premise string | hypothesis string | label int64 |
|---|---|---|
| Layer | Structure |
| --- | --- |
| conv1 | f.64×11×11;st.4×4;pad.0;LRN.;×2pool |
| conv2 | f.265×5×5;st.1×1;pad.2;LRN.;×2pool |
| conv3 | f.265×3×3;st.1×1;pad.1 | | | conv4 | f.265×3×3;st.1×1;pad.1 |
| --- | --- |
| conv5 | f.265×3×3;st.1×1;pad.1;×2pool |
| full6 | 4096 |
| full7 | 4096 |
| full8 | →k-bithashcode | | 1 |
| Layer | Structure |
| --- | --- |
| conv1 | f.64×11×11;st.4×4;pad.0;LRN.;×2pool |
| conv2 | f.265×5×5;st.1×1;pad.2;LRN.;×2pool |
| conv3 | f.265×3×3;st.1×1;pad.1 | | | layer | type | kernelsize/stride | output | layer | type |
| --- | --- | --- | --- | --- | --- |
| 1-2 | conv+relu | (3,3)/1-(1,1)/1 | (128,128,32) | 13-14 | conv+relu |
| 3-4 | conv+relu | (3,3)/2-(1,1)/1 | (64,64,32) | 15 | maxpool |
| 5 | maxpool | (3,3)/2 | (32,32,32) | 16-17 | conv+relu |
| 6-7 | conv+relu | (3,3)/1-(1,1)/1 | (32,32,64) | 18-19 | conv+relu |
| 8-9 | conv+relu | (3,3)/1-(1,1)/1 | (32,32,64) | 20 | fc+relu+dropout |
| 10 | maxpool | (3,3)/2 | (16,16,64) | 21 | fc+dropout |
| 11-12 | conv+relu | (3,3)/1-(1,1)/1 | (16,16,128) | 22 | fc | | 0 |
| Layer | Structure |
| --- | --- |
| conv1 | f.64×11×11;st.4×4;pad.0;LRN.;×2pool |
| conv2 | f.265×5×5;st.1×1;pad.2;LRN.;×2pool |
| conv3 | f.265×3×3;st.1×1;pad.1 |
| conv4 | f.265×3×3;st.1×1;pad.1 | | | conv5 | f.265×3×3;st.1×1;pad.1;×2pool |
| --- | --- |
| full6 | 4096 |
| full7 | 4096 |
| full8 | →k-bithashcode | | 1 |
| Layer | Structure |
| --- | --- |
| conv1 | f.64×11×11;st.4×4;pad.0;LRN.;×2pool |
| conv2 | f.265×5×5;st.1×1;pad.2;LRN.;×2pool |
| conv3 | f.265×3×3;st.1×1;pad.1 |
| conv4 | f.265×3×3;st.1×1;pad.1 | | | 6-7 | conv+relu | (3,3)/1-(1,1)/1 | (32,32,64) | 18-19 | conv+relu |
| --- | --- | --- | --- | --- | --- |
| 8-9 | conv+relu | (3,3)/1-(1,1)/1 | (32,32,64) | 20 | fc+relu+dropout |
| 10 | maxpool | (3,3)/2 | (16,16,64) | 21 | fc+dropout |
| 11-12 | conv+relu | (3,3)/1-(1,1)/1 | (16,16,128) | 22 | fc | | 0 |
| Micro-F1 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Algorithm | d=16 | d=32 | d=64 | d=128 | Algorithm | d=16 | d=32 | d=64 |
| Deepwalk | 0.5306 | 0.546 | 0.5832 | 0.5832 | Deepwalk | 0.4755 | 0.4981 | 0.4981 | | | Line | 0.4695 | 0.6757 | 0.6908 | 0.7044 | Line | 0.4695 | 0.6112 | 0.6355 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Node2vec | 0.5380 | 0.5524 | 0.5692 | 0.6029 | Node2vec | 0.4633 | 0.4828 | 0.5108 |
| TextOnly | 0.7030 | 0.7131 | 0.7121 | 0.7039 | TextOnly | 0.6305 | 0.6559 | 0.6683 |
| Naivecombination | 0.7070 | 0.7042 | 0.7138 | 0.7012 | Naivecombination | 0.6684 | 0.6665 | 0.6779 |
| TADW | 0.3952 | 0.5139 | 0.6751 | 0.718 | TADW | 0.3121 | 0.4465 | 0.6156 |
| Deepwalk(hete) | 0.7077 | 0.7114 | 0.7213 | 0.6993 | Deepwalk(hete) | 0.6332 | 0.6531 | 0.6725 |
| Line(hete) | 0.7159 | 0.7228 | 0.7165 | 0.7065 | Line(hete) | 0.6408 | 0.6569 | 0.655 |
| Node2vec(hete) | 0.7185 | 0.7219 | 0.7222 | 0.7077 | Node2vec(hete) | 0.6487 | 0.6645 | 0.6764 |
| GERI | 0.7427 | 0.7472 | 0.7457 | 0.7358 | GERI | 0.6794 | 0.6962 | 0.7004 | | 1 |
| Micro-F1 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Algorithm | d=16 | d=32 | d=64 | d=128 | Algorithm | d=16 | d=32 | d=64 |
| Deepwalk | 0.5306 | 0.546 | 0.5832 | 0.5832 | Deepwalk | 0.4755 | 0.4981 | 0.4981 | | | Micro-F1 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Algorithm | d=16 | d=32 | d=64 | d=128 | Algorithm | d=16 | d=32 | d=64 |
| Deepwalk | 0.5600 | 0.5769 | 0.5839 | 0.6027 | Deepwalk | 0.4552 | 0.4896 | 0.5114 |
| Line | 0.5220 | 0.4939 | 0.4895 | 0.5080 | Line | 0.4193 | 0.3920 | 0.3946 |
| Node2vec | 0.5760 | 0.5860 | 0.5952 | 0.6112 | Node2vec | 0.4858 | 0.5040 | 0.525 |
| TextOnly | 0.6113 | 0.6472 | 0.6698 | 0.6894 | TextOnly | 0.6044 | 0.6333 | 0.6521 |
| Naivecombination | 0.7440 | 0.7476 | 0.7524 | 0.7511 | Naivecombination | 0.718 | 0.7233 | 0.7284 |
| TADW | 0.5023 | 0.6031 | 0.6657 | 0.7179 | TADW | 0.4925 | 0.5904 | 0.6497 |
| Deepwalk(hete) | 0.7555 | 0.7582 | 0.7684 | 0.7771 | Deepwalk(hete) | 0.7299 | 0.7319 | 0.7451 |
| Line(hete) | 0.7669 | 0.7703 | 0.7792 | 0.7853 | Line(hete) | 0.7442 | 0.7479 | 0.7578 |
| Node2vec(hete) | 0.7553 | 0.7623 | 0.7716 | 0.7787 | Node2vec(hete) | 0.7294 | 0.7387 | 0.7495 |
| GERI | 0.7695 | 0.7786 | 0.7867 | 0.7919 | GERI | 0.7474 | 0.7548 | 0.7646 | | 0 |
| Micro-F1 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Algorithm | d=16 | d=32 | d=64 | d=128 | Algorithm | d=16 | d=32 | d=64 |
| Deepwalk | 0.5306 | 0.546 | 0.5832 | 0.5832 | Deepwalk | 0.4755 | 0.4981 | 0.4981 | | | Line | 0.4695 | 0.6757 | 0.6908 | 0.7044 | Line | 0.4695 | 0.6112 | 0.6355 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Node2vec | 0.5380 | 0.5524 | 0.5692 | 0.6029 | Node2vec | 0.4633 | 0.4828 | 0.5108 |
| TextOnly | 0.7030 | 0.7131 | 0.7121 | 0.7039 | TextOnly | 0.6305 | 0.6559 | 0.6683 |
| Naivecombination | 0.7070 | 0.7042 | 0.7138 | 0.7012 | Naivecombination | 0.6684 | 0.6665 | 0.6779 |
| TADW | 0.3952 | 0.5139 | 0.6751 | 0.718 | TADW | 0.3121 | 0.4465 | 0.6156 |
| Deepwalk(hete) | 0.7077 | 0.7114 | 0.7213 | 0.6993 | Deepwalk(hete) | 0.6332 | 0.6531 | 0.6725 |
| Line(hete) | 0.7159 | 0.7228 | 0.7165 | 0.7065 | Line(hete) | 0.6408 | 0.6569 | 0.655 |
| Node2vec(hete) | 0.7185 | 0.7219 | 0.7222 | 0.7077 | Node2vec(hete) | 0.6487 | 0.6645 | 0.6764 |
| GERI | 0.7427 | 0.7472 | 0.7457 | 0.7358 | GERI | 0.6794 | 0.6962 | 0.7004 | | 1 |
| Micro-F1 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Algorithm | d=16 | d=32 | d=64 | d=128 | Algorithm | d=16 | d=32 | d=64 |
| Deepwalk | 0.5306 | 0.546 | 0.5832 | 0.5832 | Deepwalk | 0.4755 | 0.4981 | 0.4981 | | | Line | 0.5220 | 0.4939 | 0.4895 | 0.5080 | Line | 0.4193 | 0.3920 | 0.3946 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Node2vec | 0.5760 | 0.5860 | 0.5952 | 0.6112 | Node2vec | 0.4858 | 0.5040 | 0.525 |
| TextOnly | 0.6113 | 0.6472 | 0.6698 | 0.6894 | TextOnly | 0.6044 | 0.6333 | 0.6521 |
| Naivecombination | 0.7440 | 0.7476 | 0.7524 | 0.7511 | Naivecombination | 0.718 | 0.7233 | 0.7284 |
| TADW | 0.5023 | 0.6031 | 0.6657 | 0.7179 | TADW | 0.4925 | 0.5904 | 0.6497 |
| Deepwalk(hete) | 0.7555 | 0.7582 | 0.7684 | 0.7771 | Deepwalk(hete) | 0.7299 | 0.7319 | 0.7451 |
| Line(hete) | 0.7669 | 0.7703 | 0.7792 | 0.7853 | Line(hete) | 0.7442 | 0.7479 | 0.7578 |
| Node2vec(hete) | 0.7553 | 0.7623 | 0.7716 | 0.7787 | Node2vec(hete) | 0.7294 | 0.7387 | 0.7495 |
| GERI | 0.7695 | 0.7786 | 0.7867 | 0.7919 | GERI | 0.7474 | 0.7548 | 0.7646 | | 0 |
| FOON<br>Selecte | TrialsforExperiment | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | | | REG | 53 | 15 | 28 | 42 | 17 | 48 | 19 | 42 | 53 | 18 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| EXP | 55 | 23 | 32 | 53 | 28 | 54 | 27 | 47 | 62 | 14 |
| | | | | | | | | | | | | 1 |
| FOON<br>Selecte | TrialsforExperiment | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | | | Ground<br>truth | TIFFS | | |
| --- | --- | --- | --- |
| Extract | Match | Extract | |
| 352 | 358 | 181 | 351 |
| 235 | 45 | 41 | 47 |
| 47 | 32 | 28 | 34 |
| 79 | 61 | 52 | 57 |
| 107 | 43 | 39 | 44 |
| 169 | 71 | 58 | 67 |
| 106 | 79 | 40 | 82 |
| 22 | 15 | 10 | 14 |
| 49 | 83 | 31 | 76 |
| 100 | 63 | 45 | 62 |
| 53 | 42 | 24 | 41 |
| 37 | 45 | 21 | 42 |
| 117 | 82 | 69 | 80 |
| 92 | 58 | 42 | 62 |
| 1565 | 1074 | 681 | 1060 | | 0 |
