var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"title\":\"Topic tracking with Bayesian belief network\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Bayesian belief network,Dynamic topic model,Static topic model,Topic tracking\",\"pages\":\"2164-2169\",\"volume\":\"125\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0030402613013909\",\"month\":\"5\",\"id\":\"8aa54219-cf9f-366e-96d2-10eca0e5ac0c\",\"created\":\"2015-04-11T18:56:31.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.\",\"bibtype\":\"article\",\"author\":\"Xu, Jian-min and Wu, Shu-fang and Hong, Yu\",\"doi\":\"10.1016/j.ijleo.2013.10.044\",\"journal\":\"Optik - International Journal for Light and Electron Optics\",\"number\":\"9\",\"bibtex\":\"@article{\\n title = {Topic tracking with Bayesian belief network},\\n type = {article},\\n year = {2014},\\n keywords = {Bayesian belief network,Dynamic topic model,Static topic model,Topic tracking},\\n pages = {2164-2169},\\n volume = {125},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0030402613013909},\\n month = {5},\\n id = {8aa54219-cf9f-366e-96d2-10eca0e5ac0c},\\n created = {2015-04-11T18:56:31.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.},\\n bibtype = {article},\\n author = {Xu, Jian-min and Wu, Shu-fang and Hong, Yu},\\n doi = {10.1016/j.ijleo.2013.10.044},\\n journal = {Optik - International Journal for Light and Electron Optics},\\n number = {9}\\n}\",\"author_short\":[\"Xu,  J.\",\"Wu,  S.\",\"Hong,  Y.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0030402613013909\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014\",\"role\":\"author\",\"keyword\":[\"Bayesian belief network\",\"Dynamic topic model\",\"Static topic model\",\"Topic tracking\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework\",\"type\":\"article\",\"year\":\"2010\",\"keywords\":\"Bayesian belief networks,Congestion,Inference,Link failure,Low density parity check codes,Machine learning,Measurements,Multimedia networks,Scalable diagnosis,Variational inference\",\"pages\":\"175-191\",\"volume\":\"21\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1047320309000972\",\"month\":\"2\",\"id\":\"6e08a17b-4ad4-3dee-8903-2818097bf1be\",\"created\":\"2015-04-11T18:56:31.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"In this paper, we investigate scalability and performance of measurement-based network monitoring, focusing on failure and congestion diagnosis in IP networks for network-based multimedia applications. Path-based measurements using unicast probe-packets are obtained at end-hosts, and diagnosis is performed by exploiting the spatial dependence among those measurements. We formulate network monitoring in a machine learning framework using probabilistic graphical models which perform inference of the network states (on/off) using unicast measurements. We provide fundamental limits on the relationship between the number of probe packets, the size of a network and the ability to diagnose either failed links or congested network components. Specifically, the diagnosis problem is dealt in a two-fold manner. Initially for fault diagnosis, we construct a graphical model using a Bayesian belief network for path-based measurements. We then provide a lower bound on the average number of probes per edge for link failure diagnosis using variational inference under “noisy” probe measurements. Variational inference provides a feasible approximation to address the number of spatially dependent measurements needed for diagnosis in large networks. We then develop an entropy lower (EL) bound by drawing similarities between coding over a binary symmetric channel (BSC) and link failure diagnosis. Both bounds show that the number of measurements needed for diagnosis grows linearly with respect to the number of links. The analytical results are validated by simulation. On the other hand, for congestion diagnosis, we propose a solution based on decoding of linear error control codes on a BSC. In this scenario, we consider path-based probing experiments under both noiseless and “noisy” measurements and compare its performance against the fundamental limits. To identify the congested nodes we construct a factor graph, and congestion is inferred using belief-propagation algorithm. Simulation results demonstrate the ability of our approach to perfectly localize congested nodes using a scalable number of measurements and a computationally efficient algorithm. We believe that this study can ease the problem arising due to lack of QoS support and provide good-quality broadband multimedia services.\",\"bibtype\":\"article\",\"author\":\"Narasimha, Rajesh and Dihidar, Souvik and Ji, Chuanyi and McLaughlin, Steven W.\",\"doi\":\"10.1016/j.jvcir.2009.07.007\",\"journal\":\"Journal of Visual Communication and Image Representation\",\"number\":\"2\",\"bibtex\":\"@article{\\n title = {Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework},\\n type = {article},\\n year = {2010},\\n keywords = {Bayesian belief networks,Congestion,Inference,Link failure,Low density parity check codes,Machine learning,Measurements,Multimedia networks,Scalable diagnosis,Variational inference},\\n pages = {175-191},\\n volume = {21},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1047320309000972},\\n month = {2},\\n id = {6e08a17b-4ad4-3dee-8903-2818097bf1be},\\n created = {2015-04-11T18:56:31.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {In this paper, we investigate scalability and performance of measurement-based network monitoring, focusing on failure and congestion diagnosis in IP networks for network-based multimedia applications. Path-based measurements using unicast probe-packets are obtained at end-hosts, and diagnosis is performed by exploiting the spatial dependence among those measurements. We formulate network monitoring in a machine learning framework using probabilistic graphical models which perform inference of the network states (on/off) using unicast measurements. We provide fundamental limits on the relationship between the number of probe packets, the size of a network and the ability to diagnose either failed links or congested network components. Specifically, the diagnosis problem is dealt in a two-fold manner. Initially for fault diagnosis, we construct a graphical model using a Bayesian belief network for path-based measurements. We then provide a lower bound on the average number of probes per edge for link failure diagnosis using variational inference under “noisy” probe measurements. Variational inference provides a feasible approximation to address the number of spatially dependent measurements needed for diagnosis in large networks. We then develop an entropy lower (EL) bound by drawing similarities between coding over a binary symmetric channel (BSC) and link failure diagnosis. Both bounds show that the number of measurements needed for diagnosis grows linearly with respect to the number of links. The analytical results are validated by simulation. On the other hand, for congestion diagnosis, we propose a solution based on decoding of linear error control codes on a BSC. In this scenario, we consider path-based probing experiments under both noiseless and “noisy” measurements and compare its performance against the fundamental limits. To identify the congested nodes we construct a factor graph, and congestion is inferred using belief-propagation algorithm. Simulation results demonstrate the ability of our approach to perfectly localize congested nodes using a scalable number of measurements and a computationally efficient algorithm. We believe that this study can ease the problem arising due to lack of QoS support and provide good-quality broadband multimedia services.},\\n bibtype = {article},\\n author = {Narasimha, Rajesh and Dihidar, Souvik and Ji, Chuanyi and McLaughlin, Steven W.},\\n doi = {10.1016/j.jvcir.2009.07.007},\\n journal = {Journal of Visual Communication and Image Representation},\\n number = {2}\\n}\",\"author_short\":[\"Narasimha,  R.\",\"Dihidar,  S.\",\"Ji,  C.\",\"McLaughlin,  S., W.