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This paper presents a model for semantic network representation. According to the model, the main purpose of a semantic network is to help to correctly and quickly understand the features and structure of the solutions, and then make reliable predictions about their outcomes and rewards. These objectives are achieved under constrains like limited resources (e.g., memory size and access), time limitations, and variations of solution characteristics. The paper summarizes the elements of the model and presents a set of properties that describe the effectiveness of semantic networks in creative problem solving, including design. Six examples (based on three case studies from the related literature and three experimental studies conducted by the authors) discuss the model elements, characteristics, and properties. [ABSTRACT FROM AUTHOR]

Boolean Satisfiability problem (SAT) plays a prominent role in many domains of computer science and artificial intelligence due to its significant importance in both theory and applications. Algorithms for solving SAT problems can be categorized into two main classes: complete algorithms and incomplete algorithms (typically stochastic local search (SLS) algorithms). SLS algorithms are among the most effective for solving uniform random SAT problems, while hybrid algorithms achieved great breakthroughs for solving hard random SAT (HRS) problem recently. However, there is a lack of algorithms that can effectively solve both uniform random SAT and HRS problems. In this paper, a new SLS algorithm named SelectNTS is proposed aiming at solving both uniform random SAT and HRS problem effectively. SelectNTS is essentially an improved probability selection based local search algorithm, the core of which includes new clause and variable selection heuristics: a new clause weighting scheme and a biased random walk strategy are utilized to select a clause, while a new probability selection strategy with the variation of configuration checking strategy is used to select a variable. Extensive experimental results show that SelectNTS outperforms the state-of-the-art random SAT algorithms and hybrid algorithms in solving both uniform random SAT and HRS problems effectively. [ABSTRACT FROM AUTHOR]

Currently, the research in computer science has been exponentially expanded beyond its own fields into the other research fields such as medical science, business, and social science in forms of collaborative researches. This collaborative researches stimulate a new recommending algorithm for determining a potential research collaborator under the interdisciplinary environment. Unlike other research fields, the research problems in computer science can be transformed to other known and solvable problems. In this paper, a new hybrid algorithm based on dynamic collaboration over time was proposed for recommending an appropriate collaborator. Besides considering only three basic factors concerning social proximity, friendship, and complementarity skill as employed by others’, three new additional factors related to research interest, up-to-date publication data, and seniority of researcher are involved in our analysis. A set of new measures for all six recommending factors were proposed. The experiments were conducted with real bibliographic data within six continuous years of publication and over six topics in computer science. Our results were significantly higher than the results of the other methods at 90% confidence level. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we introduce and solve several problems that arise in the single facility location field. A reverse k-influential location problem finds a region such that the location of a new facility, desirable or obnoxious, in the region guarantees a minimum k-influential value associated to the importance, attractiveness or repulsiveness, of the facility as a solution to a reverse k-nearest or farthest neighbor query. Solving reverse k-influential location problems help decision makers to progress towards suitable locations for a new facility. We present a parallel approach, to be ran on a graphics processing unit, for approximately solving reverse k-influential location problems, and also provide and discuss experimental results showing the efficiency and scalability of our approach. [Copyright &y& Elsevier]