| FOON<br>Selecte | TrialsforExperiment | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | | | REG | 53 | 15 | 28 | 42 | 17 | 48 | 19 | 42 | 53 | 18 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| EXP | 55 | 23 | 32 | 53 | 28 | 54 | 27 | 47 | 62 | 14 |
| | | | | | | | | | | | | 1 |
| FOON<br>Selecte | TrialsforExperiment | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| #1 | #2 | #3 | #4 | #5 | #6 | #7 | #8 | #9 | #10 | | | 47 | 32 | 28 | 34 |
| --- | --- | --- | --- |
| 79 | 61 | 52 | 57 |
| 107 | 43 | 39 | 44 |
| 169 | 71 | 58 | 67 |
| 106 | 79 | 40 | 82 |
| 22 | 15 | 10 | 14 |
| 49 | 83 | 31 | 76 |
| 100 | 63 | 45 | 62 |
| 53 | 42 | 24 | 41 |
| 37 | 45 | 21 | 42 |
| 117 | 82 | 69 | 80 |
| 92 | 58 | 42 | 62 |
| 1565 | 1074 | 681 | 1060 | | 0 |
| Dataset | Network | Loss | I.NetPre.? | R1 | mAP |
| --- | --- | --- | --- | --- | --- |
| Market | DGDNet | SID | YES<br>NO | 47.5<br>47.8 | 23.1<br>23.8 | | | SID+PV | YES<br>NO | 71.3<br>82.7 | 48.9<br>63.4 | | |
| --- | --- | --- | --- | --- | --- |
| GoogLeNet | SID | YES<br>NO | 76.6<br>55.0 | 51.7<br>31.4 | |
| SID+PV | YES<br>NO | 83.7<br>68.7 | 65.5<br>45.3 | | |
| VIPeR | DGDNet | SID+PV | YES<br>NO | 37.4<br>51.5 | N/A<br>N/A |
| GoogLeNet | SID+PV | YES<br>NO | 56.3<br>37.0 | N/A<br>N/A | | | 1 |
| Dataset | Network | Loss | I.NetPre.? | R1 | mAP |
| --- | --- | --- | --- | --- | --- |
| Market | DGDNet | SID | YES<br>NO | 47.5<br>47.8 | 23.1<br>23.8 | | | | K=4 | K=8 | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | GoogLeNet | VGG-16 | GoogLeNet | VGG-16 | | | | |
| | Tcomp | Tcomm | Tcomp | Tcomm | Tcomp | Tcomm | Tcomp | Tcomm |
| Plump-DP | 2.28 | 0.40 | 7.83 | 4.09 | 2.32 | 0.57 | 7.82 | 5.51 |
| Quant-DP | 0.20 | 1.47 | 0.29 | 1.93 | | | | |
| Slim-DP | 0.18 | 1.18 | 0.25 | 1.65 | | | | | | 0 |
| Dataset | Network | Loss | I.NetPre.? | R1 | mAP |
| --- | --- | --- | --- | --- | --- |
| Market | DGDNet | SID | YES<br>NO | 47.5<br>47.8 | 23.1<br>23.8 | | | SID+PV | YES<br>NO | 71.3<br>82.7 | 48.9<br>63.4 | | |
| --- | --- | --- | --- | --- | --- |
| GoogLeNet | SID | YES<br>NO | 76.6<br>55.0 | 51.7<br>31.4 | |
| SID+PV | YES<br>NO | 83.7<br>68.7 | 65.5<br>45.3 | | |
| VIPeR | DGDNet | SID+PV | YES<br>NO | 37.4<br>51.5 | N/A<br>N/A |
| GoogLeNet | SID+PV | YES<br>NO | 56.3<br>37.0 | N/A<br>N/A | | | 1 |
| Dataset | Network | Loss | I.NetPre.? | R1 | mAP |
| --- | --- | --- | --- | --- | --- |
| Market | DGDNet | SID | YES<br>NO | 47.5<br>47.8 | 23.1<br>23.8 | | | Quant-DP | 0.20 | 1.47 | 0.29 | 1.93 |
| --- | --- | --- | --- | --- |
| Slim-DP | 0.18 | 1.18 | 0.25 | 1.65 | | 0 |
| Ssrc | Stgt | | |
| --- | --- | --- | --- |
| OS | EP | JRC | |
| OS | 85.4/60.9 | 70.0/50.3 | 75.0/53.7 | | | EP | 85.5/61.3 | 83.6/60.4 | 82.1/59.4 |
| --- | --- | --- | --- |
| JRC | 90.9/64.7 | 87.9/62.1 | 87.9/61.9 | | 1 |
| Ssrc | Stgt | | |
| --- | --- | --- | --- |
| OS | EP | JRC | |
| OS | 85.4/60.9 | 70.0/50.3 | 75.0/53.7 | | | Ssrc | Stgt | | |
| --- | --- | --- | --- |
| OS | EP | JRC | |
| OS | 33.1/30.5 | 26.2/27.4 | 24.6/26.8 |
| EP | 35.4/35.9 | 38.6/37.4 | 37.7/37.0 |
| JRC | 52.6/42.9 | 55.4/44.2 | 58.9/45.9 | | 0 |
| Ssrc | Stgt | | |
| --- | --- | --- | --- |
| OS | EP | JRC | |
| OS | 85.4/60.9 | 70.0/50.3 | 75.0/53.7 | | | EP | 85.5/61.3 | 83.6/60.4 | 82.1/59.4 |
| --- | --- | --- | --- |
| JRC | 90.9/64.7 | 87.9/62.1 | 87.9/61.9 | | 1 |
| Ssrc | Stgt | | |
| --- | --- | --- | --- |
| OS | EP | JRC | |
| OS | 85.4/60.9 | 70.0/50.3 | 75.0/53.7 | | | OS | 33.1/30.5 | 26.2/27.4 | 24.6/26.8 |
| --- | --- | --- | --- |
| EP | 35.4/35.9 | 38.6/37.4 | 37.7/37.0 |
| JRC | 52.6/42.9 | 55.4/44.2 | 58.9/45.9 | | 0 |
| Methods | Accuracy | | |
| --- | --- | --- | --- |
| | 1/3 | 2/3 | cross |
| HOJ3D | 0.962 | 0.971 | 0.789 |
| HMM+DBM | - | - | 0.820 |
| EigenJoints | 0.958 | 0.977 | 0.823 |
| HMM+GMM(ourimplementation) | 0.861 | 0.929 | 0.825 |
| ActionletEnsemble(skeletondata) | - | - | 0.882 |
| SkeletalQuads | - | - | 0.898 | | | LDS | - | - | 0.900 |
| --- | --- | --- | --- |
| Cov3DJ | - | - | 0.905 |
| Our | 0.943 | 0.983 | 0.905 |
| FSFJ3D | - | - | 0.909 |
| KPLS | - | - | 0.923 | | 1 |
| Methods | Accuracy | | |
| --- | --- | --- | --- |
| | 1/3 | 2/3 | cross |
| HOJ3D | 0.962 | 0.971 | 0.789 |
| HMM+DBM | - | - | 0.820 |
| EigenJoints | 0.958 | 0.977 | 0.823 |
| HMM+GMM(ourimplementation) | 0.861 | 0.929 | 0.825 |
| ActionletEnsemble(skeletondata) | - | - | 0.882 |
| SkeletalQuads | - | - | 0.898 | | | Method | Lena |
| --- | --- |
| Bilinear | 30.13 |
| Bicubic | 31.34 |
| NEDI | 34.10 |
| WZP-Haar | 31.46 |
| WZP-Db.9/7 | 34.45 |
| Careyetal. | 34.48 |
| HMM | 34.52 |
| HMM-SR | 34.61 |
| WZP-CS | 34.93 |
| SAI | 34.74 |
| Iterative(2iter.) | 35.25 |
| Iterative(10iter.) | 37.39 |
| Hybrid(2iter.and1mod.) | 37.12 |
| Opt.Hybrid(2iter.and1mod.) | 37.41 | | 0 |
| Methods | Accuracy | | |
| --- | --- | --- | --- |
| | 1/3 | 2/3 | cross |
| HOJ3D | 0.962 | 0.971 | 0.789 |
| HMM+DBM | - | - | 0.820 |
| EigenJoints | 0.958 | 0.977 | 0.823 |
| HMM+GMM(ourimplementation) | 0.861 | 0.929 | 0.825 |
| ActionletEnsemble(skeletondata) | - | - | 0.882 |
| SkeletalQuads | - | - | 0.898 |
| LDS | - | - | 0.900 |
| Cov3DJ | - | - | 0.905 | | | Our | 0.943 | 0.983 | 0.905 |
| --- | --- | --- | --- |
| FSFJ3D | - | - | 0.909 |
| KPLS | - | - | 0.923 | | 1 |
| Methods | Accuracy | | |
| --- | --- | --- | --- |
| | 1/3 | 2/3 | cross |
| HOJ3D | 0.962 | 0.971 | 0.789 |
| HMM+DBM | - | - | 0.820 |
| EigenJoints | 0.958 | 0.977 | 0.823 |
| HMM+GMM(ourimplementation) | 0.861 | 0.929 | 0.825 |
| ActionletEnsemble(skeletondata) | - | - | 0.882 |
| SkeletalQuads | - | - | 0.898 |
| LDS | - | - | 0.900 |
| Cov3DJ | - | - | 0.905 | | | Hybrid(2iter.and1mod.) | 37.12 |
| --- | --- |
| Opt.Hybrid(2iter.and1mod.) | 37.41 | | 0 |
| Range | Parallels | CCs | Independents |
| --- | --- | --- | --- |
| 5 | 5 | 0 | 5 |
| 9 | 9 | 0 | 9 |
| 17 | 16.97 | 0.03 | 16.94 | | | 33 | 32.93 | 0.07 | 32.86 |
| --- | --- | --- | --- |
| 65 | 64.81 | 0.18 | 64.63 |
| 129 | 128.44 | 0.54 | 127.9 |
| 257 | 255.27 | 1.67 | 253.6 |
| 513 | 508.42 | 4.35 | 504.07 |
| 1025 | 1012.16 | 12.32 | 999.84 |
| 2049 | 2016.82 | 30.92 | 1985.9 |
| 4097 | 4021.72 | 72.44 | 3949.28 |
| 8193 | 8026.04 | 161.06 | 7864.98 |
| 16385 | 16036.87 | 335.95 | 15700.92 |
| 32769 | 32058.47 | 685.59 | 31372.88 |
| 65537 | 64101.76 | 1384.98 | 62716.78 | | 1 |
| Range | Parallels | CCs | Independents |
| --- | --- | --- | --- |
| 5 | 5 | 0 | 5 |
| 9 | 9 | 0 | 9 |
| 17 | 16.97 | 0.03 | 16.94 | | | | GradualandSharp | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Video | TP | FP | FN | P | R | F |
| 01811a | 60 | 7 | 4 | 0.896 | 0.938 | 0.916 |
| 6011 | 40 | 96 | 81 | 0.294 | 0.331 | 0.311 |
| 8024 | 85 | 22 | 21 | 0.794 | 0.802 | 0.798 |
| 8386 | 113 | 10 | 5 | 0.919 | 0.958 | 0.938 |
| 8401 | 26 | 5 | 5 | 0.839 | 0.839 | 0.839 |
| 10558a | 122 | 1 | 8 | 0.992 | 0.938 | 0.964 |
| 23585a | 149 | 10 | 16 | 0.937 | 0.903 | 0.92 |
| 23585b | 103 | 3 | 1 | 0.972 | 0.99 | 0.981 |
| 34921a | 70 | 4 | 5 | 0.946 | 0.933 | 0.94 |
| 34921b | 91 | 10 | 8 | 0.901 | 0.919 | 0.91 |
| 36553 | 200 | 21 | 14 | 0.905 | 0.935 | 0.92 |
| 50009 | 44 | 28 | 14 | 0.611 | 0.759 | 0.677 |
| 50028 | 81 | 17 | 12 | 0.827 | 0.871 | 0.848 |
| UGS01 | 164 | 8 | 12 | 0.953 | 0.932 | 0.943 |
| UGS04 | 218 | 25 | 5 | 0.897 | 0.978 | 0.936 |
