An Active Organisation System for Customised,Secure Agent Discovery

The area of software agents has experienced an exponential growth during the past decade, and is now being given a further boost by the introduction of global distributed computing services, such as Globus and Legion. The focus of the research has gradually shifted from single agent architectures to multi-agent systems and agent societies. In a Grid computing environment, agents should be able to discover efficiently other agents based on the computational services they offer or their characteristics (agent discovery). Existing systems either ignore this issue or use simplistic organisation models, which act as passive yellow pages thus keeping the discovery process separate from the computation. In this paper it is argued that the agent discovery can be coupled with several aspects of the computation such as access control and customisation resulting in a better sharing, use and management of the information held by the agent discovery system. A novel architecture is presented in which discovery messages and discovery paths are mutable, active entities, which interact with each other as peers making the organisation system dynamic in nature. Queries traversing the system can be reformulated while at the same time the system itself can change depending on the nature and volume of the query traffic. Furthermore it is shown that the organisation nodes of the proposed architecture can serve as re-usable components for building more complex, composite nodes from existing ones.     

End-user adoption of animated interface agentsin everyday work applications

Recognizing the potential contribution that interactive software agents bring to everyday work applications, this paper reports on end-user adoption of animated interface agents in one particular work application environment: Microsoft® Office. The paper develops and empirically tests a theoretical model of the factors affecting an end-user's choice to adopt and utilize such interface agents. From this theoretical model, a survey instrument was adapted and administered to 261 participants, familiar with animated interface agents. Results from a partial least squares (PLS) analysis indicates that a variety of factors are at play, which inhibit or foster a person's choice to utilize and adopt animated interface agents. Of significance is that: (a) both perceived usefulness and perceived enjoyment are important influencing factors; (b) users with high scores in innovativeness toward information technology are less likely to find animated interface agents enjoyable; (c) individuals with high animation predisposition scores perceive animated interface agents to be more enjoyable; and (d) users who perceive animated interface agents to be more enjoyable also perceive them to be more useful. Such insights can be used to leverage the introduction and rollout of animated interface agents in everyday work applications in ways that promote their avid adoption and use.     

A NON-PARAMETRIC APPROACH TO SIMPLICITY CLUSTERING

The simplicity principle—an updating of Ockham's razor to take into account modern information theory—states that the preferred theory for a set of data is the one that allows for the most efficient encoding of the data. We consider this in the context of classification, or clustering, as a data reduction technique that helps describe a set of objects by dividing the objects into groups. The simplicity model we present favors clusters such that the similarity of the items in the clusters is maximal, while the similarity of items between clusters is minimal. Several novel features of our clustering criterion make it especially appropriate for clustering of data derived from, psychological procedures (e.g., similarity ratings): It is non-parametric, and may be applied in situations where the metric axioms are violated without requiring (information-forgetting) transformation procedures. We illustrate the use of the criterion with a selection of data sets. A distinctive aspect of this research is that it motivates a clustering algorithm from psychological principles.     

Ontology-based sentiment analysis of twitter posts

The emergence of Web 2.0 has drastically altered the way users perceive the Internet, by improving information sharing, collaboration and interoperability. Micro-blogging is one of the most popular Web 2.0 applications and related services, like Twitter, have evolved into a practical means for sharing opinions on almost all aspects of everyday life. Consequently, micro-blogging web sites have since become rich data sources for opinion mining and sentiment analysis. Towards this direction, text-based sentiment classifiers often prove inefficient, since tweets typically do not consist of representative and syntactically consistent words, due to the imposed character limit. This paper proposes the deployment of original ontology-based techniques towards a more efficient sentiment analysis of Twitter posts. The novelty of the proposed approach is that posts are not simply characterized by a sentiment score, as is the case with machine learning-based classifiers, but instead receive a sentiment grade for each distinct notion in the post. Overall, our proposed architecture results in a more detailed analysis of post opinions regarding a specific topic.     

Lost in translation? An actor-network approach to HRIS implementation

Available evidence suggests that the adoption of IT-enabled Human Resource Information Systems (HRIS) has not produced the widely predicted transformation of Human Resources (HR) to a strategic business partner. We examine the relationship between HRIS and the HR function by applying actor-network theory (ANT) to an HRIS implementation project. The focus on how actor networks are formed and reformed during implementation may be particularly well suited to explaining why the original aims of the HRIS can be displaced or lost in translation. We suggest that the approach afforded by ANT enables us to better understand the ongoing and contingent process of HRIS implementations.     

Optimal band selection from hyperspectral data for Lantana camara discrimination

The primary objective of this research was to determine the optimal hyperspectral wavelengths based on spectroscopy data over the spectral range of 450–2500 nm for the detection of the invasive species Lantana camara L. (lantana) from seven of its co-occurring species. A procedure based on statistical analysis of the reflectance and the first derivative reflectance (FDR) identified 86 and 18 bands, respectively, where lantana significantly differed from its co-occurring species. The effectiveness of the identified optimal bands was then evaluated using Hyperion imagery. The original Hyperion image with 155 bands gave an overall accuracy of 80% compared to 77% and 76% from the 86- and 18-band spectral subsets, respectively. A pairwise comparison of the three error matrices showed no significant difference in the accuracy achieved. The FDR analysis combined with the statistical analysis proved to be a useful procedure for data reduction by refining the discrimination to fewer optimal bands for lantana detection with no adverse impact on classification accuracy.     

Investigation of direct load management scheme with consideration of end‐user comfort

A direct load management scheme with two‐way communication, with consideration of end‐user comfort, is proposed. First, the control algorithm is developed and the data required to be transmitted between system operator and controllable loads are identified. Then, the actions of controllable air conditioners and the power adjustment of dispersed generators to eliminate overloads in a substation transformer are simulated. The results indicate that the proposed load management technique performs effectively to reduce overloads on the transformer while maintaining energy consumption in each load, and the performance is improved by coordination with the output of dispersed generators. © 2011 Wiley Periodicals, Inc. Electr Eng Jpn, 176(3): 19–28, 2011; Published online in Wiley Online Library ( wileyonlinelibrary.com ). DOI 10.1002/eej.21139     

RuleML representation and simulation of Fuzzy Cognitive Maps

Fuzzy Cognitive Map (FCM) technique is a combination of Fuzzy Logic and Artificial Neural Networks that is extensively used by experts and scientists of a diversity of disciplines, for strategic planning, decision making and predictions. A standardized representation of FCMs accompanied by a system that would assist decision makers to simulate their own developed Fuzzy Cognitive Maps would be highly appreciated by them, and would help the dissemination of FCMs. In this paper, (a) a RuleML representation of FCM is proposed and (b) a system is designed and implemented in Prolog programming language to assist experts to simulate their own FCMs. This system returns results in valid RuleML syntax, making them readily available to other cooperative systems. The representation capabilities and the design choices of the implemented system are discussed and a variety of examples are given to demonstrate the use of the system.