Songs feel emotionally different to listeners depending on their lyrical contents, even when melodies are similar. Accordingly, when using features related to melody, like tempo, rhythm, tune, and musical note, it is difficult to classify emotions accurately through the existing music emotion classification methods. This paper therefore proposes a method for lyrics-based emotion classification using feature selection by partial syntactic analysis. Based on the existing emotion ontology, four kinds of syntactic analysis rules were applied to extract emotion features from lyrics. The precision and recall rates of the emotion feature extraction were 73% and 70%, respectively. The extracted emotion features along with the NB, HMM, and SVM machine learning methods were used, showing a maximum accuracy rate of 58.8%.
{"title":"Lyrics-Based Emotion Classification Using Feature Selection by Partial Syntactic Analysis","authors":"Minho Kim, H. Kwon","doi":"10.1109/ICTAI.2011.165","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.165","url":null,"abstract":"Songs feel emotionally different to listeners depending on their lyrical contents, even when melodies are similar. Accordingly, when using features related to melody, like tempo, rhythm, tune, and musical note, it is difficult to classify emotions accurately through the existing music emotion classification methods. This paper therefore proposes a method for lyrics-based emotion classification using feature selection by partial syntactic analysis. Based on the existing emotion ontology, four kinds of syntactic analysis rules were applied to extract emotion features from lyrics. The precision and recall rates of the emotion feature extraction were 73% and 70%, respectively. The extracted emotion features along with the NB, HMM, and SVM machine learning methods were used, showing a maximum accuracy rate of 58.8%.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114985216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mohamed Hilia, A. Chibani, Y. Amirat, Karim D Djouani
Enabling cross-organizational cooperation in ubiquitous computing environments poses new security challenges that concern particularly the interoperability of security management systems and the security policies of each organization. In this paper, we present a semantic framework for cooperative security management processes design in a cross-organizational context. Our framework is based on a hybrid approach that caters between the advantages of bottom-up and top down approaches. The cooperation model of our framework is based on the composition of atomic security management processes, by using speech acts and ontologies, and their mapping with internal processes views. The establishment of an e-contract between partners allows specifying common terminology for exchanging messages, cooperation security policy and process control flows. A scenario of cooperative process is presented to demonstrate the feasibility of the proposed framework.
{"title":"Cross-Organizational Cooperation Framework for Security Management in Ubiquitous Computing Environment","authors":"Mohamed Hilia, A. Chibani, Y. Amirat, Karim D Djouani","doi":"10.1109/ICTAI.2011.76","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.76","url":null,"abstract":"Enabling cross-organizational cooperation in ubiquitous computing environments poses new security challenges that concern particularly the interoperability of security management systems and the security policies of each organization. In this paper, we present a semantic framework for cooperative security management processes design in a cross-organizational context. Our framework is based on a hybrid approach that caters between the advantages of bottom-up and top down approaches. The cooperation model of our framework is based on the composition of atomic security management processes, by using speech acts and ontologies, and their mapping with internal processes views. The establishment of an e-contract between partners allows specifying common terminology for exchanging messages, cooperation security policy and process control flows. A scenario of cooperative process is presented to demonstrate the feasibility of the proposed framework.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130799783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a new heuristic for polarity selection is proposed. This heuristic is defined for parallel SAT solvers. The selected polarity is an important component of modern SAT solvers, in particular in portfolio ones. Indeed, these solvers are often based on the cooperation/competition principle. In this case, the polarity can be used to guide the solver in the search space. A criterion based on the intention notion is proposed in order to evaluate whether two solvers are to study the same search space or not. Once this criterion defined, a dynamical heuristic polarity is proposed for tuning the different solvers. Experimental results show that our approach is efficient and provides significant improvements on a range of industrial instances.
{"title":"Dynamic Polarity Adjustment in a Parallel SAT Solver","authors":"Long Guo, Jean-Marie Lagniez","doi":"10.1109/ICTAI.2011.19","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.19","url":null,"abstract":"In this paper, a new heuristic for polarity selection is proposed. This heuristic is defined for parallel SAT solvers. The selected polarity is an important component of modern SAT solvers, in particular in portfolio ones. Indeed, these solvers are often based on the cooperation/competition principle. In this case, the polarity can be used to guide the solver in the search space. A criterion based on the intention notion is proposed in order to evaluate whether two solvers are to study the same search space or not. Once this criterion defined, a dynamical heuristic polarity is proposed for tuning the different solvers. Experimental results show that our approach is efficient and provides significant improvements on a range of industrial instances.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131184806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The solving of Weighted CSP (WCSP) with global constraints relies on powerful consistency techniques, but enforcing these consistencies on soft global constraints is not a trivial task. Lee and Leung suggest that a soft global constraint can be used practically if we can find its minimum cost and perform projections/extensions on it in polynomial time, at the same time projections and extensions should not destroy those conditions. However, there are many useful constraints, whose minimum costs cannot be found in polynomial time. In this paper, we propose a special class of soft global constraints which can be modeled as integer linear programs. We show that they are soft linear projection-safe and their minimum cost can be computed by integer programming. By linear relaxation we can avoid the exponential time taken to solve the integer programs, as the approximation of their actual minimum costs can be obtained to serve as a good lower bound in enforcing the approximated consistency notions. While less pruning can be done, our approach allows much more efficient consistency enforcement, and we demonstrate the efficiency of such approaches experimentally.
