The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS ...The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS environment are presented, and a data modeling of moving object is given by using Temporal logical to extent the query language, finally the application model in cargo delivery system is shown.展开更多
In this paper, we proposed a new approach for face recognition with robust to illumination variation. The improved performance to various lights in recognition is obtained by a novel combination of multicondition reli...In this paper, we proposed a new approach for face recognition with robust to illumination variation. The improved performance to various lights in recognition is obtained by a novel combination of multicondition relighting and optimal feature selection. Multi-condition relighting provides a "coarse" compensation for the variable illumination, and then the optimal feature selection further refines the compensation, and additionally offers the robustness to shadow and highlight, by deemphasizing the local mismatches caused by imprecise lighting compensation, shadow or highlight on recognition. For evaluation, two databases with various illumination mismatches have been used. The results have demonstrated the improved robustness of the new methods.展开更多
Background:Since biological systems are complex and often involve multiple types of genomic relationships,tensor analysis methods can be utilized to elucidate these hidden complex relationships.There is a pressing nee...Background:Since biological systems are complex and often involve multiple types of genomic relationships,tensor analysis methods can be utilized to elucidate these hidden complex relationships.There is a pressing need for this,as the interpretation of the results of high-throughput experiments has advanced at a much slower pace than the accumulation of data.Results:In this review we provide an overview of some tensor analysis methods for biological systems.Conclusions:Tensors are natural and powerful generalizations of vectors and matrices to higher dimensions and play a fundamental role in physics,mathematics and many other areas.Tensor analysis methods can be used to provide the foundations of systematic approaches to distinguish significant higher order correlations among the elements of a complex systems via finding ensembles of a small number of reduced systems that provide a concise and representative summary of these correlations.展开更多
Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are ene...Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.展开更多
In this article,we survey the main achievements of moving objects with transportation modes that span the past decade.As an important kind of human behavior,transportation modes reflect characteristic movement feature...In this article,we survey the main achievements of moving objects with transportation modes that span the past decade.As an important kind of human behavior,transportation modes reflect characteristic movement features and enrich the mobility with informative knowledge.We make explicit comparisons with closely related work that investigates moving objects by incorporating into location-dependent semantics and descriptive attributes.An exhaustive survey is offered by considering the following aspects:1)modeling and representing mobility data with motion modes;2)answering spatio-temporal queries with transportation modes;3)query optimization techniques;4)predicting transportation modes from sensor data,e.g.,GPS-enabled devices.Several new and emergent issues concerning transportation modes are proposed for future research.展开更多
Authorship attribution, also known as authorship classification, is the problem of identifying the authors （reviewers） of a set of documents （reviews）. The common approach is to build a classifier using supervised...Authorship attribution, also known as authorship classification, is the problem of identifying the authors （reviewers） of a set of documents （reviews）. The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers （authors） only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.展开更多
Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different re...Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high accuracy. In this paper, we propose a personalized model to protect private information in social networks. Specifically, we break the label-structure association by slightly changing the edges in some users＇ neighborhoods. More importantly, in order to increase the usability of the published graph, we also preserve the influence value of each user during the privacy protection. We verify the effectiveness of our methods through extensive experiments. The results show that the proposed methods can protect sensitive labels against learning algorithms and at the same time, preserve certain graph utilities.展开更多
Machine learning （ML） has been instrumental for the ad- vances of both data analysis and artificial intelligence （AI）. The recent success of deep learning brings it to a new height. ML algorithms have been success...Machine learning （ML） has been instrumental for the ad- vances of both data analysis and artificial intelligence （AI）. The recent success of deep learning brings it to a new height. ML algorithms have been successfully used in almost all ar- eas of applications in industry, science, and engineering.展开更多
In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems ...In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.展开更多
基金Supported by the National Science Research Project (No.2001BA205A18)
文摘The development of spatio-temporal database systems is primarily motivated by applications which track and present mobile objects. In this paper, solutions for establishing the moving object database based on GPS/GIS environment are presented, and a data modeling of moving object is given by using Temporal logical to extent the query language, finally the application model in cargo delivery system is shown.
文摘In this paper, we proposed a new approach for face recognition with robust to illumination variation. The improved performance to various lights in recognition is obtained by a novel combination of multicondition relighting and optimal feature selection. Multi-condition relighting provides a "coarse" compensation for the variable illumination, and then the optimal feature selection further refines the compensation, and additionally offers the robustness to shadow and highlight, by deemphasizing the local mismatches caused by imprecise lighting compensation, shadow or highlight on recognition. For evaluation, two databases with various illumination mismatches have been used. The results have demonstrated the improved robustness of the new methods.
