13102622
D-S Theory-based Trust Model FIRE^+ in Multi-agent Systems
In open multi-agent systems (MAS), interaction between agents is uncertain. Trust plays an important role in deciding with whom to interact and how to interact with it. Current trust models have solved the problem of how to quantify, deduce and evaluate trust, but there are still challenges. This paper presents FIRE+ trust model using utility loss of issue to evaluate trust values. With the representation of matrix, it efficiently increases the rating information. Also by introducing D-S theory and WMA, it can handle uncertain information and dishonest witness finely. Meanwhile, our approach introduces the concept of information-amount to derive each issue 's weight so as to gain the whole trust value of the target agent. It also takes witness reliability into account. Experiments show that FIRE+ is more effective than others.
43513684
A representation theorem for recovering contraction relations satisfying wci
A notion of an image structure associated with the canonical epistemic state is introduced. Based on it, we get a representation result for recovering contraction inference relations satisfying the condition weak conjunctive inclusion (wci) in terms of F-standard epistemic AGM states. In effect, this result establishes a representation theorem for belief contraction functions satisfying AGM postulates (k-1) - (k-7), and Rott's (wci) and (k-8c), and hence generalizes Rott's corresponding result in the finite framework.
15542808
An Agent-based Policy Aware Framework for Web Services Security
The security of Web services has become one of the important research topics about the application of Web services. In this paper, we propose an agent-based policy aware framework for Web services security. In this framework, a policy language called ReiT which is a declarative language based on the rules and ontologies is introduced. The non-structural knowledge is represented by rules and the structural temporal knowledge is represented by ontology. Moreover, we propose a mixed reasoning mechanism to evaluate the ReiT policy. The access control policy including the context of the user and Web services is evaluated by the reasoner. In addition, we present a policy aware BOID agent to authorize the access control of the Web services. And we implement the policy aware BOID agent by extending the JADE platform.
2517026
On Dynamic and Concurrent Model of Web Service Components
The researches of Web service fasten on composition process, and are lack of the formalized description on dynamic attributes of service component itself. Aiming to these, a new dynamic and concurrent model of Web service component is presented. Firstly, this paper analyzes the process of state transition of service component under certain actions, and then depicts the dynamic transition process of service component with the state transition graph. At last, the physical representation method in memory and the algorithms to judge equivalence of state transition graph are given. Comparing with existing researches, the dynamic evolvement of service component under the actions is considered, and the algorithm to judge equivalence of service components provides an effective tool for model verification.
8899940
Sketch retrieval based on spatial relations
In this paper, we propose an effective approach for sketch retrieval. Given sketches are firstly decomposed into basic geometric primitives and represented as a topological graph that encodes both the intrinsic attributes of the primitives and their relationships. The graph spectrum descriptor is then adopted to translate the graph-match into the computation of vector distances. Sketches retrieval is finally done by means of similarity calculation of feature vectors based on dimensionality reduction. The relevance feedback is also introduced to refine the retrieval results. Experiments prove the effectiveness and efficiency of the proposed method in sketch content retrieval and user independent.
16775013
A Dynamic Trust Model for the Multi-agent Systems
Internet provides a fertile medium for new breeds of computer viruses. Many people who have access to a wealth of information via Internet are attacked by more computer viruses than they can effectively process. We present a dynamic computer virus detection model that can detect known viruses and previously unknown viruses to prevent information systems from damage. This model is inspired by biological immune systems that protect the body against damage from pathogens. The architecture of this model, the formal definitions of self nonself antigen, antibody, and vaccine gene library are introduced. Furthermore, the evolution of self and nonself, the generation of the antibody, the evolution of the virus vaccine gene, and the detection of the antigen are depicted. Experiment results show that this model has better capacity of self-adaptability and self-learning in detecting unknown viruses than traditional models.
