Split (1)

train · 14.7k rows

premise string	hypothesis string	label int64
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| SMD1 \| 853319 \| 1150 \| 1692741 \| 2639 \| 2178997 \| 3640 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SMD2 \| 1030789 \| 1459 \| 1557348 \| 2387 \| 2232534 \| 3787 \| \| SMD3 \| 879785 \| 1238 \| 1476206 \| 2390 \| 1978647 \| 2992 \| \| SMD4 \| 581078 \| 710 \| 1134376 \| 1687 \| 1438936 \| 2153 \| \| SMD5 \| 1217102 \| 1683 \| 2021104 \| 2966 \| 2904080 \| 3772 \| \| SMD6 \| 1303778 \| 1646 \| 2463058 \| 3301 \| 3230143 \| 4165 \|	1
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| t \| k \| maximaln \| ourresult \| result \| t \| k \| maximaln \| ourresult \| result \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 7 \| 2 \| 192 \| 551 \| 557 \| 15 \| 2 \| 49152 \| 147407 \| 147421 \| \| 8 \| 2 \| 384 \| 1124 \| 1131 \| 15 \| 3 \| 57344 \| 229266 \| 229307 \| \| 8 \| 3 \| 448 \| 1731 \| 1751 \| 15 \| 4 \| 61440 \| 306973 \| 307094 \| \| 9 \| 2 \| 768 \| 2273 \| 2281 \| 15 \| 5 \| 63488 \| 380476 \| 380778 \| \| 9 \| 3 \| 896 \| 3516 \| 3539 \| 15 \| 6 \| 64512 \| 450699 \| 451375 \| \| 10 \| 2 \| 1536 \| 4574 \| 4583 \| 16 \| 2 \| 98304 \| 294860 \| 294875 \| \| 10 \| 3 \| 1792 \| 7093 \| 7119 \| 16 \| 3 \| 114688 \| 458635 \| 458679 \| \| 10 \| 4 \| 1920 \| 9448 \| 9524 \| 16 \| 4 \| 122880 \| 614158 \| 614288 \| \| 11 \| 2 \| 3072 \| 9179 \| 9189 \| 16 \| 5 \| 126976 \| 761373 \| 761698 \| \| 11 \| 3 \| 3584 \| 14254 \| 14283 \| 16 \| 6 \| 129024 \| 902220 \| 902949 \| \| 11 \| 4 \| 3840 \| 19033 \| 19118 \| 16 \| 7 \| 130048 \| 1038539 \| 1040073 \| \| 12 \| 2 \| 6144 \| 18392 \| 18403 \| 17 \| 2 \| 196608 \| 589769 \| 589785 \| \| 12 \| 3 \| 7168 \| 28583 \| 28615 \| 17 \| 3 \| 229376 \| 917380 \| 917427 \| \| 12 \| 4 \| 7680 \| 38218 \| 38312 \| 17 \| 4 \| 245760 \| 1228543 \| 1228682 \| \| 12 \| 5 \| 7936 \| 47257 \| 47490 \| 17 \| 5 \| 253952 \| 1523198 \| 1523546 \| \| 13 \| 2 \| 12288 \| 36821 \| 36833 \| 17 \| 6 \| 258048 \| 1805325 \| 1806107 \| \| 13 \| 3 \| 14336 \| 57248 \| 57283 \| 17 \| 7 \| 260096 \| 2078796 \| 2080445 \| \| 13 \| 4 \| 15360 \| 76603 \| 76706 \| 18 \| 2 \| 393216 \| 1179590 \| 1179607 \| \| 13 \| 5 \| 15872 \| 94842 \| 95098 \| 18 \| 3 \| 458752 \| 1834877 \| 1834927 \| \| 14 \| 2 \| 24576 \| 73682 \| 73695 \| 18 \| 4 \| 491520 \| 2457328 \| 2457476 \| \| 14 \| 3 \| 28672 \| 114585 \| 114623 \| 18 \| 5 \| 507904 \| 3046879 \| 3047250 \| \| 14 \| 4 \| 30720 \| 153388 \| 153500 \| 18 \| 6 \| 516096 \| 3611598 \| 3612433 \| \| 14 \| 5 \| 31744 \| 190043 \| 190322 \| 18 \| 7 \| 520192 \| 4159437 \| 4161201 \| \| 14 \| 6 \| 32256 \| 224970 \| 225593 \| 18 \| 8 \| 522240 \| 4696076 \| 4699666 \|	0
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| SMD1 \| 853319 \| 1150 \| 1692741 \| 2639 \| 2178997 \| 3640 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SMD2 \| 1030789 \| 1459 \| 1557348 \| 2387 \| 2232534 \| 3787 \| \| SMD3 \| 879785 \| 1238 \| 1476206 \| 2390 \| 1978647 \| 2992 \| \| SMD4 \| 581078 \| 710 \| 1134376 \| 1687 \| 1438936 \| 2153 \| \| SMD5 \| 1217102 \| 1683 \| 2021104 \| 2966 \| 2904080 \| 3772 \| \| SMD6 \| 1303778 \| 1646 \| 2463058 \| 3301 \| 3230143 \| 4165 \|	1
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| 11 \| 4 \| 3840 \| 19033 \| 19118 \| 16 \| 7 \| 130048 \| 1038539 \| 1040073 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 12 \| 2 \| 6144 \| 18392 \| 18403 \| 17 \| 2 \| 196608 \| 589769 \| 589785 \| \| 12 \| 3 \| 7168 \| 28583 \| 28615 \| 17 \| 3 \| 229376 \| 917380 \| 917427 \| \| 12 \| 4 \| 7680 \| 38218 \| 38312 \| 17 \| 4 \| 245760 \| 1228543 \| 1228682 \| \| 12 \| 5 \| 7936 \| 47257 \| 47490 \| 17 \| 5 \| 253952 \| 1523198 \| 1523546 \| \| 13 \| 2 \| 12288 \| 36821 \| 36833 \| 17 \| 6 \| 258048 \| 1805325 \| 1806107 \| \| 13 \| 3 \| 14336 \| 57248 \| 57283 \| 17 \| 7 \| 260096 \| 2078796 \| 2080445 \| \| 13 \| 4 \| 15360 \| 76603 \| 76706 \| 18 \| 2 \| 393216 \| 1179590 \| 1179607 \| \| 13 \| 5 \| 15872 \| 94842 \| 95098 \| 18 \| 3 \| 458752 \| 1834877 \| 1834927 \| \| 14 \| 2 \| 24576 \| 73682 \| 73695 \| 18 \| 4 \| 491520 \| 2457328 \| 2457476 \| \| 14 \| 3 \| 28672 \| 114585 \| 114623 \| 18 \| 5 \| 507904 \| 3046879 \| 3047250 \| \| 14 \| 4 \| 30720 \| 153388 \| 153500 \| 18 \| 6 \| 516096 \| 3611598 \| 3612433 \| \| 14 \| 5 \| 31744 \| 190043 \| 190322 \| 18 \| 7 \| 520192 \| 4159437 \| 4161201 \| \| 14 \| 6 \| 32256 \| 224970 \| 225593 \| 18 \| 8 \| 522240 \| 4696076 \| 4699666 \|	0
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \|	\| bank1 \| bank2 \| \| --- \| --- \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \| \| page1 \| page2 \| \| ...onpage<unk>ofthe... \| ...wasitfrontpagenews... \| \| ...asourcetoldpagesix.... \| ...himself,tonypage,theformer... \| \| ...onpage<unk>ofthe... \| ...sectionsofthepagethatdiscuss... \| \| proﬁle1 \| proﬁle2 \| \| ...(<unk>:quote,proﬁle,research)... \| ...so<unk>theproﬁleofthecity... \| \| ...(<unk>:quote,proﬁle,research)... \| ...thehighestproﬁle<unk>heldby... \| \| ...(<unk>:quote,proﬁle,research)... \| ...fromhighi,eliteschools,... \| \| edition1 \| edition2 \| \| ...ofthesecondeditionofwindows... \| ...ofthenewyorkedition.... \| \| ...thismonth’seditionof<unk>,the... \| ...ofthenewyorkedition.... \| \| ...forthcomingd.c.editionofthehit... \| ...ofthenewyorkedition.... \| \| rodham1 \| rodham2 \| \| ...senatorshillaryrodhamclintonand... \| \| \| ...makinghillaryrodhamclintonhis... \| \| \| ...hillaryrodhamclinton’scampaignhas... \| \|	1
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \|	\| Market \| #ofBenignApps \| #ofMalware \| \| --- \| --- \| --- \| \| GooglePlay \| 39156 \| 26178 \| \| Anzhi \| 2957 \| 12260 \| \| AppChina \| 1845 \| 4154 \| \| SlideMe \| 289 \| 132 \| \| HiApk \| 65 \| 157 \| \| FDroid \| 29 \| 2 \| \| Angeeks \| 6 \| 27 \|	0
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \| \| bank1 \| bank2 \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \|	\| page1 \| page2 \| \| --- \| --- \| \| ...onpage<unk>ofthe... \| ...wasitfrontpagenews... \| \| ...asourcetoldpagesix.... \| ...himself,tonypage,theformer... \| \| ...onpage<unk>ofthe... \| ...sectionsofthepagethatdiscuss... \| \| proﬁle1 \| proﬁle2 \| \| ...(<unk>:quote,proﬁle,research)... \| ...so<unk>theproﬁleofthecity... \| \| ...(<unk>:quote,proﬁle,research)... \| ...thehighestproﬁle<unk>heldby... \| \| ...(<unk>:quote,proﬁle,research)... \| ...fromhighi,eliteschools,... \| \| edition1 \| edition2 \| \| ...ofthesecondeditionofwindows... \| ...ofthenewyorkedition.... \| \| ...thismonth’seditionof<unk>,the... \| ...ofthenewyorkedition.... \| \| ...forthcomingd.c.editionofthehit... \| ...ofthenewyorkedition.... \| \| rodham1 \| rodham2 \| \| ...senatorshillaryrodhamclintonand... \| \| \| ...makinghillaryrodhamclintonhis... \| \| \| ...hillaryrodhamclinton’scampaignhas... \| \|	1
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \| \| bank1 \| bank2 \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \|	\| FDroid \| 29 \| 2 \| \| --- \| --- \| --- \| \| Angeeks \| 6 \| 27 \|	0
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Disgust \| 60 \| \| --- \| --- \| \| Surprise \| 25 \| \| Repression \| 27 \| \| Others \| 102 \|	1
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Emotion \| Frequency \| \| --- \| --- \| \| Amusement \| 5 \| \| Sadness \| 6 \| \| Disgust \| 88 \| \| Surprise \| 20 \| \| Contempt \| 3 \| \| Fear \| 2 \| \| Repression \| 40 \| \| Tense \| 28 \|	0
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Disgust \| 60 \| \| --- \| --- \| \| Surprise \| 25 \| \| Repression \| 27 \| \| Others \| 102 \|	1
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Repression \| 40 \| \| --- \| --- \| \| Tense \| 28 \|	0
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \|	\| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 31.05.2015 \| 210 \| 526 \| 158 \| 98 \| 36 \| 2 \| 1030 \| \| Total \| 452 \| 979 \| 273 \| 185 \| 62 \| 9 \| 1960 \|	1
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \|	\| date \| truepositives \| falsepositives \| falsenegatives \| \| --- \| --- \| --- \| --- \| \| 2012-07-02 \| 1 \| 0 \| 0 \| \| 2012-07-03 \| 3 \| 0 \| 0 \| \| 2012-07-04 \| 2 \| 0 \| 0 \| \| 2012-07-05 \| 2 \| 0 \| 0 \| \| 2012-07-06 \| 3 \| 0 \| 0 \| \| 2012-07-08 \| 0 \| 0 \| 2 \| \| 2012-07-09 \| 2 \| 0 \| 0 \| \| 2012-07-10 \| 2 \| 0 \| 0 \| \| 2012-07-11 \| 0 \| 0 \| 1 \| \| 2012-07-14 \| 1 \| 0 \| 0 \| \| 2012-07-29 \| 1 \| 0 \| 0 \| \| 2012-07-30 \| 1 \| 0 \| 0 \| \| total \| 18 \| 0 \| 3 \|	0
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \| \| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \|	\| 31.05.2015 \| 210 \| 526 \| 158 \| 98 \| 36 \| 2 \| 1030 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total \| 452 \| 979 \| 273 \| 185 \| 62 \| 9 \| 1960 \|	1
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \| \| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \|	\| 2012-07-06 \| 3 \| 0 \| 0 \| \| --- \| --- \| --- \| --- \| \| 2012-07-08 \| 0 \| 0 \| 2 \| \| 2012-07-09 \| 2 \| 0 \| 0 \| \| 2012-07-10 \| 2 \| 0 \| 0 \| \| 2012-07-11 \| 0 \| 0 \| 1 \| \| 2012-07-14 \| 1 \| 0 \| 0 \| \| 2012-07-29 \| 1 \| 0 \| 0 \| \| 2012-07-30 \| 1 \| 0 \| 0 \| \| total \| 18 \| 0 \| 3 \|	0
