Abstract:Biological network alignment is an important approach in the study of organisms’ structure, function and evolution. In this review, the recent studies in the field of biological network alignment are surveyed. First, a definition of biological network alignment is defined formally. Secondly, the methods for alignment are reviewed and described into three categories according to their mathematical properties. Some models and algorithms in every category are analyzed comprehensively and comparably. Next, tools for alignment are listed and analyzed. In the end, some applications and key problems in the field are highlighted, as well as the future progress of biological network alignment.

Abstract:In this paper, an orthomodular lattice-valued pushdown automaton (e-VPDA) is introduced. This paper also provides the means of general subset-construction, and further proves the fact that an e-VPDA can accept the same l-valued language by final states and by another e-VPDA, with crisp transition relation and quantum final states at the same time. By using these relations, this paper is able to establish some algebraic level characterizations of orthomodular lattice-valued context-free languages and also focuses on the closed properties of these l-valued languages in details under standard operative conditions. Finally, this paper presents that an arbitrary orthomodular lattice-valued context-free grammar (e-VCFG) are mutually equivalently constructed with a e-VPDA, respectively.

Abstract:In this paper, the prediction model based on Bayesian is constructed for presenting cause and effect relationships between software structure features and software flexibility by consulting the experiential data and expert knowledge. In addition, the Bayesian networks learning technique is extended to find out the weak causal relationships in the software flexibility prediction model constructing. Finally the method and examples using the prediction model to assess software flexibility in the software architecture level are presented.

Abstract:As the size and complexity of mission-critical application software continues to grow, the cost of Software Testing (ST) is also increasing. The numerous methods and processes used to evaluate ST dynamically and quantitatively to improve testing efficiency serve as practice problems in the ST field. Based on multi-dimensional test coverage models, this paper proposes a dynamic evaluation method for ST and discusses it from the viewpoint of testing monitoring information, dynamic analysis and evaluation models, and testing optimized strategy. Furthermore, a concept, Synthetic Test Coverage (STC), is defined in this paper, and its empirical formulas are also presented. Examples show that the methods are useful in helping ST evaluation groups to track and control the effects of ST and for improving the user’s ability to observe and control the ST process.

Abstract:An idea of identifying unstable genes by integrative analysis of pair of different data has been proposed. This problem is modelled as a nonlinear integer programming problem. Three approximate methods have been proposed to work out the solution. An index is designed to measure and rank the instability magnitude of unstable gene. The experimental results on two lung cancer datasets from two research groups demonstrate the identified unstable genes have really unstable expression. The identified unstable genes can be used to improve the result of identifying differential expression genes and excluding these genes can effectively enhance the accuracy of microarray data classification. The findings suggest the proposed methods are effective and the unstable genes can provide valuable information for microarray data analysis.

Abstract:A period detection method called MPD(memory-constrain period detection) is proposed naively on a time series stream, where the Haar-wavelet synopsis of series stream is adopted, and an estimated period based on partial fragments is proposed to improve the detection efficiency, and the cubic spline is used to detect period of arbitrary length. The time and space complexity error bound of MPD are validated through theoretical and experimental analysis.

Abstract:This paper proposes a novel algorithm, named EMicro, to cluster uncertain data streams. Although most of the works used today mainly use the distance metric to describe the cluster quality, EMicro considers distance metric and data uncertainty together to measure the clustering quality. Another contribution of this paper is the outlier processing mechanism. Two buffers are maintained to reserve normal micro-clusters and potential outlier micro-clusters, respectively, to obtain good performance. Experimental results show that EMicro outperforms existing methods in efficiency and effectiveness.

Abstract:This paper proposes a novel adaptive model for Web image semantic automatic annotation. First, the model automatically collects training image data by exploring the associated textual data and the social tagging data of Web images, such as the Flickr’s Related Tags. Then, using a newly constrained piecewise penalty weighted regression to combine the adaptive estimation of the weight distribution of associated texts and the prior knowledge constrain together and implement the Web image semantic annotation. The proposed training data auto-generation methods and Web image annotation approaches are tested on a real-world Web image data set and promising results are achieved.

Abstract:An image segmentation approach based on immune spectral clustering algorithm, is proposed, in which the dimension reduction ability of the spectral clustering is used to attain the distribution of data in the mapping space. Next, a new immune clonal clustering algorithm is proposed to cluster the sample points in the mapping space. Compact input with low-dimension for immune clonal clustering is obtained after spectral mapping, and the immune clonal clustering algorithm, characterized by its rapid convergence to global optimum and minimal sensitivity to initialization, can obtain good clustering results. To efficiently apply the algorithm to image segmentation, Nystr?m method is used to reduce the computation complexity. Experimental results on synthetic texture images and SAR images show the validity of the algorithm in image segmentation.

Abstract:Texture images have abnormal, microscopic characteristics, but some parts of the image maintain statistical regularity from a macroscopic point of view. In order to capture these characteristics that improve image segmentation results, a new wavelet-based, multi-scale Bayesian texture image segmentation method, based on EHMM-HMT (enhanced hidden Markov model-hidden Markov tree) and MSWHMT (multi-states weighted hidden Markov tree) modes, is proposed. The image blocks’ relative interactions are described through the EHMM model effectively, and the homogenous raw segmentation, propitious to final fusion, is obtained on the coarsest scale. Subsequently, in order to reduce mislabeling the boundaries of raw segmentation and to decrease the computing complexity of the model, the MSWHMT model is proposed with better raw segmentations of high accurate boundary detection put on finer scales. Finally, a pixel level segmentation is reached through a multi-scale Bayesian fusing strategy that combines with the boundaries. The method is compared to HMTseg, HMT (boundar based+MAP), and EHMM-HMT (MAP) algorithm through several micro-texture images to demonstrate its competitive performance. It has also been found to improve the accuracy of macro-texture image segmentations.

Abstract:In this paper, a technique called Time-Varying Texel (TVT) is presented for simulating the withering effects of grassland. Time-Varying Texel is defined as an extension of the traditional texel structure by adding a time dimension, where the texel at different time is computed by using a Texel Deformation Technique. In this approach, a point based structure (PBS) is employed in combination with the Time-Varying Texel. Throuogh the introduction of PBS as a bridge, a Physical-biological Model can be applied to handling the deformation at the grass texel. In addition, a mingling algorithm is applied to enhance the diversity of grassland, and the LOD representation of TVT is developed for promoting efficiency. Experimental results show that the realistic grass time-varying simulation is well achieved with the proposed techniques.

Abstract:Image retargeting techniques aim to adapt images to the target screens with different sizes and aspect ratios. Unfortunately, image retargeting for mobile device screens suffers from the problems of limited display area and various aspect ratios. This has been one of the hot topics in multimedia research. This paper proposes a novel image retargeting approach based on region relation graph. First, energy map and sensitivity energy maps are calculated by visual attention and weighted gradient, respectively. Then, the original image is decomposed into curve-edge trapezoid meshes, further represented by region relation graph. Based on the region relation graph, image retargeting is formulated as a quadratic programming problem constrained by energy maps, which emphasizes the important image parts and reduces the visual distortion by optimally relocating the key mesh vertexes. Finally, the target image is generated based on the optimal solution. Experimental results show the effective and efficiency of the proposed approach.

Abstract:This article proposes a multivariate network visualization paradigm, MulNetVisBasc. Advanced Start Coordinates (ASC) are employed to place nodes on the basis of multivariate attributes and to devise an algorithm that that incorporates edge-merging and routing techniques to automatically lay-out edges; furthermore, a user-friendly human-computer interface is developed to assist users in further data analysis and mining. The experimental results suggest that the visualization of MulNetVisBasc not only uncovers the multivariate distributional characteristics of datasets intuitively, but also displays the associations of networks clearly and is helpful in discovering the implicit knowledge hidden behind datasets. The edge layout algorithm reduces the visual clutters caused by edge crossing and is suitable for relatively huge multivariate network datasets in virtue of its low complexity. Finally, the human-computer interface is flexible and convenient.

Abstract:Covert channel is the communication channel that allows a process to transfer information in a manner that violates the system’s security policy. It is a major threat to the secure information systems and widely exists in secure operation systems, secure networks and secure database. Covert channel analysis is generally required by secure information systems’s secure criterion, such as TCSEC. This paper firstly analysis the covert channel concept, field, techniques and classification. Next, it surveys the classic techniques and methods from the following aspects: covert channel identification, measurement, elimination, limitation, auditing, and detection. The research achievements in the past 30 years are systematically concluded, especially the new techniques of covert channel measurements and handlings in recent years. This paper attempts to give a comprehensive and clear outline for this research direction, and provides a useful reference for the researchers of this field.

Abstract:In multi-hop wireless sensor networks that contain a high density of nodes, precise data gathering makes nodes that are close to the sink incur a heavier workload, which depletes their energy faster and can easily cause a “hot spot” that would shorten the network lifetime. The problem of constructing a tree that has a maximum lifespan is NP-complete. An algorithm called MAXLAT can be used to solve this problem without the need for the location of nodes. MAXLAT starts from a tree whose root has the largest number of children. The nodes in the tree are classified into three subsets that go accordingly to their respective loads: bottleneck nodes, sub-bottleneck nodes, and rich nodes. Next, the MAXLAT continues to transfer descendants of high-load nodes to sub-trees of low-load nodes by coloring. When MAXLAT is terminated, it constructs a tree in which “bottleneck nodes” carry a lighter load. Simulation results show that the tree achieved by MAXLAT has a longer lifetime than trees created by previous algorithms.

