \| 0.84 \| 0.85 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| RouletteWheel \| 1-Point \| 0.83 \| 0.74 \| 0.91 \| 0.91 \| \| \| \| \| \| \| \| \| \| \| \| \| \| ...	\| T-2 \| 0.22 \| 0.45 \| 0.38 \| 0.43 \| \| --- \| --- \| --- \| --- \| --- \| \| T-3 \| 0.30 \| 0.40 \| 0.47 \| 0.66 \| \| T-4 \| 0.26 \| 0.44 \| 0.42 \| 0.44 \| \| T-5 \| 0.26 \| 0.45 \| 0.45 \| 0.56 \| \| T-6 \| 0.5 \| 0.49 \| 0.55 \| 0.7 \| \| T-7 \| 0.45 \| 0.53 \| 0.56 \| 0.66 \| \| T-8 \| 0.42 \| 0.61 \| 0.58 \| 0.74 \| \| T-9 \| 0.41 \| 0.50 \| 0.53 \| 0.76 \| \| ...	0
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 3N/5 \| 0.1958 \| 0.2094 \| 0.2114 \| 0.2132 \| 0.2183 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4N/5 \| 0.1944 \| 0.2085 \| 0.2115 \| 0.2119 \| 0.2167 \|	1
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2174 \| 0.2465 \| 0.2515 \| 0.2565 \| 0.2631 \| \| 2N/5 \| 0.2114 \| 0.2468 \| 0.2491 \| 0.2557 \| 0.2602 \| \| 3N/5 \| 0.21 \| 0.245 \| 0.2482 \| 0.2554 \| 0.2587 \| \| 4N/5 \| 0.2092 \| 0.2449 \| 0.2463 \| 0.2539 \| 0.2582 \|	0
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 3N/5 \| 0.1958 \| 0.2094 \| 0.2114 \| 0.2132 \| 0.2183 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 4N/5 \| 0.1944 \| 0.2085 \| 0.2115 \| 0.2119 \| 0.2167 \|	1
\| TSS \| P1 \| P2 \| P3 \| P4 \| P5 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| N/5 \| 0.2016 \| 0.2131 \| 0.2159 \| 0.2167 \| 0.2252 \| \| 2N/5 \| 0.1975 \| 0.2111 \| 0.2125 \| 0.2142 \| 0.2198 \|	\| 2N/5 \| 0.2114 \| 0.2468 \| 0.2491 \| 0.2557 \| 0.2602 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3N/5 \| 0.21 \| 0.245 \| 0.2482 \| 0.2554 \| 0.2587 \| \| 4N/5 \| 0.2092 \| 0.2449 \| 0.2463 \| 0.2539 \| 0.2582 \|	0
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Random \| 0.906 \| 0.981 \| 0.910 \| 0.947 \| 0.843 \| 0.957 \| 0.905 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LSTM \| 0.946 \| 0.985 \| 0.935 \| 0.962 \| 0.945 \| 0.997 \| 0.986 \| \| d-scLSTM \| 0.925 \| 0.984 \| 0.926 \| 0.957 \| 0.948 \| 0.997 \| 0.986 \| \| hld-scLSTM \| 0.932 \| 0.987 \| 0.942 \| 0.964 \| 0.968 \| 0.997 \| 0.989 ...	1
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| \| DSTC2 \| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| B-1 \| B-2 \| B-3 \| B-4 \| M \| RL \| B-1 \| B-2 \| B-3 \| B-4 \| M \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| \| Random \| 0.875 \| 0.843 \| 0.822 \| 0.807 \| 0.564 \| ...	0
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Random \| 0.906 \| 0.981 \| 0.910 \| 0.947 \| 0.843 \| 0.957 \| 0.905 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LSTM \| 0.946 \| 0.985 \| 0.935 \| 0.962 \| 0.945 \| 0.997 \| 0.986 \| \| d-scLSTM \| 0.925 \| 0.984 \| 0.926 \| 0.957 \| 0.948 \| 0.997 \| 0.986 \| \| hld-scLSTM \| 0.932 \| 0.987 \| 0.942 \| 0.964 \| 0.968 \| 0.997 \| 0.989 ...	1
\| \| DSTC2 \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| Greedy<br>Matching \| Skip<br>Thought \| Embedding<br>Average \| Vector<br>Extrema \| \| \| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \|	\| Gold \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| 1.00 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Random \| 0.875 \| 0.843 \| 0.822 \| 0.807 \| 0.564 \| 0.852 \| 0.872 \| 0.813 \| 0.765 \| 0.721 \| 0.504 \| \| LSTM \| 0.900 \| 0.879 \| 0.863 \| 0.851 \| 0.610 \| 0.888 \| 0.98...	0
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \|	\| d(G),d(G)uu \| degreeofnodeuinG,G \| \| --- \| --- \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributionon[0,1] \| \| r←Reσ \| asamplefromthedistributionRσ \| \| p(p)iuv \| probabilityofedgee(e)iuv \| \| np \| numberofpotentialedges,\\|E\\|=m+np \| \| A,A \| adjacencymatricesofG,G...	1