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1047320309000972\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010\",\"role\":\"author\",\"keyword\":[\"Bayesian belief networks\",\"Congestion\",\"Inference\",\"Link failure\",\"Low density parity check codes\",\"Machine learning\",\"Measurements\",\"Multimedia networks\",\"Scalable diagnosis\",\"Variational inference\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"Belief updating system,Dynamic Bayesian Signalling Game,Mobile Ad Hoc Networks,Secure routing,Vulnerabilities\",\"pages\":\"301-313\",\"volume\":\"41\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0045790614003097\",\"month\":\"1\",\"id\":\"12e10b61-df66-39fc-b57e-de9163d708a1\",\"created\":\"2015-04-11T19:07:34.000Z\",\"accessed\":\"2015-03-31\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Collaboration between mobile nodes is significant in Mobile Ad Hoc Networks (MANETs). The great challenges of MANETs are their vulnerabilities to various security attacks. Because of the lack of centralized administration, secure routing is challenging in MANETs. Effective secure routing is quite essential to protect nodes from anonymous behaviours. Game theory is currently employed as a tool to analyse, formulate and solve selfishness issues in MANETs. This work uses a Dynamic Bayesian Signalling Game to analyse strategy profiles for regular and malicious nodes. We calculate the Payoff to nodes for motivating the particular nodes involved in misbehaviour. Regular nodes monitor continuously to evaluate their neighbours by using the belief evaluation and belief updating system of the Bayes rule. Simulation results show that the proposed scheme could significantly minimize the misbehaving activities of malicious nodes and thereby enhance secure routing.\",\"bibtype\":\"article\",\"author\":\"Kaliappan, M. and Paramasivan, B.\",\"doi\":\"10.1016/j.compeleceng.2014.11.011\",\"journal\":\"Computers & Electrical Engineering\",\"bibtex\":\"@article{\\n title = {Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model},\\n type = {article},\\n year = {2015},\\n keywords = {Belief updating system,Dynamic Bayesian Signalling Game,Mobile Ad Hoc Networks,Secure routing,Vulnerabilities},\\n pages = {301-313},\\n volume = {41},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0045790614003097},\\n month = {1},\\n id = {12e10b61-df66-39fc-b57e-de9163d708a1},\\n created = {2015-04-11T19:07:34.000Z},\\n accessed = {2015-03-31},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Collaboration between mobile nodes is significant in Mobile Ad Hoc Networks (MANETs). The great challenges of MANETs are their vulnerabilities to various security attacks. Because of the lack of centralized administration, secure routing is challenging in MANETs. Effective secure routing is quite essential to protect nodes from anonymous behaviours. Game theory is currently employed as a tool to analyse, formulate and solve selfishness issues in MANETs. This work uses a Dynamic Bayesian Signalling Game to analyse strategy profiles for regular and malicious nodes. We calculate the Payoff to nodes for motivating the particular nodes involved in misbehaviour. Regular nodes monitor continuously to evaluate their neighbours by using the belief evaluation and belief updating system of the Bayes rule. Simulation results show that the proposed scheme could significantly minimize the misbehaving activities of malicious nodes and thereby enhance secure routing.},\\n bibtype = {article},\\n author = {Kaliappan, M. and Paramasivan, B.},\\n doi = {10.1016/j.compeleceng.2014.11.011},\\n journal = {Computers & Electrical Engineering}\\n}\",\"author_short\":[\"Kaliappan,  M.\",\"Paramasivan,  B.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0045790614003097\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015\",\"role\":\"author\",\"keyword\":[\"Belief updating system\",\"Dynamic Bayesian Signalling Game\",\"Mobile Ad Hoc Networks\",\"Secure routing\",\"Vulnerabilities\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"title\":\"Bayesian network-based trust model\",\"type\":\"article\",\"year\":\"2003\",\"id\":\"a7aa2096-bbc9-32ed-a144-57d42b54767c\",\"created\":\"2015-04-11T19:07:34.000Z\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\" We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.\",\"bibtype\":\"article\",\"author\":\"Wang, Y. and Vassileva, J.\",\"journal\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"bibtex\":\"@article{\\n title = {Bayesian network-based trust model},\\n type = {article},\\n year = {2003},\\n id = {a7aa2096-bbc9-32ed-a144-57d42b54767c},\\n created = {2015-04-11T19:07:34.000Z},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = { We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.},\\n bibtype = {article},\\n author = {Wang, Y. and Vassileva, J.},\\n journal = {Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)}\\n}\",\"author_short\":[\"Wang,  Y.\",\"Vassileva,  J.\"],\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"wang-vassileva-bayesiannetworkbasedtrustmodel-2003\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Large scale probabilistic available bandwidth estimation\",\"type\":\"article\",\"year\":\"2011\",\"keywords\":\"Active sampling,Bayesian inference,Belief propagation,Network monitoring\",\"pages\":\"2065-2078\",\"volume\":\"55\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1389128611000752\",\"month\":\"6\",\"id\":\"ec7f728f-66ff-3728-b2d6-b35f45a0094a\",\"created\":\"2015-04-11T19:07:35.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.\",\"bibtype\":\"article\",\"author\":\"Thouin, Frederic and Coates, Mark and Rabbat, Michael\",\"doi\":\"10.1016/j.comnet.2011.02.011\",\"journal\":\"Computer Networks\",\"number\":\"9\",\"bibtex\":\"@article{\\n title = {Large scale probabilistic available bandwidth estimation},\\n type = {article},\\n year = {2011},\\n keywords = {Active sampling,Bayesian inference,Belief propagation,Network monitoring},\\n pages = {2065-2078},\\n volume = {55},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1389128611000752},\\n month = {6},\\n id = {ec7f728f-66ff-3728-b2d6-b35f45a0094a},\\n created = {2015-04-11T19:07:35.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.},\\n bibtype = {article},\\n author = {Thouin, Frederic and Coates, Mark and Rabbat, Michael},\\n doi = {10.1016/j.comnet.2011.02.011},\\n journal = {Computer Networks},\\n number = {9}\\n}\",\"author_short\":[\"Thouin,  F.\",\"Coates,  M.\",\"Rabbat,  M.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1389128611000752\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011\",\"role\":\"author\",\"keyword\":[\"Active sampling\",\"Bayesian inference\",\"Belief propagation\",\"Network monitoring\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Consequence-based framework for electric power providers using Bayesian belief network\",\"type\":\"article\",\"year\":\"2015\",\"keywords\":\"Bayesian belief network,Consequence-based framework,Decision making,Power utility\",\"pages\":\"233-241\",\"volume\":\"64\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0142061514004669\",\"month\":\"1\",\"id\":\"12805927-7c42-3190-8aa3-aa0018c004b7\",\"created\":\"2015-04-11T19:07:36.000Z\",\"accessed\":\"2015-03-19\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.\",\"bibtype\":\"article\",\"author\":\"Buriticá, Jessica A. and Tesfamariam, Solomon\",\"doi\":\"10.1016/j.ijepes.2014.07.034\",\"journal\":\"International Journal of Electrical Power & Energy Systems\",\"bibtex\":\"@article{\\n title = {Consequence-based framework for electric power providers using Bayesian belief network},\\n type = {article},\\n year = {2015},\\n keywords = {Bayesian belief network,Consequence-based framework,Decision making,Power utility},\\n pages = {233-241},\\n volume = {64},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0142061514004669},\\n month = {1},\\n id = {12805927-7c42-3190-8aa3-aa0018c004b7},\\n created = {2015-04-11T19:07:36.000Z},\\n accessed = {2015-03-19},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.},\\n bibtype = {article},\\n author = {Buriticá, Jessica A. and Tesfamariam, Solomon},\\n doi = {10.1016/j.ijepes.2014.07.034},\\n journal = {International Journal of Electrical Power & Energy Systems}\\n}\",\"author_short\":[\"Buriticá,  J., A.\",\"Tesfamariam,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0142061514004669\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015\",\"role\":\"author\",\"keyword\":[\"Bayesian belief network\",\"Consequence-based framework\",\"Decision making\",\"Power utility\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network\",\"type\":\"article\",\"year\":\"2008\",\"keywords\":\"decision-making model,discrete dynamic Bayesian network,self-defense electronic jamming\",\"pages\":\"702-708\",\"volume\":\"19\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S1004413208601425\",\"month\":\"8\",\"id\":\"e997028e-32c9-3791-9c40-7547b328ef1e\",\"created\":\"2015-04-11T19:52:02.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.