Abstract:This paper proposes a relay-connecting coverage problem in heterogeneous wireless sensor networks. The aim is to find a minimum relay-connecting set cover (MRCSC) that satisfies the following: 1) Active nodes in a set cover fully sense the task area. Motivated by triangular lattice placement with asymptotic priority, a rule is designed to restrict abnormal spreading and to form an approximate triangular lattice; 2) Active nodes are relay-connecting, which means any active node connects at least one super node with a given success data forwarding rate. Relay-connecting prevents the interference of nodes that have low success data forwarding rates that are caused by the long path nodes take to the sink and radio channel. Theoretical analysis simulations show that the coverage of MRCSC nearly reaches that of OGCD, but relay-connectivity of active nodes is strongly reinforced with a limited number of additional nodes.

Abstract:A geographical-aware, integrated topology control, MAC, and routing data delivery protocol, called geographic forwarding protocol with reliable and energy-efficient (REEGF), is proposed to satisfy these requirements for multi-hop WSNs application. In the REEGF protocol, by using a network node with collaborative communications architecture of a dual radio, idle listening time is reduced through sending or listening to a busy tone in a wakeup channel. When adopting time synchronization algorithm of WSNs and probability-based synchronization scheduling algorithm relying on node density and residual energy, network node, in the monitoring state, is awakend synchronistically with a certain probability during any network listening cycle, so idle listening time of redundancy node is reduced and so consistency and stability of the local node’s degree of connectivity can be guaranteed. When network node transitions to a more active transfer state, while adopting the contention among receivers and selecting next-hop relaying node, which is a neighbor node toward the destination SINK with the most advancement based on the geographical location, REEGF protocol can integrate Medium Access Control (MAC), routing and topology management into a single layer, save the resources of each node in WSNs, balance the energy consumption of the nodes throughout the network, and guarantee timeliness of data delivery. The results of the theory analyzing and simulations show that REEGF outperforms GeRaF for monitoring and surveillance applications, in terms of the energy efficiency, delay of average multihop data delivery, and the uniformity of network node’s energy consumption. Accordingly, the network lifetime is prolonged considerably and increased approximately linear with node’s density.

Abstract:This paper first proposes a new information slicing and transmitting method ITNC (information slicing and transmitting with multi-path network coding) with multi-paths network coding. Next, a novel anonymous communication mechanism AC-ITNC (anonymous communication mechanism based on ITNC) without key infrastructure, which is based on ITNC, is presented. In the new mechanism, the anonymous path setup information is sliced into pieces, and every piece is coded by the random coding coefficient. The coding coefficient and coded information pieces are delivered along different paths, which make the anonymous paths be set up in the case of non-cryptographic scheme. Theoretical analysis and simulation results show that AC-ITNC can significantly improve resistance against conspiracy attacks in an anonymous communication system within complete distributed environment without key infrastructure.

Abstract:An intrusion-resilient signature scheme with provable security is presented in this paper. The scheme has a stronger security than a forward secure signature scheme and a key-insulated signature scheme. It satisfies that signatures in other time periods are secure even after arbitrarily many compromises of base and signer, as long as the compromises do not happen simultaneously. Furthermore, the intruder cannot generate signatures pertaining to previous time periods, even if she compromises base and signer simultaneously to get their secret information. The scheme has a nice average performance. There is no cost parameter for key generation time, base (user) key updating time, base (user) key refresh time, signing time, verifying time, signature size, public key size, and base (user) storage size having a complexity more than O(logT) in this scheme. At last, this scheme is proven secure in the random oracle model, assuming CDH problem is hard.

Abstract:This paper presents a multi-cycle vulnerability discovery model which shows the vulnerability discovery process and the relationship between the number of vulnerabilities and their release time. The model makes use of a cycle, which expands the scope of old models. A method is proposed to compute the parameters of this model to fit the discover process of the target software. Different rules are also given to find the initial values. Experiments are made on eight Windows operating systems. The results show that this model is more effective and more accurate than current models.

Abstract:Network attack graphs are widely used as templates to extrapolate network security state by analyzing observed intrusion evidence. Existing attack graph node belief computation methods are suffering from generality problems, high computational complexity, or the overuse of empirical formulas to solve problems. This paper improves one of the Bayesian network inference algorithms—the likelihood weighting algorithm into a novel graph node belief computation algorithm, which supports the temporal partial ordering relationship among intrusion evidences. Experiment results show that the method can achieve high computation accuracy in linear computational complexity, a feature making it feasible to be used to process large scale attack graphs in real-time.

Abstract:Based on IP path information, by analyzing a general model of AS border, the concept of AS border sequence is introduced, and a series of AS border judging rules that discover the AS border division law hidden behind IP path information are proposed. Therefore, a method on judging AS borders named JBR (judging border by rules) is put forward. The experiment results show that JBR’s judging time is shorter than the method named JBA (judging border by alias), which is based on alias resolution, and it has advantage on judging both border addresses and border links.