A secure collaboration service for dynamic virtual organizations

Nowadays, various promising paradigms of distributed computing over the Internet, such as Grids, P2P and Clouds, have emerged for resource sharing and collaboration. To enable resources sharing and collaboration across different domains in an open computing environment, virtual organizations (VOs) often need to be established dynamically. However, the dynamic and autonomous characteristics of participating domains pose great challenges to the security of virtual organizations. In this paper, we propose a secure collaboration service, called PEACE-VO, for dynamic virtual organizations management. The federation approach based on role mapping has extensively been used to build virtual organizations over multiple domains. However, there is a serious issue of potential policy conflicts with this approach, which brings a security threat to the participating domains. To address this issue, we first depict concepts of implicit conflicts and explicit conflicts that may exist in virtual organization collaboration policies. Then, we propose a fully distributed algorithm to detect potential policy conflicts. With this algorithm participating domains do not have to disclose their full local privacy policies, and is able to withhold malicious internal attacks. Finally, we present the system architecture of PEACE-VO and design two protocols for VO management and authorization. PEACE-VO services and protocols have successfully been implemented in the CROWN test bed. Comprehensive experimental study demonstrates that our approach is scalable and efficient.     

A capabilities-based model for adaptive organizations

Multiagent systems have become popular over the last few years for building complex, adaptive systems in a distributed, heterogeneous setting. Multiagent systems tend to be more robust and, in many cases, more efficient than single monolithic applications. However, unpredictable application environments make multiagent systems susceptible to individual failures that can significantly reduce its ability to accomplish its overall goal. The problem is that multiagent systems are typically designed to work within a limited set of configurations. Even when the system possesses the resources and computational power to accomplish its goal, it may be constrained by its own structure and knowledge of its member’s capabilities. To overcome these problems, we are developing a framework that allows the system to design its own organization at runtime. This paper presents a key component of that framework, a metamodel for multiagent organizations named the Organization Model for Adaptive Computational Systems. This model defines the requisite knowledge of a system’s organizational structure and capabilities that will allow it to reorganize at runtime and enable it to achieve its goals effectively in the face of a changing environment and its agent’s capabilities.     

A systematic approach for business service consolidation in virtual organizations

Various forms of partnerships have increasingly emerged among organizations, in both the manufacturing and service industry. In one type of structured partnership, called the Virtual Organization (VO), the member organizations share with each other a part of their capacities and capabilities, so that together they can seize a bigger market share and more opportunities. However, to excel the VO’s potential, the participating organizations must become fully informed about each other’s particular abilities and competencies, as represented by their shared business services (BSs). Our approach proposes the creation of a common shared pool of integrated BSs, and further optimizing it through consolidating similar and related BSs, to reduce the size of this shared pool and enhance its learning curve by the VO partners. To this end, we extend and adapt a Business Process merge technique to make it more suitable to BS consolidation and VO applicability. In this paper, a semiautomated methodology is introduced that creates an optimized, clean (i.e., anomaly-free consolidated model), and valid (i.e., approved by domain experts) set of consolidated BSs in the VO context. Design science principles are applied to this research, and a systematic literature review is also performed. Furthermore, a real case study from the construction industry is conducted to validate the introduced methodology.     

Social media information governance in multi-level organizations: How humanitarian organizations accrue social capital

Strategic social media use positively influences organizational goals such as the long-term accrual of social capital, and thus social media information governance has become an increasingly important organizational objective. It is particularly important for humanitarian nongovernmental organizations (HNGOs), whose work relies on accurate and timely information regarding socially altruistic behavior (donations, volunteerism, etc.). Despite the potential of social media for increasing social capital, tensions in governing social media information across an organization's different operational levels (regional, intermediate, and national) pose a difficult challenge. Prominent governance frameworks offer little guidance, as their focus on control and incremental policymaking is largely incompatible with the processes, roles, standards, and metrics needed for managing self-governing social media. This study offers a notion of dynamic and co-evolutionary process management of multi-level organizations as a means of conceptualizing social media information governance for the accrual of organizational social capital. Based on interviews with members of HNGOs, this study reveals tensions that emerge within eight focus areas of accruing social capital in multi-level organizations, explains how dynamic process management can ease those tensions, and proposes corresponding strategy recommendations.     

A multi-level cache model for run-time optimization of remote visualization

Remote visualization is an enabling technology aiming to resolve the barrier of physical distance. While many researchers have developed innovative algorithms for remote visualization, previous work has focused little on systematically investigating optimal configurations of remote visualization architectures. In this paper, we study caching and prefetching, an important aspect of such architecture design, in order to optimize the fetch time in a remote visualization system. Unlike a processor cache or web cache, caching for remote visualization is unique and complex. Through actual experimentation and numerical simulation, we have discovered ways to systematically evaluate and search for optimal configurations of remote visualization caches under various scenarios, such as different network speeds, sizes of data for user requests, prefetch schemes, cache depletion schemes, etc. We have also designed a practical infrastructure software to adaptively optimize the caching architecture of general remote visualization systems, when a different application is started or the network condition varies. The lower bound of achievable latency discovered with our approach can aid the design of remote visualization algorithms and the selection of suitable network layouts for a remote visualization system.     

