Detection method of emerging leading papers using time transition

To survive worldwide competitions of research and development in the current rapid increase of information, decision-makers and researchers need to be supported to find promising research fields and papers. But finding those fields from an available data in too much heavy flood of information becomes difficult. We aim to develop a methodology supporting to find emerging leading papers with a bibliometric approach. The analyses in this work are about four academic domains using our time transition analysis. In the time transition analysis, after citation networks are constructed, centralities of each paper are calculated and their changes are tracked. Then, the centralities are plotted, and the features of the leading papers are extracted. Based on the features, we proposed ways to detect the leading papers by focusing on in-degree centrality and its transition. This work will contribute to finding the leading paper, and it is useful for decision-makers and researchers to decide the worthy research topic to invest their resources.     

A Citation Analysis of the Research on Manufacturing and Supply Chain Flexibility

Manufacturing and supply chain flexibility has been discussed extensively in the scientific literature. Given the different definitions, types and dimensions of flexibility, the purpose of this paper is to explore the origins and structure of the scientific research on manufacturing and supply chain flexibility. We identified a sample of 153 internationally published papers and conducted a citation analysis to examine the connections between the many scientific papers and to explore the most influential works and their impact on flexibility research. Our analysis revealed that research on flexibility received the most scientific attention between 1996 and 2005. However, the majority of the currently valid types and dimensions of flexibility have been defined in the late 1980s and the early 1990s. The current paper provides the leading academic journals and papers in the field of flexibility research. Additionally, the results of our citation analysis indicate that flexibility research is significantly influenced by papers that have been presented at subject-specific conferences. Finally, our paper presents a clearly arranged structure of the most frequently cited papers dealing with manufacturing and supply chain flexibility. Furthermore, its contents and findings are briefly discussed to provide an overview of the previous academic research.     

The organization of scientific knowledge: the structural characteristics of keyword networks

The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.     

SwCS: Section-Wise Content Similarity Approach to Exploit Scientific Big Data

The growing collection of scientific data in various web repositories is referred to as Scientific Big Data, as it fulfills the four “V’s” of Big Data–-volume, variety, velocity, and veracity. This phenomenon has created new opportunities for startups; for instance, the extraction of pertinent research papers from enormous knowledge repositories using certain innovative methods has become an important task for researchers and entrepreneurs. Traditionally, the content of the papers are compared to list the relevant papers from a repository. The conventional method results in a long list of papers that is often impossible to interpret productively. Therefore, the need for a novel approach that intelligently utilizes the available data is imminent. Moreover, the primary element of the scientific knowledge base is a research article, which consists of various logical sections such as the Abstract, Introduction, Related Work, Methodology, Results, and Conclusion. Thus, this study utilizes these logical sections of research articles, because they hold significant potential in finding relevant papers. In this study, comprehensive experiments were performed to determine the role of the logical sections-based terms indexing method in improving the quality of results (i.e., retrieving relevant papers). Therefore, we proposed, implemented, and evaluated the logical sections-based content comparisons method to address the research objective with a standard method of indexing terms. The section-based approach outperformed the standard content-based approach in identifying relevant documents from all classified topics of computer science. Overall, the proposed approach extracted 14% more relevant results from the entire dataset. As the experimental results suggested that employing a finer content similarity technique improved the quality of results, the proposed approach has led the foundation of knowledge-based startups.     

Quantity versus impact of software engineering papers: a quantitative study

According to the data from the Scopus publication database, as analyzed in several recent studies, more than 70,000 papers have been published in the area of Software Engineering (SE) since late 1960’s. According to our recent work, 43% of those papers have received no citations at all. Since citations are the most commonly used metric for measuring research (academic) impact, these figures raise questions (doubts) about the (non-existing) impact of such a large set of papers. It is a reality that typical academic reward systems encourage researchers to publish more papers and do not place a major emphasis on research impact. To shed light on the issue of volume (quantity) versus citation-based impact of SE research papers, we conduct and report in this paper a quantitative bibliometrics assessment in four aspects: (1) quantity versus impact of different paper types (e.g., conference versus journal papers), (2) ratios of uncited (non-impactful) papers, (3) quantity versus impact of papers originating from different countries, and (4) quantity versus impact of papers by each of the top-10 authors (in terms of number of papers). To achieve the above objective, we conducted a quantitative exploratory bibliometrics assessment, comprised of four research questions, to assess quantity versus impact of SE papers with respect to the aspects discussed above. We extracted the data through a systematic, automated and repeatable process from the Scopus paper database, which we also used in two previous papers. Our results show that the distribution of SE publications has a major inequality in terms of impact overall, and also when categorized in terms of the above four aspects. The situation in the SE literature is similar to the other areas of science as studied by previous bibliometrics studies. Also, among our results is the fact that journal articles and conference papers have been cited 12.6 and 3.6 times on average, confirming the expectation that journal articles have more impact, in general, than conference papers. Also, papers originated from English-speaking countries have in general more visibility and impact (and consequently citations) when compared to papers originated from non-English-speaking countries. Our results have implications for improvement of academic reward systems, which nowadays mainly encourage researchers to publish more papers and usually neglect research impact. Also, our results can help researchers in non-English-speaking countries to consider improvements to increase their research impact of their upcoming papers.     

