premise string | hypothesis string | label int64 |
|---|---|---|
| TaobaoDataset | AmazonDataset |
| --- | --- |
| 219 | 483 |
| 212 | 467 | | | 205 | 435 |
| --- | --- |
| 191 | 408 | | 1 |
| TaobaoDataset | AmazonDataset |
| --- | --- |
| 219 | 483 |
| 212 | 467 | | | Dataset | Size | Dataset | Size |
| --- | --- | --- | --- |
| Coffeeshops1 | 1733 | Inn1 | 1108 |
| Coffeeshops2 | 1807 | Inn2 | 1265 |
| Starbucks | 149 | Home-Inn | 235 |
| Costa | 75 | JinJiang | 57 | | 0 |
| TaobaoDataset | AmazonDataset |
| --- | --- |
| 219 | 483 |
| 212 | 467 | | | 205 | 435 |
| --- | --- |
| 191 | 408 | | 1 |
| TaobaoDataset | AmazonDataset |
| --- | --- |
| 219 | 483 |
| 212 | 467 | | | Starbucks | 149 | Home-Inn | 235 |
| --- | --- | --- | --- |
| Costa | 75 | JinJiang | 57 | | 0 |
| method | CC | SAM | RMSE | ERGAS |
| --- | --- | --- | --- | --- |
| SFIM | 0.91886 | 4.2895 | 637.1451 | 3.4159 |
| MTF-GLP | 0.92397 | 4.3378 | 622.4711 | 3.2666 | | | MTF-GLP-HPM | 0.92599 | 4.2821 | 611.9161 | 3.2497 |
| --- | --- | --- | --- | --- |
| GS | 0.91262 | 4.4982 | 665.0173 | 3.5490 |
| GSA | 0.92826 | 4.1950 | 587.1322 | 3.1940 |
| PCA | 0.90350 | 5.1637 | 710.3275 | 3.8680 |
| GFPCA | 0.89042 | 4.8472 | 745.6006 | 4.0001 |
| CNMF | 0.9300 | 4.4187 | 591.3190 | 3.1762 |
| BayesianNaive | 0.95195 | 3.6428 | 489.5634 | 2.6286 |
| BayesianSparse | 0.95882 | 3.3345 | 448.1721 | 2.4712 |
| HySure | 0.9465 | 3.8767 | 511.8525 | 2.8181 | | 1 |
| method | CC | SAM | RMSE | ERGAS |
| --- | --- | --- | --- | --- |
| SFIM | 0.91886 | 4.2895 | 637.1451 | 3.4159 |
| MTF-GLP | 0.92397 | 4.3378 | 622.4711 | 3.2666 | | | Algorithm | Symm | Asymm | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.9037 | 0.8991 | 7.0246 | 0.8769 | 0.8755 | 5.8162 |
| MS-SSIM | 0.8901 | 0.8681 | 21.2322 | 0.8423 | 0.7785 | 15.1681 |
| SBIQE | 0.0006 | 0.0027 | 25.4484 | 0.0021 | 0.0051 | 12.0123 |
| BRISQUE | 0.7829 | 0.7859 | 15.8298 | 0.5411 | 0.5303 | 10.1719 |
| NIQE | 0.8499 | 0.8705 | 13.4076 | 0.6835 | 0.6929 | 8.8334 |
| STMAD | 0.7815 | 0.8000 | 10.2358 | 0.6534 | 0.6010 | 9.1614 |
| Chenetal. | 0.9435 | 0.9182 | 5.4346 | 0.8370 | 0.8376 | 6.6218 |
| STRIQE | 0.8275 | 0.8017 | 9.2105 | 0.7559 | 0.7492 | 7.9321 |
| VQUEMODES(NIQE) | 0.9285 | 0.9236 | 3.9852 | 0.8955 | 0.8490 | 6.9563 | | 0 |
| method | CC | SAM | RMSE | ERGAS |
| --- | --- | --- | --- | --- |
| SFIM | 0.91886 | 4.2895 | 637.1451 | 3.4159 |
| MTF-GLP | 0.92397 | 4.3378 | 622.4711 | 3.2666 | | | MTF-GLP-HPM | 0.92599 | 4.2821 | 611.9161 | 3.2497 |
| --- | --- | --- | --- | --- |
| GS | 0.91262 | 4.4982 | 665.0173 | 3.5490 |
| GSA | 0.92826 | 4.1950 | 587.1322 | 3.1940 |
| PCA | 0.90350 | 5.1637 | 710.3275 | 3.8680 |
| GFPCA | 0.89042 | 4.8472 | 745.6006 | 4.0001 |
| CNMF | 0.9300 | 4.4187 | 591.3190 | 3.1762 |
| BayesianNaive | 0.95195 | 3.6428 | 489.5634 | 2.6286 |
| BayesianSparse | 0.95882 | 3.3345 | 448.1721 | 2.4712 |
| HySure | 0.9465 | 3.8767 | 511.8525 | 2.8181 | | 1 |
| method | CC | SAM | RMSE | ERGAS |
| --- | --- | --- | --- | --- |
| SFIM | 0.91886 | 4.2895 | 637.1451 | 3.4159 |
| MTF-GLP | 0.92397 | 4.3378 | 622.4711 | 3.2666 | | | Chenetal. | 0.9435 | 0.9182 | 5.4346 | 0.8370 | 0.8376 | 6.6218 |
| --- | --- | --- | --- | --- | --- | --- |
| STRIQE | 0.8275 | 0.8017 | 9.2105 | 0.7559 | 0.7492 | 7.9321 |
| VQUEMODES(NIQE) | 0.9285 | 0.9236 | 3.9852 | 0.8955 | 0.8490 | 6.9563 | | 0 |
| FeatureExtractorMethod | Acronym |
| --- | --- |
| ColorLayout | CL |
| ScalableColor | SC |
| ColorStructure | CS | | | ColorTemperature | CT |
| --- | --- |
| EdgeHistogram | EH |
| TextureBrowsing | TB | | 1 |
| FeatureExtractorMethod | Acronym |
| --- | --- |
| ColorLayout | CL |
| ScalableColor | SC |
| ColorStructure | CS | | | Signals | Description |
| --- | --- |
| clk | Thisistheglobalclocksignal |
| resetn | Activelowsystemreset |
| rin[7:0] | Redcolorcomponent |
| gin[7:0] | Greencolorcomponent |
| bin[7:0] | Bluecolorcomponent |
| ro[7:0] | Enhancedredcolorcomponent |
| go[7:0] | EnhancedGreencolorcomponent |
| bo[7:0] | EnhancedBluecolorcomponent |
| pixelvalid | ValidsignalforenhancedRGBpixel | | 0 |
| FeatureExtractorMethod | Acronym |
| --- | --- |
| ColorLayout | CL |
| ScalableColor | SC | | | ColorStructure | CS |
| --- | --- |
| ColorTemperature | CT |
| EdgeHistogram | EH |
| TextureBrowsing | TB | | 1 |
| FeatureExtractorMethod | Acronym |
| --- | --- |
| ColorLayout | CL |
| ScalableColor | SC | | | resetn | Activelowsystemreset |
| --- | --- |
| rin[7:0] | Redcolorcomponent |
| gin[7:0] | Greencolorcomponent |
| bin[7:0] | Bluecolorcomponent |
| ro[7:0] | Enhancedredcolorcomponent |
| go[7:0] | EnhancedGreencolorcomponent |
| bo[7:0] | EnhancedBluecolorcomponent |
| pixelvalid | ValidsignalforenhancedRGBpixel | | 0 |
| SIFTAffineinvariant/SIFT |
| --- |
| unarytraining:0.335(0.038)0.321(0.018)<br>validation:0.346(0.027)0.337(0.015)<br>testing:0.371(0.011)0.332(0.024) |
| +learningtraining:0.277(0.024)0.286(0.024)<br>validation:0.325(0.020)0.300(0.020)<br>testing:0.371(0.011)0.302(0.016) | | | higher-ordertraining:0.233(0.047)0.205(0.043)<br>validation:0.223(0.025)0.254(0.035)<br>testing:0.289(0.045)0.294(0.034) |
| --- |
| +learningtraining:0.254(0.046)0.211(0.036)<br>validation:0.224(0.025)0.234(0.035)<br>testing:0.289(0.045)0.233(0.034) | | 1 |
| SIFTAffineinvariant/SIFT |
| --- |
| unarytraining:0.335(0.038)0.321(0.018)<br>validation:0.346(0.027)0.337(0.015)<br>testing:0.371(0.011)0.332(0.024) |
| +learningtraining:0.277(0.024)0.286(0.024)<br>validation:0.325(0.020)0.300(0.020)<br>testing:0.371(0.011)0.302(0.016) | | | Trainingset(featureset) | Accuracy | Precision | Recall | F-score1 |
| --- | --- | --- | --- | --- |
| Train-set+Dev-set(baseline) | 0.906 | 0.911 | 0.906 | 0.903 |
| Train-set+Dev-set(extended) | 0.928 | 0.937 | 0.929 | 0.930 |
| Train-set+Dev-set+AL(baseline) | 0.898 | 0.911 | 0.898 | 0.898 |
| Train-set+Dev-set+AL(extended) | 0.932 | 0.945 | 0.932 | 0.935 | | 0 |
| SIFTAffineinvariant/SIFT |
| --- |
| unarytraining:0.335(0.038)0.321(0.018)<br>validation:0.346(0.027)0.337(0.015)<br>testing:0.371(0.011)0.332(0.024) | | | +learningtraining:0.277(0.024)0.286(0.024)<br>validation:0.325(0.020)0.300(0.020)<br>testing:0.371(0.011)0.302(0.016) |