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \|	\| R \| 13513 \| 8.0 \| \| --- \| --- \| --- \| \| S \| 23551 \| 14.0 \| \| FS \| 49301 \| 29.2 \| \| RS \| 31150 \| 18.5 \|	1
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \|	\| Model \| Manuallyadded \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \| \| R \| 13513 \| 8.0 \| \| S \| 23551 \| 14.0 \|	\| FS \| 49301 \| 29.2 \| \| --- \| --- \| --- \| \| RS \| 31150 \| 18.5 \|	1
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \| \| R \| 13513 \| 8.0 \| \| S \| 23551 \| 14.0 \|	\| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| Merge \| 95.4 \| 92.5 \| 97.5 \| 97.4 \| 95.4 \| 97.2 \| 95.9 \| 71.8 \| 73.9 \| 82.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Swap \| 96.8 \| 94.1 \| 97.5 \| 98.0 \| 96.1 \| 98.2 \| 96.2 \| 83.0 \| 84.2 \| 87.2 \|	1
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| \| CRDI(-) \| CKDI(-) \| CADI(+) \| PNA(-) \| CAAI(+) \| DDI(+) \| EDI(+) \| ACC(+) \| ABC(+) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 8 \| 0.65 \| 0.53 \| 0.61 \| 0.57 \| 0.74 \| 0.55 \| 0.40 \| 0.68 \| 0.70 \| \| 9 \| 0.55 \| 0.46 \| 0.57 \| 0.57 \| 0.68 \| 0.53 \| 0.27 \| 0.63 \| 0.68 \| \| 10 \| 0.40 \| 0.46 \| 0.53 \| 0.46 \| 0.57 \| 0.42 \| 0.17 \| 0.53 \| 0.61 \| \| 11 \| 0.38 \| 0.46 \| 0.44 \| 0.38 \| 0.48 \| 0.31 \| 0.10 \| 0.38 \| 0.51 \| \| 12 \| 0.27 \| 0.34 \| 0.27 \| 0.27 \| 0.29 \| 0.23 \| 0.04 \| 0.31 \| 0.42 \|	0
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| Merge \| 95.4 \| 92.5 \| 97.5 \| 97.4 \| 95.4 \| 97.2 \| 95.9 \| 71.8 \| 73.9 \| 82.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Swap \| 96.8 \| 94.1 \| 97.5 \| 98.0 \| 96.1 \| 98.2 \| 96.2 \| 83.0 \| 84.2 \| 87.2 \|	1
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| 9 \| 0.55 \| 0.46 \| 0.57 \| 0.57 \| 0.68 \| 0.53 \| 0.27 \| 0.63 \| 0.68 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 10 \| 0.40 \| 0.46 \| 0.53 \| 0.46 \| 0.57 \| 0.42 \| 0.17 \| 0.53 \| 0.61 \| \| 11 \| 0.38 \| 0.46 \| 0.44 \| 0.38 \| 0.48 \| 0.31 \| 0.10 \| 0.38 \| 0.51 \| \| 12 \| 0.27 \| 0.34 \| 0.27 \| 0.27 \| 0.29 \| 0.23 \| 0.04 \| 0.31 \| 0.42 \|	0
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \| \| Fast-RCNN+SRBBS+RBB \| 69.6 \|	\| Fast-RCNN+SRBBS+RBB+RRoI \| 75.7 \| \| --- \| --- \| \| RRD(Ours) \| 84.3 \|	1
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \| \| Fast-RCNN+SRBBS+RBB \| 69.6 \|	\| Method \| DS1 \| DS2 \| \| --- \| --- \| --- \| \| HOG \| 50 \| 52 \| \| Fast-RCNN \| 54 \| 61 \| \| Faster-RCNN \| 60 \| 66 \| \| Yolo \| 65 \| 68 \|	0
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \|	\| Fast-RCNN+SRBBS+RBB \| 69.6 \| \| --- \| --- \| \| Fast-RCNN+SRBBS+RBB+RRoI \| 75.7 \| \| RRD(Ours) \| 84.3 \|	1
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \|	\| Faster-RCNN \| 60 \| 66 \| \| --- \| --- \| --- \| \| Yolo \| 65 \| 68 \|	0
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 2nd(deep) \| 509.70 \| 463.76 \| 484.57 \| 415.93 \| \| --- \| --- \| --- \| --- \| --- \| \| 3rd(decoder) \| 679.54 \| 623.16 \| 624.23 \| 583.21 \|	1
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 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
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 2nd(deep) \| 509.70 \| 463.76 \| 484.57 \| 415.93 \| \| --- \| --- \| --- \| --- \| --- \| \| 3rd(decoder) \| 679.54 \| 623.16 \| 624.23 \| 583.21 \|	1
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 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
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| PLPCC \| 2 \| 66.5761 \| 72.5543 \| \| --- \| --- \| --- \| --- \| \| 4 \| 76.9203 \| 81.0507 \| \| \| 8 \| 85.3080 \| 87.7717 \| \| \| 16 \| 90.6341 \| 91.9022 \| \| \| 32 \| 93.5326 \| 94.3116 \| \| \| 64 \| 94.6920 \| 95.3986 \| \| \| LFCC \| 2 \| 83.0072 \| 85.8152 \| \| 4 \| 90.3623 \| 91.7935 \| \| \| 8 \| 94.6196 \| 95.4891 \| \| \| 16 \| 96.2681 \| 96.6848 \| \| \| 32 \| 97.1014 \| 97.3551 \| \| \| 64 \| 97.2464 \| 97.6268 \| \| \| MFCC \| 2 \| 74.3116 \| 78.6051 \| \| 4 \| 84.8551 \| 86.9384 \| \| \| 8 \| 90.6703 \| 92.0290 \| \| \| 16 \| 94.1667 \| 94.6920 \| \| \| 32 \| 95.6522 \| 95.9964 \| \| \| 64 \| 96.7935 \| 97.1014 \| \|	1
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| Features \| L-SVM \| K-SVM \| NB \| DT \| RF \| LR \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IDT \| 0.7731 \| 0.6374 \| 0.5984 \| 0.5895 \| 0.5567 \| 0.6425 \| \| MicroExpression \| 0.7502 \| 0.7540 \| 0.7629 \| 0.7269 \| 0.8064 \| 0.7398 \| \| Transcript \| 0.6457 \| 0.4667 \| 0.6625 \| 0.5251 \| 0.6172 \| 0.5643 \| \| MFCC \| 0.7694 \| 0.8171 \| 0.6726 \| 0.4369 \| 0.7393 \| 0.6683 \| \| IDT+MicroExpression \| 0.8347 \| 0.7540 \| 0.7629 \| 0.7687 \| 0.8184 \| 0.7419 \| \| IDT+MicroExpression+Transcripts \| 0.8347 \| 0.7540 \| 0.7776 \| 0.7777 \| 0.8184 \| 0.7419 \| \| IDT+MicroExpression+MFCC \| 0.8596 \| 0.8233 \| 0.7629 \| 0.7687 \| 0.8477 \| 0.7894 \| \| AllModalities \| 0.8773 \| 0.8233 \| 0.7776 \| 0.7777 \| 0.8477 \| 0.7894 \|	0
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| PLPCC \| 2 \| 66.5761 \| 72.5543 \| \| --- \| --- \| --- \| --- \| \| 4 \| 76.9203 \| 81.0507 \| \| \| 8 \| 85.3080 \| 87.7717 \| \| \| 16 \| 90.6341 \| 91.9022 \| \| \| 32 \| 93.5326 \| 94.3116 \| \| \| 64 \| 94.6920 \| 95.3986 \| \| \| LFCC \| 2 \| 83.0072 \| 85.8152 \| \| 4 \| 90.3623 \| 91.7935 \| \| \| 8 \| 94.6196 \| 95.4891 \| \| \| 16 \| 96.2681 \| 96.6848 \| \| \| 32 \| 97.1014 \| 97.3551 \| \| \| 64 \| 97.2464 \| 97.6268 \| \| \| MFCC \| 2 \| 74.3116 \| 78.6051 \| \| 4 \| 84.8551 \| 86.9384 \| \| \| 8 \| 90.6703 \| 92.0290 \| \| \| 16 \| 94.1667 \| 94.6920 \| \| \| 32 \| 95.6522 \| 95.9964 \| \| \| 64 \| 96.7935 \| 97.1014 \| \|	1
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| IDT+MicroExpression+Transcripts \| 0.8347 \| 0.7540 \| 0.7776 \| 0.7777 \| 0.8184 \| 0.7419 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IDT+MicroExpression+MFCC \| 0.8596 \| 0.8233 \| 0.7629 \| 0.7687 \| 0.8477 \| 0.7894 \| \| AllModalities \| 0.8773 \| 0.8233 \| 0.7776 \| 0.7777 \| 0.8477 \| 0.7894 \|	0
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \|	\| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \| \| --- \| --- \| --- \| --- \| \| RAN-LSTM-1L \| 0.74 \| 0.82 \| 0.78 \| \| RAN-LSTM-2L \| 0.85 \| 0.81 \| 0.83 \|	1
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \|	\| Processingstep \| Hardware \| DNN \| ResNet-CNN \| BLSTM \| LACE \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| AMtraining \| GPU \| 0.012 \| 0.60 \| 0.022 \| 0.23 \| \| AMevaluation \| GPU \| 0.0064 \| 0.15 \| 0.0081 \| 0.081 \| \| AMevaluation \| CPU \| 0.052 \| 11.7 \| n/a \| 8.47 \| \| Decoding \| GPU \| 1.04 \| 1.19 \| 1.40 \| 1.38 \|	0
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \| \| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \|	\| RAN-LSTM-1L \| 0.74 \| 0.82 \| 0.78 \| \| --- \| --- \| --- \| --- \| \| RAN-LSTM-2L \| 0.85 \| 0.81 \| 0.83 \|	1
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \| \| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \|	\| AMevaluation \| CPU \| 0.052 \| 11.7 \| n/a \| 8.47 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Decoding \| GPU \| 1.04 \| 1.19 \| 1.40 \| 1.38 \|	0
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \|	\| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \| \| --- \| --- \| --- \| --- \| \| Radjenovi´c \| 6000−680=5320 \| ≥5320C+3CAD \| 3 \| \| Kitchenham \| 1704−630=1074 \| 32C+1074CDA \| 3 \|	1
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \|	\| Dataset \| c(EXIF,mm)err \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Rother \| Jiang \| Arie \| VisualSFM \| 1DSfM \| L2 \| L1 \| \| \| fountain-P11 \| 23.3 \| 14.0 \| 22.6 \| 20.7 \| 32.2 \| 6.9 \| 7.0 \| \| Herz-Jesu-P25 \| 49.5 \| 64.0 \| 47.9 \| 45.3 \| 64.9 \| 25.5 \| 26.2 \| \| castle-P30 \| 2651.8 \| 235.0 \| - \| 190.1 \| - \| 317.3 \| 166.7 \|	0
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \| \| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \|	\| Radjenovi´c \| 6000−680=5320 \| ≥5320C+3CAD \| 3 \| \| --- \| --- \| --- \| --- \| \| Kitchenham \| 1704−630=1074 \| 32C+1074CDA \| 3 \|	1
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \| \| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \|	\| Herz-Jesu-P25 \| 49.5 \| 64.0 \| 47.9 \| 45.3 \| 64.9 \| 25.5 \| 26.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| castle-P30 \| 2651.8 \| 235.0 \| - \| 190.1 \| - \| 317.3 \| 166.7 \|	0
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| f3 \| 1 \| 8 \| 7 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| \| f4 \| 4 \| 18 \| 14 \| 26 \|	1
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| k \| 1 \| 2 \| 3 \| 4 \| 5 \| odd \| even \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| d<br>Lowerboundonχ(F) \| 4 \| 13 \| 29 \| 55 \| 92 \| 5519332<br>d+d+d+<br>124124 \| 551132<br>d+d+d+1<br>1246 \| \| d<br>Upperboundonχ(F) \| 4 \| 13 \| 30 \| 65 \| 108 \| 2<br>(d+1)(cid:100)(d+1)/2(cid:101) \| \| \| d<br>Valueofχ(F)0,1 \| 4 \| 9 \| 16 \| 25 \| 36 \| 2<br>(d+1) \| \| \| d<br>Lowerboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 34 \| 52 \| 2<br>3(cid:100)(d+1)/2(cid:101)−2 \| \| \| d<br>Upperboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 36 \| 54 \| 2<br>3(cid:100)(d+1)/2(cid:101) \| \|	0
