premise string | hypothesis string | label int64 |
|---|---|---|
| Layer | Type | kernel | Activation |
| --- | --- | --- | --- |
| Input | Input(48×48) | – | – |
| Hidden1 | Conv | 3×3(64ch) | ReLU |
| Pool1 | Maxpooling | 2×2 | – |
| Hidden2 | Conv | 3×3(64ch) | ReLU |
| Pool2 | Maxpooling | 2×2 | – | | | Hidden3 | Dense | 1024 | ReLU |
| --- | --- | --- | --- |
| Output | Dense | 98 | Tanh | | 1 |
| Layer | Type | kernel | Activation |
| --- | --- | --- | --- |
| Input | Input(48×48) | – | – |
| Hidden1 | Conv | 3×3(64ch) | ReLU |
| Pool1 | Maxpooling | 2×2 | – |
| Hidden2 | Conv | 3×3(64ch) | ReLU |
| Pool2 | Maxpooling | 2×2 | – | | | Layer | Units | Filters | FilterSize | PoolSize | Activation |
| --- | --- | --- | --- | --- | --- |
| 1-input | - | - | - | - | - |
| 2-conv-1 | - | 8/16* | 5x5/11x11 | - | ReLu |
| 3-maxpool-1 | - | - | - | 2x2 | - |
| 4-conv-2 | - | 8/16* | 5x5/11x11 | - | ReLu |
| 5-maxpool-2 | - | - | - | 2x2 | - |
| 6-dropout(507-fullyconnected-1 | 256 | - | - | - | ReLu |
| 8-fullyconnected-2 | 10 | - | - | - | Sigmoid | | 0 |
| Layer | Type | kernel | Activation |
| --- | --- | --- | --- |
| Input | Input(48×48) | – | – |
| Hidden1 | Conv | 3×3(64ch) | ReLU |
| Pool1 | Maxpooling | 2×2 | – |
| Hidden2 | Conv | 3×3(64ch) | ReLU | | | Pool2 | Maxpooling | 2×2 | – |
| --- | --- | --- | --- |
| Hidden3 | Dense | 1024 | ReLU |
| Output | Dense | 98 | Tanh | | 1 |
| Layer | Type | kernel | Activation |
| --- | --- | --- | --- |
| Input | Input(48×48) | – | – |
| Hidden1 | Conv | 3×3(64ch) | ReLU |
| Pool1 | Maxpooling | 2×2 | – |
| Hidden2 | Conv | 3×3(64ch) | ReLU | | | 4-conv-2 | - | 8/16* | 5x5/11x11 | - | ReLu |
| --- | --- | --- | --- | --- | --- |
| 5-maxpool-2 | - | - | - | 2x2 | - |
| 6-dropout(507-fullyconnected-1 | 256 | - | - | - | ReLu |
| 8-fullyconnected-2 | 10 | - | - | - | Sigmoid | | 0 |
| Command | ANumber | Comments |
| --- | --- | --- |
| \alignauthor | 100 | Authoralignment | | | \numberofauthors | 200 | Authorenumeration |
| --- | --- | --- |
| \table | 300 | Fortables |
| \table* | 400 | Forwidertables | | 1 |
| Command | ANumber | Comments |
| --- | --- | --- |
| \alignauthor | 100 | Authoralignment | | | Command | ANumber | Comments |
| --- | --- | --- |
| \alignauthor | 100 | Authoralignment |
| \numberofauthors | 200 | Authorenumeration |
| \table | 300 | Fortables |
| \table* | 400 | Forwidertables | | 0 |
| Command | ANumber | Comments |
| --- | --- | --- |
| \alignauthor | 100 | Authoralignment |
| \numberofauthors | 200 | Authorenumeration | | | \table | 300 | Fortables |
| --- | --- | --- |
| \table* | 400 | Forwidertables | | 1 |
| Command | ANumber | Comments |
| --- | --- | --- |
| \alignauthor | 100 | Authoralignment |
| \numberofauthors | 200 | Authorenumeration | | | \table | 300 | Fortables |
| --- | --- | --- |
| \table* | 400 | Forwidertables | | 0 |
| Layer(type) | Parameters | PreviousLayer |
| --- | --- | --- |
| conv1(Convolution) | channels=32,kernelsize=3,padding=1 | data |
| activation1(Activation) | null | conv1 |
| conv2(Convolution) | channels=32,kernelsize=3,padding=1 | activation1 |
| activation2(Activation) | null | conv2 |
| pooling1(Pooling) | poolsize=2 | activation2 |
| dropout1(Dropout) | probability=0.2 | pooling1 |
| conv3(Convolution) | channels=64,kernelsize=3,padding=1 | dropout1 |
| activation2(Activation) | null | conv3 |
| conv4(Convolution) | channels=64,kernelsize=3,padding=1 | activation2 |
| activation4(Activation) | null | conv4 |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 |
| flatten1(Flatten) | null | dropout2 |
| fc1(FullyConnected) | #output=512 | flatten1 |
| activation5(Activation) | null | fc1 |
| dropout3(Dropout) | probability=0.2 | activation5 |
| fc2(FullyConnected) | #output=512 | dropout3 |
| activation6(Activation) | null | fc2 | | | dropout4(Dropout) | probability=0.2 | activation6 |
| --- | --- | --- |
| fc3(FullyConnected) | #output=10 | dropout4 |
| softmax(SoftmaxOutput) | null | fc3 | | 1 |
| Layer(type) | Parameters | PreviousLayer |
| --- | --- | --- |
| conv1(Convolution) | channels=32,kernelsize=3,padding=1 | data |
| activation1(Activation) | null | conv1 |
| conv2(Convolution) | channels=32,kernelsize=3,padding=1 | activation1 |
| activation2(Activation) | null | conv2 |
| pooling1(Pooling) | poolsize=2 | activation2 |
| dropout1(Dropout) | probability=0.2 | pooling1 |
| conv3(Convolution) | channels=64,kernelsize=3,padding=1 | dropout1 |
| activation2(Activation) | null | conv3 |
| conv4(Convolution) | channels=64,kernelsize=3,padding=1 | activation2 |
| activation4(Activation) | null | conv4 |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 |
| flatten1(Flatten) | null | dropout2 |
| fc1(FullyConnected) | #output=512 | flatten1 |
| activation5(Activation) | null | fc1 |
| dropout3(Dropout) | probability=0.2 | activation5 |
| fc2(FullyConnected) | #output=512 | dropout3 |
| activation6(Activation) | null | fc2 | | | Layer(type) | Parameters | PreviousLayer |
| --- | --- | --- |
| conv1(Convolution) | channels=32,kernelsize=3,padding=1 | data |
| activation1(Activation) | null | conv1 |
| conv2(Convolution) | channels=32,kernelsize=3,padding=1 | activation1 |
| activation2(Activation) | null | conv2 |
| pooling1(Pooling) | poolsize=2 | activation2 |
| dropout1(Dropout) | probability=0.2 | pooling1 |
| conv3(Convolution) | channels=64,kernelsize=3,padding=1 | dropout1 |
| activation2(Activation) | null | conv3 |
| conv4(Convolution) | channels=64,kernelsize=3,padding=1 | activation2 |
| activation4(Activation) | null | conv4 |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 |
| flatten1(Flatten) | null | dropout2 |
| fc1(FullyConnected) | #output=512 | flatten1 |
| activation5(Activation) | null | fc1 |
| dropout3(Dropout) | probability=0.2 | activation5 |
| fc2(FullyConnected) | #output=512 | dropout3 |
| activation6(Activation) | null | fc2 |
| dropout4(Dropout) | probability=0.2 | activation6 |
| fc3(FullyConnected) | #output=10 | dropout4 |
| softmax(SoftmaxOutput) | null | fc3 | | 0 |
| Layer(type) | Parameters | PreviousLayer |
| --- | --- | --- |
| conv1(Convolution) | channels=32,kernelsize=3,padding=1 | data |
| activation1(Activation) | null | conv1 |
| conv2(Convolution) | channels=32,kernelsize=3,padding=1 | activation1 |
| activation2(Activation) | null | conv2 |
| pooling1(Pooling) | poolsize=2 | activation2 |
| dropout1(Dropout) | probability=0.2 | pooling1 |
| conv3(Convolution) | channels=64,kernelsize=3,padding=1 | dropout1 |
| activation2(Activation) | null | conv3 |
| conv4(Convolution) | channels=64,kernelsize=3,padding=1 | activation2 |
| activation4(Activation) | null | conv4 |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 | | | flatten1(Flatten) | null | dropout2 |
| --- | --- | --- |
| fc1(FullyConnected) | #output=512 | flatten1 |
| activation5(Activation) | null | fc1 |
| dropout3(Dropout) | probability=0.2 | activation5 |
| fc2(FullyConnected) | #output=512 | dropout3 |
| activation6(Activation) | null | fc2 |
| dropout4(Dropout) | probability=0.2 | activation6 |
| fc3(FullyConnected) | #output=10 | dropout4 |
