12630564
Develop a Computational Trust Prototype for the Semantic Web
Nowadays, more and more research has focused on the trust management of the Semantic Web. Some mature technologies from many fields such as multiagent systems, social network analysis and artificial intelligence have been introduced to this field. However, some unique characteristics of the Semantic Web are more or less ignored. In this paper, we try to explore the semantic aspects of the trust management. We present DartTrust, a trust prototype for the semantic web in the application context of Traditional Chinese Medicine (TCM). We introduce RCSW - a Reputation-Chain trust model for the Semantic Web, which incorporates pairwise trust factor and reliable factor and constructs an edge-weighted graph to calculate trust ratings. We define a small trust ontology to illustrate the trust relationships. The trust ontology is described in OWL that can be understood and processed by machines. Therefore, DartTrust can provide reasoning functions and facilitate trust management with little human effort. As a demonstration of the prototype, we present its implementation on Traditional Chinese Medicine (TCM) at the end of this paper.
45332140
Semantic Web Development for Traditional Chinese Medicine
The present invention relates to an improved tool grinding mechanism for grinding single point diamond cutting tools to precise roundness and radius specifications. The present invention utilizes a tool holder which is longitudinally displaced with respect to the remainder of the grinding system due to contact of the tool with the grinding surface with this displacement being monitored so that any variation in the grinding of the cutting surface such as caused by crystal orientation or tool thickness may be compensated for during the grinding operation to assure the attainment of the desired cutting tool face specifications.
18767986
Applying Hypothetical Queries to E-Commerce Systems to Support Reservation and Personal Preferences
Online travel continues to be one of the most successful e-commerce categories on the Internet. As increasing number of people book online, existing e-ticket systems can not satisfy customers' growing demand. Customers complain about most of the current e-ticket systems for the lack of "reservation" functionality. They also complain that these systems are not "intelligent" enough as a traditional travel agent who can deal with more complicated personal preferences. In this paper, we propose a transaction model for e-commerce system to support reservation functionality while balancing the benefits and risks of both customers and airlines. We introduce the theory of hypothetical query to avoid unnecessary transactions and rollbacks on underlying database. Hypothetical queries can be generated automatically and transformed into relational algebra queries which can be optimized with conventional techniques. We extend the lazy approach for evaluating hypothetical queries with integrity constraints to deal with customers' personal preferences. We also provide the "watch" and "remind" mechanism to help customers to purchase their most desirable tickets. Although we have chosen ticket booking system as our target application, the framework presented in this paper is also applicable to other e-commerce systems.
16665207
RCCtrust: A Combined Trust Model for Electronic Community
Previous trust models are mainly focused on reputational mechanism based on explicit trust ratings. However, the large amount of user-generated content and community context published on Web is often ignored. Without enough information, there are several problems with previous trust models: first, they cannot determine in which field one user trusts in another, so many models assume that trust exists in all fields. Second some models are not able to delineate the variation of trust scales, therefore they regard each user trusts all his friends to the same extent. Third, since these models only focus on explicit trust ratings, so the trust matrix is very sparse. To solve these problems, we present RCCtrust – a trust model which combines Reputation-, Content- and Context-based mechanisms to provide more accurate, fine-grained and efficient trust management for the electronic community. We extract trust-related information from user-generated content and community context from Web to extend reputation-based trust models. We introduce role-based and behavior-based reasoning functionalities to infer users' interests and category-specific trust relationships. Following the study in sociology, RCCtrust exploits similarities between pairs of users to depict differentiated trust scales. The experimental results show that RCCtrust outperforms pure user similarity method and linear decay trust-aware technique in both accuracy and coverage for a Recommender System.
38299104
A Computational Trust Model for Semantic Web Based on Bayesian Decision Theory
Enabling trust to ensure more effective and efficient agent interaction is at the heart of the Semantic Web vision. We propose a computational trust model based on Bayesian decision theory in this paper. Our trust model combines a variety of sources of information to assist users with making correct decision in choosing the appropriate providers according to their preferences that expressed by prior information and utility function, and takes three types of costs (operational, opportunity and service charges) into account during trust evaluating. Our approach gives trust a strict probabilistic interpretation and lays solid foundation for trust evaluating on the Semantic Web.
14869838
A Social Network-Based Trust Model for the Semantic Web
Trust is very essential to secure and high quality interactions on the Semantic Web. In this paper, we introduce a trust model, which incorporates pairwise trust ratings and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust values. We interpret trust in two dimensions to better deal with it. We apply the theory in social network field to calculate trust and simulate the trust relationships between humans. We also take advantage of formulas in probability and statistics to analyze the similarities between agents. The algorithm of the trust model aims to be simple, efficient and flexible.
