Abstract:Mobile user requirements acquisition techniques, aiming to further improve performance accuracy and real-time by fully utilizing mobile contextual information, have recently become one of the hottest topics in the domain of mobile personalized services. This paper presents an overview of the field of mobile user requirements acquisition techniques, including key techniques, evaluation, and typical applications. The prospects for future development and suggestions for possible extensions are also discussed.

Abstract:The multi-core and concurrency technology are widely used today. How to debug concurrent programs effectively becomes an important topic. Due to nondeterministic and complex behavior of the concurrent program, traditional debugging techniques are inappropriate to apply. In bug-reports-oriented debugging scenarios, the separation of error detection and error debugging processes makes the failures more difficult to reproduce, which exacerbates the difficulty of debugging. For bug-report-oriented debugging scenarios, this paper proposes a method to visualize program behaviors to assist programmers to debug manually. The method reasons concurrent program behavior through static analysis, giving a global view to programmers to help them observe the behavior of the program and guiding their attention on suspicious operation. This approach can avoid the cost of reproducing concurrent failures. With the information in the bug report as well as the feature of the event structure model, it eliminates the state explosion problem that may arise during static analysis of concurrent programs. A prototype of interactive debugging tool for Java called JESVis Debugger, is developed as an initial realization of the proposed method.

Abstract:Def-Use faults are a very important and common type of faults. The state-of-the-art detection schemes for such faults still hardly achieve both preciseness and scalability. This paper applies the idea of combining the sensitive and insensitive detection approaches and deploying the effective range of the two approaches to achieve both high detection scalability and high precision. The study results in a new scene- sensitive detection strategy based on a classification scheme on statements that contain potential faults. The key idea is to classify these statements into different categories based on how a potential fault in these statements might be triggered. It uses polynomial flow-, field- and context-sensitive summary based scene analysis to do the classification and identifies triggering scenes based on program dependence information. Different detection schemes with different amount of overheads are then applied to different categories and thus reducing the overall overhead and achieving a higher scalability. The path-sensitive detection schemes are only performed on the necessary triggering scenes. The proposed approach is implemented in a prototype system, called Minerva. Using null pointer dereference fault detection as an example and verifying the approach through applications whose total code size exceed 2.9 million lines (one application exceeds 2 million lines), the experimental results show that the average detection time of Minerva is 3× and 46× faster than the two state-of-the-art path-sensitive detection tools, Clang-sa and Saturn, respectively. The false positive rate of Minerva is 24%, which is also a third of that of Clang-sa and Saturn's. There is no false negative on the known faults. The results show that the proposed scene-sensitive fault detection approach can achieve both high scalability and high accuracy.

Abstract:Safety critical systems (SCS) are very typical and crucial in trustworthiness study, and their evaluation and verification are test-dependent. Since SCS are usually complex and dramatically huge, manual test of SCS is unfeasible in practice, which makes automatic test approaches for SCS an important trend. This paper studies the characteristics of high order collaboration, real time and temporaries in SCS testing, and based on the domain theory, ambient and CCS calculus, it defines a formal semantics for automatic test of SCS, called AutTMSCS, which describes behaviors in SCS testing. Testing tasks, test equipments and products under test are abstracted and architected in three layers, and a procedure of automatic testing is provided in the model. Based on extended LTS, the convergency and correctness of the model are proved to demonstrate the computability of the model, which indicates that the testing process of SCS can be automated. The model had been practically applied to automatic test of spacecrafts, and the system developed based on the model had become the working platform of spacecrafts test service in daily usage.

Abstract:With the development of Web2.0, more and more user-generated content (UGC) occur in Web applications. These contents, especially reviews, are opinion-rich and play important roles in e-commerce. According to the Cone's survey published in 2011, 64% of users like to read the related reviews on goods before they make purchase decisions. It is vital to provide users the accurate, succinct and true reviews. This work surveys the research progresses on quality evaluation and control of reviews focusing on the prediction, summarization and spam review detection. At the end, some potential research topics are pointed out based on these analyses.

