Abstract:Stroke fragmentation is the core of the pen-based interaction. This paper presents a novel method of stroke fragmentation, which combines geometric features and HMM (hidden Markov model). Four geometric features are employed to describe the local geometry of strokes, and a HMM structure is designed to model the drawing context to describe the global. Furthermore, stroke data is compressed as much as possible with the least loss of information by means of global searching and the best matching algorithm. It can locate the segment point and judge the primitive type simultaneously with acceptable computation efficiency. Experimental results show the effectiveness of the proposed method.

Abstract:Constrained optimization problems (COPs) are mathematical programming problems frequently encountered in the disciplines of science and engineering application. Solving COPs has become an important research area of evolutionary computation in recent years. In this paper, the state-of-the-art of constrained optimization evolutionary algorithms (COEAs) is surveyed from two basic aspects of COEAs (i.e., constraint-handling techniques and evolutionary algorithms). In addition, this paper discusses some important issues of COEAs. More specifically, several typical algorithms are analyzed in detail. Based on the analyses, it concluded that to obtain competitive results, a proper constraint-handling technique needs to be considered in conjunction with an appropriate search algorithm. Finally, the open research issues in this field are also pointed out.

Abstract:Segmentation of left ventricle tagged MR images is the basis of ventricular motion reconstruction. In left ventricle tagged MR images, the boundaries are often obscured or corrupted by the tag lines and region inhomogeneity as well as the existence of papillary muscles. These factors increase the difficulty of segmenting the inner and outer contour of left ventricle precisely. This paper introduces texture classification information and shape statistical knowledge into the Mumford-Shah model and presents an improved texture classification and shape statistics variational approach for the segmentation of inner and outer contour of left ventricle. It uses the output of support vector machine (SVM) classifier relying on S filter banks to construct a new region-based image energy term. This approach can overcome the influence of tag lines because it makes use of the supervised classification strategy. The introduction of shape statistics can improve the segmentation with broken boundaries. Segmentation results of an entire cardiac period on an identical image layer and a comparison of mean absolute distance analysis between contours generated by this approach and that generated by hand demonstrate that this method can achieve a higher segmentation precision and a better stability than other various approaches.

Abstract:The self-adaptation to the environmental changes is one of the key issues of organization-based multi-agent systems. Dynamic reorganization of organizational structures provides an effective approach for multi-agent systems to realize organizational objectives flexibly. Based on the structural characteristics of agent organizations, this paper presents a single-rooted hierarchical graph model describing social structure, role enactment and agent coordination of the organizational structures. This model decreases effectively the complexity of reorganization for large-scale agent organizations by maintaining their structural elements based on the single- rooted and hierarchical graph approach. It formalizes the reorganization process of agent organizational structures by extending the algebraic graph transformation with the DPO (double-pushout) approach. In this formal specification, the single-rooted hierarchical graphs characterize different states of organizational structures and the derivation sequences of transformation rules formulate the transition process of organizational structures. Finally, the experimental results on reorganization simulation and matching algorithm of organization transformation rules indicate that this hierarchical graph transformation approach defines formally the reorganization process of agent organizations, and supports the graph-based design of organizational elements during the reorganization process and the reorganization computation of large-scale agent organizations.

Abstract:Network community structure is one of the most fundamental and important topological properties of complex networks, within which the links between nodes are very dense, but between which they are quite sparse. Network clustering algorithms which aim to discover all natural network communities from given complex networks are fundamentally important for both theoretical researches and practical applications, and can be used to analyze the topological structures, understand the functions, recognize the hidden patterns, and predict the behaviors of complex networks including social networks, biological networks, World Wide Webs and so on. This paper reviews the background, the motivation, the state of arts as well as the main issues of existing works related to discovering network communities, and tries to draw a comprehensive and clear outline for this new and active research area. This work is hopefully beneficial to the researchers from the communities of complex network analysis, data mining, intelligent Web and bioinformatics.

Abstract:Common methods for matching multivariate time series such as the Euclid method and PCA method have difficulties in taking advantage of the global shape of time series. The Euclid method is not robust, while the PCA method is not suitable to deal with the small-scale multivariate time series. This paper proposes a pattern matching method based on point distribution for multivariate time series, which is able to characterize the shape of series. Local important points of a multivariate time series and their distribution are used to construct the pattern vector. To match pattern of multivariate time series, the Euclid norm is used to measure the similarity between the pattern vectors. The global shape characteristic is used in the method to match patterns of series. The results of experiments show that it is easy to characterize the shape of multivariate time series with this method, with which various scales can be dealt with in series data.

