Abstract:Based on the inter-procedural dependence analysis, this paper studies the propagation behavior in software of hardware fault in heterogeneous systems. This research can be used for optimizing application-level checkpointing techniques. Experimental results demonstrate that this method is viable and can be very helpful for the research of fault tolerance optimization of heterogeneous systems.

Abstract:This paper proposes an integrated approach to facilitate mobile application development and deployment from software architecture perspective. It models, in multiple dimensions, the device parameters (like CPU, memory, screen, communication module), user preferences (like energy or performance preference), and QoS requirements (like frequency of interaction, average size of event) at architectural level. This approach will generate personalized deployment plans to meet specific requirements of mobile users. The case study and experiment results show that this approach effectively facilitates development and deployment, and improves the customizability of mobile network applications.

Abstract:This paper proposes a framework of particle swarm optimization (PSO) based pairwise testing. To systematically build pairwise test suites, two different PSO based strategies are proposed. One strategy takes on a one-test-at-a-time approach and the other takes on an IPO-like approach. In these two different strategies, PSO is used to complete the construction of a single test and research on how to formulate the search space, define the fitness function, and set some heuristic settings. To verify the effectiveness of this approach, these algorithms are implemented and some typical instances have been chosen. In this empirical study, the paper analyzes the impact factors of this framework and compares this approach to other well-known approaches in test suite size and generation time. Final empirical results show the competitiveness of this approach.

Abstract:Based on the worst-case response time (WCRT) schedulability analysis for hard real-time systems, a new scheduling algorithm called extended fault-tolerant fixed-priority with preemption threshold (FT-FPPT*) is proposed in the software fault-tolerant model. This algorithm can be used, together with the schedulability analysis, to effectively enhance the fault-tolerant capability when the traditional fault-tolerant fixed-priority preemptive (FT-FPP) scheduling and fault-tolerant fixed-priority scheduling with preemption threshold (FT-FPPT) are no longer appropriate. At length, an optimal priority assignment search algorithm (PASA) is presented. PASA is optimal in the sense that the fault resilience of task sets is maximized for the proposed analysis. The effectiveness of the proposed approach is also evaluated by simulation.

Abstract:To describe the implicit data and the implicit control in program code, CNets, one extension of Petri nets, is applied. By data view nets and control view nets, data and control of the program code are modeled. Based on the CNets specification, interactions between data flow and control flow, relations among data, operations, and resources are also captured respectively. Meanwhile, mapping rules from CNets specification to Petri nets are presented. According to the rules, from CNets specification, properties of the program are analysized through Petri nets techniques without a running a program.

Abstract:This paper proposes a new approach for the text recognition of video, whose novelty mainly lies in the color-based clustering and multiple frame integration of three phases: First, in the text detection phase, the two significant features of text block are jointly considered in a video: homogeneous color, dense edges, and color-based clustering are employed to decompose the color edge map of video frame into several edge maps, which make the text detection more accurate. Second, in text enhancement phase, the text blocks are identified and integrated with the same content by filtering the blurred text based on the proposed text-intensity map, which can obtain the clean background and clear text with a high contrast of effective text extraction. Third, in the text extraction phase, on one hand, for effective binarization of text block, instead of performing binarization in a constant color plane as in the existing methods, this approach can adaptively select the best color plane according to the text contrast difference among color planes for binarization. On the other hand, for effective text recognition, the color differences between the text and background in video frames are considred, and color-based clustering is utilized to remove the noises. Extensive experimental results have shown that this approach outperforms several existing state-of-the- art methods.

Abstract:Based on background knowledge represented as a Bayesian network, this paper presents a BN-EJTR method that computes the interestingness of frequent items and frequent attributes, and prunes. BN-EJTR seeks to find inconsistent knowledge relative to background knowledge and to resolve the problems of un-interestingness and redundancy faced by frequent pattern mining. To deal with the demand of batch reasoning in Bayesian networks during computing interestingness, BN-EJTR provides a reasoning algorithm based on extended junction tree elimination for computing the support of a large number of items in a Bayesian network. In addition, BN-EJTR is equipped with a pruning mechanism based on a threshold for topological interestingness. Experimental results demonstrate that BN-EJTR has a good time performance compared with the same classified methods, and BN-EJTR also has effective pruning results. The analysis indicates that both the pruned frequent attributes and the pruned frequent items are un-interesting in respect to background knowledge.