\",\"bibtype\":\"article\",\"author\":\"Zheng, Tang and Xiaoguang, Gao\",\"doi\":\"10.1016/S1004-4132(08)60142-5\",\"journal\":\"Journal of Systems Engineering and Electronics\",\"number\":\"4\",\"bibtex\":\"@article{\\n title = {Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network},\\n type = {article},\\n year = {2008},\\n keywords = {decision-making model,discrete dynamic Bayesian network,self-defense electronic jamming},\\n pages = {702-708},\\n volume = {19},\\n websites = {http://www.sciencedirect.com/science/article/pii/S1004413208601425},\\n month = {8},\\n id = {e997028e-32c9-3791-9c40-7547b328ef1e},\\n created = {2015-04-11T19:52:02.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.},\\n bibtype = {article},\\n author = {Zheng, Tang and Xiaoguang, Gao},\\n doi = {10.1016/S1004-4132(08)60142-5},\\n journal = {Journal of Systems Engineering and Electronics},\\n number = {4}\\n}\",\"author_short\":[\"Zheng,  T.\",\"Xiaoguang,  G.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S1004413208601425\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008\",\"role\":\"author\",\"keyword\":[\"decision-making model\",\"discrete dynamic Bayesian network\",\"self-defense electronic jamming\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning\",\"type\":\"book\",\"year\":\"2014\",\"source\":\"Academic Press Library in Signal Processing\",\"keywords\":\"Bayesian network,Factor graph,Markov network,Parameter learning,Probabilistic graphical model,Probabilistic inference\",\"pages\":\"989-1064\",\"volume\":\"1\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/B9780123965028000188\",\"publisher\":\"Elsevier\",\"series\":\"Academic Press Library in Signal Processing\",\"id\":\"2a6abaab-f5f7-34ba-a615-39f16e254899\",\"created\":\"2015-04-11T20:41:36.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes.\",\"bibtype\":\"book\",\"author\":\"Pernkopf, Franz and Peharz, Robert and Tschiatschek, Sebastian\",\"doi\":\"10.1016/B978-0-12-396502-8.00018-8\",\"bibtex\":\"@book{\\n title = {Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning},\\n type = {book},\\n year = {2014},\\n source = {Academic Press Library in Signal Processing},\\n keywords = {Bayesian network,Factor graph,Markov network,Parameter learning,Probabilistic graphical model,Probabilistic inference},\\n pages = {989-1064},\\n volume = {1},\\n websites = {http://www.sciencedirect.com/science/article/pii/B9780123965028000188},\\n publisher = {Elsevier},\\n series = {Academic Press Library in Signal Processing},\\n id = {2a6abaab-f5f7-34ba-a615-39f16e254899},\\n created = {2015-04-11T20:41:36.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes.},\\n bibtype = {book},\\n author = {Pernkopf, Franz and Peharz, Robert and Tschiatschek, Sebastian},\\n doi = {10.1016/B978-0-12-396502-8.00018-8}\\n}\",\"author_short\":[\"Pernkopf,  F.\",\"Peharz,  R.\",\"Tschiatschek,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/B9780123965028000188\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014\",\"role\":\"author\",\"keyword\":[\"Bayesian network\",\"Factor graph\",\"Markov network\",\"Parameter learning\",\"Probabilistic graphical model\",\"Probabilistic inference\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health\",\"pages\":\"576-583\",\"volume\":\"267\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0378775314007939\",\"month\":\"12\",\"id\":\"2bd03643-6f6e-3455-80aa-0f76b3dec2ad\",\"created\":\"2015-04-11T20:41:36.000Z\",\"accessed\":\"2015-01-12\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.\",\"bibtype\":\"article\",\"author\":\"He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin\",\"doi\":\"10.1016/j.jpowsour.2014.05.100\",\"journal\":\"Journal of Power Sources\",\"bibtex\":\"@article{\\n title = {Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks},\\n type = {article},\\n year = {2014},\\n keywords = {Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health},\\n pages = {576-583},\\n volume = {267},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0378775314007939},\\n month = {12},\\n id = {2bd03643-6f6e-3455-80aa-0f76b3dec2ad},\\n created = {2015-04-11T20:41:36.000Z},\\n accessed = {2015-01-12},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.},\\n bibtype = {article},\\n author = {He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin},\\n doi = {10.1016/j.jpowsour.2014.05.100},\\n journal = {Journal of Power Sources}\\n}\",\"author_short\":[\"He,  Z.\",\"Gao,  M.\",\"Ma,  G.\",\"Liu,  Y.\",\"Chen,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0378775314007939\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014\",\"role\":\"author\",\"keyword\":[\"Battery management system\",\"Dynamic Bayesian Network\",\"Lithium-ion battery\",\"State of health\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"title\":\"Multiple small objects tracking based on dynamic Bayesian networks with spatial prior\",\"type\":\"article\",\"year\":\"2014\",\"keywords\":\"Data association,Dynamic Bayesian Networks,Multi-cue integration,Multiple small objects tracking\",\"pages\":\"2243-2247\",\"volume\":\"125\",\"websites\":\"http://www.sciencedirect.com/science/article/pii/S0030402613014964\",\"month\":\"5\",\"id\":\"76b024f7-c24e-308c-8db8-e253d38e492a\",\"created\":\"2015-04-11T20:41:36.000Z\",\"accessed\":\"2015-04-11\",\"file_attached\":false,\"profile_id\":\"95e10851-cdf3-31de-9f82-1ab629e601b0\",\"group_id\":\"09500bf6-14e8-379d-a953-ea715d61ca19\",\"last_modified\":\"2017-03-14T14:28:50.201Z\",\"read\":false,\"starred\":false,\"authored\":false,\"confirmed\":\"true\",\"hidden\":false,\"private_publication\":false,\"abstract\":\"This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.\",\"bibtype\":\"article\",\"author\":\"Yao, Rui and Zhang, Yanning and Zhou, Yong and Xia, Shixiong\",\"doi\":\"10.1016/j.ijleo.2013.10.108\",\"journal\":\"Optik - International Journal for Light and Electron Optics\",\"number\":\"10\",\"bibtex\":\"@article{\\n title = {Multiple small objects tracking based on dynamic Bayesian networks with spatial prior},\\n type = {article},\\n year = {2014},\\n keywords = {Data association,Dynamic Bayesian Networks,Multi-cue integration,Multiple small objects tracking},\\n pages = {2243-2247},\\n volume = {125},\\n websites = {http://www.sciencedirect.com/science/article/pii/S0030402613014964},\\n month = {5},\\n id = {76b024f7-c24e-308c-8db8-e253d38e492a},\\n created = {2015-04-11T20:41:36.000Z},\\n accessed = {2015-04-11},\\n file_attached = {false},\\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\\n last_modified = {2017-03-14T14:28:50.201Z},\\n read = {false},\\n starred = {false},\\n authored = {false},\\n confirmed = {true},\\n hidden = {false},\\n private_publication = {false},\\n abstract = {This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.},\\n bibtype = {article},\\n author = {Yao, Rui and Zhang, Yanning and Zhou, Yong and Xia, Shixiong},\\n doi = {10.1016/j.ijleo.2013.10.108},\\n journal = {Optik - International Journal for Light and Electron Optics},\\n number = {10}\\n}\",\"author_short\":[\"Yao,  R.\",\"Zhang,  Y.\",\"Zhou,  Y.\",\"Xia,  S.\"],\"urls\":{\"Website\":\"http://www.sciencedirect.com/science/article/pii/S0030402613014964\"},\"biburl\":\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0\",\"bibbaseid\":\"yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014\",\"role\":\"author\",\"keyword\":[\"Data association\",\"Dynamic Bayesian Networks\",\"Multi-cue integration\",\"Multiple small objects tracking\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"all.bib\" href=\"data:text/bibtex;base64,@article{
 title = {Topic tracking with Bayesian belief network},
 type = {article},
 year = {2014},
 keywords = {Bayesian belief network,Dynamic topic model,Static topic model,Topic tracking},
 pages = {2164-2169},
 volume = {125},
 websites = {http://www.sciencedirect.com/science/article/pii/S0030402613013909},
 month = {5},
 id = {8aa54219-cf9f-366e-96d2-10eca0e5ac0c},
 created = {2015-04-11T18:56:31.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.},
 bibtype = {article},
 author = {Xu, Jian-min and Wu, Shu-fang and Hong, Yu},
 doi = {10.1016/j.ijleo.2013.10.044},
 journal = {Optik - International Journal for Light and Electron Optics},
 number = {9}
}

@article{
 title = {Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework},
 type = {article},
 year = {2010},
 keywords = {Bayesian belief networks,Congestion,Inference,Link failure,Low density parity check codes,Machine learning,Measurements,Multimedia networks,Scalable diagnosis,Variational inference},
 pages = {175-191},
 volume = {21},
 websites = {http://www.sciencedirect.com/science/article/pii/S1047320309000972},
 month = {2},
 id = {6e08a17b-4ad4-3dee-8903-2818097bf1be},