| --- |
| higher-ordertraining:0.233(0.047)0.205(0.043)<br>validation:0.223(0.025)0.254(0.035)<br>testing:0.289(0.045)0.294(0.034) |
| +learningtraining:0.254(0.046)0.211(0.036)<br>validation:0.224(0.025)0.234(0.035)<br>testing:0.289(0.045)0.233(0.034) | | 1 |
| SIFTAffineinvariant/SIFT |
| --- |
| unarytraining:0.335(0.038)0.321(0.018)<br>validation:0.346(0.027)0.337(0.015)<br>testing:0.371(0.011)0.332(0.024) | | | Train-set+Dev-set(extended) | 0.928 | 0.937 | 0.929 | 0.930 |
| --- | --- | --- | --- | --- |
| Train-set+Dev-set+AL(baseline) | 0.898 | 0.911 | 0.898 | 0.898 |
| Train-set+Dev-set+AL(extended) | 0.932 | 0.945 | 0.932 | 0.935 | | 0 |
| NumberofWorker | Time(minutes) | Scalability |
| --- | --- | --- |
| 1 | 4841 | 1 |
| 2 | 3039 | 1.59 |
| 4 | 1644 | 2.84 |
| 8 | 850 | 5.70 | | | 16 | 430 | 11.26 |
| --- | --- | --- |
| 32 | 333 | 14.56 | | 1 |
| NumberofWorker | Time(minutes) | Scalability |
| --- | --- | --- |
| 1 | 4841 | 1 |
| 2 | 3039 | 1.59 |
| 4 | 1644 | 2.84 |
| 8 | 850 | 5.70 | | | TotalnumberofJobs | 6064 |
| --- | --- |
| Traceduration(s) | 35032 |
| Averagenumberoftasksperjob | 26.31 |
| Minimumtaskduration(s) | 12.8 |
| Maximumtaskduration(s) | 22919.3 |
| Averagetaskduration(s) | 1179.7 | | 0 |
| NumberofWorker | Time(minutes) | Scalability |
| --- | --- | --- |
| 1 | 4841 | 1 |
| 2 | 3039 | 1.59 |
| 4 | 1644 | 2.84 | | | 8 | 850 | 5.70 |
| --- | --- | --- |
| 16 | 430 | 11.26 |
| 32 | 333 | 14.56 | | 1 |
| NumberofWorker | Time(minutes) | Scalability |
| --- | --- | --- |
| 1 | 4841 | 1 |
| 2 | 3039 | 1.59 |
| 4 | 1644 | 2.84 | | | Minimumtaskduration(s) | 12.8 |
| --- | --- |
| Maximumtaskduration(s) | 22919.3 |
| Averagetaskduration(s) | 1179.7 | | 0 |
| Dataset | Label |
| --- | --- |
| F | bully |
| F+ | bully |
| T | racism |
| T+ | racism | | | T | sexism |
| --- | --- |
| T+ | sexism |
| W | Attack |
| W+ | Attack | | 1 |
| Dataset | Label |
| --- | --- |
| F | bully |
| F+ | bully |
| T | racism |
| T+ | racism | | | Dataset | Label | P | R | F1 | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| M1 | M2 | M3 | M4 | M1 | M2 | M3 | M4 | M1 | M2 | M3 | M4 | | |
| F+ | bully | 0.93 | 0.91 | 0.91 | 0.90 | 0.90 | 0.85 | 0.81 | 0.91 | 0.91 | 0.88 | 0.86 | 0.91 |
| T+ | racism<br>sexism | 0.93<br>0.92 | 0.91<br>0.84 | 0.92<br>0.88 | 0.90<br>0.88 | 0.94<br>0.92 | 0.80<br>0.93 | 0.95<br>0.94 | 0.96<br>0.92 | 0.93<br>0.92 | 0.85<br>0.88 | 0.93<br>0.92 | 0.93<br>0.90 |
| W+ | Attack | 0.92 | 0.70 | 0.90 | 0.87 | 0.83 | 0.54 | 0.81 | 0.86 | 0.88 | 0.61 | 0.85 | 0.87 | | 0 |
| Dataset | Label |
| --- | --- |
| F | bully |
| F+ | bully |
| T | racism |
| T+ | racism | | | T | sexism |
| --- | --- |
| T+ | sexism |
| W | Attack |
| W+ | Attack | | 1 |
| Dataset | Label |
| --- | --- |
| F | bully |
| F+ | bully |
| T | racism |
| T+ | racism | | | T+ | racism<br>sexism | 0.93<br>0.92 | 0.91<br>0.84 | 0.92<br>0.88 | 0.90<br>0.88 | 0.94<br>0.92 | 0.80<br>0.93 | 0.95<br>0.94 | 0.96<br>0.92 | 0.93<br>0.92 | 0.85<br>0.88 | 0.93<br>0.92 | 0.93<br>0.90 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| W+ | Attack | 0.92 | 0.70 | 0.90 | 0.87 | 0.83 | 0.54 | 0.81 | 0.86 | 0.88 | 0.61 | 0.85 | 0.87 | | 0 |
| 97.45 | 97.55 |
| --- | --- |
| 95.02 | n/a |
| 93.35<br>91.42 | 94.67<br>92.90 | | | 0.247 | 0.233 |
| --- | --- |
| 81.8 | 86.2 | | 1 |
| 97.45 | 97.55 |
| --- | --- |
| 95.02 | n/a |
| 93.35<br>91.42 | 94.67<br>92.90 | | | 97.88 | 97.85 |
| --- | --- |
| 97.59 | 97.13 |
| 94.51<br>92.60 | 94.46<br>92.57 |
| 0.236 | 0.239 |
| 84.6 | 84.2 | | 0 |
| 97.45 | 97.55 |
| --- | --- |
| 95.02 | n/a |
| 93.35<br>91.42 | 94.67<br>92.90 | | | 0.247 | 0.233 |
| --- | --- |
| 81.8 | 86.2 | | 1 |
| 97.45 | 97.55 |
| --- | --- |
| 95.02 | n/a |
| 93.35<br>91.42 | 94.67<br>92.90 | | | 94.51<br>92.60 | 94.46<br>92.57 |
| --- | --- |
| 0.236 | 0.239 |
| 84.6 | 84.2 | | 0 |
| Type |
| --- |
| string |
| string |
| long | | | double |
| --- |
| double | | 1 |
| Type |
| --- |
| string |
| string |
| long | | | GOPSize | Codingstructure |
| --- | --- |
| 1 | IPPPPPPPPPPPPPPPPP... |
| 2 | IBPBPBPBPBPBPBPBPB... |
| 4 | IBBBPBBBPBBBPBBBPB... |
| 8 | IBBBBBBBPBBBBBBBPB... |
| 16 | IBBBBBBBBBBBBBBBPB... | | 0 |
| Type |
| --- |
| string | | | string |
| --- |
| long |
| double |
| double | | 1 |
| Type |
| --- |
| string | | | 4 | IBBBPBBBPBBBPBBBPB... |
| --- | --- |
| 8 | IBBBBBBBPBBBBBBBPB... |
| 16 | IBBBBBBBBBBBBBBBPB... | | 0 |
| Priority | Tx | Coin | SenderAddress | PriorityPoint |
| --- | --- | --- | --- | --- |
| 1 | txi | coini | addi | max |
| 2 | txj | coinj | addj | ... | | | 3 | txk | coink | addk | ... |
| --- | --- | --- | --- | --- |
| ... | ... | ... | ... | ... |
| n | txm | coinm | addm | min | | 1 |
| Priority | Tx | Coin | SenderAddress | PriorityPoint |
| --- | --- | --- | --- | --- |
| 1 | txi | coini | addi | max |
| 2 | txj | coinj | addj | ... | | | n | \|E\| | OPTbound | Random | Distributed<br>Greedy | Centralized<br>Greedy |
| --- | --- | --- | --- | --- | --- |
| 1000 | 5000 | 5000 | 3950 | 4837 | 4832 |
| 1000 | 10000 | 10000 | 6330 | 7625 | 7647 |
| 1000 | 20000 | 10000 | 8655 | 9677 | 9727 |
| 500 | 5000 | 5000 | 3951 | 4626 | 4628 |
| 500 | 10000 | 10000 | 6305 | 7277 | 7296 |
| 500 | 20000 | 10000 | 8640 | 9443 | 9470 |
| 2000 | 5000 | 5000 | 3961 | 4953 | 4954 |
| 2000 | 10000 | 10000 | 6345 | 8047 | 8068 |
| 2000 | 20000 | 10000 | 8665 | 9908 | 9959 | | 0 |
| Priority | Tx | Coin | SenderAddress | PriorityPoint |
| --- | --- | --- | --- | --- |
| 1 | txi | coini | addi | max |
| 2 | txj | coinj | addj | ... |
| 3 | txk | coink | addk | ... | | | ... | ... | ... | ... | ... |
| --- | --- | --- | --- | --- |
| n | txm | coinm | addm | min | | 1 |
| Priority | Tx | Coin | SenderAddress | PriorityPoint |
| --- | --- | --- | --- | --- |
| 1 | txi | coini | addi | max |
| 2 | txj | coinj | addj | ... |
| 3 | txk | coink | addk | ... | | | 500 | 10000 | 10000 | 6305 | 7277 | 7296 |
| --- | --- | --- | --- | --- | --- |
| 500 | 20000 | 10000 | 8640 | 9443 | 9470 |
| 2000 | 5000 | 5000 | 3961 | 4953 | 4954 |
| 2000 | 10000 | 10000 | 6345 | 8047 | 8068 |
| 2000 | 20000 | 10000 | 8665 | 9908 | 9959 | | 0 |