In quest of new document relations: evaluating co-opinion relations between co-citations and its impact on Information retrieval effectiveness

Document relational network has been effective in retrieving and evaluating papers. Despite their effectiveness, relational measures, including co-citation, are far from ideal and need improvements. The assumption underlying the co-citation relation is the content relevance and opinion relatedness of cited and citing papers. This may imply existence of some kind of co-opinionatedness between co-cited papers which may be effective in improving the measure. Therefore, the present study tries to test the existence of this phenomenon and its role in improving information retrieval. To do so, based on CITREC, a medical test collection was developed consisting of 30 queries (seed documents) and 4823 of their co-cited papers. Using NLP techniques, the co-citances of the queries and their co-cited papers were analyzed and their similarities were computed by 4 g similarity measure. Opinion scores were extracted from co-citances using SentiWordnet. Also, nDCG values were calculated and then compared in terms of the citation proximity index (CPI) and co-citedness measures before and after being normalized by the co-opinionatedness measure. The reliability of the test collection was measured by generalizability theory. The findings suggested that a majority of the co-citations exhibited a high level of co-opinionatedness in that they were mostly similar either in their opinion strengths or in their polarities. Although anti-polar co-citations were not trivial in their number, a significantly higher number of the co-citations were co-polar, with a majority being positive. The evaluation of the normalization of the CPI and co-citedness by the co-opinionatedness indicated a generally significant improvement in retrieval effectiveness. While anti-polar similarity reduced the effectiveness of the measure, the co-polar similarity proved to be effective in improving the co-citedness. Consequently, the co-opinionatedness can be presented as a new document relation and used as a normalization factor to improve retrieval performance and research evaluation.

     

Visualizing the structure and bridges of the intellectual property management and strategy literature: a document co-citation analysis

This article uses document co-citation analysis to objectively explore the underlying structure of the intellectual property research domain, taken from a managerial and strategic standpoint. The goal of this study is identifying its main research areas, understanding its current state of development and suggesting potential future directions, by analyzing the co-citations from 181 papers published between 1992 and 2011 in the most influential academic journals. Five main clusters have been identified, mapped, and labeled as follows: Economics of patent system, technological and institutional capabilities, university patenting, intellectual property exploitation, and division of labor. Their most active areas on this topic, and the most influential and co-cited papers have been identified and described. Also, intra- and inter-cluster knowledge base diversity has been assessed by using indicators stemming from the domains of information theory and biology. A t test has been performed to assess the significance of the inter-cluster diversity. The knowledge bases of these five clusters are significantly diverse, this meaning that they are five co-existing paradigms.     

Women,peace and security state-of-art: a bibliometric analysis in social sciences based on SCOPUS database

To the best of our knowledge, there is no work that has focused on analysing the development of the scientific production on women, peace and security. The main objective of this paper is to cover this research gap through a bibliometric analysis, that covers 95 years (1918–2013), of articles published in peer-reviewed journals extracted from the SCOPUS database. Bibliometric indicators and laws have been applied to better understand the patterns that govern the scientific literature on this realm. A gender perspective has also been implemented in the analysis. The analysis provides quantitative results based on 321 articles published by 478 authors in 210 scientific journals. The data showed the high dispersion of the literature, both in terms of authors and journals focused on the topic, and a low level of collaboration among both authors and institutions. Regarding the research topic, half of the papers were related to the impact of conflict on civilians and civil society. The implementation of a gender perspective shows that most of the first, second and third authors of the papers are women. In terms of methodologies, qualitative methodologies are the most relevant and women are more prolific applying these methodology. In terms of geographical region of the research, most of the studies have been performed by authors from institutions in Anglo-Saxon countries, and most of the fieldwork has been focused on the United States and the main areas of conflict in the world throughout history. Finally, important research opportunities are identified.     

Assessing manufacturing strategy definitions utilising text-mining

The variations in Manufacturing Strategy (MS) definitions create confusion and lead to lack of shared understanding between academic researchers and practitioners on its scope. The purpose of this study is to provide an empirical analysis of the paradox in the difference between academic and industry definitions of MS. Natural Language Processing (NLP) based text mining is used to extract primary elements from the various academic, and industry definitions of MS. Co-word and Principal Component Analysis (PCA) provide empirical support for the grouping into nine primary elements. We posit from the terms evolution analysis that there is a stasis currently faced in academic literature towards MS definition while the industry with its emphasis on ‘context’ has been dynamic. We believe that the proposed approach and results of the present empirical analysis can contribute to overcoming the current challenges to MS design and deployment – imprecise definition leading to its inadequate operationalisation.     

