The exponential growth in the number of handheld devices and other user equipment belonging to power class 3 as per 3GPP specifications is projected to reach 64 billion by the end of 2025. This has led to a drastic increase in the power consumption of radio units. This increase in power consumption is further enabled by the increasing demand for higher data rates and reduced latency for emerging 6G communications. Furthermore, with the expected latency of 1ms in 6G communications, the user equipment demands also change accordingly. This leads to a rapid change in requirements and therefore fluctuations in power consumption. Addressing this problem, this paper presents a novel Quantum Optimised Priority Resource Allocation (QO-PRA) algorithm designed to reduce dynamic power consumption in wireless communications. QO-PRA integrates heuristic-based multi-dimensional knapsack problem framework with quantum algorithms such as the Variational Quantum Eigensolver to optimise its parameters. This allow the QO-PRA algorithm to reduce the power fluctuations in radio units over time. Furthermore, reduced power fluctuations in radio units allow the wireless system to be more efficient, reliable and predictable. Benchmarking the QO-PRA against another widely used scheduling algorithm such as round-robin demonstrates a significant improvement in smoothening power fluctuations over time while maintaining a lower average power and average power per user per second. Furthermore, it achieves a 81.65% lower standard deviation of the first derivatives of power consumption compared to the round-robin algorithms which indicates a smoother power consumption curve.

Accurate prediction of road surface conditions can help authorities manage vehicular transportation effectively in large cities by helping to reduce congestion and the risk of accidents due to adverse weather. Image-based classification, using Convolutional Neural Networks (CNN) in combination with Transfer Learning (TL), can provide a real-time, data-driven and cost-effective solution for classifying road surfaces under varying weather, traffic and image recording conditions. This paper proposes an image classification approach leveraging TL with the ResNet50 architecture, enabling the efficient utilisation of pre-existing knowledge within deep neural networks to facilitate rapid model adaptation without extensive dataset collection. This study focuses on the challenging tropical conditions of Singapore as a use case where the performance of the proposed approach is evaluated on two distinct video footage/image datasets, namely fixed expressway cameras and dashcams. The strategy of coupling pre-trained models with TL consistently converges to better results more rapidly compared to training from scratch or with fine-tuning. The results provide valuable insights for traffic management authorities in selecting scalable architectures and training strategies for big data curation from traffic cameras, considering computational constraints for real-world deployment.

Over the last decades, the healthcare industry has been revolutionized heavily, especially after the Covid-19 surge. Various artificial intelligence (AI) approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimal robustness to unseen or untrained data. So, there is a need for new techniques which can overcome the issues mentioned earlier. Federated learning (FL) can help design specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making the use of FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% higher than that of the traditional centralized and FL-based systems without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4–5 times.

The growth of IoT, edge and mobile Artificial Intelligence (AI) is supporting urban authorities exploit the wealth of information collected by Connected and Autonomous Vehicles (CAV), to drive the development of transformative intelligent transport applications for addressing smart city challenges. A critical challenge is timely and efficient road infrastructure maintenance. This paper proposes an intelligent hierarchical framework for road infrastructure maintenance that exploits the latest developments in 6G communication technologies, deep learning techniques, and mobile edge AI training approaches. The proposed framework abides with the stringent requirements of training efficient machine learning applications for CAV, and is able to exploit the vast numbers of CAVs forecasted to be present on future road networks. At the core of our framework is a novel Convolution Neural Networks (CNN) model which fuses imagery and sensory data to perform pothole detection. Experiments show the proposed model can achieve state of the art performance in comparison to existing approaches while being simple, cost-effective and computationally efficient to deploy. The proposed system can form part of a federated learning framework for facilitating large scale real-time road surface condition monitoring and support adaptive resource allocation for road infrastructure maintenance.