 created = {2015-04-11T18:56:31.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we investigate scalability and performance of measurement-based network monitoring, focusing on failure and congestion diagnosis in IP networks for network-based multimedia applications. Path-based measurements using unicast probe-packets are obtained at end-hosts, and diagnosis is performed by exploiting the spatial dependence among those measurements. We formulate network monitoring in a machine learning framework using probabilistic graphical models which perform inference of the network states (on/off) using unicast measurements. We provide fundamental limits on the relationship between the number of probe packets, the size of a network and the ability to diagnose either failed links or congested network components. Specifically, the diagnosis problem is dealt in a two-fold manner. Initially for fault diagnosis, we construct a graphical model using a Bayesian belief network for path-based measurements. We then provide a lower bound on the average number of probes per edge for link failure diagnosis using variational inference under “noisy” probe measurements. Variational inference provides a feasible approximation to address the number of spatially dependent measurements needed for diagnosis in large networks. We then develop an entropy lower (EL) bound by drawing similarities between coding over a binary symmetric channel (BSC) and link failure diagnosis. Both bounds show that the number of measurements needed for diagnosis grows linearly with respect to the number of links. The analytical results are validated by simulation. On the other hand, for congestion diagnosis, we propose a solution based on decoding of linear error control codes on a BSC. In this scenario, we consider path-based probing experiments under both noiseless and “noisy” measurements and compare its performance against the fundamental limits. To identify the congested nodes we construct a factor graph, and congestion is inferred using belief-propagation algorithm. Simulation results demonstrate the ability of our approach to perfectly localize congested nodes using a scalable number of measurements and a computationally efficient algorithm. We believe that this study can ease the problem arising due to lack of QoS support and provide good-quality broadband multimedia services.},
 bibtype = {article},
 author = {Narasimha, Rajesh and Dihidar, Souvik and Ji, Chuanyi and McLaughlin, Steven W.},
 doi = {10.1016/j.jvcir.2009.07.007},
 journal = {Journal of Visual Communication and Image Representation},
 number = {2}
}

@article{
 title = {Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model},
 type = {article},
 year = {2015},
 keywords = {Belief updating system,Dynamic Bayesian Signalling Game,Mobile Ad Hoc Networks,Secure routing,Vulnerabilities},
 pages = {301-313},
 volume = {41},
 websites = {http://www.sciencedirect.com/science/article/pii/S0045790614003097},
 month = {1},
 id = {12e10b61-df66-39fc-b57e-de9163d708a1},
 created = {2015-04-11T19:07:34.000Z},
 accessed = {2015-03-31},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Collaboration between mobile nodes is significant in Mobile Ad Hoc Networks (MANETs). The great challenges of MANETs are their vulnerabilities to various security attacks. Because of the lack of centralized administration, secure routing is challenging in MANETs. Effective secure routing is quite essential to protect nodes from anonymous behaviours. Game theory is currently employed as a tool to analyse, formulate and solve selfishness issues in MANETs. This work uses a Dynamic Bayesian Signalling Game to analyse strategy profiles for regular and malicious nodes. We calculate the Payoff to nodes for motivating the particular nodes involved in misbehaviour. Regular nodes monitor continuously to evaluate their neighbours by using the belief evaluation and belief updating system of the Bayes rule. Simulation results show that the proposed scheme could significantly minimize the misbehaving activities of malicious nodes and thereby enhance secure routing.},
 bibtype = {article},
 author = {Kaliappan, M. and Paramasivan, B.},
 doi = {10.1016/j.compeleceng.2014.11.011},
 journal = {Computers & Electrical Engineering}
}

@article{
 title = {Bayesian network-based trust model},
 type = {article},
 year = {2003},
 id = {a7aa2096-bbc9-32ed-a144-57d42b54767c},
 created = {2015-04-11T19:07:34.000Z},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = { We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.},
 bibtype = {article},
 author = {Wang, Y. and Vassileva, J.},
 journal = {Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)}
}

@article{
 title = {Large scale probabilistic available bandwidth estimation},
 type = {article},
 year = {2011},
 keywords = {Active sampling,Bayesian inference,Belief propagation,Network monitoring},
 pages = {2065-2078},
 volume = {55},
 websites = {http://www.sciencedirect.com/science/article/pii/S1389128611000752},
 month = {6},
 id = {ec7f728f-66ff-3728-b2d6-b35f45a0094a},
 created = {2015-04-11T19:07:35.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.},
 bibtype = {article},
 author = {Thouin, Frederic and Coates, Mark and Rabbat, Michael},
 doi = {10.1016/j.comnet.2011.02.011},
 journal = {Computer Networks},
 number = {9}
}

@article{
 title = {Consequence-based framework for electric power providers using Bayesian belief network},
 type = {article},
 year = {2015},
 keywords = {Bayesian belief network,Consequence-based framework,Decision making,Power utility},
 pages = {233-241},
 volume = {64},
 websites = {http://www.sciencedirect.com/science/article/pii/S0142061514004669},
 month = {1},
 id = {12805927-7c42-3190-8aa3-aa0018c004b7},
 created = {2015-04-11T19:07:36.000Z},
 accessed = {2015-03-19},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.},
 bibtype = {article},
 author = {Buriticá, Jessica A. and Tesfamariam, Solomon},
 doi = {10.1016/j.ijepes.2014.07.034},
 journal = {International Journal of Electrical Power & Energy Systems}
}

@article{
 title = {Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network},
 type = {article},
 year = {2008},
 keywords = {decision-making model,discrete dynamic Bayesian network,self-defense electronic jamming},
 pages = {702-708},
 volume = {19},
 websites = {http://www.sciencedirect.com/science/article/pii/S1004413208601425},
 month = {8},
 id = {e997028e-32c9-3791-9c40-7547b328ef1e},
 created = {2015-04-11T19:52:02.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.},
 bibtype = {article},
 author = {Zheng, Tang and Xiaoguang, Gao},
 doi = {10.1016/S1004-4132(08)60142-5},
 journal = {Journal of Systems Engineering and Electronics},
 number = {4}
}

@book{
 title = {Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning},
 type = {book},
 year = {2014},
 source = {Academic Press Library in Signal Processing},
 keywords = {Bayesian network,Factor graph,Markov network,Parameter learning,Probabilistic graphical model,Probabilistic inference},
 pages = {989-1064},
 volume = {1},
 websites = {http://www.sciencedirect.com/science/article/pii/B9780123965028000188},
 publisher = {Elsevier},
 series = {Academic Press Library in Signal Processing},
 id = {2a6abaab-f5f7-34ba-a615-39f16e254899},
 created = {2015-04-11T20:41:36.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes.},
 bibtype = {book},
 author = {Pernkopf, Franz and Peharz, Robert and Tschiatschek, Sebastian},
 doi = {10.1016/B978-0-12-396502-8.00018-8}
}

@article{
 title = {Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks},
 type = {article},
 year = {2014},
 keywords = {Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health},
 pages = {576-583},
 volume = {267},
 websites = {http://www.sciencedirect.com/science/article/pii/S0378775314007939},
 month = {12},
 id = {2bd03643-6f6e-3455-80aa-0f76b3dec2ad},
 created = {2015-04-11T20:41:36.000Z},
 accessed = {2015-01-12},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.},
 bibtype = {article},
 author = {He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin},
 doi = {10.1016/j.jpowsour.2014.05.100},
 journal = {Journal of Power Sources}
}

@article{
 title = {Multiple small objects tracking based on dynamic Bayesian networks with spatial prior},
 type = {article},
 year = {2014},
 keywords = {Data association,Dynamic Bayesian Networks,Multi-cue integration,Multiple small objects tracking},
 pages = {2243-2247},
 volume = {125},
 websites = {http://www.sciencedirect.com/science/article/pii/S0030402613014964},
 month = {5},
 id = {76b024f7-c24e-308c-8db8-e253d38e492a},
 created = {2015-04-11T20:41:36.000Z},
 accessed = {2015-04-11},
 file_attached = {false},
 profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},
 group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},