| UGS05 | 21 | 6 | 9 | 0.778 | 0.7 | 0.737 |
| UGS09 | 169 | 12 | 24 | 0.934 | 0.876 | 0.904 |
| Total | 1756 | 285 | 244 | 0.86 | 0.878 | 0.869 | | 0 |
| Range | Parallels | CCs | Independents |
| --- | --- | --- | --- |
| 5 | 5 | 0 | 5 |
| 9 | 9 | 0 | 9 |
| 17 | 16.97 | 0.03 | 16.94 |
| 33 | 32.93 | 0.07 | 32.86 |
| 65 | 64.81 | 0.18 | 64.63 |
| 129 | 128.44 | 0.54 | 127.9 |
| 257 | 255.27 | 1.67 | 253.6 |
| 513 | 508.42 | 4.35 | 504.07 | | | 1025 | 1012.16 | 12.32 | 999.84 |
| --- | --- | --- | --- |
| 2049 | 2016.82 | 30.92 | 1985.9 |
| 4097 | 4021.72 | 72.44 | 3949.28 |
| 8193 | 8026.04 | 161.06 | 7864.98 |
| 16385 | 16036.87 | 335.95 | 15700.92 |
| 32769 | 32058.47 | 685.59 | 31372.88 |
| 65537 | 64101.76 | 1384.98 | 62716.78 | | 1 |
| Range | Parallels | CCs | Independents |
| --- | --- | --- | --- |
| 5 | 5 | 0 | 5 |
| 9 | 9 | 0 | 9 |
| 17 | 16.97 | 0.03 | 16.94 |
| 33 | 32.93 | 0.07 | 32.86 |
| 65 | 64.81 | 0.18 | 64.63 |
| 129 | 128.44 | 0.54 | 127.9 |
| 257 | 255.27 | 1.67 | 253.6 |
| 513 | 508.42 | 4.35 | 504.07 | | | UGS05 | 21 | 6 | 9 | 0.778 | 0.7 | 0.737 |
| --- | --- | --- | --- | --- | --- | --- |
| UGS09 | 169 | 12 | 24 | 0.934 | 0.876 | 0.904 |
| Total | 1756 | 285 | 244 | 0.86 | 0.878 | 0.869 | | 0 |
| method | mean() | median() | trimean() |
| --- | --- | --- | --- |
| donothing | 8.28 | 6.70 | 7.25 |
| Low-levelstatistics-basedmethods | | | |
| Gray-world(GW) | 7.87 | 6.97 | 7.14 |
| White-Patch(WP) | 6.80 | 5.30 | 5.77 |
| Shades-of-Gray | 6.14 | 5.33 | 5.51 |
| GeneralGray-World | 6.14 | 5.33 | 5.51 |
| 1st-orderGray-Edge | 5.88 | 4.65 | 5.11 |
| 2nd-orderGray-Edge | 6.10 | 4.85 | 5.28 |
| Learning-basedmethods | | | |
| Pixel-basedgamut | 7.07 | 5.81 | 6.12 |
| Edge-basedgamut | 6.81 | 5.81 | 6.03 |
| Intersection-basedgamut | 6.93 | 5.80 | 6.05 |
| NaturalImageStatistics | 5.19 | 3.93 | 4.31 |
| Exemplar-basedlearning | 4.38 | 3.43 | 3.67 |
| ColorTiger(proposed) | 5.61 | 3.39 | 4.31 |
| ColorCat(CC) | 4.22 | 3.17 | 3.46 |
| ColorDogWP,GW | 5.27 | 3.71 | 4.16 | | | SmartColorCat(SCC) | 4.62 | 3.52 | 3.80 |
| --- | --- | --- | --- |
| ColorDogSCC | 4.80 | 3.08 | 3.71 |
| ColorDogCC | 4.50 | 2.86 | 3.50 | | 1 |
| method | mean() | median() | trimean() |
| --- | --- | --- | --- |
| donothing | 8.28 | 6.70 | 7.25 |
| Low-levelstatistics-basedmethods | | | |
| Gray-world(GW) | 7.87 | 6.97 | 7.14 |
| White-Patch(WP) | 6.80 | 5.30 | 5.77 |
| Shades-of-Gray | 6.14 | 5.33 | 5.51 |
| GeneralGray-World | 6.14 | 5.33 | 5.51 |
| 1st-orderGray-Edge | 5.88 | 4.65 | 5.11 |
| 2nd-orderGray-Edge | 6.10 | 4.85 | 5.28 |
| Learning-basedmethods | | | |
| Pixel-basedgamut | 7.07 | 5.81 | 6.12 |
| Edge-basedgamut | 6.81 | 5.81 | 6.03 |
| Intersection-basedgamut | 6.93 | 5.80 | 6.05 |
| NaturalImageStatistics | 5.19 | 3.93 | 4.31 |
| Exemplar-basedlearning | 4.38 | 3.43 | 3.67 |
| ColorTiger(proposed) | 5.61 | 3.39 | 4.31 |
| ColorCat(CC) | 4.22 | 3.17 | 3.46 |
| ColorDogWP,GW | 5.27 | 3.71 | 4.16 | | | method | mean() | median() | trimean() |
| --- | --- | --- | --- |
| donothing | 15.62 | 14.00 | 14.56 |
| Low-levelstatistics-basedmethods | | | |
| Gray-world(GW) | 13.01 | 10.96 | 11.53 |
| White-Patch(WP) | 12.68 | 10.50 | 11.25 |
| Shades-of-Gray | 11.55 | 9.70 | 10.23 |
| GeneralGray-World | 11.55 | 9.70 | 10.23 |
| 1st-orderGray-Edge | 10.58 | 8.84 | 9.18 |
| 2nd-orderGray-Edge | 10.68 | 9.02 | 9.40 |
| Learning-basedmethods | | | |
| Edge-basedgamut | 12.78 | 10.88 | 11.38 |
| Pixel-basedgamut | 11.79 | 8.88 | 9.97 |
| Intersection-basedgamut | 11.81 | 8.93 | 10.00 |
| NaturalImageStatistics | 9.87 | 7.65 | 8.29 |
| ColorDogWP,GW | 10.27 | 7.33 | 8.20 |
| ColorTiger(proposed) | 9.51 | 7.11 | 7.66 |
| ColorCat(CC) | 8.73 | 7.07 | 7.43 |
| Exemplar-basedlearning | 7.97 | 6.46 | 6.77 |
| SmartColorCat(SCC) | 8.18 | 6.28 | 6.73 |
| ColorDogCC | 8.81 | 5.98 | 6.97 |
| ColorDogSCC | 8.51 | 5.55 | 6.56 | | 0 |
| method | mean() | median() | trimean() |
| --- | --- | --- | --- |
| donothing | 8.28 | 6.70 | 7.25 |
| Low-levelstatistics-basedmethods | | | |
| Gray-world(GW) | 7.87 | 6.97 | 7.14 |
| White-Patch(WP) | 6.80 | 5.30 | 5.77 |
| Shades-of-Gray | 6.14 | 5.33 | 5.51 |
| GeneralGray-World | 6.14 | 5.33 | 5.51 |
| 1st-orderGray-Edge | 5.88 | 4.65 | 5.11 |
| 2nd-orderGray-Edge | 6.10 | 4.85 | 5.28 |
| Learning-basedmethods | | | |
| Pixel-basedgamut | 7.07 | 5.81 | 6.12 |
| Edge-basedgamut | 6.81 | 5.81 | 6.03 |
| Intersection-basedgamut | 6.93 | 5.80 | 6.05 |
| NaturalImageStatistics | 5.19 | 3.93 | 4.31 |
| Exemplar-basedlearning | 4.38 | 3.43 | 3.67 |
| ColorTiger(proposed) | 5.61 | 3.39 | 4.31 |
| ColorCat(CC) | 4.22 | 3.17 | 3.46 |
| ColorDogWP,GW | 5.27 | 3.71 | 4.16 |
| SmartColorCat(SCC) | 4.62 | 3.52 | 3.80 | | | ColorDogSCC | 4.80 | 3.08 | 3.71 |
| --- | --- | --- | --- |
| ColorDogCC | 4.50 | 2.86 | 3.50 | | 1 |
| method | mean() | median() | trimean() |
| --- | --- | --- | --- |
| donothing | 8.28 | 6.70 | 7.25 |
| Low-levelstatistics-basedmethods | | | |
| Gray-world(GW) | 7.87 | 6.97 | 7.14 |
| White-Patch(WP) | 6.80 | 5.30 | 5.77 |
| Shades-of-Gray | 6.14 | 5.33 | 5.51 |
| GeneralGray-World | 6.14 | 5.33 | 5.51 |
| 1st-orderGray-Edge | 5.88 | 4.65 | 5.11 |
| 2nd-orderGray-Edge | 6.10 | 4.85 | 5.28 |
| Learning-basedmethods | | | |
| Pixel-basedgamut | 7.07 | 5.81 | 6.12 |
| Edge-basedgamut | 6.81 | 5.81 | 6.03 |
| Intersection-basedgamut | 6.93 | 5.80 | 6.05 |
| NaturalImageStatistics | 5.19 | 3.93 | 4.31 |
| Exemplar-basedlearning | 4.38 | 3.43 | 3.67 |
| ColorTiger(proposed) | 5.61 | 3.39 | 4.31 |
| ColorCat(CC) | 4.22 | 3.17 | 3.46 |
| ColorDogWP,GW | 5.27 | 3.71 | 4.16 |
| SmartColorCat(SCC) | 4.62 | 3.52 | 3.80 | | | Shades-of-Gray | 11.55 | 9.70 | 10.23 |
| --- | --- | --- | --- |
| GeneralGray-World | 11.55 | 9.70 | 10.23 |
| 1st-orderGray-Edge | 10.58 | 8.84 | 9.18 |
| 2nd-orderGray-Edge | 10.68 | 9.02 | 9.40 |
| Learning-basedmethods | | | |
| Edge-basedgamut | 12.78 | 10.88 | 11.38 |
| Pixel-basedgamut | 11.79 | 8.88 | 9.97 |
| Intersection-basedgamut | 11.81 | 8.93 | 10.00 |
| NaturalImageStatistics | 9.87 | 7.65 | 8.29 |
| ColorDogWP,GW | 10.27 | 7.33 | 8.20 |
| ColorTiger(proposed) | 9.51 | 7.11 | 7.66 |
| ColorCat(CC) | 8.73 | 7.07 | 7.43 |
| Exemplar-basedlearning | 7.97 | 6.46 | 6.77 |
| SmartColorCat(SCC) | 8.18 | 6.28 | 6.73 |
| ColorDogCC | 8.81 | 5.98 | 6.97 |
| ColorDogSCC | 8.51 | 5.55 | 6.56 | | 0 |
| | HMDB51 | Hw2 | UCF101 | UCF50 |
| --- | --- | --- | --- | --- |
| Traj | 31.9 | 42.7 | 55.2 | 69.3 |
| HOG<br>LOP | 42.0<br>47.2 | 47.4<br>54.3 | 72.4<br>79.3 | 77.5<br>83.2 | | | HOF<br>MBH<br>LOF | 49.8<br>52.4<br>51.0 | 55.0<br>60.8<br>55.4 | 74.6<br>81.4<br>81.2 | 86.1<br>87.0<br>86.8 |
| --- | --- | --- | --- | --- |
| Handcrafted<br>Traj+Learned | 59.1<br>56.1 | 64.5<br>61.1 | 85.5<br>85.9 | 90.2<br>89.3 |
| Hybrid | 62.3 | 65.5 | 87.5 | 92.0 | | 1 |
| | HMDB51 | Hw2 | UCF101 | UCF50 |
| --- | --- | --- | --- | --- |
| Traj | 31.9 | 42.7 | 55.2 | 69.3 |
| HOG<br>LOP | 42.0<br>47.2 | 47.4<br>54.3 | 72.4<br>79.3 | 77.5<br>83.2 | | | Cate. | Bench. | RPKI | RG | WPKI | WG | R/T |
| --- | --- | --- | --- | --- | --- | --- |
| FINE<br>FINE<br>FINE<br>FINE | GUPS<br>SSCA2<br>canl.<br>park. | 69.67<br>20.89<br>17.79<br>9.76 | 1.78<br>1.68<br>1.64<br>2.42 | 69.62<br>20.42<br>8.64<br>6.14 | 1.78<br>1.56<br>1.10<br>2.74 | 1.00<br>1.02<br>2.06<br>1.59 |
