Weaving an agent-based Semantic Grid for distributed collaborative manufacturing

The development and maintenance of distributed collaborative manufacturing systems is faced with increasing challenges caused by the technical difficulties and ontological issues in distributed computing, resource integration and knowledge sharing over heterogeneous computing platforms. This paper presents an agent-based Semantic Grid for distributed manufacturing collaboration across ubiquitous virtual enterprises. A hierarchy of autonomous, adaptive agents forming a ‘super peer’ based peer-to-peer network is laid out in the Semantic Grid to provide, in an open, dynamic, loosely coupled and scalable manner, the service publication, discovery and reuse for management of applications that need to utilise Grid resources. The operation and experimental evaluation of the system are presented to validate the implementation of the proposed approach.     

Analysing context-dependent deviations in interacting with safety-critical systems

Mobile technology is penetrating many areas of human life. This implies that the context of use can vary in many respects. We present a method that aims to support designers in managing the complex design space when considering applications with varying contexts and help them to identify solutions that support users in performing their activities while preserving usability and safety. The method is a novel combination of an analysis of both potential deviations in task performance and most suitable information representations based on distributed cognition. The originality of the contribution is in providing a conceptual tool for better understanding the impact of context of use on user interaction in safety-critical domains. In order to present our approach we provide an example in which the implications of introducing new support through mobile devices in a safety-critical system are identified and analysed in terms of potential hazards.     

A Survey of Accelerator Architectures for Deep Neural Networks

Recently, due to the availability of big data and the rapid growth of computing power, artificial intelligence (AI) has regained tremendous attention and investment. Machine learning (ML) approaches have been successfully applied to solve many problems in academia and in industry. Although the explosion of big data applications is driving the development of ML, it also imposes severe challenges of data processing speed and scalability on conventional computer systems. Computing platforms that are dedicatedly designed for AI applications have been considered, ranging from a complement to von Neumann platforms to a “must-have” and stand-alone technical solution. These platforms, which belong to a larger category named “domain-specific computing,” focus on specific customization for AI. In this article, we focus on summarizing the recent advances in accelerator designs for deep neural networks (DNNs)—that is, DNN accelerators. We discuss various architectures that support DNN executions in terms of computing units, dataflow optimization, targeted network topologies, architectures on emerging technologies, and accelerators for emerging applications. We also provide our visions on the future trend of AI chip designs.     

Product platforms design,selection and customisation in high-variety manufacturing

Product platforms represent an effective strategy implemented by manufacturers to cope with dynamic market demands, decrease lead-time and delay products differentiation. A decision support system (DSS) for product platforms design and selection in high-variety manufacturing is presented. It applies median-joining phylogenetic networks (MJPN) for the platforms design and phylogenetic tree decomposition for platforms selection by determining the product family phylogenetic network and defines the platforms at various levels of assembly corresponding to different trade-offs between number of platforms (variety) and number of assembly/disassembly tasks (customisation effort). Product platforms are reconfigured and customised to derive final product variants. The phylogenetic tree is decomposed in multiple levels, from the native platforms to the final variants. New Platforms Reconfiguration Index (PRI) and Platforms Customisation Index (PCI) were developed as metrics to evaluate the platforms customisation effort. A case study of a large family of plastic valves is used to demonstrate the DSS application. It shows reduction of 60% in platforms variety and increases in platform customisation assembly/disassembly tasks by only 20% leading to significant production and inventory efficiencies and cost savings. This methodology supports companies in the design and selection of best product platforms for high-variety to reduce cost and delivery time.     

Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud

In today’s world, smart phones offer various applications namely face detection, augmented-reality, image and video processing, video gaming and speech recognition. With the increasing demand for computing resources, these applications become more complicated. Cloud Computing (CC) environment provides access to unlimited resource pool with several features, including on demand self-service, elasticity, wide network access, resource pooling, low cost, and ease of use. Mobile Cloud Computing (MCC) aimed at overcoming drawbacks of smart phone devices. The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance. For remote execution, recent studies suggested downloading all or part of mobile application from mobile device. On the other hand, in offloading process, mobile device energy consumption, Central Processing Unit (CPU) utilization, execution time, remaining battery life and amount of data transmission in network were related to one or more constraints by frameworks designed. To address the issues, a Heuristic and Bent Key Exchange (H-BKE) method can be considered by both ways to optimize energy consumption as well as to improve security during offloading. First, an energy efficient offloading model is designed using Reactive Heuristic Offloading algorithm where, the secondary users are allocated with the unused primary users’ spectrum. Next, a novel AES algorithm is designed that uses a Bent function and Rijndael variant with the advantage of large block size is hard to interpret and hence is said to ensure security while accessing primary users’ unused spectrum by the secondary user. Simulations are conducted for efficient offloading in mobile cloud and performance valuations are carried on the way to demonstrate that our projected technique is successful in terms of time consumption, energy consumption along with the security aspects covered during offloading in MCC.     