 last_modified = {2017-03-14T14:28:50.201Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.},
 bibtype = {article},
 author = {Yao, Rui and Zhang, Yanning and Zhou, Yong and Xia, Shixiong},
 doi = {10.1016/j.ijleo.2013.10.108},
 journal = {Optik - International Journal for Light and Electron Optics},
 number = {10}
}\">\n            <i class=\"fa fa-download fa-fw\"></i> Download BibTeX\n          </a>\n        </li>\n        <li>\n          <a href=\"//bibbase.org/rss?bib=\">\n            <i class=\"fa fa-rss fa-fw\"></i> RSS Feed\n          </a>\n          </li>\n      </ul>\n      </li>\n\n      \n\n\n      \n    </ul>\n\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_embed\"\n         style=\"display: none;\">\n\n      Excellent! Next you can\n      <a href=\"/network/register?next=/network/newSiteFromTemplate%3Fbiburl=\"\n      >create a new website with this list</a>, or\n      embed it in an existing web page by copying &amp; pasting\n      any of the following snippets.\n\n      <div style=\"text-align: left; margin-top: 1em;\">\n        <strong>JavaScript</strong>\n        <span class=\"comment recommended\">(easiest)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;script src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/09500bf6-14e8-379d-a953-ea715d61ca19?jsonp=1&amp;jsonp=1\"&gt;&lt;/script&gt;\n          </code>\n        </div>\n\n        <strong>PHP</strong>\n        <div class=\"well well-small\">\n          <code>\n            &lt;?php\n            $contents = file_get_contents(\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/09500bf6-14e8-379d-a953-ea715d61ca19?jsonp=1\");\n            print_r($contents);\n            ?&gt;\n          </code>\n        </div>\n\n        <strong>iFrame</strong>\n        <span class=\"comment\">(not recommended)</span>\n        <div class=\"well well-small\">\n          <code>\n            &lt;iframe src=\"https://bibbase.org/service/mendeley/95e10851-cdf3-31de-9f82-1ab629e601b0/group/09500bf6-14e8-379d-a953-ea715d61ca19?jsonp=1\"&gt;&lt;/iframe&gt;\n          </code>\n        </div>\n\n        <p>\n          For more details see the <a href=\"//bibbase.org/help\">documention</a>.\n        </p>\n      </div>\n    </div>\n\n    <div class=\"alert alert-success bibbase_msg\" id=\"bibbase_msg_preview\"\n         style=\"display: none;\">\n\n      This is a preview! To use this list on your own web site\n      or create a new web site from it,\n      <a href=\"/network/register?next=/network/newAccountWithFile%3FfileId=\"\n      >create a free account</a>. The file will be added\n      and you will be able to edit it in the File Manager.\n      We will show you instructions once you've created your account.\n    </div>\n\n    <div class=\"alert alert-warning bibbase_msg\" id=\"bibbase_msg_mendeley1\"\n         style=\"display: none;\">\n\n      <p>To the site owner:</p>\n\n      <p><strong>Action required!</strong> Mendeley is changing its\n        API. In order to keep using Mendeley with BibBase past April\n        14th, you need to:\n        <ol>\n          <li>renew the authorization for BibBase on Mendeley, and</li>\n          <li>update the BibBase URL\n            in your page the same way you did when you initially set up\n            this page.\n          </li>\n        </ol>\n      </p>\n\n      <p>\n        <a href=\"http://bibbase.org/cgi-bin/mendeley2/get.py\">\n          <i class=\"fa fa-angle-double-right\"></i>\n          Fix it now</a>\n      </p>\n    </div>\n\n  </div>\n\n\n  <div id=\"bibbase_body\">\n    \n  \n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0045790614003097\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015', 'http://www.sciencedirect.com/science/article/pii/S0045790614003097', event);\">\n    Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kaliappan,  M.;  and Paramasivan,  B.\n</span>\n\n  \n\n</span>\n\n  <i>Computers & Electrical Engineering</i>, 41: 301-313. 1 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0045790614003097\"\n     onclick=\"log_download('kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015', 'http://www.sciencedirect.com/science/article/pii/S0045790614003097', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.compeleceng.2014.11.011\"\n     onclick=\"log_download('kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015', 'http://doi.org/10.1016/j.compeleceng.2014.11.011', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kaliappan-paramasivan-enhancingsecureroutinginmobileadhocnetworksusingadynamicbayesiansignallinggamemodel-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Enhancing secure routing in Mobile Ad Hoc Networks using a Dynamic Bayesian Signalling Game model},\n type = {article},\n year = {2015},\n keywords = {Belief updating system,Dynamic Bayesian Signalling Game,Mobile Ad Hoc Networks,Secure routing,Vulnerabilities},\n pages = {301-313},\n volume = {41},\n websites = {http://www.sciencedirect.com/science/article/pii/S0045790614003097},\n month = {1},\n id = {12e10b61-df66-39fc-b57e-de9163d708a1},\n created = {2015-04-11T19:07:34.000Z},\n accessed = {2015-03-31},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Collaboration between mobile nodes is significant in Mobile Ad Hoc Networks (MANETs). The great challenges of MANETs are their vulnerabilities to various security attacks. Because of the lack of centralized administration, secure routing is challenging in MANETs. Effective secure routing is quite essential to protect nodes from anonymous behaviours. Game theory is currently employed as a tool to analyse, formulate and solve selfishness issues in MANETs. This work uses a Dynamic Bayesian Signalling Game to analyse strategy profiles for regular and malicious nodes. We calculate the Payoff to nodes for motivating the particular nodes involved in misbehaviour. Regular nodes monitor continuously to evaluate their neighbours by using the belief evaluation and belief updating system of the Bayes rule. Simulation results show that the proposed scheme could significantly minimize the misbehaving activities of malicious nodes and thereby enhance secure routing.},\n bibtype = {article},\n author = {Kaliappan, M. and Paramasivan, B.},\n doi = {10.1016/j.compeleceng.2014.11.011},\n journal = {Computers &amp; Electrical Engineering}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Collaboration between mobile nodes is significant in Mobile Ad Hoc Networks (MANETs). The great challenges of MANETs are their vulnerabilities to various security attacks. Because of the lack of centralized administration, secure routing is challenging in MANETs. Effective secure routing is quite essential to protect nodes from anonymous behaviours. Game theory is currently employed as a tool to analyse, formulate and solve selfishness issues in MANETs. This work uses a Dynamic Bayesian Signalling Game to analyse strategy profiles for regular and malicious nodes. We calculate the Payoff to nodes for motivating the particular nodes involved in misbehaviour. Regular nodes monitor continuously to evaluate their neighbours by using the belief evaluation and belief updating system of the Bayes rule. Simulation results show that the proposed scheme could significantly minimize the misbehaving activities of malicious nodes and thereby enhance secure routing.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0142061514004669\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015', 'http://www.sciencedirect.com/science/article/pii/S0142061514004669', event);\">\n    Consequence-based framework for electric power providers using Bayesian belief network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Buriticá,  J., A.;  and Tesfamariam,  S.\n</span>\n\n  \n\n</span>\n\n  <i>International Journal of Electrical Power & Energy Systems</i>, 64: 233-241. 1 2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0142061514004669\"\n     onclick=\"log_download('buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015', 'http://www.sciencedirect.com/science/article/pii/S0142061514004669', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Consequence-based framework for electric power providers using Bayesian belief network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ijepes.2014.07.034\"\n     onclick=\"log_download('buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015', 'http://doi.org/10.1016/j.ijepes.2014.07.034', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('buritic-tesfamariam-consequencebasedframeworkforelectricpowerprovidersusingbayesianbeliefnetwork-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Consequence-based framework for electric power providers using Bayesian belief network},\n type = {article},\n year = {2015},\n keywords = {Bayesian belief network,Consequence-based framework,Decision making,Power utility},\n pages = {233-241},\n volume = {64},\n websites = {http://www.sciencedirect.com/science/article/pii/S0142061514004669},\n month = {1},\n id = {12805927-7c42-3190-8aa3-aa0018c004b7},\n created = {2015-04-11T19:07:36.000Z},\n accessed = {2015-03-19},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.