| MID<br>MID | lirk.<br>BFS | 22.56<br>22.36 | 3.56<br>3.10 | 15.45<br>2.44 | 3.37<br>3.49 | 1.46<br>9.16 |
| COR<br>COR<br>COR<br>COR<br>COR<br>COR<br>COR | STRM.<br>bt<br>ft<br>sp<br>ua<br>ScPC.<br>perM | 33.33<br>7.68<br>31.85<br>8.04<br>4.42<br>11.00<br>2.62 | 8.00<br>7.98<br>8.00<br>7.98<br>7.19<br>5.65<br>6.28 | 16.63<br>7.63<br>31.72<br>7.89<br>3.80<br>3.85<br>2.40 | 8.00<br>7.98<br>8.00<br>7.98<br>7.92<br>5.74<br>6.12 | 2.00<br>1.01<br>1.00<br>1.02<br>1.16<br>2.86<br>1.09 |
| Note,canl.:canneal,park.:pagerank,<br>lirk.:listrank,STRM.:STREAM,ScPC:ScaleParC. | | | | | | | | 0 |
| | HMDB51 | Hw2 | UCF101 | UCF50 |
| --- | --- | --- | --- | --- |
| Traj | 31.9 | 42.7 | 55.2 | 69.3 | | | HOG<br>LOP | 42.0<br>47.2 | 47.4<br>54.3 | 72.4<br>79.3 | 77.5<br>83.2 |
| --- | --- | --- | --- | --- |
| HOF<br>MBH<br>LOF | 49.8<br>52.4<br>51.0 | 55.0<br>60.8<br>55.4 | 74.6<br>81.4<br>81.2 | 86.1<br>87.0<br>86.8 |
| Handcrafted<br>Traj+Learned | 59.1<br>56.1 | 64.5<br>61.1 | 85.5<br>85.9 | 90.2<br>89.3 |
| Hybrid | 62.3 | 65.5 | 87.5 | 92.0 | | 1 |
| | HMDB51 | Hw2 | UCF101 | UCF50 |
| --- | --- | --- | --- | --- |
| Traj | 31.9 | 42.7 | 55.2 | 69.3 | | | MID<br>MID | lirk.<br>BFS | 22.56<br>22.36 | 3.56<br>3.10 | 15.45<br>2.44 | 3.37<br>3.49 | 1.46<br>9.16 |
| --- | --- | --- | --- | --- | --- | --- |
| COR<br>COR<br>COR<br>COR<br>COR<br>COR<br>COR | STRM.<br>bt<br>ft<br>sp<br>ua<br>ScPC.<br>perM | 33.33<br>7.68<br>31.85<br>8.04<br>4.42<br>11.00<br>2.62 | 8.00<br>7.98<br>8.00<br>7.98<br>7.19<br>5.65<br>6.28 | 16.63<br>7.63<br>31.72<br>7.89<br>3.80<br>3.85<br>2.40 | 8.00<br>7.98<br>8.00<br>7.98<br>7.92<br>5.74<br>6.12 | 2.00<br>1.01<br>1.00<br>1.02<br>1.16<br>2.86<br>1.09 |
| Note,canl.:canneal,park.:pagerank,<br>lirk.:listrank,STRM.:STREAM,ScPC:ScaleParC. | | | | | | | | 0 |
| W | F | SF | QF | R16 | R32 | R64 | R128 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GrandSlam | 2000 | 1200 | 720 | 360 | 180 | 90 | 45 | 10 |
| ATPWorldTourFinals | +500 | +400 | +200pointsforeachroundrobinmatchwin | | | | | |
| Masters1000 | 1000 | 600 | 360 | 180 | 90 | 45 | 25 | 15 | | | ATP500 | 500 | 300 | 180 | 90 | 45 | 20 | 10 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ATP250 | 250 | 150 | 90 | 45 | 20 | 10 | 5 | | | 1 |
| W | F | SF | QF | R16 | R32 | R64 | R128 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GrandSlam | 2000 | 1200 | 720 | 360 | 180 | 90 | 45 | 10 |
| ATPWorldTourFinals | +500 | +400 | +200pointsforeachroundrobinmatchwin | | | | | |
| Masters1000 | 1000 | 600 | 360 | 180 | 90 | 45 | 25 | 15 | | | m | 2,000 | 4,000 | 6,000 | 8,000 | 10,000 |
| --- | --- | --- | --- | --- | --- |
| CW | 99min | 102min | 121min | 155min | 190min |
| QC | 0.985 | 0.994 | 0.995 | 0.995 | 0.997 |
| CC | 95min | 123min | 132min | 141min | 151min |
| QD | 0.92 | 0.96 | 0.97 | 0.97 | 0.98 | | 0 |
| W | F | SF | QF | R16 | R32 | R64 | R128 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GrandSlam | 2000 | 1200 | 720 | 360 | 180 | 90 | 45 | 10 | | | ATPWorldTourFinals | +500 | +400 | +200pointsforeachroundrobinmatchwin | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Masters1000 | 1000 | 600 | 360 | 180 | 90 | 45 | 25 | 15 |
| ATP500 | 500 | 300 | 180 | 90 | 45 | 20 | 10 | |
| ATP250 | 250 | 150 | 90 | 45 | 20 | 10 | 5 | | | 1 |
| W | F | SF | QF | R16 | R32 | R64 | R128 | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| GrandSlam | 2000 | 1200 | 720 | 360 | 180 | 90 | 45 | 10 | | | CC | 95min | 123min | 132min | 141min | 151min |
| --- | --- | --- | --- | --- | --- |
| QD | 0.92 | 0.96 | 0.97 | 0.97 | 0.98 | | 0 |
| Method | MAE | MSE |
| --- | --- | --- |
| Gaussianprocessregression | 3.72 | 20.1 |
| Ridgeregression | 3.59 | 19.0 |
| Cumulativeattributeregression | 3.43 | 17.7 |
| Countforest | 2.50 | 10.0 |
| ConvLSTM-nt | 2.53 | 11.2 | | | ConvLSTM | 2.24 | 8.5 |
| --- | --- | --- |
| BidirectionalConvLSTM | 2.10 | 7.6 | | 1 |
| Method | MAE | MSE |
| --- | --- | --- |
| Gaussianprocessregression | 3.72 | 20.1 |
| Ridgeregression | 3.59 | 19.0 |
| Cumulativeattributeregression | 3.43 | 17.7 |
| Countforest | 2.50 | 10.0 |
| ConvLSTM-nt | 2.53 | 11.2 | | | Method | MAE | MSE |
| --- | --- | --- |
| KernelRidgeRegression | 2.16 | 7.45 |
| RidgeRegression | 2.25 | 7.82 |
| GaussianProcessRegression | 2.24 | 7.97 |
| CumulativeAttributeRegression | 2.07 | 6.86 |
| Cross-sceneDNN | 1.6 | 3.31 |
| OPT-RC | 2.03 | 5.97 |
| FCN-MT | 1.67 | 3.41 | | 0 |
| Method | MAE | MSE |
| --- | --- | --- |
| Gaussianprocessregression | 3.72 | 20.1 |
| Ridgeregression | 3.59 | 19.0 |
| Cumulativeattributeregression | 3.43 | 17.7 | | | Countforest | 2.50 | 10.0 |
| --- | --- | --- |
| ConvLSTM-nt | 2.53 | 11.2 |
| ConvLSTM | 2.24 | 8.5 |
| BidirectionalConvLSTM | 2.10 | 7.6 | | 1 |
| Method | MAE | MSE |
| --- | --- | --- |
| Gaussianprocessregression | 3.72 | 20.1 |
| Ridgeregression | 3.59 | 19.0 |
| Cumulativeattributeregression | 3.43 | 17.7 | | | GaussianProcessRegression | 2.24 | 7.97 |
| --- | --- | --- |
| CumulativeAttributeRegression | 2.07 | 6.86 |
| Cross-sceneDNN | 1.6 | 3.31 |
| OPT-RC | 2.03 | 5.97 |
| FCN-MT | 1.67 | 3.41 | | 0 |
| Resource/Number | Symbol |
| --- | --- |
| #PEs | P | | | internalmemory(in#elements) | M |
| --- | --- |
| #disks | D |
| blocksize(in#elements)intheEMmodel | B |
| #elements | N |
| #runs | R | | 1 |
| Resource/Number | Symbol |
| --- | --- |
| #PEs | P | | | FileSize(MB) | No.OfAttackers | TrafficVol. |
| --- | --- | --- |
| 10 | 100 | 0.22GB |
| 50 | 500 | 0.67GB |
| 100 | 1500 | 1.67GB |
| 200 | 2000 | 3.23GB |
| 400 | 4000 | 5.91GB |
| 600 | 6000 | 9.14GB |
| 800 | 8000 | 12.37GB |
| 1000 | 10,000 | 15.83GB | | 0 |
| Resource/Number | Symbol |
| --- | --- |
| #PEs | P |
| internalmemory(in#elements) | M |
| #disks | D |
| blocksize(in#elements)intheEMmodel | B | | | #elements | N |
| --- | --- |
| #runs | R | | 1 |
| Resource/Number | Symbol |
| --- | --- |
| #PEs | P |
| internalmemory(in#elements) | M |
| #disks | D |
| blocksize(in#elements)intheEMmodel | B | | | 100 | 1500 | 1.67GB |
| --- | --- | --- |
| 200 | 2000 | 3.23GB |
| 400 | 4000 | 5.91GB |
| 600 | 6000 | 9.14GB |
| 800 | 8000 | 12.37GB |
| 1000 | 10,000 | 15.83GB | | 0 |
| Method | TripAdvisorData | OpenTableData |
| --- | --- | --- |
| PMF | 0.016 | 0.142 |
| BPMF | 0.219 | 0.133 |
| URP | 0.238 | 0.177 |
| SVD++ | 0.364 | 0.201 |
| BHFree | 0.359 | 0.205 |
| LARA | 0.289 | 0.152 | | | OrdRec+SVD++ | 0.148 | 0.262 |
| --- | --- | --- |
| OrdinalAspectBias | 0.404 | 0.298 | | 1 |
| Method | TripAdvisorData | OpenTableData |
| --- | --- | --- |
| PMF | 0.016 | 0.142 |
| BPMF | 0.219 | 0.133 |
| URP | 0.238 | 0.177 |
| SVD++ | 0.364 | 0.201 |
| BHFree | 0.359 | 0.205 |
| LARA | 0.289 | 0.152 | | | Database | Onenode | 4nodeswith<br>com.time | 4nodes<br>withoutcom.<br>time | com.time | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Win | Linux | Win | Linux | Win | Linux | Win | Linux |
| Drosoph | 0.08 | 0.06 | 0.038 | 0.023 | 0.0235 | 0.0188 | 0.0145 | 0.0042 |
| Pataa | 0.5 | 0.4 | 0.1344 | 0.1 | 0.0184 | 0.014 | 0.116 | 0.086 |
| estothers | 1 | 0.8 | 0.5799 | 0.421 | 0.0343 | 0.035 | 0.5456 | 0.386 |
| envnr | 18 | 15 | 4.0308 | 3.5132 | 0.5308 | 0.5132 | 3.5 | 3 |
| Nr | 27 | 24 | 7.2077 | 6.1163 | 0.4077 | 0.6163 | 6.8 | 5.5 | | 0 |
| Method | TripAdvisorData | OpenTableData |