| R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.0001 | 0.000186 | 2 | 0.235 |
| 0.0002 | 0.000189 | 2 | 0.234 |
| 0.0003 | 0.000193 | 2 | 0.218 |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | 3 | 0.266 |
| 0.0009 | 0.000167 | 4 | 0.313 | | | 0.001 | 0.000167 | 4 | 0.312 |
| --- | --- | --- | --- |
| 0.002 | 0.000156 | 2 | 0.219 |
| 0.003 | 0.000159 | 2 | 0.234 |
| 0.004 | 0.000207 | 2 | 0.203 | | 1 |
| R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.0001 | 0.000186 | 2 | 0.235 |
| 0.0002 | 0.000189 | 2 | 0.234 |
| 0.0003 | 0.000193 | 2 | 0.218 |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | 3 | 0.266 |
| 0.0009 | 0.000167 | 4 | 0.313 | | | R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.00001 | 0.000305 | 12 | 10.953 |
| 0.00002 | 0.000305 | 13 | 10.656 |
| 0.00003 | 0.000305 | 12 | 10.516 |
| 0.00004 | 0.000305 | 12 | 11.703 |
| 0.00005 | 0.000305 | 13 | 11.610 |
| 0.00006 | 0.000305 | 13 | 11.547 |
| 0.00007 | 0.000305 | 13 | 11.672 |
| 0.00008 | 0.000305 | 13 | 11.813 |
| 0.00009 | 0.000305 | 13 | 11.813 |
| 0.0001 | 0.000305 | 13 | 11.890 |
| 0.0002 | 0.000305 | 14 | 13.110 |
| 0.0003 | 0.000306 | 14 | 13.110 |
| 0.0004 | 0.000308 | 16 | 15.407 |
| 0.0005 | 0.000310 | 24 | 22.844 |
| 0.0006 | 0.000312 | 15 | 14.250 |
| 0.0007 | 0.000315 | 15 | 14.328 |
| 0.0008 | 0.000319 | 32 | 30.563 |
| 0.0009 | 0.000322 | 32 | 30.563 |
| 0.001 | 0.000326 | 30 | 29.265 |
| 0.002 | 0.000390 | 12 | 12.140 |
| 0.003 | 0.000517 | 11 | 11.657 | | 0 |
| R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.0001 | 0.000186 | 2 | 0.235 |
| 0.0002 | 0.000189 | 2 | 0.234 |
| 0.0003 | 0.000193 | 2 | 0.218 |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | 3 | 0.266 |
| 0.0009 | 0.000167 | 4 | 0.313 | | | 0.001 | 0.000167 | 4 | 0.312 |
| --- | --- | --- | --- |
| 0.002 | 0.000156 | 2 | 0.219 |
| 0.003 | 0.000159 | 2 | 0.234 |
| 0.004 | 0.000207 | 2 | 0.203 | | 1 |
| R | Optimalvalue | iter | CPU |
| --- | --- | --- | --- |
| 0.0001 | 0.000186 | 2 | 0.235 |
| 0.0002 | 0.000189 | 2 | 0.234 |
| 0.0003 | 0.000193 | 2 | 0.218 |
| 0.0004 | 0.000182 | 3 | 0.266 |
| 0.0005 | 0.000174 | 3 | 0.266 |
| 0.0006 | 0.000173 | 4 | 0.312 |
| 0.0007 | 0.000170 | 4 | 0.313 |
| 0.0008 | 0.000167 | 3 | 0.266 |
| 0.0009 | 0.000167 | 4 | 0.313 | | | 0.0008 | 0.000319 | 32 | 30.563 |
| --- | --- | --- | --- |
| 0.0009 | 0.000322 | 32 | 30.563 |
| 0.001 | 0.000326 | 30 | 29.265 |
| 0.002 | 0.000390 | 12 | 12.140 |
| 0.003 | 0.000517 | 11 | 11.657 | | 0 |
| Corp | Baseline | Tuned | Tag | Unsup |
| --- | --- | --- | --- | --- |
| Eu | 33.0±0.3 | 35.4±0.3 | 36.2±0.3 | 36.0±0.3 |
| Op | 27.9±0.6 | 28.1±0.6 | 30.5±0.6 | 30.2±0.6 |
| Wi | 15.3±0.4 | 15.4±0.4 | 16.9±0.4 | 16.0±0.4 |
| Corp | Baseline | Tuned | Tag | Unsup |
| Eu | 0.0001/0.0001 | 0.009/0.01 | -/0.3 | 0.3/- | | | Op | 0.0001/0.0001 | 0.0001/0.0001 | -/0.1 | 0.1/- |
| --- | --- | --- | --- | --- |
| Wi | 0.0001/0.004 | 0.0001/0.009 | -/0.01 | 0.01/- | | 1 |
| Corp | Baseline | Tuned | Tag | Unsup |
| --- | --- | --- | --- | --- |
| Eu | 33.0±0.3 | 35.4±0.3 | 36.2±0.3 | 36.0±0.3 |
| Op | 27.9±0.6 | 28.1±0.6 | 30.5±0.6 | 30.2±0.6 |
| Wi | 15.3±0.4 | 15.4±0.4 | 16.9±0.4 | 16.0±0.4 |
| Corp | Baseline | Tuned | Tag | Unsup |
| Eu | 0.0001/0.0001 | 0.009/0.01 | -/0.3 | 0.3/- | | | | Gradual | Sharp | | | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Video | #T | TP | FP | FN | P | R | F | #T | TP | FP | FN | P | R |
| LNA | 198 | 147 | 11 | 51 | 0.93 | 0.742 | 0.826 | 45 | 31 | 26 | 14 | 0.544 | 0.689 |
| NFC | 77 | 57 | 11 | 20 | 0.838 | 0.74 | 0.786 | 121 | 115 | 8 | 6 | 0.935 | 0.95 |
| NEC | 94 | 88 | 3 | 6 | 0.967 | 0.936 | 0.951 | 74 | 57 | 5 | 17 | 0.919 | 0.77 |
| HNA | 124 | 107 | 4 | 17 | 0.964 | 0.863 | 0.911 | 24 | 21 | 8 | 3 | 0.724 | 0.875 |
| 3PGC | 228 | 171 | 32 | 57 | 0.842 | 0.75 | 0.794 | 132 | 112 | 48 | 20 | 0.7 | 0.848 |
| CLE | 123 | 98 | 22 | 25 | 0.817 | 0.797 | 0.807 | 244 | 236 | 33 | 8 | 0.877 | 0.967 |
| CDC | 302 | 231 | 27 | 71 | 0.895 | 0.765 | 0.825 | 139 | 129 | 65 | 10 | 0.665 | 0.928 |
| 8NNE | 214 | 184 | 44 | 30 | 0.807 | 0.86 | 0.833 | 424 | 418 | 36 | 6 | 0.921 | 0.986 |
| CLE | 37 | 28 | 7 | 9 | 0.8 | 0.757 | 0.778 | 57 | 54 | 7 | 3 | 0.885 | 0.947 |
| 5PGC | 190 | 155 | 44 | 35 | 0.779 | 0.816 | 0.797 | 81 | 75 | 30 | 6 | 0.714 | 0.926 |
| MNE | 181 | 156 | 11 | 25 | 0.934 | 0.862 | 0.897 | 339 | 323 | 21 | 16 | 0.939 | 0.953 |
| CLE | 27 | 25 | 4 | 2 | 0.862 | 0.926 | 0.893 | 44 | 42 | 0 | 2 | 1 | 0.955 |
| 1NNE | 146 | 134 | 11 | 12 | 0.924 | 0.918 | 0.921 | 120 | 118 | 5 | 2 | 0.959 | 0.983 |
| Total | 1941 | 1581 | 231 | 360 | 0.873 | 0.815 | 0.843 | 1844 | 1731 | 292 | 113 | 0.856 | 0.939 | | 0 |
| Corp | Baseline | Tuned | Tag | Unsup |
| --- | --- | --- | --- | --- |
| Eu | 33.0±0.3 | 35.4±0.3 | 36.2±0.3 | 36.0±0.3 |
| Op | 27.9±0.6 | 28.1±0.6 | 30.5±0.6 | 30.2±0.6 |
| Wi | 15.3±0.4 | 15.4±0.4 | 16.9±0.4 | 16.0±0.4 |
| Corp | Baseline | Tuned | Tag | Unsup |
| Eu | 0.0001/0.0001 | 0.009/0.01 | -/0.3 | 0.3/- | | | Op | 0.0001/0.0001 | 0.0001/0.0001 | -/0.1 | 0.1/- |
| --- | --- | --- | --- | --- |
| Wi | 0.0001/0.004 | 0.0001/0.009 | -/0.01 | 0.01/- | | 1 |
| Corp | Baseline | Tuned | Tag | Unsup |
| --- | --- | --- | --- | --- |
| Eu | 33.0±0.3 | 35.4±0.3 | 36.2±0.3 | 36.0±0.3 |
| Op | 27.9±0.6 | 28.1±0.6 | 30.5±0.6 | 30.2±0.6 |
| Wi | 15.3±0.4 | 15.4±0.4 | 16.9±0.4 | 16.0±0.4 |
| Corp | Baseline | Tuned | Tag | Unsup |
| Eu | 0.0001/0.0001 | 0.009/0.01 | -/0.3 | 0.3/- | | | HNA | 124 | 107 | 4 | 17 | 0.964 | 0.863 | 0.911 | 24 | 21 | 8 | 3 | 0.724 | 0.875 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 3PGC | 228 | 171 | 32 | 57 | 0.842 | 0.75 | 0.794 | 132 | 112 | 48 | 20 | 0.7 | 0.848 |
| CLE | 123 | 98 | 22 | 25 | 0.817 | 0.797 | 0.807 | 244 | 236 | 33 | 8 | 0.877 | 0.967 |
| CDC | 302 | 231 | 27 | 71 | 0.895 | 0.765 | 0.825 | 139 | 129 | 65 | 10 | 0.665 | 0.928 |
| 8NNE | 214 | 184 | 44 | 30 | 0.807 | 0.86 | 0.833 | 424 | 418 | 36 | 6 | 0.921 | 0.986 |
| CLE | 37 | 28 | 7 | 9 | 0.8 | 0.757 | 0.778 | 57 | 54 | 7 | 3 | 0.885 | 0.947 |
| 5PGC | 190 | 155 | 44 | 35 | 0.779 | 0.816 | 0.797 | 81 | 75 | 30 | 6 | 0.714 | 0.926 |
| MNE | 181 | 156 | 11 | 25 | 0.934 | 0.862 | 0.897 | 339 | 323 | 21 | 16 | 0.939 | 0.953 |