{"title":"Modeling Soft Global Constraints as Linear Programs in Weighted Constraint Satisfaction","authors":"Jimmy Ho-man Lee, Y. W. Shum","doi":"10.1109/ICTAI.2011.53","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.53","url":null,"abstract":"The solving of Weighted CSP (WCSP) with global constraints relies on powerful consistency techniques, but enforcing these consistencies on soft global constraints is not a trivial task. Lee and Leung suggest that a soft global constraint can be used practically if we can find its minimum cost and perform projections/extensions on it in polynomial time, at the same time projections and extensions should not destroy those conditions. However, there are many useful constraints, whose minimum costs cannot be found in polynomial time. In this paper, we propose a special class of soft global constraints which can be modeled as integer linear programs. We show that they are soft linear projection-safe and their minimum cost can be computed by integer programming. By linear relaxation we can avoid the exponential time taken to solve the integer programs, as the approximation of their actual minimum costs can be obtained to serve as a good lower bound in enforcing the approximated consistency notions. While less pruning can be done, our approach allows much more efficient consistency enforcement, and we demonstrate the efficiency of such approaches experimentally.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133954624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes an unsupervised method for automatic identification of spammers in a social network. In our approach, we first investigate the link structure of the network in order to derive a legitimacy score for each node. Then we model these scores as a mixture of beta distributions. The number of components in the mixture is determined by the integrated classification likelihood Bayesian information criterion, while the parameters of each component are estimated using the expectation-maximization algorithm. This method allows us to automatically discriminate between spam senders and legitimate users. Experimental results show the suitability of the proposed approach and compare its performance to that of a previous fully-supervised method. We also illustrate our approach through a test application to Yahoo! Answers, a large question-answering web service that is particularly rich in the amount and types of content and social interactions represented.
{"title":"An Unsupervised Approach for Identifying Spammers in Social Networks","authors":"M. Bouguessa","doi":"10.1109/ICTAI.2011.130","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.130","url":null,"abstract":"This paper proposes an unsupervised method for automatic identification of spammers in a social network. In our approach, we first investigate the link structure of the network in order to derive a legitimacy score for each node. Then we model these scores as a mixture of beta distributions. The number of components in the mixture is determined by the integrated classification likelihood Bayesian information criterion, while the parameters of each component are estimated using the expectation-maximization algorithm. This method allows us to automatically discriminate between spam senders and legitimate users. Experimental results show the suitability of the proposed approach and compare its performance to that of a previous fully-supervised method. We also illustrate our approach through a test application to Yahoo! Answers, a large question-answering web service that is particularly rich in the amount and types of content and social interactions represented.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"107 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134448645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. García-Torres, Roberto Ruiz Sánchez, B. Melián-Batista, J. Moreno-Pérez, J. M. Moreno-Vega
The aim of feature selection applied to a classification task is to find a minimal subset of features for being used in the classification. Some researches have focused their effort on selecting a useful set of attributes, others on selecting a relevant and not redundant set of attributes. We proposed a heuristic construction algorithm for selecting a useful and not redundant subset of features. The algorithm proposed belongs to the filter approach and make use of a correlation measure for the task.
{"title":"A Two-Phase Heuristic Construction of Feature Sets for Classification","authors":"M. García-Torres, Roberto Ruiz Sánchez, B. Melián-Batista, J. Moreno-Pérez, J. M. Moreno-Vega","doi":"10.1109/ICTAI.2011.175","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.175","url":null,"abstract":"The aim of feature selection applied to a classification task is to find a minimal subset of features for being used in the classification. Some researches have focused their effort on selecting a useful set of attributes, others on selecting a relevant and not redundant set of attributes. We proposed a heuristic construction algorithm for selecting a useful and not redundant subset of features. The algorithm proposed belongs to the filter approach and make use of a correlation measure for the task.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132163110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proposes a method for optimal placement of DG based on intelligent optimization technique namely particle swarm optimization (PSO). Electrical system loss is used as an index of the proper location and sizing considering the DG bus voltage limit. The results show a significant reduction in power losses and considerable voltage improvement of the IEEE-30 bus test system.
{"title":"Optimal Location of Distributed Generation Using Intelligent Optimization","authors":"A. Haidar","doi":"10.1109/ICTAI.2011.143","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.143","url":null,"abstract":"This paper proposes a method for optimal placement of DG based on intelligent optimization technique namely particle swarm optimization (PSO). Electrical system loss is used as an index of the proper location and sizing considering the DG bus voltage limit. The results show a significant reduction in power losses and considerable voltage improvement of the IEEE-30 bus test system.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132405951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the growing development of heterogeneous domain-specific ontologies, the treatment of the semantic and structural differences between such ontologies becomes more important. In addition, constant maintenance and update is required so that they can be promptly enriched with new concepts and instances. In this paper, we present a coupled statistical/semantic framework for ontology merging and enrichment. First, we prioritize the ontology merging techniques according to their significance and execution into semantic-based, name-based, and statistical-based techniques respectively. In addition, we exploit multiple knowledge bases to support the merging task. Second, we use the massive amount of information encoded in texts on the Web as a corpus to enrich the merged ontology. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based merging and enrichment systems validate our proposal.
