Dong-Luong Dinh, Hee-Sok Han, H. J. Jeon, Sungyoung Lee, Tae-Seong Kim
Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.
{"title":"Principal direction analysis-based real-time 3D human pose reconstruction from a single depth image","authors":"Dong-Luong Dinh, Hee-Sok Han, H. J. Jeon, Sungyoung Lee, Tae-Seong Kim","doi":"10.1145/2542050.2542071","DOIUrl":"https://doi.org/10.1145/2542050.2542071","url":null,"abstract":"Human pose estimation in real-time is a challenging problem in computer vision. In this paper, we present a novel approach to recover a 3D human pose in real-time from a single depth human silhouette using Principal Direction Analysis (PDA) on each recognized body part. In our work, the human body parts are first recognized from a depth human body silhouette via the trained Random Forests (RFs). On each recognized body part which is presented as a set of 3D points cloud, PDA is applied to estimate the principal direction of the body part. Finally, a 3D human pose gets recovered by mapping the principal directional vector to each body part of a 3D human body model which is created with a set of super-quadrics linked by the kinematic chains. In our experiments, we have performed quantitative and qualitative evaluations of the proposed 3D human pose reconstruction methodology. Our evaluation results show that the proposed approach performs reliably on a sequence of unconstrained poses and achieves an average reconstruction error of 7.46 degree in a few key joint angles. Our 3D pose recovery methodology should be applicable to many areas such as human computer interactions and human activity recognition.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Document classifications is essential to information retrieval and text mining. In real life, unlabeled data is readily available whereas labeled ones are often laborious, expensive and slow to obtain. This paper proposes a novel Document Classification approach based on semi-supervised vMF mixture model on document manifold, called Laplacian regularized Semi-Supervised vMF Mixture Model(LapSSvMFs), which explicitly considers the manifold structure of document space to exploit efficiently both labeled and unlabeled data for classification. We have developed a generalized mean-field variational inference algorithm for the LapSSvMFs. Experimental results show that our approach preserves the best accuracy of purely graph-based transductive methods when the data has "manifold structure". Furthermore, high accuracy are obtained even for overlapping and fairly skewed datasets in comparison with other classification algorithms.
{"title":"Document classification using semi-supervived mixture model of von Mises-Fisher distributions on document manifold","authors":"N. K. Anh, Ngo Van Linh, L. Ky, Tam The Nguyen","doi":"10.1145/2542050.2542066","DOIUrl":"https://doi.org/10.1145/2542050.2542066","url":null,"abstract":"Document classifications is essential to information retrieval and text mining. In real life, unlabeled data is readily available whereas labeled ones are often laborious, expensive and slow to obtain. This paper proposes a novel Document Classification approach based on semi-supervised vMF mixture model on document manifold, called Laplacian regularized Semi-Supervised vMF Mixture Model(LapSSvMFs), which explicitly considers the manifold structure of document space to exploit efficiently both labeled and unlabeled data for classification. We have developed a generalized mean-field variational inference algorithm for the LapSSvMFs. Experimental results show that our approach preserves the best accuracy of purely graph-based transductive methods when the data has \"manifold structure\". Furthermore, high accuracy are obtained even for overlapping and fairly skewed datasets in comparison with other classification algorithms.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116518604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a mobile peer-to-peer approach that can be applied to build social computing services in distributed environment. This approach features peer-to-peer networks with the support of mobile devices that allows users to participate in social computing services including data sharing and searching services. Due to the limitations of system resource and network connectivity, mobile peers cannot easily undertake complicated operations, such as processing complex queries, indexing and transmitting large amount of data. This approach employs a super peer peer-to-peer network to deal with the problem of peer heterogeneity. It uses workstations as peers to assist mobile peers with insufficient storage, bandwidth and processing capability in dealing with complicated operations, while mobile peers possess ordinary operations such as publishing and searching data. We have extended the Gnutella protocol to provide operations on peers and mobile peers. The evaluation of the prototyping system has performed on a number of laboratory workstations and Android emulators to investigate the feasibility and scalability of the system.
{"title":"Mobile peer-to-peer approach for social computing services in distributed environment","authors":"Ha Manh Tran, K. V. Huynh, K. D. Vo, Son Thanh Le","doi":"10.1145/2542050.2542064","DOIUrl":"https://doi.org/10.1145/2542050.2542064","url":null,"abstract":"This paper presents a mobile peer-to-peer approach that can be applied to build social computing services in distributed environment. This approach features peer-to-peer networks with the support of mobile devices that allows users to participate in social computing services including data sharing and searching services. Due to the limitations of system resource and network connectivity, mobile peers cannot easily undertake complicated operations, such as processing complex queries, indexing and transmitting large amount of data. This approach employs a super peer peer-to-peer network to deal with the problem of peer heterogeneity. It uses workstations as peers to assist mobile peers with insufficient storage, bandwidth and processing capability in dealing with complicated operations, while mobile peers possess ordinary operations such as publishing and searching data. We have extended the Gnutella protocol to provide operations on peers and mobile peers. The evaluation of the prototyping system has performed on a number of laboratory workstations and Android emulators to investigate the feasibility and scalability of the system.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123268818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To design and analyze Cyber-Physical Systems (CPSs), engineers should consider computation and physical processes at the same time. Engineers require a design description which is easy to understand. SysML provides diagrammatic notations which are independent of specific disciplines. To express numerical constraints of SysML diagrams, we extend OCL with real arithmetic. Both SysML and extended-OCL require an executable design language to check inconsistencies of constraints. To achieve the above idea, we bridge a gap between SysML and VDM-RT. We propose a framework to embed SysML/extended-OCL into VDM-RT, where we prepare some libraries for extended-OCL. It is unnecessary for engineers to know VDM-RT. Using a unified CPS design description, our approach automatically debugs inconsistencies in whole CPS design. This paper illustrates the proposed method with a car race system.
{"title":"Modeling and debugging numerical constraints of cyber-physical systems design","authors":"T. N. Tien, S. Nakajima, H. Thang","doi":"10.1145/2542050.2542068","DOIUrl":"https://doi.org/10.1145/2542050.2542068","url":null,"abstract":"To design and analyze Cyber-Physical Systems (CPSs), engineers should consider computation and physical processes at the same time. Engineers require a design description which is easy to understand. SysML provides diagrammatic notations which are independent of specific disciplines. To express numerical constraints of SysML diagrams, we extend OCL with real arithmetic. Both SysML and extended-OCL require an executable design language to check inconsistencies of constraints. To achieve the above idea, we bridge a gap between SysML and VDM-RT. We propose a framework to embed SysML/extended-OCL into VDM-RT, where we prepare some libraries for extended-OCL. It is unnecessary for engineers to know VDM-RT. Using a unified CPS design description, our approach automatically debugs inconsistencies in whole CPS design. This paper illustrates the proposed method with a car race system.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114473247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vu Lam, Sang Phan Le, T. Ngo, Duy-Dinh Le, D. Duong, S. Satoh
Violent scene detection (VSD) refers to the task of detecting shots containing violent scenes in videos. With a wide range of promising real-world applications (e.g. movies/films inspection, video on demand, semantic video indexing and retrieval), VSD has been an important research problem. A typical approach for VSD is to learn a violent scene classifier and then apply it to video shots. Finding good feature representation for video shots is therefore essential to achieving high classification accuracy. It has been shown in recent work that using low-level features results in disappointing performance, since low-level features cannot convey high-level semantic information to represent violence concept. In this paper, we propose to use mid-level features to narrow the semantic gap between low-level features and violence concept. The mid-level features of a training (or test) video shots are formulated by concatenating scores returned by attribute classifiers. Attributes related to violence concept are manually defined. Compared to the original violence concept, the attributes have smaller gap to the low-level feature. Each corresponding attribute classifier is trained by using low-level features. We conduct experiments on MediaEval VSD benchmark dataset. The results show that, by using mid-level features, our proposed method outperforms the standard approach directly using low-level features.
{"title":"Violent scene detection using mid-level feature","authors":"Vu Lam, Sang Phan Le, T. Ngo, Duy-Dinh Le, D. Duong, S. Satoh","doi":"10.1145/2542050.2542070","DOIUrl":"https://doi.org/10.1145/2542050.2542070","url":null,"abstract":"Violent scene detection (VSD) refers to the task of detecting shots containing violent scenes in videos. With a wide range of promising real-world applications (e.g. movies/films inspection, video on demand, semantic video indexing and retrieval), VSD has been an important research problem. A typical approach for VSD is to learn a violent scene classifier and then apply it to video shots. Finding good feature representation for video shots is therefore essential to achieving high classification accuracy. It has been shown in recent work that using low-level features results in disappointing performance, since low-level features cannot convey high-level semantic information to represent violence concept. In this paper, we propose to use mid-level features to narrow the semantic gap between low-level features and violence concept. The mid-level features of a training (or test) video shots are formulated by concatenating scores returned by attribute classifiers. Attributes related to violence concept are manually defined. Compared to the original violence concept, the attributes have smaller gap to the low-level feature. Each corresponding attribute classifier is trained by using low-level features. We conduct experiments on MediaEval VSD benchmark dataset. The results show that, by using mid-level features, our proposed method outperforms the standard approach directly using low-level features.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131796339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The trend of Reservoir Computing (RC) has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called reservoir, which expands the input data and historical information into a high-dimensional space. This projection is carried out in order to enhance the linear separability of the input data. Another part is a memory-less structure designed to be robust and fast in the learning process. RC models are an alternative of Turing Machines and Recurrent Neural Networks to model cognitive processing in the neural system. Additionally, they are interesting Machine Learning tools to Time Series Modeling and Forecasting. Recently a new RC model was introduced under the name of Echo State Queueing Networks (ESQN). In this model the reservoir is a dynamical system which arises from the Queueing Theory. The initialization of the reservoir parameters may influence the model performance. Recently, some unsupervised techniques were used to improve the performance of one specific RC method. In this paper, we apply these techniques to set the reservoir parameters of the ESQN model. In particular, we study the ESQN model initialization using Self-Organizing Maps. Additionally, we test the model performance initializing the reservoir employing Hebbian rules. We present an empirical comparison of these reservoir initializations using a range of time series benchmarks.
