Semantic specification and evaluation of bids in web-based markets

In recent years, electronic markets have gained much attention as institutions to allocate goods and services efficiently between buyers and sellers. By leveraging the Web as a global communication medium, electronic markets provide a platform that allows participants throughout the world to spontaneously exchange products in a flexible manner. However, ensuring interoperability and mutual understanding in such a highly dynamic and heterogenous environment can easily become very tricky, particularly if the services and goods involved are complex and described by multiple attributes. In this paper, we present a comprehensive ontology framework that allows the specification of bids for Web-based markets. By expressing utility function policies with a logic-based and standardized formalism, the framework enables a compact bid representation particularly for highly configurable goods and services while ensuring a high degree of interoperability. To facilitate matchmaking of offers and requests in the market, a method for evaluating bids based on logical reasoning is presented. In addition, as proof of concept, we show how the framework can be applied in a Web service selection scenario.     

Implicit and hybrid methods for attribute weighting in multi-attribute decision-making: a review study

Artificial Intelligence Review - Attribute weighting is a task of paramount relevance in multi-attribute decision-making (MADM). Over the years, different approaches have been developed to face...     

A logistic regression model for Semantic Web service matchmaking

Semantic Web service matchmaking,as one of the most challenging problems in Semantic Web services (SWS),aims to filter and rank a set of services with respect to a service query by using a certain matching strategy.In this paper,we propose a logistic regression based method to aggregate several matching strategies instead of a fixed integration (e.g.,the weighted sum) for SWS matchmaking.The logistic regression model is trained on training data derived from binary relevance assessments of existing test collections,and then used to predict the probability of relevance between a new pair of query and service according to their matching values obtained from various matching strategies.Services are then ranked according to the probabilities of relevance with respect to each query.Our method is evaluated on two main test collections,SAWSDL-TC2 and Jena Geography Dataset(JGD).Experimental results show that the logistic regression model can effectively predict the relevance between a query and a service,and hence can improve the effectiveness of service matchmaking.     

GAMESH: A grid architecture for scalable monitoring and enhanced dependable job scheduling

Grid computing is a largely adopted paradigm to federate geographically distributed data centers. Due to their size and complexity, grid systems are often affected by failures that may hinder the correct and timely execution of jobs, thus causing a non-negligible waste of computing resources. Despite the relevance of the problem, state-of-the-art management solutions for grid systems usually neglect the identification and handling of failures at runtime. Among the primary goals to be considered, we claim the need for novel approaches capable to achieve the objectives of scalable integration with efficient monitoring solutions and of fitting large and geographically distributed systems, where dynamic and configurable tradeoffs between overhead and targeted granularity are necessary. This paper proposes GAMESH, a Grid Architecture for scalable Monitoring and Enhanced dependable job ScHeduling. GAMESH is conceived as a completely distributed and highly efficient management infrastructure, concentrating on two crucial aspects for large-scale and multi-domain grid environments: (i) the scalable dissemination of monitoring data and (ii) the troubleshooting of job execution failures. GAMESH has been implemented and tested in a real deployment encompassing geographically distributed data centers across Europe. Experimental results show that GAMESH (i) enables the collection of measurements of both computing resources and conditions of task scheduling at geographically sparse sites, while imposing a limited overhead on the entire infrastructure, and (ii) provides a failure-aware scheduler able to improve the overall system performance, even in the presence of failures, by coordinating local job schedulers at multiple domains.     

Feedforward dynamics for the control of articulated multi-limb robots

We describe a general approach for using linearizing feedforward control inputs for large degree of freedom (dof) multi-limb robots operating in scenarios involving motion and force constraints, and under-actuated degrees of freedom arising from the task and the environment. Our solution is general and has low computational cost needed for real-time control loops. It supports the tuning of the feedforward term to meet multiple task objectives. Being structure-based, it is able to easily accommodate changes in motion and force constraints that often occur in robotics scenarios.     

