协同多媒体菱工具中演示文档的同步模型

多媒体演示文档的协同菱对文档本身的结构与同步模型提出了一定的要求。为了便于面向对象的实现以及灵活多粒度的共享，提出了一个协同多媒体著作工具中分布交互式多媒体文档的同步模型。在此模型中，文档结构分为三层：页面层、对象组层及单媒体对象层，根据各层的特点，其同步分别采用基于跳转、基于事件及基于时间的策略。     

多媒体对象的组织与结构化检索

多媒体的结构化检索具有广泛的应用前景，但多媒体的检索技术尚不成熟。多媒体数据模型的复杂性，连续媒体基于内容检索的低效，以及缺乏适用的查询语言，都使得多媒体检索困难重重。英国肯特大学新近研制的多媒体检索系统在多媒体检索的相关领域取得了一定的突破。这个系统把用户视图中的多媒体对象组织成具有层次结构的虚拟数据库，使用属性来标识数据库中多媒体对象索引的特征；查询代理机允许用户直观地构造查询过程－包括一个结     

Modeling and Visualization for a Production Planning Decision Support System

This paper describes the process of decision support systems development for the optimization of the aggregate production planning problem for an important manufacturer of applicances in Chile. This work is part of a more general research effort, whose purpose is to build a generator of software applications for logistic problems in industry, based on optimization models. We take advantage of powerful tools and approaches for the modeling stage, and for building systems with user friendly interfaces and good data management capabilities. The production planning problem is formulated as a mixed integer programming model, which is solved using CPLEX. The system is currently being used by the company for their decision processes in manufacturing.     

多语种文本自动生成系统中句子规划部分知识表示模型的构造

提出了一种多语种文本自动生成系统中句子规划阶段的知识表示模型,它以句子结构类、句法规则和语义词典确定文本的具体形式,并详细介绍了该知识表示模型的结构及其匹配准则。     

From Habermas's communicative theory to practice on the internet

Abstract. Communication plays a crucial role in influencing our social life. However, communication has often been distorted by unequal opportunities to initiate and participate in it. Such conditions have been criticized by Habermas who argues for an ideal speech situation, i.e. a situation of democratic communication with equal opportunities for social actors to communicate in an undistorted manner. This ideal situation is partially being realized by the advent of the internet. The paper describes how an internet‐based tool for collaborative authoring was conceptualized, developed and then deployed with Habermas's Critical Social Theory as a guiding principle. The internet‐based electronic forum, known by its acronym GRASS (Group Report Authoring Support System), is a web tool supporting the production of concise group reports that give their readers an up‐to‐date and credible overview of the positions of various stakeholders on a particular issue. Together with people and procedures, it is a comprehensive socio‐technical information system that can play a role in resolving societal conflicts. A prototype of GRASS has been used by an environmental group as a new way in which to create a more equal exchange and comparison of ideas among various stakeholders in the debate on genetically modified food. With the widespread use of the internet, such a forum has the potential to become an emergent form of communication for widely dispersed social actors to conduct constructive debate and discussion. The barriers to such a mode of communication still remain – in the form of entrenched power structures, and limitations to human rationality and responsibility. However, we believe that the support provided by the comprehensive system of technological functionality as well as procedural checks and balances provided by GRASS may considerably reduce the impact of these obstacles. In this way, the ideal speech situation may be approximated more closely in reality.     

基于云计算的生产决策支持系统

为辅助企业进行高效率的生产,设计并实施了一种基于云计算的生产决策支持系统,云计算的运行平台为大规模应用和庞大数据处理提供了保障。系统架构为基于MVC(Model View Controller)模式的四层体系结构,系统模型库为决策功能的实现提供了大量的模型支持,数据层作为数据存储的媒介为系统提供了数据支持。在系统设计上充分考虑了人机交互,运行基本稳定,各模块衔接良好,能有效为企业提供科学化的生产决策支持。     

Knowledge Discovery in Texts for Constructing Decision Support Systems

This paper presents a Text Mining approach for discovering knowledge in texts to later construct decision support systems. Text mining can take advantage of knowledge stored in textual documents, reducing the effort for knowledge acquisition. The approach consists in performing a mining process on concepts present in texts instead of working with words. The assumption is that concepts represent real world events and characteristics better than words, allowing the understanding and the explanation of the reasoning used in decision processes. The proposed approach extracts concepts expressed in natural phrases, and then analyzes their distributions and associations. Concepts distributions and associations are used to characterize classes or situations. After the discovery process, the obtained knowledge can be embedded in automated systems to classify elements or to suggest actions or solutions to problems. In this paper, experiments using the approach in a psychiatric domain are discussed. Concepts extracted from textual medical records represent patients' symptoms, signals and social/behavior characteristics. An automatic system was constructed with the approach: a classifier whose goal is to help physicians in disease diagnoses. Results from this system show that the approach is feasible for constructing decision support systems with satisfactory performance.     

