The use of correspondence analysis to develop a scale to measure workplace morale from multi-level data

This paper focuses on the issue of comparing social groups or collectivities using measures derived from individual-level multivariate data. In this case, groups need to be differentiated such that: (a) between-group differences are maximized; (b) within-group differences are minimised; and (c) `differences' are calibrated to a scale that reflects a set indicators or observed variables.This paper demonstrates empirically how correspondence analysis can achieve this. It presents a scale of `workplace morale' derived from the responses of employees in a large sample of workplaces to questions concerning satisfaction with various facets of their job and their workplace. The scale derived through correspondence analysis is shown to achieve the three criteria described above.

Information sharing between heterogeneous uncertain reasoning models in a multi-agent environment : a case study

The issue of information sharing and exchanging is one of the most important issues in the areas of artificial intelligence and knowledge-based systems (KBSs), or even in the broader areas of computer and information technology. This paper deals with a special case of this issue by carrying out a case study of information sharing between two well-known heterogeneous uncertain reasoning models: the certainty factor model and the subjective Bayesian method. More precisely, this paper discovers a family of exactly isomorphic transformations between these two uncertain reasoning models. More interestingly, among isomorphic transformation functions in this family, different ones can handle different degrees to which a domain expert is positive or negative when performing such a transformation task. The direct motivation of the investigation lies in a realistic consideration. In the past, expert systems exploited mainly these two models to deal with uncertainties. In other words, a lot of stand-alone expert systems which use the two uncertain reasoning models are available. If there is a reasonable transformation mechanism between these two uncertain reasoning models, we can use the Internet to couple these pre-existing expert systems together so that the integrated systems are able to exchange and share useful information with each other, thereby improving their performance through cooperation. Also, the issue of transformation between heterogeneous uncertain reasoning models is significant in the research area of multi-agent systems because different agents in a multi-agent system could employ different expert systems with heterogeneous uncertain reasonings for their action selections and the information sharing and exchanging is unavoidable between different agents. In addition, we make clear the relationship between the certainty factor model and probability theory.

A logical framework for knowledge sharing in multi-agent systems

The issue of knowledge sharing has been an important topic in multi-agent research. Knowledge sharing leads to that agents analyze, judge and synthesize the told information so as to make agents’ own knowledge. To match these applications, this paper builds a logical framework for knowledge sharing among agents. We develop a multimodal logic for reasoning about both agents’ knowledge and told information. For formalizing the relationship between knowledge and told information, we present a framework of semantics, with respect to which a sound and complete proof theory is given.

A computation trust model with trust network in multi-agent systems

Trust is a fundamental issue in multi-agent systems, especially when they are applied in e-commence. The computational models of trust play an important role in determining who and how to interact in open and dynamic environments. To this end, a computation trust model is proposed in which the confidence information based on direct prior interactions with the target agent and the reputation information from trust network are used. In this way, agents can autonomously deal with deception and identify trustworthy parties in multi-agent systems. The ontological property of trust is also considered in the model. A case study is provided to show the effectiveness of the proposed model.

Ethics guidelines for international multi-centre research involving people with intellectual disabilities

This position statement endorsed by the International Association for the Scientific Study of Intellectual Disabilities is designed to promote and facilitate research projects affecting and involving people with intellectual disabilities. The paucity of dedicated research infrastructure and expert ethical review processes to oversee research in this field, especially in developing countries, is asserted as a major issue to be addressed by both the scientific community and governments. International multicenter collaboration has been proposed as a means of addressing these problems. The statement draws on internationally recognized documents outlining the ethical considerations involved in human research activities. It interprets these documents in light of the particular needs and interests of people with intellectual disabilities and incorporates international consultation involving researchers from a variety of disciplines. It affirms the importance of ethical decision making in local communities. Specific recommendations are made concerning ethical review processes, research design considerations, consent processes and the conduct of research involving and affecting people with intellectual disabilities, their families and communities. Research proposals, especially those for international, multicenter projects, need to take into account cultural diversity among participants and differing legal requirements across jurisdictions, while at the same time maintaining the scientific rigor of the research protocol. Promoting partnerships between researchers and people with intellectual disability, together with their families, advocates and local communities are important considerations when developing research projects. Similarly, the development of strategies to both communicate findings to participants and their communities, and to promote their community's access to the benefits of these findings are all important ethical considerations.

Creating collaborative spaces : the pleasures and perils of doing multi-disciplinary, multi-partner qualitative research

This paper describes our experience of managing a two-year research project that involved University staff from two different disciplines and three industry partners. It describes the benefits we gained from the involvement of multiple parties, such as the ability to call upon diverse expertise, the capacity to study a complex issue and the ability to make a direct contribution to industry practice. It also describes some of the difficulties such as managing across University structures, maintaining the collaborators' interest in the project, gaining approval from multiple ethics committees and managing the expectations of various stakeholders. The paper concludes with a number of recommendations for senior University staff and for researchers and points to ways universities could better facilitate involvement in these types of complex research projects.

