Abstract:A new concept, Autonomous Web Service (AWS), to search requirement autonomously, is introduced in this paper. An intention-behavior-achievement mechanism based on environment ontology is proposed to specify the service request and the capability of AWS. A model of AWS aggregation driven by requirement has been figured out. The matching algorithm between the requirements and the AWS capability and the AWSs aggregating algorithm have been designed based on the intention-behavior-achievement mechanism. Finally, a case study is given to show the feasibility of this method.

Abstract:This paper proposes the decision-abstraction and issue-decomposition principles specific to the architectural level design, as well as a decision-centric architecture design method based on the principles. The method models software architectures from the perspective of decisions, accomplishing the design process from eliciting the architecturally significant issues to making decisions on the architecture solutions, it also implements the automated synthesis of the candidate architecture solutions and the capture of the design decisions and rationale. This decision-centric method accommodates the characteristics of architectural level, alleviating the complexity of architecture design and the cost of decisions and rationale capture.

Abstract:This paper proposes a model called asynchronous time consistency (ATC) based on call-back clock method in distributed interactive simulation system (DIS). This model utilizes the asynchronism of different nodes’ clocks to re-distribute the time resources and improve the overall performance without compromising the system functionality. With this model, the paper firstly designs a fast approximation method based on the global delay information, and proves the effectiveness of the algorithm; secondly, according to the actual network condition, it also designs a fast iteration method based on the partial delay information. In the end, experiments are conducted and analyze to static and dynamic performance of these methods. Experimental results show that the ATC methods can improve the interaction performance effectively.

Abstract:This paper presents hash join optimization based on shared cache CMP (chip multi-processor). Firstly, it proposes a multithreaded execution framework of hash join based on Radix-Join algorithm, and then analyzes the factors which affect the performance of multithreaded Radix-Join algorithm through two instances. Based on the analysis, the performance of various threads and their shared-cache access behaviors in the hash join multithreaded execution framework were optimized, and optimize memory access of hash join in cluster join phase. It then analyzes the speedup of multithreaded cluster partition in theory was analyzed. All of the algorithms are implemented in the INGRES and EaseDB. In the experiments, the performance of the multithreaded execution framework of hash join is tested, and the results show that the proposed algorithm could effectively resolve the cache access conflict and load balance of CMP cores in multithreaded environment and hash join performance is improved.

Abstract:To improve workflow products, handling of the validity of information, temporal workflow is presented as a new concept by introducing time into workflow concept model as a dimension, which leads to time attributes being assigned to all workflow basic concepts and relationships between them. Based on the research on the expression and calculation of temporal information and the process meta-model of temporal workflow, through mapping the concepts in the meta-model into the elements of Petri Net, a temporal workflow process model, TPWF-net, is presented, which can describe the process, resource, case and their time attributes in one model synthetically. Then, some theorems are proved, which include structural equipollence between TPWF-net and WF-net, and the soundness of free-choice synchronal TPWF-net. It is also proved that well-structured TPWF-net can be decided in polynomial time. Finally, a structured modeling method of TPWF-net and a structure-simplified method of model verification are presented. Temporal workflow makes it more comprehensive to describe and analyze the time-related problems in workflow area. A temporal workflow engine prototype based on TPWF-net has been implemented and applied in some projects supported by local government.

Abstract:Time Petri net can analyze internetware performance, but it cannot analyze internetware changing price. To further satisfy the flexible modelling and the changing price analysis demands of inernetwares, this paper defines a price time colored Petri net (PTCPN) which provides an extension in the changing price information and color information for the time Petri net. This paper redefines the semantic aspect of price time colored Petri net in terms of priced timed transition systems. A cumulate price state class is defined, and its soundness and completeness are discussed. An approach is proposed to formalize internetware polymorphism and internetware control structures based on the price time colored Petri net. Finally, the approach availability is verified with an example. The results show that it is feasible to applying price time colored Petri net to the formal modeling and the analysis of internetware.

