Preface

2021 4th International Conference on Mechatronics and Computer Technology Engineering (MCTE 2021) has been held successfully on October 15-17, 2021. Due to the COVID-19 pandemic around the world and with the strict travelling rules in China, it is still difficult to take international travel for our attendees abroad. Therefore, MCTE 2021 was held both in physical (Xi'an, China) and online (Zoom). The conference mainly focuses on mechatronics, computer technology engineering, computer science and other research fields, aiming at providing a platform for experts, scholars, engineers and technical researchers engaged in mechatronics, computer technology engineering and computer science research to share scientific research achievements and cutting-edge technologies, understand academic development trends, broaden research ideas, strengthen academic research and discussion, and promote cooperation in industrialization of academic achievements. Experts, scholars, business people and other relevant personnel from universities and research institutions at home and abroad are cordially invited to attend and exchange. During the conference, the conference model was divided into three sessions, including oral presentations, keynote speeches, and online Q&A discussion. In the first part, some scholars, whose submissions were selected as the excellent papers, were given about 5-10 minutes to perform their oral presentations one by one. Then in the second part, keynote speakers were each allocated 30-45 minutes to hold their speeches. There were 90 individuals who attended this hybrid conference. List of Committee member is available in this pdf.

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Detecting bursty terms in computer science research

Scientometrics ◽

10.1007/s11192-019-03307-5 ◽

2019 ◽

Vol 122 (1) ◽

pp. 681-699 ◽

Cited By ~ 1

Author(s):

E. Tattershall ◽

G. Nenadic ◽

R. D. Stevens

Keyword(s):

Computer Science ◽

Historical Data ◽

Science Research ◽

Detection Algorithm ◽

Trend Detection ◽

Free Text ◽

Research Topics ◽

Computer Science Research ◽

Insight Into ◽

Over Time

AbstractResearch topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

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Investigating the citation advantage of author-pays charges model in computer science research: a case study of Elsevier and Springer

Library Hi Tech ◽

10.1108/lht-05-2021-0154 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Tehmina Amjad ◽

Mehwish Sabir ◽

Azra Shamim ◽

Masooma Amjad ◽

Ali Daud

Keyword(s):

Open Access ◽

Computer Science ◽

Science Research ◽

Vital Role ◽

The Body ◽

Content Type ◽

Body Of Knowledge ◽

Computer Science Research ◽

Science Domain ◽

Citation Advantage

PurposeCitation 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.Design/methodology/approachThis 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.FindingsThe 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.Originality/valueTo 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.

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