While existing graph embedding models can generate useful embedding vectors that perform well on graph-related tasks , what valuable information can be jointly learned by a graph embedding model is less discussed . In this paper , we consider the possibility of detecting critical structures by a graph embedding model . At last , support vector machine with a composite kernel ( SVM-CK ) is adopted to classify the dimensionality-reduced HSI . Experimental results on three real hyperspectral datasets demonstrate that our method can achieve higher classification accuracy with relatively less consumed time than other graph embedding-based methods . Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or rely on a simple skip-gram based model to learn embeddings on graphs . Using hand-crafted features often fails to capture the complex dynamics in human social relations , while the graph embedding based methods only use random walks to propagate information and can not incorporate external semantic data provided . We propose a novel model that utilizes Graph Convolutional Networks ( GCNs ) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner . Our effort is to minimise this by infusing syntactic knowledge into the embeddings . We propose a graph based embedding algorithm inspired from node2vec . Experimental results have shown that our algorithm improves the syntactic strength and gives robust performance on meagre data . Learning to embed the knowledge graph has been a hot topic in research communities . As for that , TransE is a promising method that can achieve state-of-art performance for many of the benchmark tasks . However , none of the previous work considers the knowledge graph in plant domain in which case the properties of the graph are significantly different . Since the margin is used as optimal distance to distinguish valid and invalid triples , it is reasonable to permit the margin to evolve during the training process . Based on this idea , a dynamic margin ( DM ) translation principle is proposed in this paper , and by introducing this principle into the classical TransE model , a knowledge graph embedding model named TransE-DM is presented . Compared with the TransE model , a feature of TransE-DM is that the loss function can evolve during the training process . Therefore , we try several strategies to acquire the merged embeddings which may not only retain the performance of { \it entity inference } , { \it relation prediction } , and even { \it triplet classification } evaluated by the single-thread { \bf TransE } on several well-known knowledge bases such as Freebase and NELL , but also scale up the learning speed along with the number of cores within a processor . So far , the empirical studies show that we could achieve comparable results as the single-thread { \bf TransE } performs by the { \it stochastic gradient descend } ( SGD ) algorithm , as well as increase the training speed multiple times via adapting the { \it batch gradient descend } ( BGD ) algorithm for { \it MapReduce } paradigm . Lastly , the current models are not able to handle dynamic data especially the introduction of new unseen entities/relations or removal of triplets . In this paper , we propose Parallel Universe TransE ( puTransE ) , an adaptable and robust adaptation of the translational model . Our approach non-parametrically estimates the energy score of a triplet from multiple embedding spaces of structurally and semantically aware triplet selection . In this paper , we propose a translation model on subgragh representing knowledge graph . The model builds an ensemble TransE model on subgraph divided by features of relations in triplets by training the model with different parts of dataset independently . Afterwards , experimental results on link prediction show improvements on parameters compared to the state-of-the-art baselines . In this paper , we propose to add domain knowledge from the most comprehensive biomedical ontology SNOMED CT to facilitate the embedding of EMR symptoms and diagnoses for oral disease prediction . We first learn embeddings of SNOMED CT concepts by applying the TransE algorithm prevalent for representation learning of knowledge base . Secondly , the mapping from symptoms/diagnoses to biomedical concepts and the corresponding semantic relations defined in SNOMED CT are modeled mathematically . Application of such models on domain-specific KGs takes advantage of enriched meta-data and provides accurate results where the underlying domain can enormously benefit . In this work , the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata . The results show a significant shift in the accuracy and performance evaluation of the model on a dataset with scholarly metadata . This is , to our knowledge , the first computational technique for estimating this semantic concept . Additionally , the model substantially outperforms a TransE baseline in prediction of actor casting choices . In this paper , by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity , we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations . We use this principle to improve the TransE , TransR and TranSparse models respectively and build new models named TransE-DT , TransR-DT and TranSparse-DT correspondingly . Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task . The authors further prove other models are not fully expressive : DistMult , because it can not model asymmetric relations ; and transitive models such as TransE and its variants FTransE and STransE , because of certain contradictions that they impose between different relation types . By Theorem [ reference ] , we establish the bound on entity and relation embedding dimensionality ( i.e. rank of the decomposition ) that guarantees full expressiveness of TuckER . WN18 describes lexical and semantic hierarchies between concepts and contains many antisymmetric relations such as hypernymy , hyponymy , or being ” part of ” . Indeed , the DistMult and TransE models are outperformed here by ComplEx and HolE , which are on par with respective filtered MRR scores of 0.941 and 0.938 . Table [ reference ] shows the filtered test set MRR for the models considered and each relation of WN18 , confirming the advantage of our model on antisymmetric relations while losing nothing on the others . These results highlight the effectiveness of the five semantic relations described in Section [ reference ] . To further investigate external knowledge , we add TransE relation embedding , and again no further improvement is observed on both the development and test sets when TransE relation embedding is used ( concatenated ) with the semantic relation vectors . We consider this is due to the fact that TransE embedding is not specifically sensitive to inference information ; e.g. , it does not model co - hyponyms features , and its potential benefit has already been covered by the semantic relation features used . Table [ reference ] shows the performance of models on the MultiNLI dataset . Given multichannel noisy speech spectra , the full-rank or rank-1 spatial covariance matrices and PSDs of speech and noise are estimated in an unsupervised maximum-likelihood manner . Experimental results showed that the full-rank version of the proposed method was significantly better than MNMF , ILRMA , and the rank-1 version . We confirmed that the initialization-sensitivity and local-optimum problems of MNMF with many spatial parameters can be solved by incorporating the precise speech model . Experiments on a noisy speech recognition task showed the effectiveness of the proposed initialization method both for MNMF and ILRMA . For determined cases , ILRMA was faster and better than MNMF , but for overdetermined cases , even with special treatments , ILRMA was inferior to MNMF . Multichannel Non-negative Matrix Factorization ( MNMF ) is one of powerful approaches , which adopts the NMF concept for source power spectrogram modeling . This concept is also employed in Independent Low-Rank Matrix Analysis ( ILRMA ) , a special class of the MNMF framework formulated under determined conditions . These methods work reasonably for particular types of sound sources , however , one limitation is that they can fail to work for sources with spectrograms that do not comply with the NMF model . The proposed method was evaluated on a underdetermined source separation task of separating out three sources from two microphone inputs . Experimental results revealed that the generalized MVAE method achieved better performance than the MNMF method . Actually , not only the performance can be improved by effectively incorporating the time-stamped information in the clustering process on most data sets , but also we can find the evolutionary trends of the clusters with time information . In this paper , we introduce an approach for clustering time-stamped data and discovering the evolutionary trends of the clusters by using Multiple Nonnegative Matrices Factorization ( MNMF ) with smooth constraint over time . To utilize time-stamped information in the clustering process , an extra object-time matrix is constructed in our proposed method . In conventional mask-based approaches , hard or soft masks are estimated and beamforming is performed using speech and noise spatial covariance matrices calculated from masked noisy observations , but the phase information of the target speech is not adequately preserved . In the proposed method , we perform complex-domain source separation based on multi-channel NMF with rank-1 spatial model ( rank-1 MNMF ) to obtain a speech spatial covariance matrix for estimating a steering vector for the target speech utilizing the separated speech observation in each time-frequency bin . This accurate steering vector estimation is effectively combined with our novel noise mask prediction method using multi-channel robust NMF ( MRNMF ) to construct a Maximum Likelihood ( ML ) beamformer that achieved a better speech recognition performance than a state-of-the-art DNN-based beamformer with no environment-specific training . This paper deals with a multichannel audio source separation problem under underdetermined conditions . Multi-channel Non-negative Matrix Factorization ( MNMF ) is one of the powerful approaches , which adopts the NMF concept for source power spectrogram modeling . It works reasonably well for particular types of sound sources , however , one limitation is that it can fail to work for sources with spectrograms that do not comply with the NMF model . To reduce the number of matrix operations in MNMF , the proposed algorithm simultaneously diagonalizes each pair of two active source spatial covariance matrices . As a result , the computational time complexity of the fast MNMF is comparable to those of TF mask approaches without assuming the sparsity constraint of source spectra unlike them . The experimental results show that the fast MNMF is only about twice as slow as the corresponding TF mask approach while maintaining the separation performance of the original MNMF . In this paper , we investigate blind source separation for audio signals based on multichannel nonnegative matrix factorization ( MNMF ) of magnitude spectrograms in a linear demixed domain . The original magnitude MNMF by itself is less effective in general acoustic situations because it discards mutual information between input channels , which is represented by non-diagonal complex elements of the spatial covariance matrices of them . To deal with this problem , several linear transformations of the multichannel input have been proposed in order to diagonalize the covariance matrices without loss of the mutual information . Finally , the abstract features are used in the data prediction by adopting the merge layer of the neural network . The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators . The transformation from the time – space domain to the topological domain benefits from the ability of networks to characterize both the local and global relationships between the nodes , and MNMF incorporates inter-similarity across distinct variates into clustering . Furthermore , to trace the evolutionary trends of clusters , time series is transformed into a dynamic multi-relational network , thereby extending MNMF to dynamic MNMF . Extensive experiments illustrate the superiority of our approach compared with the current state-of-the-art algorithms . LINE is an open source library to analyze systems that can be modeled by means of queueing theory . Recently , a new major release of the tool ( version 2.0.0 ) has introduced several novel features , which are the focus of this demonstration . To unpack the elementary properties encoded in a node representation , we evaluate the representations on the accuracy with which they can model each of these properties . Our extensive