| --- | --- | --- |
| PMF | 0.016 | 0.142 |
| BPMF | 0.219 | 0.133 |
| URP | 0.238 | 0.177 |
| SVD++ | 0.364 | 0.201 |
| BHFree | 0.359 | 0.205 | | | LARA | 0.289 | 0.152 |
| --- | --- | --- |
| OrdRec+SVD++ | 0.148 | 0.262 |
| OrdinalAspectBias | 0.404 | 0.298 | | 1 |
| Method | TripAdvisorData | OpenTableData |
| --- | --- | --- |
| PMF | 0.016 | 0.142 |
| BPMF | 0.219 | 0.133 |
| URP | 0.238 | 0.177 |
| SVD++ | 0.364 | 0.201 |
| BHFree | 0.359 | 0.205 | | | Pataa | 0.5 | 0.4 | 0.1344 | 0.1 | 0.0184 | 0.014 | 0.116 | 0.086 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| estothers | 1 | 0.8 | 0.5799 | 0.421 | 0.0343 | 0.035 | 0.5456 | 0.386 |
| envnr | 18 | 15 | 4.0308 | 3.5132 | 0.5308 | 0.5132 | 3.5 | 3 |
| Nr | 27 | 24 | 7.2077 | 6.1163 | 0.4077 | 0.6163 | 6.8 | 5.5 | | 0 |
| No.ofKeys | Local | Global |
| --- | --- | --- |
| (Tk)key | Tkey | Tkey | | | | 2 | 4 | 2 | 4 |
| --- | --- | --- | --- | --- |
| 40 | 0.29 | 0.18 | 99.63 | 99.71 |
| 60 | 0.49 | 0.35 | 99.84 | 99.92 |
| 100 | 0.69 | 0.66 | 99.88 | 99.92 | | 1 |
| No.ofKeys | Local | Global |
| --- | --- | --- |
| (Tk)key | Tkey | Tkey | | | TPKETime(ms) | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| n | t | r | KeyGen | Encrypt | Decrypt | Combine | KeyGen | Encrypt |
| 4 | 3 | 2 | 2.184 | 0.263 | 0.145 | 0.309 | 4.978 | 0.390 |
| 4 | 3 | 2 | 9.499 | 0.517 | 0.312 | 0.566 | 11.59 | 0.839 |
| 4 | 3 | 2 | 68.08 | 1.311 | 0.797 | 1.318 | 34.53 | 1.890 | | 0 |
| No.ofKeys | Local | Global |
| --- | --- | --- |
| (Tk)key | Tkey | Tkey | | | | 2 | 4 | 2 | 4 |
| --- | --- | --- | --- | --- |
| 40 | 0.29 | 0.18 | 99.63 | 99.71 |
| 60 | 0.49 | 0.35 | 99.84 | 99.92 |
| 100 | 0.69 | 0.66 | 99.88 | 99.92 | | 1 |
| No.ofKeys | Local | Global |
| --- | --- | --- |
| (Tk)key | Tkey | Tkey | | | 4 | 3 | 2 | 9.499 | 0.517 | 0.312 | 0.566 | 11.59 | 0.839 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 4 | 3 | 2 | 68.08 | 1.311 | 0.797 | 1.318 | 34.53 | 1.890 | | 0 |
| Model | 12-relMAP | 9-relMAP |
| --- | --- | --- |
| RPA | 67.5 | - |
| TransE | 75.0 | - |
| TransR | 74.0 | - | | | TransD | 77.3 | - |
| --- | --- | --- |
| TransH | 75.1 | - |
| MINERVA | - | 88.2 |
| DeepPath | 79.6 | 80.2 |
| RNN-Chain | 79.0 | 80.2 |
| CNNPath-Reasoner | 82.0 | 82.2 |
| | 88.6 | 87.9 | | 1 |
| Model | 12-relMAP | 9-relMAP |
| --- | --- | --- |
| RPA | 67.5 | - |
| TransE | 75.0 | - |
| TransR | 74.0 | - | | | Model | 20-relMAP |
| --- | --- |
| PRA | 54.1 |
| TransE | 53.2 |
| TransR | 54.0 |
| MINERVA | 55.2 |
| DeepPath | 57.2 |
| RNN-Chain | 51.2 |
| CNNPath-Reasoner | 54.2 |
| MML | 58.7 |
| | 59.8 | | 0 |
| Model | 12-relMAP | 9-relMAP |
| --- | --- | --- |
| RPA | 67.5 | - |
| TransE | 75.0 | - |
| TransR | 74.0 | - |
| TransD | 77.3 | - |
| TransH | 75.1 | - |
| MINERVA | - | 88.2 | | | DeepPath | 79.6 | 80.2 |
| --- | --- | --- |
| RNN-Chain | 79.0 | 80.2 |
| CNNPath-Reasoner | 82.0 | 82.2 |
| | 88.6 | 87.9 | | 1 |
| Model | 12-relMAP | 9-relMAP |
| --- | --- | --- |
| RPA | 67.5 | - |
| TransE | 75.0 | - |
| TransR | 74.0 | - |
| TransD | 77.3 | - |
| TransH | 75.1 | - |
| MINERVA | - | 88.2 | | | TransE | 53.2 |
| --- | --- |
| TransR | 54.0 |
| MINERVA | 55.2 |
| DeepPath | 57.2 |
| RNN-Chain | 51.2 |
| CNNPath-Reasoner | 54.2 |
| MML | 58.7 |
| | 59.8 | | 0 |
| OriginalQuery | Method | Top-1CandidateQuery | η(R,q) | λ(q,q)0 |
| --- | --- | --- | --- | --- |
| q.40 | OurApproach(α=2) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | OurApproach(α=16) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | IntuitiveSolution | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | XO-QP(NoDist) | blame,fight,drama,squatter | 0.5 | 0.92 |
| q.40 | XO-QP(Dist) | blame,fight,drama,intruder | 0.3 | 0.94 |
| q.70 | OurApproach(α=2) | massacre,panic,betrayal | 0.2 | 0.87 |
| q.70 | OurApproach(α=16) | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | IntuitiveSolution | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | XO-QP(NoDist) | uxoricide,apprehension,betrayal | 0.1 | 0.8 |
| q.70 | XO-QP(Dist) | hit,panic,betrayal | 0.3 | 0.8 |
| q.90 | OurApproach(α=2) | call,betrayal,fear,english | 0.1 | 0.85 |
| q.90 | OurApproach(α=16) | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | IntuitiveSolution | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | XO-QP(NoDist) | bias,treachery,fear,english | 0.5 | 0.93 | | | q.90 | XO-QP(Dist) | tilt,betrayal,fear,english | 0.4 | 0.93 |
| --- | --- | --- | --- | --- |
| q.100 | OurApproach(α=2) | murderer,extortion,blood | 0.3 | 0.76 |
| q.100 | OurApproach(α=16) | crook,extortion,desolation | 0.1 | 0.94 |
| q.100 | IntuitiveSolution | crook,extortion,desolation | 0.1 | 0.94 |
| q.100 | XO-QP(NoDist) | outlaw,extortion,desolation | 0.3 | 0.94 |
| q.100 | XO-QP(Dist) | outlaw,extortion,desolation | 0.3 | 0.94 |
| q.120 | OurApproach(α=2) | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | OurApproach(α=16) | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | IntuitiveSolution | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | XO-QP(NoDist) | madness,drama,fight,triumph | 0.6 | 0.94 |
| q.120 | XO-QP(Dist) | madness,drama,fight,win | 0.4 | 0.87 | | 1 |
| OriginalQuery | Method | Top-1CandidateQuery | η(R,q) | λ(q,q)0 |
| --- | --- | --- | --- | --- |
| q.40 | OurApproach(α=2) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | OurApproach(α=16) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | IntuitiveSolution | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | XO-QP(NoDist) | blame,fight,drama,squatter | 0.5 | 0.92 |
| q.40 | XO-QP(Dist) | blame,fight,drama,intruder | 0.3 | 0.94 |
| q.70 | OurApproach(α=2) | massacre,panic,betrayal | 0.2 | 0.87 |
| q.70 | OurApproach(α=16) | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | IntuitiveSolution | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | XO-QP(NoDist) | uxoricide,apprehension,betrayal | 0.1 | 0.8 |
| q.70 | XO-QP(Dist) | hit,panic,betrayal | 0.3 | 0.8 |
| q.90 | OurApproach(α=2) | call,betrayal,fear,english | 0.1 | 0.85 |
| q.90 | OurApproach(α=16) | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | IntuitiveSolution | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | XO-QP(NoDist) | bias,treachery,fear,english | 0.5 | 0.93 | | | OriginalQuery | Method | Top-1CandidateQuery | η(R,q) | λ(q,q)0 |
| --- | --- | --- | --- | --- |
| q.10 | OurApproach(α=2) | emergency,research,system | 0.4 | 0.88 |
| q.10 | OurApproach(α=16) | emergency,research,system | 0.4 | 0.88 |
| q.10 | IntuitiveSolution | emergency,research,system | 0.4 | 0.88 |
| q.10 | XO-QP(NoDist) | pinch,breakdown,system | 0.6 | 0.94 |
| q.10 | XO-QP(Dist) | pinch,breakdown,system | 0.6 | 0.94 |
| q.70 | OurApproach(α=2) | online,content,exchange | 0.1 | 0.52 |
| q.70 | OurApproach(α=16) | online,faith,merchandising | 0.1 | 1 |
| q.70 | IntuitiveSolution | online,faith,merchandising | 0.1 | 1 |
| q.70 | XO-QP(NoDist) | online,faith,trading | 1 | 1 |
| q.70 | XO-QP(Dist) | online,faith,trading | 1 | 1 |
| q.80 | OurApproach(α=2) | impact,order,analysis,system | 0.1 | 0.69 |
| q.80 | OurApproach(α=16) | impact,order,analysis,system | 0.1 | 0.69 |
| q.80 | IntuitiveSolution | wake,variety,analysis,system | 0.1 | 0.94 |
| q.80 | XO-QP(NoDist) | wake,variety,analysis,system | 1 | 0.94 |
| q.80 | XO-QP(Dist) | wake,variety,analysis,system | 1 | 0.94 |