文摘Background:Since biological systems are complex and often involve multiple types of genomic relationships,tensor analysis methods can be utilized to elucidate these hidden complex relationships.There is a pressing need for this,as the interpretation of the results of high-throughput experiments has advanced at a much slower pace than the accumulation of data.Results:In this review we provide an overview of some tensor analysis methods for biological systems.Conclusions:Tensors are natural and powerful generalizations of vectors and matrices to higher dimensions and play a fundamental role in physics,mathematics and many other areas.Tensor analysis methods can be used to provide the foundations of systematic approaches to distinguish significant higher order correlations among the elements of a complex systems via finding ensembles of a small number of reduced systems that provide a concise and representative summary of these correlations.
文摘Mobile object tracking,which has broad applications,utilizes a large number of Internet of Things(IoT)devices to identify,record,and share the trajectory information of physical objects.Nonetheless,IoT devices are energy con-strained and not feasible for deploying advanced tracking techniques due to significant computing requirements.To address these issues,in this paper,we develop an edge computing-based multivariate time series(EC-MTS)framework to accurately track mobile objects and exploit edge computing to offload its intensive computation tasks.Specifically,EC-MTS leverages statistical technique(i.e.,vector auto regression(VAR))to conduct arbitrary historical object trajectory data revisit and fit a best-effort trajectory model for accurate mobile object location prediction.Our framework offers the benefit of offloading computation intensive tasks from IoT devices by using edge computing infrastructure.We have validated the efficacy of EC-MTS and our experimental results demon-strate that EC-MTS framework could significantly improve mobile object tracking efficacy in terms of trajectory goodness-of-fit and location prediction accuracy of mobile objects.In addition,we extend our proposed EC-MTS framework to conduct multiple objects tracking in IoT systems.
文摘In this article,we survey the main achievements of moving objects with transportation modes that span the past decade.As an important kind of human behavior,transportation modes reflect characteristic movement features and enrich the mobility with informative knowledge.We make explicit comparisons with closely related work that investigates moving objects by incorporating into location-dependent semantics and descriptive attributes.An exhaustive survey is offered by considering the following aspects:1)modeling and representing mobility data with motion modes;2)answering spatio-temporal queries with transportation modes;3)query optimization techniques;4)predicting transportation modes from sensor data,e.g.,GPS-enabled devices.Several new and emergent issues concerning transportation modes are proposed for future research.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61272275, 61232002, 61272110, 61202036, 61379004, 61472337, and 61028003, and the 111 Project of China under Grant No. B07037.
文摘Authorship attribution, also known as authorship classification, is the problem of identifying the authors （reviewers） of a set of documents （reviews）. The common approach is to build a classifier using supervised learning. This approach has several issues which hurts its applicability. First, supervised learning needs a large set of documents from each author to serve as the training data. This can be difficult in practice. For example, in the online review domain, most reviewers （authors） only write a few reviews, which are not enough to serve as the training data. Second, the learned classifier cannot be applied to authors whose documents have not been used in training. In this article, we propose a novel solution to deal with the two problems. The core idea is that instead of learning in the original document space, we transform it to a similarity space. In the similarity space, the learning is able to naturally tackle the issues. Our experiment results based on online reviews and reviewers show that the proposed method outperforms the state-of-the-art supervised and unsupervised baseline methods significantly.
基金supported in part by the Research Grants Council(RGC)of Hong Kong,China,under Grant No.NHKUST612/09the National Basic Research 973 Program of China under Grant No.2012CB316200the National Natural Science Foundation of China under Grant No.60931160444
文摘Social networks are getting more and more attention in recent years. People join social networks to share their information with others. However, due to the different cultures and backgrounds, people have different requirements on what kind of information should be published. Currently, when social network websites publish data, they just leave the information that a user feels sensitive blank. This is not enough due to the existence of the label-structure relationship. A group of analyzing algorithms can be used to learn the blank information with high accuracy. In this paper, we propose a personalized model to protect private information in social networks. Specifically, we break the label-structure association by slightly changing the edges in some users＇ neighborhoods. More importantly, in order to increase the usability of the published graph, we also preserve the influence value of each user during the privacy protection. We verify the effectiveness of our methods through extensive experiments. The results show that the proposed methods can protect sensitive labels against learning algorithms and at the same time, preserve certain graph utilities.
文摘Machine learning （ML） has been instrumental for the ad- vances of both data analysis and artificial intelligence （AI）. The recent success of deep learning brings it to a new height. ML algorithms have been successfully used in almost all ar- eas of applications in industry, science, and engineering.
文摘In classic community detection, it is assumed that communities are exclusive, in the sense of either soft clustering or hard clustering. It has come to attention in the recent literature that many real-world problems violate this assumption, and thus overlapping community detection has become a hot research topic. The existing work on this topic uses either content or link information, but not both of them. In this paper, we deal with the issue of overlapping community detection by combining content and link information. We develop an effective solution called subgraph overlapping clustering (SOC) and evaluate this new approach in comparison with several peer methods in the literature that use either content or link information. The evaluations demonstrate the effectiveness and promise of SOC in dealing with large scale real datasets.