Connected and Autonomous Vehicles (CAV) are gaining increasing importance due to the current needs of modern society for better mobility and societal impact. CAV development and adoption will be driven by Artificial Intelligence (AI) and 5G/6G technologies which will offer increased speed, reduced latency and ubiquity. However, the public is concerned with the concept of handing total control of driving to vehicles. These concerns will inhibit the adoption of CAVs when they become available to the public. In this paper, we investigated user adoption of CAVs by collecting quantitative data from potential users based on their preference and inherent concerns towards adoption. We conducted a statistical analysis and applied machine learning techniques to predict the user adoption for CAVs. Our results show that several machine learning approaches were effective in forecasting user adoption for CAVs. We have employed Neural Networks, Random Forest, Naïve Bayes and Fuzzy Logic based models and achieved accuracies of 81.76%, 83.63%, 82.15% and 86.38, respectively, in forecasting the public adoption of CAV.

The recent diagnosis of COVID-19 is based on real-time reverse-transcriptase polymerase chain reaction (RT-PCR) and is regarded as the gold standard for confirmation of infection. It has already been widely recognized that deep learning techniques can potentially have a substantial role in streamlining and accelerating the diagnosis of COVID-19 patients. Numerous open dataset enterprises have been set up over the past weeks to help the researchers develop and check methods that could contribute to countering the Corona pandemic. In order to report the above unique problems in the diagnosis of COVID-19, pioneering techniques should be developed. This special issue focuses on novel deep learning imaging analysis techniques related to COVID-19.

The vehicle instrument cluster is one of the most advanced and complicated electronic embedded control systems used in modern vehicles providing a driver with an interface to control and determine the status of the vehicle. In this paper, we develop a novel hybrid approach called Hierarchical Spatial-Temporal State Machine (HSTSM). The approach addresses a problem of spatial-temporal inference in complex dynamic systems. It is based on a memory-prediction framework and Deep Neural Networks (DNN) which is used for fault detection and isolation in automatic inspection and manufacturing of vehicle instrument cluster. The technique has been compared with existing methods namely rule-based, template-based, Bayesian, restricted Boltzmann machine and hierarchical temporal memory methods. Results show that the proposed approach can successfully diagnose and locate multiple classes of faults under real-time working conditions.

This paper describes the core computational mechanisms used by an urban flood forecasting and monitoring platform developed as part of a UK Newton Fund project in Malaysia. FLUD-FLood monitoring and forecasting platform for Urban Deployment - is a novel system aiming to deliver an effective and low cost urban flood forecasting solution, which is able to accurately forecast flood risk at street level, and deliver optimized recommendations to the relevant authorities as well as an early warning alerts to members of the public. This platform is based on a hybrid Deep Learning and Fuzzy Logic based architecture. As demonstrated by the experimental results and the analysis presented in this paper, this architecture enables the proposed system to account for factors that are not included in other modern flood forecasting systems, and simultaneously process high volumes of data originating from diverse data sources, in order to deliver accurate predictions concerning urban flood events.

Summary We proposed a novel Energy Efficient Greedy Routing Protocol (EEGRP) in CCN for MANETs to achieve the gaps in the existing infrastructure of Green Cloud Computing. Initially, the consumer node broadcasts the interest packet; each relay node verifies if it is already satisfied, and then it drops the packet. Otherwise, the provider node selects shortest path to unicast data packet within minimum delay. Each relay node receives a data packet, stores its content in content store, and forwards it to next node according to path mention in the hop‐count field. If the data packet custodian node does not find path mention in the hop‐count field, then it searches the routing table entries to choose the second best path to unicast the data packet. EEGRP is implemented in three scenarios using ns2 and compare its performance with AIRDrop routing protocol on bases of three performance parameters like packet delivery ratio, delay, and energy consumption. EEGRP achieves high PDR with minimum delay and less energy consumption as compared to AIRDrop. Therefore, EEGRP is feasible to use on green computing and cloud technology. This can save energy issue to secure energy for Green Computing and Cloud before moving to full‐scale virtualization and Cloud services.