A narrative-based reasoning with applications in decision support for social service organizations

Nowadays, there is an increasing demand for incorporating unstructured narratives in decision support for knowledge-intensive industries such as healthcare and social service organizations. However, most of the current research on decision support systems (DSS) mainly focused on dealing with structured data and are inadequate to dealing with unstructured narratives such as clients’ records and stories. This paper presents a narrative-based reasoning (NBR) algorithm which incorporates the technologies of knowledge-based system (KBS), computational linguistics, and artificial intelligence (AI) for automatic processing unstructured narratives and inferring useful knowledge for decision support. A NBR enabled DSS has been built and was evaluated through a series of experiments conducted in early intervention of mental health of a social service company in Hong Kong. The performance of NBR was measured based on recall and precision and encouraging results were obtained. High recall and precision are achieved in the reasoning of unstructured data, and high recall is achieved for the association analysis. The results show that it is possible for inferring recommendations for problem solving from unstructured narratives automatically. Based on the approach, it helps to support knowledge workers with reliable suggestions on decision making so as to increase the quality of their solutions.     

A workflow model based on parallelism for distributed organizations

Distribution has become an increasingly common characteristic for modern service and production companies. Enterprises nowadays rely on distribution of their operations for provision of their supplies, labor, and for selling their products in dynamic global markets. Much of today enterprises efforts to cope with global markets are being directed towards the finding of effective collaboration means among their operations and partners. This research proposes a model for assisting distributed enterprises in modeling their operations by optimizing and integrating their workflow to accomplish the collaborative objective. The method developed, called the distributed parallel integration evaluation model (DPIEM) models the workflow in the distributed enterprise based on three integration scenarios. DPIEM minimizes the integrated tasks total cost by adding as many parallel servers per task as possible. The method was tested for a case of distributed assembly of two part-types. A total of eight scenarios for the case were analyzed, yielding the recommended number of parallel servers per integrated task. For comparison, each scenario was also simulated with the TIE parallel-computer environment. The TIE simulation results corroborate the DPIEM recommendation based on the lowest total cost for the case analyzed.     

Data mining based multi-level aggregate service planning for cloud manufacturing

Cloud manufacturing (CMfg) promotes a dynamic distributed manufacturing environment by connecting the service providers and manages them in a centralized way. Due to the distinct production capabilities, the service providers tend to be delegated services of different granularities. Meanwhile, users of different types may be after services of different granularities. A traditional aggregate production planning method is often incapable of dealing with type of problems. For this reason, a multi-level aggregate service planning (MASP) methodology is proposed. The MASP service hierarchy is presented, which integrates the services of different granularities into a layered structure. Based on this structure, one of data mining technologies named time series is introduced to provide dynamic forecast for each layer. In this way, MASP can not only deal with the services of multi-granularity, but also meet the requirements of all related service providers irrespective of their manufacturing capabilities. A case study has been carried out, showing how MASP can be applied in a CMfg environment. The results of the prediction are considered reliable as the order of magnitude of the production for each service layer is much greater than that of the corresponding mean forecast error.     

Integrated capacity and marketing incentive contracting for capital-intensive service systems

Capacity investment and capacity allocation have always been critical management decisions, especially in the presence of agency issue for capital-intensive and congestion-prone service organizations. Prior research has often modeled only one aspect of the issue, such as proposing internal pricing scheme for capacity allocation ignoring demand uncertainty and the influence of the manager, optimizing capacity alone ignoring the agency issue, or incentive contract design ignoring capacity limit and service delay. We show that simply employing a traditional incentive contract (which often ignores service delays) for the manager responsible for promoting a center's services will provide incorrect incentives and lead to a more congested and less profitable system. When firms focus on optimizing operational capacity alone, ignoring the impact of managers on service demand, they are able to maintain the optimal utilization and service quality by balancing capacity and delay costs. However, they forgo profit-increasing opportunities, as they ignore the impact of the optimal incentive contract and do not motivate the managers enough to boost demand. To tackle the management challenges faced by modern service centers, we take an integrated capacity-contracting approach by incorporating operational delays and capacity decisions within the incentive contract design. Embedding a queuing model in a general incentive contracting framework, we present a novel approach to deriving the optimal compensation contract and operational capacity for a service center. We illustrate in numerical examples that a Pareto improvement can be achieved with our integrated contracting approach because every party, from the firm to the manager, to customers, to equipment and software vendors, benefits.     

