premise string | hypothesis string | label int64 |
|---|---|---|
| method | RAND | HC | SA | GA+HC |
| --- | --- | --- | --- | --- |
| XCORR<br>XCORR4<br>XCORR8 | 0.037<br>0.111<br>0.176 | 0.165<br>0.257<br>0.332 | 0.229<br>0.342<br>0.431 | 0.426<br>0.495<br>0.532 | | | NC<br>DC | 0.016<br>0.014 | 0.327<br>0.036 | 0.419<br>0.064 | 0.541<br>0.259 |
| --- | --- | --- | --- | --- |
| valueofEq.?? | 956.9 | 740.9 | 704.5 | 683.3 |
| time[s] | 0.0004 | 0.102 | 4.239 | 51.15 | | 1 |
| method | RAND | HC | SA | GA+HC |
| --- | --- | --- | --- | --- |
| XCORR<br>XCORR4<br>XCORR8 | 0.037<br>0.111<br>0.176 | 0.165<br>0.257<br>0.332 | 0.229<br>0.342<br>0.431 | 0.426<br>0.495<br>0.532 | | | Random<br>χ(G,W)t<br>avgstd | CSC-DSATUR<br>χ(G,W)Timet<br>avgstdavg | PSO<br>χ(G,W)Timet<br>avgstdavg | |
| --- | --- | --- | --- |
| 0.1<br>0.3<br>600.5<br>0.7<br>0.9 | 15.01.0<br>9.40.5<br>7.60.4<br>6.80.2<br>6.10.2 | 5.70.53.4ms<br>6.20.410.1ms<br>5.80.623.7ms<br>5.40.543.3ms<br>5.00.063.8ms | 7.40.54.9s<br>6.60.35.8s<br>5.80.36.8s<br>5.20.27.7s<br>5.00.08.5s |
| 0.1<br>0.3<br>700.5<br>0.7<br>0.9 | 14.71.1<br>9.10.4<br>7.70.2<br>6.80.3<br>6.00.1 | 6.10.34.6ms<br>6.00.615.4ms<br>6.10.334.6ms<br>5.40.565.6ms<br>5.00.099.1ms | 8.00.66.0s<br>6.70.37.2s<br>6.00.28.9s<br>5.30.210.3s<br>5.00.011.6s |
| 0.1<br>0.3<br>800.5<br>0.7<br>0.9 | 14.00.8<br>9.00.3<br>7.60.3<br>6.70.2<br>6.20.1 | 6.50.56.0ms<br>6.00.422.4ms<br>6.10.349.9ms<br>5.70.595.6ms<br>5.00.0145.4ms | 8.10.67.0s<br>6.70.28.7s<br>6.10.211.1s<br>5.30.112.6s<br>5.00.015.1s |
| Random<br>χ(G,W)t<br>avgstd | CSC-DSATUR<br>χ(G,W)Timet<br>avgstdavg | PSO<br>χ(G,W)Timet<br>avgstdavg | |
| 0.1<br>0.3<br>600.5<br>0.7<br>0.9 | 8.40.9<br>5.10.4<br>4.10.3<br>3.60.1<br>3.00.0 | 4.20.63.5ms<br>4.00.010.0ms<br>3.50.521.1ms<br>3.00.035.6ms<br>3.00.049.6ms | 5.10.54.6s<br>4.00.35.5s<br>3.30.36.5s<br>3.00.07.3s<br>3.00.08.6s |
| 0.1<br>0.3<br>700.5<br>0.7<br>0.9 | 8.20.8<br>5.10.3<br>4.20.2<br>3.50.2<br>3.10.1 | 4.10.54.5ms<br>4.00.014.4ms<br>3.40.532.6ms<br>3.00.052.7ms<br>3.00.074.1ms | 5.20.45.9s<br>4.00.27.2s<br>3.50.38.4s<br>3.00.09.8s<br>3.00.011.1s |
| 0.1<br>0.3<br>800.5<br>0.7<br>0.9 | 7.80.7<br>5.00.3<br>4.20.2<br>3.40.2<br>3.10.1 | 4.20.45.7ms<br>4.00.020.0ms<br>3.80.445.3ms<br>3.00.076.9ms<br>3.00.0113.4ms | 5.20.46.7s<br>4.10.28.9s<br>3.30.210.7s<br>3.00.012.1s<br>3.00.014.5s | | 0 |
| method | RAND | HC | SA | GA+HC |
| --- | --- | --- | --- | --- |
| XCORR<br>XCORR4<br>XCORR8 | 0.037<br>0.111<br>0.176 | 0.165<br>0.257<br>0.332 | 0.229<br>0.342<br>0.431 | 0.426<br>0.495<br>0.532 | | | NC<br>DC | 0.016<br>0.014 | 0.327<br>0.036 | 0.419<br>0.064 | 0.541<br>0.259 |
| --- | --- | --- | --- | --- |
| valueofEq.?? | 956.9 | 740.9 | 704.5 | 683.3 |
| time[s] | 0.0004 | 0.102 | 4.239 | 51.15 | | 1 |
| method | RAND | HC | SA | GA+HC |
| --- | --- | --- | --- | --- |
| XCORR<br>XCORR4<br>XCORR8 | 0.037<br>0.111<br>0.176 | 0.165<br>0.257<br>0.332 | 0.229<br>0.342<br>0.431 | 0.426<br>0.495<br>0.532 | | | 0.1<br>0.3<br>700.5<br>0.7<br>0.9 | 14.71.1<br>9.10.4<br>7.70.2<br>6.80.3<br>6.00.1 | 6.10.34.6ms<br>6.00.615.4ms<br>6.10.334.6ms<br>5.40.565.6ms<br>5.00.099.1ms | 8.00.66.0s<br>6.70.37.2s<br>6.00.28.9s<br>5.30.210.3s<br>5.00.011.6s |
| --- | --- | --- | --- |
| 0.1<br>0.3<br>800.5<br>0.7<br>0.9 | 14.00.8<br>9.00.3<br>7.60.3<br>6.70.2<br>6.20.1 | 6.50.56.0ms<br>6.00.422.4ms<br>6.10.349.9ms<br>5.70.595.6ms<br>5.00.0145.4ms | 8.10.67.0s<br>6.70.28.7s<br>6.10.211.1s<br>5.30.112.6s<br>5.00.015.1s |
| Random<br>χ(G,W)t<br>avgstd | CSC-DSATUR<br>χ(G,W)Timet<br>avgstdavg | PSO<br>χ(G,W)Timet<br>avgstdavg | |
| 0.1<br>0.3<br>600.5<br>0.7<br>0.9 | 8.40.9<br>5.10.4<br>4.10.3<br>3.60.1<br>3.00.0 | 4.20.63.5ms<br>4.00.010.0ms<br>3.50.521.1ms<br>3.00.035.6ms<br>3.00.049.6ms | 5.10.54.6s<br>4.00.35.5s<br>3.30.36.5s<br>3.00.07.3s<br>3.00.08.6s |
| 0.1<br>0.3<br>700.5<br>0.7<br>0.9 | 8.20.8<br>5.10.3<br>4.20.2<br>3.50.2<br>3.10.1 | 4.10.54.5ms<br>4.00.014.4ms<br>3.40.532.6ms<br>3.00.052.7ms<br>3.00.074.1ms | 5.20.45.9s<br>4.00.27.2s<br>3.50.38.4s<br>3.00.09.8s<br>3.00.011.1s |
| 0.1<br>0.3<br>800.5<br>0.7<br>0.9 | 7.80.7<br>5.00.3<br>4.20.2<br>3.40.2<br>3.10.1 | 4.20.45.7ms<br>4.00.020.0ms<br>3.80.445.3ms<br>3.00.076.9ms<br>3.00.0113.4ms | 5.20.46.7s<br>4.10.28.9s<br>3.30.210.7s<br>3.00.012.1s<br>3.00.014.5s | | 0 |
| Method | F1score | ObjectDice | ObjectHausdorff | | | |
| --- | --- | --- | --- | --- | --- | --- |
| PartA | PartB | PartA | PartB | PartA | PartB | |
| Ourmethod | 0.921 | 0.855 | 0.904 | 0.858 | 44.736 | 96.976 |
| Multichannel | 0.893 | 0.843 | 0.908 | 0.833 | 44.129 | 116.821 | | | Multichannel | 0.858 | 0.771 | 0.888 | 0.815 | 54.202 | 129.930 |
| --- | --- | --- | --- | --- | --- | --- |
| CUMedVision | 0.912 | 0.716 | 0.897 | 0.781 | 45.418 | 160.347 | | 1 |
| Method | F1score | ObjectDice | ObjectHausdorff | | | |
| --- | --- | --- | --- | --- | --- | --- |
| PartA | PartB | PartA | PartB | PartA | PartB | |
| Ourmethod | 0.921 | 0.855 | 0.904 | 0.858 | 44.736 | 96.976 |
| Multichannel | 0.893 | 0.843 | 0.908 | 0.833 | 44.129 | 116.821 | | | Methods | ROUGE-1 | ROUGE-2 | ROUGE-L | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| R | P | F1 | R | P | F1 | R | P | F1 | R | P | |
| LexRank | 11.32 | 11.16 | 11.04 | 1.32 | 1.34 | 1.31 | 10.33 | 10.16 | 10.06 | 3.41 | 3.38 |
| RMRt | 11.17 | 10.25 | 10.54 | 2.25 | 2.16 | 2.19 | 10.22 | 9.39 | 9.65 | 3.88 | 3.66 |
| CTRt | 10.74 | 9.95 | 10.19 | 2.21 | 2.14 | 2.15 | 9.91 | 9.19 | 9.41 | 3.96 | 3.64 |
| NRT | 9.39 | 17.75 | 11.64 | 1.83 | 3.39 | 2.22 | 8.70 | 16.27 | 10.74 | 3.01 | 7.06 | | 0 |
| Method | F1score | ObjectDice | ObjectHausdorff | | |
| --- | --- | --- | --- | --- | --- |
| PartA | PartB | PartA | PartB | PartA | PartB | | | Ourmethod | 0.921 | 0.855 | 0.904 | 0.858 | 44.736 | 96.976 |
| --- | --- | --- | --- | --- | --- | --- |
| Multichannel | 0.893 | 0.843 | 0.908 | 0.833 | 44.129 | 116.821 |
| Multichannel | 0.858 | 0.771 | 0.888 | 0.815 | 54.202 | 129.930 |
| CUMedVision | 0.912 | 0.716 | 0.897 | 0.781 | 45.418 | 160.347 | | 1 |
| Method | F1score | ObjectDice | ObjectHausdorff | | |
| --- | --- | --- | --- | --- | --- |
| PartA | PartB | PartA | PartB | PartA | PartB | | | CTRt | 10.74 | 9.95 | 10.19 | 2.21 | 2.14 | 2.15 | 9.91 | 9.19 | 9.41 | 3.96 | 3.64 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| NRT | 9.39 | 17.75 | 11.64 | 1.83 | 3.39 | 2.22 | 8.70 | 16.27 | 10.74 | 3.01 | 7.06 | | 0 |
| Dataset | strideD | stdσ |
| --- | --- | --- |
| Synthetic | 30 | 80 |
| IDIAPPoster | 20 | 45 | | | CocktailParty | 70 | 170 |
| --- | --- | --- |
| CoffeeBreak | 30 | 85 |
| GDet | 30 | 200 | | 1 |
| Dataset | strideD | stdσ |
| --- | --- | --- |
| Synthetic | 30 | 80 |
| IDIAPPoster | 20 | 45 | | | 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 | 5 |
| Twonorm | 7400 | 2 | 20 |
| Waveform | 5000 | 3 | 21 | | 0 |
| Dataset | strideD | stdσ |
| --- | --- | --- |
| Synthetic | 30 | 80 |
| IDIAPPoster | 20 | 45 | | | CocktailParty | 70 | 170 |
| --- | --- | --- |
| CoffeeBreak | 30 | 85 |
| GDet | 30 | 200 | | 1 |
| Dataset | strideD | stdσ |
| --- | --- | --- |
| Synthetic | 30 | 80 |
| IDIAPPoster | 20 | 45 | | | 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 | 5 |
| Twonorm | 7400 | 2 | 20 |
| Waveform | 5000 | 3 | 21 | | 0 |
| | FutureGrid | FutureGridx2 | XSEDE | XSEDEx2 | OSG | OSGx2 |
| --- | --- | --- | --- | --- | --- | --- |
| Partitions | 14 | 28 | 13 | 26 | 200 | 400 |
| SimultaneousJobs | 477 | 954 | 6600 | 13200 | 42300 | 84600 |
| JobsperHour | 78 | 154 | 1090 | 2169 | 21254 | 42455 |
| Services | 77 | 144 | 260 | 520 | 4000 | 8000 |
| Nodes | 608 | 1216 | N/A | N/A | N/A | N/A |
| NetworkLinks | 6 | 12 | N/A | N/A | N/A | N/A |
| Info.databases | 1 | 1 | 1 | 1 | 1 | 1 |
| Webportals | 1 | 1 | 1 | 1 | 1 | 1 | | | Accountingsystems | 1 | 1 | 1 | 1 | 1 | 1 |