},\n bibtype = {article},\n author = {Buriticá, Jessica A. and Tesfamariam, Solomon},\n doi = {10.1016/j.ijepes.2014.07.034},\n journal = {International Journal of Electrical Power &amp; Energy Systems}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Novel consequence-based framework for electric power providers is proposed. This framework includes six performance objectives, such as reputation, health and safety, environmental, financial, reliability, and system conditions. The six performance objectives are quantified with the consideration of 41 key performance indicators (KPIs). The framework is illustrated with a case study of 10 Canadian power utilities. Furthermore, a sensitivity analysis is undertaken to identify importance of the KPIs on the decision framework.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0030402613013909\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014', 'http://www.sciencedirect.com/science/article/pii/S0030402613013909', event);\">\n    Topic tracking with Bayesian belief network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Xu,  J.; Wu,  S.;  and Hong,  Y.\n</span>\n\n  \n\n</span>\n\n  <i>Optik - International Journal for Light and Electron Optics</i>, 125(9): 2164-2169. 5 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0030402613013909\"\n     onclick=\"log_download('xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014', 'http://www.sciencedirect.com/science/article/pii/S0030402613013909', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Topic tracking with Bayesian belief network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ijleo.2013.10.044\"\n     onclick=\"log_download('xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014', 'http://doi.org/10.1016/j.ijleo.2013.10.044', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('xu-wu-hong-topictrackingwithbayesianbeliefnetwork-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Topic tracking with Bayesian belief network},\n type = {article},\n year = {2014},\n keywords = {Bayesian belief network,Dynamic topic model,Static topic model,Topic tracking},\n pages = {2164-2169},\n volume = {125},\n websites = {http://www.sciencedirect.com/science/article/pii/S0030402613013909},\n month = {5},\n id = {8aa54219-cf9f-366e-96d2-10eca0e5ac0c},\n created = {2015-04-11T18:56:31.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.},\n bibtype = {article},\n author = {Xu, Jian-min and Wu, Shu-fang and Hong, Yu},\n doi = {10.1016/j.ijleo.2013.10.044},\n journal = {Optik - International Journal for Light and Electron Optics},\n number = {9}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (Cdet)norm by 5.5% comparing to VSM, BDTM-II decreases (Cdet)norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/B9780123965028000188\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014', 'http://www.sciencedirect.com/science/article/pii/B9780123965028000188', event);\">\n    Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Pernkopf,  F.; Peharz,  R.;  and Tschiatschek,  S.\n</span>\n\n  \n\n</span>\n\n  Volume 1 of Academic Press Library in Signal ProcessingElsevier,  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/B9780123965028000188\"\n     onclick=\"log_download('pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014', 'http://www.sciencedirect.com/science/article/pii/B9780123965028000188', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/B978-0-12-396502-8.00018-8\"\n     onclick=\"log_download('pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014', 'http://doi.org/10.1016/B978-0-12-396502-8.00018-8', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('pernkopf-peharz-tschiatschek-academicpresslibraryinsignalprocessingvolume1signalprocessingtheoryandmachinelearning-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@book{\n title = {Academic Press Library in Signal Processing: Volume 1 - Signal Processing Theory and Machine Learning},\n type = {book},\n year = {2014},\n source = {Academic Press Library in Signal Processing},\n keywords = {Bayesian network,Factor graph,Markov network,Parameter learning,Probabilistic graphical model,Probabilistic inference},\n pages = {989-1064},\n volume = {1},\n websites = {http://www.sciencedirect.com/science/article/pii/B9780123965028000188},\n publisher = {Elsevier},\n series = {Academic Press Library in Signal Processing},\n id = {2a6abaab-f5f7-34ba-a615-39f16e254899},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes.},\n bibtype = {book},\n author = {Pernkopf, Franz and Peharz, Robert and Tschiatschek, Sebastian},\n doi = {10.1016/B978-0-12-396502-8.00018-8}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Over the last decades, probabilistic graphical models have become the method of choice for representing uncertainty. They are used in many research areas such as computer vision, speech processing, time-series and sequential data modeling, cognitive science, bioinformatics, probabilistic robotics, signal processing, communications and error-correcting coding theory, and in the area of artificial intelligence. This tutorial provides an introduction to probabilistic graphical models. We review three representations of probabilistic graphical models, namely, Markov networks or undirected graphical models, Bayesian networks or directed graphical models, and factor graphs. Then, we provide an overview about structure and parameter learning techniques. In particular, we discuss maximum likelihood and Bayesian learning, as well as generative and discriminative learning. Subsequently, we overview exact inference methods and briefly cover approximate inference techniques. Finally, we present typical applications for each of the three representations, namely, Bayesian networks for expert systems, dynamic Bayesian networks for speech processing, Markov random fields for image processing, and factor graphs for decoding error-correcting codes.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0378775314007939\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014', 'http://www.sciencedirect.com/science/article/pii/S0378775314007939', event);\">\n    Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  He,  Z.; Gao,  M.; Ma,  G.; Liu,  Y.;  and Chen,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Power Sources</i>, 267: 576-583. 12 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0378775314007939\"\n     onclick=\"log_download('he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014', 'http://www.sciencedirect.com/science/article/pii/S0378775314007939', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jpowsour.2014.05.100\"\n     onclick=\"log_download('he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014', 'http://doi.org/10.1016/j.jpowsour.2014.05.100', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('he-gao-ma-liu-chen-onlinestateofhealthestimationoflithiumionbatteriesusingdynamicbayesiannetworks-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Online state-of-health estimation of lithium-ion batteries using Dynamic Bayesian Networks},\n type = {article},\n year = {2014},\n keywords = {Battery management system,Dynamic Bayesian Network,Lithium-ion battery,State of health},\n pages = {576-583},\n volume = {267},\n websites = {http://www.sciencedirect.com/science/article/pii/S0378775314007939},\n month = {12},\n id = {2bd03643-6f6e-3455-80aa-0f76b3dec2ad},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-01-12},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.},\n bibtype = {article},\n author = {He, Zhiwei and Gao, Mingyu and Ma, Guojin and Liu, Yuanyuan and Chen, Sanxin},\n doi = {10.1016/j.jpowsour.2014.05.100},\n journal = {Journal of Power Sources}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Li-ion batteries are widely used in energy storage systems, electric vehicles, communication systems, etc. The State of Health (SOH) of batteries is of great importance to the safety of these systems. This paper presents a novel online method for the estimation of the SOH of Lithium (Li)-ion batteries based on Dynamic Bayesian Networks (DBNs). The structure of the DBN model is built according to the experience of experts, with the state of charges used as hidden states and the terminal voltages used as observations in the DBN. Parameters of the DBN model are learned based on training data collected through Li-ion battery aging experiments. A forward algorithm is applied for the inference of the DBN model in order to estimate the SOH in real-time. Experimental results show that the proposed method is effective and efficient in estimating the SOH of Li-ion batteries.