A Probabilistic Framework for the Hierarchic Organisation and Classification of Document Collections

This paper presents a probabilistic mixture modeling framework for the hierarchic organisation of document collections. It is demonstrated that the probabilistic corpus model which emerges from the automatic or unsupervised hierarchical organisation of a document collection can be further exploited to create a kernel which boosts the performance of state-of-the-art support vector machine document classifiers. It is shown that the performance of such a classifier is further enhanced when employing the kernel derived from an appropriate hierarchic mixture model used for partitioning a document corpus rather than the kernel associated with a flat non-hierarchic mixture model. This has important implications for document classification when a hierarchic ordering of topics exists. This can be considered as the effective combination of documents with no topic or class labels (unlabeled data), labeled documents, and prior domain knowledge (in the form of the known hierarchic structure), in providing enhanced document classification performance.     

Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?

The choice of the kernel function is crucial to most applications of support vector machines. In this paper, however, we show that in the case of text classification, term-frequency transformations have a larger impact on the performance of SVM than the kernel itself. We discuss the role of importance-weights (e.g. document frequency and redundancy), which is not yet fully understood in the light of model complexity and calculation cost, and we show that time consuming lemmatization or stemming can be avoided even when classifying a highly inflectional language like German.     

回归型支持向量机的简化算法

针对支持向量机应用于函数估计时支持向量过多所引起的计算复杂性,提出一种简化算法,可以大幅度地减少支持向量的数量,从而简化其应用.采用简化算法还可以将最小平方支持向量机算法和串行最小化算法结合起来,达到学习效率高且生成的支持向量少的效果.     

连铸-轧制混流生产模式下轧批调度问题的分支-定价算法

研究了连铸——轧制在热装、温装和冷装混流生产模式下的一类新型轧批调度问题.以最小化温装钢坯（热钢锭）缓冷（等待）导致的热能损失和连轧机架切换带来的产能损失为目标,建立了整数规划模型.由于商业优化软件难以在有限时间内直接求得模型的最优解甚至可行解,提出利用Dantzig-Wolfe分解技术将原模型分解为主问题和子问题,采用列生成算法对主问题和子问题进行迭代求解得到原问题的紧下界,最后以列生成算法作为定界机制嵌入分支——定界框架中形成分支——定价算法,执行分支搜索过程以获得整数最优解.本文还从影响分支——定价算法性能的要素出发提出改进策略.针对主问题,提出列生成和拉格朗日松弛混合求解策略来抑制单一列生成算法的尾效应.针对价格子问题,在动态规划算法中提出了基于占优规则和标号下界计算方法来及早消除无效状态空间,加速求解过程.以钢铁企业的实际生产数据和扩展的随机算例进行了数值实验,结果显示所提出改进策略能够突破求解能力的限制,使分支——定价算法在可接受计算时间内求得工业规模问题的最优解.     

支持向量机方法用于民航安检炸药判别研究

目前的民航安检X射线透视设备无法直接检出塑料炸药，国际上初步频研究有效的γ射线共振技术可以透射瞬时测定行李中的物件的氮，氢，氧，碳含量。为与此技术配套，本工作应用对小样本集统计预报特别有效的支持向量机（support vector mahine,简称SVM）算法根据样品的氮，氢，氧，碳含量判别常见民用品和炸药，并用留一法比较SVM，Fisher法和人工神经网络算法的预报效果。结果表明SVM算法误报最少，且对所列炸药无一漏报，据此建立了炸药判别系统软件的原型，在实验室中模拟测试结果良好。     

A Requirements Analysis for Information Systems to Support Decision Making in a Managed Care Environment

Next-generation informationsystems for managed care environments must meet new standardsin the following areas: user interface, external interfaces,data storage and retrieval, and data analysis. Further, the requirementsthat will serve as the blue print for their construction mustbe grounded in new ways of looking at the process of patientcare.     