基于策略的Web服务非功能属性匹配

Web服务的非功能属性匹配对增强Web服务发现的准确性,保证Web服务调用质量具有重要作用。目前Web服务的非功能属性匹配研究一般局限于某一具体的非功能属性,缺少支持各非功能属性的统一匹配方法。该文提出了一种基于策略的Web服务非功能属性的匹配方法,把Web服务非功能属性作为服务双方的策略,利用策略匹配来实现非功能属性的匹配。采用WSPL作为策略描述语言,并提出了层次化策略的概念,利用不同的发布方式对不同层次的策略进行发布,服务发现的过程与不同层次的策略匹配结合在一起。该方法支持各种非功能属性的匹配,并可以容易地和Web服务的功能性匹配方法结合到一起,形成完整的Web服务发现匹配系统。     

基于JBoss Seam的Web应用程序设计

JBossSeam是一个集JSF、EJB3.0、AJAX和jBPM于一体的新的Web应用程序框架,以EJB3.0为核心,扩展POJO加注释的设计模式到整个Web应用中。本文研究如何建立有效集成EJB3.0和JSF的Web应用和利用jPDL实现页面间导航,以及进行单元测试和集成测试的方法,并给出具体实现思路。     

基于语义距离度量模型的语义Web服务匹配排序机制

随着语义Web服务技术研究工作的不断深入,因特网上语义Web服务数量急剧增加。如何快速便捷地定位可用语义Web服务已经成为一个迫切且关键的问题。在语义Web服务匹配技术研究中,其中一个重要的研究主题就是语义Web服务匹配结果的排序机制。本文在综合概括和分析各种关于语义Web服务匹配结果排序机制的基础上,提出了一种基于语义距离度量模型的语义Web服务匹配结果排序机制,利用该排序机制,计算待匹配语义Web服务的语义相似度量,并依据此度量对语义Web服务匹配结果进行排序。该度量模型将语义Web服务引用概念间的语义关系转换成可精确比较的量化度量值,对属于相同语义匹配类型的匹配候选服务也能够分别计算语义距离,区分出相同匹配类型的候选服务与服务请求的匹配程度,从而达到改善用户对语义Web服务的搜索体验的目的。     

基于混合多维云模型的工业绿色发展水平测度

如何测度各省工业绿色发展水平高低, 判断各省工业绿色创新能力差异, 本文从绿色投入要素和绿色产出效益视角构建集结综合属性值的工业绿色发展多指标评价体系; 并在云模型基础上提出了基于混合多维云模型的区间多属性测度方法. 该方法创新性地利用区间权重和云权重的相互转换, 在解决多指标权重不一的问题后, 再运用父云贴近度计算工业绿色发展水平, 云投影度测定工业绿色创新能力; 最后采用省际间的工业面板数据进行验证. 结果表明, 相较于一般的多指标综合评价法, 该测定方法的实证结果与实际情况更相符合, 说明了该方法既能对工业绿色发展水平的整体情况进行评价分析, 也能精确计算出各指标贡献度, 从而判断其是否具有工业绿色创新能力. 因此, 本研究可为各地区调整工业绿色发展水平测度指标、制定工业绿色发展规划提供实质性建议和理论决策依据.     

To construct a monitoring mechanism of production loss by using Fuzzy Delphi method and fuzzy regression technique – A case study of IC package testing company

The development of information technology and internet capabilities over the years has been at an accelerated pace of global intense challenges and competitions of the technologies markets. Therefore, the issue of effectively utilizing valuable information located in databases worldwide, by monitoring the performance of their operations became an important issue in the electronic business environment. Methods to address this issue were employed to quickly identify the Key Performance Indicators (KPI’s) and operational problems and clarify any relationships between them, to allow the overall business objectives to be achieved. In this paper, the Fuzzy Delphi and ranking methods were used to extract the most concerning issues through expert questionnaires. A fuzzy regression model was then constructed and applied to clarify the relationships between the KPI’s and the key management objective, the area of production loss. Therefore, the key factors for future improvements were obtained by tracking the fuzzy regression model. Finally a case study of a semiconductor assembly and testing, through a company in Taiwan, was used to illustrate the proposed framework. The results indicated that this model could be easily implemented to analyze the influence of concerned KPIs on key management objectives.     

利用EJB构造电子商务平台

EJB(Enterprise Java Bean) 是一种创建在服务端的可扩展、事务处理的、多用 户安全的企业级应用，它为开发分布式的N-Tier中间件提供了统一的组件结构框架。 利 用这种技术设计出运行在J2EE服务器上通用的电子商务平台，展示了开发网上商店的具体流 程，是EJB技术很典型的一个应用。     

Analysis and case study on multi-dimensional scalability of the Internet architecture

This paper presents the definition of multi-dimensional scalability of the Internet architecture, and puts forward a mathematical method to evaluate Internet scalability based on a variety of constraints. Then, the method is employed to study the Internet scalability problem in performance, scale and service scalability. Based on the examples, theoretical analysis and experimental simulation are conducted to address the scalability issue. The results show that the proposed definition and evaluation method of multi-dimensional Internet scalability can effectively evaluate the scalability of the Internet in every aspect, thus providing rational suggestions and methods for evaluation of the next generation Internet architecture.     

J2EE平台上消息驱动Bean对客户的响应与回传的实现

EJB是基于Java的服务器端组件架构技术,借助于EJB,开发者能够轻松构建企业的、分布式组件应用,并且能够开发架构灵活、运行可靠及安全的应用系统.在EJB规范中,客户可向中间件发送消息,而消息驱动Bean(MDB)能从中间件接收消息,从而客户与消息驱动Bean实现异步传输消息,形成松耦合的状态.介绍了消息发布客户与消息驱动Bean通过服务器互传消息的理论,给出了Java消息服务的请求/响应解决方案,并且在J2EE1.4平台上实现了消息驱动Bean将响应结果异步回传给生产消息的客户,而客户可以是应用程序,Servlet/JSP,或EJB本身.     

基于编程语言结构的通用配置代码生成方法

设计一种可以生成不同编程语言配置代码的通用生成装置。提出一种基于编程语言结构的用户描述规范,用户使用简单的语法编写一个按照该规范描述目标代码数据结构的模块即可生成相应的可配置代码,并能生成多种编程语言的可配置代码文件。该装置可以方便地移植到各种开发语言环境的软件或硬件系统中,减少软件或硬件代码配置工作的开发工作量和开发时间。     

Statics and dynamics of cognitive and qualitative matchmaking in task fulfillment

Two main ingredients related to successful task performance are cognition and quality. Supply and demand of these concepts for knowledge intensive tasks are studied in this paper to fuel successful task fulfillment. Cognitive characteristics are supplied by actors performing tasks. Organizational developments such as growing complexity and increasing customer orientation may increase cognitive load. Stakeholders of tasks have quality requirements. These requirements may be affected if actors experience an increase in cognitive load. It is observed that knowledge intensive tasks demand cognitive characteristics and supply quality factors. Actors supply cognition and stakeholders demand quality. The gap between supply and demand can be bridged by introducing several models. These models consist of a matchmaking framework, conceptual models, and dynamic models. The matchmaking framework shows how supply and demand of cognitive characteristics or quality factors can be matched. Relations and roles of the concepts involved in task fulfillment are mapped out by the conceptual models. The dynamic models show causes that have effects on the supply of cognitive characteristics and the level of quality. These insights in the relations and dependencies between cognition and quality increase our understanding of the key concepts for successful task fulfillment.     

Learning potential functions and their representations for multi-task reinforcement learning

In multi-task learning, there are roughly two approaches to discovering representations. The first is to discover task relevant representations, i.e., those that compactly represent solutions to particular tasks. The second is to discover domain relevant representations, i.e., those that compactly represent knowledge that remains invariant across many tasks. In this article, we propose a new approach to multi-task learning that captures domain-relevant knowledge by learning potential-based shaping functions, which augment a task’s reward function with artificial rewards. We address two key issues that arise when deriving potential functions. The first is what kind of target function the potential function should approximate; we propose three such targets and show empirically that which one is best depends critically on the domain and learning parameters. The second issue is the representation for the potential function. This article introduces the notion of $k$ -relevance, the expected relevance of a representation on a sample sequence of $k$ tasks, and argues that this is a unifying definition of relevance of which both task and domain relevance are special cases. We prove formally that, under certain assumptions, $k$ -relevance converges monotonically to a fixed point as $k$ increases, and use this property to derive Feature Selection Through Extrapolation of k-relevance (FS-TEK), a novel feature-selection algorithm. We demonstrate empirically the benefit of FS-TEK on artificial domains.