Abstract:Keyword search helps users to efficiently get interested information from relational databases, and users are exempted from learning the professional structural query language for relational databases, which greatly reduces the usabilitye threshold. Keyword search over relational databases commonly employs data-graph-based methods which first models a database into a graph and then uses it to identify the minimum Steiner tree. However, the available methods are not able to dynamically optimize query results according to the dynamically changing user interest. In this paper, an ant-colony-optimization-based algorithm is proposed to achieve the task of keyword search over relational databases. Furthermore, a novel approach based on the theory of concept drift is presented to capture the mutation of user interest. In addition, based on concept drift theory and ant colony optimization algorithm, a new algorithm called ACOKS* is proposed to dynamically optimize the search results according to the time-changing user interest, so as to achieve the results in more accordance with user interest. Finally, a prototype is developed to carry out extensive experiments, and the results show that our method can achieve high scalability and perform better than other state-of-the-art methods.

Abstract:As the Internet applications, such as social networks and micro-blogs, become popular, their scale of users has been increasing rapidly. Community detection in these large-scale networks could provide important insights into customer behavior for service recommendation and product marketing. The difference of these networks from traditional ones is that besides topology, they have frequent information interaction between nodes. Information flow makes these networks directed and dynamic. Traditional community detection approaches fall short in these networks because they do not consider these new characteristics. Inspired by the dynamics of infectious disease theory, this paper proposes a novel community detection approach based on information flow analysis. This approach effectively groups the nodes with frequent information interaction in the same community. Between communities, there would be little information flow. This paper experiments on real-world networks demonstrate that compared with previous community detection methods, the proposed approach is more effective at identifying the dynamics in the networks.

Abstract:Existing index structures for multivariate time series can't support similarity search under DTW distance efficiently. Firstly, a transformation method, which converts unequal-length multivariate time series into equal-length univariate time series, is proposed and a mathematical proof that the transformation satisfies lower bounding distance lemma is provided. Secondly, DTW lower bounding distance is proposed, and its character is analyzed. Thirdly, based on DTW lower bounding distance proposed above, an index structure for multivariate time series is proposed, allowing database of multivariate time series be organized. Further, similarity search algorithm and process for multivariate time series are discussed, and related mathematical proofs that false dismissals can be avoided are given. Finally, validity of proposed method is verified by experiments.

Abstract:This paper surveys the state of the art of privacy preserving for publishing social network data. First, the research background of privacy preserving for social network data is presented. Then, four important aspects of privacy preserving for social network data are summarized and analyzed in detail. The discussed topics includes privacy of social network, adversaries' background knowledge, privacy preserving technologies of social network, and data utility and experimental analysis. In addition, this paper points out the defects of privacy preserving in social networks and provides the comparisons of privacy preserving technologies. Finally, some potential future research directions are introduced. This paper aims to offer a deep insight into the mainstream methods and recent progress in this field, making detailed comparison and analysis.

Abstract:Low-Rate denial of service (LDoS) attack is a new category of denial of service attacks which may become a serious threat to Internet. It has attracted many researchers' interest and is becoming an important research topic in network security area. Since 2003, researchers have revealed several kinds of low-rate denial of service attacks, such as the shrew attack, the reduction of quality (RoQ) attack, the pulsing denial-of-service (PDoS) attack and the distributed low-rate denial of service attacks (DLDoS). They also proposed some corresponding defense and detection methods. This paper thoroughly reviews the state-of-the-art of LDoS attack and prevention research, and also analyzes the basic mechanism and attack methods of different LDoS attacks. Especially, it analyzes the security of TCP congestion avoidance mechanism, and illustrates the cause of potential security issue of such mechanism. In addition, the paper also reviews and evaluates the current LDoS attack prevention and detection approaches. Finally, the paper identifies some open research issues and points out possible future research directions in LDoS attack research area.