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \|	\| Variable \| Description \| \| --- \| --- \| \| V \| setofnodesinagraph \| \| E \| setofedgesinagraph \| \| B(e) \| edgecapacityforedgee \| \| C(v) \| nodecapacityfornodev \| \| D \| thesetofﬂowdemands \| \| δ(v) \| theedgesleavingvertexv \| \| δ(v) \| theedgesenteringvertexv \| \| P \| thesetofwalksfromsourcestodestinations \| \| p<br>i,π \| walk-...	0
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \| \| d(G),d(G)uu \| degreeofnodeuinG,G \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributi...	\| p(p)iuv \| probabilityofedgee(e)iuv \| \| --- \| --- \| \| np \| numberofpotentialedges,\\|E\\|=m+np \| \| A,A \| adjacencymatricesofG,G0 \| \| PRW \| randomwalktransitionmatrixofG0 \| \| B \| uncertainadjacencymatrix,B=AP \| \| t \| walklength \| \| S \| switchingmatrix \| \| TV \| totaldegreevariance \|	1
\| Symbol \| Deﬁnition \| \| --- \| --- \| \| )G=(V,E0G0 \| \\|truegraphwithn=\\|V\\|andm=\\|EG0 \| \| G=(V,E,p) \| uncertaingraphconstructedfromG0 \| \| G=(V,E)G \| samplegraphfromG,G(cid:118)G \| \| d(G),d(G)uu \| degreeofnodeuinG,G \| \| ∆(d) \| numberofnodeshavingdegreedinG \| \| N(u) \| neighborsofnodeuinG \| \| Rσ \| truncatednormaldistributi...	\| w(e)i \| edge-based;theamountofunprocessedﬂowi<br>thattraverseseonitswayfromstotii \| \| --- \| --- \| \| p(v)i \| edge-based;theamountofprocessingdoneat<br>nodevfortheithﬂow \|	0
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \|	\| SMsandSPs \| 13and192 \| \| --- \| --- \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	1
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \|	\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	0
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \|	\| CUDASDK \| 7.5 \| \| --- \| --- \| \| PCIeBus \| PCIex16Gen2 \|	1
\| CPUModel \| IntelXeonE5-2690 \| \| --- \| --- \| \| CPUCores \| 8 \| \| DRAMSize \| 128GB \| \| GPUModel \| TeslaK20c \| \| DeviceMemorySize \| 5GBGDDR5 \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \|	\| DeviceMemorySize \| 5GBGDDR5 \| \| --- \| --- \| \| SMsandSPs \| 13and192 \| \| ComputeCapability \| 3.5 \| \| CUDASDK \| 7.5 \| \| PCIeBus \| PCIex16Gen2 \|	0
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \|	\| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	1
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \|	\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r1 \| SMGavaskar \| 125 \| 10122 \| 236 \| s1 \| \| r2 \| LalaAmarnath \| 24 \| 878 \| 118 \| s1 \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \| \| r4 \| Gavaskar \| 125 \| 10122 \| 236 \| s2 \| \| r5 \| MohinderAmarnath \| 69 \| 4378 \| 138 \| s2 \| \|...	0
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \|	\| r6 \| YuvrajSingh \| 40 \| 1900 \| 169 \| s2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	1
\| record \| Name \| Matches \| Runs \| Highest \| Source \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r3 \| YajurvindraSingh \| 4 \| 109 \| 43 \| s1 \|	\| r8 \| SurinderAmarnath \| 10 \| 550 \| 124 \| s3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| r9 \| YograjSingh \| 1 \| 10 \| 6 \| s3 \| \| r10 \| YSingh \| 40 \| 1900 \| 169 \| s4 \|	0
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \| \| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 8...	\| Cleaning \| 26.56 \| 30.47 \| 38.28 \| 46.09 \| \| --- \| --- \| --- \| --- \| --- \| \| Socializing \| 52.85 \| 37.31 \| 31.60 \| 45.08 \| \| Shopping \| 40.16 \| 27.87 \| 63.93 \| 64.75 \| \| Biking \| 19.57 \| 23.19 \| 78.26 \| 81.88 \| \| Family \| 70.82 \| 87.42 \| 86.69 \| 90.15 \| \| Hygiene \| 52.36 \| 46.85 \| 51.57 \| 62.60 \| \| Avg.ClassAccuracy ...	1