Employing effective plagiarism detection methods are seen to be essential in the next generation web. In this paper, we present a novel approach for plagiarism detection without reference collections. The proposed approach relies on using some statistical properties of the most common words, and the Latent Semantic Analysis that is applied to extract the most common words usage patterns. This method aims to generate a model of author’s “style” by revealing a set of certain features of authorship. The model generation procedure focuses on just one author, as an attempt to summarise the aspects of an author’s style in a definitive and clear-cut manner. The feature set of the intrinsic model were based on the frequency of the most common words, their relative frequencies in the book series, and the deviation of these frequencies across all books for a particular author. The approach has been evaluated using the leave-one-out-cross-validation method on the CEN (Corpus of English Novel) data set. Results have indicated that, by integrating deep latent semantic and stylometric analyses, hidden changes can be identified when a reference collection does not exist. The results have also shown that our Multi-Layer Perceptron based approach statistically outperforms Bayesian Network, Support Vector Machine and Random Forest models, by accurately predicting the author classes with an overall accuracy of 97%.

Automated fault detection is an important part of a quality control system. It has the potential to increase the overall quality of monitored products and processes. The fault detection of automotive instrument cluster systems in computer-based manufacturing assembly lines is currently limited to simple boundary checking. The analysis of more complex nonlinear signals is performed manually by trained operators, whose knowledge is used to supervise quality checking and manual detection of faults. We present a novel approach for automated Fault Detection and Isolation (FDI) based on deep learning. The approach was tested on data generated by computer-based manufacturing systems equipped with local and remote sensing devices. The results show that the approach models the different spatial/temporal patterns found in the data. The approach can successfully diagnose and locate multiple classes of faults under real-time working conditions. The proposed method is shown to outperform other established FDI methods.

Organisations now frequently rely on virtual collaboration through the use of computer technology. After a sequence of meetings, participants may only need to refer to the most important points rather than the whole meeting proceedings. This paper addresses the need for automated meeting summarisation in virtual meeting systems. An extraction approach to summarisation is adopted and a new algorithm is proposed by extending the TextRank algorithm to include constructs representing the structure of the meeting. This helps extract the most relevant sentences from the meeting transcript. The proposed method was evaluated in the context of student-tutor meetings. Results show that harnessing and utilising the structure of a virtual meeting can lead to more relevant automated summaries.

Using short URLs in Twitter messages has increased in popularity in the past few years. This is mostly due to the fact that Twitter, as one of the most popular social media networks, imposes a 140 character limit to the messages distributed over the network. This paper analyzes the use of short URLs by Twitter users. Specifically, the goal is to examine the content pointed by the short URLs as well as the potential impact on the performance of sentiment analysis (opinion mining) tasks. Opinion mining based on Twitter feed has been used in an array of applications, including healthcare, identifying public opinion on political issues, financial modeling and advertising. Past research has however completely disregarded tweets which contain URLs. It is not hard to see how opinion mining can be improved considering the fact that Twitter users regularly post URLs pointing to articles endorsing a particular political figure, articles in important financial outlets or reviews of products. This study is based on the analysis of three distinct Twitter datasets with varying number of tweets which include short URLs. Popular machine learning techniques used in opinion mining were deployed in different experimental settings to conclude which are the most lucrative options.

Plagiarism detection is gaining increasing importance due to requirements for integrity in education. In this paper, we have developed a new integrated approach for extrinsic plagiarism detection. The proposed approach is based on four well-known models namely Bag of Words (BOW), Latent Semantic Analysis (LSA), Stylometry and Support Vector Machines (SVM). The proposed approach works by capturing usage patterns of the most common words (MCW) from books of 25 authors. Stylistic features for each author were harnessed in the method by adjusting the LSA weighting technique. The adjusted LSA method was trained in a novel manner using the leave-one-out-cross-validation technique and compared with the traditional LSA method. The results have shown that the enhanced weighting method of the adjusted LSA outperforms the traditional LSA method.