29928622
An event-state approach to handling temporal queries in an extended relational database system
Many database applications such as office information systems, computer aided design and manufacturing require temporal support. The requirement for storing all the historical information has resulted in performance and storage problems. This paper presents and illustrates an event-state approach to effectively and efficiently store and retrieve temporal data. This model demonstrates that by using the events that have occurred, facilities can be constructed that would permit the processes of 'rolling-back' to a historical state and/or 'rolling-forward' to the then current state. The proposed temporal features have been incorporated in a relational database system. Processing efficiency and temporal query capabilities have been illustrated and discussed.<<ETX>>
15262548
Multifunctional Mobile Units with a same platform for in-pipe inspection robots
Robots are efficient and economic for in-pipe inspection tasks. In this paper, we deal with versatility and multifunction of in-pipe inspection robots. A versatile platform equipped with one driving motor is the main body of multifunctional mobile unit (MMU) that performs inspection tasks. We have developed three kinds of MMU by fixing different assemblies on the proposed versatile platform. MMU1 is an adaptive mobile mechanism that changes its working mode autonomously and gets over the obstacle without any extra motorpsilas help, in the case that the pipe diameter changes. MMU2 is a mechanism that has both driving ability and detecting function; MMU3 is equipped with assemblies that integrate the wheel and propeller function together, thus, MMU3 is able to propel both in dry pipe and watery pipe. These MMUs are all constructed from a same platform, which makes little increase of the cost but realizes of more kinds of robots. Several basic experiments have been conducted to confirm the effectiveness and mobility of the MMUs.
3459442
Steganalysis of YASS
A promising steganographic method-yet another steganography scheme (YASS)-was designed to resist blind steganalysis via embedding data in randomized locations. In addition to a concrete realization which is named the YASS algorithm in this paper, a few strategies were proposed to work with the YASS algorithm in order to enhance the data embedding rate and security. In this work, the YASS algorithm and these strategies, together referred to as YASS, have been analyzed from a warden's perspective. It is observed that the embedding locations chosen by YASS are not randomized enough and the YASS embedding scheme causes detectable artifacts. We present a steganalytic method to attack the YASS algorithm, which is facilitated by a specifically selected steganalytic observation domain (SO-domain), a term to define the domain from which steganalytic features are extracted. The proposed SO-domain is not exactly, but partially accesses, the domain where the YASS algorithm embeds data. Statistical features generated from the SO-domain have demonstrated high effectiveness in detecting the YASS algorithm and identifying some embedding parameters. In addition, we discuss how to defeat the above-mentioned strategies of YASS and demonstrate a countermeasure to a new case in which the randomness of the embedding locations is enhanced. The success of detecting YASS by the proposed method indicates a properly selected SO-domain is beneficial for steganalysis and confirms that the embedding locations are of great importance in designing a secure steganographic scheme.
11982779
A Wireless Intrusion Detection Method Based on Dynamic Growing Neural Network
In this paper an intrusion detection method based on dynamic growing neural network (DGNN) for wireless networking is presented. DGNN is based on the Hebbian learning rule and adds new neurons under certain conditions. When DGNN performs supervised learning, resonance will happen if the winner can't match the training example; this rule combines the ART/ARTMAP neural network and WTA learning rule. When DGNN performs unsupervised learning, post-prune is carried out to prevent overfitting the training data just like decision tree learning. The intrusion detection method is an anomaly detection method and the feature is selected from the packets. In the experiments, we first check the ability of the neural network and then use it to perform detection in a WLAN. The results show that it can detect new intrusion behavior and some improving methods are presented in the conclusions
5644766
Transfer learning for collaborative filtering via a rating-matrix generative model
Cross-domain collaborative filtering solves the sparsity problem by transferring rating knowledge across multiple domains. In this paper, we propose a rating-matrix generative model (RMGM) for effective cross-domain collaborative filtering. We first show that the relatedness across multiple rating matrices can be established by finding a shared implicit cluster-level rating matrix, which is next extended to a cluster-level rating model. Consequently, a rating matrix of any related task can be viewed as drawing a set of users and items from a user-item joint mixture model as well as drawing the corresponding ratings from the cluster-level rating model. The combination of these two models gives the RMGM, which can be used to fill the missing ratings for both existing and new users. A major advantage of RMGM is that it can share the knowledge by pooling the rating data from multiple tasks even when the users and items of these tasks do not overlap. We evaluate the RMGM empirically on three real-world collaborative filtering data sets to show that RMGM can outperform the individual models trained separately.