| softmax(SoftmaxOutput) | null | fc3 | | 1 |
| Layer(type) | Parameters | PreviousLayer |
| --- | --- | --- |
| conv1(Convolution) | channels=32,kernelsize=3,padding=1 | data |
| activation1(Activation) | null | conv1 |
| conv2(Convolution) | channels=32,kernelsize=3,padding=1 | activation1 |
| activation2(Activation) | null | conv2 |
| pooling1(Pooling) | poolsize=2 | activation2 |
| dropout1(Dropout) | probability=0.2 | pooling1 |
| conv3(Convolution) | channels=64,kernelsize=3,padding=1 | dropout1 |
| activation2(Activation) | null | conv3 |
| conv4(Convolution) | channels=64,kernelsize=3,padding=1 | activation2 |
| activation4(Activation) | null | conv4 |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 | | | activation4(Activation) | null | conv4 |
| --- | --- | --- |
| pooling2(Pooling) | poolsize=2 | activation4 |
| dropout2(Dropout) | probability=0.2 | pooling2 |
| flatten1(Flatten) | null | dropout2 |
| fc1(FullyConnected) | #output=512 | flatten1 |
| activation5(Activation) | null | fc1 |
| dropout3(Dropout) | probability=0.2 | activation5 |
| fc2(FullyConnected) | #output=512 | dropout3 |
| activation6(Activation) | null | fc2 |
| dropout4(Dropout) | probability=0.2 | activation6 |
| fc3(FullyConnected) | #output=10 | dropout4 |
| softmax(SoftmaxOutput) | null | fc3 | | 0 |
| Model | FrequentWords | RareWords | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| man | including | relatively | unconditional | hydroplane | uploading | |
| word | person<br>anyone<br>children<br>men | like<br>featuring<br>include<br>includes | extremely<br>making<br>very<br>quite | nazi<br>fairly<br>joints<br>supreme | molybdenum<br>your<br>imperial<br>intervene | -<br>-<br>-<br>- |
| BPE<br>LSTM | ii<br>hill<br>text<br>netherlands | called<br>involve<br>like<br>creating | newly<br>never<br>essentially<br>least | unintentional<br>ungenerous<br>unanimous<br>unpalatable | emphasize<br>heartbeat<br>hybridized<br>unplatable | upbeat<br>uprising<br>handling<br>hand-colored |
| char-<br>trigrams<br>LSTM | mak<br>vill<br>cow<br>maga | include<br>includes<br>undermining<br>under | resolutely<br>regeneratively<br>reproductively<br>commonly | unconstitutional<br>constitutional<br>unimolecular<br>medicinal | selenocysteine<br>guerrillas<br>scrofula<br>seleucia | drifted<br>affected<br>conflicted<br>convicted | | | char-<br>LSTM | mayr<br>many<br>mary<br>may | inclusion<br>insularity<br>includes<br>include | relates<br>replicate<br>relativity<br>gravestones | undamaged<br>unmyelinated<br>unconditionally<br>uncoordinated | hydrolyzed<br>hydraulics<br>hysterotomy<br>hydraulic | musag`ete<br>mutualism<br>mutualists<br>meursault |
| --- | --- | --- | --- | --- | --- | --- |
| char-<br>CNN | mtn<br>mann<br>jan<br>nun | include<br>includes<br>excluding<br>included | legislatively<br>lovely<br>creatively<br>negatively | unconventional<br>unintentional<br>unconstitutional<br>untraditional | hydroxyproline<br>hydrate<br>hydrangea<br>hyena | unloading<br>loading<br>upgrading<br>upholding | | 1 |
| Model | FrequentWords | RareWords | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| man | including | relatively | unconditional | hydroplane | uploading | |
| word | person<br>anyone<br>children<br>men | like<br>featuring<br>include<br>includes | extremely<br>making<br>very<br>quite | nazi<br>fairly<br>joints<br>supreme | molybdenum<br>your<br>imperial<br>intervene | -<br>-<br>-<br>- |
| BPE<br>LSTM | ii<br>hill<br>text<br>netherlands | called<br>involve<br>like<br>creating | newly<br>never<br>essentially<br>least | unintentional<br>ungenerous<br>unanimous<br>unpalatable | emphasize<br>heartbeat<br>hybridized<br>unplatable | upbeat<br>uprising<br>handling<br>hand-colored |
| char-<br>trigrams<br>LSTM | mak<br>vill<br>cow<br>maga | include<br>includes<br>undermining<br>under | resolutely<br>regeneratively<br>reproductively<br>commonly | unconstitutional<br>constitutional<br>unimolecular<br>medicinal | selenocysteine<br>guerrillas<br>scrofula<br>seleucia | drifted<br>affected<br>conflicted<br>convicted | | | corpus:low,MRC:high | | | | |
| --- | --- | --- | --- | --- |
| WSJ | Wikipedia-E | Web-E | BNC | MICASE |
| pencil<br>noisy<br>oven<br>happiness | mileage<br>towel<br>thoughtful<br>bra | doughnut<br>coke<br>steady<br>hunger | sock<br>nickel<br>boring<br>shrimp | bedroom<br>thirsty<br>spoon<br>sunshine |
| corpus:high,MRC:low | | | | |
| lire<br>southland<br>gore<br>charter | sonata<br>hank<br>aurora<br>belle | dell<br>portal<br>fort<br>enterprise | essence<br>lorry<br>debut<br>rover | hypothesis<br>velocity<br>precipitate<br>mass | | 0 |
| Model | FrequentWords | RareWords | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| man | including | relatively | unconditional | hydroplane | uploading | |
| word | person<br>anyone<br>children<br>men | like<br>featuring<br>include<br>includes | extremely<br>making<br>very<br>quite | nazi<br>fairly<br>joints<br>supreme | molybdenum<br>your<br>imperial<br>intervene | -<br>-<br>-<br>- | | | BPE<br>LSTM | ii<br>hill<br>text<br>netherlands | called<br>involve<br>like<br>creating | newly<br>never<br>essentially<br>least | unintentional<br>ungenerous<br>unanimous<br>unpalatable | emphasize<br>heartbeat<br>hybridized<br>unplatable | upbeat<br>uprising<br>handling<br>hand-colored |
| --- | --- | --- | --- | --- | --- | --- |
| char-<br>trigrams<br>LSTM | mak<br>vill<br>cow<br>maga | include<br>includes<br>undermining<br>under | resolutely<br>regeneratively<br>reproductively<br>commonly | unconstitutional<br>constitutional<br>unimolecular<br>medicinal | selenocysteine<br>guerrillas<br>scrofula<br>seleucia | drifted<br>affected<br>conflicted<br>convicted |
| char-<br>LSTM | mayr<br>many<br>mary<br>may | inclusion<br>insularity<br>includes<br>include | relates<br>replicate<br>relativity<br>gravestones | undamaged<br>unmyelinated<br>unconditionally<br>uncoordinated | hydrolyzed<br>hydraulics<br>hysterotomy<br>hydraulic | musag`ete<br>mutualism<br>mutualists<br>meursault |
| char-<br>CNN | mtn<br>mann<br>jan<br>nun | include<br>includes<br>excluding<br>included | legislatively<br>lovely<br>creatively<br>negatively | unconventional<br>unintentional<br>unconstitutional<br>untraditional | hydroxyproline<br>hydrate<br>hydrangea<br>hyena | unloading<br>loading<br>upgrading<br>upholding | | 1 |
| Model | FrequentWords | RareWords | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| man | including | relatively | unconditional | hydroplane | uploading | |
| word | person<br>anyone<br>children<br>men | like<br>featuring<br>include<br>includes | extremely<br>making<br>very<br>quite | nazi<br>fairly<br>joints<br>supreme | molybdenum<br>your<br>imperial<br>intervene | -<br>-<br>-<br>- | | | pencil<br>noisy<br>oven<br>happiness | mileage<br>towel<br>thoughtful<br>bra | doughnut<br>coke<br>steady<br>hunger | sock<br>nickel<br>boring<br>shrimp | bedroom<br>thirsty<br>spoon<br>sunshine |
| --- | --- | --- | --- | --- |
| corpus:high,MRC:low | | | | |
