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This paper aims to describe the problems faced by MyAIS, an open access repository of scholarly content, the reasons for moving to a cloud infrastructure and the design of the system.

MyAIS is supported by an IBM HS22V multiple blade server using virtualization technology. A single blade server runs both MyAIS's back‐end that handles the processing of digital repository and front‐end that handles the web server. The whole system runs on a Linux platform. The virtualized servers deliver an infrastructure as a service (IaaS) to the Faculty of Computer Science, University of Malaya researchers as well as students.

Sharing resources in the cloud cuts maintenance cost and leaves a smaller carbon footprint due to its modular design. Virtualization makes it easy to monitor access traffic and usage of hardware resources on‐the‐fly. The scalability and expansion of MyAIS is improved. It allows for faster system recovery in case of hard disk failure and speedy file move during migration. Larger bandwidth improves reliability and management of bottlenecks.

There are still bottlenecks at the wide area network level resulting in sluggishness during peak hours.

There is significant improvement in storage expansion through shared hardware resources.

Cloud as an IaaS helps reduce the total cost of ownership and maintenance, increase system scalability to handle increased traffic, meet increasing demand for storage space for full text resources, and reduce downtime. It makes backup and disaster recovery easier. It is possible to accommodate other university repositories in future.

There is little known globally on the association among the independent shareholder, board size and merger and acquisition (M&A) performance. This paper addresses the global issue about cross-border M&A in banking sector, particularly exploring the role of difference in the independent shareholder and board size between acquirer and target banks on synergy gains based on the international study.

Based on cross-border bank M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis techniques to explore the country connectedness of the acquirer-target banks in cross-border M&As. Ordinary least squares (OLS) with robust standard errors is further used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between the acquirer and target banks.

Our results indicate that the acquiring banks are generally interconnected with the targeted banks and that some of acquiring banks are clearly concentrated in Asian countries including China, Hong Kong, and Philippines. Moreover, we find that cross-border M&As with larger difference in independent shareholders between the bidder and target bank would result in higher synergy gains in all cases of takeover premiums on 1 day, 1 week and 4 weeks. In addition, financial differences between the bidder and target banks have a significant impact on synergetic gains, a topic not explored in previous studies. There is no evidence that institutional and governance differences between bidder and target bank have significant cross-border impacts on takeover premiums with respect to 1 day, 1 week and 4 weeks, respectively.

This paper contributes to the literature by exploring the international issue about the role of difference in the degree of bank independent shareholder and board sizes between acquirer and target banks on synergy gains. Based on bank cross-border M&As data on 59 deals from 1995 to 2009, we initially apply social network analysis to explore the country connectedness of acquirer-target bank in cross-border M&As, while ten ordinary least squares (OLS) with robust standard errors is used to investigate synergy gains within the difference in the degree of bank independent shareholder and board sizes between acquirer and target banks.

The purpose of this paper is to study the users’ willingness for acceptance of background music service in university libraries based on intelligent campus and to improve the intelligence level of university libraries and provide a reference for the atmosphere.

The research method of combining theory with practice is applied, and field distribution method and network survey method are used. An algorithm model is established to investigate relevant users, and statistical analysis of the data obtained is made.

The results show that in the questionnaire survey, girls are more inclined to study in the environment of library than boys; for grade, sophomores and juniors are more inclined to go to library than other grades; through model analysis, the target users are more inclined to choose light music as background music, accounting for 65 percent. Heavy metals and other users have fewer choices, accounting for only 8 percent.

This questionnaire is mainly filled out in paper form on site, so it only selects some nearby students as the survey objects. However, the condition of university libraries across the country must be different due to regional differences, disciplinary differences and funding differences, so the representative sample may be insufficient. Therefore, in the follow-up research, the scope of the survey should be expanded, especially the geographical scope. It should collect as much data as possible for students of different types and genders, so as to expand the applicable scope and explanatory power of the model.