A multi-level model of individual information privacy beliefs

Several types of individual information privacy beliefs have been studied in literature, but their distinctions, relationships, and behavioral impacts have yet been systematically analyzed, causing difficulties in comparing and consolidating results across literature. Based on a review on various types of privacy beliefs, this study develops a multi-level model to strengthen this concept. The model consists of three levels of privacy beliefs, including: disposition to privacy, representing a person’s fundamental beliefs and overall propensity to value privacy across contexts; online privacy concern, representing a person’s overall perception of privacy risks in the online environment; and website privacy concern, representing a person’s perception of privacy risks on a particular website. An empirical test reveals that disposition to privacy has a positive impact on both online privacy concern and website privacy concern, and website privacy concern is the only significant predictor of intentions to disclose information and transact on a website. The study helps to synthesize individual information privacy beliefs and assists in understanding their impacts on online behavior.     

Ontology-driven intelligent service for configuration support in networked organizations

Nowadays, organizations must continually adapt to market and organizational changes to achieve their most important goals. Migration to business services and service-oriented architectures provides a valuable opportunity to attain the organization objectives. This migration causes evolution both in organizational structure and in technology-enabling businesses to dynamically change vendors and services. One of the forms of organizational structures is the form of networked organization. Technologies of business intelligence and Web intelligence effectively support business processes within the networked organizations. While business intelligence focuses on development of services for consumer needs recognition, information search, and evaluation of alternatives; Web intelligence addresses advancement of Web-empowered systems, services, and environments. The paper proposes a technological ontology-driven framework for configuration support as applied to networked organization. The framework integrates concepts of business intelligence and Web intelligence into a collaboration environment of a networked organization on the base of attainment of knowledge logistics purposes. This framework referred to as KSNet is based on the integration of software agent technology and Web services. Knowledge logistics functions of KSNet are complemented by technological functions of knowledge-gathering agents. The services of these agents are implemented with CAPNET, a FIPA compliant agent platform. CAPNET allows consuming services of agents in a service-oriented way. Applicability of the approach is illustrated through a “Binni scenario”-based case study of a portable field hospital configuration. Alexander Smirnov received his M.E., Ph.D. and D.Sc. degrees from St. Petersburg, Russia, in 1979, 1984, and 1994, respectively. He is a Deputy Director for Research and a Head of Computer Aided Integrated Systems Laboratory at St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS). He is a full professor at St. Petersburg State Electrical Engineering University. His current research interests include corporate knowledge management, Web-services, group decision support systems, virtual enterprises, and supply chain management. Nikolay Shilov received his M.E. from St. Petersburg State Technical University, Russia, in 1998 and his Ph.D. from SPIIRAS, in 2005. He is a senior researcher at the Computer Aided Integrated Systems Laboratory of SPIIRAS. His current research interests include virtual enterprise configuration, supply chain management, knowledge management, ontology engineering and Web-services. Tatiana Levashova received her M.E. degree from St. Petersburg State Electrical Engineering University, in 1986. She is a lead programmer at Computer Aided Integrated Systems Laboratory of SPIIRAS. Her current research is devoted to knowledge-related problems such as knowledge representation, knowledge management, and ontology management. She has published more than 96 papers in reviewed journals and proceedings of international conferences. Leonid Sheremetov received his Ph.D. in Computer Science in 1990 from St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences where he has worked as a research fellow and a senior research fellow from 1982. Now, he is a principal investigator of the Research Program on Applied Mathematics and Computing of the Mexican Petroleum Institute where he leads the Distributed Intelligent Systems Group, and is also a part-time professor of the Artificial Intelligence Laboratory of the Centre for Computing Research of the National Polytechnic Institute, Mexico. His current research interests include multiagent systems, semantic WEB, decision support systems, and enterprise information integration. His group developed CAPNET agent platform and has been involved in several projects for the energy industry ranging from petroleum exploration and production to knowledge management, with special focus on industrial exploitation of agent technology. He is also member of Editorial Boards of several journals. Miguel Contreras obtained his M.S. degree in Computer Science in 2002 from the National Polytechnic Institute of Mexico. Now, he is a Ph.D. student of the Postgraduate Studies Program at the Mexican Petroleum Institute. He is one of the principal developers of the CAPNET agent platform. His current research interests include multiagent systems, service-oriented architectures, and enterprise information integration.     