Journal peer review: a bar or bridge? An analysis of a paper’s revision history and turnaround time,and the effect on citation

Journal peer review lies at the heart of academic quality control. This article explores the journal peer review process and seeks to examine how the reviewing process might itself contribute to papers, leading them to be more highly cited and to achieve greater recognition. Our work builds on previous observations and views expressed in the literature about (a) the role of actors involved in the research and publication process that suggest that peer review is inherent in the research process and (b) on the contribution reviewers themselves might make to the content and increased citation of papers. Using data from the journal peer review process of a single journal in the Social Sciences field (Business, Management and Accounting), we examine the effects of peer review on papers submitted to that journal including the effect upon citation, a novel step in the study of the outcome of peer review. Our detailed analysis suggests, contrary to initial assumptions, that it is not the time taken to revise papers but the actual number of revisions that leads to greater recognition for papers in terms of citation impact. Our study provides evidence, albeit limited to the case of a single journal, that the peer review process may constitute a form of knowledge production and is not the simple correction of errors contained in submitted papers.     

Investigating the genealogy of the literature on digital pathology: a two-dimensional bibliometric approach

The purpose of our study is to investigate the genealogy of the literature on digital pathology (DP) by evaluating the “upstream” (source papers in the field), “midstream” (outstanding papers in the field), and “downstream” (latest papers in the field) of the research field. All analyses are carried out on a complete database, on which we performed cocitation analysis, bibliographic coupling and double-cluster analysis. Our research reveals the integral knowledge structure of DP, which will help researchers understand the trend of DP, accounting for academic prospects regarding the application of DP in clinic. In addition, as a methodological contribution, we propose a two-dimensional bibliometric approach.

     

A review of agile manufacturing systems

About a decade ago, the agile manufacturing paradigm was formulated in response to the constantly changing 'new economy' and as a basis for returning to global competitiveness. While agility means different things to different enterprises under different contexts, the following elements capture its essential concept: agility is characterized by cooperativeness and synergism (possibly resulting in virtual corporations), by a strategic vision that enables thriving in face of continuous and unpredictable change, by the responsive creation and delivery of customer-valued, high quality and mass customized goods/services, by nimble organization structures of a knowledgeable and empowered workforce, and facilitated by an information infrastructure that links constituent partners in a unified electronic network. During this period, a significant amount of attention from both the academic and industrial communities has produced a large body of results in research and development related to this topic. Each contribution has tackled a different aspect of this large field. In this paper, we review a wide range of recent literature on agile manufacturing. About 73 papers from premier scientific journals and conferences have been reviewed, and a classification scheme to organize these is proposed. We critique these bodies of work and suggest directions for additional research and identify topics where fruitful opportunities exist.     

Syntax-Based Aspect Sentiment Quad Prediction by Dual Modules Neural Network for Chinese Comments

Aspect-Based Sentiment Analysis (ABSA) is one of the essential research in the field of Natural Language Processing (NLP), of which Aspect Sentiment Quad Prediction (ASQP) is a novel and complete subtask. ASQP aims to accurately recognize the sentiment quad in the target sentence, which includes the aspect term, the aspect category, the corresponding opinion term, and the sentiment polarity of opinion. Nevertheless, existing approaches lack knowledge of the sentence’s syntax, so despite recent innovations in ASQP, it is poor for complex cyber comment processing. Also, most research has focused on processing English text, and ASQP for Chinese text is almost non-existent. Chinese usage is more casual than English, and individual characters contain more information. We propose a novel syntactically enhanced neural network framework inspired by syntax knowledge enhancement strategies in other NLP studies. In this framework, part of speech (POS) and dependency trees are input to the model as auxiliary information to strengthen its cognition of Chinese text structure. Besides, we design a relation extraction module, which provides a bridge for the overall extraction of the framework. A comparison of the designed experiments reveals that our proposed strategy outperforms the previous studies on the key metric F1. Further experiments demonstrate that the auxiliary information added to the framework improves the final performance in different ways.     

Research collaboration in groups and networks: differences across academic fields

The purpose of this paper is to give a macro-picture of collaboration in research groups and networks across all academic fields in Norwegian research universities, and to examine the relative importance of membership in groups and networks for individual publication output. To our knowledge, this is a new approach, which may provide valuable information on collaborative patterns in a particular national system, but of clear relevance to other national university systems. At the system level, conducting research in groups and networks are equally important, but there are large differences between academic fields. The research group is clearly most important in the field of medicine and health, while undertaking research in an international network is most important in the natural sciences. Membership in a research group and active participation in international networks are likely to enhance publication productivity and the quality of research.     