| lire<br>southland<br>gore<br>charter | sonata<br>hank<br>aurora<br>belle | dell<br>portal<br>fort<br>enterprise | essence<br>lorry<br>debut<br>rover | hypothesis<br>velocity<br>precipitate<br>mass | | 0 |
| ServiceName | Unit | Cost/Year |
| --- | --- | --- |
| Stratusbasesubscription | Pkg | $626.06 |
| AdditionalCPUCores | vCPU | $20.13 | | | AdditionalBlockStorage | TB | $151.95 |
| --- | --- | --- |
| PersistentSecureObjectStorage | TB | $70.35 | | 1 |
| ServiceName | Unit | Cost/Year |
| --- | --- | --- |
| Stratusbasesubscription | Pkg | $626.06 |
| AdditionalCPUCores | vCPU | $20.13 | | | NumberofUsers | 262,909 |
| --- | --- |
| NumberofServices | 39,520 |
| NumberofServicesSold | 8,862 |
| TotalRevenue | $1,349,316 |
| AverageRevenueperservice | $152 |
| AlexaGlobalRank | 12K | | 0 |
| ServiceName | Unit | Cost/Year |
| --- | --- | --- |
| Stratusbasesubscription | Pkg | $626.06 |
| AdditionalCPUCores | vCPU | $20.13 | | | AdditionalBlockStorage | TB | $151.95 |
| --- | --- | --- |
| PersistentSecureObjectStorage | TB | $70.35 | | 1 |
| ServiceName | Unit | Cost/Year |
| --- | --- | --- |
| Stratusbasesubscription | Pkg | $626.06 |
| AdditionalCPUCores | vCPU | $20.13 | | | NumberofServicesSold | 8,862 |
| --- | --- |
| TotalRevenue | $1,349,316 |
| AverageRevenueperservice | $152 |
| AlexaGlobalRank | 12K | | 0 |
| Inputframe<br>3x64x64 |
| --- |
| 7firstlayersofresnet-18(pretrained,frozenweights) |
| Reshape1x8192 |
| FC8192→128 | | | FC128→8192 |
| --- |
| Reshape128x8x8 |
| UpSamplingNearest(2),3x3Conv.128-1str.,BN,ReLU |
| UpSamplingNearest(2),3x3Conv.64-1str.,BN,ReLU |
| UpSamplingNearest(2),3x3Conv.3-1str.,BN,ReLU |
| 3sigmoid<br>Targetmask | | 1 |
| Inputframe<br>3x64x64 |
| --- |
| 7firstlayersofresnet-18(pretrained,frozenweights) |
| Reshape1x8192 |
| FC8192→128 | | | 3DConvolutional(filters=32,kernelsize=5,strides=2,activation=”relu”) |
| --- |
| 3DConvolutional(filters=32,kernelsize=5,strides=2,activation=”relu”) |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| Dense(128,activation=“relu”) |
| Dense(64,activation=“relu”) |
| Dense(1,activation=None) | | 0 |
| Inputframe<br>3x64x64 |
| --- |
| 7firstlayersofresnet-18(pretrained,frozenweights) |
| Reshape1x8192 |
| FC8192→128 |
| FC128→8192 |
| Reshape128x8x8 |
| UpSamplingNearest(2),3x3Conv.128-1str.,BN,ReLU |
| UpSamplingNearest(2),3x3Conv.64-1str.,BN,ReLU | | | UpSamplingNearest(2),3x3Conv.3-1str.,BN,ReLU |
| --- |
| 3sigmoid<br>Targetmask | | 1 |
| Inputframe<br>3x64x64 |
| --- |
| 7firstlayersofresnet-18(pretrained,frozenweights) |
| Reshape1x8192 |
| FC8192→128 |
| FC128→8192 |
| Reshape128x8x8 |
| UpSamplingNearest(2),3x3Conv.128-1str.,BN,ReLU |
| UpSamplingNearest(2),3x3Conv.64-1str.,BN,ReLU | | | 3DConvolutional(filters=32,kernelsize=3,strides=1,activation=”relu”) |
| --- |
| MaxPooling3D(poolsize=(2,2),strides=(1,1)) |
| Dense(128,activation=“relu”) |
| Dense(64,activation=“relu”) |
| Dense(1,activation=None) | | 0 |
| Methods | ρ |
| --- | --- |
| TUAW–influence-flowmodel | 0.284848 |
| TUAW–MEIBI | 0.948485 | | | TUAW–MEIBIX | 0.939394 |
| --- | --- |
| influence-flowmodel–MEIBI | 0.418182 |
| influence-flowmodel–MEIBIX | 0.357576 |
| MEIBI–MEIBIX | 0.987879 | | 1 |
| Methods | ρ |
| --- | --- |
| TUAW–influence-flowmodel | 0.284848 |
| TUAW–MEIBI | 0.948485 | | | n | ρ |
| --- | --- |
| 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 |
| 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 |
| 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 |
| 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 | | 0 |
| Methods | ρ |
| --- | --- |
| TUAW–influence-flowmodel | 0.284848 |
| TUAW–MEIBI | 0.948485 |
| TUAW–MEIBIX | 0.939394 |
| influence-flowmodel–MEIBI | 0.418182 | | | influence-flowmodel–MEIBIX | 0.357576 |
| --- | --- |
| MEIBI–MEIBIX | 0.987879 | | 1 |
| Methods | ρ |
| --- | --- |
| TUAW–influence-flowmodel | 0.284848 |
| TUAW–MEIBI | 0.948485 |
| TUAW–MEIBIX | 0.939394 |
| influence-flowmodel–MEIBI | 0.418182 | | | 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 |
| --- | --- |
| 10,000<br>30,000 | 0<br>0.3<br>0<br>0.3 | | 0 |
| Variable | Value | Description |
| --- | --- | --- |
| nDirectQueries | 1000 | numberofdirectqueries |
| directQueryDist | 248:248:248:156:100 | distributionofdirectqueries<br>(selection:approximation:aggregation:s<br>window:joiningrequest) |
| nPolicies | 900 | numberofuniquepolicies |
| nRequests | 1500 | numberofmatchingrequests | | | α | 0.223 | skewparameterforZipfdistribution |
| --- | --- | --- |
| maxRank | 300 | maximumrankofuniquerequests<br>fromwhichZipfdistributionis<br>generated | | 1 |
| Variable | Value | Description |
| --- | --- | --- |
| nDirectQueries | 1000 | numberofdirectqueries |
| directQueryDist | 248:248:248:156:100 | distributionofdirectqueries<br>(selection:approximation:aggregation:s<br>window:joiningrequest) |
| nPolicies | 900 | numberofuniquepolicies |
| nRequests | 1500 | numberofmatchingrequests | | | Description | Count |
| --- | --- |
| Requests | 252036 |
| Responses | 251802 |
| URLsaccessed | 503838 |
| DistinctURLs | 201628 |
| Distincthosts | 9580 |
| Distincthosts(combined) | 8601 | | 0 |
| Variable | Value | Description |
| --- | --- | --- |
| nDirectQueries | 1000 | numberofdirectqueries |
| directQueryDist | 248:248:248:156:100 | distributionofdirectqueries<br>(selection:approximation:aggregation:s<br>window:joiningrequest) |
| nPolicies | 900 | numberofuniquepolicies | | | nRequests | 1500 | numberofmatchingrequests |
| --- | --- | --- |
| α | 0.223 | skewparameterforZipfdistribution |
| maxRank | 300 | maximumrankofuniquerequests<br>fromwhichZipfdistributionis<br>generated | | 1 |
| Variable | Value | Description |
| --- | --- | --- |
| nDirectQueries | 1000 | numberofdirectqueries |
| directQueryDist | 248:248:248:156:100 | distributionofdirectqueries<br>(selection:approximation:aggregation:s<br>window:joiningrequest) |
| nPolicies | 900 | numberofuniquepolicies | | | DistinctURLs | 201628 |
| --- | --- |
| Distincthosts | 9580 |
| Distincthosts(combined) | 8601 | | 0 |
| Biomarker | ER | P53 | PgR |
| --- | --- | --- | --- |
| No.ofTMA | 32 | 33 | 40 |
| NAP | 42.02 | 50.68 | 48.17 |
| NNP | 43.53 | 46.72 | 48.92 | | | RGB-CNN | 24.90 | 31.57 | 38.19 |
| --- | --- | --- | --- |
| RA-CNN | 25.43 | 23.39 | 31.82 |
| RAM-CNN | 21.01 | 16.66 | 25.44 | | 1 |
| Biomarker | ER | P53 | PgR |
| --- | --- | --- | --- |
| No.ofTMA | 32 | 33 | 40 |
| NAP | 42.02 | 50.68 | 48.17 |
| NNP | 43.53 | 46.72 | 48.92 | | | Dataset | MMPC | HITON-PC | MMMB | HITON-MB | IAMB | mRMR | CMI | JMI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| prostate | 9 | 8 | 175 | 98 | 2 | 5/20 | 15/5 | 20/20 |
| dexter | 8 | 8 | 11 | 10 | 4 | 25/20 | 25/20 | 25/25 |
| arcene | 4 | 4 | 5 | 6 | 3 | 5/10 | 15/20 | 35/35 |
| dorothea | 24 | 28 | - | - | 6 | 15/15 | 40/15 | 10/10 |
| leukemia | 1014 | - | - | - | 1 | 10/20 | 20/10 | 10/10 |
| breast-cancer | 8 | 6 | 10 | 7 | 4 | 40/35 | 40/25 | 35/40 | | 0 |
| Biomarker | ER | P53 | PgR |
| --- | --- | --- | --- |
| No.ofTMA | 32 | 33 | 40 | | | NAP | 42.02 | 50.68 | 48.17 |
| --- | --- | --- | --- |
| NNP | 43.53 | 46.72 | 48.92 |
| RGB-CNN | 24.90 | 31.57 | 38.19 |