| --- | --- | --- | --- | --- | --- | --- |
| Metaschedulers | 1 | 2 | 2 | 4 | 2 | 4 |
| Monitoringsystems | 2 | 4 | 1 | 1 | 1 | 1 |
| ScienceGateways | 0 | 0 | 10 | 20 | 20 | 40 | | 1 |
| | FutureGrid | FutureGridx2 | XSEDE | XSEDEx2 | OSG | OSGx2 |
| --- | --- | --- | --- | --- | --- | --- |
| Partitions | 14 | 28 | 13 | 26 | 200 | 400 |
| SimultaneousJobs | 477 | 954 | 6600 | 13200 | 42300 | 84600 |
| JobsperHour | 78 | 154 | 1090 | 2169 | 21254 | 42455 |
| Services | 77 | 144 | 260 | 520 | 4000 | 8000 |
| Nodes | 608 | 1216 | N/A | N/A | N/A | N/A |
| NetworkLinks | 6 | 12 | N/A | N/A | N/A | N/A |
| Info.databases | 1 | 1 | 1 | 1 | 1 | 1 |
| Webportals | 1 | 1 | 1 | 1 | 1 | 1 | | | Job/Task | Scheduler | FIFO | Fair | Capacity | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| | | | | | | | |
| | | | | | | | |
| Job | CPU(ms) | 11495 | 8415 | 12647 | 9538 | 14475 | 10784 |
| Memory(10bytes | 7479 | 4530 | 7741 | 3647 | 9463 | 5486 | |
| HDFSRead(10bytes) | 9930 | 7431 | 10968 | 8762 | 12463 | 8360 | |
| HDFSWrite(10bytes) | 8583 | 5985 | 9784 | 6202 | 10285 | 7420 | |
| Task | CPU(ms) | 3855 | 2520 | 4033 | 2184 | 4170 | 2851 |
| Memory(10bytes | 1412 | 1058 | 2496 | 1741 | 2638 | 2115 | |
| HDFSRead(10bytes) | 1638 | 1215 | 1894 | 1428 | 7426 | 4541 | |
| HDFSWrite(10bytes) | 1774 | 1385 | 3643 | 2429 | 5052 | 3715 | | | 0 |
| | FutureGrid | FutureGridx2 | XSEDE | XSEDEx2 | OSG | OSGx2 |
| --- | --- | --- | --- | --- | --- | --- |
| Partitions | 14 | 28 | 13 | 26 | 200 | 400 |
| SimultaneousJobs | 477 | 954 | 6600 | 13200 | 42300 | 84600 |
| JobsperHour | 78 | 154 | 1090 | 2169 | 21254 | 42455 |
| Services | 77 | 144 | 260 | 520 | 4000 | 8000 |
| Nodes | 608 | 1216 | N/A | N/A | N/A | N/A |
| NetworkLinks | 6 | 12 | N/A | N/A | N/A | N/A |
| Info.databases | 1 | 1 | 1 | 1 | 1 | 1 |
| Webportals | 1 | 1 | 1 | 1 | 1 | 1 |
| Accountingsystems | 1 | 1 | 1 | 1 | 1 | 1 |
| Metaschedulers | 1 | 2 | 2 | 4 | 2 | 4 | | | Monitoringsystems | 2 | 4 | 1 | 1 | 1 | 1 |
| --- | --- | --- | --- | --- | --- | --- |
| ScienceGateways | 0 | 0 | 10 | 20 | 20 | 40 | | 1 |
| | FutureGrid | FutureGridx2 | XSEDE | XSEDEx2 | OSG | OSGx2 |
| --- | --- | --- | --- | --- | --- | --- |
| Partitions | 14 | 28 | 13 | 26 | 200 | 400 |
| SimultaneousJobs | 477 | 954 | 6600 | 13200 | 42300 | 84600 |
| JobsperHour | 78 | 154 | 1090 | 2169 | 21254 | 42455 |
| Services | 77 | 144 | 260 | 520 | 4000 | 8000 |
| Nodes | 608 | 1216 | N/A | N/A | N/A | N/A |
| NetworkLinks | 6 | 12 | N/A | N/A | N/A | N/A |
| Info.databases | 1 | 1 | 1 | 1 | 1 | 1 |
| Webportals | 1 | 1 | 1 | 1 | 1 | 1 |
| Accountingsystems | 1 | 1 | 1 | 1 | 1 | 1 |
| Metaschedulers | 1 | 2 | 2 | 4 | 2 | 4 | | | HDFSWrite(10bytes) | 8583 | 5985 | 9784 | 6202 | 10285 | 7420 | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Task | CPU(ms) | 3855 | 2520 | 4033 | 2184 | 4170 | 2851 |
| Memory(10bytes | 1412 | 1058 | 2496 | 1741 | 2638 | 2115 | |
| HDFSRead(10bytes) | 1638 | 1215 | 1894 | 1428 | 7426 | 4541 | |
| HDFSWrite(10bytes) | 1774 | 1385 | 3643 | 2429 | 5052 | 3715 | | | 0 |
| Tracks | Laps | Speed(Km/h) | C.error(m) | O.error() | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| µ | Max | µ | σ | µ | σ | | |
| DSP | 5 | 31.3 | 90 | 0 | 0.0808 | 0.6327 | 2.0173 | | | FOT1 | 3 | 20.4 | 28.8 | 0 | 0.0971 | -1.0397 | 1.0397 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| FOT2 | 3 | 23.9 | – | 0 | 0.088 | 0.9225 | 1.0544 | | 1 |
| Tracks | Laps | Speed(Km/h) | C.error(m) | O.error() | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| µ | Max | µ | σ | µ | σ | | |
| DSP | 5 | 31.3 | 90 | 0 | 0.0808 | 0.6327 | 2.0173 | | | Tracker | TrackPurity | TargetPurity |
| --- | --- | --- |
| Gray-scaleData | 28.43 | 04.28 |
| RGBData | 39.20 | 35.07 |
| MSData | 55.12 | 50.91 |
| OFDS | 12.66 | 12.66 |
| LoFT | 60.30 | 40.50 |
| Sum-rule | 50.17 | 45.25 |
| VarianceRatio | 48.26 | 44.56 |
| HFT | 69.78 | 60.30 |
| Ours(HLT) | 64.37 | 57.49 | | 0 |
| Tracks | Laps | Speed(Km/h) | C.error(m) | O.error() | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| µ | Max | µ | σ | µ | σ | | |
| DSP | 5 | 31.3 | 90 | 0 | 0.0808 | 0.6327 | 2.0173 | | | FOT1 | 3 | 20.4 | 28.8 | 0 | 0.0971 | -1.0397 | 1.0397 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| FOT2 | 3 | 23.9 | – | 0 | 0.088 | 0.9225 | 1.0544 | | 1 |
| Tracks | Laps | Speed(Km/h) | C.error(m) | O.error() | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| µ | Max | µ | σ | µ | σ | | |
| DSP | 5 | 31.3 | 90 | 0 | 0.0808 | 0.6327 | 2.0173 | | | Sum-rule | 50.17 | 45.25 |
| --- | --- | --- |
| VarianceRatio | 48.26 | 44.56 |
| HFT | 69.78 | 60.30 |
| Ours(HLT) | 64.37 | 57.49 | | 0 |
| CROATIAN | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Freq. | 0.99 | 0.95 | 0.96 | -0.18 | 0.93 | 0.96 | -0.15 | | | TSP | 0.01 | 0.42 | -0.03 | -0.26 | 0.39 | 0.32 | -0.28 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| ENGLISH | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR |
| Freq. | 0.93 | 0.91 | 0.86 | -0.30 | 0.84 | 0.74 | -0.17 |
| TSP | -0.26 | 0.92 | 0.73 | 0.39 | 0.04 | 0.35 | -0.27 | | 1 |
| CROATIAN | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Freq. | 0.99 | 0.95 | 0.96 | -0.18 | 0.93 | 0.96 | -0.15 | | | | CROATIAN | ENGLISH | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | CO | SIN | SHU | SYL | GR | CO | SIN | SHU | SYL | GR |
| N | 23359 | 23359 | 23359 | 2634 | 34 | 10930 | 10930 | 10930 | 2599 | 26 |
| K | 71860 | 70155 | 86214 | 18849 | 491 | 50299 | 52221 | 58920 | 6053 | 333 |
| L | 4.01 | 1.81 | 3.74 | 1.86 | 1.58 | 3.47 | 1.96 | 0.45 | 1.88 | 1.51 |
| C | 0.167 | 0.120 | 0.182 | 0.255 | 0.636 | 0.286 | 0.153 | 0.295 | 0.057 | 0.838 |
| T | 0.004 | 0.003 | 0.013 | 0.120 | 0.522 | 0.009 | 0.014 | 0.016 | 0.020 | 0.654 |
| ω | 2 | 2 | 2 | 17 | 1 | 3 | 3 | 1 | 54 | 1 | | 0 |
| CROATIAN | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Freq. | 0.99 | 0.95 | 0.96 | -0.18 | 0.93 | 0.96 | -0.15 |
| TSP | 0.01 | 0.42 | -0.03 | -0.26 | 0.39 | 0.32 | -0.28 |
| ENGLISH | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR | | | Freq. | 0.93 | 0.91 | 0.86 | -0.30 | 0.84 | 0.74 | -0.17 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TSP | -0.26 | 0.92 | 0.73 | 0.39 | 0.04 | 0.35 | -0.27 | | 1 |
| CROATIAN | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Freq. | 0.99 | 0.95 | 0.96 | -0.18 | 0.93 | 0.96 | -0.15 |
| TSP | 0.01 | 0.42 | -0.03 | -0.26 | 0.39 | 0.32 | -0.28 |
| ENGLISH | CO-SHU | CO-SIN | CO-SYL | CO-GR | SHU-SIN | SHU-SYL | SHU-GR | | | N | 23359 | 23359 | 23359 | 2634 | 34 | 10930 | 10930 | 10930 | 2599 | 26 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| K | 71860 | 70155 | 86214 | 18849 | 491 | 50299 | 52221 | 58920 | 6053 | 333 |
| L | 4.01 | 1.81 | 3.74 | 1.86 | 1.58 | 3.47 | 1.96 | 0.45 | 1.88 | 1.51 |
| C | 0.167 | 0.120 | 0.182 | 0.255 | 0.636 | 0.286 | 0.153 | 0.295 | 0.057 | 0.838 |
| T | 0.004 | 0.003 | 0.013 | 0.120 | 0.522 | 0.009 | 0.014 | 0.016 | 0.020 | 0.654 |
| ω | 2 | 2 | 2 | 17 | 1 | 3 | 3 | 1 | 54 | 1 | | 0 |
| Skip | NA | BN | IN | NA | BN |
| --- | --- | --- | --- | --- | --- |
| Dec | NA | NA | NA | BN | BN | | | PSNR<br>SSIM | 18.24<br>0.7945 | 25.67<br>0.9442 | 26.00<br>0.9414 | 25.99<br>0.9385 | 26.38<br>0.9519 |
| --- | --- | --- | --- | --- | --- |
| Skip | IN | NA | BN | IN | Perceptual<br>loss |
| Dec | BN | IN | IN | IN | |
| PSNR<br>SSIM | 26.89<br>0.9535 | 26.57<br>0.9381 | 27.67<br>0.9543 | 27.75<br>0.9549 | 27.79<br>0.9556 | | 1 |
| Skip | NA | BN | IN | NA | BN |
| --- | --- | --- | --- | --- | --- |
| Dec | NA | NA | NA | BN | BN | | | Scale | Bicubic<br>PSNR/SSIM | A+<br>PSNR/SSIM | RFL<br>PSNR/SSIM | SelfEx<br>PSNR/SSIM | SRCNN<br>PSNR/SSIM |
| --- | --- | --- | --- | --- | --- |
| ×2<br>×3<br>×4 | 33.66/0.9299<br>30.39/0.8682<br>28.42/0.8104 | 36.54/0.9544<br>32.58/0.9088<br>30.28/0.8603 | 36.54/0.9537<br>32.43/0.9057<br>30.14/0.8548 | 36.49/0.9537<br>32.58/0.9093<br>30.31/0.8619 | 36.66/0.9542<br>32.75/0.9090<br>30.48/0.8628 |
