Optimal Relay Selection for Physical-Layer Security in Cooperative Wireless Networks

In this paper, we explore the physical-layer security in cooperative wireless networks with multiple relays where both amplify-and-forward (AF) and decode-and-forward (DF) protocols are considered. We propose the AF and DF based optimal relay selection (i.e., AFbORS and DFbORS) schemes to improve the wireless security against eavesdropping attack. For the purpose of comparison, we examine the traditional AFbORS and DFbORS schemes, denoted by T-AFbORS and TDFbORS, respectively. We also investigate a so-called multiple relay combining (MRC) framework and present the traditional AF and DF based MRC schemes, called T-AFbMRC and TDFbMRC, where multiple relays participate in forwarding the source signal to destination which then combines its received signals from the multiple relays. We derive closed-form intercept probability expressions of the proposed AFbORS and DFbORS (i.e., P-AFbORS and P-DFbORS) as well as the T-AFbORS, TDFbORS, T-AFbMRC and T-DFbMRC schemes in the presence of eavesdropping attack. We further conduct an asymptotic intercept probability analysis to evaluate the diversity order performance of relay selection schemes and show that no matter which relaying protocol is considered (i.e., AF and DF), the traditional and proposed optimal relay selection approaches both achieve the diversity order M where M represents the number of relays. In addition, numerical results show that for both AF and DF protocols, the intercept probability performance of proposed optimal relay selection is strictly better than that of the traditional relay selection and multiple relay combining methods.Comment: 13 page

Anytime-Anywhere: Personalised Time Management in Networking for e-Learning

Personalisation in the provision of higher education (HE) has gained traction driven by socio-economic, demographic, and employment changes in the student population.  Concomitant with these changes is the evolving capability and ubiquity of mobile technologies.  These developments have resulted in interest in e-learning to accommodate the diverse student population and leverage the power of mobile technologies.  To address the changing educational demands ‘anytime-anywhere' personalised e-learning utilising mobile technologies is becoming ubiquitous in the domain of HE, and increasingly e-learning is embracing Web 2.0 technologies to provide networking functionality at both a pedagogic and personal level.  Personalisation requires the creation of an individual's profile (termed a context), a context defining and describing a user's current state.  This article considers personalised e-learning in a university domain with consideration of networking (in a collaborative and social networking sense).  Following consideration of the factors driving the interest in and take-up of e-Learning (in a mobile context) Web 2.0 technologies will be considered.  The nature of context and context and related research is considered followed by a brief overview of the proposed approach which is designed to enable effective personalisation with constraint satisfaction and predictable decision support.  The article closes with final observations and conclusions

A Conceptual Approach to Exploring Creativity in Requirements Engineering

Requirements engineering (RE), an early phase in software development, is the process of discovery, analysis, modelling and specification of user and business requirements for information systems. The lack of creativity theories and models within RE has been gaining increasing recognition within the RE community. This paper synthesises concepts from creativity research and RE creativity research to build a theoretical foundation for the study of creativity in RE. We argue that creativity in RE goes beyond technical aspects and involves different levels, loci, and inter-related elements including product, process, domain, people and socio-organisational context. Different facets of creativity need to be integrated within RE approaches and methods to effectively foster and support creativity in this field

Machine Learning Applications for Sustainable Manufacturing: A Bibliometric-based Review for Future Research

The role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM). In the present study, we use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing. The present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices. A comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originalityN/

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Funding Information: Griffith University Gowonda HPC Cluster; Queensland Cyber Infrastructure FoundationPeer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science.</p

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe