A framework for collaboration moderator services to support knowledge based collaboration

Knowledge sharing is a major challenge for collaborative networks and is essential to improve the productivity and quality of decisions taken by both collaborative networks and their member organisations. A critical aspect of effective knowledge sharing within virtual organizations (VOs) is the identification of the most appropriate knowledge for reuse or exploitation in a particular context, as this requires efficient tools and mechanisms for its identification, sharing or transfer. Additionally, partners need to be aware of when knowledge needs to be shared, the implications of doing so and when their decisions are likely to affect other partners within the collaboration. Therefore, tools and methods are needed for identification, acquisition, maintenance and evolution of knowledge and to support effective knowledge sharing which includes awareness of possible consequences of actions and increased awareness of other partner’s needs during the collaboration. The Collaboration Moderator Services (CMS) are designed to address these issues relating to knowledge based collaboration by providing a set of functionalities to raise users’ awareness of opportunities, problem areas and lessons learnt from and during collaborations. This paper presents the system architecture and specifications of the CMS within the context of the SYNERGY system, whose purpose is to offer interoperable service utilities to help enterprises plan, setup and run complex knowledge collaborations. The CMS are designed to support both individual organizations and collaborations as a whole throughout the VO lifecycle and the different functionalities provided by CMS to achieve this are discussed in this paper.     

A novel memetic algorithm for discovering knowledge in binary and multi class predictions based on support vector machine

In classification, every feature of the data set is an important contributor towards prediction accuracy and affects the model building cost. To extract the priority features for prediction, a suitable feature selector is schemed. This paper proposes a novel memetic based feature selection model named Shapely Value Embedded Genetic Algorithm (SVEGA). The relevance of each feature towards prediction is measured by assembling genetic algorithms with shapely value measures retrieved from SVEGA. The obtained results are then evaluated using Support Vector Machine (SVM) with different kernel configurations on 11 + 11 benchmark datasets (both binary class and multi class). Eventually, a contrasting analysis is done between SVEGA-SVM and other existing feature selection models. The experimental results with the proposed setup provides robust outcome; hence proving it to be an efficient approach for discovering knowledge via feature selection with improved classification accuracy compared to conventional methods.     

A methodology for knowledge based decision support for facilities layout planning

The area of facilities layout planning contains a mix of specialized models in addition to a requirement for human experience and subjective judgments in the decision process. In this paper, we recognize that the task of choosing a suitable facilities layout planning model for a given problem is a non-trivial one, due to the unique requirements of individual situations, and the varying nature of the available models. To facilitate the selection of an appropriate model for facilities layout problems, we propose a methodology for the design of a knowledge based Decision Support System (DSS) which is calibrated with heuristics elicited from experienced layout planners. The rationale and structure of the model are discussed, as are its mode of usage and future research issues.     

A knowledge based decision support system for computer performance management

Computer systems managers make decisions about hardware and software selection, performance evaluation, capacity planning, and other resource variables on the basis of factual data, accounting data, subjective judgements, and assumptions about the resource consumption of the jobs being run. The importance of computer resource planning calls for effective support methods. A Knowledge-Based DSS (KBDSS) will be able to assist managers in making these policy decisions by utilizing knowledge of the existing configuration and its capabilities, the organizational computing environment, available external resources, and their suppliers. Combining procedural and declarative methods, such a KBDSS may provide early warning of possible bottlenecks, forecast growth of hardware usage, and employ knowledge based inferencing to suggest suitable remedial actions to the systems manager. This paper presents a KBDSS for supporting computer resource planning decisions using a procedural/declarative framework, and illustrates the system's usage aspects.     

A support infrastructure for the smart kindergarten

Continuing progress in microelectronics has allowed the embedding of sensing, processing, and wireless communications capabilities in familiar physical objects. This enables the creation of smart environments, where communication and computation technologies facilitate interactions between people and their surroundings, instead of just person-to-person or person-to-server communication. In a collaborative project underway at the University of California, Los Angeles, called Smart Kindergarten (SmartKG), we are exploring these technologies in a sensor-instrumented environment for early childhood education. Spatially dense but unobtrusive sensors continuously capture interactions among students, teachers, and common classroom objects. The sensors deliver observations wirelessly to a wired infrastructure for analysis and storage. Two crucial building blocks of this environment are Sylph, a sensor middleware infrastructure, and iBadge, a lightweight sensor-instrumented badge worn by students and teachers.     