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0030402613014964\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014', 'http://www.sciencedirect.com/science/article/pii/S0030402613014964', event);\">\n    Multiple small objects tracking based on dynamic Bayesian networks with spatial prior.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yao,  R.; Zhang,  Y.; Zhou,  Y.;  and Xia,  S.\n</span>\n\n  \n\n</span>\n\n  <i>Optik - International Journal for Light and Electron Optics</i>, 125(10): 2243-2247. 5 2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S0030402613014964\"\n     onclick=\"log_download('yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014', 'http://www.sciencedirect.com/science/article/pii/S0030402613014964', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Multiple small objects tracking based on dynamic Bayesian networks with spatial prior [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.ijleo.2013.10.108\"\n     onclick=\"log_download('yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014', 'http://doi.org/10.1016/j.ijleo.2013.10.108', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yao-zhang-zhou-xia-multiplesmallobjectstrackingbasedondynamicbayesiannetworkswithspatialprior-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Multiple small objects tracking based on dynamic Bayesian networks with spatial prior},\n type = {article},\n year = {2014},\n keywords = {Data association,Dynamic Bayesian Networks,Multi-cue integration,Multiple small objects tracking},\n pages = {2243-2247},\n volume = {125},\n websites = {http://www.sciencedirect.com/science/article/pii/S0030402613014964},\n month = {5},\n id = {76b024f7-c24e-308c-8db8-e253d38e492a},\n created = {2015-04-11T20:41:36.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.},\n bibtype = {article},\n author = {Yao, Rui and Zhang, Yanning and Zhou, Yong and Xia, Shixiong},\n doi = {10.1016/j.ijleo.2013.10.108},\n journal = {Optik - International Journal for Light and Electron Optics},\n number = {10}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  This paper proposes an end-to-end algorithm for multiple small objects tracking in noisy video using a combination of Gaussian mixture based background segmentation along with a Dynamic Bayesian Networks (DBNs) based tracking. Background segmentation is based on an adaptive backgrounding method that models each pixel as a mixture of Gaussians with spatial prior and uses an online approximation to update the model, the spatial prior is constructed for small objects. Furthermore, we create observation model with hidden variable based on multi-cue statistical object model and employ Kalman filter as inference algorithm. Finally, we use linear assignment problem (LAP) algorithm to perform the models matching. The experimental results show the proposed method outperforms competing method, and demonstrate the effectiveness of the proposed method.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1389128611000752\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011', 'http://www.sciencedirect.com/science/article/pii/S1389128611000752', event);\">\n    Large scale probabilistic available bandwidth estimation.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Thouin,  F.; Coates,  M.;  and Rabbat,  M.\n</span>\n\n  \n\n</span>\n\n  <i>Computer Networks</i>, 55(9): 2065-2078. 6 2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1389128611000752\"\n     onclick=\"log_download('thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011', 'http://www.sciencedirect.com/science/article/pii/S1389128611000752', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Large scale probabilistic available bandwidth estimation [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.comnet.2011.02.011\"\n     onclick=\"log_download('thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011', 'http://doi.org/10.1016/j.comnet.2011.02.011', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('thouin-coates-rabbat-largescaleprobabilisticavailablebandwidthestimation-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Large scale probabilistic available bandwidth estimation},\n type = {article},\n year = {2011},\n keywords = {Active sampling,Bayesian inference,Belief propagation,Network monitoring},\n pages = {2065-2078},\n volume = {55},\n websites = {http://www.sciencedirect.com/science/article/pii/S1389128611000752},\n month = {6},\n id = {ec7f728f-66ff-3728-b2d6-b35f45a0094a},\n created = {2015-04-11T19:07:35.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.},\n bibtype = {article},\n author = {Thouin, Frederic and Coates, Mark and Rabbat, Michael},\n doi = {10.1016/j.comnet.2011.02.011},\n journal = {Computer Networks},\n number = {9}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: (i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; (ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; (iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; (iv) almost all tools use ad hoc techniques to address measurement noise; and (v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead than Pathload.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1047320309000972\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010', 'http://www.sciencedirect.com/science/article/pii/S1047320309000972', event);\">\n    Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Narasimha,  R.; Dihidar,  S.; Ji,  C.;  and McLaughlin,  S., W.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Visual Communication and Image Representation</i>, 21(2): 175-191. 2 2010.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1047320309000972\"\n     onclick=\"log_download('narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010', 'http://www.sciencedirect.com/science/article/pii/S1047320309000972', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jvcir.2009.07.007\"\n     onclick=\"log_download('narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010', 'http://doi.org/10.1016/j.jvcir.2009.07.007', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('narasimha-dihidar-ji-mclaughlin-scalablediagnosisinipnetworksusingpathbasedmeasurementandinferencealearningframework-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Scalable diagnosis in IP networks using path-based measurement and inference: A learning framework},\n type = {article},\n year = {2010},\n keywords = {Bayesian belief networks,Congestion,Inference,Link failure,Low density parity check codes,Machine learning,Measurements,Multimedia networks,Scalable diagnosis,Variational inference},\n pages = {175-191},\n volume = {21},\n websites = {http://www.sciencedirect.com/science/article/pii/S1047320309000972},\n month = {2},\n id = {6e08a17b-4ad4-3dee-8903-2818097bf1be},\n created = {2015-04-11T18:56:31.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {In this paper, we investigate scalability and performance of measurement-based network monitoring, focusing on failure and congestion diagnosis in IP networks for network-based multimedia applications. Path-based measurements using unicast probe-packets are obtained at end-hosts, and diagnosis is performed by exploiting the spatial dependence among those measurements. We formulate network monitoring in a machine learning framework using probabilistic graphical models which perform inference of the network states (on/off) using unicast measurements. We provide fundamental limits on the relationship between the number of probe packets, the size of a network and the ability to diagnose either failed links or congested network components. Specifically, the diagnosis problem is dealt in a two-fold manner. Initially for fault diagnosis, we construct a graphical model using a Bayesian belief network for path-based measurements. We then provide a lower bound on the average number of probes per edge for link failure diagnosis using variational inference under “noisy” probe measurements. Variational inference provides a feasible approximation to address the number of spatially dependent measurements needed for diagnosis in large networks. We then develop an entropy lower (EL) bound by drawing similarities between coding over a binary symmetric channel (BSC) and link failure diagnosis. Both bounds show that the number of measurements needed for diagnosis grows linearly with respect to the number of links. The analytical results are validated by simulation. On the other hand, for congestion diagnosis, we propose a solution based on decoding of linear error control codes on a BSC. In this scenario, we consider path-based probing experiments under both noiseless and “noisy” measurements and compare its performance against the fundamental limits. To identify the congested nodes we construct a factor graph, and congestion is inferred using belief-propagation algorithm. Simulation results demonstrate the ability of our approach to perfectly localize congested nodes using a scalable number of measurements and a computationally efficient algorithm. We believe that this study can ease the problem arising due to lack of QoS support and provide good-quality broadband multimedia services.