study of three state-of-the-art node representation models ( DeepWalk , node2vec and LINE ) on four different tasks and six diverse graphs reveal that node2vec and LINE best encode the network properties of sparse and dense graphs respectively . We correlate the model performance obtained for elementary property prediction tasks with the high-level downstream applications such as link prediction and node classification , and visualize the task performance vector of each model to understand the semantic similarity between the embeddings learned by various models . The following documents the changes that were required to convert the programs from CDC 6000 or 7000 programs to IBM 360 programs . In addition to the changes listed below it was , of course , necessary to include a FRAME subroutine , a LINE sub-routine , and other calls to plotting subroutines which support locally available plotting equipment . However , since plotting equipment and its software support vary from one installation to another , only those changes pertinent to the IBM 360 are listed here . If we already know the weather situation in the area to be addressed and the area to be passed during the trip , we will easily decide whether to go or not , or prepare to anticipate the situation that will occur . This study makes it easy for users to know the weather area just by opening the chatbot application used above the sosmed LINE application . MEASUREMENT SYSTEM IN LINE WEAR COATING AND TENSION BOLTS MILL INCORPORATING AT THE END THREAD OF A NUMBER SET BOLT AN ISSUER OF WAVES ACOUSTIC , A wave receiver ACOUSTIC , AN ELECTRONIC CIRCUIT , A SOURCE OF ENERGY And a communication device , IN THE VICINITY OF THE MILL MANAGER DEVICE DATA COMMUNICATIONS AND ACCUMULATION AND IN A POSITION REMOTE SYSTEM DATA PROCESSING AND DEPLOYMENT . Indirect measurement methods are available that are very accurate , if they are implemented properly and certain restrictions are observed . REFERENCING CASTER TO THE THRUST LINE Caster has been defined as follows : “ 6.2.1 Caster Angle — The angle in side elevation between the steering axis and the vertical . To facilitate students in ordering food through the LINE bot Dikampus startup , a chatbot design comparison that has a good usability score is compared . The method used is user-centered design and LINE Front-end framework as a medium of design and the system usability scale ( SUS ) as the usability standard in the design of LINE Bot Dikampus startup . Through these results , two usability scores from the design are made as to the main design assessment . THE DEMAND FOR ALL-WEATHER ROADS HAS PRODUCED A NUMBER OF PLANS FOR THE IMPROVEMENT OF THE RIDING SURFACES OF NEW OR REWORKED HIGHWAYS . ASPHALTIC LAYDOWN EQUIPMENT WITH STRING- LINE SENSORING DEVICES FOR SURFACE CONTROLS AND THE USE OF THE ELECTRONIC COMPUTER FOR THE MATHEMATICAL COMPUTATIONS REQUIRED FOR SUCH CONTROLS ARE DISCUSSED . SUFFICIENT INFORMATION IS PROVIDED RELATING TO PROGRAM CONTROL AND REASONING AND THE MECHANICS OF INPUT AND OUTPUT LINE ROUTE INVENTION INCLUDES AT LEAST ONE DIVISION EXTRACTION 1 PARTS CONTAINED IN BULK HOPPER 2 FOR A FIRST SELECTION OF PIECES IN TERMS OF HEIGHT , A SECOND SECTION 10 ALIGNMENT THESE PIECES TO GUIDE THEIR DIMENSIONS LONGITUDINAL PARALLEL WITH THE MOVEMENT DIRECTION OF THE LINE , AND ONE THIRD DIVISION November 17 WITH BLOCKING MEANS 13 , 14 PIECES , OF PROBING MEANS OF 15 AND TURNING MEANS OF PARTS 16 EN RESPONSE TO INFORMATION ISSUED BY MEANS OF PROBE . Automatic Enumeration of Types It ’s possible to exploit the _ _ LINE _ _ macro to create an easily extensible collection of types that can be accessed as an enumeration . Consider the following prototype — you can trivially map an integer index into a selector : template < int N > struct single_value : selector < false > { } ; template < > struct single_value<7 > : selector < true > // user supplied case # 1 { } ; template < > struct single_value<13 > : selector < true > // user supplied case # 2 { } ; Software development used ExpressJS and AngularJS as programming language . The result of this research are 1 ) an online test software that integrated with LINE messenger that can be used by user to access list of tryout , get information by tryout ’s detail , do an exam according to tryout that chosen , get the result of exam after submitted , and get to know the leaderboard of exam , and 2 ) the result of software testing is software has met standard based on ISO/IEC 25010 , ( 1 ) at functional suitability aspect , the function 100 % worked ( has met the standard ) , ( 2 ) performance efficiency aspect has met standard with average load time per page is 1.8 seconds , (3)usability aspect result , the software is decent with 82 % result , and get grade 0.864 at alpha-cronbach calculation that means the questionnaire is valid and reliable , ( 4 ) security aspect , software has low risk alert , and ( 5 ) compatibility , the test did n’t happened at this aspect , because no resources had been shared with another hardware or another software . Keywords : online test , mobile web app , ISO/IEC 25010 DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes . For all these methods , however , a multi - step pipeline including random walk generation and semi - supervised training is required where each step has to be optimized separately . Menon and Elkan proposed an MF model capable of incorporating side information about nodes and / or edges to demonstrate strong link prediction results on several challenging network datasets . Other recent approaches similar to MF that learn node embeddings via some encoder transformation and then use a bilinear decoder for the reconstruction include DeepWalk and its variants LINE and node2vec . DeepWalk , LINE , and node2vec do not support external node / edge features . document : LINE : Large - scale Information Network Embedding This paper studies the problem of embedding very large information networks into low - dimensional vector spaces , which is useful in many tasks such as visualization , node classification , and link prediction . In each stochastic gradient step , an edge is sampled with the probability proportional to its weight and then treated as binary for model updating . There are also two variants : LINE ( 1st ) and LINE ( 2nd ) . Like the graph factorization , both LINE ( 1st ) and LINE - SGD ( 1st ) only apply to undirected graphs . Information network