| RA-CNN | 25.43 | 23.39 | 31.82 |
| RAM-CNN | 21.01 | 16.66 | 25.44 | | 1 |
| Biomarker | ER | P53 | PgR |
| --- | --- | --- | --- |
| No.ofTMA | 32 | 33 | 40 | | | leukemia | 1014 | - | - | - | 1 | 10/20 | 20/10 | 10/10 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| breast-cancer | 8 | 6 | 10 | 7 | 4 | 40/35 | 40/25 | 35/40 | | 0 |
| 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 | | 1 |
| InputSize | Depth | Top-1 |
| --- | --- | --- |
| 224x224 | 16 | 28.5 |
| 224x224<br>224x224 | 22<br>22 | 27.8<br>25.2 | | | Depth | Para | C-10 |
| --- | --- | --- |
| - | | 7.25 |
| 110 | 1.7M | 6.61 |
| 28 | 36.5M | 4.17 |
| 1001 | 10.2M | 4.62 |
| 40<br>100<br>100<br>190 | 1.0M<br>7.0M<br>27.2M<br>25.6M | 5.24<br>4.10<br>3.74<br>3.46 |
| -<br>- | -<br>11.2M | 7.32<br>6.92 |
| 15<br>20<br>39<br>39 | 4.2M<br>2.5M<br>7.1M<br>37.4M | 5.50<br>6.01<br>4.47<br>3.65 |
| 25<br>37<br>19<br>22 | -<br>-<br>6.1M<br>39.8M | 3.60<br>3.80<br>4.38<br>3.54 | | 0 |
| 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 | | 1 |
| InputSize | Depth | Top-1 |
| --- | --- | --- |
| 224x224 | 16 | 28.5 |
| 224x224<br>224x224 | 22<br>22 | 27.8<br>25.2 | | | 28 | 36.5M | 4.17 |
| --- | --- | --- |
| 1001 | 10.2M | 4.62 |
| 40<br>100<br>100<br>190 | 1.0M<br>7.0M<br>27.2M<br>25.6M | 5.24<br>4.10<br>3.74<br>3.46 |
| -<br>- | -<br>11.2M | 7.32<br>6.92 |
| 15<br>20<br>39<br>39 | 4.2M<br>2.5M<br>7.1M<br>37.4M | 5.50<br>6.01<br>4.47<br>3.65 |
| 25<br>37<br>19<br>22 | -<br>-<br>6.1M<br>39.8M | 3.60<br>3.80<br>4.38<br>3.54 | | 0 |
| Dataset | Method | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Gavin | MDS | 6 | 60 | 169 | 384 | 644 |
| | Diverse | 3 | 19 | 111 | 373 | 622 |
| | ClusterONE | 0 | 17 | 107 | 304 | 585 |
| KroganCore | MDS | 573 | 701 | 814 | 1070 | 1443 |
| | Diverse | 483 | 626 | 739 | 918 | 1061 |
| | ClusterONE | 349 | 496 | 652 | 833 | 1048 |
| Kroganextended | MDS | 572 | 691 | 837 | 1097 | 1487 |
| | Diverse | 491 | 626 | 754 | 934 | 1078 |
| | ClusterONE | 354 | 495 | 658 | 870 | 1064 |
| Collins | MDS | 750 | 901 | 1005 | 1086 | 1210 | | | (cov/k:5) | Diverse | 702 | 846 | 963 | 1058 | 1138 |
| --- | --- | --- | --- | --- | --- | --- |
| | ClusterONE | 629 | 809 | 915 | 1019 | 1147 |
| BioGRID | MDS | 2689 | 2951 | 3181 | 3593 | 3788 |
| | Diverse | 2335 | 2623 | 2827 | 3194 | 3367 |
| | ClusterONE | 1151 | 1580 | 1776 | 2580 | 4418 |
| String | MDS | 2321 | 3422 | 4523 | 5867 | 7841 |
| | Diverse | 2063 | 3042 | 4021 | 5215 | 6970 |
| | ClusterONE | 970 | 1642 | 2381 | 3580 | 5618 | | 1 |
| Dataset | Method | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Gavin | MDS | 6 | 60 | 169 | 384 | 644 |
| | Diverse | 3 | 19 | 111 | 373 | 622 |
| | ClusterONE | 0 | 17 | 107 | 304 | 585 |
| KroganCore | MDS | 573 | 701 | 814 | 1070 | 1443 |
| | Diverse | 483 | 626 | 739 | 918 | 1061 |
| | ClusterONE | 349 | 496 | 652 | 833 | 1048 |
| Kroganextended | MDS | 572 | 691 | 837 | 1097 | 1487 |
| | Diverse | 491 | 626 | 754 | 934 | 1078 |
| | ClusterONE | 354 | 495 | 658 | 870 | 1064 |
| Collins | MDS | 750 | 901 | 1005 | 1086 | 1210 | | | Dataset | MMPC | HITON-PC | MMMB | HITON-MB | IAMB | mRMR | CMI | JMI |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| prostate | 2 | 2 | 41,568 | 54,315 | 6.37 | 2.4 | 0.09 | 4 |
| dexter | 4 | 3 | 31 | 19 | 54 | 16 | 1 | 29 |
| arcene | 3 | 3 | 16 | 15 | 20 | 1 | 0.3 | 19 |
| dorothea | 59 | 705 | - | - | 594 | 4 | 4 | 59 |
| leukemia | 10,033 | - | - | - | 5 | 2 | 0.3 | 3 |
| breast-cancer | 9 | 11 | 45 | 43 | 43.23 | 17 | 0.7 | 31 | | 0 |
| Dataset | Method | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Gavin | MDS | 6 | 60 | 169 | 384 | 644 |
| | Diverse | 3 | 19 | 111 | 373 | 622 | | | | ClusterONE | 0 | 17 | 107 | 304 | 585 |
| --- | --- | --- | --- | --- | --- | --- |
| KroganCore | MDS | 573 | 701 | 814 | 1070 | 1443 |
| | Diverse | 483 | 626 | 739 | 918 | 1061 |
| | ClusterONE | 349 | 496 | 652 | 833 | 1048 |
| Kroganextended | MDS | 572 | 691 | 837 | 1097 | 1487 |
| | Diverse | 491 | 626 | 754 | 934 | 1078 |
| | ClusterONE | 354 | 495 | 658 | 870 | 1064 |
| Collins | MDS | 750 | 901 | 1005 | 1086 | 1210 |
| (cov/k:5) | Diverse | 702 | 846 | 963 | 1058 | 1138 |
| | ClusterONE | 629 | 809 | 915 | 1019 | 1147 |
| BioGRID | MDS | 2689 | 2951 | 3181 | 3593 | 3788 |
| | Diverse | 2335 | 2623 | 2827 | 3194 | 3367 |
| | ClusterONE | 1151 | 1580 | 1776 | 2580 | 4418 |
| String | MDS | 2321 | 3422 | 4523 | 5867 | 7841 |
| | Diverse | 2063 | 3042 | 4021 | 5215 | 6970 |
| | ClusterONE | 970 | 1642 | 2381 | 3580 | 5618 | | 1 |
| Dataset | Method | 0.9 | 0.8 | 0.7 | 0.6 | 0.5 |
| --- | --- | --- | --- | --- | --- | --- |
| Gavin | MDS | 6 | 60 | 169 | 384 | 644 |
| | Diverse | 3 | 19 | 111 | 373 | 622 | | | dorothea | 59 | 705 | - | - | 594 | 4 | 4 | 59 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| leukemia | 10,033 | - | - | - | 5 | 2 | 0.3 | 3 |
| breast-cancer | 9 | 11 | 45 | 43 | 43.23 | 17 | 0.7 | 31 | | 0 |
| Model | Precision | Recall | F1 |
| --- | --- | --- | --- |
| SVMfact | 100.00 | 40.20 | 57.34 | | | NNfact | 87.14 | 59.80 | 70.93 |
| --- | --- | --- | --- |
| NNfactart | 87.18 | 66.67 | 75.56 |
| NNfactsupvart | 90.00 | 70.59 | 79.12 | | 1 |
| Model | Precision | Recall | F1 |
| --- | --- | --- | --- |
| SVMfact | 100.00 | 40.20 | 57.34 | | | Features | Precision | Recall | AUC |
| --- | --- | --- | --- |
| FP | 0.818 | 0.789 | 0.807 |
| F+FPA | 0.846 | 0.864 | 0.853 |
| F+FPE | 0.902 | 0.807 | 0.860 |
| F+F+FPEA | 0.898 | 0.927 | 0.911 | | 0 |
| Model | Precision | Recall | F1 |
| --- | --- | --- | --- |
| SVMfact | 100.00 | 40.20 | 57.34 |
| NNfact | 87.14 | 59.80 | 70.93 | | | NNfactart | 87.18 | 66.67 | 75.56 |
| --- | --- | --- | --- |
| NNfactsupvart | 90.00 | 70.59 | 79.12 | | 1 |
| Model | Precision | Recall | F1 |
| --- | --- | --- | --- |
| SVMfact | 100.00 | 40.20 | 57.34 |
| NNfact | 87.14 | 59.80 | 70.93 | | | F+FPE | 0.902 | 0.807 | 0.860 |
| --- | --- | --- | --- |
| F+F+FPEA | 0.898 | 0.927 | 0.911 | | 0 |
| Attribute | male | longhair | glasses | hat | tshirt | longsleeves | short | jeans |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DL-Pure | 80.65 | 63.23 | 30.74 | 57.21 | 37.99 | 71.76 | 35.05 | 60.18 |
| DeCAF | 79.64 | 62.29 | 31.29 | 55.17 | 41.84 | 78.77 | 80.66 | 81.46 |
| Poselets150L2 | 81.70 | 67.07 | 44.24 | 54.01 | 42.16 | 71.70 | 36.71 | 42.56 | | | DLPoselets | 92.10 | 82.26 | 76.25 | 65.55 | 44.83 | 77.31 | 43.71 | 52.52 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PANDA | 91.66 | 82.70 | 69.95 | 74.22 | 49.84 | 86.01 | 79.08 | 80.99 | | 1 |
| Attribute | male | longhair | glasses | hat | tshirt | longsleeves | short | jeans |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DL-Pure | 80.65 | 63.23 | 30.74 | 57.21 | 37.99 | 71.76 | 35.05 | 60.18 |
| DeCAF | 79.64 | 62.29 | 31.29 | 55.17 | 41.84 | 78.77 | 80.66 | 81.46 |
| Poselets150L2 | 81.70 | 67.07 | 44.24 | 54.01 | 42.16 | 71.70 | 36.71 | 42.56 | | | Attribute | male | longhair | hat | glasses | dress | sunglasses | shortsleeves | baby |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Poselets150 | 86.00 | 75.31 | 29.03 | 36.72 | 34.73 | 50.16 | 55.25 | 41.26 |