Abstract:Different from existing flash-aware buffer replacement policies that focus on the asymmetry of read and write operations, this paper addresses the “discrepancy” of the asymmetry for different flash disks which has existed for a long time. The study proposes an adaptive replacement policy (CBLRU), which assigns to each page a weighted value that combines the IO cost and the influence of pages staying in the buffer. When selecting a victim page, the one with the minimum weighted value will be selected as the victim page, thus, one can avoid the problem of occupying the valid buffer space by dirty pages that are not used for a long time. Moreover, as the cost of read and write operations will be adjusted according to different flash disks, CBLRU can wisely tune itself and adapt to different kinds of flash disks. Finally, a theorem is proposed addressing the stability of the relationship between pages, based on which CBLRU organizes all data pages into two LRU lists: one for clean pages and the other for dirty pages, and then the CPU complexity is reduced form O(klogk) to O(1). The experimental results show that compared with existing buffer-aware replacement algorithms, CBLRU is very efficient when being used for different kinds of flash disks.

Abstract:This paper proposes a idea for mining multiple-level and generalized association rules. First, an item correlation model is set up, based on the domain knowledge and clusters the items according to their correlation. Secondly, the transaction database, based on the item clusters, are reduced which make the transaction database smaller. Finally, the partitioned transaction databases are projected onto a compact structure called AFOPT-tree and find the frequent itemsets from the AFOPT. Based on the proposed idea, this paper proposes a top-down algorithm TD-CBP-MLARM and a bottom-up algorithm BU-CBP-MLARM to mine the multiple-level association rules. Additionally, this paper extends the idea to a generalized mining association rule and gives a new efficient algorithm CBP-GARM. The experiments show that the proposed algorithms not only corrects and completes mining results, but also outperform the well-known and current algorithms in mining effectiveness.

Abstract:This paper proposes a simple and efficient method for creating three-dimensional prototypes based on sketching silhouette curves and super-quadric-surface metaballs. Given a silhouette curve, it is first approximated with a set of circles or ellipses. By introducing the third dimensional parameters, a super-ellipsoidal metaball is received for each circle or ellipse. Finally, an analytic implicit surface is constructed by summing the fields of all metaballs and optimizing all parameters. The resulting shape can be conveniently modified by adjusting the positions and shape parameters of metaballs. Different components of a model can be similarly designed by sketching on the different projection planes. This method supports simple solid modeling operations, such as adding, subtracting, semi-sweeping, and is able to generate variants of shapes easily. This method can be applied to designing prototype in the conceptual design stage for computer graphics or computer aided design applications.

Abstract:This paper presents a physically based simulation algorithm for animating viscous fluid. This algorithm introduces an equivalent energy model to couple diffusion processes and projection processes into a single linear system for solving together. This model implicitly solves the viscous term while simultaneously solving pressure to guarantee incompressibility of fluid. Furthermore it automatically captures the vital zero-traction boundary conditions, eliminating artifacts caused by directly approximating this boundary condition. Furthermore, this paper utilizes the physical information taken by particles to solve the advection term for battling numerical dissipation and constructs an implicit surface of fluid based on particles. Finally, the test results show the efficiency, accuracy and stability of the algorithm, and it can nicely simulate deformation characteristics of various viscous fluids, efficiently supporting variable viscosity.

Abstract:The multi-scale orientation (MSO) features for pedestrian detection in still images are put forwarded in this paper. Extracted on randomly sampled square image blocks (units), MSO features are made up of coarse and fine features, which are calculated with a unit gradient and the Gabor wavelet magnitudes respectively. Greedy methods are employed respectively to select the features. Furthermore, the selected features are inputted into a cascade classifier with Adaboost and SVM for classification. In addition, the spatial location of MSO units can be shifted, are used to the handle multi-view problem and assembled; therefore, the occluded features are completed with average features of training positives, given an occlusion model, which enable the proposed approach to work in crowd scenes. Experimental results on INRIA testset and SDL multi-view testset report the state-of-arts results on INRIA include it is 12.4 times the faster than SVM+HOG method.

Abstract:The current NURBS system is unable to design a B-spline minimal surface effectively which is required for engineering. This paper extends the Dirchlet approach, constructing Bézier minimal surface to the design of B-spline minimal surfaces successfully. The study also proposes a model of B-spline surface which interpolates its control net at the boundary, applying the derivative formulae and cutting-angle evaluation algorithms of B-spline basis. This approach transforms the problem of computing internal control points of the minimal surface to solving a system of linear equations, avoiding the bewilderment brought by a strong nonlinear problem and advancing operational efficiency greatly. Finally, with a large number of examples, the theory and algorithms are verified.