Readiness of equipment capabilities is recognized as one of the crucial type of emergency management capabilities against flood risk events in Saudi Civil Defense (CD). This paper represents an Intelligent Information Technology (IT) system principally to assist Saudi CD in evaluating the right equipment capabilities to response and manage flood event(s). The novelty of this study stems from examine the effectiveness of using the proposed Intelligent IT System in the readiness of equipment emergency management capabilities in the Saudi CD authority. This will be achieved through an extended evaluation process of the emergency management capabilities with taking into account different flood risk zone-specific - as well condition-specific, in addition, calculation needs process based on the criteria of targeted level of readiness. A fuzzy expert system approach is used for evaluation process. The design of the IT system is evaluated via a structured interview and found applicable and appropriate for the case study.

With exponential growth of the Internet users in past few years, there is a need of context-aware information sharing among different inter-connected entities over the Internet. The prime objective of the inter-connected objects is that within a minimum use of available resources, maximum output with respect to parameters such as throughput and delay can be achieved. But, due to high velocity and irrelevant information propagation, there may be a performance degradation in some part of the network with respect to these parameters. To address these issues, we have designed novel algorithms for context-aware information propagation among the vehicles. The proposed scheme consists of algorithms for data access, data dissemination, and data suggestion. These algorithms are based on reliability of vehicle which is calculated as soon as vehicles enter the network and is updated after each successful execution of various operations of information propagation. The scheme works on by increasing the reliability which in turn solve the broadcast storm problem in which sometime irrelevant information may also be sent to the vehicles. Simulation results prove the merit of the proposed scheme over the other existing schemes with respect to parameters such as message overhead, connectivity ratio, and resources utilization.

The importance of finding relevant information for business and decision making is imperative for both individuals as well as enterprises. In this paper, we present an approach for the development of a fuzzy information retrieval (IR) system. The approach provides a new mechanism for constructing and integrating three relevancy profiles comprising of: a task profile, user profile and document profile, into a unified index, through the use of relevance feedback and fuzzy rule based summarisation. Experiments were performed from which relevance feedback and user queries were captured from 35 users on 20 predefined simulated enterprise search tasks. The captured data set was used to develop the three types of profiles and train the fuzzy system. The system shows 86% performance accuracy in correctly classifying document relevance. The overall performance of the system was evaluated based on standard precision and recall which shows significant improvements in retrieving relevant documents based on user queries.

In this paper we investigate the Affective Trajectories Hypothesis in an educational context, and its possible implications on Affective Computing. Using the results from an online survey we try to explore the relationships of the Affective Trajectories basic elements, namely one's current affective state, prediction of the future, and experienced outcomes following this prediction, with a set of education related emotions. The relations of these elements with flow, excitement, calm, boredom, stress, confusion, frustration and neutral linguistic emotional labels are presented and discussed. Their predictive power is evaluated by using these elements as inputs to different classification systems, and observing their performance in mapping different combinations of those elements to specific emotion labels. A data-driven fuzzy approach is utilized in order to linguistically model the underlying relations between the emotions, and the basic elements, by creating easily interpretable fuzzy rule bases. In our research we suggest that the basic elements are combined in a personalized way in order for an individual to choose a specific emotion label to describe his affective state. For this reason a fuzzy adaptive approach is also implemented, in order to demonstrate the importance of individual differences in this process, and the benefits of having a personalized system that can perpetuate modelling of emotional trajectories over learning tasks. Finally an overview and a basic implementation of an affective computing system which uses these elements are presented, and future research directions discussed.