| q.100 | OurApproach(α=2) | whole,study,analysis,system | 0.1 | 0.24 |
| q.100 | OurApproach(α=16) | intruder,hunt,analysis,system | 0.1 | 1 |
| q.100 | IntuitiveSolution | intruder,hunt,analysis,system | 0.1 | 1 |
| q.100 | XO-QP(NoDist) | intruder,lookup,analysis,system | 0.3 | 1 |
| q.100 | XO-QP(Dist) | intruder,lookup,analysis,system | 0.3 | 1 |
| q.110 | OurApproach(α=2) | gesture,testing,system | 0.1 | 0.72 |
| q.110 | OurApproach(α=16) | gesture,testing,system | 0.1 | 0.72 |
| q.110 | IntuitiveSolution | hint,testing,system | 0.1 | 0.9 |
| q.110 | XO-QP(NoDist) | hint,search,system | 0.7 | 0.9 |
| q.110 | XO-QP(Dist) | hint,search,system | 0.7 | 0.9 | | 0 |
| OriginalQuery | Method | Top-1CandidateQuery | η(R,q) | λ(q,q)0 |
| --- | --- | --- | --- | --- |
| q.40 | OurApproach(α=2) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | OurApproach(α=16) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | IntuitiveSolution | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | XO-QP(NoDist) | blame,fight,drama,squatter | 0.5 | 0.92 |
| q.40 | XO-QP(Dist) | blame,fight,drama,intruder | 0.3 | 0.94 |
| q.70 | OurApproach(α=2) | massacre,panic,betrayal | 0.2 | 0.87 |
| q.70 | OurApproach(α=16) | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | IntuitiveSolution | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | XO-QP(NoDist) | uxoricide,apprehension,betrayal | 0.1 | 0.8 |
| q.70 | XO-QP(Dist) | hit,panic,betrayal | 0.3 | 0.8 |
| q.90 | OurApproach(α=2) | call,betrayal,fear,english | 0.1 | 0.85 |
| q.90 | OurApproach(α=16) | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | IntuitiveSolution | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | XO-QP(NoDist) | bias,treachery,fear,english | 0.5 | 0.93 |
| q.90 | XO-QP(Dist) | tilt,betrayal,fear,english | 0.4 | 0.93 |
| q.100 | OurApproach(α=2) | murderer,extortion,blood | 0.3 | 0.76 | | | q.100 | OurApproach(α=16) | crook,extortion,desolation | 0.1 | 0.94 |
| --- | --- | --- | --- | --- |
| q.100 | IntuitiveSolution | crook,extortion,desolation | 0.1 | 0.94 |
| q.100 | XO-QP(NoDist) | outlaw,extortion,desolation | 0.3 | 0.94 |
| q.100 | XO-QP(Dist) | outlaw,extortion,desolation | 0.3 | 0.94 |
| q.120 | OurApproach(α=2) | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | OurApproach(α=16) | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | IntuitiveSolution | enthusiasm,drama,fight,triumph | 0.1 | 1 |
| q.120 | XO-QP(NoDist) | madness,drama,fight,triumph | 0.6 | 0.94 |
| q.120 | XO-QP(Dist) | madness,drama,fight,win | 0.4 | 0.87 | | 1 |
| OriginalQuery | Method | Top-1CandidateQuery | η(R,q) | λ(q,q)0 |
| --- | --- | --- | --- | --- |
| q.40 | OurApproach(α=2) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | OurApproach(α=16) | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | IntuitiveSolution | blame,fight,drama,intruder | 0.1 | 0.94 |
| q.40 | XO-QP(NoDist) | blame,fight,drama,squatter | 0.5 | 0.92 |
| q.40 | XO-QP(Dist) | blame,fight,drama,intruder | 0.3 | 0.94 |
| q.70 | OurApproach(α=2) | massacre,panic,betrayal | 0.2 | 0.87 |
| q.70 | OurApproach(α=16) | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | IntuitiveSolution | slaughter,treachery,revulsion | 0.1 | 1 |
| q.70 | XO-QP(NoDist) | uxoricide,apprehension,betrayal | 0.1 | 0.8 |
| q.70 | XO-QP(Dist) | hit,panic,betrayal | 0.3 | 0.8 |
| q.90 | OurApproach(α=2) | call,betrayal,fear,english | 0.1 | 0.85 |
| q.90 | OurApproach(α=16) | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | IntuitiveSolution | fancy,treachery,fear,english | 0.1 | 1 |
| q.90 | XO-QP(NoDist) | bias,treachery,fear,english | 0.5 | 0.93 |
| q.90 | XO-QP(Dist) | tilt,betrayal,fear,english | 0.4 | 0.93 |
| q.100 | OurApproach(α=2) | murderer,extortion,blood | 0.3 | 0.76 | | | q.80 | XO-QP(NoDist) | wake,variety,analysis,system | 1 | 0.94 |
| --- | --- | --- | --- | --- |
| q.80 | XO-QP(Dist) | wake,variety,analysis,system | 1 | 0.94 |
| q.100 | OurApproach(α=2) | whole,study,analysis,system | 0.1 | 0.24 |
| q.100 | OurApproach(α=16) | intruder,hunt,analysis,system | 0.1 | 1 |
| q.100 | IntuitiveSolution | intruder,hunt,analysis,system | 0.1 | 1 |
| q.100 | XO-QP(NoDist) | intruder,lookup,analysis,system | 0.3 | 1 |
| q.100 | XO-QP(Dist) | intruder,lookup,analysis,system | 0.3 | 1 |
| q.110 | OurApproach(α=2) | gesture,testing,system | 0.1 | 0.72 |
| q.110 | OurApproach(α=16) | gesture,testing,system | 0.1 | 0.72 |
| q.110 | IntuitiveSolution | hint,testing,system | 0.1 | 0.9 |
| q.110 | XO-QP(NoDist) | hint,search,system | 0.7 | 0.9 |
| q.110 | XO-QP(Dist) | hint,search,system | 0.7 | 0.9 | | 0 |
| Category | Numberoftokens |
| --- | --- |
| Avg.tokens | 22.2 | | | Avg.entokens | 2.1 |
| --- | --- |
| Avg.hitokens | 16.1 |
| Avg.resttokens | 4.0 | | 1 |
| Category | Numberoftokens |
| --- | --- |
| Avg.tokens | 22.2 | | | | Individualusersgroup |
| --- | --- |
| Totalnumberoftweets | 119,376 |
| Totalnumberofuniqueaccounts | 80,537 |
| Totalnumberoftokens | 1,837,304 |
| Averagenumberoftokenspertweet | 15.391 |
| Totalnumberofuniquetokens | 103,089 |
| Averagenumberofuniquetokenspertweet | 0.864 |
| Uniquetokens:tokensratio | 0.056 |
| Numberofhapaxlegomena | 69,542 |
| Averagenumberofhapaxlegomenapertweet | 0.583 | | 0 |
| Category | Numberoftokens |
| --- | --- |
| Avg.tokens | 22.2 |
| Avg.entokens | 2.1 | | | Avg.hitokens | 16.1 |
| --- | --- |
| Avg.resttokens | 4.0 | | 1 |
| Category | Numberoftokens |
| --- | --- |
| Avg.tokens | 22.2 |
| Avg.entokens | 2.1 | | | Totalnumberofuniqueaccounts | 80,537 |
| --- | --- |
| Totalnumberoftokens | 1,837,304 |
| Averagenumberoftokenspertweet | 15.391 |
| Totalnumberofuniquetokens | 103,089 |
| Averagenumberofuniquetokenspertweet | 0.864 |
| Uniquetokens:tokensratio | 0.056 |
| Numberofhapaxlegomena | 69,542 |
| Averagenumberofhapaxlegomenapertweet | 0.583 | | 0 |
| Item | NormalizedDecayedWeight |
| --- | --- |
| 2 | 198.08 |
| 3 | 96.16 | | | 5 | 72.15 |
| --- | --- |
| 6 | 37.04 | | 1 |
| Item | NormalizedDecayedWeight |
| --- | --- |
| 2 | 198.08 |
| 3 | 96.16 | | | OverallScore | Uniformweights | ActualWeights | LinearRegression | HuberRegression | OrdinaryLS |
| --- | --- | --- | --- | --- | --- |
| Final(Normalized) | 7.2368 | 6.1644 | 0.5690 | 0.5280 | 0.6804 |
| Midterm(Normalized) | 2.9898 | 3.2856 | 0.3802 | 0.3967 | 0.3161 |
| Final(Actual) | 8.0209 | 6.3726 | 0.6013 | 0.7234 | 0.7190 |
| Midterm(Actual) | 17.5650 | 17.51134 | 0.5218 | 0.5094 | 0.4338 | | 0 |
| Item | NormalizedDecayedWeight |
| --- | --- |
| 2 | 198.08 | | | 3 | 96.16 |
| --- | --- |
| 5 | 72.15 |
| 6 | 37.04 | | 1 |
| Item | NormalizedDecayedWeight |
| --- | --- |
| 2 | 198.08 | | | Midterm(Normalized) | 2.9898 | 3.2856 | 0.3802 | 0.3967 | 0.3161 |
| --- | --- | --- | --- | --- | --- |
| Final(Actual) | 8.0209 | 6.3726 | 0.6013 | 0.7234 | 0.7190 |
| Midterm(Actual) | 17.5650 | 17.51134 | 0.5218 | 0.5094 | 0.4338 | | 0 |
| Models | devset | testset |
| --- | --- | --- |
| OnestageQACNN | 66.8 | - |
| QACNN(noattention) | 69.6 | - |
| QACNN(onlyword-levelattention) | 72.5 | - | | | QACNN(onlysentence-levelattention) | 75.1 | - |
| --- | --- | --- |
| QACNN(single) | 77.6 | 75.84 |
| QACNN(ensemble) | 79.0 | 79.99 | | 1 |
| Models | devset | testset |
| --- | --- | --- |
| OnestageQACNN | 66.8 | - |
| QACNN(noattention) | 69.6 | - |
