Split (1)

train · 14.7k rows

premise string	hypothesis string	label int64
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \|	\| 5LSTMlayers \| 79.4 \| \| --- \| --- \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \| \| ResNet-152 \| 87.2 \| \| ResNet-50+8layers \| 88.2 \|	1
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \|	\| Numberoflayers \| 512width \| 1024width \| \| --- \| --- \| --- \| \| 3 \| 48.5 \| 38.1 \| \| 4 \| 38.3 \| 34.6 \| \| 5 \| 36.2 \| 31.9 \| \| 6 \| 34.5 \| 30.3 \| \| 7 \| 32.5 \| 30.6 \| \| 8 \| 31.0 \| 31.3 \| \| 9 \| 31.4 \| 29.4 \| \| 10 \| 31.5 \| 29.4 \|	0
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \| \| 5LSTMlayers \| 79.4 \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \|	\| ResNet-152 \| 87.2 \| \| --- \| --- \| \| ResNet-50+8layers \| 88.2 \|	1
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \| \| 5LSTMlayers \| 79.4 \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \|	\| 7 \| 32.5 \| 30.6 \| \| --- \| --- \| --- \| \| 8 \| 31.0 \| 31.3 \| \| 9 \| 31.4 \| 29.4 \| \| 10 \| 31.5 \| 29.4 \|	0
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| Fractal<br>LowResolution<br>Pattern \| .5820<br>.5663<br>.5638 \| .5518<br>.5418<br>.5509 \| .0302<br>.0245<br>.0129 \| \| --- \| --- \| --- \| --- \| \| Overall \| .6221 \| .5755 \| .0466 \|	1
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| \| Gradual \| Sharp \| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Video \| #T \| TP \| FP \| FN \| P \| R \| F \| #T \| TP \| FP \| FN \| P \| \| 1004ABCa \| 203 \| 177 \| 9 \| 26 \| 0.952 \| 0.872 \| 0.91 \| 224 \| 209 \| 18 \| 15 \| 0.921 \| \| 1012CNNa \| 170 \| 149 \| 23 \| 21 \|...	0
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| Fractal<br>LowResolution<br>Pattern \| .5820<br>.5663<br>.5638 \| .5518<br>.5418<br>.5509 \| .0302<br>.0245<br>.0129 \| \| --- \| --- \| --- \| --- \| \| Overall \| .6221 \| .5755 \| .0466 \|	1
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| 1101CNNa \| 204 \| 187 \| 13 \| 17 \| 0.935 \| 0.917 \| 0.926 \| 191 \| 180 \| 12 \| 11 \| 0.938 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1109ABCa \| 170 \| 159 \| 12 \| 11 \| 0.93 \| 0.935 \| 0.933 \| 257 \| 241 \| 11 \| 16 \| 0.956 \| \| 1123CNNa \| 126 \| 99 \| 32 \| 27 \| 0.756 \| 0.786 \| 0.77 \| 236 \| ...	0
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \|	\| TempCoh \| 45.4 \| 15.9 \| \| --- \| --- \| --- \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \| \| Our-Stack-of-Diﬀ. \| 60.3 \| 32.5 \| \| Randweights-Stack-of-Diﬀ. \| 51.3 \| 28.3 \| \| ImageNetweights-Stack-of-Diﬀ. \| 70.1 \| 40.8 \|	1
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \|	\| Method \| UCF101 \| HMDB \| HW2 \| \| --- \| --- \| --- \| --- \| \| IDT+FV<br>IDT+HSV<br>IDT+Actionness<br>VideoDarwin<br>RankPool+IDT \| 85.9<br>87.9<br>-<br>-<br>91.4 \| 57.2<br>61.1<br>60.4<br>63.7<br>66.9 \| 64.3<br>-<br>-<br>73.7<br>76.7 \| \| Two-stream(avg)<br>Two-stream(SVM)<br>Two-streamLSTM<br>LTC<br>TSN(2modalities)<br>...	0
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \| \| TempCoh \| 45.4 \| 15.9 \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \|	\| Our-Stack-of-Diﬀ. \| 60.3 \| 32.5 \| \| --- \| --- \| --- \| \| Randweights-Stack-of-Diﬀ. \| 51.3 \| 28.3 \| \| ImageNetweights-Stack-of-Diﬀ. \| 70.1 \| 40.8 \|	1
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \| \| TempCoh \| 45.4 \| 15.9 \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \|	\| Two-stream(avg)<br>Two-stream(SVM)<br>Two-streamLSTM<br>LTC<br>TSN(2modalities)<br>TSN(3modalities)<br>Two-streamI3D \| 86.9<br>88.0<br>88.6<br>91.7<br>94.0<br>94.2<br>98.0 \| 58.0<br>59.4<br>-<br>64.8<br>68.5<br>69.4<br>80.7 \| -<br>-<br>-<br>-<br>-<br>-<br>- \| \| --- \| --- \| --- \| --- \| \| Two-streamfusion<br>ST-ResNet ...	0
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \| \| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| Cliﬀbar \| ...	\| Surfer \| 80.32 \| 98.14 \| 70 \| 64 \| 24 \| 71 \| 61 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Sylvester \| 88.85 \| 99.03 \| 77 \| 33 \| 45 \| 77 \| 80 \| \| Cardark \| 100 \| 100 \| 36 \| 25 \| 54 \| 48 \| 18 \| \| Faceocc1 \| 100 \| 100 \| 43.4 \| 80 \| 74 \| 60 \| 82 \| \| Faceocc2 \| 100 \| 100 \| 99 \| 70 \| 66 \| 62 \| 70 \| \| Twinning \|...	1
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \| \| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| Cliﬀbar \| ...	\| Network \| n \| k \| SLPA \| std \| r \| Copra \| std \| v \| LFM \| Cﬁnder \| k \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| karate \| 34 \| 4.5 \| 0.65 \| 0.21 \| 0.33 \| 0.44 \| 0.18 \| 3 \| 0.42 \| 0.52 \| 3 \| \| dolphins \| 62 \| 5.1 \| 0.76 \| 0.03 \| 0.45 \| 0.70 \| 0.04 \| 4 \| 0.28 \| 0.66 \| 3 \| \| lesmis \| 7...	0
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \|	\| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cliﬀbar \| 98.48 \| 87.88 \| 99 \| 53 \| 47 \| 71 \| 8 \| \| Woman \| 22.39 \| 17.91 \| 11.6 \| 14 \| 17 \| 15 \| 67 \| \| Surfer \| 80.32 \| 98.14 \| 70 \| 64 \| 24 \| 71 \| 61 \| \| Sylvester \| 88.85 \| 99.03 \| 77 \| 33 \| 45 \| 77 \| 80 \| \| Cardark...	1
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \|	\| email \| 1133 \| 9.6 \| 0.64 \| 0.03 \| 0.45 \| 0.51 \| 0.22 \| 2 \| 0.25 \| 0.46 \| 3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| CA-GrQc \| 4730 \| 5.6 \| 0.76 \| 0.00 \| 0.45 \| 0.71 \| 0.01 \| 1 \| 0.45 \| 0.51 \| 3 \| \| PGP \| 10680 \| 4.5 \| 0.82 \| 0.01 \| 0.45 \| 0.78 \| 0.02 \| 9 \| 0.44 \| 0.57 \| 3 \|	0
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \| \| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \|	\| fr2/desk \| 0.009 \| 0.071 \| 0.034 \| 0.017 \| 0.057 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr2/xyz \| 0.004 \| 0.011 \| 0.029 \| 0.018 \| - \| \| fr3/oﬃce \| 0.010 \| 0.017 \| 0.030 \| 0.035 \| - \| \| fr3/nst \| 0.019 \| 0.016 \| 0.031 \| 0.018 \| - \|	1
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \| \| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \|	\| Noiselevel \| Easy \| Hard \| Tough \| \| --- \| --- \| --- \| --- \| \| SIFT \| 0.453 \| 0.193 \| 0.086 \| \| TFeat \| 0.467 \| 0.259 \| 0.131 \| \| DeepCompare \| 0.399 \| 0.199 \| 0.095 \| \| DeepDesc \| 0.430 \| 0.239 \| 0.125 \| \| HardNet \| 0.658 \| 0.479 \| 0.303 \| \| SKAR-EgoSeg \| 0.495 \| 0.226 \| 0.102 \| \| SKAR-EgoSeg* \| 0.507 \| 0.251 \| 0.12...	0
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \|	\| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \| \| fr2/desk \| 0.009 \| 0.071 \| 0.034 \| 0.017 \| 0.057 \| \| fr2/xyz \| 0.004 \| 0.011 \| 0.029 \| 0.018 \| - \| \| fr3/oﬃce \| 0.010 \| 0.017 \| 0.030 \| 0.035 \| - \| \| fr3/nst \| 0.019 \| 0.016 \| 0...	1
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \|	\| HardNet \| 0.658 \| 0.479 \| 0.303 \| \| --- \| --- \| --- \| --- \| \| SKAR-EgoSeg \| 0.495 \| 0.226 \| 0.102 \| \| SKAR-EgoSeg* \| 0.507 \| 0.251 \| 0.123 \| \| SKAR-HPatches \| 0.677 \| 0.494 \| 0.313 \| \| SKAR-HPatches* \| 0.695 \| 0.525 \| 0.342 \|	0
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| s11-f6<br>(unsat) \| t<br>n \| 391,4<br>34714 \| 402,9<br>61523 \| 412,7<br>40892 \| 416,8<br>62557 \| >1h<br>– \| >1h<br>– \| >1h<br>– \| 307,6<br>27949 \| 488,4<br>63447 \| 465,2<br>37954 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Averagedcputime \| t \| 47,9 \| 91,5 \| 68,8 \| 91,5 \| – \| – \| – \|...	1
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel<br>by#del \| fully<br>assigned \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ehi-85-0<br>(unsat) \| t<br>n \| 2,1<br>722 \| 94,2(0,15)<br>8005(4) \| 2,7<br>61 \| 11,7<br>3 \| 12,1<br>3 \| 12<br>3 \| ...	0
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| s3-f11<br>(unsat) \| t<br>n \| 9,6<br>1078 \| 41,4<br>18728 \| 9,9<br>861 \| 36,2<br>17211 \| >1h<br>– \| >1h<br>– \| >1h<br>– \| 5,3<br>641 \| 47,5<br>18862 \| 10,4<br>889 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g8-f10<br>(sat) \| t<br>n \| 15<br>4193 \| 72,5<br>26535 \| 45,2<br>6018 \| 62,4<br...	1
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| frb30-15-2<br>(sat) \| t<br>n \| 63,7<br>21330 \| 123,6(97,8)<br>38765(27458) \| 55,4<br>20019 \| 273,4<br>79936 \| 5,4<br>1306 \| 391,3<br>81911 \| 86,8<br>26596 \| 91,2<br>26296 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 40-8-753-<br>0,1(sat) \| t<br>n \| 76,5<br>21164 \| 70,9(45,9)<br>21369(13422) \| 60,...	0
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| TotalSphere \| 1015 \| 1300 \| 1528 \| 1831 \| 2102 \| 2373 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpeedupTerasort \| 2.6 \| 2.5 \| 2.4 \| 2.6 \| 2.4 \| 2.4 \| \| SpeedupTerasplit \| 4.2 \| 1.9 \| 2.0 \| 1.8 \| 1.8 \| 1.6 \| \| Speeduptotal \| 2.7 \| 2.3 \| 2.3 \| 2.3 \| 2.2 \| 2.1 \|	1