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| f3 \| 1 \| 8 \| 7 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| \| f4 \| 4 \| 18 \| 14 \| 26 \|	1
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| d<br>Valueofχ(F)0,1 \| 4 \| 9 \| 16 \| 25 \| 36 \| 2<br>(d+1) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| d<br>Lowerboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 34 \| 52 \| 2<br>3(cid:100)(d+1)/2(cid:101)−2 \| \| d<br>Upperboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 36 \| 54 \| 2<br>3(cid:100)(d+1)/2(cid:101) \|	0
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| appear \| .122 \| .076 \| .077 \| .072 \| .182 \| .112 \| .037 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	0
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| --- \| --- \| --- \| \| \| \| \|	1
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \|	0
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \| \| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \|	\| (7) \| proposedauxiliaryset \| 0.755 \| 0.771 \| 0.728 \| 0.749 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IterativeTransfer \| (8) \| iterativetransferlearning \| 0.771 \| 0.785 \| 0.75 \| 0.767 \|	1
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \| \| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \|	\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \| \| 0.838 \| 0 \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	0
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \|	\| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (7) \| proposedauxiliaryset \| 0.755 \| 0.771 \| 0.728 \| 0.749 \| \| \| IterativeTransfer \| (8) \| iterativetransferlearning \| 0.771 \| 0.785 \| 0.75 \| 0.767 \|	1
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \|	\| 0.838 \| 0 \| \| --- \| --- \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	0
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \| \| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \|	\| Flow1 \| Node1to2(PI=20) \| Flow1 \| Node1to2 \| \| --- \| --- \| --- \| --- \| \| Routingprotocol \| AODV \| SimulationTime \| 10sec \| \| Source \| CBR \| Queuelength \| 100 \| \| SuperframeTimePeriod \| 60msec \| PUActivity \| None \|	1
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \| \| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \|	\| TotalNodes \| 8 \| Packetsize \| 2000bytes \| \| --- \| --- \| --- \| --- \| \| DataSlots \| 13 \| PacketgenerationInterval \| 1msec \| \| Tc \| 1.5msec \| Queuelength \| 100 \| \| Td \| 2msec \| Routing \| AODV \| \| Ta \| 0.5msec \| Simulationtime \| 20sec \| \| Tq \| 20msec \| Source \| CBR \| \| Flow1 \| Node1to2 \| Flow3 \| Node5to6 \| \| Flow2 \| Node3to4 \| Flow4 \| Node7to8 \| \| Throughputﬂow1 \| 89.79kbps \| Throughputﬂow3 \| 91.21kbps \| \| Throughputﬂow2 \| 84.08kbps \| Throughputﬂow4 \| 89.79kbps \|	0
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \|	\| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| --- \| --- \| --- \| --- \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \| \| Flow1 \| Node1to2(PI=20) \| Flow1 \| Node1to2 \| \| Routingprotocol \| AODV \| SimulationTime \| 10sec \| \| Source \| CBR \| Queuelength \| 100 \| \| SuperframeTimePeriod \| 60msec \| PUActivity \| None \|	1
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \|	\| Flow1 \| Node1to2 \| Flow3 \| Node5to6 \| \| --- \| --- \| --- \| --- \| \| Flow2 \| Node3to4 \| Flow4 \| Node7to8 \| \| Throughputﬂow1 \| 89.79kbps \| Throughputﬂow3 \| 91.21kbps \| \| Throughputﬂow2 \| 84.08kbps \| Throughputﬂow4 \| 89.79kbps \|	0
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \| \| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \|	\| (25,30) \| 6 \| 15 \| 135 \| 25 \| 125 \| 4 \| 146 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (25,40) \| 8 \| 7 \| 193 \| 20 \| 180 \| 25 \| 175 \| \| (30,36) \| 6 \| 30 \| 150 \| 30 \| 150 \| 15 \| 525 \| \| (36,48) \| 4 \| 12 \| 132 \| 36 \| 108 \| 36 \| 108 \| \| (4,20) \| 10,5(skewed) \| 2 \| 38 \| 4 \| 36 \| 2 \| 18 \| \| (8,40) \| 10,5(skewed) \| 8 \| 72 \| 8 \| 72 \| 4 \| 36 \| \| (8,50) \| 25 \| 8 \| 192 \| 8 \| 192 \| 8 \| 192 \|	1
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \| \| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \|	\| InputSize \| Depth \| Top-1 \| \| --- \| --- \| --- \| \| 224x224 \| 16 \| 28.5 \| \| 224x224<br>224x224 \| 22<br>22 \| 27.8<br>25.2 \| \| 224x224<br>224x224 \| 50<br>152 \| 24.7<br>23.0 \| \| 224x224<br>224x224 \| 50<br>101 \| 23.6<br>20.4 \| \| 224x224<br>224x224 \| 38<br>38 \| 24.3<br>22.6 \|	0
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \|	\| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \| \| (25,30) \| 6 \| 15 \| 135 \| 25 \| 125 \| 4 \| 146 \| \| (25,40) \| 8 \| 7 \| 193 \| 20 \| 180 \| 25 \| 175 \| \| (30,36) \| 6 \| 30 \| 150 \| 30 \| 150 \| 15 \| 525 \| \| (36,48) \| 4 \| 12 \| 132 \| 36 \| 108 \| 36 \| 108 \| \| (4,20) \| 10,5(skewed) \| 2 \| 38 \| 4 \| 36 \| 2 \| 18 \| \| (8,40) \| 10,5(skewed) \| 8 \| 72 \| 8 \| 72 \| 4 \| 36 \| \| (8,50) \| 25 \| 8 \| 192 \| 8 \| 192 \| 8 \| 192 \|	1
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \|	\| 224x224<br>224x224 \| 50<br>152 \| 24.7<br>23.0 \| \| --- \| --- \| --- \| \| 224x224<br>224x224 \| 50<br>101 \| 23.6<br>20.4 \| \| 224x224<br>224x224 \| 38<br>38 \| 24.3<br>22.6 \|	0
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \|	\| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 7 \| {v,v}35 \| v3 \| v,v16 \| v3 \| 1 \| \| 8 \| {v}5 \| ∅ \| v,v16 \| v5 \| 1 \|	1
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \|	\| Step \| V \| V1 \| C \| V2 \| \| --- \| --- \| --- \| --- \| --- \| \| Initial \| v,v,v,v,v,v,v3456789 \| v1 \| ∅ \| v2 \| \| Checkv3 \| v,v,v,v,v,v456789 \| v,v13 \| ∅ \| v2 \| \| Checkv4 \| v,v,v,v,v56789 \| v,v13 \| v4 \| v2 \| \| Checkv5 \| v,v,v,v6789 \| v,v13 \| v4 \| v,v25 \| \| Checkv6 \| v,v,v789 \| v,v,v136 \| v4 \| v,v25 \| \| Checkv7 \| v,v89 \| v,v,v136 \| v,v47 \| v,v25 \| \| Checkv8 \| v9 \| v,v,v136 \| v,v,v478 \| v,v25 \| \| Checkv9 \| ∅ \| v,v,v136 \| v,v,v478 \| v,v,v259 \|	0
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \| \| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \|	\| 7 \| {v,v}35 \| v3 \| v,v16 \| v3 \| 1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 8 \| {v}5 \| ∅ \| v,v16 \| v5 \| 1 \|	1
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \| \| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \|	\| Checkv5 \| v,v,v,v6789 \| v,v13 \| v4 \| v,v25 \| \| --- \| --- \| --- \| --- \| --- \| \| Checkv6 \| v,v,v789 \| v,v,v136 \| v4 \| v,v25 \| \| Checkv7 \| v,v89 \| v,v,v136 \| v,v47 \| v,v25 \| \| Checkv8 \| v9 \| v,v,v136 \| v,v,v478 \| v,v25 \| \| Checkv9 \| ∅ \| v,v,v136 \| v,v,v478 \| v,v,v259 \|	0
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	1
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Model \| Manuallyadded \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	1
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \| \| RAM \| 20GB \|	\| Harddisk \| ToshibaSSD512G \| \| --- \| --- \| \| System \| Ubuntu16.04 \|	1
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \| \| RAM \| 20GB \|	\| GPU \| GTX1080 \| TitanX \| \| --- \| --- \| --- \| \| ComputeCapability \| 6.1 \| 6.1 \| \| GlobalMem(GB) \| 8 \| 10 \| \| #ofSMs \| 20 \| 28 \| \| CoreClock(MHz) \| 1607 \| 1417 \| \| MemClock(MHz) \| 5005 \| 5005 \| \| MemBusWidth \| 256 \| 384 \| \| L2Cache(KB) \| 2048 \| 3072 \|	0
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \|	\| RAM \| 20GB \| \| --- \| --- \| \| Harddisk \| ToshibaSSD512G \| \| System \| Ubuntu16.04 \|	1
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \|	\| GlobalMem(GB) \| 8 \| 10 \| \| --- \| --- \| --- \| \| #ofSMs \| 20 \| 28 \| \| CoreClock(MHz) \| 1607 \| 1417 \| \| MemClock(MHz) \| 5005 \| 5005 \| \| MemBusWidth \| 256 \| 384 \| \| L2Cache(KB) \| 2048 \| 3072 \|	0
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \| \| 0.0665 \| 0.0625 \|	\| 0.0639 \| 0.0603 \| \| --- \| --- \| \| 0.0767 \| 0.0761 \|	1
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \| \| 0.0665 \| 0.0625 \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| 0.575 \| 0.496 \| 0.450 \| 0.421 \| \| \| \| \| \| \| \| \| \| \| \| 0.583 \| 0.550 \| 0.536 \| 0.510 \| \| \| \| \| \| \| \| \| \| \|	0
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \|	\| 0.0665 \| 0.0625 \| \| --- \| --- \| \| 0.0639 \| 0.0603 \| \| 0.0767 \| 0.0761 \|	1
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| 0.583 \| 0.550 \| 0.536 \| 0.510 \| \| \| \| \| \| \| \| \| \| \|	0
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \|	\| 0.838 \| 0 \| \| --- \| --- \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	1
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \|	\| Accuracy/n \| 100 \| 1K \| 10K \| 50K \| \| --- \| --- \| --- \| --- \| --- \| \| 0.5 \| 1 \| 0.99 \| 0.52 \| 0.78 \| \| 0.6 \| 1 \| 0.92 \| 0.75 \| 0.88 \| \| 0.7 \| 0.99 \| 0.15 \| 0.87 \| 0.63 \| \| 0.8 \| 0.93 \| 0.49 \| 0.51 \| 0.12 \| \| 0.9 \| 0.93 \| 0.75 \| 0.28 \| 0.47 \| \| 1.0 \| 0.84 \| 0.48 \| 0.43 \| 0.64 \|	0
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \|	\| 0.831 \| 214 \| \| --- \| --- \| \| 0.836 \| 72 \| \| 0.838 \| 0 \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	1
