Aravind Sesagiri Raamkumar

Bio: Aravind Sesagiri Raamkumar is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Altmetrics & Recommender system. The author has an hindex of 7, co-authored 24 publications receiving 168 citations.

Using author-specified keywords in building an initial reading list of research papers in scientific paper retrieval and recommender systems

Abstract: The requirements for the task of building an initial reading list in literature review are re-conceptualized and a novel retrieval technique centered on author-specified keywords of papers is proposed for this task.The HITS variant of the proposed technique best satisfies the requirements of the task in an offline evaluation experiment.The proposed technique is evaluated by 132 researchers using 14 evaluation measures in a user evaluation study.Relevance, Recency and Usefulness were identified as the measures with high agreement percentages from participants.Students group were more satisfied with the results than staff group.Three predictors for user satisfaction were identified through the evaluation study. An initial reading list is prepared by researchers at the start of literature review for getting an overview of the research performed in a particular area. Prior studies have taken the approach of merely recommending seminal or popular papers to aid researchers in such a task. In this paper, we present an alternative technique called the AKR (Author-specified Keywords based Retrieval) technique for providing popular, recent, survey and a diverse set of papers as a part of the initial reading list. The AKR technique is based on a novel coverage value that has its calculation centered on author-specified keywords. We performed an offline evaluation experiment with four variants of the AKR technique along with three state-of-the-art approaches involving collaborative filtering and graph ranking algorithms. Findings show that the Hyperlink-Induced Topic Search (HITS) enhanced variant of the AKR technique performs better than other techniques, satisfying most requirements for a reading list. A user evaluation study was conducted with 132 researchers to gauge user interest on the proposed technique using 14 evaluation measures. Results show that (i) students group are more satisfied with the recommended papers than staff group, (ii) popularity measure is strongly correlated with the output quality measures and (iii) the measures familiarity, usefulness and agreeability on a good list were found to be strong predictors for user satisfaction. The AKR technique provides scope for extension in future information retrieval (IR) and content-based recommender systems (RS) studies.

Exploring prestigious citations sourced from top universities in bibliometrics and altmetrics: a case study in the computer science discipline

Abstract: Citation count is an important indicator for measuring research outputs. There have been numerous studies that have investigated factors affecting citation counts from the perspectives of cited papers and citing papers. In this paper, we focused specifically on citing papers and explored citations sourced from prestigious affiliations in the computer science discipline. The QS World University Rankings was employed to identify prestigious citations, named QS citations. We used the Microsoft Academic Graph, a massive scholarly dataset, and conducted different kinds of analysis between papers with QS citations and those without QS citations. We discovered that papers with QS citations are generally associated with higher total citation counts than those without QS citations. We extended the analysis to authors and journals, and the results indicated that when authors or journals have higher proportions of papers with QS citations, they are usually associated with higher values of the H-index or the Journal Impact Factor respectively. Additionally, papers with QS citations are also associated with a higher Altmetric Attention Score and a higher number of specific types of altmetrics such as tweet counts.

Social media presence of scholarly journals

Abstract: Recently, social media has become a potentially new way for scholarly journals to disseminate and evaluate research outputs. Scholarly journals have started promoting their research articles to a wide range of audiences via social media platforms. This article aims to investigate the social media presence of scholarly journals across disciplines. We extracted journals from Web of Science and searched for the social media presence of these journals on Facebook and Twitter. Relevant metrics and content relating to the journals' social media accounts were also crawled for data analysis. From our results, the social media presence of scholarly journals lies between 7.1% and 14.2% across disciplines; and it has shown a steady increase in the last decade. The popularity of scholarly journals on social media is distinct across disciplines. Further, we investigated whether social media metrics of journals can predict the Journal Impact Factor (JIF). We found that the number of followers and disciplines have significant effects on the JIF. In addition, a word co‐occurrence network analysis was also conducted to identify popular topics discussed by scholarly journals on social media platforms. Finally, we highlight challenges and issues faced in this study and discuss future research directions.

Reasons for Primary Medication Nonadherence: A Systematic Review and Metric Analysis.

Abstract: BACKGROUND: The behavior of medication nonadherence is distinguished into primary and secondary nonadherence. Primary nonadherence (PNA) is not as thoroughly studied as secondary nonadherence. OBJE...

Rec4LRW : scientific paper recommender system for literature review and writing

Abstract: In this paper, we introduce Rec4LRW, a recommender system (RS) for assisting researchers in finding research papers for their literature review and writing purposes. This system focuses on three researcher tasks – (1) Building a reading list of research papers, (2) Finding similar papers based on a set of papers, and (3) Shortlisting papers from the final reading list for inclusion in manuscript based on article type. A set of intermediate criteria are proposed to capture the relations between a research paper and its bibliography. The recommendation techniques for the three tasks in Rec4LRW are specifically devised on top of the intermediate criteria. The Rec4LRW workflow along with the screen designs for the three tasks is provided in this paper. The recommendation techniques in the system will be evaluated with state-of-the-art approaches along with user-based evaluation in subsequent studies.

The Eigenfactor Metrics: A network approach to assessing scholarly journals

Abstract: Limited time and budgets have created a legitimate need for quantitative measures of scholarly work The well-known journal impact factor is the leading measure of this sort; here we describe an alternative approach based on the full structure of the scholarly citation network The Eigenfactor and Article Influence Score use an iterative ranking scheme similar to Google's PageRank algorithm With this approach, citations from top journals are weighted more heavily than citations from lower-tier publications We describe these metrics and the rankings that they provide

The literature review: a step-by-step guide for students

Abstract: The literature review: a step-by-step guide for students, by Diana Ridley, London, Sage, 2008, 170 pp., £18.99 (paperback), ISBN: 978-1-4129-3426-8 In this work, Diana Ridley presents a coherent an...

Short-term traffic flow prediction in smart multimedia system for Internet of Vehicles based on deep belief network

Abstract: In the multimedia system for Internet of Vehicles (IoVs), accurate traffic flow information processing and feedback can give drivers guidance. In traditional information processing for IoVs, few researches deal with traffic flow information processing by deep learning. Specially, most of the existing prediction technologies adopt shallow neural network, and their models for chaotic time series are prone to be restricted by multiple parameters. Over the last few years, the dawning of the big data era creates opportunities for the intelligent traffic control and management. In this paper, we take Restricted Boltzmann Machine (RBM) as the method for traffic flow prediction, which is a typical algorithm based on deep learning architecture. Considering traffic big data aggregation in IoVs, multimedia technologies provide enough real sample data for model training. RBM constructs the long-term model of polymorphic for chaotic time series, using phase space reconstruction to recognize the data. To the best of our knowledge, it is the first time apply RBM model to short-term traffic flow prediction, which can improve the performance of multimedia system in IoVs. Moreover, experimental results show that the proposed method has superior performance than traditional shallow neural network prediction methods.

INNODOCT/17. International conference on innovation,documentation and education

Abstract: This paper presents an analysis of the 62 universities in Ecuador that are in the web ranking of universities, in order to evaluate the use of digital media and obtain information on the management of university e-branding. The study explores, to a large extent, the digital variables currently used for broadcasting and we include the resources of two indexed databases as a dissemination flow. All this data has been analyzed through statistics and web performance tools. The results indicate the universities in Ecuador do not present a clear use of the academic e-branding as a strategy of dissemination, exposure, and visibility improvement to increase their ranking level. However, it is clear that some have demonstrated the relevance of the uses of these systems to improve their worldwide level spread.