Multi-tier phishing email classification with an impact of classifier rescheduling

Protecting user's mailbox from infiltration of phishing email is a significant research issue now a day. Many researches are going on filtering phishing using classification based algorithms and achieve substantial performance. It has been studied and investigated with different classification algorithms and observed that the outputs of the classifiers vary from one another with same corpora. This paper presents the impact of classifier rescheduling of multi-tier classification of phishing email to observe the best scheduling in the classification process. In our method, the features of phishing email will be extracted and classified in a sequential fashion by using the multi-tier classification and the outputs will be sent to the decision fusion process. Empirical evidence proofs that the impact of rescheduling of classifiers among the tiers gives diverse outcomes in terms of accuracy as well as number of false positive instances.

MVGL analyser for multi-classifier based spam filtering system

In the last decade, the rapid growth of the Internet and email, there has been a dramatic growth in spam. Spam is commonly defined as unsolicited email messages and protecting email from the infiltration of spam is an important research issue. Classifications algorithms have been successfully used to filter spam, but with a certain amount of false positive trade-offs, which is unacceptable to users sometimes. This paper presents an approach to overcome the burden of GL (grey list) analyzer as further refinements to our multi-classifier based classification model (Islam, M. and W. Zhou 2007). In this approach, we introduce a ldquomajority voting grey list (MVGL)rdquo analyzing technique which will analyze the generated GL emails by using the majority voting (MV) algorithm. We have presented two different variations of the MV system, one is simple MV (SMV) and other is the ranked MV (RMV). Our empirical evidence proofs the improvements of this approach compared to the existing GL analyzer of multi-classifier based spam filtering process.

Incorporating fairness into development of an integrated multi-agent online dispute resolution environment

The paper describes the development of an integrated multi-agent online dispute resolution environment called IMODRE that was designed to assist parties involved in Australian family law disputes achieve legally fairer negotiated outcomes. The system extends our previous work in developing negotiation support systems Family_Winner and AssetDivider. In this environment one agent uses a Bayesian Belief Network expertly modeled with knowledge of the Australian Family Law domain to advise disputants of their Best Alternatives to Negotiated Agreements. Another agent incorporates the percentage split of marital property into an integrative bargaining process and applies heuristics and game theory to equitably distribute marital property assets and facilitate further trade-offs. We use this system to add greater fairness to Family property law negotiations.

Models of trust in business-to-consumer electronic commerce : a review of multi-disciplinary approaches

The issue of trust in Internet-based business-to-consumer electronic commerce has been explored from a number of difference perspectives. The current body of research is diverse and fragmented. This paper critically reviews recently published models pertaining to trust in business to consumer e-commerce. For analytical purposes we categorize the literature in three main streams: technological, design and sociological/psychological. Based on our analysis and our own empirical observations we raise four main areas of concern that warrant further research attention: an oversimplification of the trust concept, a uni-directional view of trust, discipline centered approaches to modelling trust and a lack of empirical grounding and testing. In the light of these concerns we recommend avenues for further research.

Optimal metric selection for improved multi-pose face recognition with group information

We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ2 norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓp norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets

Special issue : Computational intelligence models for image processing and information reasoning

Computational Intelligence (CI) models comprise robust computing methodologies with a high level of machine learning quotient. CI models, in general, are useful for designing computerized intelligent systems/machines that possess useful characteristics mimicking human behaviors and capabilities in solving complex tasks, e.g., learning, adaptation, and evolution. Examples of some popular CI models include fuzzy systems, artificial neural networks, evolutionary algorithms, multi-agent systems, decision trees, rough set theory, knowledge-based systems, and hybrid of these models. This special issue highlights how different computational intelligence models, coupled with other complementary techniques, can be used to handle problems encountered in image processing and information reasoning.

China and technical global internet governance: Beijing’s approach to multi-stakeholder governance within ICANN, WSIS and the IGF

Since the late 1990s, the Chinese government has engaged in a process of attempting to reform the technical global internet governance regime, which is currently dominated by the US government and non-state actors. This article aims to contribute to the literature on Beijing’s approach to this issue by providing a detailed empirical account of its involvement in a few core regime organisations. It argues that Beijing’s reform approach is guided by its domestically derived preferences for strong state authority and expanding China’s global power, but that its reform efforts are unlikely to succeed based on countervailing structural hard- and soft-power factors.

A novel fuzzy multi-objective framework to construct optimal prediction intervals for wind power forecast

The forecasting behavior of the high volatile and unpredictable wind power energy has always been a challenging issue in the power engineering area. In this regard, this paper proposes a new multi-objective framework based on fuzzy idea to construct optimal prediction intervals (Pis) to forecast wind power generation more sufficiently. The proposed method makes it possible to satisfy both the PI coverage probability (PICP) and PI normalized average width (PINAW), simultaneously. In order to model the stochastic and nonlinear behavior of the wind power samples, the idea of lower upper bound estimation (LUBE) method is used here. Regarding the optimization tool, an improved version of particle swam optimization (PSO) is proposed. In order to see the feasibility and satisfying performance of the proposed method, the practical data of a wind farm in Australia is used as the case study.