Middleware to enhance mobile communications for road safety and traffic mobility applications

Middleware has emerged as an important architectural component in supporting distributed applications. The role of middleware is to present a unified programming model to application writers and to mask out problems of heterogeneity and distribution. It is motivated by the convergence of the embedded sensor and mobile communication revolutions in the automobile industry. The vehicle fleet is morphing into a vast mobile sensor fleet. The authors provide a middleware architecture and implementation that addresses the needs of a distributed system of mobile sensors comprised of vehicles and intersections producing trafficrelated data for traffic safety and operations. The authors discuss the technical challenges that the middleware addresses and describe a prototype implementation. Traffic management, intersection safety and vehicle-to-vehicle safety applications are three applications described and implemented on the middleware. The authors conclude their paper with conducting performance measures that relate to the cost of overhead incurred from using the middleware. The measurements show the middleware is efficient enough for the road safety and congestion relief applications presented.     

SWMRD: a Semantic Web-based manufacturing resource discovery system for cross-enterprise collaboration

As supply chains are becoming ever more global and agile in the modern manufacturing era, enterprises are increasingly dependent upon the efficient and effective discovery of shared manufacturing resources provided by their partners, wherever they are. Enterprises are thus faced with increasing challenges caused by the technical difficulties and ontological issues in manufacturing interoperability and integration over heterogeneous computing platforms. This paper presents a prototype intelligent system SWMRD (Semantic Web-based manufacturing resource discovery) for distributed manufacturing collaboration across ubiquitous virtual enterprises. Ontology-based annotation to the distributed manufacturing resources via a new, multidisciplinary manufacturing ontology is proposed on the semantic web to convert resources into machine understandable knowledge, which is a prelude to the meaningful resource discovery for cross-enterprise multidisciplinary collaboration. An ontology-based multi-level knowledge retrieval model is devised to extend the traditional information retrieval approaches based on keyword search, with integrated capabilities of graph search, semantic search, fuzzy search and automated reasoning to realise the intelligent discovery of manufacturing resources, e.g. to facilitate more flexible, meaningful, accurate and automated resource discovery. A case study for intelligent discovery of manufacturing resources is used to demonstrate the practicality of the developed system.     

Job partition and allocation using the prediction model in non-dedicated heterogeneous wireless network environments

Adapting wireless devices to communicate within grid networks empower users. This makes deployment of a wide range of applications possible within appropriate limits, including intermittent network connectivity and non-dedicated heterogeneous system capacity. The performance prediction model is used to improve the performance of the mobile grid job scheduling algorithm (MG-JSA). The proposed algorithm predicts the response time for processing the distributed application in each mobile node, although considering wireless network environments and inherent non-dedicated heterogeneous system capacity. Using this prediction model, the algorithm partitions and allocates the distributed jobs to available mobile nodes for rapid job processing. The efficiency of the MG-JSA model is demonstrated by evaluating its performance.     

Two-level single-chain pointer forwarding strategy: a new scheme for location management in mobile communication

A new strategy called 'two-level single-chain pointer (TLSCP) Forwarding strategy is proposed to reduce location management cost for mobile users. A two-level hierarchy of level-1 and level-2 pointer forwarding chains that are reduced to a single chain in a distributed database environment to locate a mobile user is used in this strategy. An analytical model is developed to evaluate the signalling and location tracking cost. Performance analysis shows that the proposed TLSCP scheme significantly reduces the network signalling traffic cost for mobile users with low mobility.     