| CLE | 27 | 25 | 4 | 2 | 0.862 | 0.926 | 0.893 | 44 | 42 | 0 | 2 | 1 | 0.955 |
| 1NNE | 146 | 134 | 11 | 12 | 0.924 | 0.918 | 0.921 | 120 | 118 | 5 | 2 | 0.959 | 0.983 |
| Total | 1941 | 1581 | 231 | 360 | 0.873 | 0.815 | 0.843 | 1844 | 1731 | 292 | 113 | 0.856 | 0.939 | | 0 |
| Network | #Predicted | #Matched | #Derivable | #Derived | Pr |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 294<br>156<br>167<br>172 | 29<br>31<br>32<br>39 | 155<br>102<br>109<br>112 | 38<br>40<br>40<br>41 | 0.098<br>0.198<br>0.191<br>0.226 |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 297<br>149<br>162<br>168 | 39<br>38<br>41<br>41 | 155<br>102<br>109<br>112 | 49<br>51<br>52<br>54 | 0.131<br>0.255<br>0.253<br>0.244 | | | PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 156<br>144<br>165<br>128 | 41<br>31<br>43<br>39 | 155<br>102<br>109<br>112 | 56<br>59<br>60<br>59 | 0.263<br>0.215<br>0.260<br>0.304 |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 414<br>221<br>248<br>253 | 34<br>32<br>37<br>46 | 155<br>102<br>109<br>112 | 41<br>44<br>45<br>45 | 0.082<br>0.144<br>0.149<br>0.181 | | 1 |
| Network | #Predicted | #Matched | #Derivable | #Derived | Pr |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 294<br>156<br>167<br>172 | 29<br>31<br>32<br>39 | 155<br>102<br>109<br>112 | 38<br>40<br>40<br>41 | 0.098<br>0.198<br>0.191<br>0.226 |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 297<br>149<br>162<br>168 | 39<br>38<br>41<br>41 | 155<br>102<br>109<br>112 | 49<br>51<br>52<br>54 | 0.131<br>0.255<br>0.253<br>0.244 | | | Network | #Predicted | Size | #Matched | #Derivable | #Derived | Pr |
| --- | --- | --- | --- | --- | --- | --- |
| P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 294<br>338<br>102<br>138<br>261<br>429 | 7.96<br>8.66<br>15.88<br>17.14<br>10.52<br>13.01 | 29<br>19<br>29<br>33<br>42<br>57 | 155<br>164<br>119<br>122<br>158<br>164 | 38<br>23<br>38<br>44<br>54<br>56 | 0.098<br>0.056<br>0.284<br>0.239<br>0.161<br>0.133 |
| P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 297<br>342<br>136<br>141<br>296<br>484 | 7.94<br>8.34<br>9.46<br>7.44<br>9.98<br>8.72 | 39<br>25<br>41<br>48<br>49<br>81 | 155<br>164<br>119<br>122<br>158<br>164 | 49<br>29<br>57<br>61<br>61<br>71 | 0.131<br>0.073<br>0.301<br>0.340<br>0.166<br>0.167 |
| P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 156<br>306<br>136<br>252<br>127<br>429 | 11.42<br>14.39<br>12.44<br>8.91<br>11.66<br>9.80 | 41<br>33<br>36<br>51<br>45<br>80 | 155<br>164<br>119<br>122<br>158<br>164 | 56<br>41<br>48<br>63<br>60<br>66 | 0.263<br>0.108<br>0.265<br>0.202<br>0.354<br>0.186 |
| P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 414<br>510<br>111<br>131<br>269<br>419 | 5.98<br>6.68<br>10.17<br>8.90<br>7.49<br>7.61 | 34<br>28<br>39<br>43<br>55<br>79 | 155<br>164<br>119<br>122<br>158<br>164 | 41<br>34<br>54<br>60<br>67<br>74 | 0.082<br>0.055<br>0.351<br>0.328<br>0.204<br>0.189 | | 0 |
| Network | #Predicted | #Matched | #Derivable | #Derived | Pr |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 294<br>156<br>167<br>172 | 29<br>31<br>32<br>39 | 155<br>102<br>109<br>112 | 38<br>40<br>40<br>41 | 0.098<br>0.198<br>0.191<br>0.226 | | | PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 297<br>149<br>162<br>168 | 39<br>38<br>41<br>41 | 155<br>102<br>109<br>112 | 49<br>51<br>52<br>54 | 0.131<br>0.255<br>0.253<br>0.244 |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 156<br>144<br>165<br>128 | 41<br>31<br>43<br>39 | 155<br>102<br>109<br>112 | 56<br>59<br>60<br>59 | 0.263<br>0.215<br>0.260<br>0.304 |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 414<br>221<br>248<br>253 | 34<br>32<br>37<br>46 | 155<br>102<br>109<br>112 | 41<br>44<br>45<br>45 | 0.082<br>0.144<br>0.149<br>0.181 | | 1 |
| Network | #Predicted | #Matched | #Derivable | #Derived | Pr |
| --- | --- | --- | --- | --- | --- |
| PhysicalP<br>FSW(P)<br>ICD(P)<br>TCSS(P) | 294<br>156<br>167<br>172 | 29<br>31<br>32<br>39 | 155<br>102<br>109<br>112 | 38<br>40<br>40<br>41 | 0.098<br>0.198<br>0.191<br>0.226 | | | P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 156<br>306<br>136<br>252<br>127<br>429 | 11.42<br>14.39<br>12.44<br>8.91<br>11.66<br>9.80 | 41<br>33<br>36<br>51<br>45<br>80 | 155<br>164<br>119<br>122<br>158<br>164 | 56<br>41<br>48<br>63<br>60<br>66 | 0.263<br>0.108<br>0.265<br>0.202<br>0.354<br>0.186 |
| --- | --- | --- | --- | --- | --- | --- |
| P<br>P+F<br>FSW(P+F)<br>ICD(P+F)<br>TCSS(P+F)<br>Consensus | 414<br>510<br>111<br>131<br>269<br>419 | 5.98<br>6.68<br>10.17<br>8.90<br>7.49<br>7.61 | 34<br>28<br>39<br>43<br>55<br>79 | 155<br>164<br>119<br>122<br>158<br>164 | 41<br>34<br>54<br>60<br>67<br>74 | 0.082<br>0.055<br>0.351<br>0.328<br>0.204<br>0.189 | | 0 |
| Pass | SE2 | SE3 | SE13 | SE15 | ALL |
| --- | --- | --- | --- | --- | --- |
| 1 | 71.6 | 70.3 | 67.0 | 72.5 | 70.3 |
| 2 | 71.9 | 70.2 | 67.1 | 72.8 | 70.4 |
| 3 | 72.2 | 70.5 | 67.2 | 72.6 | 70.6 | | | 4 | 72.1 | 70.4 | 67.2 | 72.4 | 70.5 |
| --- | --- | --- | --- | --- | --- |
| 5 | 72.0 | 70.4 | 67.1 | 71.5 | 70.3 | | 1 |
| Pass | SE2 | SE3 | SE13 | SE15 | ALL |
| --- | --- | --- | --- | --- | --- |
| 1 | 71.6 | 70.3 | 67.0 | 72.5 | 70.3 |
| 2 | 71.9 | 70.2 | 67.1 | 72.8 | 70.4 |
| 3 | 72.2 | 70.5 | 67.2 | 72.6 | 70.6 | | | 1 | 100 | 0.5 | 35.137 |
| --- | --- | --- | --- |
| 1 | 100 | 0.5 | 82.415 |
| 1 | 100 | 0.5 | 100.414 |
| 2 | 100 | 0.5 | 35.301 |
| 3 | 100 | 0.5 | 35.564 |
| 1 | 200 | 0.5 | 36.480 |
| 1 | 300 | 0.5 | 37.645 |
| 1 | 400 | 0.5 | 41.113 |
| 1 | 100 | 0 | 33.621 |
| 1 | 100 | 0.1 | 34.934 | | 0 |
| Pass | SE2 | SE3 | SE13 | SE15 | ALL |
| --- | --- | --- | --- | --- | --- |
| 1 | 71.6 | 70.3 | 67.0 | 72.5 | 70.3 |
| 2 | 71.9 | 70.2 | 67.1 | 72.8 | 70.4 |
| 3 | 72.2 | 70.5 | 67.2 | 72.6 | 70.6 | | | 4 | 72.1 | 70.4 | 67.2 | 72.4 | 70.5 |
| --- | --- | --- | --- | --- | --- |
| 5 | 72.0 | 70.4 | 67.1 | 71.5 | 70.3 | | 1 |
| Pass | SE2 | SE3 | SE13 | SE15 | ALL |
| --- | --- | --- | --- | --- | --- |
| 1 | 71.6 | 70.3 | 67.0 | 72.5 | 70.3 |
| 2 | 71.9 | 70.2 | 67.1 | 72.8 | 70.4 |
| 3 | 72.2 | 70.5 | 67.2 | 72.6 | 70.6 | | | 1 | 100 | 0 | 33.621 |
| --- | --- | --- | --- |
| 1 | 100 | 0.1 | 34.934 | | 0 |