embedding is an effective way for efficient graph analytics . However , it still faces with computational challenges in problems such as link prediction and node recommendation , particularly with increasing scale of networks . In this study , we aim to construct a polarity dictionary specialized for the analysis of financial policies . Based on an idea that polarity words are likely located in the secondary proximity in the dependency network , we proposed an automatic dictionary construction method using secondary LINE ( Large-scale Information Network Embedding ) that is a network representation learning method to quantify relationship . The results suggested the possibility of constructing a dictionary using distributed representation by LINE . LSTM is a special recurrent neural network ( RNN ) architecture that contains three gates and one internal state to control the storage of information . Additionally , we leverage the LINE ( Large-scale Information Network Embedding ) , an embedding method to transform vertices into large scale networks , to transform locations into spatial vectors according to their distance information , which we name it “ Location2Vec ” . By feeding the summation of the dynamic location embeddings and pre-trained Location2Vec embeddings into LSTM model , it can effectively predict next locations of a trajectory and improve the predicting accuracy . In this work , we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering . In a cold-start setting , on three publicly available datasets , we evaluate approaches from four method families : ( i ) factorization-based , ( ii ) random walk-based , ( iii ) deep learning-based , and ( iv ) the Large-scale Information Network Embedding ( LINE ) approach . We find that across the four families , random-walk-based approaches consistently achieve the best accuracy . document : LINE : Large - scale Information Network Embedding This paper studies the problem of embedding very large information networks into low - dimensional vector spaces , which is useful in many tasks such as visualization , node classification , and link prediction . , the problem of Large - scale Information Network Embedding aims to represent each vertex ∈vV into a low - dimensional space Rd , i.e. , learning a function : fG→VRd , where ≪d|V|. In the space Rd , both the first - order proximity and the second - order proximity between the vertices are preserved . Next , we introduce a large - scale network embedding model that preserves both first - and second - order proximities . section : LINE : Large - scale Information Network Embedding We have explored crucial aspects of NMF on massive recognition experiments with the ORL database of faces which include intuitively clear parts constituting the whole . Using a principal changing of the learning stage structure and by formulating NMF problems for each of a priori given parts separately , we developed a novel modular NMF algorithm . Although this algorithm provides uniquely separated basis vectors which code individual face parts in accordance with the parts-based principle of the NMF methodology applied to object recognition problems , any significant improvement of recognition rates for occluded parts , predicted in several papers , was not reached .
While existing graph embedding models can generate useful embedding vectors that perform well on graph-related tasks , what valuable information can be jointly learned by a graph embedding model is less discussed . In this paper , we consider the possibility of detecting critical structures by a graph embedding model .
At last , support vector machine with a composite kernel ( SVM-CK ) is adopted to classify the dimensionality-reduced HSI . Experimental results on three real hyperspectral datasets demonstrate that our method can achieve higher classification accuracy with relatively less consumed time than other graph embedding-based methods .
Most existing methods predict relationship based on a pairwise approach using some hand-crafted features or rely on a simple skip-gram based model to learn embeddings on graphs . Using hand-crafted features often fails to capture the complex dynamics in human social relations , while the graph embedding based methods only use random walks to propagate information and can not incorporate external semantic data provided . We propose a novel model that utilizes Graph Convolutional Networks ( GCNs ) to learn user embeddings on the User Mobility Heterogeneous Graph in an unsupervised manner .
Our effort is to minimise this by infusing syntactic knowledge into the embeddings . We propose a graph based embedding algorithm inspired from node2vec . Experimental results have shown that our algorithm improves the syntactic strength and gives robust performance on meagre data .
Learning to embed the knowledge graph has been a hot topic in research communities . As for that , TransE is a promising method that can achieve state-of-art performance for many of the benchmark tasks . However , none of the previous work considers the knowledge graph in plant domain in which case the properties of the graph are significantly different .
Since the margin is used as optimal distance to distinguish valid and invalid triples , it is reasonable to permit the margin to evolve during the training process . Based on this idea , a dynamic margin ( DM ) translation principle is proposed in this paper , and by introducing this principle into the classical TransE model , a knowledge graph embedding model named TransE-DM is presented . Compared with the TransE model , a feature of TransE-DM is that the loss function can evolve during the training process .
Therefore , we try several strategies to acquire the merged embeddings which may not only retain the performance of { \it entity inference } , { \it relation prediction } , and even { \it triplet classification } evaluated by the single-thread { \bf TransE } on several well-known knowledge bases such as Freebase and NELL , but also scale up the learning speed along with the number of cores within a processor . So far , the empirical studies show that we could achieve comparable results as the single-thread { \bf TransE } performs by the { \it stochastic gradient descend } ( SGD ) algorithm , as well as increase the training speed multiple times via adapting the { \it batch gradient descend } ( BGD ) algorithm for { \it MapReduce } paradigm .