| QACNN(onlyword-levelattention) | 72.5 | - | | | Models | devset | testset |
| --- | --- | --- |
| CosineWord2Vec | 46.4 | 45.63 |
| CosineTFIDF | 47.6 | 47.36 |
| SSCBTFIDF | 48.5 | - |
| CompareAggregate | 72.1 | 72.9 |
| QACNN | 77.6 | 75.84 |
| ConvnetFusion | - | 77.63 |
| QACNN(ensemble) | 79.0 | 79.99 | | 0 |
| Models | devset | testset |
| --- | --- | --- |
| OnestageQACNN | 66.8 | - |
| QACNN(noattention) | 69.6 | - |
| QACNN(onlyword-levelattention) | 72.5 | - | | | QACNN(onlysentence-levelattention) | 75.1 | - |
| --- | --- | --- |
| QACNN(single) | 77.6 | 75.84 |
| QACNN(ensemble) | 79.0 | 79.99 | | 1 |
| Models | devset | testset |
| --- | --- | --- |
| OnestageQACNN | 66.8 | - |
| QACNN(noattention) | 69.6 | - |
| QACNN(onlyword-levelattention) | 72.5 | - | | | QACNN | 77.6 | 75.84 |
| --- | --- | --- |
| ConvnetFusion | - | 77.63 |
| QACNN(ensemble) | 79.0 | 79.99 | | 0 |
| Mode | Denver(GHz) | A57(GHz) | GPU(GHz) |
| --- | --- | --- | --- |
| Max-N | 2.0 | 2.0 | 1.30 |
| Max-Q | - | 1.2 | 0.85 | | | Max-PCore-All | 1.4 | 1.4 | 1.12 |
| --- | --- | --- | --- |
| Max-PARM | - | 2.0 | 1.12 |
| Max-PDenver | 2.0 | - | 1.12 | | 1 |
| Mode | Denver(GHz) | A57(GHz) | GPU(GHz) |
| --- | --- | --- | --- |
| Max-N | 2.0 | 2.0 | 1.30 |
| Max-Q | - | 1.2 | 0.85 | | | Work | Network | Clock | Speedup | Baseline |
| --- | --- | --- | --- | --- |
| Ly&Chow | 256×256 | 100MHz | 32× | 2.8GHzP4 |
| Kimet.al | 256×256 | 200MHz | 25× | 2.4GHzCore2 |
| DianNao | General | 0.98GHz | 117.87× | 2GHzSIMD |
| Zhanget.al | 256×256 | 100MHz | 17.42× | 2.2GHzXeon |
| DLAU | 256×256 | 200MHz | 36.1× | 2.3GHzCore2 | | 0 |
| Mode | Denver(GHz) | A57(GHz) | GPU(GHz) |
| --- | --- | --- | --- |
| Max-N | 2.0 | 2.0 | 1.30 |
| Max-Q | - | 1.2 | 0.85 | | | Max-PCore-All | 1.4 | 1.4 | 1.12 |
| --- | --- | --- | --- |
| Max-PARM | - | 2.0 | 1.12 |
| Max-PDenver | 2.0 | - | 1.12 | | 1 |
| Mode | Denver(GHz) | A57(GHz) | GPU(GHz) |
| --- | --- | --- | --- |
| Max-N | 2.0 | 2.0 | 1.30 |
| Max-Q | - | 1.2 | 0.85 | | | Kimet.al | 256×256 | 200MHz | 25× | 2.4GHzCore2 |
| --- | --- | --- | --- | --- |
| DianNao | General | 0.98GHz | 117.87× | 2GHzSIMD |
| Zhanget.al | 256×256 | 100MHz | 17.42× | 2.2GHzXeon |
| DLAU | 256×256 | 200MHz | 36.1× | 2.3GHzCore2 | | 0 |
| Numberofiteration | 1 | 5 | 10 | 20 | 40 | 80 | 81 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 6.045 | 3.540 | 3.356 | 3.260 | 3.201 | 3.091 | 3.091 | | | Estimatedmode | -6.575 | -3.572 | -3.304 | -3.175 | -3.098 | -3.047 | -3.047 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 0.905 | 2.456 | 2.680 | 2.806 | 2.887 | 2.940 | 2.941 |
| Estimatedmode | -0.575 | -2.250 | -2.512 | -2.652 | -2.738 | -2.795 | -2.797 |
| Estimatedmode | 4.457 | 3.391 | 3.273 | 3.202 | 3.155 | 3.124 | 3.123 |
| Estimatedmode | -4.759 | -3.384 | -3.234 | -3.145 | -3.087 | -3.048 | -3.047 |
| Estimatedmode | 0.588 | 2.299 | 2.589 | 2.743 | 2.837 | 2.900 | 2.901 |
| Estimatedmode | -0.602 | -2.298 | -2.553 | -2.688 | -2.771 | -2.855 | -2.856 |
| Estimatedmode | 5.076 | 3.400 | 3.236 | 3.145 | 3.088 | 3.051 | 3.050 |
| Estimatedmode | -5.160 | -3.477 | -3.308 | -3.215 | -3.157 | -3.118 | -3.117 | | 1 |
| Numberofiteration | 1 | 5 | 10 | 20 | 40 | 80 | 81 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 6.045 | 3.540 | 3.356 | 3.260 | 3.201 | 3.091 | 3.091 | | | Numberofatoms | Bestvalueobtained | Bestvalueknown |
| --- | --- | --- |
| 19 | -72.659782 | -72.659782 |
| 20 | -77.177043 | -77.177043 |
| 21 | -81.684571 | -81.684571 |
| 22 | -86.573675 | -86.809782 |
| 23 | -92.844461 | -92.844472 |
| 24 | -97.348815 | -97.348815 |
| 25 | -102.372663 | -102.372663 |
| 27 | -112.825517 | -112.873584 |
| 30 | -128.096960 | -128.286571 |
| 34 | -150.044528 | -150.044528 |
| 44 | -207.631655 | -207.688728 |
| 49 | -239.091863 | -239.091864 |
| 56 | -283.324945 | -283.643105 |
| 65 | -334.014007 | -334.971532 |
| 67 | -347.053308 | -347.252007 |
| 84 | -452.267210 | -452.6573 |
| 93 | -510.653123 | -510.8779 |
| 148 | -881.072948 | -881.072971 |
| 170 | -1024.791771 | -1024.791797 |
| 172 | -1039.154878 | -1039.154907 |
| 268 | -1706.182547 | -1706.182605 |
| 288 | -1850.010789 | -1850.010842 |
| 293 | -1888.427022 | -1888.427400 |
| 298 | -1927.638727 | -1927.638785 |
| 300 | -1942.106181 | -1942.106775 |
| 301 | -1949.340973 | -1949.341015 |
| 304 | -1971.044089 | -1971.044144 |
| 308 | -1999.983235 | -1999.983300 | | 0 |
| Numberofiteration | 1 | 5 | 10 | 20 | 40 | 80 | 81 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 6.045 | 3.540 | 3.356 | 3.260 | 3.201 | 3.091 | 3.091 |
| Estimatedmode | -6.575 | -3.572 | -3.304 | -3.175 | -3.098 | -3.047 | -3.047 |
| Estimatedmode | 0.905 | 2.456 | 2.680 | 2.806 | 2.887 | 2.940 | 2.941 |
| Estimatedmode | -0.575 | -2.250 | -2.512 | -2.652 | -2.738 | -2.795 | -2.797 |
| Estimatedmode | 4.457 | 3.391 | 3.273 | 3.202 | 3.155 | 3.124 | 3.123 |
| Estimatedmode | -4.759 | -3.384 | -3.234 | -3.145 | -3.087 | -3.048 | -3.047 |
| Estimatedmode | 0.588 | 2.299 | 2.589 | 2.743 | 2.837 | 2.900 | 2.901 | | | Estimatedmode | -0.602 | -2.298 | -2.553 | -2.688 | -2.771 | -2.855 | -2.856 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 5.076 | 3.400 | 3.236 | 3.145 | 3.088 | 3.051 | 3.050 |
| Estimatedmode | -5.160 | -3.477 | -3.308 | -3.215 | -3.157 | -3.118 | -3.117 | | 1 |
| Numberofiteration | 1 | 5 | 10 | 20 | 40 | 80 | 81 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Estimatedmode | 6.045 | 3.540 | 3.356 | 3.260 | 3.201 | 3.091 | 3.091 |
| Estimatedmode | -6.575 | -3.572 | -3.304 | -3.175 | -3.098 | -3.047 | -3.047 |
| Estimatedmode | 0.905 | 2.456 | 2.680 | 2.806 | 2.887 | 2.940 | 2.941 |
| Estimatedmode | -0.575 | -2.250 | -2.512 | -2.652 | -2.738 | -2.795 | -2.797 |
| Estimatedmode | 4.457 | 3.391 | 3.273 | 3.202 | 3.155 | 3.124 | 3.123 |
| Estimatedmode | -4.759 | -3.384 | -3.234 | -3.145 | -3.087 | -3.048 | -3.047 |
| Estimatedmode | 0.588 | 2.299 | 2.589 | 2.743 | 2.837 | 2.900 | 2.901 | | | 27 | -112.825517 | -112.873584 |
| --- | --- | --- |
| 30 | -128.096960 | -128.286571 |
| 34 | -150.044528 | -150.044528 |
| 44 | -207.631655 | -207.688728 |
| 49 | -239.091863 | -239.091864 |
| 56 | -283.324945 | -283.643105 |
| 65 | -334.014007 | -334.971532 |
| 67 | -347.053308 | -347.252007 |
| 84 | -452.267210 | -452.6573 |
| 93 | -510.653123 | -510.8779 |
| 148 | -881.072948 | -881.072971 |
| 170 | -1024.791771 | -1024.791797 |
| 172 | -1039.154878 | -1039.154907 |
| 268 | -1706.182547 | -1706.182605 |
| 288 | -1850.010789 | -1850.010842 |
| 293 | -1888.427022 | -1888.427400 |
| 298 | -1927.638727 | -1927.638785 |
| 300 | -1942.106181 | -1942.106775 |
| 301 | -1949.340973 | -1949.341015 |
| 304 | -1971.044089 | -1971.044144 |
| 308 | -1999.983235 | -1999.983300 | | 0 |
| F-measure |
| --- |
| 0.129<br>0.134<br>0.135<br>0.138<br>0.174 |
| 0.369<br>0.418 | | | 0.343<br>0.354<br>0.386 |
| --- |
| 0.443 | | 1 |
| F-measure |
| --- |
| 0.129<br>0.134<br>0.135<br>0.138<br>0.174 |
| 0.369<br>0.418 | | | 97.88 |
| --- |
| 97.59 |
| 94.51<br>92.60 |
| 0.236 |
| 84.6 | | 0 |
| F-measure |
| --- |
| 0.129<br>0.134<br>0.135<br>0.138<br>0.174 | | | 0.369<br>0.418 |
| --- |
| 0.343<br>0.354<br>0.386 |
| 0.443 | | 1 |
| F-measure |
| --- |
| 0.129<br>0.134<br>0.135<br>0.138<br>0.174 | | | 0.236 |
| --- |
| 84.6 | | 0 |