| DPD | 85.84 | 72.40 | 27.55 | 23.94 | 48.55 | 34.36 | 54.75 | 41.38 |
| DeCAF | 82.47 | 65.03 | 19.15 | 14.91 | 44.68 | 26.91 | 56.40 | 50.19 |
| DL-DPM | 88.27 | 77.64 | 43.44 | 36.70 | 55.72 | 55.03 | 67.95 | 64.89 |
| PANDA | 94.10 | 83.17 | 39.52 | 72.25 | 59.41 | 66.62 | 72.09 | 78.76 | | 0 |
| Attribute | male | longhair | glasses | hat | tshirt | longsleeves | short | jeans |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DL-Pure | 80.65 | 63.23 | 30.74 | 57.21 | 37.99 | 71.76 | 35.05 | 60.18 |
| DeCAF | 79.64 | 62.29 | 31.29 | 55.17 | 41.84 | 78.77 | 80.66 | 81.46 | | | Poselets150L2 | 81.70 | 67.07 | 44.24 | 54.01 | 42.16 | 71.70 | 36.71 | 42.56 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DLPoselets | 92.10 | 82.26 | 76.25 | 65.55 | 44.83 | 77.31 | 43.71 | 52.52 |
| PANDA | 91.66 | 82.70 | 69.95 | 74.22 | 49.84 | 86.01 | 79.08 | 80.99 | | 1 |
| Attribute | male | longhair | glasses | hat | tshirt | longsleeves | short | jeans |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| DL-Pure | 80.65 | 63.23 | 30.74 | 57.21 | 37.99 | 71.76 | 35.05 | 60.18 |
| DeCAF | 79.64 | 62.29 | 31.29 | 55.17 | 41.84 | 78.77 | 80.66 | 81.46 | | | DL-DPM | 88.27 | 77.64 | 43.44 | 36.70 | 55.72 | 55.03 | 67.95 | 64.89 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PANDA | 94.10 | 83.17 | 39.52 | 72.25 | 59.41 | 66.62 | 72.09 | 78.76 | | 0 |
| Algorithm | IRCCYNDatabase | LFOVIADatabase | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.6359 | 0.2465 | 1.0264 | 0.8816 | 0.8828 | 6.1104 |
| MS-SSIM | 0.9100 | 0.8534 | 0.6512 | 0.8172 | 0.7888 | 8.9467 |
| SBIQE | 0.0081 | 0.0054 | 1.2712 | 0.0010 | 0.0043 | 16.0311 |
| BRISQUE | 0.7535 | 0.8145 | 0.6535 | 0.6182 | 0.6000 | 12.6001 |
| NIQE | 0.5729 | 0.5664 | 0.8464 | 0.7206 | 0.7376 | 11.1138 |
| STMAD | 0.6400 | 0.3495 | 0.9518 | 0.6802 | 0.6014 | 9.4918 |
| FLOSIM | 0.9178 | 0.9111 | 0.4918 | - | - | - | | | Chenetal. | 0.7886 | 0.7861 | 0.7464 | 0.8573 | 0.8588 | 6.6655 |
| --- | --- | --- | --- | --- | --- | --- |
| STRIQE | 0.7931 | 0.6400 | 0.7544 | 0.7543 | 0.7485 | 8.5011 |
| VQUEMODES(NIQE) | 0.9697 | 0.9637 | 0.2635 | 0.8943 | 0.8890 | 5.9124 | | 1 |
| Algorithm | IRCCYNDatabase | LFOVIADatabase | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.6359 | 0.2465 | 1.0264 | 0.8816 | 0.8828 | 6.1104 |
| MS-SSIM | 0.9100 | 0.8534 | 0.6512 | 0.8172 | 0.7888 | 8.9467 |
| SBIQE | 0.0081 | 0.0054 | 1.2712 | 0.0010 | 0.0043 | 16.0311 |
| BRISQUE | 0.7535 | 0.8145 | 0.6535 | 0.6182 | 0.6000 | 12.6001 |
| NIQE | 0.5729 | 0.5664 | 0.8464 | 0.7206 | 0.7376 | 11.1138 |
| STMAD | 0.6400 | 0.3495 | 0.9518 | 0.6802 | 0.6014 | 9.4918 |
| FLOSIM | 0.9178 | 0.9111 | 0.4918 | - | - | - | | | Algorithm | Symm | Asymm | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.9037 | 0.8991 | 7.0246 | 0.8769 | 0.8755 | 5.8162 |
| MS-SSIM | 0.8901 | 0.8681 | 21.2322 | 0.8423 | 0.7785 | 15.1681 |
| SBIQE | 0.0006 | 0.0027 | 25.4484 | 0.0021 | 0.0051 | 12.0123 |
| BRISQUE | 0.7829 | 0.7859 | 15.8298 | 0.5411 | 0.5303 | 10.1719 |
| NIQE | 0.8499 | 0.8705 | 13.4076 | 0.6835 | 0.6929 | 8.8334 |
| STMAD | 0.7815 | 0.8000 | 10.2358 | 0.6534 | 0.6010 | 9.1614 |
| Chenetal. | 0.9435 | 0.9182 | 5.4346 | 0.8370 | 0.8376 | 6.6218 |
| STRIQE | 0.8275 | 0.8017 | 9.2105 | 0.7559 | 0.7492 | 7.9321 |
| VQUEMODES(NIQE) | 0.9285 | 0.9236 | 3.9852 | 0.8955 | 0.8490 | 6.9563 | | 0 |
| Algorithm | IRCCYNDatabase | LFOVIADatabase | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.6359 | 0.2465 | 1.0264 | 0.8816 | 0.8828 | 6.1104 |
| MS-SSIM | 0.9100 | 0.8534 | 0.6512 | 0.8172 | 0.7888 | 8.9467 |
| SBIQE | 0.0081 | 0.0054 | 1.2712 | 0.0010 | 0.0043 | 16.0311 | | | BRISQUE | 0.7535 | 0.8145 | 0.6535 | 0.6182 | 0.6000 | 12.6001 |
| --- | --- | --- | --- | --- | --- | --- |
| NIQE | 0.5729 | 0.5664 | 0.8464 | 0.7206 | 0.7376 | 11.1138 |
| STMAD | 0.6400 | 0.3495 | 0.9518 | 0.6802 | 0.6014 | 9.4918 |
| FLOSIM | 0.9178 | 0.9111 | 0.4918 | - | - | - |
| Chenetal. | 0.7886 | 0.7861 | 0.7464 | 0.8573 | 0.8588 | 6.6655 |
| STRIQE | 0.7931 | 0.6400 | 0.7544 | 0.7543 | 0.7485 | 8.5011 |
| VQUEMODES(NIQE) | 0.9697 | 0.9637 | 0.2635 | 0.8943 | 0.8890 | 5.9124 | | 1 |
| Algorithm | IRCCYNDatabase | LFOVIADatabase | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| LCC | SROCC | RMSE | LCC | SROCC | RMSE | |
| SSIM | 0.6359 | 0.2465 | 1.0264 | 0.8816 | 0.8828 | 6.1104 |
| MS-SSIM | 0.9100 | 0.8534 | 0.6512 | 0.8172 | 0.7888 | 8.9467 |
| SBIQE | 0.0081 | 0.0054 | 1.2712 | 0.0010 | 0.0043 | 16.0311 | | | BRISQUE | 0.7829 | 0.7859 | 15.8298 | 0.5411 | 0.5303 | 10.1719 |
| --- | --- | --- | --- | --- | --- | --- |
| NIQE | 0.8499 | 0.8705 | 13.4076 | 0.6835 | 0.6929 | 8.8334 |
| STMAD | 0.7815 | 0.8000 | 10.2358 | 0.6534 | 0.6010 | 9.1614 |
| Chenetal. | 0.9435 | 0.9182 | 5.4346 | 0.8370 | 0.8376 | 6.6218 |
| STRIQE | 0.8275 | 0.8017 | 9.2105 | 0.7559 | 0.7492 | 7.9321 |
| VQUEMODES(NIQE) | 0.9285 | 0.9236 | 3.9852 | 0.8955 | 0.8490 | 6.9563 | | 0 |
| | TheFSMCModel | TheMeasurementData | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| pk,k−1 | pk,k | pk,k+1 | pk,k−1 | pk,k | pk,k+1 | |
| k=1 | - | 0.75 | 0.25 | - | 0.78 | 0.22 |
| k=2 | 0.25 | 0.5 | 0.25 | 0.269 | 0.47 | 0.26 |
| k=3 | 0.25 | 0.5 | 0.25 | 0.23 | 0.5 | 0.26 |
| k=4 | 0.22 | 0.66 | 0.11 | 0.22 | 0.65 | 0.12 |
| k=5 | 0.125 | 0.5 | 0.25 | 0.126 | 0.63 | 0.24 |
| k=6 | 0.095 | 0.81 | 0.048 | 0.089 | 0.86 | 0.049 | | | k=7 | 0.13 | 0.6 | 0.27 | 0.12 | 0.61 | 0.26 |
| --- | --- | --- | --- | --- | --- | --- |
| k=8 | 0.013 | 0.98 | - | 0.013 | 0.98 | - | | 1 |
| | TheFSMCModel | TheMeasurementData | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| pk,k−1 | pk,k | pk,k+1 | pk,k−1 | pk,k | pk,k+1 | |
| k=1 | - | 0.75 | 0.25 | - | 0.78 | 0.22 |
| k=2 | 0.25 | 0.5 | 0.25 | 0.269 | 0.47 | 0.26 |
| k=3 | 0.25 | 0.5 | 0.25 | 0.23 | 0.5 | 0.26 |
| k=4 | 0.22 | 0.66 | 0.11 | 0.22 | 0.65 | 0.12 |
| k=5 | 0.125 | 0.5 | 0.25 | 0.126 | 0.63 | 0.24 |
| k=6 | 0.095 | 0.81 | 0.048 | 0.089 | 0.86 | 0.049 | | | K1 | k2 | k3 | k4 |
| --- | --- | --- | --- |
| 0.6 | 1.2 | 0.1 | 0.001 |
| 0.864 | 0.981 | 0.005 | 0.016 |
| 0.108 | 0.735 | 0.016 | 0.013 |
| 0.263 | 0.299 | 0 | 0 |
| 1.207 | 1.909 | 0.008 | 0.014 |
| 0.648 | 1.64 | 0.027 | 0.016 |
| 0.047 | 0.325 | 0.084 | 0 |
| 0.425 | 1.055 | 0.023 | 0.013 |
| 0.63 | 0.842 | 0.092 | 0.014 | | 0 |
| | TheFSMCModel | TheMeasurementData | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| pk,k−1 | pk,k | pk,k+1 | pk,k−1 | pk,k | pk,k+1 | |
| k=1 | - | 0.75 | 0.25 | - | 0.78 | 0.22 |
| k=2 | 0.25 | 0.5 | 0.25 | 0.269 | 0.47 | 0.26 |
| k=3 | 0.25 | 0.5 | 0.25 | 0.23 | 0.5 | 0.26 |
| k=4 | 0.22 | 0.66 | 0.11 | 0.22 | 0.65 | 0.12 | | | k=5 | 0.125 | 0.5 | 0.25 | 0.126 | 0.63 | 0.24 |
| --- | --- | --- | --- | --- | --- | --- |
| k=6 | 0.095 | 0.81 | 0.048 | 0.089 | 0.86 | 0.049 |
| k=7 | 0.13 | 0.6 | 0.27 | 0.12 | 0.61 | 0.26 |
| k=8 | 0.013 | 0.98 | - | 0.013 | 0.98 | - | | 1 |
| | TheFSMCModel | TheMeasurementData | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| pk,k−1 | pk,k | pk,k+1 | pk,k−1 | pk,k | pk,k+1 | |