},\n bibtype = {article},\n author = {Narasimha, Rajesh and Dihidar, Souvik and Ji, Chuanyi and McLaughlin, Steven W.},\n doi = {10.1016/j.jvcir.2009.07.007},\n journal = {Journal of Visual Communication and Image Representation},\n number = {2}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In this paper, we investigate scalability and performance of measurement-based network monitoring, focusing on failure and congestion diagnosis in IP networks for network-based multimedia applications. Path-based measurements using unicast probe-packets are obtained at end-hosts, and diagnosis is performed by exploiting the spatial dependence among those measurements. We formulate network monitoring in a machine learning framework using probabilistic graphical models which perform inference of the network states (on/off) using unicast measurements. We provide fundamental limits on the relationship between the number of probe packets, the size of a network and the ability to diagnose either failed links or congested network components. Specifically, the diagnosis problem is dealt in a two-fold manner. Initially for fault diagnosis, we construct a graphical model using a Bayesian belief network for path-based measurements. We then provide a lower bound on the average number of probes per edge for link failure diagnosis using variational inference under “noisy” probe measurements. Variational inference provides a feasible approximation to address the number of spatially dependent measurements needed for diagnosis in large networks. We then develop an entropy lower (EL) bound by drawing similarities between coding over a binary symmetric channel (BSC) and link failure diagnosis. Both bounds show that the number of measurements needed for diagnosis grows linearly with respect to the number of links. The analytical results are validated by simulation. On the other hand, for congestion diagnosis, we propose a solution based on decoding of linear error control codes on a BSC. In this scenario, we consider path-based probing experiments under both noiseless and “noisy” measurements and compare its performance against the fundamental limits. To identify the congested nodes we construct a factor graph, and congestion is inferred using belief-propagation algorithm. Simulation results demonstrate the ability of our approach to perfectly localize congested nodes using a scalable number of measurements and a computationally efficient algorithm. We believe that this study can ease the problem arising due to lack of QoS support and provide good-quality broadband multimedia services.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2008\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2008')\"\n      onkeypress=\"toggleGroup('group_2008')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2008</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1004413208601425\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008', 'http://www.sciencedirect.com/science/article/pii/S1004413208601425', event);\">\n    Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Zheng,  T.;  and Xiaoguang,  G.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of Systems Engineering and Electronics</i>, 19(4): 702-708. 8 2008.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S1004413208601425\"\n     onclick=\"log_download('zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008', 'http://www.sciencedirect.com/science/article/pii/S1004413208601425', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network [link]\"\n       title=\"Website\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Website</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/S1004-4132(08)60142-5\"\n     onclick=\"log_download('zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008', 'http://doi.org/10.1016/S1004-4132(08)60142-5', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zheng-xiaoguang-researchontheselfdefenceelectronicjammingdecisionmakingbasedonthediscretedynamicbayesiannetwork-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Research on the self-defence electronic jamming decision-making based on the discrete dynamic Bayesian network},\n type = {article},\n year = {2008},\n keywords = {decision-making model,discrete dynamic Bayesian network,self-defense electronic jamming},\n pages = {702-708},\n volume = {19},\n websites = {http://www.sciencedirect.com/science/article/pii/S1004413208601425},\n month = {8},\n id = {e997028e-32c9-3791-9c40-7547b328ef1e},\n created = {2015-04-11T19:52:02.000Z},\n accessed = {2015-04-11},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = {The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.},\n bibtype = {article},\n author = {Zheng, Tang and Xiaoguang, Gao},\n doi = {10.1016/S1004-4132(08)60142-5},\n journal = {Journal of Systems Engineering and Electronics},\n number = {4}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The manner and conditions of running the decision-making system with self-defense electronic jammingare given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making withself-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discretedynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discretedynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesizedifferent targets which are not predominant. In this way, various features at the same time, as well as the samefeature appearing at different time complement mutually; in addition, the accuracy and reliability of electronicjamming decision making are enhanced significantly.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2003\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2003')\"\n      onkeypress=\"toggleGroup('group_2003')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2003</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"wang-vassileva-bayesiannetworkbasedtrustmodel-2003\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Bayesian network-based trust model.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wang,  Y.;  and Vassileva,  J.\n</span>\n\n  \n\n</span>\n\n  <i>Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)</i>.  2003.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wang-vassileva-bayesiannetworkbasedtrustmodel-2003\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wang-vassileva-bayesiannetworkbasedtrustmodel-2003')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{\n title = {Bayesian network-based trust model},\n type = {article},\n year = {2003},\n id = {a7aa2096-bbc9-32ed-a144-57d42b54767c},\n created = {2015-04-11T19:07:34.000Z},\n file_attached = {false},\n profile_id = {95e10851-cdf3-31de-9f82-1ab629e601b0},\n group_id = {09500bf6-14e8-379d-a953-ea715d61ca19},\n last_modified = {2017-03-14T14:28:50.201Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n abstract = { We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents&#x27; behaviors. The agent&#x27;s needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent&#x27;s capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.},\n bibtype = {article},\n author = {Wang, Y. and Vassileva, J.},\n journal = {Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)}\n}</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n   We propose a Bayesian network-based trust model. Since trust is multifaceted, even in the same context, agents still need to develop differentiated trust in different aspects of other agents' behaviors. The agent's needs are different in different situations. Depending on the situation, an agent may need to consider its trust in a specific aspect of another agent's capability or in a combination of multiple aspects. Bayesian networks provide a flexible method to present differentiated trust and combine different aspects of trust. A Bayesian network-based trust model is presented for a file sharing peer-to-peer application.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);