Cogent and Energy Efficient Authentication Protocol for WSN in IoT

Given the accelerating development of Internet of things (IoT), a secure and robust authentication mechanism is urgently required as a critical architectural component. The IoT has improved the quality of everyday life for numerous people in many ways. Owing to the predominantly wireless nature of the IoT, connected devices are more vulnerable to security threats compared to wired networks. User authentication is thus of utmost importance in terms of security on the IoT. Several authentication protocols have been proposed in recent years, but most prior schemes do not provide sufficient security for these wireless networks. To overcome the limitations of previous schemes, we propose an efficient and lightweight authentication scheme called the Cogent Biometric-Based Authentication Scheme (COBBAS). The proposed scheme is based on biometric data, and uses lightweight operations to enhance the efficiency of the network in terms of time, storage, and battery consumption. A formal security analysis of COBBAS using Burrows–Abadi–Needham logic proves that the proposed protocol provides secure mutual authentication. Formal security verification using the Automated Validation of Internet Security Protocols and Applications tool shows that the proposed protocol is safe against man-in-the-middle and replay attacks. Informal security analysis further shows that COBBAS protects wireless sensor networks against several security attacks such as password guessing, impersonation, stolen verifier attacks, denial-of-service attacks, and errors in biometric recognition. This protocol also provides user anonymity, confidentiality, integrity, and biometric recovery in acceptable time with reasonable computational cost.     

Significant difference of dependence upon scientific knowledge among different technologies

Summary The authors have constructed an original database of the full text of the Japanese Patent Gazette published since 1994. The database includes not only the front page but also the body text of more than 880,000 granted Japanese patents. By reading the full texts of all 1,500 patent samples, we found that some inventors cite many academic papers in addition to earlier patents in the body texts of their Japanese patents. Using manually extracted academic paper citations and patent citations as “right” answers, we fine-tuned a search algorithm that automatically retrieves cited scientific papers and patents from the entire texts of all the Japanese patents in the database. An academic paper citation in a patent text indicates that the inventor used scientific knowledge in the cited paper when he/she invented the idea codified in the citing patent. The degree of science linkage, as measured by the number of research papers cited in patent documents, is particularly strong in biotechnology. Among other types of technology, those related to photographic-sensitized material, cryptography, optical computing, and speech recognition also show strong science linkage. This suggests that the degree of dependence on scientific knowledge differs from technology to technology and therefore, different ways of university-industry collaboration are necessary for different technology fields.     

Bidirectional relationship between network position and knowledge creation in Scientometrics

Based on the 3100 papers published in the international journal Scientometrics retrieved from Web of Science database over the period 1996–2015, we utilize three-stage least squares (3SLS) to investigate the bidirectional relationship between authors’ network position and knowledge creation. Furthermore, our research is of great benefit to better understanding of the interplay of knowledge and collaboration networks on knowledge creation. The empirical results confirm that the prolific co-authorship and international co-authorship have positive and significant effects on the knowledge creation. The results from the 3SLS estimation models using the number of publications and citations as dependent variables also show that the structural holes of an author in the collaboration networks and her/his knowledge elements in knowledge networks have positive effects on her/his knowledge creation. These findings suggest that structural capital of an author and his/her knowledge elements are important influence factors of the quantity and quality of research output. The results of the models in this paper suggest that authors with better performance in knowledge creation are more likely to attract collaborators and occupy structural holes. In summary, we fill the research gap in exploring the bidirectional relationship between authors’ network positions (in terms of degree centrality and structural holes) and research output.     

中国包装产业大数据知识图谱应用系统的设计

目的针对目前包装产业存在的产业链长、数据大而散、包装领域知识不全面等问题,设计一个中国包装产业大数据知识图谱应用系统。方法从行业高度定义涵盖包装领域全生态的分类体系,结合人工智能中知识图谱的最新技术,对政府信息、工商信息、行业信息、学术论文、全球包装专利等互联网上各类数据进行自动采集汇聚,抽取出知识信息,融合成一个涵盖资讯、政策、会议、标准、论文、专利、企业、产品、高校、机构和专家等十几类信息的包装知识图谱知识库。结果系统主要功能包括数据采集、知识图谱和终端应用,实现了包装产业大数据的图谱探索、产业链图、数据报告和关联搜索。结论该系统使用方便,可从多视图、多维度获取包装产业相关数据,提升行业的数字化和信息化水平,加速中国包装行业的智能化产业升级,促进包装产业逐步向智能、绿色、集约、创新方向发展。