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This paper presents a different scheme of meeting scheduling negotiation among a large number of personnel in a heterogeneous community. This scheme, named Host-to-Host Negotiation, attempts to produce a stable schedule under uncertain personnel preferences. By collecting information from hosts’ inter organizational meeting, this study intends to guarantee personnel availability. As a consequence, personnel’s and meeting’s profile in this scheme are stored in a centralized manner. This study considers personnel preferences by adapting the Clarke Tax Mechanism, which is categorized as a non manipulated mechanism design. Finally, this paper introduces negotiation strategies based on the conflict handling mode. A host-to-host scheme can give notification if any conflict exist and lead to negotiation process with acceptable disclosed information. Nevertheless, a complete negotiation process will be more elaborated in the future works.

Since abstract of scientific paper is author biased, readers’ required information may not be included in the abstract. Tailored summary may help them to get a summary based on their information needs. This research is the first one that implements tailored summary system for scientific paper. Tailored summary applies information extraction that transforms a scientific paper into Rhetorical Document Profile, a structured representation of paper content based on rhetorical scheme of fifteen slots. This research adapted building plan that used rhetorical scheme of seven slots. We also implement tailored summary system. After generating initial summary, surface repair is conducted to improve summary readability. Each sentence in initial summary is combined with template phrase based on syntax-tree combination method. There are five groups of template phrases provided in surface repair. We construct evaluation standards by asking five human raters. The best method for sentence selection subsystem that uses Maximal Marginal Importance-Multi Sentence is employing TF.IDF weighting system with precision/recall of 0.61. The surface repair subsystem has acceptance of 0.91.

Learning unlabeled data in a drifting environment still receives little attention. This paper presents a concept tracker algorithm for learning concept drift that exploits unlabeled data. In the absence of complete labeled data, instance classes are identified using a concept hierarchy that is incrementally constructed from data stream (mostly unlabeled data) in unsupervised mode. The persistence assumption in temporal reasoning is then applied to infer target concepts. Empirical evaluation that has been conducted on information-filtering domains demonstrates the effectiveness of this approach.