| Dataset | Ohsumed(M=105) | 20-Newsgroup(M=36) | Reuters(M=40) |
| --- | --- | --- | --- |
| OurModel | 1.85(0.01) | 1.14(0.01) | 1.17(0.01) | | | LDA | 10.55(0.37) | 6.42(0.28) | 4.97(0.13) |
| --- | --- | --- | --- |
| LDAbackground | 12.49(0.31) | 5.27(0.12) | 4.18(0.74) |
| STC | 10.22(0.50) | 2.67(0.06) | 3.01(0.20) | | 1 |
| Dataset | Ohsumed(M=105) | 20-Newsgroup(M=36) | Reuters(M=40) |
| --- | --- | --- | --- |
| OurModel | 1.85(0.01) | 1.14(0.01) | 1.17(0.01) | | | Dataset | logβ | α | τ |
| --- | --- | --- | --- |
| N=50 | | | |
| SL | 2.39(0.17) | 0.31(0.02) | 0.98(0.02) |
| BP | 0.93(0.12) | 0.08(0.04) | 0.92(0.00) |
| MN | 1.66(0.16) | 0.03(0.01) | 0.72(0.04) |
| WK | −0.21(0.81) | 0.00(0.00) | 0.40(0.19) |
| N=5000 | | | |
| SL | 2.39(0.01) | 0.31(0.01) | 0.98(0.00) |
| BP | 0.96(0.02) | 0.08(0.00) | 0.92(0.00) |
| MN | 1.69(0.03) | 0.02(0.00) | 0.74(0.01) |
| WK | 0.39(0.22) | 0.00(0.00) | 0.60(0.01) | | 0 |
| Dataset | Ohsumed(M=105) | 20-Newsgroup(M=36) | Reuters(M=40) |
| --- | --- | --- | --- |
| OurModel | 1.85(0.01) | 1.14(0.01) | 1.17(0.01) | | | LDA | 10.55(0.37) | 6.42(0.28) | 4.97(0.13) |
| --- | --- | --- | --- |
| LDAbackground | 12.49(0.31) | 5.27(0.12) | 4.18(0.74) |
| STC | 10.22(0.50) | 2.67(0.06) | 3.01(0.20) | | 1 |
| Dataset | Ohsumed(M=105) | 20-Newsgroup(M=36) | Reuters(M=40) |
| --- | --- | --- | --- |
| OurModel | 1.85(0.01) | 1.14(0.01) | 1.17(0.01) | | | WK | −0.21(0.81) | 0.00(0.00) | 0.40(0.19) |
| --- | --- | --- | --- |
| N=5000 | | | |
| SL | 2.39(0.01) | 0.31(0.01) | 0.98(0.00) |
| BP | 0.96(0.02) | 0.08(0.00) | 0.92(0.00) |
| MN | 1.69(0.03) | 0.02(0.00) | 0.74(0.01) |
| WK | 0.39(0.22) | 0.00(0.00) | 0.60(0.01) | | 0 |
| | Firm1 | Firm2 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | F=10,H=1,K=25 | F=10,H=1,K=25 | | | | | | | | |
| Period(t) | at | bt | yt | xt | ht | qt | yt | xt | ht | qt |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>1<br>0<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 | | | | Π=−55.44 | Π=−55.44 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>0<br>1<br>0<br>1<br>0 | 6.33<br>0.00<br>9.33<br>0.00<br>12.66<br>0.00 | 3.00<br>0.00<br>5.33<br>0.00<br>5.33<br>0.00 | 3.34<br>3.00<br>4.00<br>5.33<br>7.33<br>5.33 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 |
| | Π=−23.77 | Π=−15.01 | | | | | | | | |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>1<br>0<br>1<br>1<br>1 | 8.33<br>5.89<br>0.00<br>11.31<br>12.67<br>7.45 | 4.99<br>5.44<br>2.10<br>0.00<br>5.33<br>0.00 | 3.34<br>5.44<br>3.34<br>13.41<br>7.34<br>12.78 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>3.37<br>12.67<br>7.56<br>7.34 | 5.44<br>2.10<br>0.00<br>5.33<br>0.00<br>0.00 | 5.44<br>3.34<br>5.47<br>7.34<br>12.89<br>7.34 |
| | Π=−53.17 | Π=−45.65 | | | | | | | | | | 1 |
| | Firm1 | Firm2 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | F=10,H=1,K=25 | F=10,H=1,K=25 | | | | | | | | |
| Period(t) | at | bt | yt | xt | ht | qt | yt | xt | ht | qt |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>1<br>0<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 | | | | Firm1 | Firm2 | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | F=10,H=1,K=25 | F=10,H=1,K=25 | | | | | | | | |
| Period(t) | at | bt | yt | xt | ht | qt | yt | xt | ht | qt |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 0<br>0<br>0<br>0<br>0<br>0 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 1<br>0<br>1<br>1<br>1<br>1 | 10.888<br>0.000<br>5.469<br>13.409<br>12.889<br>12.769 | 5.449<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 5.439<br>5.449<br>5.469<br>13.409<br>12.889<br>12.769 |
| | Π=0 | Π=155.119 | | | | | | | | | | 0 |
| | Firm1 | Firm2 |
| --- | --- | --- |
| | F=10,H=1,K=25 | F=10,H=1,K=25 | | | Period(t) | at | bt | yt | xt | ht | qt | yt | xt | ht | qt |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>1<br>0<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 |
| | Π=−55.44 | Π=−55.44 | | | | | | | | |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>0<br>1<br>0<br>1<br>0 | 6.33<br>0.00<br>9.33<br>0.00<br>12.66<br>0.00 | 3.00<br>0.00<br>5.33<br>0.00<br>5.33<br>0.00 | 3.34<br>3.00<br>4.00<br>5.33<br>7.33<br>5.33 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>5.47<br>13.41<br>12.89<br>12.78 | 5.44<br>0.00<br>0.00<br>0.00<br>0.00<br>0.00 | 5.44<br>5.44<br>5.47<br>13.41<br>12.89<br>12.78 |
| | Π=−23.77 | Π=−15.01 | | | | | | | | |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 1<br>1<br>0<br>1<br>1<br>1 | 8.33<br>5.89<br>0.00<br>11.31<br>12.67<br>7.45 | 4.99<br>5.44<br>2.10<br>0.00<br>5.33<br>0.00 | 3.34<br>5.44<br>3.34<br>13.41<br>7.34<br>12.78 | 1<br>0<br>1<br>1<br>1<br>1 | 10.88<br>0.00<br>3.37<br>12.67<br>7.56<br>7.34 | 5.44<br>2.10<br>0.00<br>5.33<br>0.00<br>0.00 | 5.44<br>3.34<br>5.47<br>7.34<br>12.89<br>7.34 |
| | Π=−53.17 | Π=−45.65 | | | | | | | | | | 1 |
| | Firm1 | Firm2 |
| --- | --- | --- |
| | F=10,H=1,K=25 | F=10,H=1,K=25 | | | Period(t) | at | bt | yt | xt | ht | qt | yt | xt | ht | qt |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1<br>2<br>3<br>4<br>5<br>6 | 10<br>10<br>10<br>10<br>10<br>10 | 1<br>1<br>1<br>0.5<br>0.5<br>0.5 | 0<br>0<br>0<br>0<br>0<br>0 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 0.000<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 1<br>0<br>1<br>1<br>1<br>1 | 10.888<br>0.000<br>5.469<br>13.409<br>12.889<br>12.769 | 5.449<br>0.000<br>0.000<br>0.000<br>0.000<br>0.000 | 5.439<br>5.449<br>5.469<br>13.409<br>12.889<br>12.769 |
| | Π=0 | Π=155.119 | | | | | | | | | | 0 |
| | | MSCOCO | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | Measure | Beamsize | Layers | BLEU | METEOR | TER | | | Seq-to-Seq | - | 10 | 2 | 16.5 | 15.4 | 67.1 |
| --- | --- | --- | --- | --- | --- | --- |
| WithAttention | - | 10 | 2 | 18.6 | 16.8 | 63.0 |
| Seq-toSeq | - | 10 | 4 | 28.9 | 23.2 | 56.3 |
| Bi-directional | - | 10 | 4 | 32.8 | 24.9 | 53.7 |
| WithAttention | - | 10 | 4 | 33.4 | 25.2 | 53.8 |
| ResidualLSTM | - | 10 | 4 | 37.0 | 27.0 | 51.6 |
| Unsupervised | - | Nobeam | 1,2 | 12.8 | 17.5 | 78.8 |
| VAE-S | Avg | Nobeam | 1,2 | 7.0 | 14.0 | 82.3 |
| | | | | | | |
| | | | | | | |