Developing strategies for overcoming barriers to knowledge sharing based on conversational knowledge management: A case study of a financial company

Knowledge management involves the systematic management of vital knowledge resources and the associated processes of creating, gathering, organizing, diffusion, utilizing and exploiting information. A key challenge emerging for organizations is how to encourage knowledge sharing within an organization because knowledge is an organization’s intellectual capital and is of increasing importance in gaining a competitive business advantage. Isolated initiatives for promoting knowledge sharing and team collaboration without taking into consideration the limitations and constraints of knowledge sharing can halt any further development in the KM culture of an operation. This article investigates knowledge sharing bottlenecks and proposes the use of conversational knowledge sharing as an effective instrument for knowledge sharing. And to develop strategies, this paper determines the causes and effects of knowledge barriers and proposes solutions by using HOQ. The article introduces a financial company case study as a best practice example of conversational knowledge sharing. Then, the paper analyzes the case study to provide evidence for the feasibility and effectiveness of the proposed approach.     

A study of application platform for smart contract visualization based blockchain

The Journal of Supercomputing - In recent years, blockchain, which is the base technology of the 4th industrial revolution, is rapidly emerging as an alternative to the centralized data management...     

A meta-model for knowledge configuration management to support collaborative engineering

This paper focuses on industrial design and simulation processes especially in automotive and aerospace areas. Designers use business models (called expert models) such as CAD (computed aided design) and CAE (computed aided engineering) models to optimize and streamline the engineering process. Each expert model contains information such as parameters, expert rules, mathematic relations (parametric models, for example) which are shared by several users and in several different domains (mechanical, thermal, acoustic, fluid, etc.). This information is exploited at the same time in a concurrent engineering context. It is the basis of an imperfect collaboration process due to the fact that existing tools do not manage encapsulated information well and are unable to ensure that parameters and rules are consistent (same value of parameters for example) throughout different heterogeneous expert models. In this context, we propose an approach to manage knowledge using configurations synchronized with expert models which enable designers to use parameters consistently in a collaborative context. Our approach is called KCModel (knowledge configuration model): it allows acquisition, traceability, re-use and consistency of explicit knowledge used in configuration.     

A knowledge based component/service repository to enhance analysts’ domain knowledge for requirements analysis

Knowledge of the business domain (e.g., insurance claim, human resources) is crucial to analysts’ ability to conduct good requirements analysis (RA). However, current practices afford little assistance to analysts in acquiring domain knowledge. We argue that traditional reuse repositories could be augmented by adding rich faceted information on component/services and artifacts such as business-process templates to help analysts acquire domain knowledge during RA. In this paper, we present the design of a Knowledge Based Component Repository (KBCR) for facilitating RA. Then, we report on the design and development of a KBCR prototype. We illustrate its application in a system that is populated with components and process templates for the auto insurance claim domain. An empirical study was conducted to assess its effectiveness in improving RA. Results showed that KBCR enhanced analysts’ business domain knowledge and helped them better prepare for RA. Our key research contribution is to offer analysts a rich repository (i.e., KBCR) containing domain knowledge that they could utilize to acquire domain knowledge that is crucial for carrying out RA. While repositories of reusable components have been employed for some time, no one has used such repositories to help analysts acquire domain knowledge in order improve the RA of the system.     

基于光纤智能夹层的车架有限元分析与试验研究

以某型轻卡车架为研究对象,建立了车架有限元模型,提出采用光纤智能夹层结构分析弯曲、扭转工况下车架的应力分布。采用波分复用形式构建了分布式光纤智能夹层传感器网络,对车架在弯曲、扭转工况下车架关键点的应力分布进行了试验研究,并用Patran软件进行了车架结构静强度的有限元分析。试验结果与有限元模拟结果吻合得较好。该研究结果为同型车型开发、改型及优化设计提供了技术手段和依据。     

应用精确代价因子的两层邮件过滤模型

分析了一种基于直线几何分割的朴素贝叶斯邮件过滤模型LGDNBF,用更为精确的代价因子描述了分类器误判的代价。定义了高风险决策区域,对高风险决策区域中的邮件引入SVM方法进行二次分类,提出了基于精确代价因子的两层邮件过滤模型。在中文邮件语料集上的实验结果证明了这一两层过滤模型的分类效果较之朴素贝叶斯邮件过滤模型有明显的改进。     

Supporting multi-criteria decisions based on a hierarchical structure by taking advantage of acquired knowledge

In this paper, an approach to decision making is described. It combines a knowledge acquisition technique, a multi-attribute decision making technique, a validation technique and a machine learning algorithm. The suggested system is an extension of a previous decision support system based on the fuzzy repertory table technique. The aim is to increase its efficiency when dealing with a great amount of alternatives and criteria. The solution is based on a mechanism to divide the original problem into several simpler sub-problems. Moreover, a case study is presented to illustrate how the proposed system is used to design a Product Search Assistant. It will be integrated into a multi-agent architecture developed to give support to an e-marketplace.     

