Discovering novel knowledge using granule mining

This paper presents an extended granule mining based methodology, to effectively describe the relationships between granules not only by traditional support and confidence, but by diversity and condition diversity as well. Diversity measures how diverse of a granule associated with the other granules, it provides a kind of novel knowledge in databases. We also provide an algorithm to implement the proposed methodology. The experiments conducted to characterize a real network traffic data collection show that the proposed concepts and algorithm are promising.

Design science research and the core of information systems

Design Science Research (DSR) has emerged as an important approach in Information Systems (IS) research, evidenced by the plethora of recent related articles in recognized IS outlets. Nonetheless, discussion continues on the value of DSR for IS and how to conduct strong DSR, with further discussion necessary to better position DSR as a mature and stable research paradigm appropriate for IS. This paper contributes to address this need, by providing a comprehensive conceptual and argumentative positioning of DSR relative to the core of IS. This paper seeks to argue the relevance of DSR as a paradigm that addresses the core of IS discipline well. Here we use the framework defined by Wand and Weber, to position what the core of IS is.

Interface design in interactive science courseware for the Malaysian Smart School Project

With the goal of improving the academic performance of primary and secondary students in Malaysia by 2020, the Malaysian Ministry of Education has made a significant investment in developing a Smart School Project. The aim of this project is to introduce interactive courseware into primary and secondary schools across Malaysia. As has been the case around the world, interactive courseware is regarded as a tool to motivate students to learn meaningfully and enhance learning experiences. Through an initial pilot phase, the Malaysian government has commissioned the development of interactive courseware by a number of developers and has rolled this courseware out to selected schools over the past 12 years. However, Ministry reports and several independent researchers have concluded that its uptake has been limited, and that much of the courseware has not been used effectively in schools. This has been attributed to weaknesses in the interface design of the courseware, which, it has been argued, fails to accommodate the needs of students and teachers. Taking the Smart School Project's science courseware as a sample, this research project has investigated the extent, nature, and reasons for the problems that have arisen. In particular, it has focused on examining the quality and effectivity of the interface design in facilitating interaction and supporting learning experiences. The analysis has been conducted empirically, by first comparing the interface design principles, characteristics and components of the existing courseware against best practice, as described in the international literature, as well as against the government guidelines provided to the developers. An ethnographic study was then undertaken to observe how the courseware is used and received in the classroom, and to investigate the stakeholders' (school principal, teachers and students') perceptions of its usability and effectivity. Finally, to understand how issues may have arisen, a review of the development process has been undertaken and it has been compared to development methods recommended in the literature, as well as the guidelines provided to the developers. The outcomes of the project include an empirical evaluation of the quality of the interface design of the Smart School Project's science courseware; the identification of other issues that have affected its uptake; an evaluation of the development process and, out of this, an extended set of principles to guide the design and development of future Smart School Project courseware to ensure that it accommodates the various stakeholders' needs.

Discovering relevant features for effective query formulation

The quality of discovered features in relevance feedback (RF) is the key issue for effective search query. Most existing feedback methods do not carefully address the issue of selecting features for noise reduction. As a result, extracted noisy features can easily contribute to undesirable effectiveness. In this paper, we propose a novel feature extraction method for query formulation. This method first extract term association patterns in RF as knowledge for feature extraction. Negative RF is then used to improve the quality of the discovered knowledge. A novel information filtering (IF) model is developed to evaluate the proposed method. The experimental results conducted on Reuters Corpus Volume 1 and TREC topics confirm that the proposed model achieved encouraging performance compared to state-of-the-art IF models.

Quantum key distribution in the classical authenticated key exchange framework

Key establishment is a crucial primitive for building secure channels in a multi-party setting. Without quantum mechanics, key establishment can only be done under the assumption that some computational problem is hard. Since digital communication can be easily eavesdropped and recorded, it is important to consider the secrecy of information anticipating future algorithmic and computational discoveries which could break the secrecy of past keys, violating the secrecy of the confidential channel. Quantum key distribution (QKD) can be used generate secret keys that are secure against any future algorithmic or computational improvements. QKD protocols still require authentication of classical communication, although existing security proofs of QKD typically assume idealized authentication. It is generally considered folklore that QKD when used with computationally secure authentication is still secure against an unbounded adversary, provided the adversary did not break the authentication during the run of the protocol. We describe a security model for quantum key distribution extending classical authenticated key exchange (AKE) security models. Using our model, we characterize the long-term security of the BB84 QKD protocol with computationally secure authentication against an eventually unbounded adversary. By basing our model on traditional AKE models, we can more readily compare the relative merits of various forms of QKD and existing classical AKE protocols. This comparison illustrates in which types of adversarial environments different quantum and classical key agreement protocols can be secure.

A parallel cooperative co-evolutionary genetic algorithm for the composite SaaS placement problem in Cloud computing

A composite SaaS (Software as a Service) is a software that is comprised of several software components and data components. The composite SaaS placement problem is to determine where each of the components should be deployed in a cloud computing environment such that the performance of the composite SaaS is optimal. From the computational point of view, the composite SaaS placement problem is a large-scale combinatorial optimization problem. Thus, an Iterative Cooperative Co-evolutionary Genetic Algorithm (ICCGA) was proposed. The ICCGA can find reasonable quality of solutions. However, its computation time is noticeably slow. Aiming at improving the computation time, we propose an unsynchronized Parallel Cooperative Co-evolutionary Genetic Algorithm (PCCGA) in this paper. Experimental results have shown that the PCCGA not only has quicker computation time, but also generates better quality of solutions than the ICCGA.

Operational semantics of aspects in business process management

Aspect orientation is an important approach to address complexity of cross-cutting concerns in Information Systems. This approach encapsulates these concerns separately and compose them to the main module when needed. Although there a different works which shows how this separation should be performed in process models, the composition of them is an open area. In this paper, we demonstrate the semantics of a service which enables this composition. The result can also be used as a blueprint to implement the service to support aspect orientation in Business Process Management area.

Eliciting Expert Knowledge in Conservation Science

Expert knowledge is used widely in the science and practice of conservation because of the complexity of problems, relative lack of data, and the imminent nature of many conservation decisions. Expert knowledge is substantive information on a particular topic that is not widely known by others. An expert is someone who holds this knowledge and who is often deferred to in its interpretation. We refer to predictions by experts of what may happen in a particular context as expert judgments. In general, an expert-elicitation approach consists of five steps: deciding how information will be used, determining what to elicit, designing the elicitation process, performing the elicitation, and translating the elicited information into quantitative statements that can be used in a model or directly to make decisions. This last step is known as encoding. Some of the considerations in eliciting expert knowledge include determining how to work with multiple experts and how to combine multiple judgments, minimizing bias in the elicited information, and verifying the accuracy of expert information. We highlight structured elicitation techniques that, if adopted, will improve the accuracy and information content of expert judgment and ensure uncertainty is captured accurately. We suggest four aspects of an expert elicitation exercise be examined to determine its comprehensiveness and effectiveness: study design and context, elicitation design, elicitation method, and elicitation output. Just as the reliability of empirical data depends on the rigor with which it was acquired so too does that of expert knowledge.