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The 9th ACIS/IEEE International Conference on Computer Science and Information Science, held in Kaminoyama, Japan on August 18-20 is aimed at bringing together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. This publication captures 18 of the conference's most promising papers, and we impatiently await the important contributions that we know these authors will bring to the ?eld. In chapter 1, Taewan Gu et al. propose a method of software reliability estimation based on IEEE Std. 1633 which is adaptive in the face of frequent changes to software requirements, and show why the adaptive approach is necessary when software requirements are changed frequently through a case study. In chapter 2, Keisuke Matsuno et al. investigate the capacity of incremental learning in chaotic neural networks, varying both the refractory parameter and the learning parameter with network size. This approach is investigated through simulations, which ?nd that capacity can be increased in greater than direct proportion to size. In chapter 3, Hongwei Zeng and Huaikou Miao extend the classical labeled transition system models to make both abstraction and compositional reasoning applicable to deadlock detection for parallel composition of components, and propose a compositional abstraction re?nement approach.
Résumé
The 9th ACIS/IEEE International Conference on Computer Science and Information Science, held in Kaminoyama, Japan on August 18-20 is aimed at bringing together researchers and scientists, businessmen and entrepreneurs, teachers and students to discuss the numerous fields of computer science, and to share ideas and information in a meaningful way. This publication captures 18 of the conference’s most promising papers, and we impatiently await the important contributions that we know these authors will bring to the ?eld. In chapter 1, Taewan Gu et al. propose a method of software reliability estimation based on IEEE Std. 1633 which is adaptive in the face of frequent changes to software requirements, and show why the adaptive approach is necessary when software requirements are changed frequently through a case study. In chapter 2, Keisuke Matsuno et al. investigate the capacity of incremental learning in chaotic neural networks, varying both the refractory parameter and the learning parameter with network size. This approach is investigated through simulations, which ?nd that capacity can be increased in greater than direct proportion to size. In chapter 3, Hongwei Zeng and Huaikou Miao extend the classical labeled transition system models to make both abstraction and compositional reasoning applicable to deadlock detection for parallel composition of components, and propose a compositional abstraction re?nement approach.
