Hao Lan Zhang

Topological analysis of AOCD-based agent networks and experimental results

Topological analysis of intelligent agent networks provides crucial information about the structure of agent distribution over a network. Performance analysis of agent network topologies helps multi-agent system developers to understand the impact of topology on system efficiency and effectiveness. Appropriate topology analysis enables the adoption of suitable frameworks for specific multi-agent systems. In this paper, we systematically classify agent network topologies and propose a novel hybrid topology for distributed multi-agent systems. We compare the performance of this topology with two other common agent network topologies-centralised and decentralised topologies-within a new multi-agent framework, called Agent-based Open Connectivity for DSS (AOCD). Three major aspects are studied for estimating topology performance, which include (i) transmission time for a set of requests; (ii) waiting time for processing requests; and (iii) memory consumption for storing agent information. We also conduct a set of AOCD topological experiments to compare the performance of hybrid and centralised agent network topologies and illustrate our experimental results in this paper.

Classification of intelligent agent network topologies and a new topological description language for agent networks

Topological theory of intelligent agent networks provides crucial information about the structure of agent distribution over a network. Agent network topologies not only take agent distribution into consideration but also consider agent mobility and intelligence in a network. Current research in the agent network topology area adopts topological theory from the distributed system and computing network fields without considering mobility and intelligence aspects. Moreover, current agent network topology theory is not systematic and relies on graph-based methodology, which is inefficient in describing large-scale agent networks. In this paper, we systematically classify the agent network topologies and propose a new description language called Topological Description Language for Agent networks (TDLA), which incorporates the mobility and intelligence characteristics in an agent network.

AOCD: A Multi-agent Based Open Architecture for Decision Support Systems

Despite the critical need for an open architecture in decision support system (DSS) design, present DSS architectural design are unable to provide a fundamental solution to enhance the flexibility, connectivity, compatibility, and intelligence of a DSS. In this paper, we present a novel multi-agent-based architecture for DSS, called agent-based open connectivity for decision support systems (AOCD). The AOCD architecture adopts a hybrid agent network topology that makes use of a unique feature called matrix-agent connection. This architecture is able to overcome the difficulties in concurrent control and synchronous communication problems that plague many decentralized systems. Performance analysis has been carried out on this architecture and we find that it is able to provide a high degree of flexibility and efficiency compared with other architectures

An association rule analysis framework for complex physiological and genetic data

Physiological and genetic information has been critical to the successful diagnosis and prognosis of complex diseases. In this paper, we introduce a support-confidence-correlation framework to accurately discover truly meaningful and interesting association rules between complex physiological and genetic data for disease factor analysis, such as type II diabetes (T2DM). We propose a novel Multivariate and Multidimensional Association Rule mining system based on Change Detection (MMARCD). Given a complex data set ui (e.g. u1 numerical data streams, u2 images, u3 videos, u4 DNA/RNA sequences) observed at each time tick t, MMARCD incrementally finds correlations and hidden variables that summarise the key relationships across the entire system. Based upon MMARCD, we are able to construct a correlation network for human diseases.

Performance analysis of network topologies in agent-based open connectivity architecture for DSS

Performance analysis of agent network topologies helps multi-agent system developers to understand the impact of topology on system efficiency and effectiveness. Appropriate topology analysis enables the adoption of suitable frameworks for the specific multi-agent systems. In this paper, we propose a novel hybrid topology for distributed multi-agent systems, and compare the performance of this topology with two other common agent network topologies within the new multi-agent framework, agent-based open connectivity for DSS (AOCD). Three major aspects are studied for estimating topology performance, which include (i) transmission time for a set of requests; (ii) waiting time for processing requests; and (iii) memory consumption for storing agent information.

Topological sorts on DAGs

In this article, we will study the topological sorts of two directed acyclic graphs (DAGs) and the associated properties. More specifically, we will study under what conditions a certain single (or some, or every) topological sort(s) of a DAG can be extended into the topological sort(s) of another DAG. We first give the necessary and sufficient conditions for the problems. We then indicate that these problems are solvable either in linear time cost or in the same time cost as to compute the transitive closure. Indicates when two DAGs have a shared topological sort.Indicate when a given topological sort of a DAG can be extended into another DAG.Indicate when every topological sorts of a DAG can be extended into another DAG.Provide the time complexity of the above.

A Novel Method in Extracranial Removal of Brain MR Images

Abstract The removal of extracranial elements from brain M R images provides doctors a reliable method to analyze and diagnose dynamic data in E-Health. In traditional segmentation, experts are required to identify every element of the whole image, subjectivity and large amount of works make the mission uncertain and intolerant. GVF Snake model leads to efficiently dealing with the segmentation by giving an initial contour around the final object, but manual participation is still inevitable. This paper proposes an automatic morphology -based algorithm to generate the initial contour for active contour model to implement the removal accurately. To achieve the removal, initial contours will be produced by the original contour generator, which are utilized to approach more precise contours implemented by improved GVF Snake model. After a self-correction among the elements, segmentation missions are done. Experimental result shows that with simple steps and little time, the proposed algorithm can complete the segmentation task successfully, and is of good robustness as well as high accuracy.

Detecting cyberbullying in social networks using multi-agent system

State-of-the-art studies on cyberbullying detection, using text classification, predominantly take it for granted that streaming text can be completely labelled. However, the rapid growth of unlabelled data generated in real time from online content renders this virtually impossible. In this paper, we propose a session-based framework for automatic detection of cyberbullying within the large volume of unlabelled streaming text. Given that the streaming data from Social Networks arrives in large volume at the server system, we incorporate an ensemble of one-class classifiers in the session-based framework. System uses Multi-Agent distributed environment to process streaming data from multiple social network sources. The proposed strategy tackles real world situations, where only a few positive instances of cyberbullying are available for initial training. Our main contribution in this paper is to automatically detect cyberbullying in real world situations, where labelled data is not readily available. Initial results indicate the suggested approach is reasonably effective for detecting cyberbullying automatically on social networks. The experiments indicate that the ensemble learner outperforms the single window and fixed window approaches, while the learning process is based on positive and unlabelled data only, no negative data is available for training.

Online action recognition by template matching

Human action recognition from video has attracted great attentions from various communities due to its wide applications. Regarded as an effective way to analyze human movements, human skeleton is extracted and represents human body as dots and lines, Recently, depth-cameras make skeleton tracking become practical. Based on the extraction and template matching, we develop a system for online human action segmentation and recognition in this paper. We proposed a method to generate action templates that can be used to represent intra-class variations. We then adopted efficient subsequence matching algorithm for online process. The experimental results demonstrated the effectiveness and efficiency of our system.

An Optimised Design for Agent Capability Reuse

In spite of the rising demands for reusable information systems, current designs are still insufficient in providing efficient reusable mechanisms for system design. One of the major problems hindering the development of information reuse in most traditional systems, which include component-based systems and object-oriented systems, is the lack of the self-organising ability among the system components or subsystems. The emergence of intelligent agent-based technology is able to solve the problems plaguing many traditional systems. In this paper we introduce an optimised design for agent-based systems, which is able to provide an efficient process for agent capability reuse. An experimental program is developed to evaluate the performance of the proposed design.