| ×2<br>×3<br>×4 | 30.24/0.8688<br>27.55/0.7742<br>26.00/0.7027 | 32.28/0.9056<br>29.13/0.8188<br>27.32/0.7491 | 32.26/0.9040<br>29.05/0.8164<br>27.24/0.7451 | 32.22/0.9034<br>29.16/0.8196<br>27.40/0.7518 | 32.42/0.9063<br>29.28/0.8209<br>27.49/0.7503 |
| ×2<br>×3<br>×4 | 29.56/0.8431<br>27.21/0.7385<br>25.96/0.6675 | 31.21/0.8863<br>28.29/0.7835<br>26.82/0.7087 | 31.16/0.8840<br>28.22/0.7806<br>26.75/0.7054 | 31.18/0.8855<br>28.29/0.7840<br>26.84/0.7106 | 31.36/0.8879<br>28.41/0.7863<br>26.90/0.7101 |
| ×2<br>×3<br>×4 | 26.88/0.8403<br>24.46/0.7349<br>23.14/0.6577 | 29.20/0.8938<br>26.03/0.7973<br>24.32/0.7183 | 29.11/0.8904<br>25.86/0.7900<br>24.19/0.7096 | 29.54/0.8967<br>26.44/0.8088<br>24.79/0.7374 | 29.50/0.8946<br>26.24/0.7989<br>24.52/0.7221 | | 0 |
| Skip | NA | BN | IN | NA | BN |
| --- | --- | --- | --- | --- | --- |
| Dec | NA | NA | NA | BN | BN |
| PSNR<br>SSIM | 18.24<br>0.7945 | 25.67<br>0.9442 | 26.00<br>0.9414 | 25.99<br>0.9385 | 26.38<br>0.9519 |
| Skip | IN | NA | BN | IN | Perceptual<br>loss | | | Dec | BN | IN | IN | IN | |
| --- | --- | --- | --- | --- | --- |
| PSNR<br>SSIM | 26.89<br>0.9535 | 26.57<br>0.9381 | 27.67<br>0.9543 | 27.75<br>0.9549 | 27.79<br>0.9556 | | 1 |
| Skip | NA | BN | IN | NA | BN |
| --- | --- | --- | --- | --- | --- |
| Dec | NA | NA | NA | BN | BN |
| PSNR<br>SSIM | 18.24<br>0.7945 | 25.67<br>0.9442 | 26.00<br>0.9414 | 25.99<br>0.9385 | 26.38<br>0.9519 |
| Skip | IN | NA | BN | IN | Perceptual<br>loss | | | ×2<br>×3<br>×4 | 29.56/0.8431<br>27.21/0.7385<br>25.96/0.6675 | 31.21/0.8863<br>28.29/0.7835<br>26.82/0.7087 | 31.16/0.8840<br>28.22/0.7806<br>26.75/0.7054 | 31.18/0.8855<br>28.29/0.7840<br>26.84/0.7106 | 31.36/0.8879<br>28.41/0.7863<br>26.90/0.7101 |
| --- | --- | --- | --- | --- | --- |
| ×2<br>×3<br>×4 | 26.88/0.8403<br>24.46/0.7349<br>23.14/0.6577 | 29.20/0.8938<br>26.03/0.7973<br>24.32/0.7183 | 29.11/0.8904<br>25.86/0.7900<br>24.19/0.7096 | 29.54/0.8967<br>26.44/0.8088<br>24.79/0.7374 | 29.50/0.8946<br>26.24/0.7989<br>24.52/0.7221 | | 0 |
| h | N | SearchLDS | SkipList | JumpSearchLDS | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| β=0.8 | β=0.9 | β=0.95 | β=1.0 | | | | |
| 10 | 55 | 0.048 | 0.059 | 0.064 | 0.061 | 0.061 | 0.060 | | | 50 | 1275 | 0.190 | 0.144 | 0.215 | 0.155 | 0.134 | 0.128 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 100 | 5050 | 0.347 | 0.207 | 0.370 | 0.223 | 0.176 | 0.157 |
| 500 | 125250 | 1.840 | 0.481 | 1.686 | 0.678 | 0.357 | 0.228 |
| 1000 | 500500 | 3.990 | 0.780 | 3.600 | 1.284 | 0.557 | 0.267 | | 1 |
| h | N | SearchLDS | SkipList | JumpSearchLDS | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| β=0.8 | β=0.9 | β=0.95 | β=1.0 | | | | |
| 10 | 55 | 0.048 | 0.059 | 0.064 | 0.061 | 0.061 | 0.060 | | | β | 5@NDCG | 10@NDCG | 20@NDCG | 40@NDCG |
| --- | --- | --- | --- | --- |
| 0.5 | 0.3885 | 0.2888 | 0.2139 | 0.1478 |
| 1.5 | 0.3392 | 0.2745 | 0.2035 | 0.1370 |
| 2.5 | 0.3392 | 0.2863 | 0.2036 | 0.1379 |
| 3.5 | 0.3392 | 0.2752 | 0.1988 | 0.1362 | | 0 |
| h | N | SearchLDS | SkipList | JumpSearchLDS | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| β=0.8 | β=0.9 | β=0.95 | β=1.0 | | | | |
| 10 | 55 | 0.048 | 0.059 | 0.064 | 0.061 | 0.061 | 0.060 | | | 50 | 1275 | 0.190 | 0.144 | 0.215 | 0.155 | 0.134 | 0.128 |
| --- | --- | --- | --- | --- | --- | --- | --- |
| 100 | 5050 | 0.347 | 0.207 | 0.370 | 0.223 | 0.176 | 0.157 |
| 500 | 125250 | 1.840 | 0.481 | 1.686 | 0.678 | 0.357 | 0.228 |
| 1000 | 500500 | 3.990 | 0.780 | 3.600 | 1.284 | 0.557 | 0.267 | | 1 |
| h | N | SearchLDS | SkipList | JumpSearchLDS | | | |
| --- | --- | --- | --- | --- | --- | --- | --- |
| β=0.8 | β=0.9 | β=0.95 | β=1.0 | | | | |
| 10 | 55 | 0.048 | 0.059 | 0.064 | 0.061 | 0.061 | 0.060 | | | 2.5 | 0.3392 | 0.2863 | 0.2036 | 0.1379 |
| --- | --- | --- | --- | --- |
| 3.5 | 0.3392 | 0.2752 | 0.1988 | 0.1362 | | 0 |
| Work | DataGeneration | EvaluationMetrics |
| --- | --- | --- |
| Pirttikangasetal. | Semi-Non-Overlapping-Window | Accuracy | | | Suutalaetal. | Semi-Non-Overlapping-Window | Accuracy,Precision,Recall |
| --- | --- | --- |
| Kwapiszetal. | Unknown | Accuracy |
| Cataletal. | Unknown | Accuracy,AUC,F-Measure |
| Kimetal. | Semi-Non-Overlapping-Window | F-measure |
| KimandChoi | Semi-Non-Overlapping-Window | Accuracy,F-measure |
| ChenandXue | Semi-Non-Overlapping-Window | Accuracy |
| JiangandYin | Unknown | Accuracy |
| Haetal. | Semi-Non-Overlapping-Window | Accuracy |
| HaandChoi | Semi-Non-Overlapping-Window | Accuracy |
| Yaoetal. | Semi-Non-Overlapping-Window | Accuracy |
| Panetal. | Semi-Non-Overlapping-Window | Accuracy |
| Yangetal. | Unknown | Accuracy,F-Measure | | 1 |
| Work | DataGeneration | EvaluationMetrics |
| --- | --- | --- |
| Pirttikangasetal. | Semi-Non-Overlapping-Window | Accuracy | | | Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.77 | 0.70 | 0.74 |
| Zhangetal. | 0.83 | 0.67 | 0.74 |
| Yinetal. | 0.81 | 0.63 | 0.71 |
| Kangetal. | 0.71 | 0.62 | 0.66 |
| Yaoetal. | 0.63 | 0.63 | 0.60 | | 0 |
| Work | DataGeneration | EvaluationMetrics |
| --- | --- | --- |
| Pirttikangasetal. | Semi-Non-Overlapping-Window | Accuracy |
| Suutalaetal. | Semi-Non-Overlapping-Window | Accuracy,Precision,Recall | | | Kwapiszetal. | Unknown | Accuracy |
| --- | --- | --- |
| Cataletal. | Unknown | Accuracy,AUC,F-Measure |
| Kimetal. | Semi-Non-Overlapping-Window | F-measure |
| KimandChoi | Semi-Non-Overlapping-Window | Accuracy,F-measure |
| ChenandXue | Semi-Non-Overlapping-Window | Accuracy |
| JiangandYin | Unknown | Accuracy |
| Haetal. | Semi-Non-Overlapping-Window | Accuracy |
| HaandChoi | Semi-Non-Overlapping-Window | Accuracy |
| Yaoetal. | Semi-Non-Overlapping-Window | Accuracy |
| Panetal. | Semi-Non-Overlapping-Window | Accuracy |
| Yangetal. | Unknown | Accuracy,F-Measure | | 1 |
| Work | DataGeneration | EvaluationMetrics |
| --- | --- | --- |
| Pirttikangasetal. | Semi-Non-Overlapping-Window | Accuracy |
| Suutalaetal. | Semi-Non-Overlapping-Window | Accuracy,Precision,Recall | | | Yinetal. | 0.81 | 0.63 | 0.71 |
| --- | --- | --- | --- |
| Kangetal. | 0.71 | 0.62 | 0.66 |
| Yaoetal. | 0.63 | 0.63 | 0.60 | | 0 |
| Table | NMMsize | MMSize |
| --- | --- | --- |
| phrase-table | 119KB | 585KB |
| reordering-table | 74KB | 1,576KB | | | LMfile | 238KB | 238KB |
| --- | --- | --- |
| TOTAL | 431KB | 2,399KB | | 1 |
| Table | NMMsize | MMSize |
| --- | --- | --- |
| phrase-table | 119KB | 585KB |
| reordering-table | 74KB | 1,576KB | | | Totalvocabularysize | 768,298 |
| --- | --- |
| Mediandocumentlength | 346characters |
| Mediannumberofwords | 54 |
| Mediansentencelength | 51characters |
| MedianNbrofwordsinsentence | 8 | | 0 |
| Table | NMMsize | MMSize |
| --- | --- | --- |
| phrase-table | 119KB | 585KB |
| reordering-table | 74KB | 1,576KB | | | LMfile | 238KB | 238KB |
| --- | --- | --- |
| TOTAL | 431KB | 2,399KB | | 1 |
| Table | NMMsize | MMSize |
| --- | --- | --- |
| phrase-table | 119KB | 585KB |
| reordering-table | 74KB | 1,576KB | | | Mediansentencelength | 51characters |
| --- | --- |
| MedianNbrofwordsinsentence | 8 | | 0 |
| Database | ModelOrder | IdentificationAccuracy |
| --- | --- | --- |
| POLYCOST | 2 | 19.4960 |
| 4 | 21.6180 | |
| 8 | 19.0981 | |
| 16 | 22.4138 | |
| YOHO | 2 | 16.8841 |
| 4 | 18.2246 | |
| 8 | 15.1268 | |
| 16 | 18.2246 | | | | 32 | 21.2138 |
| --- | --- |
| 64 | 21.9565 | | 1 |
| Database | ModelOrder | IdentificationAccuracy |
| --- | --- | --- |
| POLYCOST | 2 | 19.4960 |
| 4 | 21.6180 | |
| 8 | 19.0981 | |
| 16 | 22.4138 | |
| YOHO | 2 | 16.8841 |
| 4 | 18.2246 | |
| 8 | 15.1268 | |
| 16 | 18.2246 | | | | Model | C | d | Q |
| --- | --- | --- | --- |
| Full | 0.14 | 4.0 | 0.40 |
| Nodistance | 0.13 | 3.8 | 0.33 |