AB-HCI: an interface multi-agent system to support human-centred computing

Currently, new types of information are available to applications. This incoming stream of data can be processed to improve user experience. User experience can be enhanced to present contents to users in a better manner, or to provide software artefacts to improve their interaction with the environment. The wide range of interaction design capabilities offered by this data requires methods and run-time architectures able to cope with the integration of the incoming data from the interaction environment into the applications. Human-centred computing takes into account what the state of user interaction is and how this data can be used to actually make it useful. A multi-agent system architecture is described. This architecture is able to tackle the arrival of context data by means of a set of sensors and interface agents that filter and process the context-of-use data to produce new presentations. The context of use provides information about the user, the platform, the physical environment and the current task the user is performing. These new presentations produced are adapted according to the context-of-use information gathered by applying a mixed-initiative approach to overcome some of the usual flaws found in full-adaptive applications.     

Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach

Blockchain, and cryptocurrencies such as Bitcoin, Ethereum, and Litecoin, are innovative FinTech technologies that speedily invade the finance market and changing the power of the global economy. However, there was a low acceptance of these technologies among consumers. There is a clear gap in-between that has not yet been taken into consideration and has been misunderstood on many platforms. This paper intends to estimate the usability of blockchain technology by investigating behavioral elements that affect customers' intention towards blockchain-based cryptocurrency transactions. Given the complexity of the technology, this study proposes a new integration model: The Technology Acceptance Model (TAM) together with new external variables regarding blockchain adoption characteristics such as trust, regulatory support, social influence, design, and experience. Surveys were conducted among international users to identify the impacts of these variables on their intention.Our findings indicate two powerful constructs (regulatory support and experience) that encourage customer trust towards blockchain-based applications. People surveyed agreed on a great sense of security and can trust the Blockchain-based applications when they are regulated and insured by the local government. Also, at a certain level of experience, users feel confident to use blockchain-based applications, a high level of trust supports technology adoption. As such, governments and businesses can dedicate efforts to enhance customers' trust and ultimately promote better acceptance of blockchain technology and its applications.     

Trusted virtual machine monitor-based group signature architecture

Group communication is an important technique for many network computing applications. In group communication, a member in a group sends a message to others normally by multicast. Group signature guarantees the integrity of the exchanged data and provides source authentication. In a virtual machine (VMs) based computing system, a virtual machine monitor (VMM) allows applications to run in different VMs strongly isolated from each other. A trusted VMM (TVMM) based platform can provide stronger security protection for group signature systems than traditional computing platforms can. The authors first introduce a TVMM-based group signature architecture and a TVMM security protection mechanism for group signature components. Then, the authors propose a group signature scheme using the function of message checking based on the discrete logarithm problem. Finally, the authors prove the correctness of the group signature scheme and analyse its security in virtual computing environments.     

用于网络化制造的智能化ASP平台

在ASP平台推广应用中遇到智能化的需求,采用移动Agent技术构建5层结构的ASP平台,以工作流管理来组织协调平台智能运作;以构件技术搭建资源构件层,保证可扩展,满足分布性,实现软件装配;以智能管理层调用应用服务层和资源构件层,整合平台资源,实现平台智能化。     

A grid-enabled lightweight computational steering client: a .NET PDA implementation

The grid has been developed to support large-scale computer simulations in a diverse range of scientific and engineering fields. Consequently, the increasing availability of powerful distributed computing resources is changing how scientists undertake large-scale modelling/simulation. Instead of being limited to local computing resources, scientists are now able to make use of supercomputing facilities around the world. These grid resources comprise specialized distributed three-dimensional visualization environments through to massive computational systems. The scientist usually accesses these resources from reasonably high-end desktop computers. Even though most modern desktop computers are provided with reasonably powerful three-dimensional graphical hardware, not all scientific applications require high-end three-dimensional visualization because the data of interest is essentially numerical or two-dimensional graphical data. For these applications, a much simpler two-dimensional graphical displays can be used. Since large jobs can take many hours to complete the scientist needs access to a technology that will allow them to still monitor and control their job while away from their desks. This paper describes an effective method of monitoring and controlling a set of chained computer simulations by means of a lightweight steering client based on a small personal digital assistant (PDA). The concept of using a PDA to steer a series of computational jobs across a supercomputing resource may seem strange at first but when scientists realize they can use these devices to connect to their computation wherever there is a wireless network (or cellular phone network) the concept becomes very compelling. Apart from providing a much needed easy-to-use interface, the PDA-based steering client has the benefit of freeing the scientist from the desktop. It is during this monitoring stage that the hand-held PDA client is of particular value as it gives the application scientist greater freedom to leave his or her desk but still communicate with their simulation, with the proviso that they remain within the range of a wireless network.     