基于区块链的众包物流分级多层智能服务交易监管架构

针对现代服务业的支柱产业众包物流缺乏交易监管致使存在的伪造、欺骗、隐私泄露和不可追溯等安全威胁,提出了基于区块链的众包物流分级多层智能服务交易监管架构。首先,该架构采用了国家授权认证中心对物流服务平台监管与物流服务平台对物流参与主体监管的二级监管体系。之后,在该监管体系架构下,实现了群智合约、合法匿名的身份认证、智能交易匹配、异常数据分析与检测、隐私保护及可追溯等功能。然后,通过安全性分析与交易监管构件软件验证交易监管架构的安全可控及运行效率。最后,在真实的众包物流企业平台上运行该软件构件进行实际测量,测量结果显示,所提出的众包物流分级多层智能服务交易监管架构是安全可控的,能够保障用户和数据的隐私,防范伪造和欺骗,实现用户行为和用户数据的可审计和可追溯。     

基于多级表征线索注意模型的轻量化抠图方法

面对主流计算平台对框架轻量化的需求,设计一种基于多任务结构的轻量化抠图框架.将总体任务拆分为两类子任务,其中一类任务用来在语义层面上为高级特征分类,区分前景背景与未知区域的特征;另一类任务用于计算前景与背景图层的线性组合权重.通过与特征分类任务共享高级特征网络的权值获得精准的前景特征,再与低级别卷积特征相融合.所提出的模型能够生成精准的抠图掩膜,同时优化卷积神经网络来实现模型轻量化.在Composition1K数据集上对比不同方法的实验结果:在分辨率为$640\times640$的输入条件下,所提方法比DIM(deep image matting)和AdaMatting(adaptation and matting)方法分别减少19%和81%的空间消耗;对于同样的数据输入,所提方法需要的处理时间只有DIM消耗时间的五分之一.     

A requirements data model for product service systems

Product service systems (PSS) are bundles of physical technological elements and service elements that are integrated to solve customer problems. In practice, most components of PSS are developed independently from each other, which leads to problems with coordination of development activities and integration of PSS components. Therefore, an integrated requirements engineering for PSS is needed that deals with the involvement of developers from product engineering, software engineering, and service engineering, as well as the inherent complexity of the PSS and the development process. In a case study with the development department of a PSS provider, we analyzed requirements documents and conducted expert interviews. We identified problems in the development, for example, that requirements on different levels of abstraction are intermingled, rationales for requirements are missing, and the concretization of requirements is unclear. To solve these problems, we propose a requirements data model (RDMod) for requirements to PSS. An RDMod describes different types of requirements and the relations between them. Thus, it is a scheme for the concretization of the requirements, which especially addresses the problems of structuring the requirements, enabling traceability, and finding conflicts. We then used an analytical evaluation, a feature-based evaluation and a retrospective application with requirements analysts of the industry partner. In a joint workshop, we specified requirements for a PSS with the RDMod. In structured interviews, we analyzed the perceived advantages of the RDMod. The experts confirmed that the RDMod is applicable in their development and it provides a clear structure for the requirements and therefore helps overcoming the identified problems.     

A reputation assessment model for trustful service recommendation

Nowadays, software systems are mainly Web front-based, Cloud-deployed and accessible by a wide audience over the Internet. These online systems commonly rely on Service-oriented Architecture principles, where they are built as orchestrations of RESTful (and in some rare cases as SOAP-based) services. Integrating new services in an existing orchestration is a challenging and risky task because trustworthiness of these services is not guaranteed throughout their lifetime. Reputation of services is a good indicator about the overall quality of services, because it reflects consumer satisfaction regarding the service-offered functionality and quality. Thus, reputation of services could be considered in the selection and recommendation of trustworthy services. In this paper, we present a framework for the management of web service reputation to conduct a better service recommendation. We present a reputation assessment model that aggregates fair user feedback ratings. The model includes a mechanism that prevents the introduction of malicious feedback ratings, by penalizing detected specious users. In addition, this framework includes a bootstrapping technique for estimating reputation of newcomer services based on neighbor similarity and initial advertised QoS. A set of experiments has been conducted to evaluate the effectiveness of the proposed framework. The results of these experiments highlighted the potential of our framework. These are presented at the end of the paper.     

A replacement schedule model for airborne service helicopters

The focus of this study is airborne service helicopter replacement scheduling, a problem which has not yet been adequately addressed in past studies. We use a time–space network technique to develop a network model that integrates purchase, retirement and performance upgrade schedules as well as fleet deployments for airborne service helicopters. The performance of the model is demonstrated by numerical testing in reference to the operations of a Taiwan organization.     

适于OMAP的多级启动Boot Loader

通过对OMAP启动方式的分析,针对OMAP需要从外部Flash启动、耗时大、风险高的缺点,提出了一种多级启动的Boot Loader设计方案.该方案通过两级启动,在RAM中运行Boot Loader,降低了代码运行的风险,减小了Boot过程的耗时.实验证明,使用该方法拷贝程序的耗时能够减少20%左右.