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \| \| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 8...	\| Classes \| NumberofImages \| PercentofDataset \| \| --- \| --- \| --- \| \| Chores \| 725 \| 1.79 \| \| Driving \| 1031 \| 2.54 \| \| Cooking \| 759 \| 1.87 \| \| Exercising \| 502 \| 1.24 \| \| Reading \| 1414 \| 3.48 \| \| Presentation \| 848 \| 2.09 \| \| Dogs \| 1149 \| 2.83 \| \| Resting \| 106 \| 0.26 \| \| Eating \| 4699 \| 11.58 \| \| Working \| 13895 \|...	0
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \|	\| Cooking \| 25.66 \| 35.53 \| 47.37 \| 60.53 \| \| --- \| --- \| --- \| --- \| --- \| \| Exercising \| 44.00 \| 63.00 \| 69.00 \| 73.00 \| \| Reading \| 68.55 \| 49.12 \| 30.04 \| 53.36 \| \| Presentation \| 80.00 \| 72.35 \| 80.59 \| 87.06 \| \| Dogs \| 62.17 \| 44.35 \| 55.65 \| 66.09 \| \| Resting \| 72.73 \| 54.55 \| 27.27 \| 45.45 \| \| Eating \| 77.14 \| ...	1
\| \| kNN \| RDF \| CNN \| CNN+LF \| \| --- \| --- \| --- \| --- \| --- \| \| Chores \| 33.10 \| 17.24 \| 00.69 \| 20.00 \| \| Driving \| 55.07 \| 60.87 \| 98.55 \| 96.62 \|	\| Cleaning \| 642 \| 1.59 \| \| --- \| --- \| --- \| \| Socializing \| 970 \| 2.39 \| \| Shopping \| 606 \| 1.49 \| \| Biking \| 696 \| 1.71 \| \| Family \| 8267 \| 20.37 \| \| Hygiene \| 1266 \| 3.12 \|	0
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \| \| ............ \| \| ............ \| \| ............ \|	\| ............ \| \| --- \| \| 1x8100(HOGFeatureofimage(N-1)) \| \| 1x8100(HOGFeatureofimageN) \|	1
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \| \| ............ \| \| ............ \| \| ............ \|	\| image \| base \| mtbdd<br>(1)(2) \| \| --- \| --- \| --- \| \| image0 \| 3574 \| 134.612236 \| \| image1 \| 32.01 \| 1.7841.90 \| \| image2 \| 350.72 \| 13.88285.48 \| \| image3 \| 4000 \| 214.641898.21 \| \| image4 \| sameasimage3 \| 148.95533.52 \|	0
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \|	\| ............ \| \| --- \| \| ............ \| \| ............ \| \| ............ \| \| 1x8100(HOGFeatureofimage(N-1)) \| \| 1x8100(HOGFeatureofimageN) \|	1
\| 1x8100(HOGFeatureofimage1) \| \| --- \| \| 1x8100(HOGFeatureofimage2) \| \| 1x8100(HOGFeatureofimage3) \| \| 1x8100(HOGFeatureofimage4) \|	\| image2 \| 350.72 \| 13.88285.48 \| \| --- \| --- \| --- \| \| image3 \| 4000 \| 214.641898.21 \| \| image4 \| sameasimage3 \| 148.95533.52 \|	0
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| MBCR \| n=d+t,d=k,t≥1 \| \| \| --- \| --- \| --- \| \| MBCR \| n=d+t,d≥k,t≥1 \| \| \| MSCR \| n=d+2,k=t=2 \| \| \| MSCR \| n=2k,d=n−2,k≥2,t=2 \| \| \| MSCR \| n=2k,d=n−t,k≥2,k≥t≥2<br>(repairofsystematicnodesonly) \| \|	1
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| Code(n=2k-1,d=2k-2) \| k=4 \| k=8 \| k=16 \| \| --- \| --- \| --- \| --- \| \| VanillaPMMSR(non-systematic) \| 50 \| 75 \| 87 \| \| SparsePMMSR(non-systematic) \| 64 \| 85 \| 93 \| \| VanillaPMMSR(systematic) \| 0 \| 0 \| 0 \| \| SparsePMMSR(systematic) \| 50 \| 75 \| 88 \|	0
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| MBCR \| n=d+t,d=k,t≥1 \| \| \| --- \| --- \| --- \| \| MBCR \| n=d+t,d≥k,t≥1 \| \| \| MSCR \| n=d+2,k=t=2 \| \| \| MSCR \| n=2k,d=n−2,k≥2,t=2 \| \| \| MSCR \| n=2k,d=n−t,k≥2,k≥t≥2<br>(repairofsystematicnodesonly) \| \|	1
\| Type \| CodeParameters \| Ref. \| \| --- \| --- \| --- \| \| MBCR \| n≥d+t,d≥k,t≥1 \| \|	\| VanillaPMMSR(systematic) \| 0 \| 0 \| 0 \| \| --- \| --- \| --- \| --- \| \| SparsePMMSR(systematic) \| 50 \| 75 \| 88 \|	0