| k=1 | - | 0.75 | 0.25 | - | 0.78 | 0.22 |
| k=2 | 0.25 | 0.5 | 0.25 | 0.269 | 0.47 | 0.26 |
| k=3 | 0.25 | 0.5 | 0.25 | 0.23 | 0.5 | 0.26 |
| k=4 | 0.22 | 0.66 | 0.11 | 0.22 | 0.65 | 0.12 | | | 0.263 | 0.299 | 0 | 0 |
| --- | --- | --- | --- |
| 1.207 | 1.909 | 0.008 | 0.014 |
| 0.648 | 1.64 | 0.027 | 0.016 |
| 0.047 | 0.325 | 0.084 | 0 |
| 0.425 | 1.055 | 0.023 | 0.013 |
| 0.63 | 0.842 | 0.092 | 0.014 | | 0 |
| Years | Numberofbooks |
| --- | --- |
| 1520-1699 | 1,243 |
| 1700-1799 | 44,059 | | | 1800-1899 | 5,518,213 |
| --- | --- |
| 1900-2008 | 31,823,074 | | 1 |
| Years | Numberofbooks |
| --- | --- |
| 1520-1699 | 1,243 |
| 1700-1799 | 44,059 | | | Papers | Year |
| --- | --- |
| | 2005 |
| | 2006 |
| | 2007 |
| | 2008 |
| | 2010 |
| | 2011 |
| | 2012 |
| | 2013 |
| | 2014 |
| | 2015 |
| | 2016 |
| | 2017 | | 0 |
| Years | Numberofbooks |
| --- | --- |
| 1520-1699 | 1,243 | | | 1700-1799 | 44,059 |
| --- | --- |
| 1800-1899 | 5,518,213 |
| 1900-2008 | 31,823,074 | | 1 |
| Years | Numberofbooks |
| --- | --- |
| 1520-1699 | 1,243 | | | | 2007 |
| --- | --- |
| | 2008 |
| | 2010 |
| | 2011 |
| | 2012 |
| | 2013 |
| | 2014 |
| | 2015 |
| | 2016 |
| | 2017 | | 0 |
| | P-average(mm) | D-average(mm) | R(pixels) |
| --- | --- | --- | --- |
| Plane1 | 6.942 | 0.8080 | 2.716 |
| Plane2 | 4.206 | 3.7432 | 0.854 |
| Plane3 | 4.864 | 4.5740 | 1.656 | | | Plane4 | 1.640 | 0.1930 | 0.353 |
| --- | --- | --- | --- |
| Plane5 | 833.587 | 1482 | 17.741 | | 1 |
| | P-average(mm) | D-average(mm) | R(pixels) |
| --- | --- | --- | --- |
| Plane1 | 6.942 | 0.8080 | 2.716 |
| Plane2 | 4.206 | 3.7432 | 0.854 |
| Plane3 | 4.864 | 4.5740 | 1.656 | | | | P-average(mm) | D-average(mm) | R(pixels) |
| --- | --- | --- | --- |
| Plane1 | 5.802 | 2.015 | 1.512 |
| Plane2 | 5.166 | 4.117 | 1.215 |
| Plane3 | 3.002 | 3.481 | 0.405 |
| Plane4 | 2.479 | 0.483 | 1.171 |
| Plane5 | 7.632 | 14.022 | 2.117 | | 0 |
| | P-average(mm) | D-average(mm) | R(pixels) |
| --- | --- | --- | --- |
| Plane1 | 6.942 | 0.8080 | 2.716 |
| Plane2 | 4.206 | 3.7432 | 0.854 |
| Plane3 | 4.864 | 4.5740 | 1.656 | | | Plane4 | 1.640 | 0.1930 | 0.353 |
| --- | --- | --- | --- |
| Plane5 | 833.587 | 1482 | 17.741 | | 1 |
| | P-average(mm) | D-average(mm) | R(pixels) |
| --- | --- | --- | --- |
| Plane1 | 6.942 | 0.8080 | 2.716 |
| Plane2 | 4.206 | 3.7432 | 0.854 |
| Plane3 | 4.864 | 4.5740 | 1.656 | | | Plane4 | 2.479 | 0.483 | 1.171 |
| --- | --- | --- | --- |
| Plane5 | 7.632 | 14.022 | 2.117 | | 0 |
| Dataset | WSim(SIM) | WSim(REL) | | |
| --- | --- | --- | --- | --- |
| Window | 2 | 3 | 2 | 3 |
| CosFreq | 0.335 | 0.334 | 0.03 | 0.05 |
| CosLMI | 0.638 | 0.663 | 0.293 | 0.34 |
| CosPPMI | 0.672 | 0.675 | 0.441 | 0.446 | | | CosSVD-Freq300 | 0.35 | 0.363 | -0.013 | 0.001 |
| --- | --- | --- | --- | --- |
| CosSVD-LMI300 | 0.604 | 0.626 | 0.222 | 0.286 |
| CosSVD-PPMI300 | 0.72 | 0.725 | 0.444 | 0.486 |
| APSynLMI-1000 | 0.609 | 0.609 | 0.317 | 0.36 |
| APSynLMI-500 | 0.599 | 0.601 | 0.289 | 0.344 |
| APSynLMI-100 | 0.566 | 0.574 | 0.215 | 0.271 |
| APSynPPMI-1000 | 0.692 | 0.726 | 0.507 | 0.568 |
| APSynPPMI-500 | 0.699 | 0.742 | 0.508 | 0.571 |
| APSynPPMI-100 | 0.66 | 0.692 | 0.482 | 0.516 | | 1 |
| Dataset | WSim(SIM) | WSim(REL) | | |
| --- | --- | --- | --- | --- |
| Window | 2 | 3 | 2 | 3 |
| CosFreq | 0.335 | 0.334 | 0.03 | 0.05 |
| CosLMI | 0.638 | 0.663 | 0.293 | 0.34 |
| CosPPMI | 0.672 | 0.675 | 0.441 | 0.446 | | | Dataset | WSim(SIM) | WSim(REL) | | |
| --- | --- | --- | --- | --- |
| Window | 2 | 3 | 2 | 3 |
| CosFreq | 0.208 | 0.158 | 0.167 | 0.175 |
| CosLMI | 0.416 | 0.395 | 0.251 | 0.269 |
| CosPPMI | 0.52 | 0.496 | 0.378 | 0.396 |
| CosSVD-Freq300 | 0.240 | 0.214 | 0.051 | 0.084 |
| CosSVD-LMI300 | 0.418 | 0.393 | 0.141 | 0.151 |
| CosSVD-PPMI300 | 0.550 | 0.522 | 0.325 | 0.323 |
| APSynLMI-1000 | 0.32 | 0.29 | 0.259 | 0.241 |
| APSynLMI-500 | 0.355 | 0.319 | 0.261 | 0.284 |
| APSynLMI-100 | 0.388 | 0.335 | 0.233 | 0.27 |
| APSynPPMI-1000 | 0.519 | 0.525 | 0.337 | 0.397 |
| APSynPPMI-500 | 0.564 | 0.546 | 0.361 | 0.382 |
| PMIAPSynPPMI-100 | 0.562 | 0.553 | 0.287 | 0.309 | | 0 |
| Dataset | WSim(SIM) | WSim(REL) | | |
| --- | --- | --- | --- | --- |
| Window | 2 | 3 | 2 | 3 |
| CosFreq | 0.335 | 0.334 | 0.03 | 0.05 |
| CosLMI | 0.638 | 0.663 | 0.293 | 0.34 |
| CosPPMI | 0.672 | 0.675 | 0.441 | 0.446 | | | CosSVD-Freq300 | 0.35 | 0.363 | -0.013 | 0.001 |
| --- | --- | --- | --- | --- |
| CosSVD-LMI300 | 0.604 | 0.626 | 0.222 | 0.286 |
| CosSVD-PPMI300 | 0.72 | 0.725 | 0.444 | 0.486 |
| APSynLMI-1000 | 0.609 | 0.609 | 0.317 | 0.36 |
| APSynLMI-500 | 0.599 | 0.601 | 0.289 | 0.344 |
| APSynLMI-100 | 0.566 | 0.574 | 0.215 | 0.271 |
| APSynPPMI-1000 | 0.692 | 0.726 | 0.507 | 0.568 |
| APSynPPMI-500 | 0.699 | 0.742 | 0.508 | 0.571 |
| APSynPPMI-100 | 0.66 | 0.692 | 0.482 | 0.516 | | 1 |
| Dataset | WSim(SIM) | WSim(REL) | | |
| --- | --- | --- | --- | --- |
| Window | 2 | 3 | 2 | 3 |
| CosFreq | 0.335 | 0.334 | 0.03 | 0.05 |
| CosLMI | 0.638 | 0.663 | 0.293 | 0.34 |
| CosPPMI | 0.672 | 0.675 | 0.441 | 0.446 | | | CosFreq | 0.208 | 0.158 | 0.167 | 0.175 |
| --- | --- | --- | --- | --- |
| CosLMI | 0.416 | 0.395 | 0.251 | 0.269 |
| CosPPMI | 0.52 | 0.496 | 0.378 | 0.396 |
| CosSVD-Freq300 | 0.240 | 0.214 | 0.051 | 0.084 |
| CosSVD-LMI300 | 0.418 | 0.393 | 0.141 | 0.151 |
| CosSVD-PPMI300 | 0.550 | 0.522 | 0.325 | 0.323 |
| APSynLMI-1000 | 0.32 | 0.29 | 0.259 | 0.241 |
| APSynLMI-500 | 0.355 | 0.319 | 0.261 | 0.284 |
| APSynLMI-100 | 0.388 | 0.335 | 0.233 | 0.27 |
| APSynPPMI-1000 | 0.519 | 0.525 | 0.337 | 0.397 |
| APSynPPMI-500 | 0.564 | 0.546 | 0.361 | 0.382 |
| PMIAPSynPPMI-100 | 0.562 | 0.553 | 0.287 | 0.309 | | 0 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.8259 | 0.8019 | 0.8137 |
| AccurateTumorSegmentation(PHP+CNN) | 0.8311 | 0.8235 | 0.8273 | | | HyMap | 0.6469 | 0.7228 | 0.6827 |
| --- | --- | --- | --- |
| ConvNetCNN3 | 0.6927 | 0.8446 | 0.7612 |
| MCTA | 0.7050 | 0.7419 | 0.7229 |
| TVIA | 0.6993 | 0.7240 | 0.7114 | | 1 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.8259 | 0.8019 | 0.8137 |
| AccurateTumorSegmentation(PHP+CNN) | 0.8311 | 0.8235 | 0.8273 | | | Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.772 | 0.7890 | 0.7804 |
| AccurateTumorSegmentation(PHP+CNN) | 0.7413 | 0.8172 | 0.7774 |
| HyMap | 0.6469 | 0.7228 | 0.6827 |
| ConvNetCNN3 | 0.6234 | 0.8167 | 0.7071 |
| MCTA | 0.5429 | 0.9586 | 0.6932 |
| TVIA | 0.5334 | 0.9747 | 0.6895 | | 0 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.8259 | 0.8019 | 0.8137 |
| AccurateTumorSegmentation(PHP+CNN) | 0.8311 | 0.8235 | 0.8273 | | | HyMap | 0.6469 | 0.7228 | 0.6827 |
| --- | --- | --- | --- |
| ConvNetCNN3 | 0.6927 | 0.8446 | 0.7612 |
| MCTA | 0.7050 | 0.7419 | 0.7229 |
| TVIA | 0.6993 | 0.7240 | 0.7114 | | 1 |
| Method | Precision | Recall | F1-score |
| --- | --- | --- | --- |
| FastTumorSegmentation(PHP) | 0.8259 | 0.8019 | 0.8137 |
| AccurateTumorSegmentation(PHP+CNN) | 0.8311 | 0.8235 | 0.8273 | | | AccurateTumorSegmentation(PHP+CNN) | 0.7413 | 0.8172 | 0.7774 |