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \|	\| 0.8 \| 0.93 \| 0.49 \| 0.51 \| 0.12 \| \| --- \| --- \| --- \| --- \| --- \| \| 0.9 \| 0.93 \| 0.75 \| 0.28 \| 0.47 \| \| 1.0 \| 0.84 \| 0.48 \| 0.43 \| 0.64 \|	0
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| OKmedicalII \| 0.369 \| 0.465 \| \| --- \| --- \| --- \| \| OKmedical \| 0.357 \| 0.430 \| \| Lazaretal. \| 0.355 \| 0.449 \| \| GIB \| 0.322 \| 0.399 \| \| Fujita \| 0.310 \| 0.378 \| \| IRIA \| 0.264 \| 0.368 \| \| Waikato \| 0.206 \| 0.273 \|	1
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| Region \| L \| T \| mi \| vii \| siii \| κiiii \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| HKG \| -41.3 \| 2.1 \| 0.67 \| 8.7 \| 0.39 \| 5.1 \| \| USA \| -16.1 \| 2.7 \| -2.0 \| 4.8 \| 0.11 \| -0.15 \| \| CAN \| -15.2 \| 3.1 \| -1.7 \| 4.8 \| 0.45 \| 0.78 \| \| SIN \| -8.5 \| 1.6 \| -0.77 \| 3.4 \| -0.41 \| 0.24 \| \| ROC \| -8.1 \| 2.7 \| -1.7 \| 3.2 \| 0.36 \| -0.16 \| \| MAS \| -4.5 \| 3.1 \| -1.5 \| 2.1 \| 1.2 \| 3.8 \| \| VIE \| -3.6 \| 1.5 \| -0.89 \| 2.4 \| -2.0 \| 6.4 \| \| GER \| -2.4 \| 2.1 \| -0.99 \| 1.4 \| 0.29 \| -0.81 \| \| FRA \| -2.4 \| 2.0 \| -0.85 \| 1.5 \| 0.70 \| 0.47 \| \| THI \| -2.3 \| 1.8 \| -0.76 \| 1.7 \| 0.32 \| 0.12 \| \| GBR \| -1.4 \| 1.4 \| -0.42 \| 1.5 \| 0.78 \| 1.6 \| \| Average \| -9.6 \| 2.2 \| -1.0 \| 3.2 \| 0.20 \| 1.6 \|	0
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| OKmedicalII \| 0.369 \| 0.465 \| \| --- \| --- \| --- \| \| OKmedical \| 0.357 \| 0.430 \| \| Lazaretal. \| 0.355 \| 0.449 \| \| GIB \| 0.322 \| 0.399 \| \| Fujita \| 0.310 \| 0.378 \| \| IRIA \| 0.264 \| 0.368 \| \| Waikato \| 0.206 \| 0.273 \|	1
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| VIE \| -3.6 \| 1.5 \| -0.89 \| 2.4 \| -2.0 \| 6.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GER \| -2.4 \| 2.1 \| -0.99 \| 1.4 \| 0.29 \| -0.81 \| \| FRA \| -2.4 \| 2.0 \| -0.85 \| 1.5 \| 0.70 \| 0.47 \| \| THI \| -2.3 \| 1.8 \| -0.76 \| 1.7 \| 0.32 \| 0.12 \| \| GBR \| -1.4 \| 1.4 \| -0.42 \| 1.5 \| 0.78 \| 1.6 \| \| Average \| -9.6 \| 2.2 \| -1.0 \| 3.2 \| 0.20 \| 1.6 \|	0
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \|	\| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| --- \| --- \| --- \| --- \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \| \| Bristol \| 0.0145(6) \| Cardiﬀ \| 0.0038(6) \| \| Norwich \| 0.0180(11) \| London \| 0.0042(2) \|	1
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \|	\| London \| Manchester \| Birmingham \| Leeds \| \| --- \| --- \| --- \| --- \| \| liverpool(613) \| brexit(694) \| brexit(803) \| brexit(682) \| \| sleep(369) \| government(612) \| disabled(663) \| eu(594) \| \| trending(337) \| tory(572) \| extremely(654) \| leaving(570) \| \| ﬀ(316) \| nhs(506) \| inclusion(654) \| nhs(539) \| \| topic(304) \| eu(493) \| wwfc(518) \| ref(520) \| \| co(-396) \| wigan(-475) \| thursday(-491) \| pop(-497) \| \| brighton(-423) \| chester(-504) \| pigeonswoop(-504) \| south(-504) \| \| lunch(-449) \| lunch(-515) \| villa(-557) \| thursday(-512) \| \| awards(-472) \| gt(-564) \| art(-692) \| council(-564) \| \| greater(-818) \| mufc(-606) \| blues(-700) \| bradford(-586) \|	0
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \| \| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \|	\| Bristol \| 0.0145(6) \| Cardiﬀ \| 0.0038(6) \| \| --- \| --- \| --- \| --- \| \| Norwich \| 0.0180(11) \| London \| 0.0042(2) \|	1
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \| \| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \|	\| co(-396) \| wigan(-475) \| thursday(-491) \| pop(-497) \| \| --- \| --- \| --- \| --- \| \| brighton(-423) \| chester(-504) \| pigeonswoop(-504) \| south(-504) \| \| lunch(-449) \| lunch(-515) \| villa(-557) \| thursday(-512) \| \| awards(-472) \| gt(-564) \| art(-692) \| council(-564) \| \| greater(-818) \| mufc(-606) \| blues(-700) \| bradford(-586) \|	0
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \| \| Filepath \| 788 \| 315 \|	\| Fileextensions \| 1170 \| 316 \| \| --- \| --- \| --- \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Totalfeaturesize \| 31699 \| 15497 \|	1
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \| \| Filepath \| 788 \| 315 \|	\| FeatureType \| Top100 \| Top500 \| \| --- \| --- \| --- \| \| Extension \| 27 \| 79 \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Text \| 68 \| 416 \|	0
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \|	\| Filepath \| 788 \| 315 \| \| --- \| --- \| --- \| \| Fileextensions \| 1170 \| 316 \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Totalfeaturesize \| 31699 \| 15497 \|	1
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \|	\| Filesizerelated \| 2 \| 2 \| \| --- \| --- \| --- \| \| Timerelated \| 3 \| 3 \| \| Text \| 68 \| 416 \|	0
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \| \| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \|	\| 9 \| HMM \| 147GBhtmlﬁle \| 1841 \| ourimplementation \| \| --- \| --- \| --- \| --- \| --- \| \| 10 \| PageRank \| 187GBwebpage \| 18470 \| Mahout \| \| 11 \| Hive-bench \| 156GBDBtable \| 3659 \| Hivebench \|	1
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \| \| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \|	\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| 1-7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| 70 \| \| HadoopTerasort \| 645 \| 766 \| 768 \| 773 \| 815 \| 882 \| 901 \| \| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \| 630 \| 760 \| 891 \| 990 \| 1099 \| 1194 \| \| SpeedupTerasort \| 1.6 \| 1.9 \| 1.9 \| 1.8 \| 1.9 \| 2.0 \| 2.0 \| \| SpeedupTerasplit \| 1.5 \| 1.2 \| 1.2 \| 1.2 \| 1.2 \| 1.4 \| 1.5 \| \| Speeduptotal \| 1.6 \| 1.6 \| 1.6 \| 1.5 \| 1.5 \| 1.6 \| 1.7 \|	0
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \|	\| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| --- \| --- \| --- \| --- \| --- \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \| \| 9 \| HMM \| 147GBhtmlﬁle \| 1841 \| ourimplementation \| \| 10 \| PageRank \| 187GBwebpage \| 18470 \| Mahout \| \| 11 \| Hive-bench \| 156GBDBtable \| 3659 \| Hivebench \|	1
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \|	\| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \| 630 \| 760 \| 891 \| 990 \| 1099 \| 1194 \| \| SpeedupTerasort \| 1.6 \| 1.9 \| 1.9 \| 1.8 \| 1.9 \| 2.0 \| 2.0 \| \| SpeedupTerasplit \| 1.5 \| 1.2 \| 1.2 \| 1.2 \| 1.2 \| 1.4 \| 1.5 \| \| Speeduptotal \| 1.6 \| 1.6 \| 1.6 \| 1.5 \| 1.5 \| 1.6 \| 1.7 \|	0

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premise string	hypothesis string	label int64
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| SMD1 \| 853319 \| 1150 \| 1692741 \| 2639 \| 2178997 \| 3640 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SMD2 \| 1030789 \| 1459 \| 1557348 \| 2387 \| 2232534 \| 3787 \| \| SMD3 \| 879785 \| 1238 \| 1476206 \| 2390 \| 1978647 \| 2992 \| \| SMD4 \| 581078 \| 710 \| 1134376 \| 1687 \| 1438936 \| 2153 \| \| SMD5 \| 1217102 \| 1683 \| 2021104 \| 2966 \| 2904080 \| 3772 \| \| SMD6 \| 1303778 \| 1646 \| 2463058 \| 3301 \| 3230143 \| 4165 \|	1
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| t \| k \| maximaln \| ourresult \| result \| t \| k \| maximaln \| ourresult \| result \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 7 \| 2 \| 192 \| 551 \| 557 \| 15 \| 2 \| 49152 \| 147407 \| 147421 \| \| 8 \| 2 \| 384 \| 1124 \| 1131 \| 15 \| 3 \| 57344 \| 229266 \| 229307 \| \| 8 \| 3 \| 448 \| 1731 \| 1751 \| 15 \| 4 \| 61440 \| 306973 \| 307094 \| \| 9 \| 2 \| 768 \| 2273 \| 2281 \| 15 \| 5 \| 63488 \| 380476 \| 380778 \| \| 9 \| 3 \| 896 \| 3516 \| 3539 \| 15 \| 6 \| 64512 \| 450699 \| 451375 \| \| 10 \| 2 \| 1536 \| 4574 \| 4583 \| 16 \| 2 \| 98304 \| 294860 \| 294875 \| \| 10 \| 3 \| 1792 \| 7093 \| 7119 \| 16 \| 3 \| 114688 \| 458635 \| 458679 \| \| 10 \| 4 \| 1920 \| 9448 \| 9524 \| 16 \| 4 \| 122880 \| 614158 \| 614288 \| \| 11 \| 2 \| 3072 \| 9179 \| 9189 \| 16 \| 5 \| 126976 \| 761373 \| 761698 \| \| 11 \| 3 \| 3584 \| 14254 \| 14283 \| 16 \| 6 \| 129024 \| 902220 \| 902949 \| \| 11 \| 4 \| 3840 \| 19033 \| 19118 \| 16 \| 7 \| 130048 \| 1038539 \| 1040073 \| \| 12 \| 2 \| 6144 \| 18392 \| 18403 \| 17 \| 2 \| 196608 \| 589769 \| 589785 \| \| 12 \| 3 \| 7168 \| 28583 \| 28615 \| 17 \| 3 \| 229376 \| 917380 \| 917427 \| \| 12 \| 4 \| 7680 \| 38218 \| 38312 \| 17 \| 4 \| 245760 \| 1228543 \| 1228682 \| \| 12 \| 5 \| 7936 \| 47257 \| 47490 \| 17 \| 5 \| 253952 \| 1523198 \| 1523546 \| \| 13 \| 2 \| 12288 \| 36821 \| 36833 \| 17 \| 6 \| 258048 \| 1805325 \| 1806107 \| \| 13 \| 3 \| 14336 \| 57248 \| 57283 \| 17 \| 7 \| 260096 \| 2078796 \| 2080445 \| \| 13 \| 4 \| 15360 \| 76603 \| 76706 \| 18 \| 2 \| 393216 \| 1179590 \| 1179607 \| \| 13 \| 5 \| 15872 \| 94842 \| 95098 \| 18 \| 3 \| 458752 \| 1834877 \| 1834927 \| \| 14 \| 2 \| 24576 \| 73682 \| 73695 \| 18 \| 4 \| 491520 \| 2457328 \| 2457476 \| \| 14 \| 3 \| 28672 \| 114585 \| 114623 \| 18 \| 5 \| 507904 \| 3046879 \| 3047250 \| \| 14 \| 4 \| 30720 \| 153388 \| 153500 \| 18 \| 6 \| 516096 \| 3611598 \| 3612433 \| \| 14 \| 5 \| 31744 \| 190043 \| 190322 \| 18 \| 7 \| 520192 \| 4159437 \| 4161201 \| \| 14 \| 6 \| 32256 \| 224970 \| 225593 \| 18 \| 8 \| 522240 \| 4696076 \| 4699666 \|	0