Starting from the library scene, this research studies the acceptance intention of users of background music service in the library, which provides reference for the improvement of the intellectualization of university libraries and their atmosphere. Although different university libraries have different operation modes and service characteristics, the conclusions of this study have certain practical significance for the library industry because the library industry has many commonalities.

At present, the research on the background music service of University Library based on the smart campus is relatively rare and limited to the theoretical stage. Few people have deeply explored the background music service of the library, and no scholars have quantitatively studied it. In this study, based on the questionnaire and from the perspective of users, the acceptance intention of background music service users is investigated, which provides a reference for the improvement of the intellectualization of university libraries and its atmosphere. It is a research topic of great practical significance.

In order to solve the current imbalance of academic resources within the discipline, this article builds a three-dimensional talent evaluation model based on the topic–author–citation based on the z index and proposes the ZAS index to evaluate scholars on different research topics within the discipline.

Based on the sample data of the CSSCI journals in the discipline of physical education in the past five years, the keywords were classified into 13 categories of research topics including female sports. The ZAS index of scholars on topic of female sports and so on was calculated, and quantitative indexes such as h index p index and z index were calculated. Comparative analysis of the evaluation effect was performed.

It is found that compared with the h index and p index, the z index achieves a better balance between the quantity, quality and citation distribution of scholars' results and effectively recognizes that the citation quality is higher and the number of citations of each paper is more balanced. In addition, compared to the z index, this article is based on a ZAS index model with an improved three-dimensional topic–author–citation relationship in research fields such as female sports.

It can identify some outstanding scholars who are engaged in small-scale or emerging topic research such as female sports and are excellent in different research areas. Talents create an objective and fair evaluation environment. At the same time, the ranking ability of ZAS indicators in the evaluation of talents is the strongest, and it is expected to be used in practical evaluations.

In today’s digital era, data pertaining to scientific research have attracted considerable attention of researchers. Data of scientific publications can be modeled in the form of networks such as citation networks, co-citation networks, collaboration networks, and others. Identification and ranking of important nodes in such networks is useful in many applications, such as finding most influential papers, most productive researchers, pattern of citation, and many more. The paper aims to discuss this issue.

A number of methods are available in literature for node ranking, and K-shell decomposition is one such method. This method categorizes nodes in different groups based on their topological position. The shell number of a node provides useful insights about the node’s importance in the network. It has been found that shells produced by the K-shell method need to be further refined to quantify the influence of the nodes aptly. In this work, a method has been developed, which ranks nodes by taking the core(s) as the origin and second-order neighborhood of a node as its immediate sphere of influence.

It is found that the performance of the proposed technique is either comparable or better than other methods in terms of correctness and accuracy. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods. The proposed method can be used to rank authors, research articles, and fields of research.

The proposed method ranks nodes by their global position in a network as well as their local sphere of information. It leads to better quantification of a node’s impact. This method is found to be better in terms of accuracy and correctness. In case of assigning different ranks to nodes, the performance of the proposed technique is far more superior to existing methods.

The purpose of this paper is to present a novel approach for mining scientific trends using topics from Call for Papers (CFP). The work contributes a valuable input for researchers, academics, funding institutes and research administration departments by sharing the trends to set directions of research path.

The authors procure an innovative CFP data set to analyse scientific evolution and prestige of conferences that set scientific trends using scientific publications indexed in DBLP. Using the Field of Research code 804 from Australian Research Council, the authors identify 146 conferences (from 2006 to 2015) into different thematic areas by matching the terms extracted from publication titles with the Association for Computing Machinery Computing Classification System. Furthermore, the authors enrich the vocabulary of terms from the WordNet dictionary and Growbag data set. To measure the significance of terms, the authors adopt the following weighting schemas: probabilistic, gram, relative, accumulative and hierarchal.

