Shaowen Yao

A Review on Computational Trust Models for Multi-agent Systems

Trust plays important roles on effective interaction and cooperation for multi-agent systems(MAS). This study aims at finding out the current situation and future trends of computational trustfor multi-agent systems. Through defining seven common compositional elements for the computational trust models, the study points out significant weaknesses in the current design. Finally, the paper figures out the future research trends through discussion and analysis around the strengths and weaknesses identified. Also the paper proposes an idea of using ontology and XML technologies such as RDF that allow systems to provide both human and machine readable annotations for trust models.

A Banking Based Grid Recourse Allocation Scheduling

One of major bottlenecks in grid computing is grid resource allocation. There are many existing ways to solve the problem economical models are effective approaches to help manage and evaluate the resource allocation. Inspired by the banking marketing theory (BMT) comes a new way to study grid resources allocate. The key issues for meeting the requirement of BMT is try to find a best scheduling algorithm to deliver great value in the grid resource allocation. In this paper, the researcher found that the cost based scheduling algorithm is possible method to do that job. The essentially problem is pricing all the available resources in the transaction and maximum all participants based on the dynamic cost functions.

An overview of topic modeling and its current applications in bioinformatics

BackgroundWith the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, so-called topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics.DescriptionThis paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications.ConclusionTopic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers’ ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.

Combine Elliptic Curve Cryptography with Digital Watermark for OWL-Based Ontology Encryption

Although progress has been made on Semantic Web during the past decades, security is still one of the major challenges in the development of Semantic Web. Elliptic curve cryptography (ECC) serves as an excellent candidate because of its small key size and high security protection. Digital watermark is the practice of hiding a message within that work itself. In this paper, a novel method is proposed to combine elliptic curve cryptography with digital watermark for OWL-based ontology encryption.

Investigating Workflow Resource Patterns in term of Pi-calculus

Workflow resource patterns focus on the various ways in which resources are represented and utilized in workflows. This paper uses Pi-calculus to model workflow resource patterns. The main goal is to explore expressive capabilities of Pi-calculus regarding business process and resource. The formalizations can be used as a foundation for pattern-based workflow system as well as a basis for future research on workflow-related patterns.

A Quantifiable Trust Model for Multi-agent System Based on Equal Relations

The paper first analyzes trust relations, combining the characteristic of multi-agent system, investigates the equal-relations based trust mechanism for multi- agent system, and proposes a possible trust model to quantify and manage the reliability of agent in multi- agent system through building trust database and network of agent. Simulation experiment show that the model is able to promptly evaluate and update trust relation among agents, and improve the reliability and efficiency of interaction between agents, providing a solution for the trust problem of multi-agent system.

On the (In)Security of Recent Group Key Distribution Protocols

A typical stateful (resp. stateless) group key distribution (GKD) protocol is composed of a secret assignment algorithm, and stateful join/leave rekeying algorithms (resp. a stateless group rekeying algorithm). Any design flaw in any of these algorithms could lead to attacks on GKD protocols. We show how two recently-proposed stateful GKD protocols based on asymmetric cryptographic primitives suffer from collusion attacks due to security flaws in either secret assignment algorithms or leave rekeying algorithms. A variety of single-user attacks and improvements on stateless group rekeying algorithms of a number of GKD protocols based on Shamir’s Secret-Sharing Scheme (SSS) have been put forward. We show the stateless group rekeying algorithms of one improved protocol and its variant (proposed by us) still suffer from attacks. In addition, we prove a lower bound on the size of a user’s long-term secret for perfectly secure multi-session stateless GKD protocols. This bound reveals that (i) it is impossible to design an infinite-session stateless GKD protocol that is both perfectly secure and practical; (ii) all the considered SSS-based stateless GKD protocols are bound to be either incorrect or vulnerable to attacks. This work highlights the urgent necessity of adopting the provable security approach in this research field.

The QoS resource quantification based on the grid banking model

Grid applications must provide exceptional quality of service but because of the complexity and diversity of grid environment, the quality of service (QoS) can not be guaranteed. In order to solve the problem of quality of service in grid, QoS mechanisms must be introduced, that is, the QoS parameters of the problem description. At present, there are a lot of QoS researches, but very little research related to quantitative QoS, and even fewer with the principles of economics to analyze the problem. In this paper, a QoS-based banking model grid is introduced to make use of the hierarchical idea, logic resources QoS, system QoS, security QoS, trust QoS in each level, analyzing QoS attributes and proposing measurement of the five categories of QoS. Finally, according to the measurement results, Service Level Agreement (SLA) is added so that the SLA becomes the basis for exchanges between the parties. In this way, it can be widely used in evaluation, selection and optimization on grid QoS control strategy.

Predicting protein function via multi-label supervised topic model on gene ontology

ABSTRACT As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions.

The Framework of Infrared Video Mining Based on Topic Model

We proposed a framework of infrared video mining based on topic model. It aims to learn motion patterns for a crowded and complicated infrared scene. After video preprocessing, motion features are extracted from each pair of consecutive frames at first, and quantized into visual words. Motion pattern are modeled as distributions over visual words in topic model. Experiments about BOVW demonstrate the feasibility of the framework.