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| 1-7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| 70 \| \| HadoopTerasort \| 645 \| 766 \| 768 \| 773 \| 815 \| 882 \| 901 \| \| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| HadoopTerasplit \| 141 \| 266 \| 410 \|...	0
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| TotalSphere \| 1015 \| 1300 \| 1528 \| 1831 \| 2102 \| 2373 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpeedupTerasort \| 2.6 \| 2.5 \| 2.4 \| 2.6 \| 2.4 \| 2.4 \| \| SpeedupTerasplit \| 4.2 \| 1.9 \| 2.0 \| 1.8 \| 1.8 \| 1.6 \| \| Speeduptotal \| 2.7 \| 2.3 \| 2.3 \| 2.3 \| 2.2 \| 2.1 \|	1
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \|...	0
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.056 \| 0.049 \| 0.061 \| 0.041 \| 0.053 \| 0.339 \| 0.23 \| 0.041 \| 0.065 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.08 \| 0.067 \| 0.054 \| 0.035 \| 0.03 \| 0.041 \| 0.332 \| 0.204 \| 0.077 \| \| 0.065 \| 0.074 \| 0.082 \| 0.043 \| 0.03 \| 0.04 \| 0.048 \| 0.339 \| 0.203 \| \| 0.05 \| 0.044 \| 0.066 \| 0.065 \| 0.048 \| 0.033 ...	1
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 200 \| 400 \| 600 \| 800 \| \| --- \| --- \| --- \| --- \| \| 0.965 \| 0.967 \| 0.967 \| 0.967 \| \| 0.968 \| 0.970 \| 0.970 \| 0.968 \| \| 0.968 \| 0.968 \| 0.967 \| 0.968 \| \| 0.969 \| 0.972 \| 0.970 \| 0.969 \| \| 0.831 \| 0.842 \| 0.845 \| 0.846 \| \| 0.846 \| 0.851 \| 0.852 \| 0.853 \| \| 0.842 \| 0.849 \| 0.854 \| 0.849 \| \| 0.876 \| 0.874 \| 0.866 \| 0.86...	0
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.05 \| 0.044 \| 0.066 \| 0.065 \| 0.048 \| 0.033 \| 0.048 \| 0.044 \| 0.488 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.035 \| 0.036 \| 0.055 \| 0.048 \| 0.039 \| 0.048 \| 0.061 \| 0.06 \| 0.106 \|	1
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.846 \| 0.851 \| 0.852 \| 0.853 \| \| --- \| --- \| --- \| --- \| \| 0.842 \| 0.849 \| 0.854 \| 0.849 \| \| 0.876 \| 0.874 \| 0.866 \| 0.868 \| \| 0.658 \| 0.650 \| 0.654 \| 0.655 \| \| 0.712 \| 0.705 \| 0.701 \| 0.702 \| \| 0.680 \| 0.690 \| 0.694 \| 0.695 \| \| 0.715 \| 0.709 \| 0.714 \| 0.715 \|	0
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \| \| Mediannumberofwords \| 54 \|	\| Mediansentencelength \| 51characters \| \| --- \| --- \| \| MedianNbrofwordsinsentence \| 8 \|	1
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \| \| Mediannumberofwords \| 54 \|	\| Totalnumberofdialogues \| 1092 \| \| --- \| --- \| \| Totalnumberofutterances \| 10114 \| \| Totalnumberofwords \| 33477 \| \| Totalnumberofsemanticunits \| 14584 \| \| Diﬀerentclassesofsemanticunits \| 38 \|	0
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \|	\| Mediannumberofwords \| 54 \| \| --- \| --- \| \| Mediansentencelength \| 51characters \| \| MedianNbrofwordsinsentence \| 8 \|	1
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \|	\| Totalnumberofsemanticunits \| 14584 \| \| --- \| --- \| \| Diﬀerentclassesofsemanticunits \| 38 \|	0
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \| \| 15agents,randompaths \| 0.5033 \|	\| 05agents,tightgoals,nodiversiﬁcation \| 0.5462 \| \| --- \| --- \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.4970 \| \| 15agents,tightgoals,nodiversiﬁcation \| 0.5001 \| \| 05agents,loosegoals,nodiversiﬁcation \| 0.5487 \| \| 10agents,loosegoals,nodiversiﬁcation \| 0.5120 \| \| 15agents,loosegoals,nodiversiﬁcation \| 0.4619 \| \| 05ag...	1
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \| \| 15agents,randompaths \| 0.5033 \|	\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 10agents,randompaths \| 0.2573 \| \| 20agents,randompaths \| 0.2428 \| \| 30agents,randompaths \| 0.2423 \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.3774 \| \| 20agents,tightgoals,nodiversiﬁcation \| 0.2858 \| \| 30agents,tightgoals,nodiversiﬁcation \| 0.4390 \| \| 10agents,l...	0
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \|	\| 15agents,randompaths \| 0.5033 \| \| --- \| --- \| \| 05agents,tightgoals,nodiversiﬁcation \| 0.5462 \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.4970 \| \| 15agents,tightgoals,nodiversiﬁcation \| 0.5001 \| \| 05agents,loosegoals,nodiversiﬁcation \| 0.5487 \| \| 10agents,loosegoals,nodiversiﬁcation \| 0.5120 \| \| 15agents,loosegoals,...	1
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \|	\| 10agents,loosegoals,diversiﬁcation \| 0.3254 \| \| --- \| --- \| \| 20agents,loosegoals,diversiﬁcation \| 0.2949 \| \| 30agents,loosegoals,diversiﬁcation \| 0.3026 \|	0
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \|	\| RangeofMemoryvaluesforresources \| (2-16) \| \| --- \| --- \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofNetworkvaluesforutilization \| (0-1000) \| \| RangeofrevenuevaluesforexecutingVMs \| (0.1-1.5) \| \| RangeofSLAvalues...	1
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \|	\| DeviceSpeciﬁcations \| ValueUnit \| Example \| \| --- \| --- \| --- \| \| accountaddress \| string \| 0xc968efa8019d(hashvalue) \| \| networkport \| int \| 42024 \| \| input/outputdata \| string \| video,audio,text \| \| bandwidthrequest \| double \| [minValue,maxValue] \| \| CPUrequest \| double \| [minValue,maxValue] \| \| memoryrequest \| dou...	0
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \| \| RangeofMemoryvaluesforresources \| (2-16) \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofN...	\| RangeofnumberofVMsforcloudservices \| (1-6) \| \| --- \| --- \| \| Numberofcloudservices \| (1-1) \| \| Probabilitydistribution \| Random \|	1
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \| \| RangeofMemoryvaluesforresources \| (2-16) \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofN...	\| coinbalance* \| double \| 200.00 \| \| --- \| --- \| --- \| \| credit* \| int \| 100 \| \| isBlocked* \| bool \| false \| \| isRegistered* \| bool \| false \| \| lastrequestid* \| string \| 0xcf30613db6a84(hashvalue) \|	0
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \|	\| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpinImages(B.distance) \| 0.58 \| 0.55 \| 0.70 \| 0.67 \| 0.16 \| 0.65 \| \| SHOT(χdistance) \| 0.23 \| 0.21 \| 0.23 \| 0.34 \| 0.09 \| 0.31 \| \| FPFH(EMDdistance) \| 0.23 \| 0.23 \| 0.32 \| 0.43 \| 0.1 \| 0.39 \|	1
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \|	\| N \| 1 \| 2 \| 5 \| 10 \| 20 \| 100 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Situate \| 0.37(0.09) \| 0.50(0.07) \| 0.56(0.07) \| 0.65(0.04) \| 0.77(0.05) \| 0.93(0.02) \| \| Uniform \| 0.29(0.07)) \| 0.32(0.07) \| 0.44(0.06) \| 0.54(0.04) \| 0.65(0.03) \| 0.79(0.03) \| \| Faster-RCNN \| 0.24 \| 0.25 \| 0.28 \| 0.38 \| 0.54 \| 0.91 \| \| I...	0
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \| \| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \|	\| SpinImages(B.distance) \| 0.58 \| 0.55 \| 0.70 \| 0.67 \| 0.16 \| 0.65 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SHOT(χdistance) \| 0.23 \| 0.21 \| 0.23 \| 0.34 \| 0.09 \| 0.31 \| \| FPFH(EMDdistance) \| 0.23 \| 0.23 \| 0.32 \| 0.43 \| 0.1 \| 0.39 \|	1
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \| \| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \|	\| Faster-RCNN \| 0.24 \| 0.25 \| 0.28 \| 0.38 \| 0.54 \| 0.91 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IRSG \| 0.24 \| 0.24 \| 0.27 \| 0.37 \| 0.55 \| 0.87 \|	0
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \|	\| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LogisticRegression \| 89.52 \| 62.04 \| 73.79 \| 92.52 \| 71.05 \| 62.61 \| \| MultilayerPerceptron(MLP) \| 87.91 \| 63.62 \| 68.5 \| 90.16 \| 76.72 \| 75.39 \| \| GaussianNaiveBayes \| 88.33 \| 73.98 \| 41.67 \| 88.34 \| ...	1
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \|	\| \| Performance \| RocAuc \| F1 \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Classiﬁer \| mean \| std \| mean \| std \| mean \| std \| \| LogisticRegression \| 0.7500 \| 0.2738 \| 0.7000 \| 0.4153 \| 0.6166 \| 0.4349 \| \| LDA \| 0.6750 \| 0.2968 \| 0.7000 \| 0.4153 \| 0.6066 \| 0.4103 \| \| SVC \| 0.6500 \| 0.3000 \| 0.7000 \| 0.4153 ...	0
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \| \| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 ...	\| LogisticRegression \| 89.52 \| 62.04 \| 73.79 \| 92.52 \| 71.05 \| 62.61 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| MultilayerPerceptron(MLP) \| 87.91 \| 63.62 \| 68.5 \| 90.16 \| 76.72 \| 75.39 \| \| GaussianNaiveBayes \| 88.33 \| 73.98 \| 41.67 \| 88.34 \| 73.98 \| 41.67 \| \| MultinomialNaiveBayes \| 83.96 \| 65.52 \| 59.90 \| 90.97 \|...	1
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \| \| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 ...	\| LDA \| 0.6750 \| 0.2968 \| 0.7000 \| 0.4153 \| 0.6066 \| 0.4103 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SVC \| 0.6500 \| 0.3000 \| 0.7000 \| 0.4153 \| 0.5766 \| 0.4060 \| \| GaussianNB \| 0.6500 \| 0.3000 \| 0.6750 \| 0.4038 \| 0.5766 \| 0.4060 \| \| DecisionTreeClassiﬁer \| 0.5500 \| 0.2915 \| 0.5250 \| 0.2610 \| 0.5533 \| 0.3357 \| \| G...	0
