Jianming Yong

A survey on bio-inspired algorithms for web service composition

Web service composition has become a promising technology in a variety of e-science or e-business areas. There are a variety of models and methods to deal with this issue from different aspects. Bio-inspired algorithms are becoming main approaches and solutions. This paper reviews the current researches on web service composition based on bio-inspired algorithms, such as Ant Colony Optimization (ACO), Genetic Algorithm(GA), Evolutionary Algorithm (EA) and Particle Swarm Optimization(PSO). By analyzing and investigating different approaches, this paper gives an overview about the researches on bio-inspired algorithm in web service composition and point out future directions.

Semantic access control for cloud computing based on e-Healthcare

With the increased development of cloud computing, access control policies have become an important issue in the security filed of cloud computing. Semantic web is the extension of current Web which aims at automation, integration and reuse of data among different web applications such as clouding computing. However, Semantic web applications pose some new requirements for security mechanisms especially in the access control models. In this paper, we analyse existing access control methods and present a semantic based access control model which considers semantic relations among different entities in cloud computing environment. We have enriched the research for semantic web technology with role-based access control that is able to be applied in the field of medical information system or e-Healthcare system. This work demonstrates how the semantic web technology provides efficient solutions for the management of complex and distributed data in heterogeneous systems, and it can be used in the medical information systems as well.

A trust degree based access control in grid environments

The purpose of grid computing is to enable coordinated resource sharing and support cooperative work between different domains in dynamic grid environments. In order to protect each participants privilege and security, a secure and efficient access control is essential. This paper presents a new approach of access mechanism based on trust relationships across domains. A new calculation method of trust in grid is proposed and the difference between intro-domain trust and inter-domain trust is analyzed. In addition, a novel access control framework combined with trust degree is given from this proposal. It is shown to be adaptive for both intro-domain and inter-domain conditions. Hence, a prototype system based on the proposed model is introduced; furthermore, it has been shown as a dynamic and fine-granularity access control method through performance analyses and has also been demonstrated as a suitable system for grid environments.

Sentiment analysis on tweets for social events

Sentiment analysis or opinion mining is an important type of text analysis that aims to support decision making by extracting and analyzing opinion oriented text, identifying positive and negative opinions, and measuring how positively or negatively an entity (i.e., people, organization, event, location, product, topic, etc.) is regarded. As more and more users express their political and religious views on Twitter, tweets become valuable sources of peoples opinions. Tweets data can be efficiently used to infer peoples opinions for marketing or social studies. This paper proposes a Tweets Sentiment Analysis Model (TSAM) that can spot the societal interest and general peoples opinions in regard to a social event. In this paper, Australian federal election 2010 event was taken as an example for sentiment analysis experiments. We are primarily interested in the sentiment of the specific political candidates, i.e., two primary minister candidates - Julia Gillard and Tony Abbot. Our experimental results demonstrate the effectiveness of the system.

On Secure Wireless Communications for IoT Under Eavesdropper Collusion

Wireless communication is one of the key technologies that actualize the Internet of Things (IoT) concept into the real world. Understanding the security performance of wireless communications lays the foundation for the security management of IoT. Eavesdropper collusion represents a significant threat to wireless communication security, while physical-layer security serves as a promising approach to providing a strong form of security guarantee. This paper studies the important secrecy outage performance of wireless communications under eavesdropper collusion, where the physical layer security is adopted to counteract such attack. Based on the classical Probability Theory, we first conduct analysis on the secrecy outage of the simple noncolluding case in which eavesdroppers do not collude and operate independently. For the secrecy outage analysis of the more hazardous M-colluding scenario, where any M eavesdroppers can combine their observations to decode the message, the techniques of Laplace transform, keyhole contour integral, and Cauchy Integral Theorem are jointly adopted to work around the highly cumbersome multifold convolution problem involved in such analysis, such that the related signal-to-interference ratio modeling for all colluding eavesdroppers can be conducted and thus the corresponding secrecy outage probability can be analytically determined. Finally, simulation and numerical results are provided to illustrate our theoretical achievements. An interesting observation suggests that the SOP increases first superlinearly and then sublinearly with M.