| --- | --- | --- | --- |
| HyMap | 0.6469 | 0.7228 | 0.6827 |
| ConvNetCNN3 | 0.6234 | 0.8167 | 0.7071 |
| MCTA | 0.5429 | 0.9586 | 0.6932 |
| TVIA | 0.5334 | 0.9747 | 0.6895 | | 0 |
| Method | Direction | Discuss | Eat | Greet | Phone | Pose | Purchase | Sit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Yasin | 60.0 | 54.7 | 71.6 | 67.5 | 63.8 | 61.9 | 55.7 | 73.9 | | | X\|gt(Ours) | 53.27 | 46.75 | 58.63 | 61.21 | 55.98 | 58.13 | 48.85 | 55.60 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Method | Smoke | Photo | Wait | Walk | WalkDog | WalkPair | Avg. | Median |
| Yasin | 78.9 | 96.9 | 67.9 | 47.5 | 89.3 | 53.4 | 70.5 | - |
| Ours | 60.25 | 76.05 | 62.19 | 35.76 | 61.93 | 51.08 | 57.50 | 51.93 | | 1 |
| Method | Direction | Discuss | Eat | Greet | Phone | Pose | Purchase | Sit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Yasin | 60.0 | 54.7 | 71.6 | 67.5 | 63.8 | 61.9 | 55.7 | 73.9 | | | Method | Direction | Discuss | Eat | Greet | Phone | Pose | Purchase | Sit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Zhou<br>Tekin | 87.36<br>102.41 | 109.31<br>147.72 | 87.05<br>88.83 | 103.16<br>125.38 | 116.18<br>118.02 | 106.88<br>112.38 | 99.78<br>129.17 | 124.52<br>138.89 |
| Ours | 89.87 | 97.57 | 89.98 | 107.87 | 107.31 | 93.56 | 136.09 | 133.14 |
| Method | Smoke | Photo | Wait | Walk | WalkDog | WalkPair | Avg. | Median |
| Zhou<br>Tekin | 107.42<br>118.42 | 139.46<br>182.73 | 118.09<br>138.75 | 79.39<br>55.07 | 114.23<br>126.29 | 97.70<br>65.76 | 113.01<br>124.97 | -<br>- |
| Ours | 106.65 | 139.17 | 106.21 | 87.03 | 114.05 | 90.55 | 114.18 | 93.05 | | 0 |
| Method | Direction | Discuss | Eat | Greet | Phone | Pose | Purchase | Sit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Yasin | 60.0 | 54.7 | 71.6 | 67.5 | 63.8 | 61.9 | 55.7 | 73.9 |
| X\|gt(Ours) | 53.27 | 46.75 | 58.63 | 61.21 | 55.98 | 58.13 | 48.85 | 55.60 | | | Method | Smoke | Photo | Wait | Walk | WalkDog | WalkPair | Avg. | Median |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Yasin | 78.9 | 96.9 | 67.9 | 47.5 | 89.3 | 53.4 | 70.5 | - |
| Ours | 60.25 | 76.05 | 62.19 | 35.76 | 61.93 | 51.08 | 57.50 | 51.93 | | 1 |
| Method | Direction | Discuss | Eat | Greet | Phone | Pose | Purchase | Sit |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Yasin | 60.0 | 54.7 | 71.6 | 67.5 | 63.8 | 61.9 | 55.7 | 73.9 |
| X\|gt(Ours) | 53.27 | 46.75 | 58.63 | 61.21 | 55.98 | 58.13 | 48.85 | 55.60 | | | Method | Smoke | Photo | Wait | Walk | WalkDog | WalkPair | Avg. | Median |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Zhou<br>Tekin | 107.42<br>118.42 | 139.46<br>182.73 | 118.09<br>138.75 | 79.39<br>55.07 | 114.23<br>126.29 | 97.70<br>65.76 | 113.01<br>124.97 | -<br>- |
| Ours | 106.65 | 139.17 | 106.21 | 87.03 | 114.05 | 90.55 | 114.18 | 93.05 | | 0 |
| Team | Model | Performance |
| --- | --- | --- |
| Le-Hong | ME | 88.78 |
| [Anonymous] | CRF | 86.62 |
| Nguyenetal. | ME | 84.08 | | | Nguyenetal. | LSTM | 83.80 |
| --- | --- | --- |
| Leetal. | CRF | 78.40 | | 1 |
| Team | Model | Performance |
| --- | --- | --- |
| Le-Hong | ME | 88.78 |
| [Anonymous] | CRF | 86.62 |
| Nguyenetal. | ME | 84.08 | | | Model | KL-weight | D-ScoreMNIST | D-ScoreChairs |
| --- | --- | --- | --- |
| Ours | 1 | 6.71 | 1.79 |
| Ours | 2 | 6.74 | 1.73 |
| Ours | 4 | 6.82 | 1.81 |
| Slow | 1 | 4.70 | 1.19 |
| Slow | 2 | 5.78 | 1.57 |
| Slow | 4 | 6.38 | 1.39 |
| VAE | 1 | 1.08 | 1.27 |
| VAE | 2 | 1.70 | 1.24 |
| VAE | 4 | 1.71 | 1.35 | | 0 |
| Team | Model | Performance |
| --- | --- | --- |
| Le-Hong | ME | 88.78 |
| [Anonymous] | CRF | 86.62 | | | Nguyenetal. | ME | 84.08 |
| --- | --- | --- |
| Nguyenetal. | LSTM | 83.80 |
| Leetal. | CRF | 78.40 | | 1 |
| Team | Model | Performance |
| --- | --- | --- |
| Le-Hong | ME | 88.78 |
| [Anonymous] | CRF | 86.62 | | | Ours | 2 | 6.74 | 1.73 |
| --- | --- | --- | --- |
| Ours | 4 | 6.82 | 1.81 |
| Slow | 1 | 4.70 | 1.19 |
| Slow | 2 | 5.78 | 1.57 |
| Slow | 4 | 6.38 | 1.39 |
| VAE | 1 | 1.08 | 1.27 |
| VAE | 2 | 1.70 | 1.24 |
| VAE | 4 | 1.71 | 1.35 | | 0 |
| SystemName | BlogSum<br>R-2 | MEAD<br>R-2 | TAC-Best<br>R-2 |
| --- | --- | --- | --- |
| Baseline | 0.102⇓ | 0.041⇓ | 0.130 |
| w/oIllustration | 0.107⇓ | 0.022⇓ | 0.112⇓ |
| w/oContingency | 0.093⇓ | 0.025⇓ | 0.102⇓ |
| w/oComparison | 0.103⇓ | 0.033⇓ | 0.113⇓ |
| w/oAttributive | 0.113⇓ | 0.050 | 0.124 |
| w/oTopic-opinion | 0.112⇓ | 0.049 | 0.123 | | | w/oAttribution | 0.118⇓ | 0.051⇓ | 0.128 |
| --- | --- | --- | --- |
| withallRelations | 0.125 | .053 | 0.138 | | 1 |
| SystemName | BlogSum<br>R-2 | MEAD<br>R-2 | TAC-Best<br>R-2 |
| --- | --- | --- | --- |
| Baseline | 0.102⇓ | 0.041⇓ | 0.130 |
| w/oIllustration | 0.107⇓ | 0.022⇓ | 0.112⇓ |
| w/oContingency | 0.093⇓ | 0.025⇓ | 0.102⇓ |
| w/oComparison | 0.103⇓ | 0.033⇓ | 0.113⇓ |
| w/oAttributive | 0.113⇓ | 0.050 | 0.124 |
| w/oTopic-opinion | 0.112⇓ | 0.049 | 0.123 | | | Database | Onenode | 4nodeswith<br>com.time | 4nodes<br>withoutcom.<br>time | com.time | | | | |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| | Win | Linux | Win | Linux | Win | Linux | Win | Linux |
| Drosoph | 0.08 | 0.06 | 0.038 | 0.023 | 0.0235 | 0.0188 | 0.0145 | 0.0042 |
| Pataa | 0.5 | 0.4 | 0.1344 | 0.1 | 0.0184 | 0.014 | 0.116 | 0.086 |
| estothers | 1 | 0.8 | 0.5799 | 0.421 | 0.0343 | 0.035 | 0.5456 | 0.386 |
| envnr | 18 | 15 | 4.0308 | 3.5132 | 0.5308 | 0.5132 | 3.5 | 3 |
| Nr | 27 | 24 | 7.2077 | 6.1163 | 0.4077 | 0.6163 | 6.8 | 5.5 | | 0 |
| SystemName | BlogSum<br>R-2 | MEAD<br>R-2 | TAC-Best<br>R-2 |
| --- | --- | --- | --- |
| Baseline | 0.102⇓ | 0.041⇓ | 0.130 |
| w/oIllustration | 0.107⇓ | 0.022⇓ | 0.112⇓ | | | w/oContingency | 0.093⇓ | 0.025⇓ | 0.102⇓ |
| --- | --- | --- | --- |
| w/oComparison | 0.103⇓ | 0.033⇓ | 0.113⇓ |
| w/oAttributive | 0.113⇓ | 0.050 | 0.124 |
| w/oTopic-opinion | 0.112⇓ | 0.049 | 0.123 |
| w/oAttribution | 0.118⇓ | 0.051⇓ | 0.128 |
| withallRelations | 0.125 | .053 | 0.138 | | 1 |
| SystemName | BlogSum<br>R-2 | MEAD<br>R-2 | TAC-Best<br>R-2 |
| --- | --- | --- | --- |
| Baseline | 0.102⇓ | 0.041⇓ | 0.130 |
| w/oIllustration | 0.107⇓ | 0.022⇓ | 0.112⇓ | | | Drosoph | 0.08 | 0.06 | 0.038 | 0.023 | 0.0235 | 0.0188 | 0.0145 | 0.0042 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Pataa | 0.5 | 0.4 | 0.1344 | 0.1 | 0.0184 | 0.014 | 0.116 | 0.086 |
| estothers | 1 | 0.8 | 0.5799 | 0.421 | 0.0343 | 0.035 | 0.5456 | 0.386 |
| envnr | 18 | 15 | 4.0308 | 3.5132 | 0.5308 | 0.5132 | 3.5 | 3 |
| Nr | 27 | 24 | 7.2077 | 6.1163 | 0.4077 | 0.6163 | 6.8 | 5.5 | | 0 |
| | Input | Train | Test | mAP | FPS |
| --- | --- | --- | --- | --- | --- |
| YOLO<br>YOLOv2<br>YOLOv2544x544<br>FasterR-CNN<br>R-FCN(ResNet-101) | 448<br>416<br>544 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 63.4<br>76.8<br>78.6<br>73.2<br>80.5 | 45<br>67<br>40<br>5<br>5.9 | | | SSD*<br>DSSD(ResNet-101)<br>ISSD*<br>ours(SSDpooling)*<br>ours(SSDdeconvolution)* | 300<br>321<br>300<br>300<br>300 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 77.7<br>78.6<br>78.1<br>77.1<br>77.3 | 61.1<br>9.5<br>26.9<br>48.3<br>39.9 |