Abstract:Cognitive radio technology is regarded as the most promising technique to solve the problem of ultra-low utilization efficiency of wireless spectrum resources. Based on this technology, cognitive radio networks (CRNs) effectively improve utilization efficiency of licensed spectrum through dynamic spectrum access. However, the resultant dynamic channel availability significantly increases the difficulty in networking for CRNs. Rendezvous aims at providing common media for communication between users, which is the foundation of networking in wireless networks. This paper introduces the basic concept and characteristics of rendezvous in CRNs and dissect the challenges and concerned performance metrics in design of rendezvous schemes. According to the proposed classification criteria and system models, the related research work on rendezvous schemes are analyzed in depth. Finally, open problems in rendezvous schemes design are discussed to point out future research trends and focuses.

Abstract:Low-Duty-Cycle wireless sensor networks (LDC-WSN) can effectively increase the lifecycle of wireless sensor networks. However, in traditional LDC-WSN, not only is end-to-end delay very large, but the quality of link is also not considered. In order to solve these two problems, this paper proposes link-quality and energy-aware based scheduling scheme algorithm (LES). Simulation shows that, compared with traditional networks, LES can achieve significant performance improvement in terms of energy-saving and therefore increase life cycle.

Abstract:BGP hijacking is one of the most severe threats facing current inter-domain routing system, but yet there still lack effective countermeasures. This paper models AS (autonomous system) level immunity to BGP hijacking as the possibility of the victim AS learning bogus routes via local BGP routing information, and presents the sufficient condition and necessary condition for an AS to be immune in the presence of BGP hijacking, as well as the upper bound of such immunity. Evaluation results show that more than 80% of ASes have no immunity to BGP hijacking at all and only less than 0.26% of ASes have immunity higher than 85%. Further analysis pinpoints the root cause of such low immunity—provider barrier that victim AS' providers prefer customer routes and prevent the propagation of bogus route to the victim. To tackle this barrier and improve AS level immunity against BGP hijacking, this study designs a cooperation based monitoring mechanism, and proposes a lightweight heuristic approach for each participant to select AS cooperators. This proposed mechanism is completely compatible to BGP, and is incrementally deployable. Experimental results show that by peering with only 25 cautiously selected ASes, one AS can significantly improve its immunity to 95%.

Abstract:Providing a provable and verifiable execution environment for the tenants is a very important problem in the cloud computing mode. This paper proposes a dynamic trustworthiness verification mechanism for the tenants' virtual execution environment, named TCEE (trusted cloud execution environment), which extends the current trusted chain into virtual machine's architecture stack. It cyclically verifies the trustworthiness of the memory and file systems within the virtual execution environments. TCEE introduces a TTP (trusted third party) to perform the verification and audit action against tenants' virtual machines to avoid heavy involvement of end tenants and unnecessary information leakage of the cloud providers. A prove-of-concept prototype is implemented according to TCEE to evaluate the effectiveness and the performance overhead incurred. Experimental results show that TCEE is effective and its performance overhead is minor.

Abstract:Edge information is often the key to the detection of objects. Traditional edge detection algorithms calculate gradient omnidirectionally, which usually results in calculation of redundancy. Inspired by Weber local descriptor, this study proposes a saliency texture structure descriptor that simulates divergent and significant characteristics of human eyes observing things. First of all, it calculates the sum of relative differences between intensity of a center pixel and those of its neighborhood pixels, and divide the sum by the center pixel's intensity to get its local saliency factor. Then, it extracts its local texture structure though a divergent gray level co-occurrence matrix. At last, it constructs a two dimensional histogram as the feature vectors by combining saliency factor and texture structure. Experimental results show that the saliency texture structure descriptor has the ability of good edge detection and powerful structural expression, and is robust to noise and light and shadow changes. When used in pedestrian detection, the saliency texture structure descriptor gets much higher detection rate than other local descriptors such as CENTRIST and HOG. This descriptor can find its high application value in vehicle active safety system.