Optimization of data intensive applications is affected greatly by the nature of the platform, the distributed file system, the co-location of data and programs, and the proximity of resources. Data intensive applications running on a public cloud have been shown to exhibit degraded performance compared to a private cluster. This performance degradation is as a result of the inefficient resource allocation adopted in a virtualized, volatile and multi-tenancy environment such as a cloud. The placement of virtual machines is critical for improving performance in geo-clouds environment. We address degradation in performance by designing a scheduling algorithm that dynamically distributes virtual machines based on the characteristics of the job, the network characteristics and the real time performance of the data centers. Our algorithm also finds the best node(s) to host virtual machines that will yield maximum resource utilization and minimize bandwidth consumption. Our results show that incorporating these characteristics in a resource scheduling algorithm increases the performance of data intensive application than known traditional scheduling.

Increased competition, changing technology, and process re-engineering have changed the traditional employees' practice and capability. To meet such demands, organizations and businesses are relying on Information and communication technology (ICT) to monitor and improve employee performance and productivity. In this paper we examine the influence of cultural forces in accepting the implementation of such a system that deals with assessment and evaluation of government employees, to facilitate the transitional process from manual to e-performance assessment in governmental organizations in the United Arab Emirates (UAE). A user study consisting of pilot tests and surveys was carried out in order to test the effectiveness and acceptability of the e-performance system. It is envisaged that this research provides better understanding of e-performance management system in countries like the UAE.

Software developers produce a significant amount of knowledge, everyday facing a significant amount of engineering challenges and resolving them using a significant number of information resources. Once the task that they are facing is complete and the results of their effort are embedded into the source code this knowledge is very rarely shared with the software development community. As a consequence other software developers when faced with the same or similar problem have to solve it by themselves wasting a significant amount of time and resources every day. This paper proposes a Collaborative Information Recommender (CIR) system which intends to address the problem outlined above and facilitate the exchange of information between software developers. The proposed CIR system not only supports a direct reuse of the code related information found by other developers but also supports the creation and management of very problem focused social and knowledge networks.

In order to enhance students' problem-solving competences it is necessary to develop their technical skill as well as their soft skills such as business, communication and team working. In this paper, we present an Activity-Led Learning (ALL) approach and analyze its impact on students' engagement (time-on-task), satisfaction and group performance. We propose a Group Performance Model (GPM) to deploy ALL effectively in the master-level, Network Planning and Management, module. The model provides a structure within which students are introduced to the ALL pedagogical methodology. The model systematically helps to facilitate group formation and allows group integration and cooperation by developing `common ground' amongst group members. In order to evaluate the usage of GPM in ALL, we conducted group performance analysis using a fuzzy rule-based classification model. The results of the analysis showed that the application of GPM resulted in a reduction in overall time spent on tasks, while achieving better grades. This indicates that GPM can help groups to develop common ground, coordinate their activities and overcome inter-personal issues to achieve better overall performance in shorter times as opposed to groups in which GPM has not been applied. Students' direct feedback on module also shows the effectiveness of ALL on students' performance in general.

The aim of this paper is to examine the value of creating an information technology (IT) system to assist Saudi Arabia in predicting and preparing the right training capabilities to manage scalable flood events. This paper sets forth the scope of emergency response training capabilities needed to manage low-, medium-, and high-intensity flooding events in Jeddah. With the use of primary data from local responders in Jeddah, the need for an emergency response training capabilities IT system that can be defined as able to map human resource training needs was assessed. This IT system could help decision makers calibrate the right response criteria in the event of scalable flash flooding across Jeddah.

This paper presents a novel system for automatic detection and recognition of faulty audio signaling devices as part of an automated industrial manufacturing process. The system uses historical data labeled by human experts in detecting faulty signaling devices to train an artificial neural network based classifier for modeling their decision making process. The neural network is implemented on a real time embedded micro controller which can be more efficiently incorporated into an automated production line eliminating the need for a manual inspection within the manufacturing process. We present real world experiments based on data pertaining to the production and manufacture of audio signaling components used in car instrument clusters. Our results show that the proposed expert system is able to successfully classify faulty audio signaling devices to a high degree of accuracy. The results can be generalized to other signaling devices where an output signal is represented by a complex and changing frequency spectrum even with significant environmental noise.