| Nocategories | 0.01 | 3.8 | 0.29 |
| Notriadicclosure | 0.05 | 4.0 | 0.38 | | 0 |
| Database | ModelOrder | IdentificationAccuracy |
| --- | --- | --- |
| POLYCOST | 2 | 19.4960 |
| 4 | 21.6180 | |
| 8 | 19.0981 | |
| 16 | 22.4138 | |
| YOHO | 2 | 16.8841 | | | 4 | 18.2246 |
| --- | --- |
| 8 | 15.1268 |
| 16 | 18.2246 |
| 32 | 21.2138 |
| 64 | 21.9565 | | 1 |
| Database | ModelOrder | IdentificationAccuracy |
| --- | --- | --- |
| POLYCOST | 2 | 19.4960 |
| 4 | 21.6180 | |
| 8 | 19.0981 | |
| 16 | 22.4138 | |
| YOHO | 2 | 16.8841 | | | Nocategories | 0.01 | 3.8 | 0.29 |
| --- | --- | --- | --- |
| Notriadicclosure | 0.05 | 4.0 | 0.38 | | 0 |
| TestLibrary | Global | Princeton | CMU-IBM | MINLP |
| --- | --- | --- | --- | --- |
| Numberofproblems | 369 | 980 | 142 | 249 | | | Avg.no.ofcontinuousvariables | 1092 | 1355 | 369 | 346 |
| --- | --- | --- | --- | --- |
| Avg.no.ofbinaryvariables | 0 | 0 | 139 | 235 |
| Avg.no.ofintegervariables | 0 | 0 | 0 | 24 |
| Avg.no.ofconstraints | 785 | 836 | 956 | 534 | | 1 |
| TestLibrary | Global | Princeton | CMU-IBM | MINLP |
| --- | --- | --- | --- | --- |
| Numberofproblems | 369 | 980 | 142 | 249 | | | Binary | Coverage | Crashes | TotalPaths | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| AFL | AFL-d | AFLCB | AFL | AFL-d | AFLCB | AFL | AFL-d | AFLCB | |
| addr2line | 6.23 | 6.78 | 6.23 | 4 | 5 | 3 | 2427 | 3115 | 2351 |
| cxxfilt | 10.15 | 10.78 | 9.36 | 532 | 1422 | 369 | 8422 | 13592 | 8198 |
| elfedit | 0.18 | 0.18 | 0.18 | 0 | 0 | 0 | 21 | 21 | 21 |
| nm | 12.53 | 15.12 | 11.31 | 109 | 1179 | 44 | 4841 | 11933 | 4282 |
| objcopy | 12.53 | 14.57 | 11.98 | 30 | 94 | 33 | 3053 | 6277 | 4155 |
| objdump | 10.32 | 11.69 | 10.68 | 3 | 86 | 27 | 4099 | 7198 | 5490 |
| readelf | 12.43 | 15.45 | 12.99 | 0 | 0 | 0 | 5706 | 13342 | 7683 |
| size | 6.41 | 7.15 | 6.60 | 1 | 0 | 0 | 2359 | 3247 | 2452 |
| strings | 0.15 | 0.15 | 0.15 | 0 | 0 | 0 | 72 | 72 | 72 |
| strip-new | 11.16 | 12.98 | 11.34 | 8 | 22 | 10 | 2836 | 5860 | 4099 |
| gif2png | 1.74 | 1.76 | 1.74 | 125 | 133 | 132 | 1504 | 1599 | 1538 |
| libxml2 | 8.74 | 9.42 | 9.34 | 0 | 0 | 0 | 3211 | 5233 | 3933 |
| libpng | 5.05 | 5.32 | 5.07 | 0 | 0 | 0 | 2112 | 2679 | 1957 |
| mpg321 | 0.44 | 0.44 | 0.44 | 14 | 14 | 14 | 168 | 169 | 169 |
| tcpdump | 15.62 | 16.36 | 17.83 | 170 | 304 | 289 | 6471 | 11018 | 8135 | | 0 |
| TestLibrary | Global | Princeton | CMU-IBM | MINLP |
| --- | --- | --- | --- | --- |
| Numberofproblems | 369 | 980 | 142 | 249 | | | Avg.no.ofcontinuousvariables | 1092 | 1355 | 369 | 346 |
| --- | --- | --- | --- | --- |
| Avg.no.ofbinaryvariables | 0 | 0 | 139 | 235 |
| Avg.no.ofintegervariables | 0 | 0 | 0 | 24 |
| Avg.no.ofconstraints | 785 | 836 | 956 | 534 | | 1 |
| TestLibrary | Global | Princeton | CMU-IBM | MINLP |
| --- | --- | --- | --- | --- |
| Numberofproblems | 369 | 980 | 142 | 249 | | | elfedit | 0.18 | 0.18 | 0.18 | 0 | 0 | 0 | 21 | 21 | 21 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| nm | 12.53 | 15.12 | 11.31 | 109 | 1179 | 44 | 4841 | 11933 | 4282 |
| objcopy | 12.53 | 14.57 | 11.98 | 30 | 94 | 33 | 3053 | 6277 | 4155 |
| objdump | 10.32 | 11.69 | 10.68 | 3 | 86 | 27 | 4099 | 7198 | 5490 |
| readelf | 12.43 | 15.45 | 12.99 | 0 | 0 | 0 | 5706 | 13342 | 7683 |
| size | 6.41 | 7.15 | 6.60 | 1 | 0 | 0 | 2359 | 3247 | 2452 |
| strings | 0.15 | 0.15 | 0.15 | 0 | 0 | 0 | 72 | 72 | 72 |
| strip-new | 11.16 | 12.98 | 11.34 | 8 | 22 | 10 | 2836 | 5860 | 4099 |
| gif2png | 1.74 | 1.76 | 1.74 | 125 | 133 | 132 | 1504 | 1599 | 1538 |
| libxml2 | 8.74 | 9.42 | 9.34 | 0 | 0 | 0 | 3211 | 5233 | 3933 |
| libpng | 5.05 | 5.32 | 5.07 | 0 | 0 | 0 | 2112 | 2679 | 1957 |
| mpg321 | 0.44 | 0.44 | 0.44 | 14 | 14 | 14 | 168 | 169 | 169 |
| tcpdump | 15.62 | 16.36 | 17.83 | 170 | 304 | 289 | 6471 | 11018 | 8135 | | 0 |
| | Search:IronMan;Path:MAM | Search:IronMan;Path:MTM | Search:SylvesterStallone;Path:AMTM | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Rank | Movie | Score | Movie | Score | Movie | Score | | | 1 | IronMan | 1.0000 | IronMan | 1.0000 | Rocky | 0.1023 |
| --- | --- | --- | --- | --- | --- | --- |
| 2 | TheKiteRunner | 0.2185 | TheIncredibaleHulk | 0.8752 | MillionDollorBaby | 0.0981 |
| 3 | TheGoodNight | 0.1894 | TMNT | 0.8531 | TheWrestlet | 0.0932 |
| 4 | SeeSpotRun | 0.1894 | Spawn | 0.8256 | Hardball | 0.0895 |
| 5 | Proof | 0.1894 | Batman | 0.8171 | OutCold | 0.0887 | | 1 |
| | Search:IronMan;Path:MAM | Search:IronMan;Path:MTM | Search:SylvesterStallone;Path:AMTM | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Rank | Movie | Score | Movie | Score | Movie | Score | | | Category | SVM<br>Baseline | SVM<br>Best | NN<br>Baseline | NN<br>Best |
| --- | --- | --- | --- | --- |
| airconditioner | 39.3 | 45.1 | 49.9 | 53.2 |
| carhorn | 52.4 | 53.0 | 51.6 | 52.8 |
| childrenplaying | 53.8 | 54.3 | 65.1 | 65.2 |
| dogbark | 76.2 | 75.9 | 81.7 | 82.0 |
| drilling | 56.6 | 57.2 | 63.4 | 63.0 |
| engineidling | 53.8 | 54.1 | 68.0 | 69.8 |
| gunshot | 67.8 | 69.1 | 80.4 | 81.9 |
| jackhammer | 60.2 | 62.3 | 63.7 | 66.2 |
| siren | 72.2 | 72.8 | 80.2 | 80.4 |
| streetmusic | 46.0 | 46.4 | 58.5 | 59.0 |
| MeanAP | 57.8 | 59.0 | 66.3 | 67.5 | | 0 |
| | Search:IronMan;Path:MAM | Search:IronMan;Path:MTM | Search:SylvesterStallone;Path:AMTM | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Rank | Movie | Score | Movie | Score | Movie | Score |
| 1 | IronMan | 1.0000 | IronMan | 1.0000 | Rocky | 0.1023 |
| 2 | TheKiteRunner | 0.2185 | TheIncredibaleHulk | 0.8752 | MillionDollorBaby | 0.0981 | | | 3 | TheGoodNight | 0.1894 | TMNT | 0.8531 | TheWrestlet | 0.0932 |
| --- | --- | --- | --- | --- | --- | --- |
| 4 | SeeSpotRun | 0.1894 | Spawn | 0.8256 | Hardball | 0.0895 |
| 5 | Proof | 0.1894 | Batman | 0.8171 | OutCold | 0.0887 | | 1 |
| | Search:IronMan;Path:MAM | Search:IronMan;Path:MTM | Search:SylvesterStallone;Path:AMTM | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Rank | Movie | Score | Movie | Score | Movie | Score |
| 1 | IronMan | 1.0000 | IronMan | 1.0000 | Rocky | 0.1023 |
| 2 | TheKiteRunner | 0.2185 | TheIncredibaleHulk | 0.8752 | MillionDollorBaby | 0.0981 | | | childrenplaying | 53.8 | 54.3 | 65.1 | 65.2 |
| --- | --- | --- | --- | --- |
| dogbark | 76.2 | 75.9 | 81.7 | 82.0 |
| drilling | 56.6 | 57.2 | 63.4 | 63.0 |
| engineidling | 53.8 | 54.1 | 68.0 | 69.8 |
| gunshot | 67.8 | 69.1 | 80.4 | 81.9 |
| jackhammer | 60.2 | 62.3 | 63.7 | 66.2 |
| siren | 72.2 | 72.8 | 80.2 | 80.4 |
| streetmusic | 46.0 | 46.4 | 58.5 | 59.0 |
| MeanAP | 57.8 | 59.0 | 66.3 | 67.5 | | 0 |
| ArticleType | #ofArticles | MedianError(km) |
| --- | --- | --- |
| airport | 60 | 6 |
| railwaystation | 178 | 6 |
| waterbody | 76 | 42 |
| city | 2414 | 44 |
| mountain | 124 | 54 |
| landmark | 1109 | 65 |
| NULL | 970 | 94 | | | edu | 187 | 110 |
| --- | --- | --- |
| river | 86 | 187 |
| adm2nd | 105 | 295 | | 1 |
| ArticleType | #ofArticles | MedianError(km) |
| --- | --- | --- |
| airport | 60 | 6 |
| railwaystation | 178 | 6 |
| waterbody | 76 | 42 |
| city | 2414 | 44 |
| mountain | 124 | 54 |
| landmark | 1109 | 65 |
| NULL | 970 | 94 | | | Methods | aeroplane | bicycle | boat | bottle | bus | car | chair | diningtable | motorbike | sofa | train | tvmonitor | Average |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ObjectDetection(AP) | | | | | | | | | | | | | |
| DPM | 42.2 | 49.6 | 6.0 | 20.0 | 54.1 | 38.3 | 15.0 | 9.0 | 33.1 | 18.9 | 36.4 | 33.2 | 29.6 |
| R-CNN | 72.4 | 68.7 | 34.0 | – | 73.0 | 62.3 | 33.0 | 35.2 | 70.7 | 49.6 | 70.1 | 57.2 | 56.9 |
| Oursw/oExtra | 76.3 | 73.4 | 43.4 | 44.7 | 74.5 | 63.3 | 35.4 | 32.4 | 74.9 | 51.9 | 74.1 | 60.9 | 58.8 |
| OursFull | 76.5 | 74.0 | 42.4 | 47.0 | 74.5 | 64.7 | 38.5 | 38.6 | 76.7 | 55.1 | 74.8 | 65.3 | 60.7 |
