Extracting significant Website Key Objects: A Semantic Web mining approach

Web mining has been traditionally used in different application domains in order to enhance the content that Web users are accessing. Likewise, Website administrators are interested in finding new approaches to improve their Website content according to their users' preferences. Furthermore, the Semantic Web has been considered as an alternative to represent Web content in a way which can be used by intelligent techniques to provide the organization, meaning, and definition of Web content. In this work, we define the Website Key Object Extraction problem, whose solution is based on a Semantic Web mining approach to extract from a given Website core ontology, new relations between objects according to their Web user interests. This methodology was applied to a real Website, whose results showed that the automatic extraction of Key Objects is highly competitive against traditional surveys applied to Web users.     

Michigan Particle Swarm Optimization for Prototype Reduction in Classification Problems

This paper presents a new approach to Particle Swarm Optimization, called Michigan Approach PSO (MPSO), and its application to continuous classification problems as a Nearest Prototype (NP) classifier. In Nearest Prototype classifiers, a collection of prototypes has to be found that accurately represents the input patterns. The classifier then assigns classes based on the nearest prototype in this collection. The MPSO algorithm is used to process training data to find those prototypes. In the MPSO algorithm each particle in a swarm represents a single prototype in the solution and it uses modified movement rules with particle competition and cooperation that ensure particle diversity. The proposed method is tested both with artificial problems and with real benchmark problems and compared with several algorithms of the same family. Results show that the particles are able to recognize clusters, find decision boundaries and reach stable situations that also retain adaptation potential. The MPSO algorithm is able to improve the accuracy of 1-NN classifiers, obtains results comparable to the best among other classifiers, and improves the accuracy reported in literature for one of the problems.

基于遗传算法的高考志愿填报排序问题的研究

针对高考志愿填报录取最优化、最佳匹配问题,提出了基于遗传算法搜索最优解的解决方案。该方案模拟物种自然选择和遗传进化过程,将不同考生的高考志愿按录取结果利益最大化进行排序。在可选择院校数量相同的情况下,对不同考生考试成绩的数据通过程序不断进行智能优化和迭代,志愿排序结果趋于稳定,且达到最佳匹配。其不但满足考生的实际需求,而且达到志愿填报利益最大化的目的。该方法采用涵盖985、211、普通院校的10所高校的实际数据进行测试,结果表明,遗传算法可以用来求解最优高考志愿填报排序问题,且具有很高的准确率和适应度。     

复杂系统的仿人智能控制

复杂控制系统难以建立较精确的数学模型,文章给出了其描述模型,讨论了被控对象的静态属性设置与动态特征状态识别两个基本问题,简述了仿人智能控制器设计的基本思想与原则,为解决复杂系统的控制问题提供了理论基础。     

Interactive learning and management of visual information via human-like software robot

To achieve smooth real-world interaction between people and computers, we developed a system that displays a three-dimensional computer-graphic human-like image from the waist up (anthropomorphic software robot: hereinafter “robot”) on the display, that interactively sees and hears, and that has fine and detailed control functions such as facial expressions, line of sight, and pointing at targets with its finger. The robot visually searches and identifies persons and objects in real space that it has learned in advance (registered space, which was our office in this case), manages the history information of the places and times it found objects and/or persons, and tells the user, indicating their three-dimensional positions with line of sight and its finger. It interactively learns new objects and persons with line of with their names and owners. By using this function, the robot can engage in simple dialogue (do a task) with the user. Osamu Hasegawa, Ph.D.: He received the B.E. and M.E. degrees from the Science University of Tokyo, in 1988, 1990 respectively. He received Ph.D. degree from the University of Tokyo, in 1993. Currently, he is a senior research scientist at the Electrotechnical Laboratory (ETL), Tsukuba, Japan. His research interests include Computer Vision and Multi-modal Human Interface. Dr. Hasegawa is a member of the AAAI, the Institute of Electronics, Information and Communication Engineers, Japan (IEICE), Information Processing Society of Japan and others. Katsuhiko Sakaue, Ph.D.: He received the B.E., M.E., and Ph.D. degrees all in electronic engineering from the University of Tokyo, in 1976, 1978 and 1981, respectively. In 1981, he joined the Electrotechnical Laboratory, Ministry of International Trade and Industry, and engaged in researches in image processing and computer vision. He received the Encouragement Prize in 1979 from IEICE, and the Paper Award in 1985 from Information Processing Society of Japan. He is a member of IEICE, IEEE, IPSJ, ITE. Satoru Hayamizu, Ph.D.: He is a leader of Interactive Intermodal Integration Lab. at Electrotechnical Laboratory. He received the B.E., M.E., Ph.D. degrees from Tokyo University. Since 1981, he has been working on speech recognition, spoken dialogue, and communication with artifacts. From 1989 to 1990, he was a visiting scholar in Carnegie Mellon University and in 1994 a visiting scientist in LIMSI/CNRS.     