Lastly , the current models are not able to handle dynamic data especially the introduction of new unseen entities/relations or removal of triplets . In this paper , we propose Parallel Universe TransE ( puTransE ) , an adaptable and robust adaptation of the translational model . Our approach non-parametrically estimates the energy score of a triplet from multiple embedding spaces of structurally and semantically aware triplet selection .
In this paper , we propose a translation model on subgragh representing knowledge graph . The model builds an ensemble TransE model on subgraph divided by features of relations in triplets by training the model with different parts of dataset independently . Afterwards , experimental results on link prediction show improvements on parameters compared to the state-of-the-art baselines .
In this paper , we propose to add domain knowledge from the most comprehensive biomedical ontology SNOMED CT to facilitate the embedding of EMR symptoms and diagnoses for oral disease prediction . We first learn embeddings of SNOMED CT concepts by applying the TransE algorithm prevalent for representation learning of knowledge base . Secondly , the mapping from symptoms/diagnoses to biomedical concepts and the corresponding semantic relations defined in SNOMED CT are modeled mathematically .
Application of such models on domain-specific KGs takes advantage of enriched meta-data and provides accurate results where the underlying domain can enormously benefit . In this work , the TransE embedding model is reconciled for a specific link prediction task on scholarly metadata . The results show a significant shift in the accuracy and performance evaluation of the model on a dataset with scholarly metadata .
This is , to our knowledge , the first computational technique for estimating this semantic concept . Additionally , the model substantially outperforms a TransE baseline in prediction of actor casting choices .
In this paper , by introducing parameter vectors into the translation principle which treats each relation as a translation from the head entity to the tail entity , we propose a novel dynamic translation principle which supports flexible translation between the embeddings of entities and relations . We use this principle to improve the TransE , TransR and TranSparse models respectively and build new models named TransE-DT , TransR-DT and TranSparse-DT correspondingly . Experimental results show that our dynamic translation principle achieves great improvement in both the link prediction task and the triple classification task .
The authors further prove other models are not fully expressive : DistMult , because it can not model asymmetric relations ; and transitive models such as TransE and its variants FTransE and STransE , because of certain contradictions that they impose between different relation types . By Theorem [ reference ] , we establish the bound on entity and relation embedding dimensionality ( i.e. rank of the decomposition ) that guarantees full expressiveness of TuckER .
WN18 describes lexical and semantic hierarchies between concepts and contains many antisymmetric relations such as hypernymy , hyponymy , or being ” part of ” . Indeed , the DistMult and TransE models are outperformed here by ComplEx and HolE , which are on par with respective filtered MRR scores of 0.941 and 0.938 . Table [ reference ] shows the filtered test set MRR for the models considered and each relation of WN18 , confirming the advantage of our model on antisymmetric relations while losing nothing on the others .
These results highlight the effectiveness of the five semantic relations described in Section [ reference ] . To further investigate external knowledge , we add TransE relation embedding , and again no further improvement is observed on both the development and test sets when TransE relation embedding is used ( concatenated ) with the semantic relation vectors . We consider this is due to the fact that TransE embedding is not specifically sensitive to inference information ; e.g. , it does not model co - hyponyms features , and its potential benefit has already been covered by the semantic relation features used . Table [ reference ] shows the performance of models on the MultiNLI dataset .
Given multichannel noisy speech spectra , the full-rank or rank-1 spatial covariance matrices and PSDs of speech and noise are estimated in an unsupervised maximum-likelihood manner . Experimental results showed that the full-rank version of the proposed method was significantly better than MNMF , ILRMA , and the rank-1 version . We confirmed that the initialization-sensitivity and local-optimum problems of MNMF with many spatial parameters can be solved by incorporating the precise speech model .
Experiments on a noisy speech recognition task showed the effectiveness of the proposed initialization method both for MNMF and ILRMA . For determined cases , ILRMA was faster and better than MNMF , but for overdetermined cases , even with special treatments , ILRMA was inferior to MNMF .
Multichannel Non-negative Matrix Factorization ( MNMF ) is one of powerful approaches , which adopts the NMF concept for source power spectrogram modeling . This concept is also employed in Independent Low-Rank Matrix Analysis ( ILRMA ) , a special class of the MNMF framework formulated under determined conditions . These methods work reasonably for particular types of sound sources , however , one limitation is that they can fail to work for sources with spectrograms that do not comply with the NMF model .
The proposed method was evaluated on a underdetermined source separation task of separating out three sources from two microphone inputs . Experimental results revealed that the generalized MVAE method achieved better performance than the MNMF method .
Actually , not only the performance can be improved by effectively incorporating the time-stamped information in the clustering process on most data sets , but also we can find the evolutionary trends of the clusters with time information . In this paper , we introduce an approach for clustering time-stamped data and discovering the evolutionary trends of the clusters by using Multiple Nonnegative Matrices Factorization ( MNMF ) with smooth constraint over time . To utilize time-stamped information in the clustering process , an extra object-time matrix is constructed in our proposed method .