| JointObjectDetectionandPoseEstimation(4ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 34.6 | 41.7 | 1.5 | – | 26.1 | 20.2 | 6.8 | 3.1 | 30.4 | 5.1 | 10.7 | 34.7 | 19.5 |
| DPM-VOC+VP | 39.4 | 43.9 | 0.3 | – | 49.1 | 37.6 | 6.1 | 3.0 | 32.2 | 11.8 | 12.5 | 33.2 | 24.5 |
| Oursw/oExtra | 62.3 | 56.6 | 18.0 | – | 62.0 | 40.9 | 19.3 | 14.9 | 62.3 | 44.1 | 58.1 | 58.5 | 45.2 |
| OursFull | 61.4 | 60.4 | 21.1 | – | 63.0 | 48.7 | 23.8 | 17.4 | 60.7 | 47.8 | 55.9 | 62.3 | 47.5 |
| JointObjectDetectionandPoseEstimation(8ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 23.4 | 36.5 | 1.0 | – | 35.5 | 23.5 | 5.8 | 3.6 | 25.1 | 12.5 | 10.9 | 27.4 | 18.7 |
| DPM-VOC+VP | 29.7 | 42.6 | 0.4 | – | 39.5 | 36.8 | 9.4 | 2.6 | 32.9 | 11.0 | 10.3 | 28.6 | 22.2 |
| Oursw/oExtra | 45.9 | 25.5 | 11.1 | – | 37.7 | 34.6 | 15.2 | 7.4 | 37.1 | 33.0 | 42.5 | 24.3 | 28.6 |
| OursFull | 48.8 | 36.3 | 16.4 | – | 39.8 | 37.2 | 19.1 | 13.2 | 37.0 | 32.1 | 44.4 | 26.9 | 31.9 |
| JointObjectDetectionandPoseEstimation(16ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 15.4 | 18.4 | 0.5 | – | 46.9 | 18.1 | 6.0 | 2.2 | 16.1 | 10.0 | 22.1 | 16.3 | 15.6 |
| DPM-VOC+VP | 17.0 | 24.7 | 1.0 | – | 49.0 | 30.1 | 6.6 | 3.0 | 17.2 | 7.7 | 20.4 | 20.2 | 17.9 |
| Oursw/oExtra | 23.3 | 19.2 | 8.4 | – | 52.6 | 27.0 | 9.9 | 5.1 | 23.6 | 20.9 | 27.4 | 27.9 | 22.3 |
| OursFull | 28.0 | 23.7 | 10.7 | – | 50.8 | 31.4 | 14.3 | 9.4 | 23.4 | 19.5 | 30.7 | 27.8 | 24.5 |
| JointObjectDetectionandPoseEstimation(24ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 8.0 | 14.3 | 0.3 | – | 39.2 | 13.7 | 4.4 | 3.6 | 10.1 | 8.2 | 20.0 | 11.2 | 12.1 |
| DPM-VOC+VP | 10.6 | 16.7 | 2.2 | – | 43.5 | 25.4 | 4.4 | 2.3 | 11.3 | 4.9 | 22.4 | 14.4 | 14.4 |
| Oursw/oExtra | 18.9 | 10.5 | 6.7 | – | 34.3 | 23.3 | 8.3 | 6.5 | 20.6 | 17.5 | 33.8 | 17.0 | 17.9 |
| OursFull | 20.7 | 16.4 | 7.9 | – | 34.6 | 24.6 | 9.4 | 7.6 | 19.9 | 20.0 | 32.7 | 18.2 | 19.3 | | 0 |
| ArticleType | #ofArticles | MedianError(km) |
| --- | --- | --- |
| airport | 60 | 6 | | | railwaystation | 178 | 6 |
| --- | --- | --- |
| waterbody | 76 | 42 |
| city | 2414 | 44 |
| mountain | 124 | 54 |
| landmark | 1109 | 65 |
| NULL | 970 | 94 |
| edu | 187 | 110 |
| river | 86 | 187 |
| adm2nd | 105 | 295 | | 1 |
| ArticleType | #ofArticles | MedianError(km) |
| --- | --- | --- |
| airport | 60 | 6 | | | DPM | 42.2 | 49.6 | 6.0 | 20.0 | 54.1 | 38.3 | 15.0 | 9.0 | 33.1 | 18.9 | 36.4 | 33.2 | 29.6 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| R-CNN | 72.4 | 68.7 | 34.0 | – | 73.0 | 62.3 | 33.0 | 35.2 | 70.7 | 49.6 | 70.1 | 57.2 | 56.9 |
| Oursw/oExtra | 76.3 | 73.4 | 43.4 | 44.7 | 74.5 | 63.3 | 35.4 | 32.4 | 74.9 | 51.9 | 74.1 | 60.9 | 58.8 |
| OursFull | 76.5 | 74.0 | 42.4 | 47.0 | 74.5 | 64.7 | 38.5 | 38.6 | 76.7 | 55.1 | 74.8 | 65.3 | 60.7 |
| JointObjectDetectionandPoseEstimation(4ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 34.6 | 41.7 | 1.5 | – | 26.1 | 20.2 | 6.8 | 3.1 | 30.4 | 5.1 | 10.7 | 34.7 | 19.5 |
| DPM-VOC+VP | 39.4 | 43.9 | 0.3 | – | 49.1 | 37.6 | 6.1 | 3.0 | 32.2 | 11.8 | 12.5 | 33.2 | 24.5 |
| Oursw/oExtra | 62.3 | 56.6 | 18.0 | – | 62.0 | 40.9 | 19.3 | 14.9 | 62.3 | 44.1 | 58.1 | 58.5 | 45.2 |
| OursFull | 61.4 | 60.4 | 21.1 | – | 63.0 | 48.7 | 23.8 | 17.4 | 60.7 | 47.8 | 55.9 | 62.3 | 47.5 |
| JointObjectDetectionandPoseEstimation(8ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 23.4 | 36.5 | 1.0 | – | 35.5 | 23.5 | 5.8 | 3.6 | 25.1 | 12.5 | 10.9 | 27.4 | 18.7 |
| DPM-VOC+VP | 29.7 | 42.6 | 0.4 | – | 39.5 | 36.8 | 9.4 | 2.6 | 32.9 | 11.0 | 10.3 | 28.6 | 22.2 |
| Oursw/oExtra | 45.9 | 25.5 | 11.1 | – | 37.7 | 34.6 | 15.2 | 7.4 | 37.1 | 33.0 | 42.5 | 24.3 | 28.6 |
| OursFull | 48.8 | 36.3 | 16.4 | – | 39.8 | 37.2 | 19.1 | 13.2 | 37.0 | 32.1 | 44.4 | 26.9 | 31.9 |
| JointObjectDetectionandPoseEstimation(16ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 15.4 | 18.4 | 0.5 | – | 46.9 | 18.1 | 6.0 | 2.2 | 16.1 | 10.0 | 22.1 | 16.3 | 15.6 |
| DPM-VOC+VP | 17.0 | 24.7 | 1.0 | – | 49.0 | 30.1 | 6.6 | 3.0 | 17.2 | 7.7 | 20.4 | 20.2 | 17.9 |
| Oursw/oExtra | 23.3 | 19.2 | 8.4 | – | 52.6 | 27.0 | 9.9 | 5.1 | 23.6 | 20.9 | 27.4 | 27.9 | 22.3 |
| OursFull | 28.0 | 23.7 | 10.7 | – | 50.8 | 31.4 | 14.3 | 9.4 | 23.4 | 19.5 | 30.7 | 27.8 | 24.5 |
| JointObjectDetectionandPoseEstimation(24ViewsAVP) | | | | | | | | | | | | | |
| VDPM | 8.0 | 14.3 | 0.3 | – | 39.2 | 13.7 | 4.4 | 3.6 | 10.1 | 8.2 | 20.0 | 11.2 | 12.1 |
| DPM-VOC+VP | 10.6 | 16.7 | 2.2 | – | 43.5 | 25.4 | 4.4 | 2.3 | 11.3 | 4.9 | 22.4 | 14.4 | 14.4 |
| Oursw/oExtra | 18.9 | 10.5 | 6.7 | – | 34.3 | 23.3 | 8.3 | 6.5 | 20.6 | 17.5 | 33.8 | 17.0 | 17.9 |
| OursFull | 20.7 | 16.4 | 7.9 | – | 34.6 | 24.6 | 9.4 | 7.6 | 19.9 | 20.0 | 32.7 | 18.2 | 19.3 | | 0 |
| GS-01 | GS-02 | | |
| --- | --- | --- | --- |
| referencepoint | hours | referencepoint | hours |
| R1 | 0.000040 | R1 | 0.00013 |
| R2 | 1.52 | R2 | 4.99 |
| R3 | 1.28 | R3 | 4.2 | | | R4 | 1.23 | R4 | 3.53 |
| --- | --- | --- | --- |
| R5 | 1.12 | R5 | 3.36 |
| R6 | 1.0 | R6 | 3.07 |
| R7 | 1.0 | R7 | 2.52 |
| | | R8 | 2,61 | | 1 |
| GS-01 | GS-02 | | |
| --- | --- | --- | --- |
| referencepoint | hours | referencepoint | hours |
| R1 | 0.000040 | R1 | 0.00013 |
| R2 | 1.52 | R2 | 4.99 |
| R3 | 1.28 | R3 | 4.2 | | | Data(hrs) | AM | LM | SWB | CH |
| --- | --- | --- | --- | --- |
| 300 | A2W | - | 20.8 | 30.4 |
| 300 | PhoneCTC | Small | 14.5 | 25.1 |
| 2000 | A2W | - | 13.0 | 18.8 |
| 2000 | PhoneCTC | Big | 9.6 | 16.0 | | 0 |
| GS-01 | GS-02 | | |
| --- | --- | --- | --- |
| referencepoint | hours | referencepoint | hours | | | R1 | 0.000040 | R1 | 0.00013 |
| --- | --- | --- | --- |
| R2 | 1.52 | R2 | 4.99 |
| R3 | 1.28 | R3 | 4.2 |
| R4 | 1.23 | R4 | 3.53 |
| R5 | 1.12 | R5 | 3.36 |
| R6 | 1.0 | R6 | 3.07 |
| R7 | 1.0 | R7 | 2.52 |
| | | R8 | 2,61 | | 1 |
| GS-01 | GS-02 | | |
| --- | --- | --- | --- |
| referencepoint | hours | referencepoint | hours | | | 300 | PhoneCTC | Small | 14.5 | 25.1 |
| --- | --- | --- | --- | --- |
| 2000 | A2W | - | 13.0 | 18.8 |
| 2000 | PhoneCTC | Big | 9.6 | 16.0 | | 0 |
| Category | Numberoftweets |
| --- | --- |
| Totaltweets | 3545 | | | Tweetsinfavor | 964 |
| --- | --- |
| Tweetsagainst | 647 |
| Neutraltweets | 1934 | | 1 |
| Category | Numberoftweets |
| --- | --- |
| Totaltweets | 3545 | | | Numbers | Category0 | Categoryvoid | | |
| --- | --- | --- | --- | --- |
| | Passed | Failed | Passed | Failed |
| Twitter | 12,681 | 2,228 | 21,443 | 4,123 |
| Facebook | 7,586 | 1,287 | 21,715 | 3,579 |
| Total | 20,267 | 3,515 | 43,158 | 7,702 | | 0 |
| Category | Numberoftweets |
| --- | --- |
| Totaltweets | 3545 |
| Tweetsinfavor | 964 | | | Tweetsagainst | 647 |
| --- | --- |
| Neutraltweets | 1934 | | 1 |
| Category | Numberoftweets |
| --- | --- |
| Totaltweets | 3545 |
| Tweetsinfavor | 964 | | | Facebook | 7,586 | 1,287 | 21,715 | 3,579 |
| --- | --- | --- | --- | --- |
| Total | 20,267 | 3,515 | 43,158 | 7,702 | | 0 |
| Datasets | Scene-15 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | NMI | ACC | AR | F-score | Precision | Recall | NMI | ACC | AR |
| SPCbest<br>LRRbest | 0.421<br>0.426 | 0.437<br>0.445 | 0.270<br>0.272 | 0.321<br>0.324 | 0.314<br>0.316 | 0.329<br>0.333 | 0.559<br>0.226 | 0.443<br>0.120 | 0.304<br>0.031 |
| Co-reg<br>RMSC | 0.470<br>0.564 | 0.503<br>0.507 | 0.334<br>0.394 | 0.380<br>0.437 | 0.382<br>0.425 | 0.378<br>0.450 | 0.270<br>0.342 | 0.149<br>0.232 | 0.054<br>0.110 | | | DiMSC<br>LTMSC<br>ECMSC | 0.269<br>0.571<br>0.463 | 0.300<br>0.574<br>0.457 | 0.117<br>0.424<br>0.303 | 0.181<br>0.465<br>0.357 | 0.173<br>0.452<br>0.318 | 0.190<br>0.479<br>0.408 | 0.383<br>0.226<br>0.590 | 0.246<br>0.120<br>0.469 | 0.128<br>0.031<br>0.323 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| t-SVD-MSC<br>ETLMSC | 0.858<br>0.902 | 0.812<br>0.878 | 0.771<br>0.851 | 0.788<br>0.862 | 0.743<br>0.848 | 0.839<br>0.877 | 0.750<br>0.899 | 0.684<br>0.775 | 0.555<br>0.729 | | 1 |