Abstract:This paper addresses the problem of paraphrase collocation extraction by using “OBJ” relationship as a case study. Specifically, the proposed method recasts paraphrase collocation extraction as a binary classification problem, which combines multiple features based on translation, thesaurus, polarity words, and web mining. Experimental results show that the binary classification-based method is effective for paraphrase collocation extraction. Especially, the exploited features are all helpful for improving the extraction performance. With the proposed method, more than 280 000 pairs of paraphrase collocations are extracted, the precision of which is above 70%. Further experiments show that nearly 40% of sentences can be paraphrased by using the extracted paraphrase collocations, which demonstrates that the proposed method is useful in practice.

Abstract:To efficiently utilize the discriminant information in the range space of locality preserving total scatter, this paper proposes a complete discriminant locality preserving projections (CDLPP) algorithm for face recognition. Since Fisher discriminant analysis and locality preserving projections (LPP) have been widely used in face recognition, CDLPP algorithm integrates them together and analyzes the discriminant information contained in the principal spaces and null spaces of locality preserving within-class scatter, locality preserving between-class scatter and locality preserving total scatter. First, CDLPP algorithm removes the null space of locality preserving total scatter, in which no discriminant information is contained, using singular value decomposition (SVD). Then, regular discriminant features and irregular discriminant features of CDLPP are extracted severally in the null space and principal space of the locality preserving within-class scatter. Finally, both regular discriminant features and irregular discriminant features are concatenated to be used for face recognition. Extensive experiments on ORL face database, FERET subset and PIE subset illustrate that the performances of CDLPP outperform those of current subspace face recognition algorithms, such as LDA, LPP and discriminant LPP, which proves the effectiveness of the proposed algorithm.

Abstract:Word sense disambiguation (WSD) can be thought as a classification problem. Feature selection is of great importance in such a task. In general, features are selected manually, which requires a deep understanding of the task itself and the employed classification model. In this paper, the effect of feature template on Chinese WSD is studied, and an automatic feature selection algorithm based on maximum entropy model (MEM) is proposed, including uniform feature template selection for all ambiguous words and customized feature template selection for each word. Experimental result shows that automatic feature selection can reduce feature size and improve Chinese WSD performance. Compared with the best evaluation results of SemEval 2007: task #5, this method gets MicroAve (micro-average accuracy）) increase 3.10% and MacroAve (macro-average accuracy）) 2.96% respectively.

Abstract:This paper presents a hybrid self-adaptive orthogonal genetic algorithm (HSOGA) based on orthogonal experimental design method for solving global optimization problems. In HSOGA, the orthogonal experimental design method is utilized to design crossover operator, and as a result, a self-adaptive orthogonal crossover operator is proposed. The self-adaptive orthogonal crossover operator self-adaptively adjusts the number of orthogonal array’s factors and the location for dividing the parents into several sub-vectors according to the similarity of the two parents, in order to produce a small but representative set of points as the potential offspring. In addition, in HSOGA the self-adaptive orthogonal crossover operator is also adopted to generate an initial population that is scattered uniformly over the feasible solution space in order to maintain the diversity. Moreover, a local search scheme is incorporated into HSOGA in the purpose of enhancing the local search ability and speeding up the convergence of HSOGA. HSOGA is tested with fourteen benchmark functions. The experimental results suggest that HSOGA is generic and effective.

Abstract:In wireless mesh networks, multicast routing is one of the supporting technologies for its practical application, and has become the research focus. Since the traditional multicast routing protocols don’t adapt themselves directly to the structure characteristic and performance requirement of wireless mesh networks, a variety of protocols for wireless mesh networks are studied. This paper summarizes the design targets, principles and classification methods for protocols. The fundamental mechanism, characteristics and performance of existing representative protocols are discussed and compared in detail. Finally, the influences of multi-radio multi-channel and multi-rate on the design of multicast routing protocol are analyzed, and the importance of establishing the cross-layer optimization model and the mechanism of the multicast routing based on actual environment and practical design are pointed out.