In conventional mask-based approaches , hard or soft masks are estimated and beamforming is performed using speech and noise spatial covariance matrices calculated from masked noisy observations , but the phase information of the target speech is not adequately preserved . In the proposed method , we perform complex-domain source separation based on multi-channel NMF with rank-1 spatial model ( rank-1 MNMF ) to obtain a speech spatial covariance matrix for estimating a steering vector for the target speech utilizing the separated speech observation in each time-frequency bin . This accurate steering vector estimation is effectively combined with our novel noise mask prediction method using multi-channel robust NMF ( MRNMF ) to construct a Maximum Likelihood ( ML ) beamformer that achieved a better speech recognition performance than a state-of-the-art DNN-based beamformer with no environment-specific training .
This paper deals with a multichannel audio source separation problem under underdetermined conditions . Multi-channel Non-negative Matrix Factorization ( MNMF ) is one of the powerful approaches , which adopts the NMF concept for source power spectrogram modeling . It works reasonably well for particular types of sound sources , however , one limitation is that it can fail to work for sources with spectrograms that do not comply with the NMF model .
To reduce the number of matrix operations in MNMF , the proposed algorithm simultaneously diagonalizes each pair of two active source spatial covariance matrices . As a result , the computational time complexity of the fast MNMF is comparable to those of TF mask approaches without assuming the sparsity constraint of source spectra unlike them . The experimental results show that the fast MNMF is only about twice as slow as the corresponding TF mask approach while maintaining the separation performance of the original MNMF .
In this paper , we investigate blind source separation for audio signals based on multichannel nonnegative matrix factorization ( MNMF ) of magnitude spectrograms in a linear demixed domain . The original magnitude MNMF by itself is less effective in general acoustic situations because it discards mutual information between input channels , which is represented by non-diagonal complex elements of the spatial covariance matrices of them . To deal with this problem , several linear transformations of the multichannel input have been proposed in order to diagonalize the covariance matrices without loss of the mutual information .
Finally , the abstract features are used in the data prediction by adopting the merge layer of the neural network . The experimental results show that the proposed MNMF model has better performance compared with the other methods in many evaluation indicators .
The transformation from the time – space domain to the topological domain benefits from the ability of networks to characterize both the local and global relationships between the nodes , and MNMF incorporates inter-similarity across distinct variates into clustering . Furthermore , to trace the evolutionary trends of clusters , time series is transformed into a dynamic multi-relational network , thereby extending MNMF to dynamic MNMF . Extensive experiments illustrate the superiority of our approach compared with the current state-of-the-art algorithms .
LINE is an open source library to analyze systems that can be modeled by means of queueing theory . Recently , a new major release of the tool ( version 2.0.0 ) has introduced several novel features , which are the focus of this demonstration .
To unpack the elementary properties encoded in a node representation , we evaluate the representations on the accuracy with which they can model each of these properties . Our extensive study of three state-of-the-art node representation models ( DeepWalk , node2vec and LINE ) on four different tasks and six diverse graphs reveal that node2vec and LINE best encode the network properties of sparse and dense graphs respectively . We correlate the model performance obtained for elementary property prediction tasks with the high-level downstream applications such as link prediction and node classification , and visualize the task performance vector of each model to understand the semantic similarity between the embeddings learned by various models .
The following documents the changes that were required to convert the programs from CDC 6000 or 7000 programs to IBM 360 programs . In addition to the changes listed below it was , of course , necessary to include a FRAME subroutine , a LINE sub-routine , and other calls to plotting subroutines which support locally available plotting equipment . However , since plotting equipment and its software support vary from one installation to another , only those changes pertinent to the IBM 360 are listed here .
If we already know the weather situation in the area to be addressed and the area to be passed during the trip , we will easily decide whether to go or not , or prepare to anticipate the situation that will occur . This study makes it easy for users to know the weather area just by opening the chatbot application used above the sosmed LINE application .
MEASUREMENT SYSTEM IN LINE WEAR COATING AND TENSION BOLTS MILL INCORPORATING AT THE END THREAD OF A NUMBER SET BOLT AN ISSUER OF WAVES ACOUSTIC , A wave receiver ACOUSTIC , AN ELECTRONIC CIRCUIT , A SOURCE OF ENERGY And a communication device , IN THE VICINITY OF THE MILL MANAGER DEVICE DATA COMMUNICATIONS AND ACCUMULATION AND IN A POSITION REMOTE SYSTEM DATA PROCESSING AND DEPLOYMENT .
Indirect measurement methods are available that are very accurate , if they are implemented properly and certain restrictions are observed . REFERENCING CASTER TO THE THRUST LINE Caster has been defined as follows : “ 6.2.1 Caster Angle — The angle in side elevation between the steering axis and the vertical .
To facilitate students in ordering food through the LINE bot Dikampus startup , a chatbot design comparison that has a good usability score is compared . The method used is user-centered design and LINE Front-end framework as a medium of design and the system usability scale ( SUS ) as the usability standard in the design of LINE Bot Dikampus startup . Through these results , two usability scores from the design are made as to the main design assessment .