| --- | --- | --- | --- | --- | --- |
| ours(R-SSD)*<br>ours(R-SSDoneclassifier(4boxes))*<br>ours(R-SSDoneclassifier(6boxes))* | 300<br>300<br>300 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007 | 78.5<br>76.2<br>77.0 | 35.0<br>34.8<br>35.4 |
| SSD*<br>DSSD(ResNet-101)<br>ours(R-SSD)* | 512<br>513<br>512 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007 | 79.8<br>81.5<br>80.8 | 25.2<br>5.5<br>16.6 | | 1 |
| | Input | Train | Test | mAP | FPS |
| --- | --- | --- | --- | --- | --- |
| YOLO<br>YOLOv2<br>YOLOv2544x544<br>FasterR-CNN<br>R-FCN(ResNet-101) | 448<br>416<br>544 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 63.4<br>76.8<br>78.6<br>73.2<br>80.5 | 45<br>67<br>40<br>5<br>5.9 | | | Cat | Method | Feature | Metric | | |
| --- | --- | --- | --- | --- | --- |
| | | | | | |
| A | XQDA<br>GOG<br>NFST<br>SCS | LOMO<br>GOG<br>LOMO,KCCA<br>CHS | -<br>-<br>-<br>- | XQDA<br>XQDA<br>NSFT<br>SCS | -<br>-<br>-<br>- |
| B | DCNN+<br>X-Corr<br>MTDnet | -<br>-<br>- | DCNN+<br>X-Corr<br>MTDnet | DVM<br>DVM<br>DVM,L2 | -<br>-<br>- |
| C | S-CNN<br>DGD<br>MCP<br>JLML(Ours) | -<br>-<br>-<br>- | S-CNN<br>DGD<br>MCP<br>JLML | -<br>-<br>-<br>- | L2<br>L2<br>L2<br>L2 | | 0 |
| | Input | Train | Test | mAP | FPS |
| --- | --- | --- | --- | --- | --- |
| YOLO<br>YOLOv2<br>YOLOv2544x544<br>FasterR-CNN<br>R-FCN(ResNet-101) | 448<br>416<br>544 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 63.4<br>76.8<br>78.6<br>73.2<br>80.5 | 45<br>67<br>40<br>5<br>5.9 |
| SSD*<br>DSSD(ResNet-101)<br>ISSD*<br>ours(SSDpooling)*<br>ours(SSDdeconvolution)* | 300<br>321<br>300<br>300<br>300 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 77.7<br>78.6<br>78.1<br>77.1<br>77.3 | 61.1<br>9.5<br>26.9<br>48.3<br>39.9 | | | ours(R-SSD)*<br>ours(R-SSDoneclassifier(4boxes))*<br>ours(R-SSDoneclassifier(6boxes))* | 300<br>300<br>300 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007 | 78.5<br>76.2<br>77.0 | 35.0<br>34.8<br>35.4 |
| --- | --- | --- | --- | --- | --- |
| SSD*<br>DSSD(ResNet-101)<br>ours(R-SSD)* | 512<br>513<br>512 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007 | 79.8<br>81.5<br>80.8 | 25.2<br>5.5<br>16.6 | | 1 |
| | Input | Train | Test | mAP | FPS |
| --- | --- | --- | --- | --- | --- |
| YOLO<br>YOLOv2<br>YOLOv2544x544<br>FasterR-CNN<br>R-FCN(ResNet-101) | 448<br>416<br>544 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 63.4<br>76.8<br>78.6<br>73.2<br>80.5 | 45<br>67<br>40<br>5<br>5.9 |
| SSD*<br>DSSD(ResNet-101)<br>ISSD*<br>ours(SSDpooling)*<br>ours(SSDdeconvolution)* | 300<br>321<br>300<br>300<br>300 | VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012<br>VOC2007+2012 | 2007<br>2007<br>2007<br>2007<br>2007 | 77.7<br>78.6<br>78.1<br>77.1<br>77.3 | 61.1<br>9.5<br>26.9<br>48.3<br>39.9 | | | B | DCNN+<br>X-Corr<br>MTDnet | -<br>-<br>- | DCNN+<br>X-Corr<br>MTDnet | DVM<br>DVM<br>DVM,L2 | -<br>-<br>- |
| --- | --- | --- | --- | --- | --- |
| C | S-CNN<br>DGD<br>MCP<br>JLML(Ours) | -<br>-<br>-<br>- | S-CNN<br>DGD<br>MCP<br>JLML | -<br>-<br>-<br>- | L2<br>L2<br>L2<br>L2 | | 0 |
| Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.88 | 0.78 | 0.83 |
| Zhangetal. | 0.88 | 0.74 | 0.80 |
| Tianetal. | 0.85 | 0.76 | 0.80 |
| Luetal. | 0.89 | 0.70 | 0.78 |
| iwrr2014 | 0.86 | 0.70 | 0.77 |
| USTBTexStar | 0.88 | 0.66 | 0.76 |
| TextSpotter | 0.88 | 0.65 | 0.74 |
| Yinetal. | 0.84 | 0.65 | 0.73 | | | CASIANLPR | 0.79 | 0.68 | 0.73 |
| --- | --- | --- | --- |
| TextDetectorCASIA | 0.85 | 0.63 | 0.72 |
| I2RNUSFAR | 0.75 | 0.69 | 0.72 |
| I2RNUS | 0.73 | 0.66 | 0.69 |
| TH-TextLoc | 0.70 | 0.65 | 0.67 | | 1 |
| Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.88 | 0.78 | 0.83 |
| Zhangetal. | 0.88 | 0.74 | 0.80 |
| Tianetal. | 0.85 | 0.76 | 0.80 |
| Luetal. | 0.89 | 0.70 | 0.78 |
| iwrr2014 | 0.86 | 0.70 | 0.77 |
| USTBTexStar | 0.88 | 0.66 | 0.76 |
| TextSpotter | 0.88 | 0.65 | 0.74 |
| Yinetal. | 0.84 | 0.65 | 0.73 | | | Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.7651 | 0.7531 | 0.7591 |
| Zhangetal. | 0.83 | 0.67 | 0.74 |
| Yinetal. | 0.81 | 0.63 | 0.71 |
| Kangetal. | 0.71 | 0.62 | 0.66 |
| Yinetal. | 0.71 | 0.61 | 0.66 |
| Unified | 0.64 | 0.62 | 0.61 |
| TD-Mixture | 0.63 | 0.63 | 0.60 |
| TD-ICDAR | 0.53 | 0.52 | 0.50 |
| Epshteinetal. | 0.25 | 0.25 | 0.25 |
| Chenetal. | 0.05 | 0.05 | 0.05 | | 0 |
| Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.88 | 0.78 | 0.83 |
| Zhangetal. | 0.88 | 0.74 | 0.80 |
| Tianetal. | 0.85 | 0.76 | 0.80 |
| Luetal. | 0.89 | 0.70 | 0.78 |
| iwrr2014 | 0.86 | 0.70 | 0.77 |
| USTBTexStar | 0.88 | 0.66 | 0.76 |
| TextSpotter | 0.88 | 0.65 | 0.74 |
| Yinetal. | 0.84 | 0.65 | 0.73 |
| CASIANLPR | 0.79 | 0.68 | 0.73 |
| TextDetectorCASIA | 0.85 | 0.63 | 0.72 |
| I2RNUSFAR | 0.75 | 0.69 | 0.72 | | | I2RNUS | 0.73 | 0.66 | 0.69 |
| --- | --- | --- | --- |
| TH-TextLoc | 0.70 | 0.65 | 0.67 | | 1 |
| Algorithm | Precision | Recall | F-measure |
| --- | --- | --- | --- |
| Proposed | 0.88 | 0.78 | 0.83 |
| Zhangetal. | 0.88 | 0.74 | 0.80 |
| Tianetal. | 0.85 | 0.76 | 0.80 |
| Luetal. | 0.89 | 0.70 | 0.78 |
| iwrr2014 | 0.86 | 0.70 | 0.77 |
| USTBTexStar | 0.88 | 0.66 | 0.76 |
| TextSpotter | 0.88 | 0.65 | 0.74 |
| Yinetal. | 0.84 | 0.65 | 0.73 |
| CASIANLPR | 0.79 | 0.68 | 0.73 |
| TextDetectorCASIA | 0.85 | 0.63 | 0.72 |
| I2RNUSFAR | 0.75 | 0.69 | 0.72 | | | Yinetal. | 0.71 | 0.61 | 0.66 |
| --- | --- | --- | --- |
| Unified | 0.64 | 0.62 | 0.61 |
| TD-Mixture | 0.63 | 0.63 | 0.60 |
| TD-ICDAR | 0.53 | 0.52 | 0.50 |
| Epshteinetal. | 0.25 | 0.25 | 0.25 |
| Chenetal. | 0.05 | 0.05 | 0.05 | | 0 |
| Model | Year | NDCG@20 |
| --- | --- | --- |
| ARIMA(1,1,1) | 2015 | 0.5546 |
| NaiveExponentialSmoothing | 2015 | 0.6487 |
| NaiveExponentialSmoothing | 2014 | 0.7343 | | | NaiveExponentialSmoothing | 2013 | 0.7263 |
| --- | --- | --- |
| ExponentialSmoothing | 2015 | 0.6405 |
| ExponentialSmoothing | 2014 | 0.8086 |
| ExponentialSmoothing | 2013 | 0.7391 | | 1 |
| Model | Year | NDCG@20 |
| --- | --- | --- |
| ARIMA(1,1,1) | 2015 | 0.5546 |
| NaiveExponentialSmoothing | 2015 | 0.6487 |
| NaiveExponentialSmoothing | 2014 | 0.7343 | | | Model | Year | NDCG@20 |
| --- | --- | --- |
| NaiveExponentialSmoothing | 2015 | 0.8110 |
| NaiveExponentialSmoothing | 2014 | 0.7411 |
| NaiveExponentialSmoothing | 2013 | 0.8342 |
| ARIMA(1,1,1) | 2015 | 0.7249 |
| ARIMA(0,1,1) | 2015 | 0.7250 | | 0 |
| Model | Year | NDCG@20 |
| --- | --- | --- |
| ARIMA(1,1,1) | 2015 | 0.5546 |
| NaiveExponentialSmoothing | 2015 | 0.6487 |
| NaiveExponentialSmoothing | 2014 | 0.7343 | | | NaiveExponentialSmoothing | 2013 | 0.7263 |
| --- | --- | --- |
| ExponentialSmoothing | 2015 | 0.6405 |
| ExponentialSmoothing | 2014 | 0.8086 |
| ExponentialSmoothing | 2013 | 0.7391 | | 1 |
| Model | Year | NDCG@20 |
| --- | --- | --- |
| ARIMA(1,1,1) | 2015 | 0.5546 |
| NaiveExponentialSmoothing | 2015 | 0.6487 |
| NaiveExponentialSmoothing | 2014 | 0.7343 | | | NaiveExponentialSmoothing | 2013 | 0.8342 |
| --- | --- | --- |
| ARIMA(1,1,1) | 2015 | 0.7249 |
| ARIMA(0,1,1) | 2015 | 0.7250 | | 0 |
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