Even though there exists a number of search solutions targetted at software engineers the literature suggests that they are not widely used by the people engaged in code delivery [26]. Moreover, current code focused information retrieval systems such as Google Code Search (discontinued), Codeplex or Koders produce results based on specific keywords and therefore they do not take into account user context such as location, browsing history, previous interaction patterns and domain expertise. In this paper we discuss the development of task-specific information retrieval systems for software engineers. We discuss how software engineers interact with information and information retrieval systems and investigate to what extent a domain-specific search and recommendation system can be developed in order to support their work related activities. We have conducted a user study: a questionnaire and an automated observation of user interactions with the browser and software development environment. We discuss factors that can be used as implicit feedback indicators for further collaborative filtering and discuss how these parameters can be analysed using Computational Intelligence based techniques.

Many enterprises rely on a wide variety of collaborative applications in order to support their everyday activities and to share resources. The collaborative applications are typically designed from scratch if the existing applications do not meet the enterprise's evolving needs. This incurs significant costs, and inconvenience. In this paper we present a case study of six applications (Sage 200, Gold-Vision CRM system, E-Commerce System, Gold-Vision Connect System, Realex Transaction and Spindle Document Automation Tool) within an enterprise. These applications are working in isolation. Therefore, sharing of information and data among these applications is carried out manually which imposes additional burden on their users and causes performance degradation. In this paper, we address this problem by integration and optimization of these applications. We also address the usability problems of these applications. We present comparative evaluation results that show significant improvement in ease and performance of user tasks using integrated applications.

Monitoring students' activity and performance is vital to enable educators to provide effective teaching and learning in order to better engage students with the subject and improve their understanding of the material being taught. We describe the use of a fuzzy Linguistic Summarisation (LS) technique for extracting linguistically interpretable scaled fuzzy weighted rules from student data describing prominent relationships between activity / engagement characteristics and achieved performance. We propose an intelligent framework for monitoring individual or group performance during activity and problem based learning tasks. The system can be used to more effectively evaluate new teaching approaches and methodologies, identify weaknesses and provide more personalised feedback on learner's progress. We present a case study and initial experiments in which we apply the fuzzy LS technique for analysing the effectiveness of using a Group Performance Model (GPM) to deploy Activity Led Learning (ALL) in a Master-level module. Results show that the fuzzy weighted rules can identify useful relationships between student engagement and performance providing a mechanism allowing educators to transparently evaluate teaching and factors effecting student performance, which can be incorporated as part of an automated intelligent analysis and feedback system.

In order to enhance students' problem-solving competences it is necessary to develop their technical skill as well as their soft skills such as business, communication and team working. In this paper, we introduce an Activity-Led Learning (ALL) approach and analyze its impact on students' engagement (time-on-task), satisfaction and group performance. We also propose a Group Performance Model (GPM) in order to deploy ALL successfully in the master-level module, Network Planning and Management. The model facilitates group formation and allows group integration and cooperation by developing `common ground' amongst group members. This paper also presents students evaluation from two perspectives; students direct feedback on the module and indirect measures based on common ground and Task Performance Time (TPT) analysis.

An important aspect of redesign for usability is to evaluate the system with its real users in order to identify key areas for improvement. In this paper we discuss the redesign of a project management system using a user-centred design methodology. The system is used by a wide range of users; academic staff, students, external clients and administrative staff. The original system was not fulfilling its needs as it had not captured work practices in a way that was recognizable to the users. The advantages of the redesign included: improved usefulness; improved efficiency and productivity; reduced learning time; improved usability; and increased acceptance among users. We evaluated the current system and analysed work practices using a user-centred design and evaluation philosophy. We present comparative evaluation results that show significant improvements in performance of user tasks for the redesigned project management system.