THE DEMAND FOR ALL-WEATHER ROADS HAS PRODUCED A NUMBER OF PLANS FOR THE IMPROVEMENT OF THE RIDING SURFACES OF NEW OR REWORKED HIGHWAYS . ASPHALTIC LAYDOWN EQUIPMENT WITH STRING- LINE SENSORING DEVICES FOR SURFACE CONTROLS AND THE USE OF THE ELECTRONIC COMPUTER FOR THE MATHEMATICAL COMPUTATIONS REQUIRED FOR SUCH CONTROLS ARE DISCUSSED . SUFFICIENT INFORMATION IS PROVIDED RELATING TO PROGRAM CONTROL AND REASONING AND THE MECHANICS OF INPUT AND OUTPUT
LINE ROUTE INVENTION INCLUDES AT LEAST ONE DIVISION EXTRACTION 1 PARTS CONTAINED IN BULK HOPPER 2 FOR A FIRST SELECTION OF PIECES IN TERMS OF HEIGHT , A SECOND SECTION 10 ALIGNMENT THESE PIECES TO GUIDE THEIR DIMENSIONS LONGITUDINAL PARALLEL WITH THE MOVEMENT DIRECTION OF THE LINE , AND ONE THIRD DIVISION November 17 WITH BLOCKING MEANS 13 , 14 PIECES , OF PROBING MEANS OF 15 AND TURNING MEANS OF PARTS 16 EN RESPONSE TO INFORMATION ISSUED BY MEANS OF PROBE .
Automatic Enumeration of Types It ’s possible to exploit the _ _ LINE _ _ macro to create an easily extensible collection of types that can be accessed as an enumeration . Consider the following prototype — you can trivially map an integer index into a selector : template < int N > struct single_value : selector < false > { } ; template < > struct single_value<7 > : selector < true > // user supplied case # 1 { } ; template < > struct single_value<13 > : selector < true > // user supplied case # 2 { } ;
Software development used ExpressJS and AngularJS as programming language . The result of this research are 1 ) an online test software that integrated with LINE messenger that can be used by user to access list of tryout , get information by tryout ’s detail , do an exam according to tryout that chosen , get the result of exam after submitted , and get to know the leaderboard of exam , and 2 ) the result of software testing is software has met standard based on ISO/IEC 25010 , ( 1 ) at functional suitability aspect , the function 100 % worked ( has met the standard ) , ( 2 ) performance efficiency aspect has met standard with average load time per page is 1.8 seconds , (3)usability aspect result , the software is decent with 82 % result , and get grade 0.864 at alpha-cronbach calculation that means the questionnaire is valid and reliable , ( 4 ) security aspect , software has low risk alert , and ( 5 ) compatibility , the test did n’t happened at this aspect , because no resources had been shared with another hardware or another software . Keywords : online test , mobile web app , ISO/IEC 25010
DeepWalk perozzi2014deepwalk learns embeddings via the prediction of the local neighborhood of nodes , sampled from random walks on the graph . LINE tang2015line and node2vec grovernode2vec extend DeepWalk with more sophisticated random walk or breadth - first search schemes . For all these methods , however , a multi - step pipeline including random walk generation and semi - supervised training is required where each step has to be optimized separately .
Menon and Elkan proposed an MF model capable of incorporating side information about nodes and / or edges to demonstrate strong link prediction results on several challenging network datasets . Other recent approaches similar to MF that learn node embeddings via some encoder transformation and then use a bilinear decoder for the reconstruction include DeepWalk and its variants LINE and node2vec . DeepWalk , LINE , and node2vec do not support external node / edge features .
document : LINE : Large - scale Information Network Embedding This paper studies the problem of embedding very large information networks into low - dimensional vector spaces , which is useful in many tasks such as visualization , node classification , and link prediction .
In each stochastic gradient step , an edge is sampled with the probability proportional to its weight and then treated as binary for model updating . There are also two variants : LINE ( 1st ) and LINE ( 2nd ) . Like the graph factorization , both LINE ( 1st ) and LINE - SGD ( 1st ) only apply to undirected graphs .
Information network embedding is an effective way for efficient graph analytics . However , it still faces with computational challenges in problems such as link prediction and node recommendation , particularly with increasing scale of networks .
In this study , we aim to construct a polarity dictionary specialized for the analysis of financial policies . Based on an idea that polarity words are likely located in the secondary proximity in the dependency network , we proposed an automatic dictionary construction method using secondary LINE ( Large-scale Information Network Embedding ) that is a network representation learning method to quantify relationship . The results suggested the possibility of constructing a dictionary using distributed representation by LINE .
LSTM is a special recurrent neural network ( RNN ) architecture that contains three gates and one internal state to control the storage of information . Additionally , we leverage the LINE ( Large-scale Information Network Embedding ) , an embedding method to transform vertices into large scale networks , to transform locations into spatial vectors according to their distance information , which we name it “ Location2Vec ” . By feeding the summation of the dynamic location embeddings and pre-trained Location2Vec embeddings into LSTM model , it can effectively predict next locations of a trajectory and improve the predicting accuracy .
