This special issue comprises of 8 selected papers from the International Workshop on Computer Science and Engineering 2009 (WCSE 2009) & International Symposium on Information Science and Engineering 2009(ISISE 2009). The conferences received 1350 paper submissions from 17 countries and regions, of which 512 were selected for presentation after a rigorous review process. From these 512 research papers, through two rounds of reviewing, the guest editors selected 8 as the best papers on the Computers track of the Conference. The candidates of the Special Issue are all the authors, whose papers have been accepted and presented at the WCSE 2009 and ISISE 2009, with the contents not been published elsewhere before.
   The WCSE 2009 are sponsored by Qingdao University of Science & Technology, China; Peoples’ Friendship University of Russia, Russia; Nanchang HangKong University, China; National Chung Hsing University,Taiwan;Hunan Agricultural University , China; Guangdong University of Business Studies, China;Jiaxing University, China. Technical Co-Sponsors of the conference are IEEE, IEEE Shandong Section, and IEEE Shanghai Section.
   “A Study of Fault Diagnostic based on Artificial Immune Systems”, by WANG Chu-Jiao, XIA Shi-Xiong and WANG Hai-zhi, propose a novel ideal to apply semantics in immune models. Similar to Learning Classifier Systems, a better understanding of AIS can be obtained when they are analyzed under the perspective of complex Semantic system.
  “An Interest Management Architecture by ALM and Region Partition for Large-Scale Distributed Virtual Environment”, by Yaping Lu, Yunjia Wang and Houquan Liu, propose an interest management architecture that supports a large number of users on the Internet by dividing the virtual environment into multiple adjacent hexagonal regions. In the architecture, messaging among entities is based on a multi-server communication infrastructure by the Application Layer Multicasting (ALM). And each region is mapped to an ALM tree with a master node constructing the overlay tree and managing nodes that lie in its region.
  “An Interest Management Architecture by ALM and Region Partition for Large-Scale Distributed Virtual Environment”, by Yaping Lu, Yunjia Wang and Houquan Liu, propose an interest management architecture that supports a large number of users on the Internet by dividing the virtual environment into multiple adjacent hexagonal regions. In the architecture, messaging among entities is based on a multi-server communication infrastructure by the Application Layer Multicasting (ALM). And each region is mapped to an ALM tree with a master node constructing the overlay tree and managing nodes that lie in its region.
   “Study on Aggregation Tree Construction Based on Grid”, by Jian Shu, Yebin Chen, Linlan Liu, Sheng Zhang and Jun Li, proposes an aggregation tree construction method based on grid named ATCBG which makes some improvements over GROUP. The results of simulation experiment show that the average energy consumption of ATCBG is evidently lower than GROUP, and the lifetime of the network is much longer than GROUP before the emergence of node death.
“An Auto-revocation Supported Delegation Model”, by Chunxiao Ye and Xiang Li, proposes an Attribute-Based Delegation Model (ABDM), in which delegatee must satisfy both delegation prerequisite condition (CR) and delegation attribute expression (DAE) when assigned to a delegation role. ABDM introduce some auto revocation mechanics to support two new types of auto revocation: revocation triggered by the change of user’s delegation attribute expression and revocation triggered by the change of delegated permission’s delegation attribute expression, which are different from existing revocations.
   “An Iterative Method of Extracting Chinese ISA Relations for Ontology Learning”, by Lei Liu, Sen Zhang, Luhong Diao and Cungen Cao, proposes an iterative method extracting ISA relations from large Chinese free text for ontology learning. Experimental results demonstrate good performance of the method for extracting ISA relation from large Chinese corpus.
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