Abstract:This paper presents a new scheme based on the dynamical characteristics of P2P systems. First, the differences in node availability are taken into consideration, and a new data placement algorithm using less redundancy to guarantee target data availability is proposed. Second, permanent failure detector is used to distinguish between permanent and transient failures, which can reduce data recovery cost by decreasing the number of unnecessary repairs. Results from a trace-driven simulation suggest that this scheme can reduce about 80% maintenance bandwidth, compared with traditional methods.

Abstract:This paper surveys the mathematics behind Bloom filters, some important variations and network-related applications of Bloom filters. The current researches show that although Bloom filters start drawing significant attention from the academic community and there has been considerable progress, there are still many unknown dimensions to be explorered. The research trends of Bloom filter algorithm are foreseen in the end.

Abstract:This paper presents the basic concept of topology's properties and modeling metrics; categorizes and analyzes both AS-level models and router-lever models. Moreover, this paper summarizes current research achievements on Internet topology's modeling, especially at the router-level. Finally, it identifies future directions and open problems of the topology modeling research.

Abstract:The appearance of plenty of intelligent devices equipped for short-range wireless communications boosts the fast rise of wireless ad hoc networks application. However, in many realistic application environments, nodes form a disconnected network for most of the time due to nodal mobility, low density, lossy link, etc. Conventional communication model of mobile ad hoc network (MANET) requires at least one path existing from source to destination nodes, which results in communication failure in these scenarios. Opportunistic networks utilize the communication opportunities arising from node movement to forward messages in a hop-by-hop way, and implement communications between nodes based on the "store-carry-forward" routing pattern. This networking approach, totally different from the traditional communication model, captures great interests from researchers. This paper first introduces the conceptions and theories of opportunistic networks and some current typical applications. Then it elaborates the popular research problems including opportunistic forwarding mechanism, mobility model and opportunistic data dissemination and retrieval. Some other interesting research points such as communication middleware, cooperation and security problem and new applications are stated briefly. Finally, the paper concludes and looks forward to the possible research focuses for opportunistic networks in the future.

Abstract:A key function of a host-based intrusion detection system is to monitor program execution. Models constructed based on static analysis have the advantage of not producing false alarms; still, they can not be put into practice due to imprecision or inefficiency of the model. The prior work has shown a trade-off between efficiency and precision. In particular, models based upon non-deterministic finite state automaton (DFA) are efficient but lack precision. More accurate models based upon pushdown automaton (PDA) are very inefficient to operate due to non-determinism in stack activity. DYCK model, VPStatic model and IMA use some subtle approaches to achieve more determinism by extracting information about stack activity of the program or inserting code to expose program state or just inline the local automaton but still can not solve the problem of indirect call/JMP. This paper presents a new training-free model (hybrid finite automaton, HFA) to gain more determinism and resolves indirect call/JMP through static-dynamic hybrid approach. The results show that in run-time, these models slowed the execution of the test programs by 5% to 10%. This paper also formally compares HFA with some typical models and proves that HFA is more accurate than others and it is more suitable for dynamic linked applications. Some technical details of the protocol type system on Linux and experimental results are also presented in the paper.

Abstract:Resource discovery is one of the important research issues in grid computing. As computational resources are fundamental resources supporting various grid applications, it is of great importance to study computational resource organization and discovery. However, existing approaches are limited in terms of efficiency, scalability, adaptability and the ability to support a wide range of query mechanisms. After analyzing the requirement of application resources, this paper introduces the primary attribute (PA), and describes a computational resource. Then it proposes resource category tree (RCT) to organize and discover the computational resources. This paper presents the mechanism of organizing resources based on RCT, including the basic concepts and rationale, the self-organizing design to support resource dynamics, the load-aware self adaptation and primary-backup based fault tolerance. It also designs four searching algorithms to support comprehensive query types in the framework of resource organization by using RCT, and a theoretical complexity analysis is presented as well. In the end, the paper evaluates the performance of RCT through simulations.

Abstract:When localizing a mobile node in sensor networks, it is possible that a seed node is passed by the mobile node during its sleeping mode, which might lead to failed localization. This paper proposes a dynamic node scheduling scheme P-SWIM based on proactive wakeup and sleep. In P-SWIM, each seed is proactively notified if a mobile node is moving toward it. Hence, only these seeds remain active in full duty when the mobile node passes by them, while the other seeds still stay in sleeping mode. Simulation results indicate that localization algorithms based on P-SWIM can achieve better localization performance than those based on the other two static node scheduling schemes, RIS and GAF. Moreover, P-SWIM incurs least running overhead to the network overall power consumption among the three schemes. In addition, the paper evaluates the effect of node scheduling schemes on localization performance by tuning the parameters of each scheme, which presents guidelines for efficient network deployment for mobile sensor positioning applications.