Large scale knowledge based systems for airborne decision support

At ES 96 during the Keynote address, the point was forcefully made that software houses have contributed little to the advancement of KBS. In the defence area, especially that of aerospace systems, extensive use has been made of the expertise of software and system houses in developing validation methodologies (VORTEX), real time (MUSE) and multi-agent (D-MUSE) software and together with Universities, a knowledge acquisition toolkit (PC PACK). In the UK at DERA Farnborough within the Airborne Decision Support Group, Air Sector, these software and tools have been developed and applied to problems in building Decision Support Systems for Maritime Air applications. The demanding aircrew tasks are characterised by the need for assimilation and interpretation of multi-sensor data to devise tactical responses in real time based on prevailing tactical doctrine and aircrew experience. The applications include Decision Support for Anti-Submarine Warfare (ASW), Anti-Surface Warfare (ASuW), Airborne Early Warning (AEW) together with ASW/ASuW and the proposed AEW technology demonstrators. Currently the transition is being made from the laboratory concept demonstrators to large scale technology demonstrator programmes as a risk reduction exercise prior to specification for airborne use. The proposed AEW TDP includes extensive modularity to support extensibility and component reuse.     

基于支持向量机的一类混沌系统自适应逆控制

本文研究了基于支持向量机回归自适应逆控制的混沌控制方法,用支持向量机建立系统的辨识器,同时在控制过程可逆的条件下设计基于支持向量回归的系统逆控制器.将该自适应逆控制的方法应用于Lorenz混沌系统的控制,仿真结果表明在系统带有不确定性和测量噪声的情况下,该方法可以有效的将混沌系统的状态控制到给定状态.     

Analysis and assessment of a knowledge based smart city architecture providing service APIs

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs.Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.     

Kave: a tool for knowledge acquisition to support artificial ventilation

A decision support system for artificial ventilation is being developed. One of the fundamental goals for this system is the application of the system when a domain expert is not present. Such a system requires a rich knowledge base. The knowledge acquisition process is often considered to be the bottleneck in acquiring such a complete knowledge base. Since no single available method, for example interviewing domain experts, is sufficient for removing this bottleneck, we have chosen a combination of different methods. The different backgrounds of knowledge engineers and domain experts could cause communication restrictions and difficulties between them, e.g. they might not understand each others knowledge domain and this will affect formulation of the knowledge. To solve this problem we needed a tool which supports both the knowledge engineer and the domain expert already from the initial phase of developing the knowledge base. We have developed a knowledge acquisition system called KAVE to elicit knowledge from domain experts and storing it in the knowledge base. KAVE is based on a domain specific conceptual model which is a result of cooperation between knowledge engineers and domain experts during identification, design and structuring of knowledge for this domain. KAVE includes a patient simulator to help validate knowledge in the knowledge base and a knowledge editor to facilitate refinement and maintenance of the knowledge base.     

基于类向心度的模糊支持向量机

传统支持向量机(SVM)训练含有噪声或野值点的数据时,容易产生过拟合,而模糊支持向量机可以有效地处理这种问题。针对使用样本与类中心之间的距离关系来构建模糊支持向量机隶属度函数的不足,提出了一种基于类向心度的模糊支持向量机(CCD FSVM)。该方法不仅考虑到样本与类中心之间的关系,还考虑到类中各个样本之间的联系,并用类向心度来表示。将类向心度应用于模糊隶属度函数的设计,能够很好地将有效样本与噪声、野值点样本区分开来,而且可以通过向心度的大小,对混合度比较高的样本进行区分,从而达到提高分类精度的效果。实验结果表明,基于类向心度的模糊支持向量机其分类正确率比支持向量机高,在使用三种不同隶属度函数的FSVM中,该方法的抗噪性能最好,分类性能最强。     

基于支持向量数据描述算法的SVM多分类新方法*

提出一种基于支持向量数据描述算法(SVDD)的多分类方法(S-MSVM).受SVDD的启发,该方法对每类样本建立一个超球来界定,但训练好的超球在所有情况下都是相交的.选择相交区域的样本单独建立超球,重复该步骤,直到相交区域消失或相交区域内没有样本点.给出了该方法的时间复杂度分析,并通过实验验证了该方法具有相对较好的训练精度.     

基于情境感知技术的智慧城市服务系统研究

随着城市规模越来越复杂,全国各级政府都在进行城市物联网和信息化建设,目前虽然搭建了互联骨干网和部署了大量的传感器,收集了众多的城市行为数据,但落后的信息管理模式难以体现信息价值,将信息体现在服务提升之中,造成信息资源的极大浪费。因此,将情境感知技术引入到智慧城市服务的应用之中,构建了一个基于情境感知的城市服务系统,并通过情境信息采集、情境信息推理和服务配置模型等关键技术,实时感知城市内的情境需求,从而提供智能化的业务服务组合。最后通过一社区智慧街道管理系统来验证本文所设计的系统效果。