| Datasets | Scene-15 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | NMI | ACC | AR | F-score | Precision | Recall | NMI | ACC | AR |
| SPCbest<br>LRRbest | 0.421<br>0.426 | 0.437<br>0.445 | 0.270<br>0.272 | 0.321<br>0.324 | 0.314<br>0.316 | 0.329<br>0.333 | 0.559<br>0.226 | 0.443<br>0.120 | 0.304<br>0.031 |
| Co-reg<br>RMSC | 0.470<br>0.564 | 0.503<br>0.507 | 0.334<br>0.394 | 0.380<br>0.437 | 0.382<br>0.425 | 0.378<br>0.450 | 0.270<br>0.342 | 0.149<br>0.232 | 0.054<br>0.110 | | | Datasets | BBC-Sport | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | NMI | ACC | AR | F-score | Precision | Recall | NMI | ACC | AR |
| SPCbest<br>LRRbest | 0.735<br>0.747 | 0.853<br>0.886 | 0.744<br>0.725 | 0.798<br>0.789 | 0.804<br>0.803 | 0.792<br>0.776 | 0.642<br>0.768 | 0.731<br>0.871 | 0.545<br>0.736 |
| Co-reg<br>RMSC | 0.771<br>0.808 | 0.849<br>0.912 | 0.783<br>0.837 | 0.829<br>0.871 | 0.836<br>0.879 | 0.822<br>0.864 | 0.804<br>0.822 | 0.780<br>0.915 | 0.755<br>0.789 |
| DiMSC<br>ECMSC | 0.814<br>0.090 | 0.901<br>0.408 | 0.843<br>0.060 | 0.880<br>0.391 | 0.875<br>0.267 | 0.882<br>0.942 | 0.772<br>0.780 | 0.703<br>0.718 | 0.652<br>0.672 |
| t-SVD-MSC<br>ETLMSC | 0.830<br>0.984 | 0.941<br>0.978 | 0.853<br>0.967 | 0.888<br>0.977 | 0.881<br>0.963 | 0.896<br>0.998 | 0.932<br>0.977 | 0.955<br>0.958 | 0.924<br>0.953 | | 0 |
| Datasets | Scene-15 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | NMI | ACC | AR | F-score | Precision | Recall | NMI | ACC | AR |
| SPCbest<br>LRRbest | 0.421<br>0.426 | 0.437<br>0.445 | 0.270<br>0.272 | 0.321<br>0.324 | 0.314<br>0.316 | 0.329<br>0.333 | 0.559<br>0.226 | 0.443<br>0.120 | 0.304<br>0.031 | | | Co-reg<br>RMSC | 0.470<br>0.564 | 0.503<br>0.507 | 0.334<br>0.394 | 0.380<br>0.437 | 0.382<br>0.425 | 0.378<br>0.450 | 0.270<br>0.342 | 0.149<br>0.232 | 0.054<br>0.110 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DiMSC<br>LTMSC<br>ECMSC | 0.269<br>0.571<br>0.463 | 0.300<br>0.574<br>0.457 | 0.117<br>0.424<br>0.303 | 0.181<br>0.465<br>0.357 | 0.173<br>0.452<br>0.318 | 0.190<br>0.479<br>0.408 | 0.383<br>0.226<br>0.590 | 0.246<br>0.120<br>0.469 | 0.128<br>0.031<br>0.323 |
| t-SVD-MSC<br>ETLMSC | 0.858<br>0.902 | 0.812<br>0.878 | 0.771<br>0.851 | 0.788<br>0.862 | 0.743<br>0.848 | 0.839<br>0.877 | 0.750<br>0.899 | 0.684<br>0.775 | 0.555<br>0.729 | | 1 |
| Datasets | Scene-15 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Methods | NMI | ACC | AR | F-score | Precision | Recall | NMI | ACC | AR |
| SPCbest<br>LRRbest | 0.421<br>0.426 | 0.437<br>0.445 | 0.270<br>0.272 | 0.321<br>0.324 | 0.314<br>0.316 | 0.329<br>0.333 | 0.559<br>0.226 | 0.443<br>0.120 | 0.304<br>0.031 | | | DiMSC<br>ECMSC | 0.814<br>0.090 | 0.901<br>0.408 | 0.843<br>0.060 | 0.880<br>0.391 | 0.875<br>0.267 | 0.882<br>0.942 | 0.772<br>0.780 | 0.703<br>0.718 | 0.652<br>0.672 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| t-SVD-MSC<br>ETLMSC | 0.830<br>0.984 | 0.941<br>0.978 | 0.853<br>0.967 | 0.888<br>0.977 | 0.881<br>0.963 | 0.896<br>0.998 | 0.932<br>0.977 | 0.955<br>0.958 | 0.924<br>0.953 | | 0 |
| Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set(baseline) | 0.885 | 0.888 | 0.885 | 0.879 |
| Train-set(extended) | 0.889 | 0.904 | 0.889 | 0.889 | | | Train-set+AL(baseline) | 0.890 | 0.896 | 0.890 | 0.885 |
| --- | --- | --- | --- | --- |
| Train-set+AL(extended) | 0.896 | 0.914 | 0.896 | 0.897 | | 1 |
| Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set(baseline) | 0.885 | 0.888 | 0.885 | 0.879 |
| Train-set(extended) | 0.889 | 0.904 | 0.889 | 0.889 | | | Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set(baseline) | 0.853 | 0.857 | 0.853 | 0.848 |
| Train-set(extended) | 0.860 | 0.871 | 0.860 | 0.858 |
| Train-set+AL(baseline) | 0.867 | 0.883 | 0.867 | 0.868 |
| Train-set+AL(extended) | 0.884 | 0.899 | 0.885 | 0.886 | | 0 |
| Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set(baseline) | 0.885 | 0.888 | 0.885 | 0.879 |
| Train-set(extended) | 0.889 | 0.904 | 0.889 | 0.889 | | | Train-set+AL(baseline) | 0.890 | 0.896 | 0.890 | 0.885 |
| --- | --- | --- | --- | --- |
| Train-set+AL(extended) | 0.896 | 0.914 | 0.896 | 0.897 | | 1 |
| Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set(baseline) | 0.885 | 0.888 | 0.885 | 0.879 |
| Train-set(extended) | 0.889 | 0.904 | 0.889 | 0.889 | | | Train-set+AL(baseline) | 0.867 | 0.883 | 0.867 | 0.868 |
| --- | --- | --- | --- | --- |
| Train-set+AL(extended) | 0.884 | 0.899 | 0.885 | 0.886 | | 0 |
| tol | Method | Translation | Rotation | | |
| --- | --- | --- | --- | --- | --- |
| Time(s) | Iterations | Time(s) | Iterations | | |
| −2<br>10 | PALM<br>PDIPA<br>L1LS | 0.18<br>0.40<br>2.71 | 11.31<br>11.77<br>11.60 | 0.13<br>0.28<br>1.96 | 8.44<br>9.21<br>8.75 | | | −3<br>10 | PALM<br>PDIPA<br>L1LS<br>DALM | 0.27<br>0.49<br>3.76<br>5.44 | 11.52<br>11.52<br>11.50<br>14.35 | 0.17<br>0.36<br>2.84<br>6.29 | 8.69<br>8.79<br>8.71<br>14.79 |
| --- | --- | --- | --- | --- | --- |
| −4<br>10 | PALM<br>PDIPA<br>L1LS<br>DALM<br>FISTA<br>TFOCS | 0.62<br>0.53<br>4.90<br>11.39<br>8.75<br>249.92 | 11.56<br>11.52<br>11.50<br>11.77<br>11.60<br>14.46 | 0.36<br>0.39<br>3.59<br>9.61<br>6.62<br>232.78 | 8.75<br>8.77<br>8.67<br>9.10<br>8.85<br>12.85 | | 1 |
| tol | Method | Translation | Rotation | | |
| --- | --- | --- | --- | --- | --- |
| Time(s) | Iterations | Time(s) | Iterations | | |
| −2<br>10 | PALM<br>PDIPA<br>L1LS | 0.18<br>0.40<br>2.71 | 11.31<br>11.77<br>11.60 | 0.13<br>0.28<br>1.96 | 8.44<br>9.21<br>8.75 | | | Pattern<br>Size | Algorithm | System1 | System2 | System3 |
| --- | --- | --- | --- | --- |
| 2 | ABM<br>HAL<br>L<br>SF<br>TBM | 8.89665<br>8.26117<br>6.08718<br>4.28357<br>10.5142 | 24.6946<br>24.6946<br>24.6946<br>9.87784<br>32.9261 | 32.9261<br>32.9261<br>32.9261<br>24.6946<br>32.9261 |
| 4 | ABM<br>HAL<br>L<br>SF<br>TBM | 20.4425<br>23.3995<br>6.52724<br>4.29622<br>21.2602 | 46.7838<br>51.0369<br>27.8712<br>9.84923<br>49.123 | 68.9446<br>83.6137<br>38.9093<br>23.3919<br>71.4517 |
| 6 | ABM<br>HAL<br>L<br>SF<br>TBM | 28.1637<br>31.2569<br>6.45279<br>4.28142<br>29.2294 | 60.2832<br>63.6323<br>27.4015<br>9.84005<br>62.249 | 89.4829<br>108.055<br>37.9265<br>22.1973<br>93.8837 |
| 8 | ABM<br>HAL<br>L<br>SF<br>TBM | 33.7463<br>37.0999<br>6.34086<br>4.23323<br>35.3437 | 69.2828<br>73.0482<br>26.6684<br>9.78229<br>72.2627 | 106.674<br>126.801<br>36.5241<br>22.0342<br>112.007 |
| 10 | ABM<br>HAL<br>L<br>SF<br>TBM | 39.6329<br>42.5986<br>6.32525<br>4.22537<br>41.1973 | 76.2308<br>80.5134<br>26.6383<br>9.74924<br>78.7439 | 117.47<br>135.202<br>36.1904<br>21.9134<br>125.714 |
| 14 | ABM<br>HAL<br>L<br>SF<br>TBM | 47.7986<br>49.8997<br>6.22037<br>4.189<br>49.3573 | 89.1214<br>92.9962<br>25.9262<br>9.72233<br>92.9962 | 129.631<br>147.511<br>33.6837<br>21.1774<br>142.594 |
| 18 | ABM<br>HAL<br>L<br>SF<br>TBM | 50.1514<br>50.1514<br>5.86185<br>4.05173<br>51.2912 | 97.859<br>101.773<br>24.7023<br>9.63763<br>97.859 | 141.352<br>159.021<br>31.4115<br>21.0275<br>149.667 | | 0 |
| tol | Method | Translation | Rotation | | |
| --- | --- | --- | --- | --- | --- |
| Time(s) | Iterations | Time(s) | Iterations | | |
| −2<br>10 | PALM<br>PDIPA<br>L1LS | 0.18<br>0.40<br>2.71 | 11.31<br>11.77<br>11.60 | 0.13<br>0.28<br>1.96 | 8.44<br>9.21<br>8.75 | | | −3<br>10 | PALM<br>PDIPA<br>L1LS<br>DALM | 0.27<br>0.49<br>3.76<br>5.44 | 11.52<br>11.52<br>11.50<br>14.35 | 0.17<br>0.36<br>2.84<br>6.29 | 8.69<br>8.79<br>8.71<br>14.79 |
| --- | --- | --- | --- | --- | --- |
| −4<br>10 | PALM<br>PDIPA<br>L1LS<br>DALM<br>FISTA<br>TFOCS | 0.62<br>0.53<br>4.90<br>11.39<br>8.75<br>249.92 | 11.56<br>11.52<br>11.50<br>11.77<br>11.60<br>14.46 | 0.36<br>0.39<br>3.59<br>9.61<br>6.62<br>232.78 | 8.75<br>8.77<br>8.67<br>9.10<br>8.85<br>12.85 | | 1 |
| tol | Method | Translation | Rotation | | |
| --- | --- | --- | --- | --- | --- |
| Time(s) | Iterations | Time(s) | Iterations | | |
