Search results

A decade and a half after the debut of pervasive computing, a large number of prototypes, applications, and interaction interfaces have emerged. However, there is a lack of consensus about the best approaches to create such systems or how to evaluate them. To address these issues, this paper aims to develop a performance evaluation framework for pervasive computing systems.

Based on the authors' experience in the Gator Tech Smart House – an assistive environment for the elderly, they established a reference scenario that was used to guide the analysis of the large number of systems they studied. An extensive survey of the literature was conducted, and through a thorough analysis, the authors derived and arrived at a broad taxonomy that could form a basic framework for evaluating existing and future pervasive computing systems.

A taxonomy of pervasive systems is instrumental to their successful evaluation and assessment. The process of creating such taxonomy is cumbersome, and as pervasive systems evolve with new technological advances, such taxonomy is bound to change by way of refinement or extension. This paper found that a taxonomy for something so broad as pervasive systems is very complex. It overcomes the complexity by focusing the classifications on key aspects of pervasive systems, decided purely empirically and based on the authors own experience in a real‐life, large‐scale pervasive system project.

There are currently no methods or frameworks for comparing, classifying, or evaluating pervasive systems. The paper establishes a taxonomy – a first step toward a larger evaluation methodology. It also provides a wealth of information, derived from a survey of a broad collection of pervasive systems.

The starting point for this paper is that the researcher is intimately bound up in all aspects of the research process. This idea of what is a critical aspect of much interpretive methodology has been challenged by some proponents of the interpretive accounting research (IAR) project. The authors suggest that adopting some of the views expounded in the IAR project may lead to the accounting research community becoming isolated from other interpretive methodology inspired disciplines. The paper aims to discuss these issues.

Currently popular views on IAR are informed by selective theoretical insights from interpretive sociology. The authors argue that these insights cannot provide a general frame with which to encapsulate accounting research that may be reasonably termed “interpretive.”

The authors’ reading of the literature suggests that the some of the IAR literature exhibits: a tendency to routinely make overly specific claims for what it is possible for interpretive research to achieve; the promotion of a somewhat reductionist view of what the bounds of interpretive research are. The authors suggest that these tendencies detract from the strengths of (adopting a broad view of) IAR.

In expressing the authors’ concerns, the authors do not wish to make an exclusive argument for what IAR is and is not. This would not be in line with writing an interpretive paper. While the authors do not eschew the possibility of a limited building of knowledge by applying interpretive methodological stances neither do the authors see such activity as a central plank of interpretive research.

The authors believe that positivistic commentaries on qualitative enquiry should not be taken as exemplary of interpretive research (in accounting – or elsewhere). The authors feel that IAR needs to be more open to an array of subjectivist motivations, if it is to provide useful critique of the nature of day-to-day accounting practice.

The authors seek to go beyond the rather unhelpful debate about whether IAR should be seen to possess both objective and subjective elements. The authors argue that IAR suffers more from a lack of engagement and debate than it faces dangers from areas of interpretive methodology that adopt positions considered to be too subjectivist.

This study aims to investigate the key elements that influence knowledge sharing practice, primarily the relationship between knowledge sharing practice and organizational performance within the oil and gas (OG) industry.

A sample of 203 responses was collected from the OG industry using an online questionnaire. Data were analyzed using applied structural equation modeling to validate the model and test the hypotheses.

The results indicate that significant relationships exist among the model constructs. These findings provide a better understanding of the factors that influence knowledge sharing practices within the OG industry. These findings prove that knowledge sharing practices positively impact organizational performance through cost reduction, organization growth and intangible benefits.

This study demonstrates that organizations in the OG industry may increase performance by adopting knowledge sharing practices. This study also provides practitioners with important information to enhance knowledge sharing practice within their organizations. For instance, managers should focus on Web 2.0 and other knowledge sharing systems to facilitate both tacit and explicit knowledge sharing. The findings provide empirical evidence that knowledge sharing practices allow organizations to transfer expert knowledge to younger generations of employees. As a result, organizations will be able to capture knowledge and alleviate the negative impact of high staff turnover within the OG industry.

The lack of knowledge sharing practices and the eminent loss of technical knowledge within the (OG) industry, because of retirements and turnover, create a difficult challenge for practitioners. Research on knowledge sharing within the OG industry is limited. Therefore, this study provides an in-depth analysis regarding the critical knowledge sharing practices and valuable information to researcher and practitioners’ knowledge sharing practices within the OG industry.

The purpose of this study is to propose a new method for the end-to-end classification of steel surface defects.

This study proposes an AM-AoN-SNN algorithm, which combines an attention mechanism (AM) with an All-optical Neuron-based spiking neural network (AoN-SNN). The AM enhances network learning and extracts defective features, while the AoN-SNN predicts both the labels of the defects and the final labels of the images. Compared to the conventional Leaky-Integrated and Fire SNN, the AoN-SNN has improved the activation of neurons.

The experimental findings on Northeast University (NEU)-CLS demonstrate that the proposed neural network detection approach outperforms other methods. Furthermore, the network’s effectiveness was tested, and the results indicate that the proposed method can achieve high detection accuracy and strong anti-interference capabilities while maintaining a basic structure.

This study introduces a novel approach to classifying steel surface defects using a combination of a shallow AoN-SNN and a hybrid AM with different network architectures. The proposed method is the first study of SNN networks applied to this task.

The ranking of universities and other research-intensive institutions in global ranking systems is based on numerous indicators, including number of articles with external collaboration, number of articles with international collaboration, number of articles with industry collaboration as well as co-patents with industry. The purpose of this paper is to examine university–industry research collaboration in Ghana, with the aim of exploring the relationship between the research output collaborations in the top four universities in Ghana and industry across different geographical scales.

This study’s data was obtained from the SciVal database, which drawn its data from the Scopus bibliographic and citation database. The bibliographic and citation data were extracted using a search of the publications affiliated to the University of Ghana, for the period 2011–2020.

Key findings demonstrate a constant rise in the number of research publications by the selected universities over time. Research collaboration intensity in the selected universities in terms of co-authored publications was higher as compared to single-authored publications. University–industry research co-authorships were, however, lower when compared to university–university research co-authorships. The university–industry research co-authorships occurred mostly with Europe, Asia-Pacific and North American-based institutions as opposed to African-based institutions. In Ghana, four industry-based institutions were engaged in intensive research with the selected universities.

This study demonstrates that, for each selected university, it is possible to measure the performance of individual universities in both intra-regional and international collaboration. Such results may be useful in informing policy as well as merit-based public funding of universities in Ghana.