Bibliometric Indicators for Assessing the Quality of Scholarly Communications: A Case Study on International Journal of Cooperative Information Systems

This paper analyses various bibliometric dimensions of the journal literature such as authors’ productivity, geographical distribution, citation pattern, institution-wise distribution of articles, discipline-wise distributions of articles, productive institutions, Productivity Index (PI), Activity Index (AI), Domestic Collaborative Index (DCI) and International Collaborative Index (ICI) etc. It also explores the applicability of Lotka’s Inverse Square Law and Zipf’s Law to examine the observed rank – frequency pattern of Keywords and Subject Terms of Information Systems (IS) literature. To illustrate these bibliometric indicators pertinent information on the field of Information Systems (IS) collected from EBSCO database for the International Journal of Cooperative Information Systems (IJCIS). Results indicated that a high level of collaboration exists among the authors, USA occupies the dominant position in terms of high productive authors, institutions and tops the list with highest number of domestic collaboration. Authors’ productivity confirms to Lotka’s law and the Frequency distribution of both Subject Terms and Keywords in IJCIS journal literature follow Zipf’s distribution

A logical deduction based clause learning algorithm for Boolean satisfiability problems

Clause learning is the key component of modern SAT solvers, while conflict analysis based on the implication graph is the mainstream technology to generate the learnt clauses. Whenever a clause in the clause database is falsified by the current variable assignments, the SAT solver will try to analyze the reason by using different cuts (i.e., the Unique Implication Points) on the implication graph. Those schemes reflect only the conflict on the current search subspace, does not reflect the inherent conflict directly involved in the rest space. In this paper, we propose a new advanced clause learning algorithm based on the conflict analysis and the logical deduction, which reconstructs a linear logical deduction by analyzing the relationship of different decision variables between the backjumping level and the current decision level. The logical deduction result is then added into the clause database as a newly learnt clause. The resulting implementation in Minisat improves the state-of-the-art performance in SAT solving

Critical infrastructure systems : security analysis and modelling approach

A system security analysis and system modelling framework tool is proposed adopting an associated conceptual methodology as the basis for assessing security and conceptually modelling a critical infrastructure system incident. The intent is to identify potential system security issues and gain operational insights that will contribute to improving system resilience, contingency planning, disaster recovery and ameliorating incident management responses for critical infrastructure system incidents. The aforementioned system security analysis and modelling framework is applied to an adverse critical infrastructure system incident case study. This paper reports on the practical application of the framework to a case study of an actual critical infrastructure system failure and the resultant incident implications for the system and the wider regional communities.<br /

Motivational Social Visualizations for Personalized E-Learning

A large number of educational resources is now available on the Web to support both regular classroom learning and online learning. However, the abundance of available content produces at least two problems: how to help students find the most appropriate resources, and how to engage them into using these resources and benefiting from them. Personalized and social learning have been suggested as potential methods for addressing these problems. Our work presented in this paper attempts to combine the ideas of personalized and social learning. We introduce Progressor + , an innovative Web-based interface that helps students find the most relevant resources in a large collection of self-assessment questions and programming examples. We also present the results of a classroom study of the Progressor +  in an undergraduate class. The data revealed the motivational impact of the personalized social guidance provided by the system in the target context. The interface encouraged students to explore more educational resources and motivated them to do some work ahead of the course schedule. The increase in diversity of explored content resulted in improving students’ problem solving success. A deeper analysis of the social guidance mechanism revealed that it is based on the leading behavior of the strong students, who discovered the most relevant resources and created trails for weaker students to follow. The study results also demonstrate that students were more engaged with the system: they spent more time in working with self-assessment questions and annotated examples, attempted more questions, and achieved higher success rates in answering them

Fairs for e-commerce: the benefits of aggregating buyers and sellers

In recent years, many new and interesting models of successful online business have been developed. Many of these are based on the competition between users, such as online auctions, where the product price is not fixed and tends to rise. Other models, including group-buying, are based on cooperation between users, characterized by a dynamic price of the product that tends to go down. There is not yet a business model in which both sellers and buyers are grouped in order to negotiate on a specific product or service. The present study investigates a new extension of the group-buying model, called fair, which allows aggregation of demand and supply for price optimization, in a cooperative manner. Additionally, our system also aggregates products and destinations for shipping optimization. We introduced the following new relevant input parameters in order to implement a double-side aggregation: (a) price-quantity curves provided by the seller; (b) waiting time, that is, the longer buyers wait, the greater discount they get; (c) payment time, which determines if the buyer pays before, during or after receiving the product; (d) the distance between the place where products are available and the place of shipment, provided in advance by the buyer or dynamically suggested by the system. To analyze the proposed model we implemented a system prototype and a simulator that allow to study effects of changing some input parameters. We analyzed the dynamic price model in fairs having one single seller and a combination of selected sellers. The results are very encouraging and motivate further investigation on this topic

Immune network algorithm in monthly streamflow prediction at Johor river

This study proposes an alternative method in generating future stream flow data with single-point river stage. Prediction of stream flow data is important in water resources engineering for planning and design purposes in order to estimate long term forecasting. This paper utilizes Artificial Immune System (AIS) in modelling the stream flow of one stations of Johor River. AIS has the abilities of self-organizing, memory, recognition, adaptive and ability of learning inspired from the immune system. Immune Network Algorithm is part of the three main algorithm in AIS. The model of Immune Network Algorithm used in this study is aiNet. The training process in aiNet is partly inspired by clonal selection principle and the other part uses antibody interactions for removing redundancy and finding data patterns. Like any other traditional statistical and stochastic techniques, results from this study, exhibit that, Immune Network Algorithm is capable of producing future stream flow data at monthly duration with various advantages