Web services have capabilities that make them suitable to meet the requirements posed by collaborative editing applications (CEAs). In this paper we describe how AuTrA(Adaptable user defined Transaction relaxing Approach), a system for composing web applications that allows user-specified varying of transaction characteristics, can be usefully applied to collaborative editing. We show how the application of AuTrA can be efficacious for business processing by increasing work throughput. A major feature of AuTrA is support for relaxation of any ACID property of transactions in certain circumstances.

The high cost of air travel and accommodation together with the increasing capacity and speed of internet networks has resulted in large multinational companies considering virtual meetings as an alternative to face-to-face meetings. The hardware and software to support virtual meetings is becoming more sophisticated and more accessible, as some Hotel Chains are offering the facility to businesses as part of their portfolio of services. With the rise in popularity of this type of meeting environment the need for a facilitator to co-ordinate this type of activity has become more acute but the technology to support this has lagged behind. With the emergence of the Virtual Design Office as a response to multi-cultural design teams working on global projects the need for virtual meetings becomes more important in time critical situations. This paper presents work that has been completed in developing the requirements for an agent based virtual meeting support system, with particular emphasis on the role of the facilitator.

An important aspect of Ubiquitous Computing (UbiComp) is augmenting people and environments with computational resources which provide information and services unobtrusively whenever and wherever required. In line with the vision of UbiComp, we have developed a multi-user location-awareness system by following a user-centred design and evaluation method. In this paper we discuss the development of such a system that allows users to share informative feedback about their current geographical location. The proposed system can be used by various users, for example family members, relatives or a group of friends, in order to share the information related to their locations and to interact with each other. It is intended that this type of system in the future will become a typical part of the Smart Home environment.

This paper presents an agent based computational model in order to address the long acknowledged problem of translating ethnographic findings into system design. This model is based on an ethnographic framework consisting of three dimensions, distributed coordination, awareness of work and plans and procedures; and the BDI (belief, desire and intention) model of intelligent agents. The ethnographic framework is used to organise ethnographically derived information into the three dimensions; whereas the BDI model allows the information to be mapped onto the concepts of multi-agent systems. Focusing on a case study, the usefulness of the proposed model is demonstrated by showing that ethnographic accounts can systematically inform system design and development. Evaluation and generalisability of the model is discussed in the paper.

An important aspect of redesign for usability is to evaluate the system with its real users in order to explore implications for better design. In this paper we discuss the redesign of a support management system deployed in a small and medium sized enterprise (SME) in the UK. The system is used to support complex and distributed cooperative activities taking place in the SME. We evaluate the current system and analyse work practices using a user-centred design and evaluation philosophy. Following that we discuss how user needs are incorporated into the enhanced design of the support management system. The user-centred design techniques used in this research include interviews, questionnaires, observations and user tests. Finally, we present comparative evaluation results that show significant improvement in performance of user tasks using the redesigned support management system.

This paper examines how web services can be used to aid co-operative working for design applications, particularly with regard to transaction management. Design applications classically are long running. Users typically check documents out to work on them, and then check them back in later. While checked out other users cannot work on them. We propose a model which will increase throughput. We achieve this by optionally relaxing all ACID properties. The model will be adaptive to meet different situations with different characteristics. For instance in some cases it will be appropriate to just relax atomicity. In others it may be appropriate to relax isolation and atomicity while maintaining consistency. In our model we will explore how far ACID properties of a transaction and combinations thereof can be relaxed in collaborating design applications whilst still maintaining a reliable user service. The proposed model can be used in many areas of application.

In an increasingly sophisticated global business environment there is often a need to be able to bring together professionals from all types of organizations from all over the world. Those professionals operate under severe time constraints and a conventional face-to-face meeting is both impractical and expensive. Virtual meetings have proved to be a convenient alternative, offering a flexible meeting domain more suitable for the busy executive. This paper explores the concept of using a peer-to-peer network and multi-agent systems to facilitate virtual meetings, managing the artifacts required for the session. By the use of a laptop, PDA or similar electronic device the meeting participant can join the meeting and collaborate in the discussion.