17429523
A Novel Trust-based Cooperative Node Selection Scheme in Wireless Sensor Networks
For communication between the cluster head node (CHN) and the distant data fusion center (DFC) with the cooperation of intra-cluster nodes in wireless sensor networks (WSNs), reliable handling of packets by the cooperative node (CN) is difficult to ensure. In this paper, we propose a novel CN selection scheme based on trust for WSNs. This scheme can choose one of high trust nodes to assist in the transmission of CHN, which aims to improve the transmission performance over cooperative communication in the presence of potential malicious nodes. First, we establish a trust model to improve the reliability of transmission by CN in WSNs, which is based on both the direct trust and the indirect trust of nodes. Then the CHN selects a node based on the proposed trust model to aid the CHN in transmission. Simulation results show that the proposed scheme can mitigate the impact of the potential malicious nodes on transmission performance and the simulation results also demonstrate the efficiency of the proposed scheme.
1039556
Indexing Noncrashing Failures: A Dynamic Program Slicing-Based Approach
Recent software systems usually feature an automated failure reporting component, with which a huge number of failures are collected from software end-users. With a proper support of failure indexing, which identifies failures due to the same fault, the collected failure data can help developers prioritize failure diagnosis, among other utilities of the failure data. Since crashing failures can be effectively indexed by program crashing venues, current practice has seen great success in prioritizing crashing failures. A recent study of bug characteristics indicates that as excellent memory checking tools are widely adopted, semantic bugs and the resulting noncrashing failures have become dominant. Unfortunately, the problem of how to index non-crashing failures has not been seriously studied before. In previous study, two techniques have been proposed to index noncrashing failures, and they are T-Proximity and R-Proximity. However, as T-Proximity indexes failures by the profile of the entire execution, it is generally not effective because most information in the profile is fault-irrelevant. On the other hand, although R-Proximity is more effective than T-Proximity, it relies on a sufficient number of correct executions that may not be available in practice. In this paper, we propose a dynamic slicing-based approach, which does not require any correct executions, and is comparably effective as R-Proximity. A detailed case study with gzip is reported, which clearly strates the advantages of the proposed approach.
27052355
HFSM: Hierarchical Facial Shape Modeling Algorithm for Realistic Facial Expression Animation
We propose a hierarchical 3D facial modeling technique for realistic facial expression animation from the anatomical specification. We analyze the real human face to construct the facial model with a hierarchical biomechanical structure, incorporating a physically-based approximation to facial skin tissue, a set of anatomically-motivated facial muscle actuators and underlying skull structure. The deformable skin model has multi-layer structure to approximate different types of soft tissue. It takes into account the nonlinear stress-strain relationship of the skin and the fact that soft tissue is almost incompressible. Different kinds of muscle models have been developed to simulate the distribution of the muscle force exerted to the skin due to muscle contraction. The 3D face model incorporates a skull structure which extends the scope of facial motion and facilitates facial muscle construction. The facial muscle construction is achieved by a muscle mapping approach which efficiently locates facial muscles at the anatomically correct positions between the skin and skull layers. To generate facial expressions, the resulting hierarchical facial model is animated by solving the governing dynamic equation. For computational efficiency, we use a localized, semi-implicit integration method in the numerical simulation. The dynamic facial animation algorithm runs at an interactive rate and synthesizes various realistic facial expressions.
7343144
A New Recovery Escalation Algorithm with Load Balancing and Backup Resources Sharing in Path Protected WDM Optical Networks
This paper proposes a new survivable algorithm, enhanced shared-path protection (ESPP), to tolerate multi-link failures in WDM optical networks. In ESPP, we consider the load balancing to reduce the blocking probability, use resources sharing to save backup resources, and perform recovery escalation to carry the affected traffic. Compared with the conventional algorithm, ESPP has better resource utilization ratio, lower blocking probability and higher protection ability. Simulation results are shown to be promising.
8620934
Certifying Concurrent Programs Using Transactional Memory
Transactional memory (TM) is a new promising concurrency-control mechanism that can avoid many of the pitfalls of the traditional lock-based techniques. TM systems handle data races between threads automatically so that programmers do not have to reason about the interaction of threads manually. TM provides a programming model that may make the development of multi-threaded programs easier. Much work has been done to explore the various implementation strategies of TM systems and to achieve better performance, but little has been done on how to formally reason about programs using TM and how to make sure that such reasoning is sound. In this paper, we focus on the semantics of transactional memory and present a proof-carrying code (PCC) system for reasoning about programs using TM. We formalize our reasoning with respect to the TM semantics, prove its soundness, and use examples to demonstrate its effectiveness.