From genetic to bacteriological algorithms for mutation‐based testing

The level of confidence in a software component is often linked to the quality of its test cases. This quality can in turn be evaluated with mutation analysis: faults are injected into the software component (making mutants of it) to check the proportion of mutants detected (‘killed’) by the test cases. But while the generation of a set of basic test cases is easy, improving its quality may require prohibitive effort. This paper focuses on the issue of automating the test optimization. The application of genetic algorithms would appear to be an interesting way of tackling it. The optimization problem is modelled as follows: a test case can be considered as a predator while a mutant program is analogous to a prey. The aim of the selection process is to generate test cases able to kill as many mutants as possible, starting from an initial set of predators, which is the test cases set provided by the programmer. To overcome disappointing experimentation results, on .Net components and unit Eiffel classes, a slight variation on this idea is studied, no longer at the ‘animal’ level (lions killing zebras, say) but at the bacteriological level. The bacteriological level indeed better reflects the test case optimization issue: it mainly differs from the genetic one by the introduction of a memorization function and the suppression of the crossover operator. The purpose of this paper is to explain how the genetic algorithms have been adapted to fit with the issue of test optimization. The resulting algorithm differs so much from genetic algorithms that it has been given another name: bacteriological algorithm. Copyright © 2005 John Wiley & Sons, Ltd.     

From critiquing to representational talkback: computer support for revealing features in design

Computational critiquing mechanisms support designers in refining a partial design solution in an alternating cycle of reflection and action. This paper argues that critiquing mechanisms can support designers not only in refining a partial solution, but also in gaining a better understanding of the problem. We further argue that different types of critiquing are possible, ranging from conventional rule-based messages to an implicit type of critiquing based on the notion of “representational talkback” – representations that can reveal to the user otherwise implicit features of a design. We support these claims with user studies of three types of design support systems: Kid, IAM-eMMa, and Art.     

支持向量机及其在密闭鼓风炉故障诊断中的应用

针对密闭鼓风炉故障诊断中难以获得大量故障数据样本以及特征提取和诊断知识获取困难等不足,提出了应用支持向量机(SVM)进行故障诊断的新方法.采用改进"1对其余"算法构建多个SVM,利用可靠性数据分析技术中一些基本概念处理原始样本数据作为特征向量,输入到由多个SVM构成的多类分类器中进行故障分类.经实验证明,该方法简单,重复训练量少,训练、分类速度快,准确度高.     

Exploiting user feedback to compensate for the unreliability of user models

Natural Language is a powerful medium for interacting with users, and sophisticated computer systems using natural language are becoming more prevalent. Just as human speakers show an essential, inbuilt responsiveness to their hearers, computer systems must tailor their utterances to users. Recognizing this, researchers devised user models and strategies for exploiting them in order to enable systems to produce the best answer for a particular user.Because these efforts were largely devoted to investigating how a user model could be exploited to produce better responses, systems employing them typically assumed that a detailed and correct model of the user was available a priori, and that the information needed to generate appropriate responses was included in that model. However, in practice, the completeness and accuracy of a user model cannot be guaranteed. Thus, unless systems can compensate for incorrect or incomplete user models, the impracticality of building user models will prevent much of the work on tailoring from being successfully applied in real systems. In this paper, we argue that one way for a system to compensate for an unreliable user model is to be able to react to feedback from users about the suitability of the texts it produces. We also discuss how such a capability can actually alleviate some of the burden now placed on user modeling. Finally, we present a text generation system that employs whatever information is available in its user model in an attempt to produce satisfactory texts, but is also capable of responding to the user's follow-up questions about the texts it produces.Dr. Johanna D. Moore holds interdisciplinary appointments as an Assistant Professor of Computer Science and as a Research Scientist at the Learning Research and Development Center at the University of Pittsburgh. Her research interests include natural language generation, discourse, expert system explanation, human-computer interaction, user modeling, intelligent tutoring systems, and knowledge representation. She received her MS and PhD in Computer Science from the University of California at Los Angeles, and her BS in Mathematics and Computer Science from the University of California at Los Angeles. She is a member of the Cognitive Science Society, ACL, AAAI, ACM, IEEE, and Phi Beta Kappa. Readers can reach Dr. Moore at the Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15260.Dr. Cecile Paris is the project leader of the Explainable Expert System project at USC's information Sciences Institute. She received her PhD and MS in Computer Science from Columbia University (New York) and her bachelor's degree from the University of California in Berkeley. Her research interests include natural language generation and user modeling, discourse, expert system explanation, human-computer interaction, intelligent tutoring systems, machine learning, and knowledge acquisition. At Columbia University, she developed a natural language generation system capable of producing multi-sentential texts tailored to the users level of expertise about the domain. At ISI, she has been involved in designing a flexible explanation facility that supports dialogue for an expert system shell. Dr. Paris is a member of the Association for Computational Linguistics (ACL), the American Association for Artificial Intelligence (AAAI), the Cognitive Science Society, ACM, IEEE, and Phi Kappa Phi. Readers can reach Dr. Paris at USC/ISI, 4676 Admiralty Way, Marina Del Rey, California, 90292