The results indicate the rise of “big data analytics” from CFP topics in the last few years. Whereas the topics related to “privacy and security” show an exponential increase, the topics related to “semantic web” show a downfall in recent years. While analysing publication output in DBLP that matches CFP indexed in ERA Core A* to C rank conference, the authors identified that A* and A tier conferences not merely set publication trends, since B or C tier conferences target similar CFP.

Overall, the analyses presented in this research are prolific for the scientific community and research administrators to study research trends and better data management of digital libraries pertaining to the scientific literature.

The aim of this study is to explore students' expectations and perceived effectiveness of computer-assisted review tools, and the differences in reliability and validity between human evaluation and automatic evaluation, to find a way to improve students' English writing ability.

Based on the expectancy disconfirmation theory (EDT) and Intelligent Computer-Assisted Language Learning (ICALL) theory, an experiment is conducted through the observation method, semistructured interview method and questionnaire survey method. In the experiment, respondents were asked to write and submit four essays on three online automated essay evaluation (AEE) systems in total, one essay every two weeks. Also, two teacher raters were invited to score the first and last papers of each student. The respondents' feedbacks were investigated to confirm the effectiveness of the AEE system; the evaluation results of the AEE systems and teachers were compared; descriptive statistics was used to analyze the experimental data.

The experiment revealed that the respondents held high expectations for the computer-assisted evaluation tools, and the effectiveness of computer scoring feedback on students was higher than that of teacher scoring feedback. Moreover, at the end of the writing project, the students' independent learning ability and English writing ability were significantly improved. Besides, there was a positive correlation between students' initial expectations of computer-assisted learning tools and the final evaluation of learning results.

The innovation lies in the use of observation methods, questionnaire survey methods, data analysis, and other methods for the experiment, and the combination of deep learning theory, EDT and descriptive statistics, which has particular reference value for future works.

The purpose of this paper is to solve the problem of information privacy and security of social users. Mobile internet and social network are more and more deeply integrated into people’s daily life, especially under the interaction of the fierce development momentum of the Internet of Things and diversified personalized services, more and more private information of social users is exposed to the network environment actively or unintentionally. In addition, a large amount of social network data not only brings more benefits to network application providers, but also provides motivation for malicious attackers. Therefore, under the social network environment, the research on the privacy protection of user information has great theoretical and practical significance.

In this study, based on the social network analysis, combined with the attribute reduction idea of rough set theory, the generalized reduction concept based on multi-level rough set from the perspectives of positive region, information entropy and knowledge granularity of rough set theory were proposed. Furthermore, it was traversed on the basis of the hierarchical compatible granularity space of the original information system and the corresponding attribute values are coarsened. The selected test data sets were tested, and the experimental results were analyzed.

The results showed that the algorithm can guarantee the anonymity requirement of data publishing and improve the effect of classification modeling on anonymous data in social network environment.

In the test and verification of privacy protection algorithm and privacy protection scheme, the efficiency of algorithm and scheme needs to be tested on a larger data scale. However, the data in this study are not enough. In the following research, more data will be used for testing and verification.

In the context of social network, the hierarchical structure of data is introduced into rough set theory as domain knowledge by referring to human granulation cognitive mechanism, and rough set modeling for complex hierarchical data is studied for hierarchical data of decision table. The theoretical research results are applied to hierarchical decision rule mining and k-anonymous privacy protection data mining research, which enriches the connotation of rough set theory and has important theoretical and practical significance for further promoting the application of this theory. In addition, combined the theory of secure multi-party computing and the theory of attribute reduction in rough set, a privacy protection feature selection algorithm for multi-source decision table is proposed, which solves the privacy protection problem of feature selection in distributed environment. It provides a set of effective rough set feature selection method for privacy protection classification mining in distributed environment, which has practical application value for promoting the development of privacy protection data mining.

In this study, the proposed algorithm and scheme can effectively protect the privacy of social network data, ensure the availability of social network graph structure and realize the need of both protection and sharing of user attributes and relational data.