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \|	\| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br>1.9 \| 1.2<br>0.8<br>0.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL+Periodic \| k=100M<br>k=50M<br>k=25M \| 3.8<br>3.4<br>0.5 \| 18.4<br>19.5<br>18.1 \| 3.6<br>4.4<br>2.7 \| 12...	1
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \|	\| Req. \| Pseudorandom<br>PFNC \| ModelcheckingTA<br>PFNC \| BDIagents<br>PFNC \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| CooperativeManufacturingAssistant(160testspermethod) \| \| \| \| \| \| \| \| \| \| \| 1<br>2<br>3<br>4<br>5<br>6 \| 19<br>3<br>24<br>57<br>33<br>160 \| 4<br>0<br>0<...	0
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \| \| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br...	\| LBL+Periodic \| k=100M<br>k=50M<br>k=25M \| 3.8<br>3.4<br>0.5 \| 18.4<br>19.5<br>18.1 \| 3.6<br>4.4<br>2.7 \| 12.0<br>18.6<br>21.3 \| 4.8<br>0.6<br>–3.7 \| 1.3<br>1.9<br>0.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL+Retroﬁtting \| – \| 5.7 \| 15.6 \| 5.5 \| 18.6 \| 14.7 \| 0.9 \|	1
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \| \| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br...	\| 1<br>2<br>3<br>4<br>5<br>6 \| 19<br>3<br>24<br>57<br>33<br>160 \| 4<br>0<br>0<br>0<br>31<br>0 \| 137<br>157<br>136<br>103<br>96<br>0 \| 37<br>35<br>97<br>99<br>128<br>160 \| 50<br>10<br>0<br>0<br>2<br>0 \| 73<br>115<br>63<br>61<br>30<br>0 \| 45<br>31<br>63<br>72<br>79<br>160 \| 16<br>5<br>2<br>0<br>4<br>0 \| 99<br>124<br>95<b...	0
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Imagepre-processing \| 2.77 \| 2.90 \| 2.87 \| 0.04 \| \| --- \| --- \| --- \| --- \| --- \| \| CNNinferencing \| 18.49 \| 19.30 \| 18.99 \| 0.20 \| \| TotalTime \| 22.86 \| 23.74 \| 23.38 \| 0.20 \|	1
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| \| facedetection \| eyedetection \| Time(s) \| \| --- \| --- \| --- \| --- \| \| Originalimage \| 0.297 \| 0.58 \| NULL \| \| Lowrank+sparsity \| 0.375 \| 0.70 \| 10 \| \| Multiscalelowrank \| 0.359 \| 1.00 \| 4472 \| \| IBTSVT \| 0.844 \| 1.00 \| 715 \|	0
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Imagepre-processing \| 2.77 \| 2.90 \| 2.87 \| 0.04 \| \| --- \| --- \| --- \| --- \| --- \| \| CNNinferencing \| 18.49 \| 19.30 \| 18.99 \| 0.20 \| \| TotalTime \| 22.86 \| 23.74 \| 23.38 \| 0.20 \|	1
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Lowrank+sparsity \| 0.375 \| 0.70 \| 10 \| \| --- \| --- \| --- \| --- \| \| Multiscalelowrank \| 0.359 \| 1.00 \| 4472 \| \| IBTSVT \| 0.844 \| 1.00 \| 715 \|	0
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \|	\| C \| thesetofconcepts \| \| --- \| --- \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfunction \| \| w \| aweightingfunction(notconsideringtemporaldecay) \| \| f \| adecayfunction \| \| w \|...	1
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \|	\| Notation \| Deﬁnition \| \| --- \| --- \| \| T<br>(p) \| Timecomplexityofamultiplication \| \| T<br>(p) \| Timecomplexityofanaddition \| \| T<br>(p) \| Timecomplexityofanexponentiation \| \| T<br>(p) \| Timecomplexityofaninversion \| \| T<br>(p) \| Timecomplexityofanellipticcurvescalarmultiplication \| \| T<br>(p) \| Timecomplexityofanell...	0
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \| \| C \| thesetofconcepts \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfu...	\| f \| adecayfunction \| \| --- \| --- \| \| w \| aweightingfunctionthatextendswwithtemporaldecay \| \| σ \| asimilarityfunction \|	1
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \| \| C \| thesetofconcepts \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfu...	\| T<br>(p) \| Timecomplexityofanellipticcurvescalarmultiplication \| \| --- \| --- \| \| T<br>(p) \| Timecomplexityofanellipticcurvepointsaddition \|	0
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| Cheminot \| RIV<br>Cu \| 0.273<br>0.844 \| 0.165<br>0.848 \| 0.147<br>0.808 \| 0.060<br>0.932 \| 0.203<br>0.829 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cropland \| RIV<br>Cu \| 0.046<br>0.337 \| 0.037<br>0.412 \| 0.038<br>0.415 \| 0.028<br>0.426 \| 0.048<br>0.423 \|	1
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| CIFAR-10 \| NUS-WIDE \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 12bits \| 24bits \| 32bits \| 48bits \| 12bits \| 24bits \| 32bits \| 48bits \| 12bits \| \| 0.162<br>0.131<br>0.121<br>0.138 \| 0.169<br>0.135<br>0.126<br>0.141 \| 0.172<br>0.133<br>0.120<br>0.146 \| 0.175<br>0.130<br>0.120<br>0....	0
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| Cheminot \| RIV<br>Cu \| 0.273<br>0.844 \| 0.165<br>0.848 \| 0.147<br>0.808 \| 0.060<br>0.932 \| 0.203<br>0.829 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cropland \| RIV<br>Cu \| 0.046<br>0.337 \| 0.037<br>0.412 \| 0.038<br>0.415 \| 0.028<br>0.426 \| 0.048<br>0.423 \|	1
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| 0.196<br>0.174<br>0.101<br>0.212 \| 0.246<br>0.205<br>0.128<br>0.247 \| 0.289<br>0.220<br>0.132<br>0.256 \| 0.301<br>0.232<br>0.169<br>0.281 \| 0.435<br>0.433<br>0.401<br>0.549 \| 0.435<br>0.426<br>0.442<br>0.614 \| 0.548<br>0.426<br>0.480<br>0.653 \| 0.435<br>0.423<br>0.471<br>0.678 \| 0.553<br>0.550<br>0.543<br>0.552 \| \| -...	0
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| Dropout(0.5) \| \| --- \| \| fully-connected(1024) \| \| fully-connected(10) \| \| SoftMax \|	1
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	0
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| \| \| \| conv(24,32) \| conv(24,32)<br>maxpool(2) \| conv(24,32)<br>maxpool(2) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| conv(32,40) \| conv(32,40)<br>maxpool(2) \| \| \| \| \| \| \| conv(40,48) \| \| Dropout(0.5) \| \| \| \| \| \| \| fully-connected(1024) \| \| \| \| \| \| \| fully-connected(10) \| \| \| \| \| \| \| ...	1
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| fully-connected(4) \| \| --- \| \| Normalize \|	0
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \|	\| AMI \| 700 \| 100 \| \| --- \| --- \| --- \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \| \| CP \| 102 \| 17 \| \| UERCTrain \| 2304 \| 166 \| \| Multi-PIEEar \| 17183 \| 205 \|	1
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \|	\| Dataset \| #ofsamples \| #ofclasses \| #ofattributes \| \| --- \| --- \| --- \| --- \| \| Breastcancer \| 286 \| 2 \| 9 \| \| Diabetes \| 768 \| 2 \| 8 \| \| SolarFlare \| 144 \| 3 \| 9 \| \| German \| 1000 \| 2 \| 20 \| \| Heart \| 270 \| 2 \| 13 \| \| Image \| 2310 \| 7 \| 19 \| \| Ringnorm \| 7400 \| 2 \| 20 \| \| Splice \| 3190 \| 3 \| 60 \| \| Thyroid \| 215 \| 3...	0
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \| \| AMI \| 700 \| 100 \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \|	\| CP \| 102 \| 17 \| \| --- \| --- \| --- \| \| UERCTrain \| 2304 \| 166 \| \| Multi-PIEEar \| 17183 \| 205 \|	1
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \| \| AMI \| 700 \| 100 \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \|	\| Thyroid \| 215 \| 3 \| 5 \| \| --- \| --- \| --- \| --- \| \| Twonorm \| 7400 \| 2 \| 20 \| \| Waveform \| 5000 \| 3 \| 21 \|	0
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| PART \| 0.69/569 \| \| --- \| --- \| \| Logistic \| 0.59/512 \|	1
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| Model/Algorithm \| MNIST \| Caltech101 \| US-CT \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DC \| mIoU \| DC \| mIoU \| DC \| mIoU \| \| DetectionAccuracy \| 0.87 \| 0.78 \| 0.95 \| 0.91 \| 0.97 \| .0.94 \| \| MatchingAccuracy \| 0.82 \| 0.71 \| 0.96 \| 0.93 \| 0.97 \| 0.95 \| \| DSPaccw/oseg \| 0.58 \| 0.44 \| 0.60 \| 0.48 \| 0.29...	0
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| PART \| 0.69/569 \| \| --- \| --- \| \| Logistic \| 0.59/512 \|	1
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| DSPaccw/oseg \| 0.58 \| 0.44 \| 0.60 \| 0.48 \| 0.29 \| 0.17 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DSPaccwithseg \| - \| - \| - \| - \| 0.63 \| 0.49 \|	0
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \|	\| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| classsize(withoutteacher) \| 25 \| 22 \| 22 \| 24 \| 26 \| 22 \| 23 \| 21 \| 23 \| 23 \| \| teacherfound \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| \| missingnodes \| 0 \| 1 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 ...	1
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \|	\| Class \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 10 \| 11 \| 12 \| 13 \| 14 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 88 \| 85 \| 91 \| 90 \| 91 \| 100 \| 100 \| 95 \| 83 \| 83 \| 71 \| 70 \| 96 \| 75 \| \| 2. \| 53 \| 70 \| 88 \| 90 \| 100 \| 100 \| 100 \| 100 \| 83 \| 71 \| 63 \| 63 \| 87 \| 47 ...	0