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| SMD1 \| 853319 \| 1150 \| 1692741 \| 2639 \| 2178997 \| 3640 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SMD2 \| 1030789 \| 1459 \| 1557348 \| 2387 \| 2232534 \| 3787 \| \| SMD3 \| 879785 \| 1238 \| 1476206 \| 2390 \| 1978647 \| 2992 \| \| SMD4 \| 581078 \| 710 \| 1134376 \| 1687 \| 1438936 \| 2153 \| \| SMD5 \| 1217102 \| 1683 \| 2021104 \| 2966 \| 2904080 \| 3772 \| \| SMD6 \| 1303778 \| 1646 \| 2463058 \| 3301 \| 3230143 \| 4165 \|	1
\| Pr.No. \| Best \| Median \| Worst \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \| TotalLL<br>FE \| TotalUL<br>FE \|	\| 11 \| 4 \| 3840 \| 19033 \| 19118 \| 16 \| 7 \| 130048 \| 1038539 \| 1040073 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 12 \| 2 \| 6144 \| 18392 \| 18403 \| 17 \| 2 \| 196608 \| 589769 \| 589785 \| \| 12 \| 3 \| 7168 \| 28583 \| 28615 \| 17 \| 3 \| 229376 \| 917380 \| 917427 \| \| 12 \| 4 \| 7680 \| 38218 \| 38312 \| 17 \| 4 \| 245760 \| 1228543 \| 1228682 \| \| 12 \| 5 \| 7936 \| 47257 \| 47490 \| 17 \| 5 \| 253952 \| 1523198 \| 1523546 \| \| 13 \| 2 \| 12288 \| 36821 \| 36833 \| 17 \| 6 \| 258048 \| 1805325 \| 1806107 \| \| 13 \| 3 \| 14336 \| 57248 \| 57283 \| 17 \| 7 \| 260096 \| 2078796 \| 2080445 \| \| 13 \| 4 \| 15360 \| 76603 \| 76706 \| 18 \| 2 \| 393216 \| 1179590 \| 1179607 \| \| 13 \| 5 \| 15872 \| 94842 \| 95098 \| 18 \| 3 \| 458752 \| 1834877 \| 1834927 \| \| 14 \| 2 \| 24576 \| 73682 \| 73695 \| 18 \| 4 \| 491520 \| 2457328 \| 2457476 \| \| 14 \| 3 \| 28672 \| 114585 \| 114623 \| 18 \| 5 \| 507904 \| 3046879 \| 3047250 \| \| 14 \| 4 \| 30720 \| 153388 \| 153500 \| 18 \| 6 \| 516096 \| 3611598 \| 3612433 \| \| 14 \| 5 \| 31744 \| 190043 \| 190322 \| 18 \| 7 \| 520192 \| 4159437 \| 4161201 \| \| 14 \| 6 \| 32256 \| 224970 \| 225593 \| 18 \| 8 \| 522240 \| 4696076 \| 4699666 \|	0
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \|	\| bank1 \| bank2 \| \| --- \| --- \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \| \| page1 \| page2 \| \| ...onpage<unk>ofthe... \| ...wasitfrontpagenews... \| \| ...asourcetoldpagesix.... \| ...himself,tonypage,theformer... \| \| ...onpage<unk>ofthe... \| ...sectionsofthepagethatdiscuss... \| \| proﬁle1 \| proﬁle2 \| \| ...(<unk>:quote,proﬁle,research)... \| ...so<unk>theproﬁleofthecity... \| \| ...(<unk>:quote,proﬁle,research)... \| ...thehighestproﬁle<unk>heldby... \| \| ...(<unk>:quote,proﬁle,research)... \| ...fromhighi,eliteschools,... \| \| edition1 \| edition2 \| \| ...ofthesecondeditionofwindows... \| ...ofthenewyorkedition.... \| \| ...thismonth’seditionof<unk>,the... \| ...ofthenewyorkedition.... \| \| ...forthcomingd.c.editionofthehit... \| ...ofthenewyorkedition.... \| \| rodham1 \| rodham2 \| \| ...senatorshillaryrodhamclintonand... \| \| \| ...makinghillaryrodhamclintonhis... \| \| \| ...hillaryrodhamclinton’scampaignhas... \| \|	1
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \|	\| Market \| #ofBenignApps \| #ofMalware \| \| --- \| --- \| --- \| \| GooglePlay \| 39156 \| 26178 \| \| Anzhi \| 2957 \| 12260 \| \| AppChina \| 1845 \| 4154 \| \| SlideMe \| 289 \| 132 \| \| HiApk \| 65 \| 157 \| \| FDroid \| 29 \| 2 \| \| Angeeks \| 6 \| 27 \|	0
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \| \| bank1 \| bank2 \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \|	\| page1 \| page2 \| \| --- \| --- \| \| ...onpage<unk>ofthe... \| ...wasitfrontpagenews... \| \| ...asourcetoldpagesix.... \| ...himself,tonypage,theformer... \| \| ...onpage<unk>ofthe... \| ...sectionsofthepagethatdiscuss... \| \| proﬁle1 \| proﬁle2 \| \| ...(<unk>:quote,proﬁle,research)... \| ...so<unk>theproﬁleofthecity... \| \| ...(<unk>:quote,proﬁle,research)... \| ...thehighestproﬁle<unk>heldby... \| \| ...(<unk>:quote,proﬁle,research)... \| ...fromhighi,eliteschools,... \| \| edition1 \| edition2 \| \| ...ofthesecondeditionofwindows... \| ...ofthenewyorkedition.... \| \| ...thismonth’seditionof<unk>,the... \| ...ofthenewyorkedition.... \| \| ...forthcomingd.c.editionofthehit... \| ...ofthenewyorkedition.... \| \| rodham1 \| rodham2 \| \| ...senatorshillaryrodhamclintonand... \| \| \| ...makinghillaryrodhamclintonhis... \| \| \| ...hillaryrodhamclinton’scampaignhas... \| \|	1
\| rock1 \| rock2 \| \| --- \| --- \| \| ...atthe<unk>pop,rockandjazz... \| ...includinghsbc,northernrockand... \| \| ...alittlebit<unk>rock,... \| ...pakistanhasa<unk>rockmusicscene... \| \| ...onlightrockand<unk>stations... \| ...spokesmanforroundrock,<unk>... \| \| bank1 \| bank2 \| \| ...beingabankholidayin... \| ...thebankofenglandhas... \| \| ...alltheusbankrunsand... \| ...withtheroyalbankofscotland... \| \| ...bygettingthebank’sinterests... \| ...developmentbankofjapanandthe... \|	\| FDroid \| 29 \| 2 \| \| --- \| --- \| --- \| \| Angeeks \| 6 \| 27 \|	0
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Disgust \| 60 \| \| --- \| --- \| \| Surprise \| 25 \| \| Repression \| 27 \| \| Others \| 102 \|	1
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Emotion \| Frequency \| \| --- \| --- \| \| Amusement \| 5 \| \| Sadness \| 6 \| \| Disgust \| 88 \| \| Surprise \| 20 \| \| Contempt \| 3 \| \| Fear \| 2 \| \| Repression \| 40 \| \| Tense \| 28 \|	0
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Disgust \| 60 \| \| --- \| --- \| \| Surprise \| 25 \| \| Repression \| 27 \| \| Others \| 102 \|	1
\| Emotion \| Frequency \| \| --- \| --- \| \| Happiness \| 33 \|	\| Repression \| 40 \| \| --- \| --- \| \| Tense \| 28 \|	0
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \|	\| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 31.05.2015 \| 210 \| 526 \| 158 \| 98 \| 36 \| 2 \| 1030 \| \| Total \| 452 \| 979 \| 273 \| 185 \| 62 \| 9 \| 1960 \|	1
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \|	\| date \| truepositives \| falsepositives \| falsenegatives \| \| --- \| --- \| --- \| --- \| \| 2012-07-02 \| 1 \| 0 \| 0 \| \| 2012-07-03 \| 3 \| 0 \| 0 \| \| 2012-07-04 \| 2 \| 0 \| 0 \| \| 2012-07-05 \| 2 \| 0 \| 0 \| \| 2012-07-06 \| 3 \| 0 \| 0 \| \| 2012-07-08 \| 0 \| 0 \| 2 \| \| 2012-07-09 \| 2 \| 0 \| 0 \| \| 2012-07-10 \| 2 \| 0 \| 0 \| \| 2012-07-11 \| 0 \| 0 \| 1 \| \| 2012-07-14 \| 1 \| 0 \| 0 \| \| 2012-07-29 \| 1 \| 0 \| 0 \| \| 2012-07-30 \| 1 \| 0 \| 0 \| \| total \| 18 \| 0 \| 3 \|	0
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \| \| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \|	\| 31.05.2015 \| 210 \| 526 \| 158 \| 98 \| 36 \| 2 \| 1030 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Total \| 452 \| 979 \| 273 \| 185 \| 62 \| 9 \| 1960 \|	1
\| Date \| 1 \| 2 \| 3 \| 4 \| 5 \| 5+ \| Total \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 29.05.2015 \| 144 \| 190 \| 53 \| 33 \| 11 \| 4 \| 435 \| \| 30.05.2015 \| 98 \| 263 \| 62 \| 54 \| 15 \| 3 \| 495 \|	\| 2012-07-06 \| 3 \| 0 \| 0 \| \| --- \| --- \| --- \| --- \| \| 2012-07-08 \| 0 \| 0 \| 2 \| \| 2012-07-09 \| 2 \| 0 \| 0 \| \| 2012-07-10 \| 2 \| 0 \| 0 \| \| 2012-07-11 \| 0 \| 0 \| 1 \| \| 2012-07-14 \| 1 \| 0 \| 0 \| \| 2012-07-29 \| 1 \| 0 \| 0 \| \| 2012-07-30 \| 1 \| 0 \| 0 \| \| total \| 18 \| 0 \| 3 \|	0
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \|	\| R \| 13513 \| 8.0 \| \| --- \| --- \| --- \| \| S \| 23551 \| 14.0 \| \| FS \| 49301 \| 29.2 \| \| RS \| 31150 \| 18.5 \|	1
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \|	\| Model \| Manuallyadded \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \| \| R \| 13513 \| 8.0 \| \| S \| 23551 \| 14.0 \|	\| FS \| 49301 \| 29.2 \| \| --- \| --- \| --- \| \| RS \| 31150 \| 18.5 \|	1
\| Viewpoint \| No.intotal \| No.permodel \| \| --- \| --- \| --- \| \| F \| 18431 \| 10.9 \| \| R \| 13513 \| 8.0 \| \| S \| 23551 \| 14.0 \|	\| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| Merge \| 95.4 \| 92.5 \| 97.5 \| 97.4 \| 95.4 \| 97.2 \| 95.9 \| 71.8 \| 73.9 \| 82.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Swap \| 96.8 \| 94.1 \| 97.5 \| 98.0 \| 96.1 \| 98.2 \| 96.2 \| 83.0 \| 84.2 \| 87.2 \|	1
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| \| CRDI(-) \| CKDI(-) \| CADI(+) \| PNA(-) \| CAAI(+) \| DDI(+) \| EDI(+) \| ACC(+) \| ABC(+) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 8 \| 0.65 \| 0.53 \| 0.61 \| 0.57 \| 0.74 \| 0.55 \| 0.40 \| 0.68 \| 0.70 \| \| 9 \| 0.55 \| 0.46 \| 0.57 \| 0.57 \| 0.68 \| 0.53 \| 0.27 \| 0.63 \| 0.68 \| \| 10 \| 0.40 \| 0.46 \| 0.53 \| 0.46 \| 0.57 \| 0.42 \| 0.17 \| 0.53 \| 0.61 \| \| 11 \| 0.38 \| 0.46 \| 0.44 \| 0.38 \| 0.48 \| 0.31 \| 0.10 \| 0.38 \| 0.51 \| \| 12 \| 0.27 \| 0.34 \| 0.27 \| 0.27 \| 0.29 \| 0.23 \| 0.04 \| 0.31 \| 0.42 \|	0
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| Merge \| 95.4 \| 92.5 \| 97.5 \| 97.4 \| 95.4 \| 97.2 \| 95.9 \| 71.8 \| 73.9 \| 82.8 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Swap \| 96.8 \| 94.1 \| 97.5 \| 98.0 \| 96.1 \| 98.2 \| 96.2 \| 83.0 \| 84.2 \| 87.2 \|	1
\| \| ODINDATA \| SMULTRONDATA \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| DE \| GD \| HA \| MG \| KO \| CY \| YAQ \| DE \| SV \| DE-SV \| \| Baseline \| 76.7 \| 72.0 \| 54.4 \| 57.4 \| 56.0 \| 75.4 \| 54.4 \| 40.7 \| 37.5 \| 43.3 \| \| Remove \| 93.9 \| 87.8 \| 95.7 \| 88.9 \| 88.1 \| 95.1 \| 90.9 \| 63.6 \| 62.2 \| 73.5 \|	\| 9 \| 0.55 \| 0.46 \| 0.57 \| 0.57 \| 0.68 \| 0.53 \| 0.27 \| 0.63 \| 0.68 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 10 \| 0.40 \| 0.46 \| 0.53 \| 0.46 \| 0.57 \| 0.42 \| 0.17 \| 0.53 \| 0.61 \| \| 11 \| 0.38 \| 0.46 \| 0.44 \| 0.38 \| 0.48 \| 0.31 \| 0.10 \| 0.38 \| 0.51 \| \| 12 \| 0.27 \| 0.34 \| 0.27 \| 0.27 \| 0.29 \| 0.23 \| 0.04 \| 0.31 \| 0.42 \|	0