An architecture for IoT-enabled intelligent process-aware cloud production platform: a case study in a networked cloud clinical laboratory

Cloud production is an emerging paradigm that supports co-designing and co-producing integrated solutions with customers. The realisation of this paradigm requires integrated platforms that enable parties collaborating within a production ecosystem to inter-operate networked business processes. Previous research has proposed different architectures for cloud production platforms from different perspectives like virtualiseng and servitiseng manufacturing resources, distributed and networked sensing supported by IoT technologies, and service-oriented and process-centred computing to compose and enact networked production services. However, an integrated architecture that brings together insights from service-oriented cloud manufacturing, IoT-enabled intelligence, and networked process-centred service composition and enactment has not been sufficiently addressed in previous research. In order to incorporate insights from the mentioned different perspectives, in this paper architectural analysis, synthesis, and evaluation steps are conducted to propose a conceptual architecture for IoT-enabled intelligent process-aware cloud production platforms. This architecture describes design-time and run-time components of a cloud production platform that can sense and intelligently respond to events within a value network. To evaluate the applicability of the proposed architecture within real-life scenarios, a case study is conducted in a cloud clinical laboratory in Tehran, Iran. Within this case study, a concrete cloud clinical laboratory platform has been instantiated.     

支持ATM的高级通讯环境EACE的设计和实现

研究实时CORBA 以适应开发有高通信性能要求的应用是近两年网络分布计算机模型的研究热点。ACE( Advanced Communication Environment) 是CORBA 实现TAO( The ACE ORB) 的一个底层通信环境。本文在分析ACE 的基础上, 设计并实现了EACE( Enhanced ACE) , 在ACE 中实现了ATM 协议, 为构建支持QoS 的CORBA 建立了一个高效的底层通信框架。     

基于情境感知的移动购物应用设计研究

目的探索情境感知在移动购物APP设计中的应用,以期设计出更好的移动购物体验。方法以情境感知理论为基础,分析移动购物中的用户情境、环境情境、资源情境因素,对关键情境进行提取分析,阐述关键情境下的需求获取方法,提出设计方法和基于情感化的设计策略。结论情境感知理论的运用帮助移动购物应用成为用户的私人购物助手,有助于在移动购物场景中为用户提供个性化、更优的移动购物体验。     

Task-Based Resource Allocation Bid in Edge Computing Micro Datacenter

Edge computing attracts online service providers (SP) to offload services to edge computing micro datacenters that are close to end users. Such offloads reduce packet-loss rates, delays and delay jitter when responding to service requests. Simultaneously, edge computing resource providers (RP) are concerned with maximizing incomes by allocating limited resources to SPs. Most works on this topic make a simplified assumption that each SP has a fixed demand; however, in reality, SPs themselves may have multiple task-offloading alternatives. Thus, their demands could be flexibly changed, which could support finer-grained allocations and further improve the incomes for RPs. Here, we propose a novel resource bidding mechanism for the RP in which each SP bids resources based on the demand of a single task (task-based) rather than the whole service (service-based) and then the RP allocates resources to these tasks with following the resource constraints at edge servers and the sequential rule of task-offloading to guarantee the interest of SPs. We set the incomes of the RP as our optimization target and then formulate the resource allocation problem. Two typical greedy algorithms are adopted to solve this problem and analyze the performance differences using two different bidding methods. Comprehensive results show that our proposal optimizes resource utilization and improves the RP’s incomes when resources in the edge computing datacenter are limited.     

Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array

Cyber security has become a serious concern as the internet penetrates every corner of our life over the last two decades. The rapidly developing human–machine interfacing calls for an effective and continuous authentication solution. Herein, we developed a two-factor, pressure-enhanced keystroke-dynamics-based security system that is capable of authenticating and even identifying users through their unique typing behavior. The system consists of a rationally designed triboelectric keystroke device that converts typing motions into analog electrical signals, and a support vector machine (SVM) algorithm-based software platform for user classification. This unconventional keystroke device is self-powered, stretchable and water/dust proof, which makes it highly mobile and applicable to versatile working environments. The promising application of this novel system in the financial and computing industry can push cyber security to the next level, where leaked passwords would possibly be of no concern.