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \| \| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \|	\| classsize(withoutteacher) \| 25 \| 22 \| 22 \| 24 \| 26 \| 22 \| 23 \| 21 \| 23 \| 23 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| teacherfound \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| \| missingnodes \| 0 \| 1 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \|	1
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \| \| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \|	\| 2 \| 59 \| 72 \| 59 \| 85 \| 95 \| 95 \| 66 \| 95 \| 45 \| 40 \| 69 \| 100 \| 78 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3 \| 49 \| 75 \| 49 \| 55 \| 90 \| 90 \| 69 \| 95 \| 15 \| 50 \| 72 \| 100 \| 83 \| \|	0
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \|	\| DatabaseServer \| MySQL \| SysBench \| \| --- \| --- \| --- \| \| MailServer \| Postﬁx \| Postal \| \| FileServer \| Sambasmbd \| DBench \| \| StreamingServer \| ﬀserver \| openRTSP \| \| ApplicationServer \| Tomcat \| Apacheab \| \| ComputeServer \| libsvm \| a1a.t \| \| IdleServer \| - \| - \|	1
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \|	\| AC \| AuthenticatedModule \| \| --- \| --- \| \| AIK \| AttestationIdentityKey \| \| CA \| CertiﬁcateAuthority \| \| DRTM \| DynamicRootofTrustforMeasurement \| \| MITM \| Man-in-the-Middle \| \| MLE \| MeasuredLaunchEnvironment \| \| PAL \| ProgramApplicationLogic \| \| PCR \| PlatformConﬁgurationRegister \| \| SKINIT \| SecureKernelInit \| \| S...	0
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \| \| DatabaseServer \| MySQL \| SysBench \| \| MailServer \| Postﬁx \| Postal \|	\| FileServer \| Sambasmbd \| DBench \| \| --- \| --- \| --- \| \| StreamingServer \| ﬀserver \| openRTSP \| \| ApplicationServer \| Tomcat \| Apacheab \| \| ComputeServer \| libsvm \| a1a.t \| \| IdleServer \| - \| - \|	1
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \| \| DatabaseServer \| MySQL \| SysBench \| \| MailServer \| Postﬁx \| Postal \|	\| SLB \| SecureLoaderBlock \| \| --- \| --- \| \| SML \| StoredMeasurementLog \| \| SRK \| StorageRootKey \| \| TCB \| TrustedComputingBase \| \| TPM \| TrustedPlatformModule \| \| TXT \| TrustedExecutionTechnology \| \| PM \| ProxyModule \| \| DB \| Database \|	0
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \| \| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \|	\| ANPs(ﬁltered) \| 107.8 \| 14.75 \| 21.21 \| 18 \| \| --- \| --- \| --- \| --- \| --- \| \| VNPs(complete) \| 70.88 \| 8.91 \| 15.48 \| 12 \| \| VNPs(ﬁltered) \| 58.72 \| 7.87 \| 14.59 \| 11 \|	1
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \| \| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \|	\| \| AFLWDataset(21pts) \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| [0,30] \| (30,60] \| (60,90] \| mean \| std \| \| AN \| 4.88 \| 5.55 \| 7.10 \| 5.84 \| 1.14 \| \| AN+LR \| 4.00 \| 4.48 \| 5.89 \| 4.79 \| 0.98 \| \| VGG \| 4.15 \| 4.64 \| 5.96 \| 4.92 \| 0.94 \| \| VGG+LR \| 3.46 \| 3.78 \| 4.77 \| 4.00 \| 0.69 \|	0
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \|	\| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \| \| --- \| --- \| --- \| --- \| --- \| \| ANPs(ﬁltered) \| 107.8 \| 14.75 \| 21.21 \| 18 \| \| VNPs(complete) \| 70.88 \| 8.91 \| 15.48 \| 12 \| \| VNPs(ﬁltered) \| 58.72 \| 7.87 \| 14.59 \| 11 \|	1
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \|	\| AN \| 4.88 \| 5.55 \| 7.10 \| 5.84 \| 1.14 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| AN+LR \| 4.00 \| 4.48 \| 5.89 \| 4.79 \| 0.98 \| \| VGG \| 4.15 \| 4.64 \| 5.96 \| 4.92 \| 0.94 \| \| VGG+LR \| 3.46 \| 3.78 \| 4.77 \| 4.00 \| 0.69 \|	0
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| CelebFaces \| public \| 10K \| 202K \| \| --- \| --- \| --- \| --- \| \| CASIA-WebFace \| public \| 10K \| 500K \| \| Ours \| public \| 100K \| about10M \| \| Facebook \| private \| 4K \| 4400K \| \| Google \| private \| 8M \| 100-200M \|	1
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| DataSet \| RealNetwork \| RD \| SW \| SF \| HK \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| HighestDegreeofaNode \| \| \| \| \| \| Blog \| 351 \| 46 \| 47 \| 211 \| 321 \| \| Twitter \| 237 \| 27 \| 27 \| 253 \| 319 \| \| Epinions \| 1192 \| 77 \| 72 \| 373 \| 560 \| \| Author \| 102 \| 15 \| 16 \| 201 \| 183 \| \| \| ClusteringCoeﬃcient \| \| \| \| \|...	0
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| CelebFaces \| public \| 10K \| 202K \| \| --- \| --- \| --- \| --- \| \| CASIA-WebFace \| public \| 10K \| 500K \| \| Ours \| public \| 100K \| about10M \| \| Facebook \| private \| 4K \| 4400K \| \| Google \| private \| 8M \| 100-200M \|	1
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| Twitter \| 237 \| 27 \| 27 \| 253 \| 319 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Epinions \| 1192 \| 77 \| 72 \| 373 \| 560 \| \| Author \| 102 \| 15 \| 16 \| 201 \| 183 \| \| \| ClusteringCoeﬃcient \| \| \| \| \| \| Blog \| 0.32 \| 0.02 \| 0.56 \| 0.07 \| 0.24 \| \| Twitter \| 0.13 \| 0.005 \| 0.49 \| 0.03 \| 0.27 \| \| Epinions \| 0.27 \| 0.02 \| 0.5...	0
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \| \| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| ...	\| NLPR \| 0.1165 \| 0.1087 \| 0.1784 \| 0.1669 \| \| --- \| --- \| --- \| --- \| --- \| \| OURS \| 0.0794 \| 0.0860 \| 0.1779 \| 0.1589 \|	1
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \| \| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| ...	\| Methods \| MAE \| F-<br>measure \| Max-P \| Min-P \| Max-R \| Min-R \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2804 \| 0.3875 \| 0.1262 \| 1 \| 0 \| \| SIM \| 0.3740 \| 0.3345 \| 0.5076 \| 0.1262 \| 1 \| 0 \| \| HS \| 0.1849 \| 0.5361 \| 0.6581 \| 0.1262 \| 1 \| 0.1187 \| \| BSCA \| 0.1851 \| 0.5826 \| 0.7977 \| 0.1262 \| 1 \| 0....	0
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \|	\| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| --- \| --- \| --- \| --- \| --- \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| \| DES \| 0.3079 \| 0.3207 \| 0.3132 \| 0.4465 \| \| NLPR \| 0.1165 \| 0.1087 \| 0.1784 \| 0.1669 \| \| OURS \| 0.0794 \| 0.086...	1
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \|	\| OURS1 \| 0.1065 \| 0.5357 \| 0.9000 \| 0.0074 \| 1 \| 0 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| OURS2 \| 0.0880 \| 0.6881 \| 0.9249 \| 0.1262 \| 1 \| 0.1118 \| \| OURS \| 0.0781 \| 0.7230 \| 0.9181 \| 0.1262 \| 1 \| 0.2669 \|	0

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premise string	hypothesis string	label int64
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \|	\| 5LSTMlayers \| 79.4 \| \| --- \| --- \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \| \| ResNet-152 \| 87.2 \| \| ResNet-50+8layers \| 88.2 \|	1
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \|	\| Numberoflayers \| 512width \| 1024width \| \| --- \| --- \| --- \| \| 3 \| 48.5 \| 38.1 \| \| 4 \| 38.3 \| 34.6 \| \| 5 \| 36.2 \| 31.9 \| \| 6 \| 34.5 \| 30.3 \| \| 7 \| 32.5 \| 30.6 \| \| 8 \| 31.0 \| 31.3 \| \| 9 \| 31.4 \| 29.4 \| \| 10 \| 31.5 \| 29.4 \|	0
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \| \| 5LSTMlayers \| 79.4 \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \|	\| ResNet-152 \| 87.2 \| \| --- \| --- \| \| ResNet-50+8layers \| 88.2 \|	1
\| Structure \| CS \| \| --- \| --- \| \| 1LSTMlayer \| 71.8 \| \| 2LSTMlayers \| 75.2 \| \| 3LSTMlayers \| 76.0 \| \| 4LSTMlayers \| 76.0 \| \| 5LSTMlayers \| 76.7 \| \| 6LSTMlayers \| 76.7 \| \| 5LSTMlayers \| 79.4 \| \| ResNet-18 \| 86.1 \| \| ResNet-50 \| 87.5 \| \| ResNet-101 \| 87.3 \|	\| 7 \| 32.5 \| 30.6 \| \| --- \| --- \| --- \| \| 8 \| 31.0 \| 31.3 \| \| 9 \| 31.4 \| 29.4 \| \| 10 \| 31.5 \| 29.4 \|	0
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| Fractal<br>LowResolution<br>Pattern \| .5820<br>.5663<br>.5638 \| .5518<br>.5418<br>.5509 \| .0302<br>.0245<br>.0129 \| \| --- \| --- \| --- \| --- \| \| Overall \| .6221 \| .5755 \| .0466 \|	1
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| \| Gradual \| Sharp \| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Video \| #T \| TP \| FP \| FN \| P \| R \| F \| #T \| TP \| FP \| FN \| P \| \| 1004ABCa \| 203 \| 177 \| 9 \| 26 \| 0.952 \| 0.872 \| 0.91 \| 224 \| 209 \| 18 \| 15 \| 0.921 \| \| 1012CNNa \| 170 \| 149 \| 23 \| 21 \|...	0
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| Fractal<br>LowResolution<br>Pattern \| .5820<br>.5663<br>.5638 \| .5518<br>.5418<br>.5509 \| .0302<br>.0245<br>.0129 \| \| --- \| --- \| --- \| --- \| \| Overall \| .6221 \| .5755 \| .0466 \|	1
