Search results

This study reviews the methods found in the literature for the ranking of authors, identifies the pros and cons of these methods, discusses and compares these methods. The purpose of this paper is to study is to find the challenges and future directions of ranking of academic objects, especially authors, for future researchers.

This study reviews the methods found in the literature for the ranking of authors, classifies them into subcategories by studying and analyzing their way of achieving the objectives, discusses and compares them. The data sets used in the literature and the evaluation measures applicable in the domain are also presented.

The survey identifies the challenges involved in the field of ranking of authors and future directions.

To the best of the knowledge, this is the first survey that studies the author ranking problem in detail and classifies them according to their key functionalities, features and way of achieving the objective according to the requirement of the problem.

Tags are used to annotate resources on social media platforms. Most tag recommendation methods use popular tags, but in the case of new resources that are as yet untagged (the cold start problem), popularity-based tag recommendation methods fail to work. The purpose of this paper is to propose a novel model for tag recommendation called multi-feature space latent Dirichlet allocation (MFS-LDA) for cold start problem.

MFS-LDA is a novel latent Dirichlet allocation (LDA)-based model which exploits multiple feature spaces (title, contents, and tags) for recommending tags. Exploiting multiple feature spaces allows MFS-LDA to recommend tags even if data from a feature space is missing (the cold start problem).

Evaluation of a publicly available data set consisting of around 20,000 Wikipedia articles that are tagged on a social bookmarking website shows a significant improvement over existing LDA-based tag recommendation methods.

The originality of MFS-LDA lies in segregation of features for removing bias toward dominant features and in synchronization of multiple feature space for tag recommendation.

Citation analysis is an important measure for the assessment of quality and impact of academic entities (authors, papers and publication venues) used for ranking of research articles, authors and publication venues. It is a common observation that high-level publication venues, with few exceptions (Nature, Science and PLOS ONE), are usually topic specific. The purpose of this paper is to investigate the claim correlation analysis between topic specificity and citation count of different types of publication venues (journals, conferences and workshops).

The topic specificity was calculated using the information theoretic measure of entropy (which tells us about the disorder of the system). The authors computed the entropy of the titles of the papers published in each venue type to investigate their topic specificity.

It was observed that venues usually with higher citations (high-level publication venues) have low entropy and venues with lesser citations (not-high-level publication venues) have high entropy. Low entropy means less disorder and more specific to topic and vice versa. The input data considered here were DBLP-V7 data set for the last 10 years. Experimental analysis shows that topic specificity and citation count of publication venues are negatively correlated to each other.

This paper is the first attempt to discover correlation between topic sensitivity and citation counts of publication venues. It also used topic specificity as a feature to rank academic entities.

Link prediction in social networks refers toward inferring the new interactions among the users in near future. Citation networks are constructed based on citing each other papers. Reciprocal link prediction in citations networks refers toward inferring about getting a citation from an author, whose work is already cited by you. The paper aims to discuss these issues.

In this paper, the authors study the extent to which the information of a two-way citation relationship (called reciprocal) is predictable. The authors propose seven different features based on papers, their authors and citations of each paper to predict reciprocal links.

Extensive experiments are performed on CiteSeer data set by using three classification algorithms (decision trees, Naive Bayes, and support vector machines) to analyze the impact of individual, category wise and combination of features. The results reveal that it is likely to precisely predict 96 percent of reciprocal links. The study delivers convincing evidence of presence of the underlying equilibrium amongst reciprocal links.

It is not a generic method for link prediction which can work for different networks with relevant features and parameters.

This paper predicts the reciprocal links to show who is citing your work to collaborate with them in future.

The proposed method will be helpful in finding collaborators and developing academic links.

The proposed method uses reciprocal link prediction for bibliographic networks in a novel way.

Academic rankings are facing various issues, including the use of data sources that are not publicly verifiable, subjective parameters, a narrow focus on research productivity and regional biases and so forth. This research work is intended to enhance creditability of the ranking process by using the objective indicators based on publicly verifiable data sources.

The proposed ranking methodology – OpenRank – drives the objective indicators from two well-known publicly verifiable data repositories: the ArnetMiner and DBpedia.

The resultant academic ranking reflects common tendencies of the international academic rankings published by the Shanghai Ranking Consultancy (SRC), Quacquarelli Symonds (QS) and Times Higher Education (THE). Evaluation of the proposed methodology advocates its effectiveness and quick reproducibility with low cost of data collection.

Implementation of the OpenRank methodology faced the issue of availability of the quality data. In future, accuracy of the academic rankings can be improved further by employing more relevant public data sources like the Microsoft Academic Graph, millions of graduate's profiles available in the LinkedIn repositories and the bibliographic data maintained by Association for Computing Machinery and Scopus and so forth.

The suggested use of open data sources would offer new dimensions to evaluate academic performance of the higher education institutions (HEIs) and having comprehensive understanding of the catalyst factors in the higher education.

The research work highlighted the need of a purposely built, publicly verifiable electronic data source for performance evaluation of the global HEIs. Availability of such a global database would help in better academic planning, monitoring and analysis. Definitely, more transparent, reliable and less controversial academic rankings can be generated by employing the aspired data source.