QoS is an issue of intrinsic importance in service oriented applications. Although many service oriented applications deliver their promised services yet it is rather difficult for them to maintain the QoS from user perspective. In this paper we develop an approach based on multi-agent system that helps to achieve the desired level of QoS by negotiating and coordinating between agents at the application layer. These service oriented applications may use the Web services of an agent based service in order to realize a service oriented architecture. In this paper, we propose an agent based approach to address QoS issues in service oriented applications. This approach is based on a unified framework that is applicable to both Web services and agent based services.

This paper presents some reflection upon the use of a flexible scenario-based method for the assessment of a third level module in databases in a UK university. The method is designed to encourage problem-solving skills amongst students by carrying out scenario-based assessments which are highly flexible. By the use of this method, we intend to address the surface learning strategy demonstrated by many students - a problem identified by many studies in higher education. The method is applied to assess the intended learning outcomes and performance of students. The key benefits of this method are its applicability to courses with large cohorts, flexibility and time saving in marking. In this paper, we report our experience of using the technique of scenario-based learning as a key pedagogical method for assessing practical databases issues concerning data protection, data legislations and object-oriented databases.

One of the major issues in dealing with exceptions in open multi-agent systems (MAS) is lack of uniform representation of exceptions and their shared semantics. In the absence of a uniform framework different business organization may use different representations for the same exception or may interpret the same exception in different way. In order to address this issue we present an ontological approach to provide a uniform way of representing and interpreting exceptions in cross-organizational settings. This helps agents from different organizations to interpret exceptional situations in an unambiguous way and exchange exception related information using standard structures. We believe that exception ontology along with domain ontology increases the open MAS reliability and also enhances its fault tolerance capability.

Design for emerging technologies such as ubiquitous computing, calm technology and Grid computing requires knowledge and expertise from different disciplines. Such knowledge helps to improve our individual information processing resources and thus overcome our limitations. However, integrating design knowledge and expertise of different team members coming from different disciplines is a complex task. Today, we are facing two problems. Firstly, how to enable the multidisciplinary design team to solve problems given the need to build common ground amongst team members? Secondly, how to structure the information which is held about projects so that issues can be progressed and resolved, in particular those arising as a result of the diversity of team members. In this paper, we propose an approach based on common ground theory and IBIS (Issue Based Information System) to address issues encountered by the multidisciplinary team and to enable the decision making process. We also provide a means of evaluating the approach by using statistical analysis.

While we have seen a successful move towards a rapid proliferation of CSCW integration through Web technology including artificial intelligence techniques, still a significant work needs to be done in order to provide quality of service (QoS), particularly addressing the issues of QoS in terms of user requirements. In this paper, we present an approach based on ethnography and multi-agents systems to address these issues in an effective way by mapping a user mental model onto agents. We apply ethnographic approach in order to understand and explain the semantics, functionality and detailed user requirements of CSCW systems. Secondly, we employ artificial intelligent agents for communication and collaboration purposes based on user profile and preferences. In this paper, we also demonstrate the use/illness of this approach by presenting a real life case study of two CSCW (computer supported cooperative work) systems

This paper reports on the need for integration and different aspects of interoperability in CSCW. This is a part of our research towards investigating and developing an integrative framework for CSCW applications. The framework will enable different applications to communicate with each other to share information and to support organizational activities. It will be flexible enough to accommodate the various and varying needs of the user community. We discuss different types of integration and interoperability in CSCW and consider different models of CSCW systems. A framework for CSCW integration is presented. The framework includes ontological, coordination, user interface, security and transaction models. An example application scenario involving integration is given. The work is novel as no integrative framework for CSCW exists currently.