\| Class \| Ours \| Others \| Diﬀerence \| \| --- \| --- \| --- \| --- \| \| Social<br>Cartoon<br>Aﬀective \| .6607<br>.6586<br>.6646 \| .5764<br>.5918<br>.6018 \| .0843<br>.0668<br>.0628 \| \| Other14categories \| - \| - \| - \|	\| 1101CNNa \| 204 \| 187 \| 13 \| 17 \| 0.935 \| 0.917 \| 0.926 \| 191 \| 180 \| 12 \| 11 \| 0.938 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1109ABCa \| 170 \| 159 \| 12 \| 11 \| 0.93 \| 0.935 \| 0.933 \| 257 \| 241 \| 11 \| 16 \| 0.956 \| \| 1123CNNa \| 126 \| 99 \| 32 \| 27 \| 0.756 \| 0.786 \| 0.77 \| 236 \| ...	0
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \|	\| TempCoh \| 45.4 \| 15.9 \| \| --- \| --- \| --- \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \| \| Our-Stack-of-Diﬀ. \| 60.3 \| 32.5 \| \| Randweights-Stack-of-Diﬀ. \| 51.3 \| 28.3 \| \| ImageNetweights-Stack-of-Diﬀ. \| 70.1 \| 40.8 \|	1
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \|	\| Method \| UCF101 \| HMDB \| HW2 \| \| --- \| --- \| --- \| --- \| \| IDT+FV<br>IDT+HSV<br>IDT+Actionness<br>VideoDarwin<br>RankPool+IDT \| 85.9<br>87.9<br>-<br>-<br>91.4 \| 57.2<br>61.1<br>60.4<br>63.7<br>66.9 \| 64.3<br>-<br>-<br>73.7<br>76.7 \| \| Two-stream(avg)<br>Two-stream(SVM)<br>Two-streamLSTM<br>LTC<br>TSN(2modalities)<br>...	0
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \| \| TempCoh \| 45.4 \| 15.9 \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \|	\| Our-Stack-of-Diﬀ. \| 60.3 \| 32.5 \| \| --- \| --- \| --- \| \| Randweights-Stack-of-Diﬀ. \| 51.3 \| 28.3 \| \| ImageNetweights-Stack-of-Diﬀ. \| 70.1 \| 40.8 \|	1
\| Method \| UCF101-split1 \| HMDB51-split1 \| \| --- \| --- \| --- \| \| DrLim \| 45.7 \| 16.3 \| \| TempCoh \| 45.4 \| 15.9 \| \| Obj.Patch \| 40.7 \| 15.6 \| \| Seq.Ver. \| 50.9 \| 19.8 \|	\| Two-stream(avg)<br>Two-stream(SVM)<br>Two-streamLSTM<br>LTC<br>TSN(2modalities)<br>TSN(3modalities)<br>Two-streamI3D \| 86.9<br>88.0<br>88.6<br>91.7<br>94.0<br>94.2<br>98.0 \| 58.0<br>59.4<br>-<br>64.8<br>68.5<br>69.4<br>80.7 \| -<br>-<br>-<br>-<br>-<br>-<br>- \| \| --- \| --- \| --- \| --- \| \| Two-streamfusion<br>ST-ResNet ...	0
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \| \| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| Cliﬀbar \| ...	\| Surfer \| 80.32 \| 98.14 \| 70 \| 64 \| 24 \| 71 \| 61 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Sylvester \| 88.85 \| 99.03 \| 77 \| 33 \| 45 \| 77 \| 80 \| \| Cardark \| 100 \| 100 \| 36 \| 25 \| 54 \| 48 \| 18 \| \| Faceocc1 \| 100 \| 100 \| 43.4 \| 80 \| 74 \| 60 \| 82 \| \| Faceocc2 \| 100 \| 100 \| 99 \| 70 \| 66 \| 62 \| 70 \| \| Twinning \|...	1
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \| \| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| Cliﬀbar \| ...	\| Network \| n \| k \| SLPA \| std \| r \| Copra \| std \| v \| LFM \| Cﬁnder \| k \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| karate \| 34 \| 4.5 \| 0.65 \| 0.21 \| 0.33 \| 0.44 \| 0.18 \| 3 \| 0.42 \| 0.52 \| 3 \| \| dolphins \| 62 \| 5.1 \| 0.76 \| 0.03 \| 0.45 \| 0.70 \| 0.04 \| 4 \| 0.28 \| 0.66 \| 3 \| \| lesmis \| 7...	0
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \|	\| Jumping \| 100 \| 100 \| 10 \| 12 \| 71 \| 21 \| 63 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cliﬀbar \| 98.48 \| 87.88 \| 99 \| 53 \| 47 \| 71 \| 8 \| \| Woman \| 22.39 \| 17.91 \| 11.6 \| 14 \| 17 \| 15 \| 67 \| \| Surfer \| 80.32 \| 98.14 \| 70 \| 64 \| 24 \| 71 \| 61 \| \| Sylvester \| 88.85 \| 99.03 \| 77 \| 33 \| 45 \| 77 \| 80 \| \| Cardark...	1
\| Sequence \| RCACF \| CA \| FCT \| VTD \| IVT \| MIL \| Struck \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tiger2 \| 86.85 \| 28.49 \| 72 \| 13 \| 19 \| 44 \| 62 \| \| Tiger1 \| 100 \| 90.14 \| 52 \| 78 \| 8 \| 34 \| 87 \| \| Animal \| 97.18 \| 100 \| 92 \| 96 \| 4 \| 87 \| 96 \|	\| email \| 1133 \| 9.6 \| 0.64 \| 0.03 \| 0.45 \| 0.51 \| 0.22 \| 2 \| 0.25 \| 0.46 \| 3 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| CA-GrQc \| 4730 \| 5.6 \| 0.76 \| 0.00 \| 0.45 \| 0.71 \| 0.01 \| 1 \| 0.45 \| 0.51 \| 3 \| \| PGP \| 10680 \| 4.5 \| 0.82 \| 0.01 \| 0.45 \| 0.78 \| 0.02 \| 9 \| 0.44 \| 0.57 \| 3 \|	0
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \| \| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \|	\| fr2/desk \| 0.009 \| 0.071 \| 0.034 \| 0.017 \| 0.057 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr2/xyz \| 0.004 \| 0.011 \| 0.029 \| 0.018 \| - \| \| fr3/oﬃce \| 0.010 \| 0.017 \| 0.030 \| 0.035 \| - \| \| fr3/nst \| 0.019 \| 0.016 \| 0.031 \| 0.018 \| - \|	1
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \| \| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \|	\| Noiselevel \| Easy \| Hard \| Tough \| \| --- \| --- \| --- \| --- \| \| SIFT \| 0.453 \| 0.193 \| 0.086 \| \| TFeat \| 0.467 \| 0.259 \| 0.131 \| \| DeepCompare \| 0.399 \| 0.199 \| 0.095 \| \| DeepDesc \| 0.430 \| 0.239 \| 0.125 \| \| HardNet \| 0.658 \| 0.479 \| 0.303 \| \| SKAR-EgoSeg \| 0.495 \| 0.226 \| 0.102 \| \| SKAR-EgoSeg* \| 0.507 \| 0.251 \| 0.12...	0
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \|	\| fr1/desk2 \| 0.022 \| 0.048 \| 0.071 \| 0.046 \| - \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/room \| 0.047 \| 0.068 \| 0.075 \| 0.043 \| 0.087 \| \| fr2/desk \| 0.009 \| 0.071 \| 0.034 \| 0.017 \| 0.057 \| \| fr2/xyz \| 0.004 \| 0.011 \| 0.029 \| 0.018 \| - \| \| fr3/oﬃce \| 0.010 \| 0.017 \| 0.030 \| 0.035 \| - \| \| fr3/nst \| 0.019 \| 0.016 \| 0...	1
\| Sequence \| ORB-SLAM2<br>(RGB-D) \| Elastic-<br>Fusion \| Kintinuous \| DVO<br>SLAM \| RGBD<br>SLAM \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| fr1/desk \| 0.016 \| 0.020 \| 0.037 \| 0.021 \| 0.026 \|	\| HardNet \| 0.658 \| 0.479 \| 0.303 \| \| --- \| --- \| --- \| --- \| \| SKAR-EgoSeg \| 0.495 \| 0.226 \| 0.102 \| \| SKAR-EgoSeg* \| 0.507 \| 0.251 \| 0.123 \| \| SKAR-HPatches \| 0.677 \| 0.494 \| 0.313 \| \| SKAR-HPatches* \| 0.695 \| 0.525 \| 0.342 \|	0
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| s11-f6<br>(unsat) \| t<br>n \| 391,4<br>34714 \| 402,9<br>61523 \| 412,7<br>40892 \| 416,8<br>62557 \| >1h<br>– \| >1h<br>– \| >1h<br>– \| 307,6<br>27949 \| 488,4<br>63447 \| 465,2<br>37954 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Averagedcputime \| t \| 47,9 \| 91,5 \| 68,8 \| 91,5 \| – \| – \| – \|...	1
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel<br>by#del \| fully<br>assigned \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ehi-85-0<br>(unsat) \| t<br>n \| 2,1<br>722 \| 94,2(0,15)<br>8005(4) \| 2,7<br>61 \| 11,7<br>3 \| 12,1<br>3 \| 12<br>3 \| ...	0
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| s3-f11<br>(unsat) \| t<br>n \| 9,6<br>1078 \| 41,4<br>18728 \| 9,9<br>861 \| 36,2<br>17211 \| >1h<br>– \| >1h<br>– \| >1h<br>– \| 5,3<br>641 \| 47,5<br>18862 \| 10,4<br>889 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| g8-f10<br>(sat) \| t<br>n \| 15<br>4193 \| 72,5<br>26535 \| 45,2<br>6018 \| 62,4<br...	1
\| Instance \| \| d/wdeg \| d/wdeg<br>r.probe \| d/wdeg<br>RSC \| d/wdeg<br>r.probe<br>RSC \| Impact \| Node<br>Impact \| Impact<br>RSC \| alldel \| alldel<br>r.probe \| alldel<br>RSC \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| s2-f25<br>(unsat) \| t<br>n \| 7,4<br>1195 \| 29,2<br>16317 \| 14,2<br>26...	\| frb30-15-2<br>(sat) \| t<br>n \| 63,7<br>21330 \| 123,6(97,8)<br>38765(27458) \| 55,4<br>20019 \| 273,4<br>79936 \| 5,4<br>1306 \| 391,3<br>81911 \| 86,8<br>26596 \| 91,2<br>26296 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 40-8-753-<br>0,1(sat) \| t<br>n \| 76,5<br>21164 \| 70,9(45,9)<br>21369(13422) \| 60,...	0
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| TotalSphere \| 1015 \| 1300 \| 1528 \| 1831 \| 2102 \| 2373 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpeedupTerasort \| 2.6 \| 2.5 \| 2.4 \| 2.6 \| 2.4 \| 2.4 \| \| SpeedupTerasplit \| 4.2 \| 1.9 \| 2.0 \| 1.8 \| 1.8 \| 1.6 \| \| Speeduptotal \| 2.7 \| 2.3 \| 2.3 \| 2.3 \| 2.2 \| 2.1 \|	1
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| 1-7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| 70 \| \| HadoopTerasort \| 645 \| 766 \| 768 \| 773 \| 815 \| 882 \| 901 \| \| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| HadoopTerasplit \| 141 \| 266 \| 410 \|...	0
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| TotalSphere \| 1015 \| 1300 \| 1528 \| 1831 \| 2102 \| 2373 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpeedupTerasort \| 2.6 \| 2.5 \| 2.4 \| 2.6 \| 2.4 \| 2.4 \| \| SpeedupTerasplit \| 4.2 \| 1.9 \| 2.0 \| 1.8 \| 1.8 \| 1.6 \| \| Speeduptotal \| 2.7 \| 2.3 \| 2.3 \| 2.3 \| 2.2 \| 2.1 \|	1
\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| \| Locations \| 1 \| 2 \| 3 \| \| \| \| \| HadoopTerasort \| 2312 \| 2401 \| 2623 \| 3228 \| 3358 \| 3532 \| \| SphereTerasort \| 905 \| 980 \| 1106 \| 1260 \| 1401 \| 1450 \| \| HadoopTerasplit \| 4...	\| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \|...	0
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.056 \| 0.049 \| 0.061 \| 0.041 \| 0.053 \| 0.339 \| 0.23 \| 0.041 \| 0.065 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.08 \| 0.067 \| 0.054 \| 0.035 \| 0.03 \| 0.041 \| 0.332 \| 0.204 \| 0.077 \| \| 0.065 \| 0.074 \| 0.082 \| 0.043 \| 0.03 \| 0.04 \| 0.048 \| 0.339 \| 0.203 \| \| 0.05 \| 0.044 \| 0.066 \| 0.065 \| 0.048 \| 0.033 ...	1
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 200 \| 400 \| 600 \| 800 \| \| --- \| --- \| --- \| --- \| \| 0.965 \| 0.967 \| 0.967 \| 0.967 \| \| 0.968 \| 0.970 \| 0.970 \| 0.968 \| \| 0.968 \| 0.968 \| 0.967 \| 0.968 \| \| 0.969 \| 0.972 \| 0.970 \| 0.969 \| \| 0.831 \| 0.842 \| 0.845 \| 0.846 \| \| 0.846 \| 0.851 \| 0.852 \| 0.853 \| \| 0.842 \| 0.849 \| 0.854 \| 0.849 \| \| 0.876 \| 0.874 \| 0.866 \| 0.86...	0
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.05 \| 0.044 \| 0.066 \| 0.065 \| 0.048 \| 0.033 \| 0.048 \| 0.044 \| 0.488 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.035 \| 0.036 \| 0.055 \| 0.048 \| 0.039 \| 0.048 \| 0.061 \| 0.06 \| 0.106 \|	1
\| 0.414 \| 0.245 \| 0.033 \| 0.018 \| 0.012 \| 0.032 \| 0.043 \| 0.064 \| 0.075 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 0.058 \| 0.297 \| 0.246 \| 0.037 \| 0.021 \| 0.043 \| 0.051 \| 0.089 \| 0.091 \| \| 0.031 \| 0.053 \| 0.298 \| 0.227 \| 0.049 \| 0.042 \| 0.042 \| 0.049 \| 0.132 \| \| 0.031 \| 0.041 \| 0.089 \| 0.345 \| 0.182 \| ...	\| 0.846 \| 0.851 \| 0.852 \| 0.853 \| \| --- \| --- \| --- \| --- \| \| 0.842 \| 0.849 \| 0.854 \| 0.849 \| \| 0.876 \| 0.874 \| 0.866 \| 0.868 \| \| 0.658 \| 0.650 \| 0.654 \| 0.655 \| \| 0.712 \| 0.705 \| 0.701 \| 0.702 \| \| 0.680 \| 0.690 \| 0.694 \| 0.695 \| \| 0.715 \| 0.709 \| 0.714 \| 0.715 \|	0
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \| \| Mediannumberofwords \| 54 \|	\| Mediansentencelength \| 51characters \| \| --- \| --- \| \| MedianNbrofwordsinsentence \| 8 \|	1
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \| \| Mediannumberofwords \| 54 \|	\| Totalnumberofdialogues \| 1092 \| \| --- \| --- \| \| Totalnumberofutterances \| 10114 \| \| Totalnumberofwords \| 33477 \| \| Totalnumberofsemanticunits \| 14584 \| \| Diﬀerentclassesofsemanticunits \| 38 \|	0
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \|	\| Mediannumberofwords \| 54 \| \| --- \| --- \| \| Mediansentencelength \| 51characters \| \| MedianNbrofwordsinsentence \| 8 \|	1
\| Totalvocabularysize \| 768,298 \| \| --- \| --- \| \| Mediandocumentlength \| 346characters \|	\| Totalnumberofsemanticunits \| 14584 \| \| --- \| --- \| \| Diﬀerentclassesofsemanticunits \| 38 \|	0
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \| \| 15agents,randompaths \| 0.5033 \|	\| 05agents,tightgoals,nodiversiﬁcation \| 0.5462 \| \| --- \| --- \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.4970 \| \| 15agents,tightgoals,nodiversiﬁcation \| 0.5001 \| \| 05agents,loosegoals,nodiversiﬁcation \| 0.5487 \| \| 10agents,loosegoals,nodiversiﬁcation \| 0.5120 \| \| 15agents,loosegoals,nodiversiﬁcation \| 0.4619 \| \| 05ag...	1
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \| \| 15agents,randompaths \| 0.5033 \|	\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 10agents,randompaths \| 0.2573 \| \| 20agents,randompaths \| 0.2428 \| \| 30agents,randompaths \| 0.2423 \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.3774 \| \| 20agents,tightgoals,nodiversiﬁcation \| 0.2858 \| \| 30agents,tightgoals,nodiversiﬁcation \| 0.4390 \| \| 10agents,l...	0
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \|	\| 15agents,randompaths \| 0.5033 \| \| --- \| --- \| \| 05agents,tightgoals,nodiversiﬁcation \| 0.5462 \| \| 10agents,tightgoals,nodiversiﬁcation \| 0.4970 \| \| 15agents,tightgoals,nodiversiﬁcation \| 0.5001 \| \| 05agents,loosegoals,nodiversiﬁcation \| 0.5487 \| \| 10agents,loosegoals,nodiversiﬁcation \| 0.5120 \| \| 15agents,loosegoals,...	1
\| SyntheticCrowdCharacteristics \| Score \| \| --- \| --- \| \| 05agents,randompaths \| 0.5506 \| \| 10agents,randompaths \| 0.5080 \|	\| 10agents,loosegoals,diversiﬁcation \| 0.3254 \| \| --- \| --- \| \| 20agents,loosegoals,diversiﬁcation \| 0.2949 \| \| 30agents,loosegoals,diversiﬁcation \| 0.3026 \|	0
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \|	\| RangeofMemoryvaluesforresources \| (2-16) \| \| --- \| --- \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofNetworkvaluesforutilization \| (0-1000) \| \| RangeofrevenuevaluesforexecutingVMs \| (0.1-1.5) \| \| RangeofSLAvalues...	1
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \|	\| DeviceSpeciﬁcations \| ValueUnit \| Example \| \| --- \| --- \| --- \| \| accountaddress \| string \| 0xc968efa8019d(hashvalue) \| \| networkport \| int \| 42024 \| \| input/outputdata \| string \| video,audio,text \| \| bandwidthrequest \| double \| [minValue,maxValue] \| \| CPUrequest \| double \| [minValue,maxValue] \| \| memoryrequest \| dou...	0
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \| \| RangeofMemoryvaluesforresources \| (2-16) \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofN...	\| RangeofnumberofVMsforcloudservices \| (1-6) \| \| --- \| --- \| \| Numberofcloudservices \| (1-1) \| \| Probabilitydistribution \| Random \|	1
\| InputData \| Value(Min-Max) \| \| --- \| --- \| \| Workloadtraceduration(t) \| (4-4) \| \| RangeofCPUvaluesforresources \| (4-10) \| \| RangeofMemoryvaluesforresources \| (2-16) \| \| RangeofNetworkvaluesforresources \| (100-1000) \| \| RangeofCPUvaluesforutilization \| (2-10) \| \| RangeofMemoryvaluesforutilization \| (1-16) \| \| RangeofN...	\| coinbalance* \| double \| 200.00 \| \| --- \| --- \| --- \| \| credit* \| int \| 100 \| \| isBlocked* \| bool \| false \| \| isRegistered* \| bool \| false \| \| lastrequestid* \| string \| 0xcf30613db6a84(hashvalue) \|	0
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \|	\| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SpinImages(B.distance) \| 0.58 \| 0.55 \| 0.70 \| 0.67 \| 0.16 \| 0.65 \| \| SHOT(χdistance) \| 0.23 \| 0.21 \| 0.23 \| 0.34 \| 0.09 \| 0.31 \| \| FPFH(EMDdistance) \| 0.23 \| 0.23 \| 0.32 \| 0.43 \| 0.1 \| 0.39 \|	1
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \|	\| N \| 1 \| 2 \| 5 \| 10 \| 20 \| 100 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Situate \| 0.37(0.09) \| 0.50(0.07) \| 0.56(0.07) \| 0.65(0.04) \| 0.77(0.05) \| 0.93(0.02) \| \| Uniform \| 0.29(0.07)) \| 0.32(0.07) \| 0.44(0.06) \| 0.54(0.04) \| 0.65(0.03) \| 0.79(0.03) \| \| Faster-RCNN \| 0.24 \| 0.25 \| 0.28 \| 0.38 \| 0.54 \| 0.91 \| \| I...	0
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \| \| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \|	\| SpinImages(B.distance) \| 0.58 \| 0.55 \| 0.70 \| 0.67 \| 0.16 \| 0.65 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SHOT(χdistance) \| 0.23 \| 0.21 \| 0.23 \| 0.34 \| 0.09 \| 0.31 \| \| FPFH(EMDdistance) \| 0.23 \| 0.23 \| 0.32 \| 0.43 \| 0.1 \| 0.39 \|	1
\| Method \| NN \| 1-Tier \| 2-Tier \| mAP \| e \| DCG \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| meshLBP(B.distance) \| 0.77 \| 0.74 \| 0.86 \| 0.73 \| 0.17 \| 0.71 \| \| edgeLBP-Run1(B.distance) \| 0.87 \| 0.82 \| 0.99 \| 0.82 \| 0.17 \| 0.80 \| \| edgeLBP-Run2(B.distance) \| 0.87 \| 0.87 \| 0.99 \| 0.82 \| 0.17 \| 0.81 \|	\| Faster-RCNN \| 0.24 \| 0.25 \| 0.28 \| 0.38 \| 0.54 \| 0.91 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IRSG \| 0.24 \| 0.24 \| 0.27 \| 0.37 \| 0.55 \| 0.87 \|	0
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \|	\| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LogisticRegression \| 89.52 \| 62.04 \| 73.79 \| 92.52 \| 71.05 \| 62.61 \| \| MultilayerPerceptron(MLP) \| 87.91 \| 63.62 \| 68.5 \| 90.16 \| 76.72 \| 75.39 \| \| GaussianNaiveBayes \| 88.33 \| 73.98 \| 41.67 \| 88.34 \| ...	1
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \|	\| \| Performance \| RocAuc \| F1 \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Classiﬁer \| mean \| std \| mean \| std \| mean \| std \| \| LogisticRegression \| 0.7500 \| 0.2738 \| 0.7000 \| 0.4153 \| 0.6166 \| 0.4349 \| \| LDA \| 0.6750 \| 0.2968 \| 0.7000 \| 0.4153 \| 0.6066 \| 0.4103 \| \| SVC \| 0.6500 \| 0.3000 \| 0.7000 \| 0.4153 ...	0
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \| \| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 ...	\| LogisticRegression \| 89.52 \| 62.04 \| 73.79 \| 92.52 \| 71.05 \| 62.61 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| MultilayerPerceptron(MLP) \| 87.91 \| 63.62 \| 68.5 \| 90.16 \| 76.72 \| 75.39 \| \| GaussianNaiveBayes \| 88.33 \| 73.98 \| 41.67 \| 88.34 \| 73.98 \| 41.67 \| \| MultinomialNaiveBayes \| 83.96 \| 65.52 \| 59.90 \| 90.97 \|...	1