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \| \| Fast-RCNN+SRBBS+RBB \| 69.6 \|	\| Fast-RCNN+SRBBS+RBB+RRoI \| 75.7 \| \| --- \| --- \| \| RRD(Ours) \| 84.3 \|	1
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \| \| Fast-RCNN+SRBBS+RBB \| 69.6 \|	\| Method \| DS1 \| DS2 \| \| --- \| --- \| --- \| \| HOG \| 50 \| 52 \| \| Fast-RCNN \| 54 \| 61 \| \| Faster-RCNN \| 60 \| 66 \| \| Yolo \| 65 \| 68 \|	0
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \|	\| Fast-RCNN+SRBBS+RBB \| 69.6 \| \| --- \| --- \| \| Fast-RCNN+SRBBS+RBB+RRoI \| 75.7 \| \| RRD(Ours) \| 84.3 \|	1
\| Methods \| mAP \| \| --- \| --- \| \| Fast-RCNN+SRBBS \| 55.7 \|	\| Faster-RCNN \| 60 \| 66 \| \| --- \| --- \| --- \| \| Yolo \| 65 \| 68 \|	0
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 2nd(deep) \| 509.70 \| 463.76 \| 484.57 \| 415.93 \| \| --- \| --- \| --- \| --- \| --- \| \| 3rd(decoder) \| 679.54 \| 623.16 \| 624.23 \| 583.21 \|	1
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 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
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 2nd(deep) \| 509.70 \| 463.76 \| 484.57 \| 415.93 \| \| --- \| --- \| --- \| --- \| --- \| \| 3rd(decoder) \| 679.54 \| 623.16 \| 624.23 \| 583.21 \|	1
\| \| 32×32 \| 64×64 \| \| \| \| --- \| --- \| --- \| --- \| --- \| \| HiddenLayer \| 8Projections \| 16Projections \| 8Projections \| 16Projections \| \| 1st(encoder) \| 519.52 \| 453.33 \| 466.92 \| 392.09 \|	\| 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
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| PLPCC \| 2 \| 66.5761 \| 72.5543 \| \| --- \| --- \| --- \| --- \| \| 4 \| 76.9203 \| 81.0507 \| \| \| 8 \| 85.3080 \| 87.7717 \| \| \| 16 \| 90.6341 \| 91.9022 \| \| \| 32 \| 93.5326 \| 94.3116 \| \| \| 64 \| 94.6920 \| 95.3986 \| \| \| LFCC \| 2 \| 83.0072 \| 85.8152 \| \| 4 \| 90.3623 \| 91.7935 \| \| \| 8 \| 94.6196 \| 95.4891 \| \| \| 16 \| 96.2681 \| 96.6848 \| \| \| 32 \| 97.1014 \| 97.3551 \| \| \| 64 \| 97.2464 \| 97.6268 \| \| \| MFCC \| 2 \| 74.3116 \| 78.6051 \| \| 4 \| 84.8551 \| 86.9384 \| \| \| 8 \| 90.6703 \| 92.0290 \| \| \| 16 \| 94.1667 \| 94.6920 \| \| \| 32 \| 95.6522 \| 95.9964 \| \| \| 64 \| 96.7935 \| 97.1014 \| \|	1
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| Features \| L-SVM \| K-SVM \| NB \| DT \| RF \| LR \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IDT \| 0.7731 \| 0.6374 \| 0.5984 \| 0.5895 \| 0.5567 \| 0.6425 \| \| MicroExpression \| 0.7502 \| 0.7540 \| 0.7629 \| 0.7269 \| 0.8064 \| 0.7398 \| \| Transcript \| 0.6457 \| 0.4667 \| 0.6625 \| 0.5251 \| 0.6172 \| 0.5643 \| \| MFCC \| 0.7694 \| 0.8171 \| 0.6726 \| 0.4369 \| 0.7393 \| 0.6683 \| \| IDT+MicroExpression \| 0.8347 \| 0.7540 \| 0.7629 \| 0.7687 \| 0.8184 \| 0.7419 \| \| IDT+MicroExpression+Transcripts \| 0.8347 \| 0.7540 \| 0.7776 \| 0.7777 \| 0.8184 \| 0.7419 \| \| IDT+MicroExpression+MFCC \| 0.8596 \| 0.8233 \| 0.7629 \| 0.7687 \| 0.8477 \| 0.7894 \| \| AllModalities \| 0.8773 \| 0.8233 \| 0.7776 \| 0.7777 \| 0.8477 \| 0.7894 \|	0
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| PLPCC \| 2 \| 66.5761 \| 72.5543 \| \| --- \| --- \| --- \| --- \| \| 4 \| 76.9203 \| 81.0507 \| \| \| 8 \| 85.3080 \| 87.7717 \| \| \| 16 \| 90.6341 \| 91.9022 \| \| \| 32 \| 93.5326 \| 94.3116 \| \| \| 64 \| 94.6920 \| 95.3986 \| \| \| LFCC \| 2 \| 83.0072 \| 85.8152 \| \| 4 \| 90.3623 \| 91.7935 \| \| \| 8 \| 94.6196 \| 95.4891 \| \| \| 16 \| 96.2681 \| 96.6848 \| \| \| 32 \| 97.1014 \| 97.3551 \| \| \| 64 \| 97.2464 \| 97.6268 \| \| \| MFCC \| 2 \| 74.3116 \| 78.6051 \| \| 4 \| 84.8551 \| 86.9384 \| \| \| 8 \| 90.6703 \| 92.0290 \| \| \| 16 \| 94.1667 \| 94.6920 \| \| \| 32 \| 95.6522 \| 95.9964 \| \| \| 64 \| 96.7935 \| 97.1014 \| \|	1
\| Feature \| ModelOrder \| BaselineSystem \| FusedSystem \| \| --- \| --- \| --- \| --- \| \| LPCC \| 2 \| 80.9420 \| 84.7101 \| \| 4 \| 88.9855 \| 91.0870 \| \| \| 8 \| 93.8949 \| 94.7826 \| \| \| 16 \| 95.6884 \| 96.2862 \| \| \| 32 \| 96.5399 \| 97.1014 \| \| \| 64 \| 96.7391 \| 97.2826 \| \|	\| IDT+MicroExpression+Transcripts \| 0.8347 \| 0.7540 \| 0.7776 \| 0.7777 \| 0.8184 \| 0.7419 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IDT+MicroExpression+MFCC \| 0.8596 \| 0.8233 \| 0.7629 \| 0.7687 \| 0.8477 \| 0.7894 \| \| AllModalities \| 0.8773 \| 0.8233 \| 0.7776 \| 0.7777 \| 0.8477 \| 0.7894 \|	0
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \|	\| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \| \| --- \| --- \| --- \| --- \| \| RAN-LSTM-1L \| 0.74 \| 0.82 \| 0.78 \| \| RAN-LSTM-2L \| 0.85 \| 0.81 \| 0.83 \|	1
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \|	\| Processingstep \| Hardware \| DNN \| ResNet-CNN \| BLSTM \| LACE \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| AMtraining \| GPU \| 0.012 \| 0.60 \| 0.022 \| 0.23 \| \| AMevaluation \| GPU \| 0.0064 \| 0.15 \| 0.0081 \| 0.081 \| \| AMevaluation \| CPU \| 0.052 \| 11.7 \| n/a \| 8.47 \| \| Decoding \| GPU \| 1.04 \| 1.19 \| 1.40 \| 1.38 \|	0
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \| \| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \|	\| RAN-LSTM-1L \| 0.74 \| 0.82 \| 0.78 \| \| --- \| --- \| --- \| --- \| \| RAN-LSTM-2L \| 0.85 \| 0.81 \| 0.83 \|	1
\| Network \| Precision \| Recall \| F1-Score \| \| --- \| --- \| --- \| --- \| \| AlexNet \| 0.28 \| 0.67 \| 0.40 \| \| RAN-CNN-FC6-3L \| 0.77 \| 0.82 \| 0.79 \| \| RAN-CNN-FC6-5L \| 0.82 \| 0.81 \| 0.81 \| \| RAN-CNN-FC7-5L \| 0.79 \| 0.81 \| 0.80 \| \| RAN-CNN-FC6-5L-D \| 0.81 \| 0.83 \| 0.82 \|	\| AMevaluation \| CPU \| 0.052 \| 11.7 \| n/a \| 8.47 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Decoding \| GPU \| 1.04 \| 1.19 \| 1.40 \| 1.38 \|	0
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \|	\| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \| \| --- \| --- \| --- \| --- \| \| Radjenovi´c \| 6000−680=5320 \| ≥5320C+3CAD \| 3 \| \| Kitchenham \| 1704−630=1074 \| 32C+1074CDA \| 3 \|	1
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \|	\| Dataset \| c(EXIF,mm)err \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Rother \| Jiang \| Arie \| VisualSFM \| 1DSfM \| L2 \| L1 \| \| \| fountain-P11 \| 23.3 \| 14.0 \| 22.6 \| 20.7 \| 32.2 \| 6.9 \| 7.0 \| \| Herz-Jesu-P25 \| 49.5 \| 64.0 \| 47.9 \| 45.3 \| 64.9 \| 25.5 \| 26.2 \| \| castle-P30 \| 2651.8 \| 235.0 \| - \| 190.1 \| - \| 317.3 \| 166.7 \|	0
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \| \| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \|	\| Radjenovi´c \| 6000−680=5320 \| ≥5320C+3CAD \| 3 \| \| --- \| --- \| --- \| --- \| \| Kitchenham \| 1704−630=1074 \| 32C+1074CDA \| 3 \|	1
\| Datasets \| #StudiesReviewed \| ReviewCost \| #MissingRelevant \| \| --- \| --- \| --- \| --- \| \| Wahono \| 7002−670=6332 \| ≥6332C+4CAD \| 4 \| \| Hall \| 8991−350=8641 \| ≥8641C+6CAD \| 6 \|	\| Herz-Jesu-P25 \| 49.5 \| 64.0 \| 47.9 \| 45.3 \| 64.9 \| 25.5 \| 26.2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| castle-P30 \| 2651.8 \| 235.0 \| - \| 190.1 \| - \| 317.3 \| 166.7 \|	0
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| f3 \| 1 \| 8 \| 7 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| \| f4 \| 4 \| 18 \| 14 \| 26 \|	1
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| k \| 1 \| 2 \| 3 \| 4 \| 5 \| odd \| even \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| d<br>Lowerboundonχ(F) \| 4 \| 13 \| 29 \| 55 \| 92 \| 5519332<br>d+d+d+<br>124124 \| 551132<br>d+d+d+1<br>1246 \| \| d<br>Upperboundonχ(F) \| 4 \| 13 \| 30 \| 65 \| 108 \| 2<br>(d+1)(cid:100)(d+1)/2(cid:101) \| \| \| d<br>Valueofχ(F)0,1 \| 4 \| 9 \| 16 \| 25 \| 36 \| 2<br>(d+1) \| \| \| d<br>Lowerboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 34 \| 52 \| 2<br>3(cid:100)(d+1)/2(cid:101)−2 \| \| \| d<br>Upperboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 36 \| 54 \| 2<br>3(cid:100)(d+1)/2(cid:101) \| \|	0
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| f3 \| 1 \| 8 \| 7 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| \| f4 \| 4 \| 18 \| 14 \| 26 \|	1
\| fi \| f(0)i \| f(1)i \| f(1)−f(0)ii \| i<br>(cid:80)<br>f(1)−f(0)jj<br>j=1 \| \| --- \| --- \| --- \| --- \| --- \| \| f1 \| 3 \| 5 \| 2 \| 2 \| \| f2 \| 7 \| 10 \| 3 \| 5 \|	\| d<br>Valueofχ(F)0,1 \| 4 \| 9 \| 16 \| 25 \| 36 \| 2<br>(d+1) \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| d<br>Lowerboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 34 \| 52 \| 2<br>3(cid:100)(d+1)/2(cid:101)−2 \| \| d<br>Upperboundonχ(F)0,2 \| 4 \| 13 \| 24 \| 36 \| 54 \| 2<br>3(cid:100)(d+1)/2(cid:101) \|	0
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	1
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| appear \| .122 \| .076 \| .077 \| .072 \| .182 \| .112 \| .037 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	0
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| --- \| --- \| --- \| \| \| \| \|	1
\| \| \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| state \| .407 \| .296 \| .672 \| .461 \| .286 \| .269 \| .238 \| .355 \| .268 \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \| \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| \| \| \| \| \| \| \| \| \|	0