{"title":"Initializing reservoirs with exhibitory and inhibitory signals using unsupervised learning techniques","authors":"Sebastián Basterrech, V. Snás̃el","doi":"10.1145/2542050.2542087","DOIUrl":"https://doi.org/10.1145/2542050.2542087","url":null,"abstract":"The trend of Reservoir Computing (RC) has been gaining prominence in the Neural Computation community since the 2000s. In a RC model there are at least two well-differentiated structures. One is a recurrent part called reservoir, which expands the input data and historical information into a high-dimensional space. This projection is carried out in order to enhance the linear separability of the input data. Another part is a memory-less structure designed to be robust and fast in the learning process. RC models are an alternative of Turing Machines and Recurrent Neural Networks to model cognitive processing in the neural system. Additionally, they are interesting Machine Learning tools to Time Series Modeling and Forecasting. Recently a new RC model was introduced under the name of Echo State Queueing Networks (ESQN). In this model the reservoir is a dynamical system which arises from the Queueing Theory. The initialization of the reservoir parameters may influence the model performance. Recently, some unsupervised techniques were used to improve the performance of one specific RC method. In this paper, we apply these techniques to set the reservoir parameters of the ESQN model. In particular, we study the ESQN model initialization using Self-Organizing Maps. Additionally, we test the model performance initializing the reservoir employing Hebbian rules. We present an empirical comparison of these reservoir initializations using a range of time series benchmarks.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"238 1-2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132118535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we focus on two aspects of time series mining: first on the transformation of numerical data to symbolic data; then on the search for frequent patterns in the resulting symbolic time series. We are thus interested in some patterns which have a high frequency in our database of time series and might help to generate candidates for various tasks in the area of time series mining. During the symbolization phase, we transform the numerical time series into a symbolic time series by i) splitting this latter into consecutive subsequences, ii) using a clustering algorithm to cluster these subsequences, each subsequence being then replaced by the name of its cluster to produce the symbolic time series. In the second phase, we use a sliding window to create a collection of transactions from the symbolic time series, then we use some algorithm for mining sequential pattern to find out some interesting motifs in the original time series. An example experiment based on environmental data is presented.
{"title":"Time series symbolization and search for frequent patterns","authors":"Mai Van Hoan, M. Exbrayat","doi":"10.1145/2542050.2542057","DOIUrl":"https://doi.org/10.1145/2542050.2542057","url":null,"abstract":"In this paper, we focus on two aspects of time series mining: first on the transformation of numerical data to symbolic data; then on the search for frequent patterns in the resulting symbolic time series. We are thus interested in some patterns which have a high frequency in our database of time series and might help to generate candidates for various tasks in the area of time series mining. During the symbolization phase, we transform the numerical time series into a symbolic time series by i) splitting this latter into consecutive subsequences, ii) using a clustering algorithm to cluster these subsequences, each subsequence being then replaced by the name of its cluster to produce the symbolic time series. In the second phase, we use a sliding window to create a collection of transactions from the symbolic time series, then we use some algorithm for mining sequential pattern to find out some interesting motifs in the original time series. An example experiment based on environmental data is presented.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122592828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Traditional surgical planning is challenged by limited spatial information of 2-D medical images or separated planning phases between 3-D model based virtual planning and its in situ registration. This paper presents an augmented surgical planning method with model-section views of an actual patient body and direct augmented interaction to realize spatially interactive planning in situ. A projector-Kinect system is proposed to construct a spatially augmented reality surgical environment directly on the patient body through surgical models' projection, correction and registration. Hand gesture based direct augmented interaction is developed to provide surgeons with interactive guidance during planning. The physical patient's body is augmented by the integration of medical information like 3-D anatomic and pathologic models as well as the 2-D medical images. Compared to conventional planning methods, this augmented planning enables surgical planning in 3-D space on the actual patient, and allows the planning to be integrated into an in situ robot-assisted surgical environment. Experimental results demonstrate that the method can facilitate spatial information perception and convey virtual planning on the physical patient in situ.
{"title":"In situ spatial AR surgical planning using projector-Kinect system","authors":"Rong Wen, Binh P. Nguyen, Chin-Boon Chng, C. Chui","doi":"10.1145/2542050.2542060","DOIUrl":"https://doi.org/10.1145/2542050.2542060","url":null,"abstract":"Traditional surgical planning is challenged by limited spatial information of 2-D medical images or separated planning phases between 3-D model based virtual planning and its in situ registration. This paper presents an augmented surgical planning method with model-section views of an actual patient body and direct augmented interaction to realize spatially interactive planning in situ. A projector-Kinect system is proposed to construct a spatially augmented reality surgical environment directly on the patient body through surgical models' projection, correction and registration. Hand gesture based direct augmented interaction is developed to provide surgeons with interactive guidance during planning. The physical patient's body is augmented by the integration of medical information like 3-D anatomic and pathologic models as well as the 2-D medical images. Compared to conventional planning methods, this augmented planning enables surgical planning in 3-D space on the actual patient, and allows the planning to be integrated into an in situ robot-assisted surgical environment. Experimental results demonstrate that the method can facilitate spatial information perception and convey virtual planning on the physical patient in situ.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124383132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emotion classification from speech signal is an interesting subject of machine learning applications that can provide the emotional or psychological states from speakers. This implicit information is helpful for machine to understand human behavior in more comprehensive way. Many feature extraction and classification methods have being proposed to find the most accurate and efficient method, but this is still an open question for researchers. In this paper, we propose a novel method to select features and classify emotions in hierarchical way using genetic algorithm and support vector machine classifiers in order to find the most accurate binary classification tree. We show the efficiency and robustness of our method by applying and analyzing on Berlin dataset of emotional speech and the experiment results show that our method achieves high accuracy and efficiency.
{"title":"Hierarchical emotion classification using genetic algorithms","authors":"Ba-Vui Le, J. Bang, Sungyoung Lee","doi":"10.1145/2542050.2542075","DOIUrl":"https://doi.org/10.1145/2542050.2542075","url":null,"abstract":"Emotion classification from speech signal is an interesting subject of machine learning applications that can provide the emotional or psychological states from speakers. This implicit information is helpful for machine to understand human behavior in more comprehensive way. Many feature extraction and classification methods have being proposed to find the most accurate and efficient method, but this is still an open question for researchers. In this paper, we propose a novel method to select features and classify emotions in hierarchical way using genetic algorithm and support vector machine classifiers in order to find the most accurate binary classification tree. We show the efficiency and robustness of our method by applying and analyzing on Berlin dataset of emotional speech and the experiment results show that our method achieves high accuracy and efficiency.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128995361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Uncertainty is ubiquitous in the outcome of many automatic processes (such as information extraction, natural language analysis, machine learning, data integration) or for all tasks that involve human judgment, contradicting information, or measurement errors. This uncertainty can be captured by probabilistic models -- probabilistic information can now be stored, queried, updated, aggregated in a well-founded manner. This talk will provide concrete motivation for probabilistic data management, review some of the most important models for probabilistic data (tables, trees) and present some of the important results in this research area, both theoretical and applied. A concrete example of the use of a probabilistic data management system will also be demonstrated.
{"title":"Probabilistic models for uncertain data","authors":"P. Senellart","doi":"10.1145/2542050.2542051","DOIUrl":"https://doi.org/10.1145/2542050.2542051","url":null,"abstract":"Uncertainty is ubiquitous in the outcome of many automatic processes (such as information extraction, natural language analysis, machine learning, data integration) or for all tasks that involve human judgment, contradicting information, or measurement errors. This uncertainty can be captured by probabilistic models -- probabilistic information can now be stored, queried, updated, aggregated in a well-founded manner. This talk will provide concrete motivation for probabilistic data management, review some of the most important models for probabilistic data (tables, trees) and present some of the important results in this research area, both theoretical and applied. A concrete example of the use of a probabilistic data management system will also be demonstrated.","PeriodicalId":246033,"journal":{"name":"Proceedings of the 4th Symposium on Information and Communication Technology","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117097542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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