\| \| NSL-KDD \| UNSW-NB15 \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| ACC \| PPV \| TPR \| ACC \| PPV \| TPR \| \| \| DecisionTree \| 87.91 \| 63.62 \| 68.50 \| 93.78 \| 76.42 \| 68.92 \| \| K-means \| 82.78 \| 84.96 \| 56.95 \| 87.05 \| 74.01 \| 35.23 \| \| KNeighboursClassiﬁer \| 88.56 \| 77.19 \| 71.39 \| 94.31 \| 77.42 \| 71.52 ...	\| LDA \| 0.6750 \| 0.2968 \| 0.7000 \| 0.4153 \| 0.6066 \| 0.4103 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SVC \| 0.6500 \| 0.3000 \| 0.7000 \| 0.4153 \| 0.5766 \| 0.4060 \| \| GaussianNB \| 0.6500 \| 0.3000 \| 0.6750 \| 0.4038 \| 0.5766 \| 0.4060 \| \| DecisionTreeClassiﬁer \| 0.5500 \| 0.2915 \| 0.5250 \| 0.2610 \| 0.5533 \| 0.3357 \| \| G...	0
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \|	\| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br>1.9 \| 1.2<br>0.8<br>0.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL+Periodic \| k=100M<br>k=50M<br>k=25M \| 3.8<br>3.4<br>0.5 \| 18.4<br>19.5<br>18.1 \| 3.6<br>4.4<br>2.7 \| 12...	1
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \|	\| Req. \| Pseudorandom<br>PFNC \| ModelcheckingTA<br>PFNC \| BDIagents<br>PFNC \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| CooperativeManufacturingAssistant(160testspermethod) \| \| \| \| \| \| \| \| \| \| \| 1<br>2<br>3<br>4<br>5<br>6 \| 19<br>3<br>24<br>57<br>33<br>160 \| 4<br>0<br>0<...	0
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \| \| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br...	\| LBL+Periodic \| k=100M<br>k=50M<br>k=25M \| 3.8<br>3.4<br>0.5 \| 18.4<br>19.5<br>18.1 \| 3.6<br>4.4<br>2.7 \| 12.0<br>18.6<br>21.3 \| 4.8<br>0.6<br>–3.7 \| 1.3<br>1.9<br>0.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL+Retroﬁtting \| – \| 5.7 \| 15.6 \| 5.5 \| 18.6 \| 14.7 \| 0.9 \|	1
\| Method \| k,γ \| MEN-3k \| RG-65 \| WS-353 \| TOEFL \| SYN-REL \| SA \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| LBL(Baseline) \| k=∞,γ=0 \| 58.0 \| 42.7 \| 53.6 \| 66.7 \| 31.5 \| 72.5 \| \| LBL+Lazy \| γ=1<br>γ=0.1<br>γ=0.01 \| –0.4<br>0.7<br>0.7 \| 4.2<br>8.1<br>9.5 \| 0.6<br>0.4<br>1.7 \| –0.1<br>–1.4<br>2.6 \| 0.6<br>0.7<br...	\| 1<br>2<br>3<br>4<br>5<br>6 \| 19<br>3<br>24<br>57<br>33<br>160 \| 4<br>0<br>0<br>0<br>31<br>0 \| 137<br>157<br>136<br>103<br>96<br>0 \| 37<br>35<br>97<br>99<br>128<br>160 \| 50<br>10<br>0<br>0<br>2<br>0 \| 73<br>115<br>63<br>61<br>30<br>0 \| 45<br>31<br>63<br>72<br>79<br>160 \| 16<br>5<br>2<br>0<br>4<br>0 \| 99<br>124<br>95<b...	0
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Imagepre-processing \| 2.77 \| 2.90 \| 2.87 \| 0.04 \| \| --- \| --- \| --- \| --- \| --- \| \| CNNinferencing \| 18.49 \| 19.30 \| 18.99 \| 0.20 \| \| TotalTime \| 22.86 \| 23.74 \| 23.38 \| 0.20 \|	1
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| \| facedetection \| eyedetection \| Time(s) \| \| --- \| --- \| --- \| --- \| \| Originalimage \| 0.297 \| 0.58 \| NULL \| \| Lowrank+sparsity \| 0.375 \| 0.70 \| 10 \| \| Multiscalelowrank \| 0.359 \| 1.00 \| 4472 \| \| IBTSVT \| 0.844 \| 1.00 \| 715 \|	0
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Imagepre-processing \| 2.77 \| 2.90 \| 2.87 \| 0.04 \| \| --- \| --- \| --- \| --- \| --- \| \| CNNinferencing \| 18.49 \| 19.30 \| 18.99 \| 0.20 \| \| TotalTime \| 22.86 \| 23.74 \| 23.38 \| 0.20 \|	1
\| Operation \| Mean \| Max \| 99pct. \| Stdev. \| \| --- \| --- \| --- \| --- \| --- \| \| Imagecapture \| 1.61 \| 1.81 \| 1.75 \| 0.05 \|	\| Lowrank+sparsity \| 0.375 \| 0.70 \| 10 \| \| --- \| --- \| --- \| --- \| \| Multiscalelowrank \| 0.359 \| 1.00 \| 4472 \| \| IBTSVT \| 0.844 \| 1.00 \| 715 \|	0
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \|	\| C \| thesetofconcepts \| \| --- \| --- \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfunction \| \| w \| aweightingfunction(notconsideringtemporaldecay) \| \| f \| adecayfunction \| \| w \|...	1
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \|	\| Notation \| Deﬁnition \| \| --- \| --- \| \| T<br>(p) \| Timecomplexityofamultiplication \| \| T<br>(p) \| Timecomplexityofanaddition \| \| T<br>(p) \| Timecomplexityofanexponentiation \| \| T<br>(p) \| Timecomplexityofaninversion \| \| T<br>(p) \| Timecomplexityofanellipticcurvescalarmultiplication \| \| T<br>(p) \| Timecomplexityofanell...	0
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \| \| C \| thesetofconcepts \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfu...	\| f \| adecayfunction \| \| --- \| --- \| \| w \| aweightingfunctionthatextendswwithtemporaldecay \| \| σ \| asimilarityfunction \|	1
\| u \| auser \| \| --- \| --- \| \| i \| asocialmediaitem \| \| Iu \| thesetofu’ssocialmediaitems \| \| c \| aconcept \| \| C \| thesetofconcepts \| \| d \| acandidateitem(scientiﬁcpublication) \| \| D \| thesetofcandidateitems \| \| t,tid \| thetimestampofiandd,respectively \| \| Pu \| u’suserproﬁle \| \| Pd \| d’sdocumentproﬁle \| \| Φ \| aproﬁlingfu...	\| T<br>(p) \| Timecomplexityofanellipticcurvescalarmultiplication \| \| --- \| --- \| \| T<br>(p) \| Timecomplexityofanellipticcurvepointsaddition \|	0
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| Cheminot \| RIV<br>Cu \| 0.273<br>0.844 \| 0.165<br>0.848 \| 0.147<br>0.808 \| 0.060<br>0.932 \| 0.203<br>0.829 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cropland \| RIV<br>Cu \| 0.046<br>0.337 \| 0.037<br>0.412 \| 0.038<br>0.415 \| 0.028<br>0.426 \| 0.048<br>0.423 \|	1
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| CIFAR-10 \| NUS-WIDE \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 12bits \| 24bits \| 32bits \| 48bits \| 12bits \| 24bits \| 32bits \| 48bits \| 12bits \| \| 0.162<br>0.131<br>0.121<br>0.138 \| 0.169<br>0.135<br>0.126<br>0.141 \| 0.172<br>0.133<br>0.120<br>0.146 \| 0.175<br>0.130<br>0.120<br>0....	0
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| Cheminot \| RIV<br>Cu \| 0.273<br>0.844 \| 0.165<br>0.848 \| 0.147<br>0.808 \| 0.060<br>0.932 \| 0.203<br>0.829 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Cropland \| RIV<br>Cu \| 0.046<br>0.337 \| 0.037<br>0.412 \| 0.038<br>0.415 \| 0.028<br>0.426 \| 0.048<br>0.423 \|	1
\| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Vancouver \| RIV<br>Cu \| 0.273<br>0.304 \| 0.198<br>0.236 \| 0.179<br>0.284 \| 0.195<br>0.281 \| 0.179<br>0.314 \| \| Bayard \| RIV<br>Cu \| 0.273<br>0.770 \| 0.178<br>0.744 \| 0.157<br>0.736 \| 0.073<br>0.838 \| 0.203<br>0.750 \|	\| 0.196<br>0.174<br>0.101<br>0.212 \| 0.246<br>0.205<br>0.128<br>0.247 \| 0.289<br>0.220<br>0.132<br>0.256 \| 0.301<br>0.232<br>0.169<br>0.281 \| 0.435<br>0.433<br>0.401<br>0.549 \| 0.435<br>0.426<br>0.442<br>0.614 \| 0.548<br>0.426<br>0.480<br>0.653 \| 0.435<br>0.423<br>0.471<br>0.678 \| 0.553<br>0.550<br>0.543<br>0.552 \| \| -...	0
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| Dropout(0.5) \| \| --- \| \| fully-connected(1024) \| \| fully-connected(10) \| \| SoftMax \|	1
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	0
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| \| \| \| conv(24,32) \| conv(24,32)<br>maxpool(2) \| conv(24,32)<br>maxpool(2) \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| conv(32,40) \| conv(32,40)<br>maxpool(2) \| \| \| \| \| \| \| conv(40,48) \| \| Dropout(0.5) \| \| \| \| \| \| \| fully-connected(1024) \| \| \| \| \| \| \| fully-connected(10) \| \| \| \| \| \| \| ...	1
\| 8 \| 16 \| 32 \| 64 \| 128 \| 256 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| conv(1,8) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| conv(1,8)<br>maxpool(2) \| \| \| conv(8,16) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8,16)<br>maxpool(2) \| conv(8...	\| fully-connected(4) \| \| --- \| \| Normalize \|	0
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \|	\| AMI \| 700 \| 100 \| \| --- \| --- \| --- \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \| \| CP \| 102 \| 17 \| \| UERCTrain \| 2304 \| 166 \| \| Multi-PIEEar \| 17183 \| 205 \|	1
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \|	\| Dataset \| #ofsamples \| #ofclasses \| #ofattributes \| \| --- \| --- \| --- \| --- \| \| Breastcancer \| 286 \| 2 \| 9 \| \| Diabetes \| 768 \| 2 \| 8 \| \| SolarFlare \| 144 \| 3 \| 9 \| \| German \| 1000 \| 2 \| 20 \| \| Heart \| 270 \| 2 \| 13 \| \| Image \| 2310 \| 7 \| 19 \| \| Ringnorm \| 7400 \| 2 \| 20 \| \| Splice \| 3190 \| 3 \| 60 \| \| Thyroid \| 215 \| 3...	0
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \| \| AMI \| 700 \| 100 \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \|	\| CP \| 102 \| 17 \| \| --- \| --- \| --- \| \| UERCTrain \| 2304 \| 166 \| \| Multi-PIEEar \| 17183 \| 205 \|	1
\| Dataset \| #Images \| #Subjects \| \| --- \| --- \| --- \| \| AWE \| 1000 \| 100 \| \| AMI \| 700 \| 100 \| \| WPUT \| 2071 \| 501 \| \| IITD \| 493 \| 125 \|	\| Thyroid \| 215 \| 3 \| 5 \| \| --- \| --- \| --- \| --- \| \| Twonorm \| 7400 \| 2 \| 20 \| \| Waveform \| 5000 \| 3 \| 21 \|	0
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| PART \| 0.69/569 \| \| --- \| --- \| \| Logistic \| 0.59/512 \|	1
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| Model/Algorithm \| MNIST \| Caltech101 \| US-CT \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DC \| mIoU \| DC \| mIoU \| DC \| mIoU \| \| DetectionAccuracy \| 0.87 \| 0.78 \| 0.95 \| 0.91 \| 0.97 \| .0.94 \| \| MatchingAccuracy \| 0.82 \| 0.71 \| 0.96 \| 0.93 \| 0.97 \| 0.95 \| \| DSPaccw/oseg \| 0.58 \| 0.44 \| 0.60 \| 0.48 \| 0.29...	0
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| PART \| 0.69/569 \| \| --- \| --- \| \| Logistic \| 0.59/512 \|	1
\| Algorithm \| WAVGinK=1 \| \| --- \| --- \| \| J48 \| 0.65/569 \| \| NaiveBayes \| 0.80/569 \|	\| DSPaccw/oseg \| 0.58 \| 0.44 \| 0.60 \| 0.48 \| 0.29 \| 0.17 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| DSPaccwithseg \| - \| - \| - \| - \| 0.63 \| 0.49 \|	0
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \|	\| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| classsize(withoutteacher) \| 25 \| 22 \| 22 \| 24 \| 26 \| 22 \| 23 \| 21 \| 23 \| 23 \| \| teacherfound \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| \| missingnodes \| 0 \| 1 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 ...	1
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \|	\| Class \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 10 \| 11 \| 12 \| 13 \| 14 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| 88 \| 85 \| 91 \| 90 \| 91 \| 100 \| 100 \| 95 \| 83 \| 83 \| 71 \| 70 \| 96 \| 75 \| \| 2. \| 53 \| 70 \| 88 \| 90 \| 100 \| 100 \| 100 \| 100 \| 83 \| 71 \| 63 \| 63 \| 87 \| 47 ...	0
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \| \| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \|	\| classsize(withoutteacher) \| 25 \| 22 \| 22 \| 24 \| 26 \| 22 \| 23 \| 21 \| 23 \| 23 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| teacherfound \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| 1 \| \| missingnodes \| 0 \| 1 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \| 0 \|	1
\| component \| 1 \| 2 \| 3 \| 4 \| 5 \| 6 \| 7 \| 8 \| 9 \| 11 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| componentsize \| 26 \| 22 \| 23 \| 25 \| 27 \| 23 \| 24 \| 22 \| 24 \| 24 \| \| class \| 1B \| 5A \| 3B \| 5B \| 2B \| 2A \| 3A \| 4A \| 1A \| 4B \|	\| 2 \| 59 \| 72 \| 59 \| 85 \| 95 \| 95 \| 66 \| 95 \| 45 \| 40 \| 69 \| 100 \| 78 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 3 \| 49 \| 75 \| 49 \| 55 \| 90 \| 90 \| 69 \| 95 \| 15 \| 50 \| 72 \| 100 \| 83 \| \|	0
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \|	\| DatabaseServer \| MySQL \| SysBench \| \| --- \| --- \| --- \| \| MailServer \| Postﬁx \| Postal \| \| FileServer \| Sambasmbd \| DBench \| \| StreamingServer \| ﬀserver \| openRTSP \| \| ApplicationServer \| Tomcat \| Apacheab \| \| ComputeServer \| libsvm \| a1a.t \| \| IdleServer \| - \| - \|	1
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \|	\| AC \| AuthenticatedModule \| \| --- \| --- \| \| AIK \| AttestationIdentityKey \| \| CA \| CertiﬁcateAuthority \| \| DRTM \| DynamicRootofTrustforMeasurement \| \| MITM \| Man-in-the-Middle \| \| MLE \| MeasuredLaunchEnvironment \| \| PAL \| ProgramApplicationLogic \| \| PCR \| PlatformConﬁgurationRegister \| \| SKINIT \| SecureKernelInit \| \| S...	0
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \| \| DatabaseServer \| MySQL \| SysBench \| \| MailServer \| Postﬁx \| Postal \|	\| FileServer \| Sambasmbd \| DBench \| \| --- \| --- \| --- \| \| StreamingServer \| ﬀserver \| openRTSP \| \| ApplicationServer \| Tomcat \| Apacheab \| \| ComputeServer \| libsvm \| a1a.t \| \| IdleServer \| - \| - \|	1
\| \| Server-sideBenchmarks \| Client-sideDrivingTool \| \| --- \| --- \| --- \| \| WebServer \| Apachehttpd \| Apacheab \| \| DatabaseServer \| MySQL \| SysBench \| \| MailServer \| Postﬁx \| Postal \|	\| SLB \| SecureLoaderBlock \| \| --- \| --- \| \| SML \| StoredMeasurementLog \| \| SRK \| StorageRootKey \| \| TCB \| TrustedComputingBase \| \| TPM \| TrustedPlatformModule \| \| TXT \| TrustedExecutionTechnology \| \| PM \| ProxyModule \| \| DB \| Database \|	0
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \| \| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \|	\| ANPs(ﬁltered) \| 107.8 \| 14.75 \| 21.21 \| 18 \| \| --- \| --- \| --- \| --- \| --- \| \| VNPs(complete) \| 70.88 \| 8.91 \| 15.48 \| 12 \| \| VNPs(ﬁltered) \| 58.72 \| 7.87 \| 14.59 \| 11 \|	1
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \| \| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \|	\| \| AFLWDataset(21pts) \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| [0,30] \| (30,60] \| (60,90] \| mean \| std \| \| AN \| 4.88 \| 5.55 \| 7.10 \| 5.84 \| 1.14 \| \| AN+LR \| 4.00 \| 4.48 \| 5.89 \| 4.79 \| 0.98 \| \| VGG \| 4.15 \| 4.64 \| 5.96 \| 4.92 \| 0.94 \| \| VGG+LR \| 3.46 \| 3.78 \| 4.77 \| 4.00 \| 0.69 \|	0
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \|	\| ANPs(complete) \| 112.84 \| 14.77 \| 21.84 \| 19 \| \| --- \| --- \| --- \| --- \| --- \| \| ANPs(ﬁltered) \| 107.8 \| 14.75 \| 21.21 \| 18 \| \| VNPs(complete) \| 70.88 \| 8.91 \| 15.48 \| 12 \| \| VNPs(ﬁltered) \| 58.72 \| 7.87 \| 14.59 \| 11 \|	1
\| \| DurationperFile \| TagsperFile \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| \| Mean \| Median \| Mean \| Median \|	\| AN \| 4.88 \| 5.55 \| 7.10 \| 5.84 \| 1.14 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| AN+LR \| 4.00 \| 4.48 \| 5.89 \| 4.79 \| 0.98 \| \| VGG \| 4.15 \| 4.64 \| 5.96 \| 4.92 \| 0.94 \| \| VGG+LR \| 3.46 \| 3.78 \| 4.77 \| 4.00 \| 0.69 \|	0
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| CelebFaces \| public \| 10K \| 202K \| \| --- \| --- \| --- \| --- \| \| CASIA-WebFace \| public \| 10K \| 500K \| \| Ours \| public \| 100K \| about10M \| \| Facebook \| private \| 4K \| 4400K \| \| Google \| private \| 8M \| 100-200M \|	1
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| DataSet \| RealNetwork \| RD \| SW \| SF \| HK \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| HighestDegreeofaNode \| \| \| \| \| \| Blog \| 351 \| 46 \| 47 \| 211 \| 321 \| \| Twitter \| 237 \| 27 \| 27 \| 253 \| 319 \| \| Epinions \| 1192 \| 77 \| 72 \| 373 \| 560 \| \| Author \| 102 \| 15 \| 16 \| 201 \| 183 \| \| \| ClusteringCoeﬃcient \| \| \| \| \|...	0
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| CelebFaces \| public \| 10K \| 202K \| \| --- \| --- \| --- \| --- \| \| CASIA-WebFace \| public \| 10K \| 500K \| \| Ours \| public \| 100K \| about10M \| \| Facebook \| private \| 4K \| 4400K \| \| Google \| private \| 8M \| 100-200M \|	1
\| Dataset \| Available \| people \| images \| \| --- \| --- \| --- \| --- \| \| IJB-A \| public \| 500 \| 5712 \| \| LFW \| public \| 5K \| 13K \| \| YFD \| public \| 1595 \| 3425videos \|	\| Twitter \| 237 \| 27 \| 27 \| 253 \| 319 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Epinions \| 1192 \| 77 \| 72 \| 373 \| 560 \| \| Author \| 102 \| 15 \| 16 \| 201 \| 183 \| \| \| ClusteringCoeﬃcient \| \| \| \| \| \| Blog \| 0.32 \| 0.02 \| 0.56 \| 0.07 \| 0.24 \| \| Twitter \| 0.13 \| 0.005 \| 0.49 \| 0.03 \| 0.27 \| \| Epinions \| 0.27 \| 0.02 \| 0.5...	0
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \| \| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| ...	\| NLPR \| 0.1165 \| 0.1087 \| 0.1784 \| 0.1669 \| \| --- \| --- \| --- \| --- \| --- \| \| OURS \| 0.0794 \| 0.0860 \| 0.1779 \| 0.1589 \|	1
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \| \| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| ...	\| Methods \| MAE \| F-<br>measure \| Max-P \| Min-P \| Max-R \| Min-R \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2804 \| 0.3875 \| 0.1262 \| 1 \| 0 \| \| SIM \| 0.3740 \| 0.3345 \| 0.5076 \| 0.1262 \| 1 \| 0 \| \| HS \| 0.1849 \| 0.5361 \| 0.6581 \| 0.1262 \| 1 \| 0.1187 \| \| BSCA \| 0.1851 \| 0.5826 \| 0.7977 \| 0.1262 \| 1 \| 0....	0
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \|	\| SIM \| 0.3740 \| 0.3957 \| 0.4184 \| 0.4012 \| \| --- \| --- \| --- \| --- \| --- \| \| HS \| 0.1849 \| 0.1909 \| 0.2582 \| 0.2516 \| \| BSCA \| 0.1851 \| 0.1754 \| 0.2386 \| 0.2148 \| \| LPS \| 0.1406 \| 0.1252 \| 0.1960 \| 0.2059 \| \| DES \| 0.3079 \| 0.3207 \| 0.3132 \| 0.4465 \| \| NLPR \| 0.1165 \| 0.1087 \| 0.1784 \| 0.1669 \| \| OURS \| 0.0794 \| 0.086...	1
\| \| RGBD135Dataset \| NLPRDataset \| SSD100Dataset \| NJU2000Dataset \| \| --- \| --- \| --- \| --- \| --- \| \| FT \| 0.2049 \| 0.2168 \| 0.2738 \| 0.2637 \|	\| OURS1 \| 0.1065 \| 0.5357 \| 0.9000 \| 0.0074 \| 1 \| 0 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| OURS2 \| 0.0880 \| 0.6881 \| 0.9249 \| 0.1262 \| 1 \| 0.1118 \| \| OURS \| 0.0781 \| 0.7230 \| 0.9181 \| 0.1262 \| 1 \| 0.2669 \|	0