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \| \| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \|	\| (7) \| proposedauxiliaryset \| 0.755 \| 0.771 \| 0.728 \| 0.749 \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| IterativeTransfer \| (8) \| iterativetransferlearning \| 0.771 \| 0.785 \| 0.75 \| 0.767 \|	1
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \| \| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \|	\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \| \| 0.838 \| 0 \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	0
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \|	\| ModelTransfer \| (6) \| ImageNetset \| 0.745 \| 0.758 \| 0.723 \| 0.74 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (7) \| proposedauxiliaryset \| 0.755 \| 0.771 \| 0.728 \| 0.749 \| \| \| IterativeTransfer \| (8) \| iterativetransferlearning \| 0.771 \| 0.785 \| 0.75 \| 0.767 \|	1
\| \| \| Method \| Accuracy \| FakeImages \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Precision \| Recall \| F1 \| \| \| \| \| \| Low-levelFeature \| (1) \| Text-based \| 0.672 \| 0.682 \| 0.649 \| 0.665 \| \| (2) \| BoVW \| 0.685 \| 0.685 \| 0.691 \| 0.689 \| \| \| DataTransfer \| (3) \| proposedauxiliaryset \| 0.66 \| 0.664 \| 0.653 \| 0.658 \| \| FeatureTransfer \| (4) \| ImageNetset \| 0.719 \| 0.73 \| 0.699 \| 0.714 \| \| (5) \| proposedauxiliaryset \| 0.741 \| 0.747 \| 0.733 \| 0.74 \| \|	\| 0.838 \| 0 \| \| --- \| --- \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	0
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \| \| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \|	\| Flow1 \| Node1to2(PI=20) \| Flow1 \| Node1to2 \| \| --- \| --- \| --- \| --- \| \| Routingprotocol \| AODV \| SimulationTime \| 10sec \| \| Source \| CBR \| Queuelength \| 100 \| \| SuperframeTimePeriod \| 60msec \| PUActivity \| None \|	1
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \| \| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \|	\| TotalNodes \| 8 \| Packetsize \| 2000bytes \| \| --- \| --- \| --- \| --- \| \| DataSlots \| 13 \| PacketgenerationInterval \| 1msec \| \| Tc \| 1.5msec \| Queuelength \| 100 \| \| Td \| 2msec \| Routing \| AODV \| \| Ta \| 0.5msec \| Simulationtime \| 20sec \| \| Tq \| 20msec \| Source \| CBR \| \| Flow1 \| Node1to2 \| Flow3 \| Node5to6 \| \| Flow2 \| Node3to4 \| Flow4 \| Node7to8 \| \| Throughputﬂow1 \| 89.79kbps \| Throughputﬂow3 \| 91.21kbps \| \| Throughputﬂow2 \| 84.08kbps \| Throughputﬂow4 \| 89.79kbps \|	0
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \|	\| Tc \| 1.0msec \| ChannelBandwidth \| 1Mbps \| \| --- \| --- \| --- \| --- \| \| Td \| 1.0msec \| Packetsize \| 1000Bytes \| \| Ta \| 0.5msec \| PacketFrequency \| 5ms \| \| Flow1 \| Node1to2(PI=20) \| Flow1 \| Node1to2 \| \| Routingprotocol \| AODV \| SimulationTime \| 10sec \| \| Source \| CBR \| Queuelength \| 100 \| \| SuperframeTimePeriod \| 60msec \| PUActivity \| None \|	1
\| AdaptiveTDMAMAC \| CCCMACprotocol \| \| \| \| --- \| --- \| --- \| --- \| \| Nodes \| 6-12 \| Nodes \| 6-12 \| \| Channel \| 1 \| Channels \| 2 \|	\| Flow1 \| Node1to2 \| Flow3 \| Node5to6 \| \| --- \| --- \| --- \| --- \| \| Flow2 \| Node3to4 \| Flow4 \| Node7to8 \| \| Throughputﬂow1 \| 89.79kbps \| Throughputﬂow3 \| 91.21kbps \| \| Throughputﬂow2 \| 84.08kbps \| Throughputﬂow4 \| 89.79kbps \|	0
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \| \| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \|	\| (25,30) \| 6 \| 15 \| 135 \| 25 \| 125 \| 4 \| 146 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (25,40) \| 8 \| 7 \| 193 \| 20 \| 180 \| 25 \| 175 \| \| (30,36) \| 6 \| 30 \| 150 \| 30 \| 150 \| 15 \| 525 \| \| (36,48) \| 4 \| 12 \| 132 \| 36 \| 108 \| 36 \| 108 \| \| (4,20) \| 10,5(skewed) \| 2 \| 38 \| 4 \| 36 \| 2 \| 18 \| \| (8,40) \| 10,5(skewed) \| 8 \| 72 \| 8 \| 72 \| 4 \| 36 \| \| (8,50) \| 25 \| 8 \| 192 \| 8 \| 192 \| 8 \| 192 \|	1
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \| \| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \|	\| InputSize \| Depth \| Top-1 \| \| --- \| --- \| --- \| \| 224x224 \| 16 \| 28.5 \| \| 224x224<br>224x224 \| 22<br>22 \| 27.8<br>25.2 \| \| 224x224<br>224x224 \| 50<br>152 \| 24.7<br>23.0 \| \| 224x224<br>224x224 \| 50<br>101 \| 23.6<br>20.4 \| \| 224x224<br>224x224 \| 38<br>38 \| 24.3<br>22.6 \|	0
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \|	\| (8,16) \| 2 \| 8 \| 8 \| 8 \| 8 \| 4 \| 12 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (9,12) \| 4 \| 6 \| 30 \| 9 \| 27 \| 3 \| 33 \| \| (12,16) \| 4 \| 12 \| 36 \| 12 \| 36 \| 12 \| 36 \| \| (16,20) \| 5 \| 10 \| 70 \| 16 \| 64 \| 16 \| 64 \| \| (20,25) \| 5 \| 20 \| 80 \| 20 \| 80 \| 5 \| 95 \| \| (25,30) \| 6 \| 15 \| 135 \| 25 \| 125 \| 4 \| 146 \| \| (25,40) \| 8 \| 7 \| 193 \| 20 \| 180 \| 25 \| 175 \| \| (30,36) \| 6 \| 30 \| 150 \| 30 \| 150 \| 15 \| 525 \| \| (36,48) \| 4 \| 12 \| 132 \| 36 \| 108 \| 36 \| 108 \| \| (4,20) \| 10,5(skewed) \| 2 \| 38 \| 4 \| 36 \| 2 \| 18 \| \| (8,40) \| 10,5(skewed) \| 8 \| 72 \| 8 \| 72 \| 4 \| 36 \| \| (8,50) \| 25 \| 8 \| 192 \| 8 \| 192 \| 8 \| 192 \|	1
\| Redistribution<br>conﬁguration \| Communication<br>steps \| Nearlysquare<br>CopySend/Recv \| 1Dimensional<br>CopySend/Recv \| Skewed-rectangle<br>CopySend/Recv \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| (2,4) \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| 2 \| \| (4,6) \| 3 \| 3 \| 9 \| 4 \| 8 \| 3 \| 9 \| \| (4,8) \| 2 \| 2 \| 6 \| 4 \| 4 \| 2 \| 6 \| \| (6,9) \| 3 \| 6 \| 12 \| 6 \| 12 \| 3 \| 15 \|	\| 224x224<br>224x224 \| 50<br>152 \| 24.7<br>23.0 \| \| --- \| --- \| --- \| \| 224x224<br>224x224 \| 50<br>101 \| 23.6<br>20.4 \| \| 224x224<br>224x224 \| 38<br>38 \| 24.3<br>22.6 \|	0
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \|	\| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 7 \| {v,v}35 \| v3 \| v,v16 \| v3 \| 1 \| \| 8 \| {v}5 \| ∅ \| v,v16 \| v5 \| 1 \|	1
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \|	\| Step \| V \| V1 \| C \| V2 \| \| --- \| --- \| --- \| --- \| --- \| \| Initial \| v,v,v,v,v,v,v3456789 \| v1 \| ∅ \| v2 \| \| Checkv3 \| v,v,v,v,v,v456789 \| v,v13 \| ∅ \| v2 \| \| Checkv4 \| v,v,v,v,v56789 \| v,v13 \| v4 \| v2 \| \| Checkv5 \| v,v,v,v6789 \| v,v13 \| v4 \| v,v25 \| \| Checkv6 \| v,v,v789 \| v,v,v136 \| v4 \| v,v25 \| \| Checkv7 \| v,v89 \| v,v,v136 \| v,v47 \| v,v25 \| \| Checkv8 \| v9 \| v,v,v136 \| v,v,v478 \| v,v25 \| \| Checkv9 \| ∅ \| v,v,v136 \| v,v,v478 \| v,v,v259 \|	0
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \| \| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \|	\| 7 \| {v,v}35 \| v3 \| v,v16 \| v3 \| 1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 8 \| {v}5 \| ∅ \| v,v16 \| v5 \| 1 \|	1
\| i \| U \| L∩U \| S \| Selectednode \| Case \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| {v,v,v,v,v,v}123456 \| ∅ \| ∅ \| v2 \| 3 \| \| 2 \| {v,v,v,v,v,v}123456 \| v2 \| ∅ \| v1 \| 2 \| \| 3 \| {v,v,v,v,v}23456 \| v2 \| v1 \| v4 \| 1 \| \| 4 \| {v,v,v,v}2356 \| v2 \| v1 \| v2 \| 1 \| \| 5 \| {v,v,v}356 \| ∅ \| v1 \| v3 \| 3 \| \| 6 \| {v,v,v}356 \| v3 \| v1 \| v6 \| 2 \|	\| Checkv5 \| v,v,v,v6789 \| v,v13 \| v4 \| v,v25 \| \| --- \| --- \| --- \| --- \| --- \| \| Checkv6 \| v,v,v789 \| v,v,v136 \| v4 \| v,v25 \| \| Checkv7 \| v,v89 \| v,v,v136 \| v,v47 \| v,v25 \| \| Checkv8 \| v9 \| v,v,v136 \| v,v,v478 \| v,v25 \| \| Checkv9 \| ∅ \| v,v,v136 \| v,v,v478 \| v,v,v259 \|	0
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	1
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Model \| Manuallyadded \| \| \| \| \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \| \| \| \| \| \| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	1
\| Model \| Manuallyadded \| \| \| \| \| \| --- \| --- \| --- \| --- \| --- \| --- \| \| Tagset \| full \| reduced \| full \| \| \| \| Previouslytrainedmodel \| – \| – \| no \| yes \| no \|	\| Maximumsuﬃxlenght<br>parameter \| – \| – \| 15 \| 5 \| 15 \| 5 \| 15 \| 5 \| 15 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| Numberofsuﬃxes<br>Maximumsuﬃxlenght<br>Totalnumberoftags<br>Tagspersuﬃx \| 208<br>–<br>376<br>1.8 \| 208<br>–<br>362<br>1.7 \| 16<br>1<br>97<br>6 \| 94<br>4<br>445<br>4.7 \| 16<br>1<br>97<br>6 \| 100<br>4<br>340<br>3.4 \| 16<br>1<br>87<br>5.4 \| 78<br>4<br>418<br>5.4 \| 16<br>2<br>51<br>3.2 \|	0
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \| \| RAM \| 20GB \|	\| Harddisk \| ToshibaSSD512G \| \| --- \| --- \| \| System \| Ubuntu16.04 \|	1
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \| \| RAM \| 20GB \|	\| GPU \| GTX1080 \| TitanX \| \| --- \| --- \| --- \| \| ComputeCapability \| 6.1 \| 6.1 \| \| GlobalMem(GB) \| 8 \| 10 \| \| #ofSMs \| 20 \| 28 \| \| CoreClock(MHz) \| 1607 \| 1417 \| \| MemClock(MHz) \| 5005 \| 5005 \| \| MemBusWidth \| 256 \| 384 \| \| L2Cache(KB) \| 2048 \| 3072 \|	0
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \|	\| RAM \| 20GB \| \| --- \| --- \| \| Harddisk \| ToshibaSSD512G \| \| System \| Ubuntu16.04 \|	1
\| CPU \| Intel(R)Core(TM)i7-4790K4.00Hz \| \| --- \| --- \| \| GPU \| GeForceGTX1080Ti \|	\| GlobalMem(GB) \| 8 \| 10 \| \| --- \| --- \| --- \| \| #ofSMs \| 20 \| 28 \| \| CoreClock(MHz) \| 1607 \| 1417 \| \| MemClock(MHz) \| 5005 \| 5005 \| \| MemBusWidth \| 256 \| 384 \| \| L2Cache(KB) \| 2048 \| 3072 \|	0