| VAE-SVG(our) | Avg | Nobeam | 1,2 | 39.2 | 29.2 | 43.6 |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| VAE-SVG-eq(our) | Avg | Nobeam | 1,2 | 37.3 | 28.5 | 45.1 |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | | | 1 |
| | | MSCOCO | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | Measure | Beamsize | Layers | BLEU | METEOR | TER | | | Model | BLEU-1 | BLEU-2 | BLEU-3 | BLEU-4 | METEOR | | | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| c5 | c40 | c5 | c40 | c5 | c40 | c5 | c40 | c5 | c40 | c5 | |
| SCN-LSTM | 0.740 | 0.917 | 0.575 | 0.839 | 0.436 | 0.739 | 0.331 | 0.631 | 0.257 | 0.348 | 0.543 |
| ATT | 0.731 | 0.900 | 0.565 | 0.815 | 0.424 | 0.709 | 0.316 | 0.599 | 0.250 | 0.335 | 0.535 |
| OV | 0.713 | 0.895 | 0.542 | 0.802 | 0.407 | 0.694 | 0.309 | 0.587 | 0.254 | 0.346 | 0.530 |
| MSRCap | 0.715 | 0.907 | 0.543 | 0.819 | 0.407 | 0.710 | 0.308 | 0.601 | 0.248 | 0.339 | 0.526 | | 0 |
| | | MSCOCO | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | Measure | Beamsize | Layers | BLEU | METEOR | TER |
| Seq-to-Seq | - | 10 | 2 | 16.5 | 15.4 | 67.1 |
| WithAttention | - | 10 | 2 | 18.6 | 16.8 | 63.0 |
| Seq-toSeq | - | 10 | 4 | 28.9 | 23.2 | 56.3 |
| Bi-directional | - | 10 | 4 | 32.8 | 24.9 | 53.7 |
| WithAttention | - | 10 | 4 | 33.4 | 25.2 | 53.8 |
| ResidualLSTM | - | 10 | 4 | 37.0 | 27.0 | 51.6 |
| Unsupervised | - | Nobeam | 1,2 | 12.8 | 17.5 | 78.8 |
| VAE-S | Avg | Nobeam | 1,2 | 7.0 | 14.0 | 82.3 |
| | | | | | | |
| | | | | | | |
| VAE-SVG(our) | Avg | Nobeam | 1,2 | 39.2 | 29.2 | 43.6 | | | | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| VAE-SVG-eq(our) | Avg | Nobeam | 1,2 | 37.3 | 28.5 | 45.1 |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | |
| | | | | | | | | 1 |
| | | MSCOCO | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| Model | Measure | Beamsize | Layers | BLEU | METEOR | TER |
| Seq-to-Seq | - | 10 | 2 | 16.5 | 15.4 | 67.1 |
| WithAttention | - | 10 | 2 | 18.6 | 16.8 | 63.0 |
| Seq-toSeq | - | 10 | 4 | 28.9 | 23.2 | 56.3 |
| Bi-directional | - | 10 | 4 | 32.8 | 24.9 | 53.7 |
| WithAttention | - | 10 | 4 | 33.4 | 25.2 | 53.8 |
| ResidualLSTM | - | 10 | 4 | 37.0 | 27.0 | 51.6 |
| Unsupervised | - | Nobeam | 1,2 | 12.8 | 17.5 | 78.8 |
| VAE-S | Avg | Nobeam | 1,2 | 7.0 | 14.0 | 82.3 |
| | | | | | | |
| | | | | | | |
| VAE-SVG(our) | Avg | Nobeam | 1,2 | 39.2 | 29.2 | 43.6 | | | OV | 0.713 | 0.895 | 0.542 | 0.802 | 0.407 | 0.694 | 0.309 | 0.587 | 0.254 | 0.346 | 0.530 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| MSRCap | 0.715 | 0.907 | 0.543 | 0.819 | 0.407 | 0.710 | 0.308 | 0.601 | 0.248 | 0.339 | 0.526 | | 0 |
| Model | Sensitivity | Specificity | PPV | Accuracy |
| --- | --- | --- | --- | --- |
| K−means | 0.6555 | 0.7185 | 0.4473 | 0.6997 |
| LOF | 0.0909 | 0.9519 | 0.5820 | 0.6959 | | | OC-SVM | 1.0 | 0.9015 | 0.8227 | 0.9308 |
| --- | --- | --- | --- | --- |
| WeightedOC-SVM | 1.0 | 0.9481 | 0.8975 | 0.9635 | | 1 |
| Model | Sensitivity | Specificity | PPV | Accuracy |
| --- | --- | --- | --- | --- |
| K−means | 0.6555 | 0.7185 | 0.4473 | 0.6997 |
| LOF | 0.0909 | 0.9519 | 0.5820 | 0.6959 | | | Model | Sensitivity | Specificity | Accuracy |
| --- | --- | --- | --- |
| SVM(best) | 0.8259 | 0.8324 | 0.8291 |
| LR(best) | 0.7121 | 0.6879 | 0.6991 |
| RF(best) | 0.6901 | 0.6850 | 0.6861 |
| RNNs(best) | 0.9886 | 0.9836 | 0.9763 | | 0 |
| Model | Sensitivity | Specificity | PPV | Accuracy |
| --- | --- | --- | --- | --- |
| K−means | 0.6555 | 0.7185 | 0.4473 | 0.6997 | | | LOF | 0.0909 | 0.9519 | 0.5820 | 0.6959 |
| --- | --- | --- | --- | --- |
| OC-SVM | 1.0 | 0.9015 | 0.8227 | 0.9308 |
| WeightedOC-SVM | 1.0 | 0.9481 | 0.8975 | 0.9635 | | 1 |
| Model | Sensitivity | Specificity | PPV | Accuracy |
| --- | --- | --- | --- | --- |
| K−means | 0.6555 | 0.7185 | 0.4473 | 0.6997 | | | LR(best) | 0.7121 | 0.6879 | 0.6991 |
| --- | --- | --- | --- |
| RF(best) | 0.6901 | 0.6850 | 0.6861 |
| RNNs(best) | 0.9886 | 0.9836 | 0.9763 | | 0 |
| Model | PredictionType | TrainMSE | TestMSE |
| --- | --- | --- | --- |
| ANFIS | Pointpredictions | −9<br>3.84×10 | 7.857 | | | SVR | Pointpredictions | −2<br>2.31×10 | 1.22 |
| --- | --- | --- | --- |
| NeuralNetA | Pointpredictions | −2<br>7.31×10 | 10.378 |
| NeuralNetB | Pointpredictions | −1<br>5.38×10 | 0.810 |
| FBL | Predictivedistribution | −5<br>5.93×10 | −5<br>7.64×10 | | 1 |
| Model | PredictionType | TrainMSE | TestMSE |
| --- | --- | --- | --- |
| ANFIS | Pointpredictions | −9<br>3.84×10 | 7.857 | | | Model | Accuracy | Precision<br>(0,1) | Recall<br>(0,1) | F-score<br>(0,1) |
| --- | --- | --- | --- | --- |
| NB | 94.11 | 95,92.32 | 96,90 | 95.5,91.4 |
| SVM | 93.31 | 97.13,87.18 | 92.4,94.91 | 94.74,90.90 |
| E-RNN | 95.4 | 98.42,90.37 | 94.47,97.12 | 96.40,93.62 |
| CNN-RNN | 96.01 | 96.22,95.56 | 97.69,92.81 | 96.94,94.16 |
| EC-RNN | 98.1 | 99.0,96.07 | 97.85,98.27 | 98.42,97.16 | | 0 |
| Model | PredictionType | TrainMSE | TestMSE |
| --- | --- | --- | --- |
| ANFIS | Pointpredictions | −9<br>3.84×10 | 7.857 |
| SVR | Pointpredictions | −2<br>2.31×10 | 1.22 | | | NeuralNetA | Pointpredictions | −2<br>7.31×10 | 10.378 |
| --- | --- | --- | --- |
| NeuralNetB | Pointpredictions | −1<br>5.38×10 | 0.810 |
| FBL | Predictivedistribution | −5<br>5.93×10 | −5<br>7.64×10 | | 1 |
| Model | PredictionType | TrainMSE | TestMSE |
| --- | --- | --- | --- |
| ANFIS | Pointpredictions | −9<br>3.84×10 | 7.857 |
| SVR | Pointpredictions | −2<br>2.31×10 | 1.22 | | | E-RNN | 95.4 | 98.42,90.37 | 94.47,97.12 | 96.40,93.62 |
| --- | --- | --- | --- | --- |
| CNN-RNN | 96.01 | 96.22,95.56 | 97.69,92.81 | 96.94,94.16 |
| EC-RNN | 98.1 | 99.0,96.07 | 97.85,98.27 | 98.42,97.16 | | 0 |
| Featureindex | Description |
| --- | --- |
| 1 | Whetherthecandidateisinthesamesentencewiththeconnective |
| 2 | Connectiveword |
| 3 | Down-caseconnectiveword | | | 4 | Candidateword |
| --- | --- |
| 5 | Beforeoraftertheconnectiveword |
| 6 | Connectivewhereinthesentence(beginning,middle,end) |
| 7 | 2,6 |
| 8 | Headtoconnectivethroughtheconstituenttree |
| 9 | Collapsedpathwithoutpart-of-speech |
| 10 | Thelengthof8 |
| 11 | Dependencypathfromargumenttoconnective |
| 12 | Typeofconnective | | 1 |
| Featureindex | Description |
| --- | --- |