Using automated individual white-list to protect web digital identities

The theft attacks of web digital identities, e.g., phishing, and pharming, could result in severe loss to users and vendors, and even hold users back from using online services, e-business services, especially. In this paper, we propose an approach, referred to as automated individual white-list (AIWL), to protect users web digital identities. AIWL leverages a Naive Bayesian classifier to automatically maintain an individual white-list of a user. If the user tries to submit his or her account information to a web site that does not match the white-list, AIWL will alert the user of the possible attack. Furthermore, AIWL keeps track of the features of login pages (e.g., IP addresses, document object model (DOM) paths of input widgets) in the individual white-list. By checking the legitimacy of these features, AIWL can efficiently defend users against hard attacks, especially pharming, and even dynamic pharming. Our experimental results and user studies show that AIWL is an efficient tool for protecting web digital identities.

Effective Pruning for the Discovery of Conditional Functional Dependencies

Conditional functional dependencies (CFDs) have been proposed as a new type of semantic rules extended from traditional functional dependencies. They have shown great potential for detecting and repairing inconsistent data. Constant CFDs are 100% confidence association rules. The theoretical search space for the minimal set of CFDs is the set of minimal generators and their closures in data. This search space has been used in the currently most efficient constant CFD discovery algorithm. In this paper, we propose pruning criteria to further prune the theoretic search space, and design a fast algorithm for constant CFD discovery. We evaluate the proposed algorithm on a number of media to large real-world data sets. The proposed algorithm is faster than the currently most efficient constant CFD discovery algorithm, and has linear time performance in the size of a data set.

DeDu: Building a deduplication storage system over cloud computing

This paper presents a deduplication storage system over cloud computing. Our deduplication storage system consists of two major components, a front-end deduplication application and Hadoop Distributed File System. Hadoop Distributed File System is common back-end distribution file system, which is used with a Hadoop database. We use Hadoop Distributed File System to build up a mass storage system and use a Hadoop database to build up a fast indexing system. With the deduplication applications, a scalable and parallel deduplicated cloud storage system can be effectively built up. We further use VMware to generate a simulated cloud environment. The simulation results demonstrate that our deduplication cloud storage system is more efficient than traditional deduplication approaches.

A novel approach to data deduplication over the engineering-oriented cloud systems

This paper presents a duplication-less storage system over the engineering-oriented cloud computing platforms. Our deduplication storage system, which manages data and duplication over the cloud system, consists of two major components, a front-end deduplication application and a mass storage system as back-end. Hadoop distributed file system HDFS is a common distribution file system on the cloud, which is used with Hadoop database HBase. We use HDFS to build up a mass storage system and employ HBase to build up a fast indexing system. With a deduplication application, a scalable and parallel deduplicated cloud storage system can be effectively built up. We further use VMware to generate a simulated cloud environment. The simulation results demonstrate that our deduplication storage system is sufficiently accurate and efficient for distributed and cooperative data intensive engineering applications.

Workflow-based e-learning platform

E-learning has become one of most important means for the future education, especially for universities. This paper is based on our e-learning teaching experience which comes from one of the leading e-learning university, the University of Southern Queensland. We use the workflow mechanism to analyse the e-learning system. The whole e-learning system is divided into four sub-workflow systems, teaching sub-workflow, learning sub-workflow, admin sub-workflow and infrastructure sub-workflow. Through a coherent analyse of co-relationship of main activities in four sub-workflow systems, some activities are identified as the key elements for the e-learning system. By enhancing these key elements, the performance of all the e-learning workflow gets a significant improvement and e-learning students get better grades than on-campus and traditional distance students. The workflow-based e-learning system can effectively reach the expected achievement of e-learning.