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MOOsburg is a community‐oriented multi‐user domain. It was created to enrich the Blacksburg Electronic Village by providing real‐time, situated, interaction, and a place‐based information model for community information. We are experimenting with an implementation fundamentally different from classic multi‐user domains object‐oriented (MOOs), supporting distributed system development and management, and a direct manipulation approach to navigation. To guide the development of MOOsburg, we are focusing on a set of community‐oriented applications, including a virtual science fair.

The first article in the series (The Electronic Library, 3, 3, July, 1985 pp. 200–209) introduced the reader to microcomputer terminology and to the main components—hardware and software—of a microcomputer system. This second article is largely devoted to more detailed description of the main hardware components: processors, internal memory, buses, external memory, printers and communications hardware. The importance of ergonomic factors in vdu and keyboard design is stressed. Multi‐user and network configurations are compared. Finally, the discussion of the role of the operating system, introduced in the first article, is extended.

Artificial Intelligence (AI) has surpassed expectations in opening up different possibilities for machines from different walks of life. Cloud service providers are pushing. Edge computing reduces latency, improving availability and saving bandwidth.

The exponential growth in tensor processing unit (TPU) and graphics processing unit (GPU) combined with different types of sensors has enabled the pairing of medical technology with deep learning in providing the best patient care. A significant role of pushing and pulling data from the cloud, big data comes into play as velocity, veracity and volume of data with IoT assisting doctors in predicting the abnormalities and providing customized treatment based on the patient electronic health record (EHR).

The primary focus of edge computing is decentralizing and bringing intelligent IoT devices to provide real-time computing at the point of presence (PoP). The impact of the PoP in healthcare gains importance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients. The impact edge computing of the PoP in healthcare gains significance as wearable devices and mobile apps are entrusted with real-time monitoring and diagnosis of patients.

The utility value of sensors data improves through the Laplacian mechanism of preserved PII response to each query from the ODL. The scalability is at 50% with respect to the sensitivity and preservation of the PII values in the local ODL.

This article investigates the evolution of computing, with specific reference to the security issues involved. These issues are then taken further to determine the need for education in the workplace through an information security awareness program. Techniques borrowed from the field of social psychology, which have been largely ignored in current awareness programs, are highlighted in order to show how they could be utilized to improve the effectiveness of the awareness program.

A parallel‐processor computer contains multiple CPUs that share such system resources as memory and disk storage. A parallel‐processor computer is expanded not by adding another computer, but by plugging another CPU into the computer. This technology offers expandability, compact size, high performance, high reliability, and moderate cost. The Sequent Balance Parallel‐Processor Computer is described in some detail. A fully configured Balance 21000 can execute 21 MIPS (million instructions per second). It implements the UNIX operating system, which has been widely adopted. As a result, many software packages for word processing and other applications are available from third‐party vendors. Performance tests conducted by CLSI, Inc. indicate that twenty concurrent users on a parallel‐processor system can perform CPU‐intense functions up to seven times faster than on a single‐processor system.

Cloud users can access services at anytime from anywhere in the world. On average, Google now processes more than 40,000 searches every second, which is approximately 3.5 billion searches per day. The diverse and vast amounts of data are generated with the development of next-generation information technologies such as cryptocurrency, internet of things and big data. To execute such applications, it is needed to design an efficient scheduling algorithm that considers the quality of service parameters like utilization, makespan and response time. Therefore, this paper aims to propose a novel Efficient Static Task Allocation (ESTA) algorithm, which optimizes average utilization.

Cloud computing provides resources such as virtual machine, network, storage, etc. over the internet. Cloud computing follows the pay-per-use billing model. To achieve efficient task allocation, scheduling algorithm problems should be interacted and tackled through efficient task distribution on the resources. The methodology of ESTA algorithm is based on minimum completion time approach. ESTA intelligently maps the batch of independent tasks (cloudlets) on heterogeneous virtual machines and optimizes their utilization in infrastructure as a service cloud computing.

To evaluate the performance of ESTA, the simulation study is compared with Min-Min, load balancing strategy with migration cost, Longest job in the fastest resource-shortest job in the fastest resource, sufferage, minimum completion time (MCT), minimum execution time and opportunistic load balancing on account of makespan, utilization and response time.

The simulation result reveals that the ESTA algorithm consistently superior performs under varying of batch independent of cloudlets and the number of virtual machines’ test conditions.

Presents a basic idea about a flexible robotic hand for handling fabric pieces in garment manufacture, which is multi‐functional and useful for picking a fabric piece up correctly, transferring and setting it without slipping to any three‐dimensional point. The robotic hand employs a strain gauge sensor and is capable of sensing touch, of measuring the thickness of fabric and its tension. Robotic hands have more applications in computer integrated manufacturing.

Web‐based technologies are rapidly becoming an integral part of the business fabric, interwoven into most departments and functional areas. However, the consequences of adopting these innovations, such as anticipated versus unanticipated and desirable versus undesirable consequences, warrant further exploration. This paper describes the experience of a contract service business after adopting a Web‐based collaborative technology to share information throughout the firm. User perceptions and attitudes towards this technology and its impact on time, control, and organisational change were explored via qualitative interviews. The results suggest that there were a number of consequences relating to these dimensions. We reflect on these consequences and offer suggestions based on the generalisability of the results. Further studies are identified that extend the exploration into the impact of Web‐based collaboration.

Cloud computing gives several on-demand infrastructural services by dynamically pooling heterogeneous resources to cater to users’ applications. The task scheduling needs to be done optimally to achieve proficient results in a cloud computing environment. While satisfying the user’s requirements in a cloud environment, scheduling has been proven an NP-hard problem. Therefore, it leaves scope to develop new allocation models for the problem. The aim of the study is to develop load balancing method to maximize the resource utilization in cloud environment.

In this paper, the parallelized task allocation with load balancing (PTAL) hybrid heuristic is proposed for jobs coming from various users. These jobs are allocated on the resources one by one in a parallelized manner as they arrive in the cloud system. The novel algorithm works in three phases: parallelization, task allocation and task reallocation. The proposed model is designed for efficient task allocation, reallocation of resources and adequate load balancing to achieve better quality of service (QoS) results.

The acquired empirical results show that PTAL performs better than other scheduling strategies under various cases for different QoS parameters under study.

The outcome has been examined for the real data set to evaluate it with different state-of-the-art heuristics having comparable objective parameters.

Load balancing is an important issue for a heterogeneous distributed computing system environment that has been proven to be a nondeterministic polynomial time hard problem. This paper aims to propose a resource-aware load balancing (REAL) model for a batch of independent tasks with a centralized load balancer to make the solution appropriate for a practical heterogeneous distributed environment having a migration cost with the objective of maximizing the level of load balancing considering bandwidth requirements for migration of the tasks.

To achieve the effective schedule, load balancing issues should be addressed and tackled through efficient workload distribution. In this approach, the migration has been carried out in two phases, namely, initial migration and best-fit migration. Using the best-fit policy in migrations helps in the possible performance improvement by minimizing the remaining idle slots on underloaded nodes that remain unentertained during the initial migration.

The experimental results reveal that the proposed model exhibits a superior performance among the other strategies on considered parameters such as makespan, average utilization and level of load balancing under study for a heterogeneous distributed environment.

Design of the REAL model and a comparative performance evaluation with LBSM and ITSLB have been conducted by using MATLAB 8.5.0.