基于分布式智能代理的反网络洗钱技术研究

本文针对当前洗钱犯罪活动迅速发展并呈现网络化的问题,提出了基于分布式智能代理框架的反网络洗钱技术,并结合关系图熵矩阵的数据挖掘技术,开发了反网络洗钱的仿真实验原型系统进行仿真验证。     

一种面向中转换乘的铁路网建模方法研究

铁路旅行依然属于人们首选出行方式,但种类繁多的车型与庞杂路网却经常导致无谓地花费不少钱财和时间。怎样实现旅行方案制定合理化、科学化与兼顾个人独特追求,并且切实地做到最优化评价指标妥善协调,即构成了民众普遍乞盼。结合铁路运营实际,围绕路网建模当中面临的结构体系、关键难题懈决对策等展开深入讨论,给出了问题处置策略、指导原则与技术路线。研究实验表明,通过文中方法对铁路路网进行建模,可以有效地解决铁路路网路径搜索面临的诸多难题,保证了数据完备性、有效性以及路径规划的实时性与高效性。     

一种快速实用的车牌字符识别方法

提出了一种新的车牌字符识别算法.该方法先对车牌中的单个字符进行预处理,然后采用Hausdorff距离模板匹配方法识别,对于特定位置的特殊字符,再进行校正识别.实验表明,该方法有效可行。     

Standardising vulnerability categories

Each vulnerability scanner (VS) represents, identifies and classifies vulnerabilities in its own way, thus making the different scanners difficult to study and compare. Despite numerous efforts by researchers and organisations to solve the disparity in vulnerability names used in the different VSs, vulnerability categories have still not been standardised. This paper highlights the importance of having a standard vulnerability category set. It also outlines an approach towards achieving this goal by generating a standard set of vulnerability categories. A data-clustering algorithm that employs artificial intelligence is used for this purpose. The significance of this research results from having an intelligent technique that aids in the generation of standardised vulnerability categories in a relatively fast way. In addition, the technique is generic in the sense that it allows one to accommodate any VS currently known on the market to create such vulnerability categories. Another benefit is that the approach followed in this paper allows one to also compare various VSs currently available on the market. A prototype is presented to verify the concept.     

Sequence-based clustering for Web usage mining: A new experimental framework and ANN-enhanced K-means algorithm

We develop a general sequence-based clustering method by proposing new sequence representation schemes in association with Markov models. The resulting sequence representations allow for calculation of vector-based distances (dissimilarities) between Web user sessions and thus can be used as inputs of various clustering algorithms. We develop an evaluation framework in which the performances of the algorithms are compared in terms of whether the clusters (groups of Web users who follow the same Markov process) are correctly identified using a replicated clustering approach. A series of experiments is conducted to investigate whether clustering performance is affected by different sequence representations and different distance measures as well as by other factors such as number of actual Web user clusters, number of Web pages, similarity between clusters, minimum session length, number of user sessions, and number of clusters to form. A new, fuzzy ART-enhanced K-means algorithm is also developed and its superior performance is demonstrated.     

New product development team intelligence: Antecedents and consequences

Our study investigated the effect of team knowledge on new product development (NPD). By investigating 207 NPD projects, we found that the declarative and procedural knowledge of the team and their use of IT had a positive influence on the team's knowledge base; and that the higher the functional diversity of the project team, the greater their overall knowledge. We also found that team knowledge positively impacted new product creativity and success in the market place.     

Prognostics of machine condition using soft computing

This paper presents a system for monitoring and prognostics of machine conditions using soft computing (SC) techniques. The machine condition is assessed through a suitable ‘monitoring index’ extracted from the vibration signals. The progression of the monitoring index is predicted using an SC technique, namely adaptive neuro-fuzzy inference system (ANFIS). Comparison with a machine learning method, namely support vector regression (SVR), is also presented. The proposed prediction procedures have been evaluated through benchmark data sets. The prognostic effectiveness of the techniques has been illustrated through previously published data on several types of faults in machines. The performance of SVR was found to be better than ANFIS for the data sets used. The results are helpful in understanding the relationship of machine conditions, the corresponding indicating features, the level of damage/degradation and their progression.     

Enacting agent-based services for automated procurement

Negotiation events in industrial procurement involving multiple, highly customisable goods pose serious challenges to buying agents when trying to determine the best set of providing agents’ offers. Typically, a buying agent's decision involves a large variety of constraints that may involve attributes of a very same item as well as attributes of different, multiple items. In this paper we present iBundler, an agent-aware service offered to buying agents to help them determine the optimal bundle of received offers based on their business rules. In this way, buying agents are relieved with the burden of solving too hard a problem and concentrate on strategic issues. iBundler is intended as a negotiation service for buying agents and as a winner determination service for reverse combinatorial auctions with side constraints. Furthermore, we assess the computational cost added by employing agent technology in the development of iBundler to characterise the type of negotiation scenarios that it can acceptably handle.     

Business intelligence approach to supporting strategy-making of ISP service management

The recent deregulation of telecommunication industry by the Taiwanese government has brought about the acute competition for Internet Service Providers (ISP). Taiwan’s ISP industry is characterized by the heavy pressure for raising revenue after hefty capital investments of last decade and the lack of knowledge to develop competitive strategies. To attract subscribers, all ISP dealers are making an all-out effort to improve their service management. This study proposes a Business Intelligence process for ISP dealers in Taiwan to assist management in developing effective service management strategies. We explore the customers’ usage characteristics and preference knowledge through applying the attribute-oriented induction (AOI) method on IP traffic data of users. Using the self-organizing map (SOM) method, we are able to divide customers into clusters with different usage behavior patterns. We then apply RFM modeling to calibrate customers’ value of each cluster, which will enable the management to develop direct and effective marketing strategies. For network resource management, this research mines the facility utilization over various administrative districts of the region, which could assist management in planning for effective network facilities investment. With actual data from one major ISP, we develop a BI decision support system with visual presentation, which is well received by its management staff.     

环绕智能研究综述

新一代的计算模式代替PC时代的计算模式是不可逆转的发展趋势,理解AmI的概念、应对AmI提出的技术与非技术挑战非常关键.首先讨论了环绕智能概念的起源,并比较了AmI概念与Ubiquitous Computing等相关概念的异同.然后综合分析了AmI领域的国内外研究现状,提出了一种针对AmI领域的研究内容的层次划分方法,并且讨论了AmI研究面临的技术挑战.随后,分析比较了世界IT强国针对AmI远景制定的工作计划,总结了AmI研究近期研究热点和未来发展趋势.