{"title":"A Unified Ontology Merging and Enrichment Framework","authors":"Mohammed Maree, S. Alhashmi, M. Belkhatir","doi":"10.1109/ICTAI.2011.106","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.106","url":null,"abstract":"With the growing development of heterogeneous domain-specific ontologies, the treatment of the semantic and structural differences between such ontologies becomes more important. In addition, constant maintenance and update is required so that they can be promptly enriched with new concepts and instances. In this paper, we present a coupled statistical/semantic framework for ontology merging and enrichment. First, we prioritize the ontology merging techniques according to their significance and execution into semantic-based, name-based, and statistical-based techniques respectively. In addition, we exploit multiple knowledge bases to support the merging task. Second, we use the massive amount of information encoded in texts on the Web as a corpus to enrich the merged ontology. An experimental instantiation of the framework and comparisons with state-of-the-art syntactic and semantic-based merging and enrichment systems validate our proposal.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133417815","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Manfredotti, David J. Fleet, Howard J. Hamilton, Sandra Zilles
Many tracking problems involve several distinct objects interacting with each other. We develop a framework that takes into account interactions between objects allowing the recognition of complex activities. In contrast to classic approaches that consider distinct phases of tracking and activity recognition, our framework performs these two tasks simultaneously. In particular, we adopt a Bayesian standpoint where the system maintains a joint distribution of the positions, the interactions and the possible activities. This turns out to be advantegeous, as information about the ongoing activities can be used to improve the prediction step of the tracking, while, at the same time, tracking information can be used for online activity recognition. Experimental results in two different settings show that our approach 1) decreases the error rate and improves the identity maintenance of the positional tracking and 2) identifies the correct activity with higher accuracy than standard approaches.
{"title":"Simultaneous Tracking and Activity Recognition","authors":"C. Manfredotti, David J. Fleet, Howard J. Hamilton, Sandra Zilles","doi":"10.1109/ICTAI.2011.36","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.36","url":null,"abstract":"Many tracking problems involve several distinct objects interacting with each other. We develop a framework that takes into account interactions between objects allowing the recognition of complex activities. In contrast to classic approaches that consider distinct phases of tracking and activity recognition, our framework performs these two tasks simultaneously. In particular, we adopt a Bayesian standpoint where the system maintains a joint distribution of the positions, the interactions and the possible activities. This turns out to be advantegeous, as information about the ongoing activities can be used to improve the prediction step of the tracking, while, at the same time, tracking information can be used for online activity recognition. Experimental results in two different settings show that our approach 1) decreases the error rate and improves the identity maintenance of the positional tracking and 2) identifies the correct activity with higher accuracy than standard approaches.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"10 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132127723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Various evolutionary multiobjective optimization algorithms (EMOAs) have adopted indicator-based selection operators that augment or replace dominance ranking with quality indicators. A quality indicator measures the goodness of each solution candidate. Many quality indicators have been proposed with the intention to capture different preferences in optimization. Therefore, indicator-based selection operators tend to have biased selection pressures that evolve solution candidates toward particular regions in the objective space. An open question is whether a set of existing indicator based selection operators can create a single operator that outperforms those existing ones. To address this question, this paper studies a method to aggregate (or boost) existing indicator-based selection operators. Experimental results show that a boosted selection operator outperforms exiting ones in optimality, diversity and convergence velocity. It also exhibits robustness against different characteristics in different optimization problems and yields stable performance to solve them.
{"title":"Boosting Indicator-Based Selection Operators for Evolutionary Multiobjective Optimization Algorithms","authors":"Dung H. Phan, J. Suzuki","doi":"10.1109/ICTAI.2011.49","DOIUrl":"https://doi.org/10.1109/ICTAI.2011.49","url":null,"abstract":"Various evolutionary multiobjective optimization algorithms (EMOAs) have adopted indicator-based selection operators that augment or replace dominance ranking with quality indicators. A quality indicator measures the goodness of each solution candidate. Many quality indicators have been proposed with the intention to capture different preferences in optimization. Therefore, indicator-based selection operators tend to have biased selection pressures that evolve solution candidates toward particular regions in the objective space. An open question is whether a set of existing indicator based selection operators can create a single operator that outperforms those existing ones. To address this question, this paper studies a method to aggregate (or boost) existing indicator-based selection operators. Experimental results show that a boosted selection operator outperforms exiting ones in optimality, diversity and convergence velocity. It also exhibits robustness against different characteristics in different optimization problems and yields stable performance to solve them.","PeriodicalId":332661,"journal":{"name":"2011 IEEE 23rd International Conference on Tools with Artificial Intelligence","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133884831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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