Abstract:With the rapid development of Internet technology, the existing routing system has been confronting many serious challenges of scalability, mobility, multi-homing and traffic engineering. Based on the idea of Identifier/Locator Split, the concept of identifier-based routing is proposed, and its research scope is accurately defined in this paper. On the basis of the design goals of identifier-based routing, some related researches are introduced and compared. Finally, some key issues and the development trends are discussed.

Abstract:The trust decision-making is very important to people in Electronic Commerce transactions. The approaches that assist people to finish trust decision-making become a basic issue that researcher must face and solve imminently in the field. A new quantificational subjective trust evaluation approach based on the cloud model is presented. The expected value and hyper entropy of subjective cloud is used to evaluate the reputation of trust objects, and a trust change cloud model is designed to depict the reputation change style of trust objects in order to provide additional evidence. The result of simulating experiments shows the validity and effectiveness of this approach.

Abstract:Most strategies of location management are based on the hypothesis that the cellular network is regular in size, shape and distribution. This paper proposes a location management strategy based on irregular cellular network topology, including macro-cell, micro-cell and pico-cell. The terminal will compute the physical distance between the current cell and the last reported cell by regrouping neighbor cells and comparing the CCs (cell coordinates) which are broadcasted by each base station. In the paper a dynamic location update algorithm based on distance and direction angle is presented. Finally, the formula for the total cost of location management between two paging arrivals is deduced by a semi-Markov decision process. The numerical analysis shows that the total costs of location updating and paging of individuation sector location management scheme is superior to that of circle location cell when the mobile user moves frequently.

Abstract:Wireless sensor networks usually have limited energy and transmission capacity, and they can’t match the transmission of a great deal of data. So, it is necessary to approximate or aggregate raw data sampled by sensors in networks. By designing an error tree and solving the regression equations set, this paper proposes a data compression scheme with infinite norm error bound for wireless sensor networks. The algorithms in the scheme can simultaneously explore the temporal and multiple-streams correlations among the sensory data. The temporal correlation in one stream is captured by the 1D Haar wavelet transform. For multivariate monitoring sensor networks, some streams from one sensor are selected as the bases according to the correlation coefficient matrix, and the other streams from the same sensor node can be expressed with one of these bases using linear regression. Theoretically and experimentally, it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-streams correlations on the same sensor node and achieve significant data reduction.

Abstract:The existing methods are mostly concentrated on single flow network selection. In this paper, a multi-flow network selection model scheme based on bigraph is proposed. By calculating the satisfactions between flows and networks this scheme can distribute the flows with the most satisfaction on the condition of being fair. Analysis of the performance of the algorithm proves the correctness of Algorithm. Numerical results show that the proposed schemes achieve significant satisfaction and Fair coefficient.

Abstract:As networks expanded largely and link speeds grow rapidly, keeping a counter for each flow is too expensive. Estan et al. propose two algorithms: sample and hold and multistage filters, to identify large flows, which have obvious shortcomings: sample and hold discard packets randomly while multistage filters simultaneously calculate 5～6 hash functions that is unsuitable for hardware implementation. This paper proposes two algorithms, Hits and Holds, to identify large flows quickly and correctly, which overcome the shortcomings of Estan’s algorithms, while effectively reducing false positive and false negative errors.

Abstract:In this paper, a cluster-based location management scheme is introduced, which has fully considered the characteristic of mesh structure and then utilizes the similarity of signaling transmission path of cells within a cluster to distribute the signaling based on the quasi-multicast fashion to improve location management efficiency within wireless mesh networks. The numerical result reveals that the proposed scheme has significantly lessened the signaling cost.

Abstract:Because finding a minimum connected domination set (MCDS) for a general connected network is NP-complete, a topology-aware heuristic algorithm is proposed in this paper whose correctness is proved. By taking advantage of the topology characteristic of nodes, the algorithm can reduce the blindness in the process of selecting dominating nodes, and form a smaller CDS (connected domination set) based on 2-hop local information, consequently obtain a virtue backbone network with the CDS. The simulation results show that the algorithm is superior to other distributed CDS algorithms, and closer to minimum CDS.