| n | CountofSubsets<br>ofXwithn<br>S=2n | n | ofXwithn<br>n(n+1)<br>S=(−n) |
| --- | --- | --- | --- |
| 6 | 2 | 6 | 2 |
| 7 | 5 | 7 | 5 |
| 8 | 8 | 8 | 8 |
| 9 | 13 | 9 | 13 |
| 10 | 134 | 10 | 24 |
| 50 | 416868 | 15 | 521 |
| 100 | 482240364 | 20 | 11812 |
| 150 | 114613846376 | 30 | 7206286 | | | 200 | 11954655830925 | 40 | 5076120114 |
| --- | --- | --- | --- |
| 250 | 732839540340934 | 50 | 3831141038816 | | 1 |
| n | CountofSubsets<br>ofXwithn<br>S=2n | n | ofXwithn<br>n(n+1)<br>S=(−n) |
| --- | --- | --- | --- |
| 6 | 2 | 6 | 2 |
| 7 | 5 | 7 | 5 |
| 8 | 8 | 8 | 8 |
| 9 | 13 | 9 | 13 |
| 10 | 134 | 10 | 24 |
| 50 | 416868 | 15 | 521 |
| 100 | 482240364 | 20 | 11812 |
| 150 | 114613846376 | 30 | 7206286 | | | n | tn |
| --- | --- |
| 8 | 560 |
| 9 | 5040 |
| 10 | 957600 |
| 11 | 123354000 |
| 12 | 16842764400 |
| 13 | 2764379217600 |
| 14 | 527554510282800 |
| 15 | 114387072405606000 |
| 16 | 27728561968887780000 |
| 17 | 7418031804967840056000 |
| 18 | 2167306256125914230527200 |
| 19 | 685709965521372865035362400 |
| 20 | 233306923207078035272369412000 | | 0 |
| n | CountofSubsets<br>ofXwithn<br>S=2n | n | ofXwithn<br>n(n+1)<br>S=(−n) |
| --- | --- | --- | --- |
| 6 | 2 | 6 | 2 | | | 7 | 5 | 7 | 5 |
| --- | --- | --- | --- |
| 8 | 8 | 8 | 8 |
| 9 | 13 | 9 | 13 |
| 10 | 134 | 10 | 24 |
| 50 | 416868 | 15 | 521 |
| 100 | 482240364 | 20 | 11812 |
| 150 | 114613846376 | 30 | 7206286 |
| 200 | 11954655830925 | 40 | 5076120114 |
| 250 | 732839540340934 | 50 | 3831141038816 | | 1 |
| n | CountofSubsets<br>ofXwithn<br>S=2n | n | ofXwithn<br>n(n+1)<br>S=(−n) |
| --- | --- | --- | --- |
| 6 | 2 | 6 | 2 | | | 14 | 527554510282800 |
| --- | --- |
| 15 | 114387072405606000 |
| 16 | 27728561968887780000 |
| 17 | 7418031804967840056000 |
| 18 | 2167306256125914230527200 |
| 19 | 685709965521372865035362400 |
| 20 | 233306923207078035272369412000 | | 0 |
| MRCRSUBJMPQATREC | MSRP(Acc/F1) |
| --- | --- |
| 71.578.490.183.485.2<br>71.979.390.885.288.8<br>73.680.291.485.789.0 | 72.1/81.7<br>72.8/81.5<br>73.5/82.1 | | | 72.177.890.584.286.0<br>72.979.390.685.287.6<br>74.080.491.586.187.8 | 71.5/80.8<br>72.9/81.6<br>73.7/81.7 |
| --- | --- |
| 70.875.990.482.787.2<br>71.178.490.883.789.6<br>72.478.691.584.388.7 | 73.3/81.7<br>73.2/81.7<br>75.4/83.0 |
| 68.476.589.481.481.6<br>70.076.489.681.486.0<br>71.678.090.783.283.2 | 72.2/81.4<br>72.8/81.4<br>73.2/81.4 |
| 73.380.090.784.984.8<br>75.080.591.186.587.4<br>75.881.891.986.888.6 | 73.8/81.9<br>72.3/81.3<br>75.0/82.5 | | 1 |
| MRCRSUBJMPQATREC | MSRP(Acc/F1) |
| --- | --- |
| 71.578.490.183.485.2<br>71.979.390.885.288.8<br>73.680.291.485.789.0 | 72.1/81.7<br>72.8/81.5<br>73.5/82.1 | | | | scale | CT-SRCNN-13 | Trim-S1 | Trim-S2 | Trim-S3 |
| --- | --- | --- | --- | --- | --- |
| Param. | - | 149,344 | 137,424 | 123,600 | 109,776 |
| Set5 | 2<br>3<br>4 | 37.61/0.9590/0.019<br>33.76/0.9219/0.019<br>31.49/0.8849/0.020 | 37.61/0.9591/0.018<br>33.77/0.9219/0.018<br>31.49/0.8849/0.018 | 37.60/0.9590/0.016<br>33.75/0.9218/0.016<br>31.49/0.8848/0.017 | 37.59/0.9589/0.015<br>33.74/0.9216/0.014<br>31.48/0.8847/0.017 |
| Set14 | 2<br>3<br>4 | 33.37/0.9131/0.032<br>29.91/0.8324/0.034<br>28.20/0.7680/0.032 | 33.38/0.9130/0.030<br>29.90/0.8326/0.031<br>28.20/0.7681/0.029 | 33.36/0.9130/0.028<br>29.89/0.8324/0.028<br>28.19/0.7679/0.027 | 33.36/0.9128/0.025<br>29.88/0.8322/0.026<br>28.18/0.7677/0.025 |
| BSD | 2<br>3<br>4 | 31.87/0.8962/0.020<br>28.80/0.7980/0.021<br>27.30/0.7253/0.020 | 31.86/0.8964/0.018<br>28.81/0.7980/0.019<br>27.30/0.7254/0.018 | 31.86/0.8962/0.017<br>28.80/0.7979/0.017<br>27.30/0.7251/0.016 | 31.84/0.8960/0.015<br>28.78/0.7978/0.016<br>27.28/0.7249/0.015 | | 0 |
| MRCRSUBJMPQATREC | MSRP(Acc/F1) |
| --- | --- |
| 71.578.490.183.485.2<br>71.979.390.885.288.8<br>73.680.291.485.789.0 | 72.1/81.7<br>72.8/81.5<br>73.5/82.1 |
| 72.177.890.584.286.0<br>72.979.390.685.287.6<br>74.080.491.586.187.8 | 71.5/80.8<br>72.9/81.6<br>73.7/81.7 |
| 70.875.990.482.787.2<br>71.178.490.883.789.6<br>72.478.691.584.388.7 | 73.3/81.7<br>73.2/81.7<br>75.4/83.0 | | | 68.476.589.481.481.6<br>70.076.489.681.486.0<br>71.678.090.783.283.2 | 72.2/81.4<br>72.8/81.4<br>73.2/81.4 |
| --- | --- |
| 73.380.090.784.984.8<br>75.080.591.186.587.4<br>75.881.891.986.888.6 | 73.8/81.9<br>72.3/81.3<br>75.0/82.5 | | 1 |
| MRCRSUBJMPQATREC | MSRP(Acc/F1) |
| --- | --- |
| 71.578.490.183.485.2<br>71.979.390.885.288.8<br>73.680.291.485.789.0 | 72.1/81.7<br>72.8/81.5<br>73.5/82.1 |
| 72.177.890.584.286.0<br>72.979.390.685.287.6<br>74.080.491.586.187.8 | 71.5/80.8<br>72.9/81.6<br>73.7/81.7 |
| 70.875.990.482.787.2<br>71.178.490.883.789.6<br>72.478.691.584.388.7 | 73.3/81.7<br>73.2/81.7<br>75.4/83.0 | | | Set14 | 2<br>3<br>4 | 33.37/0.9131/0.032<br>29.91/0.8324/0.034<br>28.20/0.7680/0.032 | 33.38/0.9130/0.030<br>29.90/0.8326/0.031<br>28.20/0.7681/0.029 | 33.36/0.9130/0.028<br>29.89/0.8324/0.028<br>28.19/0.7679/0.027 | 33.36/0.9128/0.025<br>29.88/0.8322/0.026<br>28.18/0.7677/0.025 |
| --- | --- | --- | --- | --- | --- |
| BSD | 2<br>3<br>4 | 31.87/0.8962/0.020<br>28.80/0.7980/0.021<br>27.30/0.7253/0.020 | 31.86/0.8964/0.018<br>28.81/0.7980/0.019<br>27.30/0.7254/0.018 | 31.86/0.8962/0.017<br>28.80/0.7979/0.017<br>27.30/0.7251/0.016 | 31.84/0.8960/0.015<br>28.78/0.7978/0.016<br>27.28/0.7249/0.015 | | 0 |
| ML | TPR | FPR | TNR | FNR | W-FM |
| --- | --- | --- | --- | --- | --- |
| SVM-linear | 0.909 | 0.109 | 0.891 | 0.091 | 0.9 |
| NB | 0.596 | 0.102 | 0.898 | 0.404 | 0.73 |
| SL | 0.899 | 0.102 | 0.898 | 0.101 | 0.899 | | | MLP | 0.924 | 0.098 | 0.902 | 0.076 | 0.914 |
| --- | --- | --- | --- | --- | --- |
| PART | 0.896 | 0.109 | 0.891 | 0.104 | 0.894 |
| RF | 0.909 | 0.068 | 0.932 | 0.091 | 0.919 |
| J48 | 0.88 | 0.125 | 0.875 | 0.12 | 0.878 | | 1 |
| ML | TPR | FPR | TNR | FNR | W-FM |
| --- | --- | --- | --- | --- | --- |
| SVM-linear | 0.909 | 0.109 | 0.891 | 0.091 | 0.9 |
| NB | 0.596 | 0.102 | 0.898 | 0.404 | 0.73 |
| SL | 0.899 | 0.102 | 0.898 | 0.101 | 0.899 | | | ML | TPR | FPR | TNR | FNR | W-FM |
| --- | --- | --- | --- | --- | --- |
| SVM-linear | 0.905 | 0.109 | 0.891 | 0.095 | 0.907 |
| NB | 0.596 | 0.102 | 0.898 | 0.404 | 0.73 |
| SL | 0.902 | 0.098 | 0.902 | 0.098 | 0.902 |
| MLP | 0.905 | 0.072 | 0.928 | 0.095 | 0.916 |
| PART | 0.899 | 0.106 | 0.894 | 0.101 | 0.897 |
| RF | 0.918 | 0.064 | 0.936 | 0.082 | 0.926 |
| J48 | 0.88 | 0.125 | 0.875 | 0.12 | 0.878 | | 0 |
| ML | TPR | FPR | TNR | FNR | W-FM |
| --- | --- | --- | --- | --- | --- |
| SVM-linear | 0.909 | 0.109 | 0.891 | 0.091 | 0.9 |
| NB | 0.596 | 0.102 | 0.898 | 0.404 | 0.73 |
| SL | 0.899 | 0.102 | 0.898 | 0.101 | 0.899 | | | MLP | 0.924 | 0.098 | 0.902 | 0.076 | 0.914 |
| --- | --- | --- | --- | --- | --- |
| PART | 0.896 | 0.109 | 0.891 | 0.104 | 0.894 |
| RF | 0.909 | 0.068 | 0.932 | 0.091 | 0.919 |
| J48 | 0.88 | 0.125 | 0.875 | 0.12 | 0.878 | | 1 |
| ML | TPR | FPR | TNR | FNR | W-FM |
| --- | --- | --- | --- | --- | --- |
| SVM-linear | 0.909 | 0.109 | 0.891 | 0.091 | 0.9 |
| NB | 0.596 | 0.102 | 0.898 | 0.404 | 0.73 |
| SL | 0.899 | 0.102 | 0.898 | 0.101 | 0.899 | | | MLP | 0.905 | 0.072 | 0.928 | 0.095 | 0.916 |
| --- | --- | --- | --- | --- | --- |
| PART | 0.899 | 0.106 | 0.894 | 0.101 | 0.897 |
| RF | 0.918 | 0.064 | 0.936 | 0.082 | 0.926 |
| J48 | 0.88 | 0.125 | 0.875 | 0.12 | 0.878 | | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.