ALBERT

Description: Loosely coupled and cross-platform features make Web services accessible and increasingly popular on the Internet. However, efficient service discovery and automated service composition are still challenges under the conventional practice where services are organized into categories. In this paper, we propose a graph-based method to organize Web services into a service ecosystem interlaced with service relationships at the semantic level. First, Web services are modelled as a set of interfaces, whose input and output parameters are annotated with well-defined ontologies. Secondly, semantic associations and interactions between Web services are mined, and services are constructed into a Web services network (SN), a variant of bipartite graph, by projecting the functional aspects of concrete Web services onto the abstract service layer. Thirdly, from the complex network perspective, the services relations are investigated and the structure of SN is analysed. To demonstrate the basic topological properties of SN, an empirical study is conducted on two data sets for comparative purposes, 10 000+ Web services collected from the Internet and 1231 Web services provided by Titan system of Zhejiang University. The experimental results reveal that SNs, which are built by different data sets on the semantic level, exhibit the same features such as small-world and scale-free. In addition, our results yield valuable insight for developing service discovery and automated composition algorithms, and characterizing the evolution of the entire Web service ecosystem.

Description: Multimedia social networks (MSNs) services and tools provide a convenient platform for users to share multimedia contents, such as electronic book, digital image, audio and video, with each other. However, in an open network, uncontrolled sharing and transmission mode of digital content between users create considerable problems regarding digital rights management (DRM). This paper aims to explore potential paths on the propagation of copyrighted contents. An approach to mining credible potential paths is proposed for MSNs. The formal descriptions were primarily based on rough set theory for mining potential paths. Trust was also measured to find credible potential paths. We presented related algorithms for mining two kinds of paths between any two nodes. Finally, we conducted an experiment based on three non-overlapped sharing communities multiplied by 150 nodes. In the communities found by using a representative real-world MSN YouTube dataset, we further mine the general and credible potential paths based on the simulated trust assessment values. The proposed method could effectively and accurately mine two kinds of potential paths of copyrighted digital content distribution and sharing, which can help to resolve critical DRM issues.

Description: In this paper, we study the first-order consensus algorithm in the small-world Farey graph where agents are driven by white noise, aiming to unveil the effect of small-world topology on the robustness of the consensus algorithm. We characterize the coherence of the Farey graph in terms of the $H_2$ -norm of the system, the square of which equals the steady-state variance and thus captures how closely agents track the consensus value. Based on the particular network structure, we derive an exact expression for the coherence in the Farey graph, whose dominant behavior scales logarithmically with the system size. To uncover the role of small-world topology, we also derive an analytical solution for the coherence of first-order consensus in the regular ring lattice sharing the same average degree as the Farey graph, whose dominant term grows linearly with the system size, implying that the small-world structure strongly affects the performance of the consensus algorithm.

Description: In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series. Based on the original SBF, an incremental feature selection algorithm is conducted to form a compact and discriminative feature representation. Finally, a multi-class support vector machine is trained to classify the test time series. Extensive experiments on different time series datasets, including one synthetic control dataset, two sign language datasets and one gait dynamics dataset, have been performed to evaluate the proposed SBF method. Compared with other state-of-the-art methods, our approach achieves superior classification performance, which clearly validates the advantages of the proposed method.

Description: Predicting the propagation path of a network worm is highly beneficial for taking appropriate countermeasures in advance. Traditional worm propagation models mainly deal with the total number of infected hosts during a period of time, which cannot indicate a worm's track. We choose the worm using random scanning to study, as it was the basic type and all the others were derived from it. A novel model proposed in this paper locates the subnets going to be infected at a given time based on the infection measurement of the subnet. The time and frequency for victims in the subnet to increase were calculated according to common characteristics of worm diffusion and the relationship between malicious traffic and bandwidth usage. Taking the two factors above as input, fuzzy reasoning was adopted to deduce the real-time infection situation for each subnet. The bigger the value of infection situation, the more likely the corresponding subnet would be attacked in a short time. Simulation experimental results show that the model estimates the worm's track dynamically with acceptable accuracy. Furthermore, the increase interval of victims in subnet is much longer for worm with slower spread speed, which provides sufficient time to carry out pertinent response.

Description: In this paper, we introduce a general paradigm called identity-based extractable hash proof system (IB-EHPS), which is an extension of extractable hash proof system (EHPS) proposed by Wee (CRYPTO'10). We show how to construct identity-based key encapsulation mechanism (IB-KEM) from IB-EHPS in a simple and modular fashion. Our construction provides a generic method of building and interpreting CCA-secure IB-KEMs based on computational assumptions. As instantiations, we realize IB-EHPS from the bilinear Diffie–Hellman assumption and the modified bilinear Diffie–Hellman assumption, respectively. Besides, we carefully investigate the relation between EHPS and IB-EHPS, and indicate possible refinement and generalization of EHPS.