| −2<br>10 | PALM<br>PDIPA<br>L1LS | 0.18<br>0.40<br>2.71 | 11.31<br>11.77<br>11.60 | 0.13<br>0.28<br>1.96 | 8.44<br>9.21<br>8.75 | | | 4 | ABM<br>HAL<br>L<br>SF<br>TBM | 20.4425<br>23.3995<br>6.52724<br>4.29622<br>21.2602 | 46.7838<br>51.0369<br>27.8712<br>9.84923<br>49.123 | 68.9446<br>83.6137<br>38.9093<br>23.3919<br>71.4517 |
| --- | --- | --- | --- | --- |
| 6 | ABM<br>HAL<br>L<br>SF<br>TBM | 28.1637<br>31.2569<br>6.45279<br>4.28142<br>29.2294 | 60.2832<br>63.6323<br>27.4015<br>9.84005<br>62.249 | 89.4829<br>108.055<br>37.9265<br>22.1973<br>93.8837 |
| 8 | ABM<br>HAL<br>L<br>SF<br>TBM | 33.7463<br>37.0999<br>6.34086<br>4.23323<br>35.3437 | 69.2828<br>73.0482<br>26.6684<br>9.78229<br>72.2627 | 106.674<br>126.801<br>36.5241<br>22.0342<br>112.007 |
| 10 | ABM<br>HAL<br>L<br>SF<br>TBM | 39.6329<br>42.5986<br>6.32525<br>4.22537<br>41.1973 | 76.2308<br>80.5134<br>26.6383<br>9.74924<br>78.7439 | 117.47<br>135.202<br>36.1904<br>21.9134<br>125.714 |
| 14 | ABM<br>HAL<br>L<br>SF<br>TBM | 47.7986<br>49.8997<br>6.22037<br>4.189<br>49.3573 | 89.1214<br>92.9962<br>25.9262<br>9.72233<br>92.9962 | 129.631<br>147.511<br>33.6837<br>21.1774<br>142.594 |
| 18 | ABM<br>HAL<br>L<br>SF<br>TBM | 50.1514<br>50.1514<br>5.86185<br>4.05173<br>51.2912 | 97.859<br>101.773<br>24.7023<br>9.63763<br>97.859 | 141.352<br>159.021<br>31.4115<br>21.0275<br>149.667 | | 0 |
| -classe1800(attendus=169,ramenes=98.00,corrects=5.00)<br>rappel=0.030precision=0.051f-mesure=0.037 |
| --- |
| -classe1810(attendus=169,ramenes=203.00,corrects=28.00)<br>rappel=0.166precision=0.138f-mesure=0.151 |
| -classe1820(attendus=169,ramenes=287.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| -classe1830(attendus=169,ramenes=479.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 | | | -classe1840(attendus=169,ramenes=72.00,corrects=10.00)<br>rappel=0.059precision=0.139f-mesure=0.083 |
| --- |
| -classe1850(attendus=169,ramenes=193.00,corrects=13.00)<br>rappel=0.077precision=0.067f-mesure=0.072 |
| -classe1860(attendus=170,ramenes=101.00,corrects=8.00)<br>rappel=0.047precision=0.079f-mesure=0.059 |
| -classe1870(attendus=169,ramenes=70.00,corrects=8.00)<br>rappel=0.047precision=0.114f-mesure=0.067 |
| -classe1880(attendus=169,ramenes=115.00,corrects=14.00)<br>rappel=0.083precision=0.122f-mesure=0.099 |
| -classe1890(attendus=197,ramenes=76.00,corrects=8.00)<br>rappel=0.041precision=0.105f-mesure=0.059 |
| -classe1900(attendus=203,ramenes=122.00,corrects=10.00)<br>rappel=0.049precision=0.082f-mesure=0.062 |
| -classe1910(attendus=201,ramenes=61.00,corrects=5.00)<br>rappel=0.025precision=0.082f-mesure=0.038 |
| -classe1920(attendus=200,ramenes=189.00,corrects=16.00)<br>rappel=0.080precision=0.085f-mesure=0.082 |
| -classe1930(attendus=200,ramenes=71.00,corrects=7.00)<br>rappel=0.035precision=0.099f-mesure=0.052 |
| -classe1940(attendus=198,ramenes=584.00,corrects=84.00)<br>rappel=0.424precision=0.144f-mesure=0.215 |
| -surl’ensembledes15classes<br>macrorappel=0.109macroprecision=0.102macroF-mesure=0.105 | | 1 |
| -classe1800(attendus=169,ramenes=98.00,corrects=5.00)<br>rappel=0.030precision=0.051f-mesure=0.037 |
| --- |
| -classe1810(attendus=169,ramenes=203.00,corrects=28.00)<br>rappel=0.166precision=0.138f-mesure=0.151 |
| -classe1820(attendus=169,ramenes=287.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| -classe1830(attendus=169,ramenes=479.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 | | | -classe1800(attendus=169,ramenes=98.00,corrects=5.00)<br>rappel=0.030precision=0.051f-mesure=0.037 |
| --- |
| -classe1810(attendus=169,ramenes=203.00,corrects=28.00)<br>rappel=0.166precision=0.138f-mesure=0.151 |
| -classe1820(attendus=169,ramenes=288.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| -classe1830(attendus=169,ramenes=478.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 |
| -classe1840(attendus=169,ramenes=72.00,corrects=10.00)<br>rappel=0.059precision=0.139f-mesure=0.083 |
| -classe1850(attendus=169,ramenes=193.00,corrects=13.00)<br>rappel=0.077precision=0.067f-mesure=0.072 |
| -classe1860(attendus=170,ramenes=101.00,corrects=8.00)<br>rappel=0.047precision=0.079f-mesure=0.059 |
| -classe1870(attendus=169,ramenes=70.00,corrects=8.00)<br>rappel=0.047precision=0.114f-mesure=0.067 |
| -classe1880(attendus=169,ramenes=115.00,corrects=14.00)<br>rappel=0.083precision=0.122f-mesure=0.099 |
| -classe1890(attendus=197,ramenes=76.00,corrects=8.00)<br>rappel=0.041precision=0.105f-mesure=0.059 |
| -classe1900(attendus=203,ramenes=122.00,corrects=10.00)<br>rappel=0.049precision=0.082f-mesure=0.062 |
| -classe1910(attendus=201,ramenes=61.00,corrects=5.00)<br>rappel=0.025precision=0.082f-mesure=0.038 |
| -classe1920(attendus=200,ramenes=189.00,corrects=16.00)<br>rappel=0.080precision=0.085f-mesure=0.082 |
| -classe1930(attendus=200,ramenes=71.00,corrects=7.00)<br>rappel=0.035precision=0.099f-mesure=0.052 |
| -classe1940(attendus=198,ramenes=584.00,corrects=84.00)<br>rappel=0.424precision=0.144f-mesure=0.215 |
| -surl’ensembledes15classes<br>macrorappel=0.109macroprecision=0.102macroF-mesure=0.105 | | 0 |
| -classe1800(attendus=169,ramenes=98.00,corrects=5.00)<br>rappel=0.030precision=0.051f-mesure=0.037 |
| --- |
| -classe1810(attendus=169,ramenes=203.00,corrects=28.00)<br>rappel=0.166precision=0.138f-mesure=0.151 |
| -classe1820(attendus=169,ramenes=287.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| -classe1830(attendus=169,ramenes=479.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 | | | -classe1840(attendus=169,ramenes=72.00,corrects=10.00)<br>rappel=0.059precision=0.139f-mesure=0.083 |
| --- |
| -classe1850(attendus=169,ramenes=193.00,corrects=13.00)<br>rappel=0.077precision=0.067f-mesure=0.072 |
| -classe1860(attendus=170,ramenes=101.00,corrects=8.00)<br>rappel=0.047precision=0.079f-mesure=0.059 |
| -classe1870(attendus=169,ramenes=70.00,corrects=8.00)<br>rappel=0.047precision=0.114f-mesure=0.067 |
| -classe1880(attendus=169,ramenes=115.00,corrects=14.00)<br>rappel=0.083precision=0.122f-mesure=0.099 |
| -classe1890(attendus=197,ramenes=76.00,corrects=8.00)<br>rappel=0.041precision=0.105f-mesure=0.059 |
| -classe1900(attendus=203,ramenes=122.00,corrects=10.00)<br>rappel=0.049precision=0.082f-mesure=0.062 |
| -classe1910(attendus=201,ramenes=61.00,corrects=5.00)<br>rappel=0.025precision=0.082f-mesure=0.038 |
| -classe1920(attendus=200,ramenes=189.00,corrects=16.00)<br>rappel=0.080precision=0.085f-mesure=0.082 |
| -classe1930(attendus=200,ramenes=71.00,corrects=7.00)<br>rappel=0.035precision=0.099f-mesure=0.052 |
| -classe1940(attendus=198,ramenes=584.00,corrects=84.00)<br>rappel=0.424precision=0.144f-mesure=0.215 |
| -surl’ensembledes15classes<br>macrorappel=0.109macroprecision=0.102macroF-mesure=0.105 | | 1 |
| -classe1800(attendus=169,ramenes=98.00,corrects=5.00)<br>rappel=0.030precision=0.051f-mesure=0.037 |
| --- |
| -classe1810(attendus=169,ramenes=203.00,corrects=28.00)<br>rappel=0.166precision=0.138f-mesure=0.151 |
| -classe1820(attendus=169,ramenes=287.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| -classe1830(attendus=169,ramenes=479.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 | | | -classe1820(attendus=169,ramenes=288.00,corrects=34.00)<br>rappel=0.201precision=0.118f-mesure=0.149 |
| --- |
| -classe1830(attendus=169,ramenes=478.00,corrects=47.00)<br>rappel=0.278precision=0.098f-mesure=0.145 |
| -classe1840(attendus=169,ramenes=72.00,corrects=10.00)<br>rappel=0.059precision=0.139f-mesure=0.083 |
| -classe1850(attendus=169,ramenes=193.00,corrects=13.00)<br>rappel=0.077precision=0.067f-mesure=0.072 |
| -classe1860(attendus=170,ramenes=101.00,corrects=8.00)<br>rappel=0.047precision=0.079f-mesure=0.059 |
| -classe1870(attendus=169,ramenes=70.00,corrects=8.00)<br>rappel=0.047precision=0.114f-mesure=0.067 |
| -classe1880(attendus=169,ramenes=115.00,corrects=14.00)<br>rappel=0.083precision=0.122f-mesure=0.099 |