Abstract:In this paper, a novel method is presented to solve these problems. This method processes the X86 executable programs statically, so it has a higher code coverage than dynamic methods. Besides, it employs a data flow analysis method to identify the jump targets for indirect jumps. It also utilizes optimized tainting mark rules based on the operation semantic of branch conditions. Experiments on 103 real malwares and 7 benign softwares show that the proposed method has the following advantages: For Trojan-spy program detection, it can reduce the false negatives caused by the explicit-flow-sensitive method, and it is effective in dealing with information steal behaviors triggered by some particular conditions. For benign program analysis, it can reduce most of the tainted branches that should be tracked in the original implicit-flow-sensitive method without optimization.

Abstract:This paper presents a rapid method to align large sets of 3D scanned data automatically. The method incorporates the technique of image registration into the pair-wise registration. Firstly, it retrieves two texture images from the scanned data to align. A method is proposed to generate the texture image from the range image when scanned data do not contain the texture information. Secondly, it detects the features using SIFT (scale-invariant feature transform) on texture images, and a set of potential corresponding pixels is selected by means of pre-filter and cross validation. Then a matching algorithm, based on RANSAC (random sample consensus) algorithm, is applied to specify the matching pixel pairs between two images. All matches obtained are mapped to 3D space and used to estimate the rigid transformation. Finally, a modified ICP (iterative closest point) algorithm is applied to refine the result. The paper also presents a method to create model graph rapidly for multi-view registration which avoids aligning all pairs of range images. This reconstruction technique achieves a robust and high performance in the application of automatic rebuilding 3D models of culture heritages.

Abstract:This paper presents an unsupervised texture segmentation method based on wavelet analysis and mean shift. The adaptive multiscale segmentation is realized by applying mean shift cluster algorithm to features which generated by wavelet pyramid. The unsupervised texture segmentation method does not require either training or prior knowledge of the number of textures. With a proper strategy, those features are propagated through finer scales adaptively. The center of a homogenous texture is analyzed by using features in coarse resolution, and its border is detected in finer resolution so as to locate the boundary accurately. This method has an analogy with human psychophysical measurements of image appearance. Experiments on synthetic and real images demonstrate that the proposed method leads to a successful unsupervised segmentation.

Abstract:Star coordinates (SC), a traditional multivariate data visualization technique, loses much information due to oversimple dimension reduction algorithm. And the SC visualization can’t offer the dimension distribution information. Moreover, the manual dimension axis configuration of SC is too complex. To address these problems, the paper proposes the advanced star coordinates (ASC), which uses the diameter instead of the radius as the dimension axis, designs the dimension configuration strategy to optimize the order and the angle of dimension axes, and projects the multi-dimensional object to low dimension visual space through the dimension reduction algorithm. And the dimension reduction process is meaningful to user and the algorithm uses the minimum of the object coordinates variation between the multi-dimensional coordinates and the advanced star coordinates as criterion. Experimental results show that the dimension reduction algorithm is highly efficient and suitable for the aggregation with a great amount of high-dimensional data. The dimension configuration strategy relieves the user’s operation burden greatly and helps them detect the connotative characteristics of the multidimensional information aggregation quickly and exactly. The visualization is easy to understand and can express the dimension distribution information effectively, which is helpful for user to view the multi-dimensional information and to discover the implicit information in knowledge discovery process.

Abstract:This paper presents an optimal algorithm to compute multi-degree reduction of Said-Bézier generalized Ball curves (SBGB) with endpoints constraints in the L₂-norm. Based on the relations between Said-Bézier basis, Power basis and Jacobi basis, this paper deduces the explicit transformation matrix from SBGB basis to Jacobi basis and in reverse order. Then based on the inverse matrix of the above matrix and the orthogonality of Jacobi basis, an explicit constrained algorithm for multi-degree reduction of SBGB curves in the L₂-norm is put forward. This algorithm can be used in not only Said-Ball curve and Bézier curve but also the large class curves located between the two curves. This paper proves that the algorithm has some superiorities, including approximating optimal error of the degree reduction estimated beforehand, high order interpolation in the endpoints and multi-degree reduction in one time. Numerical examples demonstrate its validity and superiorities.