Novel information discovery for intelligence and counterterrorism

Intelligence analysts construct hypotheses from large volumes of data, but are often limited by social and organizational norms and their own preconceptions and biases. The use of exploratory data mining technology can mitigate these limitations by requiring fewer assumptions. We present the design of the ATHENS system, which discovers novel information, relative to a specified set of existing knowledge, in large information repositories such as the World Wide Web. We illustrate the use of the system by starting from the terms “al Qaeda” and “bin Laden”" and running the ATHENS system as if on September 12th, 2001. This provides a picture of what novel information could have been known at the time. This is of some intrinsic interest, but also serves to validate the performance of the system since much of this novel information has been discovered by conventional means in the intervening years.     

一种基于群体智能的多主体聚类算法*

目前聚类算法普遍存在对初始化参数和异常数据敏感,难以找到最优聚类以及聚类的有效性等问题.利用群体智能和多主体系统具有的自组织性、健壮性、可扩展性和简单性等优点,给出了一种新型的优化聚类算法.在三维空间搭建主体运行环境,丰富主体的记忆、通信以及信息协调能力,增强主体的分析和判断能力.实验证明,该新型聚类算法具有运行速度快,准确性高以及对数据的输入顺序不敏感,能应付异常数据,处理高维、高复杂性数据等优点,可应用于图像处理、模式识别、文档归类等多个领域.     

A trace metamodel proposal based on the model driven architecture framework for the traceability of user requirements in data warehouses

The complexity of the data warehouse (DW) development process requires to follow a methodological approach in order to be successful. A widely accepted approach for this development is the hybrid one, in which requirements and data sources must be accommodated to a new DW model. The main problem is that we lose the relationships between requirements, elements in the multidimensional (MD) conceptual models and data sources in the process, since no traceability is explicitly specified. Therefore, this hurts requirements validation capability and increases the complexity of Extraction, Transformation and Loading processes. In this paper, we propose a novel trace metamodel for DWs and focus on the relationships between requirements and MD conceptual models. We propose a set of Query/View/Transformation rules to include traceability in DWs in an automatic way, allowing us to obtain a MD conceptual model of the DW, as well as a trace model. Therefore, we are able to trace every requirement to the MD elements, further increasing user satisfaction. Finally, we show the implementation in our Lucentia BI tool.     

基于人工智能的CPS系统架构研究

随着计算机技术、通信技术、传感器技术和自动化控制技术的日益成熟,信息物理融合系统开始在世界范围内出现。基于人工智能构建CPS系统,将信息物理融合系统拟人化,设计应用智能体实现人工神经元功能,云计算实现对应神经元的大脑反射区,多智能体通过总线广泛相连使得系统活灵活现。将基于人工智能产生式CPS系统架构应用到智能建筑环境测控系统中,这在将来智能建筑的发展中是必不可少的,符合未来智能建筑产业精神,也是本文研究意义所在。