| -classe1890(attendus=197,ramenes=76.00,corrects=8.00)<br>rappel=0.041precision=0.105f-mesure=0.059 |
| -classe1900(attendus=203,ramenes=122.00,corrects=10.00)<br>rappel=0.049precision=0.082f-mesure=0.062 |
| -classe1910(attendus=201,ramenes=61.00,corrects=5.00)<br>rappel=0.025precision=0.082f-mesure=0.038 |
| -classe1920(attendus=200,ramenes=189.00,corrects=16.00)<br>rappel=0.080precision=0.085f-mesure=0.082 |
| -classe1930(attendus=200,ramenes=71.00,corrects=7.00)<br>rappel=0.035precision=0.099f-mesure=0.052 |
| -classe1940(attendus=198,ramenes=584.00,corrects=84.00)<br>rappel=0.424precision=0.144f-mesure=0.215 |
| -surl’ensembledes15classes<br>macrorappel=0.109macroprecision=0.102macroF-mesure=0.105 | | 0 |
| Version | Problem | Squarespersecond |
| --- | --- | --- |
| Standard(Algorithms??and??) | DLS9 | 1.8×10 |
| Bitarithmetic(Algorithm??) | DLS9 | 2.6×10 | | | Optimizedbitarithmetic<br>(Algorithm??) | DLS9 | 6.8×10 |
| --- | --- | --- |
| LS8 | 9×10 | |
| DLS8 | 5.8×10 | |
| LS9 | 8.0×10 | |
| LS10 | 6.3×10 | |
| DLS10 | 6.0×10 | | | 1 |
| Version | Problem | Squarespersecond |
| --- | --- | --- |
| Standard(Algorithms??and??) | DLS9 | 1.8×10 |
| Bitarithmetic(Algorithm??) | DLS9 | 2.6×10 | | | Problemsize | Processorconfigurations |
| --- | --- |
| 8000(LU,MM) | 1×2,2×2,2×4,4×4,4×5,5×5,5×8 |
| 12000(LU,MM) | 1×2,2×2,2×3,3×3,3×4,4×4,4×5,5×5,5×6,6×6,6×8 |
| 14000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×7,7×7 |
| 16000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×8 |
| 20000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×8 |
| 21000(LU,MM) | 2×2,2×3,3×3,3×4,4×5,5×5,5×6,6×6,6×7,7×7 |
| 24000(LU,MM) | 2×4,3×4,4×4,4×5,5×5,5×6,6×6,6×8 |
| 8000(Jacobi) | 4,8,10,16,20,32,40,50 |
| 8192(FFT) | 2,4,8,16,32 |
| 20000(Master-worker) | 4,6,8,10,12,14,16,18,20,22 | | 0 |
| Version | Problem | Squarespersecond |
| --- | --- | --- |
| Standard(Algorithms??and??) | DLS9 | 1.8×10 | | | Bitarithmetic(Algorithm??) | DLS9 | 2.6×10 |
| --- | --- | --- |
| Optimizedbitarithmetic<br>(Algorithm??) | DLS9 | 6.8×10 |
| LS8 | 9×10 | |
| DLS8 | 5.8×10 | |
| LS9 | 8.0×10 | |
| LS10 | 6.3×10 | |
| DLS10 | 6.0×10 | | | 1 |
| Version | Problem | Squarespersecond |
| --- | --- | --- |
| Standard(Algorithms??and??) | DLS9 | 1.8×10 | | | 12000(LU,MM) | 1×2,2×2,2×3,3×3,3×4,4×4,4×5,5×5,5×6,6×6,6×8 |
| --- | --- |
| 14000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×7,7×7 |
| 16000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×8 |
| 20000(LU,MM) | 2×2,2×4,4×4,4×5,5×5,5×8 |
| 21000(LU,MM) | 2×2,2×3,3×3,3×4,4×5,5×5,5×6,6×6,6×7,7×7 |
| 24000(LU,MM) | 2×4,3×4,4×4,4×5,5×5,5×6,6×6,6×8 |
| 8000(Jacobi) | 4,8,10,16,20,32,40,50 |
| 8192(FFT) | 2,4,8,16,32 |
| 20000(Master-worker) | 4,6,8,10,12,14,16,18,20,22 | | 0 |
| Approach | Input | Actions | Evaluations | Rate |
| --- | --- | --- | --- | --- |
| IkizlerandDuyugulu | Silhouette | 9 | LOSO | 100 |
| TranandSorokin | Silhouette | 10 | LOSO | 100 | | | Eweiwietal. | Silhouette | 10 | LOSO | 100 |
| --- | --- | --- | --- | --- |
| Harnandezetal. | Images | 10 | LASO | 90.3 |
| Cheemaetal. | Silhouette | 9 | LOSO | 91.6 |
| Chaaraouietal. | Silhouette | 9 | LOSO | 92.8 |
| Proposed | Silhouette | 9 | NoTraining | 95.06 | | 1 |
| Approach | Input | Actions | Evaluations | Rate |
| --- | --- | --- | --- | --- |
| IkizlerandDuyugulu | Silhouette | 9 | LOSO | 100 |
| TranandSorokin | Silhouette | 10 | LOSO | 100 | | | Approach | Input | Actions | Evaluations | SuccessRate |
| --- | --- | --- | --- | --- |
| Singhetal. | Silhouette | 14 | LOSO | 82.4 |
| Eweiwietal. | Silhouette | 14 | LOSO | 91.9 |
| Cheemaetal. | Silhouette | 14 | LOSO | 86.0 |
| Chaaraouietal. | Silhouette | 14 | LOSO | 92.8 |
| Proposed | Silhouette | 10 | NoTraining | 93.75 | | 0 |
| Approach | Input | Actions | Evaluations | Rate |
| --- | --- | --- | --- | --- |
| IkizlerandDuyugulu | Silhouette | 9 | LOSO | 100 | | | TranandSorokin | Silhouette | 10 | LOSO | 100 |
| --- | --- | --- | --- | --- |
| Eweiwietal. | Silhouette | 10 | LOSO | 100 |
| Harnandezetal. | Images | 10 | LASO | 90.3 |
| Cheemaetal. | Silhouette | 9 | LOSO | 91.6 |
| Chaaraouietal. | Silhouette | 9 | LOSO | 92.8 |
| Proposed | Silhouette | 9 | NoTraining | 95.06 | | 1 |
| Approach | Input | Actions | Evaluations | Rate |
| --- | --- | --- | --- | --- |
| IkizlerandDuyugulu | Silhouette | 9 | LOSO | 100 | | | Eweiwietal. | Silhouette | 14 | LOSO | 91.9 |
| --- | --- | --- | --- | --- |
| Cheemaetal. | Silhouette | 14 | LOSO | 86.0 |
| Chaaraouietal. | Silhouette | 14 | LOSO | 92.8 |
| Proposed | Silhouette | 10 | NoTraining | 93.75 | | 0 |
| Inputs | Dataset | MAEs |
| --- | --- | --- |
| GroundTruthfaces(setA) | FGNET | 5.89 |
| Synthesizedfaces(setB) | FGNET | 5.96 | | | IAAP’ssynthesizedfaces(setB’) | FGNET | 6.29 |
| --- | --- | --- |
| GroundTruthfaces(setC) | MORPH | 4.84 |
| Synthesizedfaces(setD) | MORPH | 5.17 | | 1 |
| Inputs | Dataset | MAEs |
| --- | --- | --- |
| GroundTruthfaces(setA) | FGNET | 5.89 |
| Synthesizedfaces(setB) | FGNET | 5.96 | | | Inputs | MAEs(years) |
| --- | --- |
| DAMs-Mod | 4.67 |
| AAMs-Mod | 4.81 |
| DAMs-Rec | 5.67 |
| AAMs-Rec | 6.14 |
| DLF-CNN | 4.26 |
| CA-SVR | 4.67 |
| PLO | 4.82 | | 0 |
| Inputs | Dataset | MAEs |
| --- | --- | --- |
| GroundTruthfaces(setA) | FGNET | 5.89 |
| Synthesizedfaces(setB) | FGNET | 5.96 |
| IAAP’ssynthesizedfaces(setB’) | FGNET | 6.29 | | | GroundTruthfaces(setC) | MORPH | 4.84 |
| --- | --- | --- |
| Synthesizedfaces(setD) | MORPH | 5.17 | | 1 |
| Inputs | Dataset | MAEs |
| --- | --- | --- |
| GroundTruthfaces(setA) | FGNET | 5.89 |
| Synthesizedfaces(setB) | FGNET | 5.96 |
| IAAP’ssynthesizedfaces(setB’) | FGNET | 6.29 | | | DAMs-Rec | 5.67 |
| --- | --- |
| AAMs-Rec | 6.14 |
| DLF-CNN | 4.26 |
| CA-SVR | 4.67 |
| PLO | 4.82 | | 0 |
| Embeddingsize | d=300 |
| --- | --- |
| HiddenlayersizeofLSTM | m=100 | | | Batchsize | δ=32 |
| --- | --- |
| Initiallearningrate | η=0.1 |
| Regularizationweight | λ=10 | | 1 |
| Embeddingsize | d=300 |
| --- | --- |
| HiddenlayersizeofLSTM | m=100 | | | LayerTypeLayerSize | ParameterSGD |
| --- | --- |
| Conv+ReLU3x3x32 | LearningRate0.1 |
| Conv+ReLU3x3x32 | Momentum0.9 |
| MaxPooling2x2 | DelayRate- |
| Conv+ReLU3x3x64 | Dropout0.5 |
| Conv+ReLU3x3x64 | BatchSize128 |
| MaxPooling2x2 | Epochs50 |
| Dense+ReLU200 | |
| Dense+ReLU200 | |
| Softmax+ReLU10 | | | 0 |
| Embeddingsize | d=300 |
| --- | --- |
| HiddenlayersizeofLSTM | m=100 |
| Batchsize | δ=32 | | | Initiallearningrate | η=0.1 |
| --- | --- |
| Regularizationweight | λ=10 | | 1 |
| Embeddingsize | d=300 |
| --- | --- |
| HiddenlayersizeofLSTM | m=100 |
| Batchsize | δ=32 | | | Dense+ReLU200 | |
| --- | --- |
| Softmax+ReLU10 | | | 0 |
| lossfunctionused | testerrorrate |
| --- | --- |
| L | 6.78 |
| (Grad-CAM++)L | 6.74 | | | L(Grad-CAM) | 6.86 |
| --- | --- |
| L+L | 5.68 |
| L(Grad-CAM++)+L | 5.56 |
| L(Grad-CAM)+L | 5.8 | | 1 |
| lossfunctionused | testerrorrate |
| --- | --- |
| L | 6.78 |
| (Grad-CAM++)L | 6.74 | | | Parameters | Value |
| --- | --- |
| Inputdescriptionlength | 50words |
| Outputsummarylength | 15words |
| OptimizationMethod | StochasticGradientDescent<br>withmomentum |
| Learningrate | 0.1;reducedtohalfafter<br>everythirdepoch. |
| Batchsize | 128 |
| LSTMParameters | Uniformdistributionfrom[-0.1,0.1] | | 0 |
| lossfunctionused | testerrorrate |
| --- | --- |
| L | 6.78 | | | (Grad-CAM++)L | 6.74 |
| --- | --- |
| L(Grad-CAM) | 6.86 |
| L+L | 5.68 |
| L(Grad-CAM++)+L | 5.56 |
| L(Grad-CAM)+L | 5.8 | | 1 |
| lossfunctionused | testerrorrate |
| --- | --- |
| L | 6.78 | | | Learningrate | 0.1;reducedtohalfafter<br>everythirdepoch. |
| --- | --- |
| Batchsize | 128 |
| LSTMParameters | Uniformdistributionfrom[-0.1,0.1] | | 0 |
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