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \| \| 0.0665 \| 0.0625 \|	\| 0.0639 \| 0.0603 \| \| --- \| --- \| \| 0.0767 \| 0.0761 \|	1
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \| \| 0.0665 \| 0.0625 \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| 0.575 \| 0.496 \| 0.450 \| 0.421 \| \| \| \| \| \| \| \| \| \| \| \| 0.583 \| 0.550 \| 0.536 \| 0.510 \| \| \| \| \| \| \| \| \| \| \|	0
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \|	\| 0.0665 \| 0.0625 \| \| --- \| --- \| \| 0.0639 \| 0.0603 \| \| 0.0767 \| 0.0761 \|	1
\| All \| Cataract \| \| --- \| --- \| \| 0.0657 \| 0.0626 \|	\| \| \| \| \| \| --- \| --- \| --- \| --- \| \| 0.583 \| 0.550 \| 0.536 \| 0.510 \| \| \| \| \| \| \| \| \| \| \|	0
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \|	\| 0.838 \| 0 \| \| --- \| --- \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	1
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \| \| 0.831 \| 214 \| \| 0.836 \| 72 \|	\| Accuracy/n \| 100 \| 1K \| 10K \| 50K \| \| --- \| --- \| --- \| --- \| --- \| \| 0.5 \| 1 \| 0.99 \| 0.52 \| 0.78 \| \| 0.6 \| 1 \| 0.92 \| 0.75 \| 0.88 \| \| 0.7 \| 0.99 \| 0.15 \| 0.87 \| 0.63 \| \| 0.8 \| 0.93 \| 0.49 \| 0.51 \| 0.12 \| \| 0.9 \| 0.93 \| 0.75 \| 0.28 \| 0.47 \| \| 1.0 \| 0.84 \| 0.48 \| 0.43 \| 0.64 \|	0
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \|	\| 0.831 \| 214 \| \| --- \| --- \| \| 0.836 \| 72 \| \| 0.838 \| 0 \| \| 0.842 \| 0 \| \| 0.846 \| 137 \|	1
\| Accuracy \| Features \| \| --- \| --- \| \| 0.823 \| 41 \|	\| 0.8 \| 0.93 \| 0.49 \| 0.51 \| 0.12 \| \| --- \| --- \| --- \| --- \| --- \| \| 0.9 \| 0.93 \| 0.75 \| 0.28 \| 0.47 \| \| 1.0 \| 0.84 \| 0.48 \| 0.43 \| 0.64 \|	0
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| OKmedicalII \| 0.369 \| 0.465 \| \| --- \| --- \| --- \| \| OKmedical \| 0.357 \| 0.430 \| \| Lazaretal. \| 0.355 \| 0.449 \| \| GIB \| 0.322 \| 0.399 \| \| Fujita \| 0.310 \| 0.378 \| \| IRIA \| 0.264 \| 0.368 \| \| Waikato \| 0.206 \| 0.273 \|	1
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| Region \| L \| T \| mi \| vii \| siii \| κiiii \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| HKG \| -41.3 \| 2.1 \| 0.67 \| 8.7 \| 0.39 \| 5.1 \| \| USA \| -16.1 \| 2.7 \| -2.0 \| 4.8 \| 0.11 \| -0.15 \| \| CAN \| -15.2 \| 3.1 \| -1.7 \| 4.8 \| 0.45 \| 0.78 \| \| SIN \| -8.5 \| 1.6 \| -0.77 \| 3.4 \| -0.41 \| 0.24 \| \| ROC \| -8.1 \| 2.7 \| -1.7 \| 3.2 \| 0.36 \| -0.16 \| \| MAS \| -4.5 \| 3.1 \| -1.5 \| 2.1 \| 1.2 \| 3.8 \| \| VIE \| -3.6 \| 1.5 \| -0.89 \| 2.4 \| -2.0 \| 6.4 \| \| GER \| -2.4 \| 2.1 \| -0.99 \| 1.4 \| 0.29 \| -0.81 \| \| FRA \| -2.4 \| 2.0 \| -0.85 \| 1.5 \| 0.70 \| 0.47 \| \| THI \| -2.3 \| 1.8 \| -0.76 \| 1.7 \| 0.32 \| 0.12 \| \| GBR \| -1.4 \| 1.4 \| -0.42 \| 1.5 \| 0.78 \| 1.6 \| \| Average \| -9.6 \| 2.2 \| -1.0 \| 3.2 \| 0.20 \| 1.6 \|	0
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| OKmedicalII \| 0.369 \| 0.465 \| \| --- \| --- \| --- \| \| OKmedical \| 0.357 \| 0.430 \| \| Lazaretal. \| 0.355 \| 0.449 \| \| GIB \| 0.322 \| 0.399 \| \| Fujita \| 0.310 \| 0.378 \| \| IRIA \| 0.264 \| 0.368 \| \| Waikato \| 0.206 \| 0.273 \|	1
\| Team \| CPM \| AUC \| \| --- \| --- \| --- \| \| DRSCREEN \| 0.434 \| 0.551 \| \| Niemeijeretal. \| 0.395 \| 0.469 \| \| LaTIM \| 0.381 \| 0.489 \| \| ISMV \| 0.375 \| 0.435 \|	\| VIE \| -3.6 \| 1.5 \| -0.89 \| 2.4 \| -2.0 \| 6.4 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| GER \| -2.4 \| 2.1 \| -0.99 \| 1.4 \| 0.29 \| -0.81 \| \| FRA \| -2.4 \| 2.0 \| -0.85 \| 1.5 \| 0.70 \| 0.47 \| \| THI \| -2.3 \| 1.8 \| -0.76 \| 1.7 \| 0.32 \| 0.12 \| \| GBR \| -1.4 \| 1.4 \| -0.42 \| 1.5 \| 0.78 \| 1.6 \| \| Average \| -9.6 \| 2.2 \| -1.0 \| 3.2 \| 0.20 \| 1.6 \|	0
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \|	\| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| --- \| --- \| --- \| --- \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \| \| Bristol \| 0.0145(6) \| Cardiﬀ \| 0.0038(6) \| \| Norwich \| 0.0180(11) \| London \| 0.0042(2) \|	1
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \|	\| London \| Manchester \| Birmingham \| Leeds \| \| --- \| --- \| --- \| --- \| \| liverpool(613) \| brexit(694) \| brexit(803) \| brexit(682) \| \| sleep(369) \| government(612) \| disabled(663) \| eu(594) \| \| trending(337) \| tory(572) \| extremely(654) \| leaving(570) \| \| ﬀ(316) \| nhs(506) \| inclusion(654) \| nhs(539) \| \| topic(304) \| eu(493) \| wwfc(518) \| ref(520) \| \| co(-396) \| wigan(-475) \| thursday(-491) \| pop(-497) \| \| brighton(-423) \| chester(-504) \| pigeonswoop(-504) \| south(-504) \| \| lunch(-449) \| lunch(-515) \| villa(-557) \| thursday(-512) \| \| awards(-472) \| gt(-564) \| art(-692) \| council(-564) \| \| greater(-818) \| mufc(-606) \| blues(-700) \| bradford(-586) \|	0
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \| \| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \|	\| Bristol \| 0.0145(6) \| Cardiﬀ \| 0.0038(6) \| \| --- \| --- \| --- \| --- \| \| Norwich \| 0.0180(11) \| London \| 0.0042(2) \|	1
\| Region \| si \| Region \| Pi \| \| --- \| --- \| --- \| --- \| \| Newcastle \| -0.0116(8) \| Manchester \| -0.0108(3) \| \| Nottingham \| -0.0059(5) \| Newcastle \| -0.0049(7) \| \| Manchester \| 0.0012(4) \| Norwich \| -0.0023(8) \| \| Leeds \| 0.0036(5) \| Leeds \| -0.0006(4) \| \| London \| 0.0072(2) \| Nottingham \| -0.0006(5) \| \| Birmingham \| 0.0130(5) \| Bristol \| 0.0014(4) \| \| Cardiﬀ \| 0.0145(6) \| Birmingham \| 0.0038(4) \|	\| co(-396) \| wigan(-475) \| thursday(-491) \| pop(-497) \| \| --- \| --- \| --- \| --- \| \| brighton(-423) \| chester(-504) \| pigeonswoop(-504) \| south(-504) \| \| lunch(-449) \| lunch(-515) \| villa(-557) \| thursday(-512) \| \| awards(-472) \| gt(-564) \| art(-692) \| council(-564) \| \| greater(-818) \| mufc(-606) \| blues(-700) \| bradford(-586) \|	0
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \| \| Filepath \| 788 \| 315 \|	\| Fileextensions \| 1170 \| 316 \| \| --- \| --- \| --- \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Totalfeaturesize \| 31699 \| 15497 \|	1
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \| \| Filepath \| 788 \| 315 \|	\| FeatureType \| Top100 \| Top500 \| \| --- \| --- \| --- \| \| Extension \| 27 \| 79 \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Text \| 68 \| 416 \|	0
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \|	\| Filepath \| 788 \| 315 \| \| --- \| --- \| --- \| \| Fileextensions \| 1170 \| 316 \| \| Filesizerelated \| 2 \| 2 \| \| Timerelated \| 3 \| 3 \| \| Totalfeaturesize \| 31699 \| 15497 \|	1
\| Featurecategory \| datasetI \| datasetII \| \| --- \| --- \| --- \| \| Filename \| 29736 \| 14861 \|	\| Filesizerelated \| 2 \| 2 \| \| --- \| --- \| --- \| \| Timerelated \| 3 \| 3 \| \| Text \| 68 \| 416 \|	0
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \| \| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \|	\| 9 \| HMM \| 147GBhtmlﬁle \| 1841 \| ourimplementation \| \| --- \| --- \| --- \| --- \| --- \| \| 10 \| PageRank \| 187GBwebpage \| 18470 \| Mahout \| \| 11 \| Hive-bench \| 156GBDBtable \| 3659 \| Hivebench \|	1
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \| \| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \|	\| NodesUsed \| 1 \| 1-2 \| 1-3 \| 1-4 \| 1-5 \| 1-6 \| 1-7 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SizeofDataset(GB) \| 10 \| 20 \| 30 \| 40 \| 50 \| 60 \| 70 \| \| HadoopTerasort \| 645 \| 766 \| 768 \| 773 \| 815 \| 882 \| 901 \| \| SphereTerasort \| 408 \| 409 \| 410 \| 429 \| 430 \| 436 \| 440 \| \| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \| 630 \| 760 \| 891 \| 990 \| 1099 \| 1194 \| \| SpeedupTerasort \| 1.6 \| 1.9 \| 1.9 \| 1.8 \| 1.9 \| 2.0 \| 2.0 \| \| SpeedupTerasplit \| 1.5 \| 1.2 \| 1.2 \| 1.2 \| 1.2 \| 1.4 \| 1.5 \| \| Speeduptotal \| 1.6 \| 1.6 \| 1.6 \| 1.5 \| 1.5 \| 1.6 \| 1.7 \|	0
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \|	\| 7 \| FuzzyK-means \| 150GBvector \| 15470 \| Mahout \| \| --- \| --- \| --- \| --- \| --- \| \| 8 \| IBCF \| 147GBratingsdata \| 32340 \| Mahout \| \| 9 \| HMM \| 147GBhtmlﬁle \| 1841 \| ourimplementation \| \| 10 \| PageRank \| 187GBwebpage \| 18470 \| Mahout \| \| 11 \| Hive-bench \| 156GBDBtable \| 3659 \| Hivebench \|	1
\| No. \| Workload \| InputDataSize \| #RetiredInstructions(Billions) \| Source \| \| --- \| --- \| --- \| --- \| --- \| \| 1 \| Sort \| 150GBdocuments \| 4578 \| Hadoopexample \| \| 2 \| WordCount \| 154GBdocuments \| 3533 \| Hadoopexample \| \| 3 \| Grep \| 154GBdocuments \| 1499 \| Hadoopexample \| \| 4 \| NaiveBayes \| 147GBtext \| 68131 \| Mahout \| \| 5 \| SVM \| 148GBhtmlﬁle \| 2051 \| ourimplementation \| \| 6 \| K-means \| 150GBvector \| 3227 \| Mahout \|	\| HadoopTerasplit \| 141 \| 266 \| 410 \| 544 \| 671 \| 901 \| 1133 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| SphereTerasplit \| 96 \| 221 \| 350 \| 462 \| 560 \| 663 \| 754 \| \| TotalHadoop \| 786 \| 1032 \| 1178 \| 1317 \| 1486 \| 1784 \| 2034 \| \| TotalSphere \| 504 \| 630 \| 760 \| 891 \| 990 \| 1099 \| 1194 \| \| SpeedupTerasort \| 1.6 \| 1.9 \| 1.9 \| 1.8 \| 1.9 \| 2.0 \| 2.0 \| \| SpeedupTerasplit \| 1.5 \| 1.2 \| 1.2 \| 1.2 \| 1.2 \| 1.4 \| 1.5 \| \| Speeduptotal \| 1.6 \| 1.6 \| 1.6 \| 1.5 \| 1.5 \| 1.6 \| 1.7 \|	0