We suggested a satisfying solution for improvement of the HEIs' ranking process by making the following contributions: (1) enhancing creditability of the ranking results by merely employing the objective performance indicators extracted from the publicly verifiable data sources, (2) developing an academic ranking methodology based on the objective indicators using two well-known data repositories, the DBpedia and ArnetMiner and (3) demonstrating effectiveness of the proposed ranking methodology on the real data sources.

Citation is an important measure of quality, and it plays a vital role in evaluating scientific research. However, citation advantage varies from discipline to discipline, subject to subject and topic to topic. This study aims to compare the citation advantage of open access and toll access articles from four subfields of computer science.

This research studies the articles published by two prestigious publishers: Springer and Elsevier in the author-pays charges model from 2011 to 2015. For experimentation, four sub-domains of computer science are selected including (a) artificial intelligence, (b) human–computer interaction, (c) computer vision and graphics, and (d) software engineering. The open-access and toll-based citation advantage is studied and analyzed at the micro level within the computer science domain by performing independent sample t-tests.

The results of the study highlight that open access articles have a higher citation advantage as compared to toll access articles across years and sub-domains. Further, an increase in open access articles has been observed from 2011 to 2015. The findings of the study show that the citation advantage of open access articles varies among different sub-domains of a subject. The study contributed to the body of knowledge by validating the positive movement toward open access articles in the field of computer science and its sub-domains. Further, this work added the success of the author-pays charges model in terms of citation advantage to the literature of open access.

To the best of the authors’ knowledge, this is the first study to examine the citation advantage of the author-pays charges model at a subject level (computer science) along with four sub-domains of computer science.

In addition to agriculture, energy production, and industries, potable water plays a significant role in many fields, further increasing the demand for potable water. Purification and desalination play a major role in meeting the need for clean drinking water. Clean water is necessary in different areas, such as agriculture, industry, food industries, energy generation and in everyday chores.

The authors have used the different search engines like Google Scholar, Web of Science, Scopus and PubMed to find the relevant articles and prepared this mini review.

The various stages of water purification include coagulation and flocculation, coagulation, sedimentation and disinfection, which have been discussed in this mini review. Using nanotechnology in wastewater purification plants can minimize the cost of wastewater treatment plants by combining several conventional procedures into a single package.

In society, we need to avail clean water to meet our everyday, industrial and agricultural needs. Purification of grey water can meet the clean water scarcity and make the environment sustainable.

This mini review will encourage the researchers to find out ways in water remediation to meet the need of pure water in our planet and maintain sustainability.

The goal of this paper is to model the activated carbon adsorption capacity of cadmium using a polynomial regression model. The properties that influence the removal of heavy metals by activated carbon must be taken into consideration in order to synthesize materials specific to the characteristics of the pollutant.

Multivariable analysis applications allow a comprehensive description of the relationship between activated carbon surface properties and heavy metal adsorption.

The authors use a second-grade polynomial regression model to determine the influence of porosity and surface acidity in cadmium adsorption by activated carbon.

The authors propose a statistic analysis to correlate the carbon properties with its cadmium adsorption capacity. Model coefficient analysis will give a better comprehension of the influence of activated carbon porosity and surface acidity of cadmium removal.

The main purpose of this study was to evaluate students’ perception of blended learning instructional design in a Higher Education Institution (HEI) in the Sultanate of Oman. A course in a bachelor degree was offered using blended learning instructional mode, and students’ feedback was obtained on the impact of blended learning. The study was conducted in Ibra College of Technology, one of the seven colleges under the Ministry of Manpower in the Sultanate of Oman. A course in Business Strategy and HR Management was offered during Summer 2016 using a combination of traditional and online instructional design. A structured questionnaire was administered at the end of the semester to solicit feedback from students. The results of the study highlighted a positive attitude among students towards blended learning courses. The research stressed the requirements for proper infrastructure, training and development initiatives for staff and students, shift to a more focused practical assessment methods to measure the graduate attributes. Student engagement, learner autonomy, connection of learning to real life environments and flexibility all appeared to benefit from the blended learning course.

In rapidly changing global village, individuals, organizations and the society are faced with various unforeseen challenges every day, and these challenges continuously trigger and test the instincts for survival, and higher education is of no exception. In the context of today’s most critical uncertainty, i.e. COVID-19, the purpose of this study is to highlight the significance of two leadership styles, i.e. adaptive and academic leadership, and assessing readiness for change among higher education institutions (HEIs) of Punjab, Pakistan.

An online survey was conducted to collect data from 404 permanent faculty members in the public sector universities identified using stratified random sampling. The hypotheses developed were tested using co-variance-based structural equation modeling.

As per the findings, both leadership styles as exogenous constructs and the presence of organizational learning capability as mediators contributed positively in crafting organizational readiness for change (ORC) among HEIs in the course of unpredictable circumstances.

The COVID-19 episode globally has reiterated the importance of change, and the role of leadership in this regard cannot be undermined. This study, for that matter, stresses on the importance and benefits of academic and adaptive leadership dealing with uncertainties or change and the readiness of HEIs for change. Several institutions faced challenges in doing so, and the transition was not smooth, except for institutions where leaders were the differentiating factor. On top of it, institutions that had timely invested in digital systems and had enhanced organizations learning capacity survived in these turbulent times.

COVID-19 has placed tremendous challenges on HEIs to adapt with the rapidly changing conditions. Hence, this study is unique in understanding the academic and adaptive leadership styles in context of ORC. This study further helps in understanding that how public sector universities that are already influenced by stringent bureaucratic structures react to change.