In this work , we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering . In a cold-start setting , on three publicly available datasets , we evaluate approaches from four method families : ( i ) factorization-based , ( ii ) random walk-based , ( iii ) deep learning-based , and ( iv ) the Large-scale Information Network Embedding ( LINE ) approach . We find that across the four families , random-walk-based approaches consistently achieve the best accuracy .
document : LINE : Large - scale Information Network Embedding This paper studies the problem of embedding very large information networks into low - dimensional vector spaces , which is useful in many tasks such as visualization , node classification , and link prediction .
, the problem of Large - scale Information Network Embedding aims to represent each vertex ∈vV into a low - dimensional space Rd , i.e. , learning a function : fG→VRd , where ≪d|V|. In the space Rd , both the first - order proximity and the second - order proximity between the vertices are preserved . Next , we introduce a large - scale network embedding model that preserves both first - and second - order proximities . section : LINE : Large - scale Information Network Embedding
We have explored crucial aspects of NMF on massive recognition experiments with the ORL database of faces which include intuitively clear parts constituting the whole . Using a principal changing of the learning stage structure and by formulating NMF problems for each of a priori given parts separately , we developed a novel modular NMF algorithm . Although this algorithm provides uniquely separated basis vectors which code individual face parts in accordance with the parts-based principle of the NMF methodology applied to object recognition problems , any significant improvement of recognition rates for occluded parts , predicted in several papers , was not reached .
Ego-CNN: An Ego Network-based Representation of Graphs Detecting Critical Structures Spatial-spectral neighbour graph for dimensionality reduction of hyperspectral image classification Graph Convolutional Networks on User Mobility Heterogeneous Graphs for Social Relationship Inference Syntree2Vec - An algorithm to augment syntactic hierarchy into word embeddings Path-Based Learning for Plant Domain Knowledge Graph Representation Learning for Knowledge Graph with Dynamic Margin Parallel Knowledge Embedding with MapReduce on a Multi-core Processor Non-Parametric Estimation of Multiple Embeddings for Link Prediction on Dynamic Knowledge Graphs Knowledge Graph Embedding by Translation Model on Subgraph Augmenting Embedding with Domain Knowledge for Oral Disease Diagnosis Prediction Soft Marginal TransE for Scholarly Knowledge Graph Completion Learning Joint Gaussian Representations for Movies, Actors, and Literary Characters Knowledge Graph Embedding by Dynamic Translation TuckER: Tensor Factorization for Knowledge Graph Completion Complex Embeddings for Simple Link Prediction Neural Natural Language Inference Models Enhanced with External Knowledge Semi-Supervised Multichannel Speech Enhancement With a Deep Speech Prior Multi-channel Non-negative Matrix Factorization Initialized with Full-rank and Rank-1 Spatial Correlation Matrix for Speech Recognition An Evaluation of Underdetermined Source Separation Based on Multichannel Variational Autoencoder Clustering time-stamped data using multiple nonnegative matrices factorization Combined Multi-Channel NMF-Based Robust Beamforming for Noisy Speech Recognition Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation Fast Multichannel Nonnegative Matrix Factorization with Constraints on Active Source Candidates Linear demixed domain multichannel nonnegative matrix factorization for speech enhancement Data prediction model in wireless sensor networks based on bidirectional LSTM Clustering Multivariate Time Series Data via Multi-Nonnegative Matrix Factorization in Multi-Relational Networks Automated Multi-paradigm Analysis of Extended and Layered Queueing Models with LINE Towards Interpretation of Node Embeddings Remark and certification on algorithms 446 LINE Chatbot Informasi Cuaca Wilayah Indonesia Online measurement system wear coatings and tension bolts mill by ultrasonographic sensor inserted into the donut end bolts. Steering Geometry and Caster Measurement Perancangan Line Bot Layanan Pesan Antar Warung Makan “dikampus” Menggunakan Metode User Centered Design (studi Kasus Layanan Pesan Antar Makanan Untuk Wilayah Kampus Universitas Telkom) A PROGRAM FOR SMOOTHER ROADS parts routing line for distribution oriented in recent Advanced Metaprogramming in Classic C++ SOFTWARE DEVELOPMENT OF ONLINE TEST THAT INTEGRATED WITH LINE MESSENGER Semi-Supervised Classification with Graph Convolutional Networks Learning to Make Predictions on Graphs with Autoencoders LINE: Large-scale Information Network Embedding High-order Proximity Preserving Information Network Hashing Lexicon Creation for Financial Sentiment Analysis Using Network Embedding Combining Location2Vec with LSTM to predict next locations from trajectory data Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering LINE: Large-scale Information Network Embedding A modular non-negative matrix factorization for parts-based object recognition using subspace representation
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IJCAI CCKS ISCID (2) AAAI HCC SmartCom EMNLP/IJCNLP ICML ACL ISPACS INTERSPEECH IWAENC ICASSP ICPE Companion WWW (Companion Volume) Advanced Metaprogramming in Classic C++ ICLR 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) WWW '15 KDD WWW '15
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