| 1 | Whetherthecandidateisinthesamesentencewiththeconnective |
| 2 | Connectiveword |
| 3 | Down-caseconnectiveword | | | # | Features | Description |
| --- | --- | --- |
| 1 | TF | Termfrequencyofwintheshorttext |
| 2 | IDF | Inversedocumentfrequencyofwinthewholecollection |
| 3 | SF | Numberofsentencesintheshorttextthatcontainw |
| 4 | First | Whetherwexistsinthefirstsentence |
| 5 | Last | Whetherwexistsinthelastsentence |
| 6 | NE | Whetherwisanamedentity(NE) |
| 7 | NEFirst | WhetherwisNEinthefirstsentence |
| 8 | NELast | WhetherwisNEinthelastsentence |
| 9 | POS | Partofspeechofw | | 0 |
| Featureindex | Description |
| --- | --- |
| 1 | Whetherthecandidateisinthesamesentencewiththeconnective |
| 2 | Connectiveword |
| 3 | Down-caseconnectiveword |
| 4 | Candidateword |
| 5 | Beforeoraftertheconnectiveword | | | 6 | Connectivewhereinthesentence(beginning,middle,end) |
| --- | --- |
| 7 | 2,6 |
| 8 | Headtoconnectivethroughtheconstituenttree |
| 9 | Collapsedpathwithoutpart-of-speech |
| 10 | Thelengthof8 |
| 11 | Dependencypathfromargumenttoconnective |
| 12 | Typeofconnective | | 1 |
| Featureindex | Description |
| --- | --- |
| 1 | Whetherthecandidateisinthesamesentencewiththeconnective |
| 2 | Connectiveword |
| 3 | Down-caseconnectiveword |
| 4 | Candidateword |
| 5 | Beforeoraftertheconnectiveword | | | 5 | Last | Whetherwexistsinthelastsentence |
| --- | --- | --- |
| 6 | NE | Whetherwisanamedentity(NE) |
| 7 | NEFirst | WhetherwisNEinthefirstsentence |
| 8 | NELast | WhetherwisNEinthelastsentence |
| 9 | POS | Partofspeechofw | | 0 |
| GraphModel | Community | SF | Random | Music |
| --- | --- | --- | --- | --- |
| Sum | Non-sybil | 0.89 | 1.04 | 0.70 | | | Sybil | 0.08 | 0.08 | 0.08 | |
| --- | --- | --- | --- | --- |
| Entropy | Non-sybil | 1.54 | 1.15 | 0.60 |
| Sybil | 0.04 | 0.07 | 0.05 | | | 1 |
| GraphModel | Community | SF | Random | Music |
| --- | --- | --- | --- | --- |
| Sum | Non-sybil | 0.89 | 1.04 | 0.70 | | | L | GraphQMF | nonzeroDCgraphBior | zeroDCgraphBior | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | SNR(dB) | Θ | SNR(dB) | Θ | SNR(dB) | Θ |
| 4 | 32.20 | 0.98 | 286.84 | 0.88 | 286.54 | 0.70 |
| 8 | 32.25 | 0.98 | 282.89 | 0.87 | 282.71 | 0.66 |
| 10 | 42.17 | 1.00 | 270.05 | 0.81 | 270.00 | 0.65 |
| 14 | 48.09 | 1.00 | 230.83 | 0.85 | 230.73 | 0.64 |
| 16 | 44.78 | 0.99 | 222.08 | 0.94 | 222.05 | 0.64 |
| 18 | 45.23 | 0.99 | 190.53 | 0.92 | 190.43 | 0.63 |
| 20 | 54.61 | 1.00 | 170.78 | 0.94 | 170.68 | 0.63 | | 0 |
| GraphModel | Community | SF | Random | Music |
| --- | --- | --- | --- | --- |
| Sum | Non-sybil | 0.89 | 1.04 | 0.70 |
| Sybil | 0.08 | 0.08 | 0.08 | | | | Entropy | Non-sybil | 1.54 | 1.15 | 0.60 |
| --- | --- | --- | --- | --- |
| Sybil | 0.04 | 0.07 | 0.05 | | | 1 |
| GraphModel | Community | SF | Random | Music |
| --- | --- | --- | --- | --- |
| Sum | Non-sybil | 0.89 | 1.04 | 0.70 |
| Sybil | 0.08 | 0.08 | 0.08 | | | | 8 | 32.25 | 0.98 | 282.89 | 0.87 | 282.71 | 0.66 |
| --- | --- | --- | --- | --- | --- | --- |
| 10 | 42.17 | 1.00 | 270.05 | 0.81 | 270.00 | 0.65 |
| 14 | 48.09 | 1.00 | 230.83 | 0.85 | 230.73 | 0.64 |
| 16 | 44.78 | 0.99 | 222.08 | 0.94 | 222.05 | 0.64 |
| 18 | 45.23 | 0.99 | 190.53 | 0.92 | 190.43 | 0.63 |
| 20 | 54.61 | 1.00 | 170.78 | 0.94 | 170.68 | 0.63 | | 0 |
| Syndrome | ErrorPatterns(inHex) | | | |
| --- | --- | --- | --- | --- |
| 00 | 00 | 05 | 11 | 41 | | | 01 | 01 | 04 | 10 | 40 |
| --- | --- | --- | --- | --- |
| 10 | 03 | 09 | 21 | 81 |
| 11 | 02 | 08 | 20 | 80 | | 1 |
| Syndrome | ErrorPatterns(inHex) | | | |
| --- | --- | --- | --- | --- |
| 00 | 00 | 05 | 11 | 41 | | | Benign | | |
| --- | --- | --- |
| Min. | Max. | Min. |
| 43 | 57 | 44 |
| 3,020 | 26,924 | 3,020 |
| 941 | 8,387 | 939 |
| 941 | 14,040 | 941 |
| 0.3 | 110 | 0.3 |
| 2.9 | 737 | 2.9 |
| 4 | 1,116 | 4 |
| 0.4 | 1,434 | 0.4 | | 0 |
| Syndrome | ErrorPatterns(inHex) | | | |
| --- | --- | --- | --- | --- |
| 00 | 00 | 05 | 11 | 41 | | | 01 | 01 | 04 | 10 | 40 |
| --- | --- | --- | --- | --- |
| 10 | 03 | 09 | 21 | 81 |
| 11 | 02 | 08 | 20 | 80 | | 1 |
| Syndrome | ErrorPatterns(inHex) | | | |
| --- | --- | --- | --- | --- |
| 00 | 00 | 05 | 11 | 41 | | | 43 | 57 | 44 |
| --- | --- | --- |
| 3,020 | 26,924 | 3,020 |
| 941 | 8,387 | 939 |
| 941 | 14,040 | 941 |
| 0.3 | 110 | 0.3 |
| 2.9 | 737 | 2.9 |
| 4 | 1,116 | 4 |
| 0.4 | 1,434 | 0.4 | | 0 |
| ASRsystem | |
| --- | --- |
| Inputunits | 23 |
| Hiddenunits | 512 |
| Outputunits | 27293 |
| LSTMlayerdepth | 2 |
| MTsystem | |
| Sourcevocabulary | 27293 |
| Targetvocabulary | 33155 |
| Embedsize | 128 |
| Inputunits | 128 |
| Hiddenunits | 512 |
| Outputunits | 33155 |
| LSTMlayerdepth | 2 | | | Optimization | |
| --- | --- |
| Initiallearningrate | 0.001000 |
| Learningdescendrate | 1.800000 |
| Optimizingmethod | Adam | | 1 |
| ASRsystem | |
| --- | --- |
| Inputunits | 23 |
| Hiddenunits | 512 |
| Outputunits | 27293 |
| LSTMlayerdepth | 2 |
| MTsystem | |
| Sourcevocabulary | 27293 |
| Targetvocabulary | 33155 |
| Embedsize | 128 |
| Inputunits | 128 |
| Hiddenunits | 512 |
| Outputunits | 33155 |
| LSTMlayerdepth | 2 | | | Hyperparameter | NMT | SPF | Ours |
| --- | --- | --- | --- |
| BatchSize | 128 | 100 | 128 |
| HiddenLayerSize | 512 | 600 | 512 |
| EncoderLayer | 2 | 2 | 2 |
| DecoderLayer | 2 | 1 | 2 |
| Optimizer | ADAM | ADAM | ADAM |
| LearningRate | 0.001 | 0.001 | 0.001 |
| BidirectionalEncoder | Used | Used | Used |
| EncoderDropoutRate | 0.2 | 0.4 | 0.2 |
| DecoderDropoutRate | 0.2 | 0.5 | 0.2 |
| BeamSearchSize | - | 5 | - | | 0 |
| ASRsystem | |
| --- | --- |
| Inputunits | 23 |
| Hiddenunits | 512 |
| Outputunits | 27293 | | | LSTMlayerdepth | 2 |
| --- | --- |
| MTsystem | |
| Sourcevocabulary | 27293 |
| Targetvocabulary | 33155 |
| Embedsize | 128 |
| Inputunits | 128 |
| Hiddenunits | 512 |
| Outputunits | 33155 |
| LSTMlayerdepth | 2 |
| Optimization | |
| Initiallearningrate | 0.001000 |
| Learningdescendrate | 1.800000 |
| Optimizingmethod | Adam | | 1 |
| ASRsystem | |
| --- | --- |
| Inputunits | 23 |
| Hiddenunits | 512 |
| Outputunits | 27293 | | | EncoderLayer | 2 | 2 | 2 |
| --- | --- | --- | --- |
| DecoderLayer | 2 | 1 | 2 |
| Optimizer | ADAM | ADAM | ADAM |
| LearningRate | 0.001 | 0.001 | 0.001 |
| BidirectionalEncoder | Used | Used | Used |
| EncoderDropoutRate | 0.2 | 0.4 | 0.2 |
| DecoderDropoutRate | 0.2 | 0.5 | 0.2 |
| BeamSearchSize | - | 5 | - | | 0 |
| Entity | Ni | Vi | Mi | StructureType |
| --- | --- | --- | --- | --- |
| Account | 11446187 | 11446187 | 1 | Vestigial |
| CPUHours | 11446187 | 11446187 | 2752964 | Identity |
| DefaultDepartment | 11446187 | 11446187 | 1 | Vestigial |
| JobName | 11446187 | 11446187 | 90491 | Organization |
| JobNumber | 11446187 | 11446187 | 485212 | Identity |
| MemoryUsage | 11446187 | 11446187 | 5241559 | Identity | | | Priority | 11446187 | 11446187 | 1 | Vestigial |
| --- | --- | --- | --- | --- |
| TaskNumber | 11446187 | 11446187 | 7491889 | Identity |
| UserName | 11446187 | 11446187 | 8388 | Organization | | 1 |
| Entity | Ni | Vi | Mi | StructureType |
| --- | --- | --- | --- | --- |
| Account | 11446187 | 11446187 | 1 | Vestigial |
| CPUHours | 11446187 | 11446187 | 2752964 | Identity |
| DefaultDepartment | 11446187 | 11446187 | 1 | Vestigial |
| JobName | 11446187 | 11446187 | 90491 | Organization |
| JobNumber | 11446187 | 11446187 | 485212 | Identity |
| MemoryUsage | 11446187 | 11446187 | 5241559 | Identity | | | Entity | Ni | Vi | Mi | StructureType |
| --- | --- | --- | --- | --- |
| latlon | 1624984 | 1625197 | 1506465 | Identity |
| lat | 1624984 | 1625192 | 1504469 | Identity |
| lon | 1625061 | 1625725 | 1504619 | Identity |
| place | 1741337 | 1741516 | 1504619 | Identity |
| retweetID | 636455 | 636644 | 627163 | Identity |
| reuserID | 720624 | 722148 | 676616 | Identity |
| time | 2020000 | 2020000 | 35176 | Organization |
| userID | 2020000 | 2020000 | 1711141 | Identity |
| user | 2020000 | 2020000 | 1711143 | Identity |
| word | 1976746 | 17180314 | 7838862 | Authority | | 0 |
| Entity | Ni | Vi | Mi | StructureType |
| --- | --- | --- | --- | --- |
| Account | 11446187 | 11446187 | 1 | Vestigial |
| CPUHours | 11446187 | 11446187 | 2752964 | Identity |
| DefaultDepartment | 11446187 | 11446187 | 1 | Vestigial |
| JobName | 11446187 | 11446187 | 90491 | Organization |
| JobNumber | 11446187 | 11446187 | 485212 | Identity |
| MemoryUsage | 11446187 | 11446187 | 5241559 | Identity | | | Priority | 11446187 | 11446187 | 1 | Vestigial |
| --- | --- | --- | --- | --- |
| TaskNumber | 11446187 | 11446187 | 7491889 | Identity |
| UserName | 11446187 | 11446187 | 8388 | Organization | | 1 |
| Entity | Ni | Vi | Mi | StructureType |
| --- | --- | --- | --- | --- |
| Account | 11446187 | 11446187 | 1 | Vestigial |
| CPUHours | 11446187 | 11446187 | 2752964 | Identity |
| DefaultDepartment | 11446187 | 11446187 | 1 | Vestigial |
| JobName | 11446187 | 11446187 | 90491 | Organization |
| JobNumber | 11446187 | 11446187 | 485212 | Identity |
| MemoryUsage | 11446187 | 11446187 | 5241559 | Identity | | | place | 1741337 | 1741516 | 1504619 | Identity |
| --- | --- | --- | --- | --- |
| retweetID | 636455 | 636644 | 627163 | Identity |
| reuserID | 720624 | 722148 | 676616 | Identity |
| time | 2020000 | 2020000 | 35176 | Organization |
| userID | 2020000 | 2020000 | 1711141 | Identity |
| user | 2020000 | 2020000 | 1711143 | Identity |
| word | 1976746 | 17180314 | 7838862 | Authority | | 0 |
| Dataset | FCT | EPa | EP |
| --- | --- | --- | --- |
| Flight | 797.2 | 731.2 | 836.9 |
| Electricity | 11600.3 | 9002.5 | 11402.5 | | | RotatingHyperplane | 5647.8 | 5413.8 | 5804.5 |
| --- | --- | --- | --- |
| Spam | 4.2 | 3.9 | 4.2 | | 1 |
| Dataset | FCT | EPa | EP |
| --- | --- | --- | --- |
| Flight | 797.2 | 731.2 | 836.9 |
| Electricity | 11600.3 | 9002.5 | 11402.5 | | | Dataset | AveragePoolMemory(inKBs) | | |
| --- | --- | --- | --- |
| | FCT | EPa | EP |
| Flight | 32.1 | 20.2 | 18.1 |
| Electricity | 31.6 | 16.1 | 14.1 |
| Rot.Hyperplane | 48.4 | 38.6 | 27.9 |
| Spam | 17.3 | 17.2 | 16.4 | | 0 |
| Dataset | FCT | EPa | EP |
| --- | --- | --- | --- |
| Flight | 797.2 | 731.2 | 836.9 | | | Electricity | 11600.3 | 9002.5 | 11402.5 |
| --- | --- | --- | --- |
| RotatingHyperplane | 5647.8 | 5413.8 | 5804.5 |
| Spam | 4.2 | 3.9 | 4.2 | | 1 |
| Dataset | FCT | EPa | EP |
| --- | --- | --- | --- |
| Flight | 797.2 | 731.2 | 836.9 | | | Rot.Hyperplane | 48.4 | 38.6 | 27.9 |
| --- | --- | --- | --- |
| Spam | 17.3 | 17.2 | 16.4 | | 0 |
| #Dataset | #Triples | #(S∩O) | #P |
| --- | --- | --- | --- |
| WatDiv100M | 108997714 | 10250947 | 86 |
| WatDiv200M | 219783842 | 20296483 | 86 |
| WatDiv300M | 329827477 | 30221812 | 86 |
| WatDiv400M | 439433765 | 40040420 | 86 | | | WatDiv500M | 549246141 | 49771433 | 86 |
| --- | --- | --- | --- |
| Yago | 200737655 | 38734252 | 46 |
| DBpedia | 120978080 | 42966066 | 4282 | | 1 |
| #Dataset | #Triples | #(S∩O) | #P |
| --- | --- | --- | --- |
| WatDiv100M | 108997714 | 10250947 | 86 |
| WatDiv200M | 219783842 | 20296483 | 86 |
| WatDiv300M | 329827477 | 30221812 | 86 |
| WatDiv400M | 439433765 | 40040420 | 86 | | | Dataset | \|Triples\| | \|Predicates\| | MK-tree | IK-tree | RDF-3X |
| --- | --- | --- | --- | --- | --- |
| Jamendo | 1,049,639 | 28 | 0.74 | 0.74 | 37.73 |
| Dblp | 46,597,620 | 27 | 82.48 | 84.04 | 1,643.31 |
| Geonames | 112,235,492 | 26 | 152.20 | 156.01 | 3,584.80 |
| DBpedia | 232,542,405 | 39,672 | 931.44 | 788.19 | 9,757.58 | | 0 |
| #Dataset | #Triples | #(S∩O) | #P |
| --- | --- | --- | --- |
| WatDiv100M | 108997714 | 10250947 | 86 |
| WatDiv200M | 219783842 | 20296483 | 86 |
| WatDiv300M | 329827477 | 30221812 | 86 | | | WatDiv400M | 439433765 | 40040420 | 86 |
| --- | --- | --- | --- |
| WatDiv500M | 549246141 | 49771433 | 86 |
| Yago | 200737655 | 38734252 | 46 |
| DBpedia | 120978080 | 42966066 | 4282 | | 1 |
| #Dataset | #Triples | #(S∩O) | #P |
| --- | --- | --- | --- |
| WatDiv100M | 108997714 | 10250947 | 86 |
| WatDiv200M | 219783842 | 20296483 | 86 |
| WatDiv300M | 329827477 | 30221812 | 86 | | | Dblp | 46,597,620 | 27 | 82.48 | 84.04 | 1,643.31 |
| --- | --- | --- | --- | --- | --- |
| Geonames | 112,235,492 | 26 | 152.20 | 156.01 | 3,584.80 |
| DBpedia | 232,542,405 | 39,672 | 931.44 | 788.19 | 9,757.58 | | 0 |
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