scholarly journals Connectivity Analysis of Computer Science Centers based on Scientific Publications Datafor Major Russian Cities

2014 ◽  
Vol 31 ◽  
pp. 892-899 ◽  
Author(s):  
Fedor Krasnov ◽  
Evgeniya Vlasova ◽  
Rostislav Yavorskiy
2016 ◽  
Vol 2 ◽  
pp. e41 ◽  
Author(s):  
Yan Wu ◽  
Srinivasan Venkatramanan ◽  
Dah Ming Chiu

Academic publication metadata can be used to analyze the collaboration, productivity and hot topic trends of a research community. In this paper, we study a specific group of authors, namely the top active authors. They are defined as the top 1% authors with uninterrupted and continuous presence in scientific publications over a time window. We take the top active authors in the Computer Science (CS) community over different time windows in the past 50 years, and use them to analyze collaboration, productivity and topic trends. We show that (a) the top active authors are representative of the overall population; (b) the community is increasingly moving in the direction of Team Research, with increased level and degree of collaboration; and (c) the research topics are increasingly inter-related. By focusing on the top active authors, it helps visualize these trends better. Besides, the observations from top active authors also shed light on design of better evaluation framework and resource management for policy makers in academia.


Author(s):  
Francisco V. Cipolla-Ficarra ◽  
Donald Nilson ◽  
Jacqueline Alma

In the current appendix present a first heuristic study about the scientific publications related to computer science and the human factors that make that some contents travel through highways and others in back roads of scientific information. We also present the first elements which generate that parallel information of the scientific work for financial and/or commercial reasons. Finally, a set of rhetoric questions link two decades of experiences in the university educational context, research and development (R&D) and Transfer of Technology (TOT) in the Mediterranean South and make up a first evaluation guide.


Author(s):  
Maryna Gutnyk ◽  
Volodymyr Sklyar ◽  
Serhii Radohuz ◽  
Nataliia Volosnikova ◽  
Elena Tverytnykova

Author(s):  
B.N. Chigarev

This study aims to reveal and analyze the landscape of China’s scientific publications in 2018–2020 on the subject “Energy Engineering and Power Technology” using bibliometric data from the Lens platform. Bibliometric data of 26,623 scholarly works that satisfy the query: “Filters: Year Published = (2018–); Publication Type = (journal article); Subject = (Energy Engineering and Power Technology); Institution Country/Region = (China)” were used to analyze their main topics disclosed by Fields of Study and Subject; the leading contributors to these R&D activities were also detected. Chinese Academy of Sciences, China University of Petroleum, Tsinghua University, Xi’an Jiaotong University, China University of Mining and Technology are the leading institutions in the subject. Most research works were funded by National Natural Science Foundation of China. China carries out its research not only in conjunction with the leading economies: United States, United Kingdom, Australia and Canada, but also with the developing countries: Pakistan, Iran, Saudi Arabia and Viet Nam. Materials science, Chemical engineering, Computer science, Chemistry, Catalysis, Environmental science are the top Fields of Study. Analysis of co-occurrence of Fields of Study allowed to identify 5 thematic clusters: 1. Thermal efficiency and environmental science; 2. Materials science for energy storage and hydrogen production; 3. Catalysis and pyrolysis for better fossil fuels; 4. Computer science and control theory for renewable energy; 5. Petroleum engineering for new fossil fuel resources and composite materials. The results of the work can serve as a reference material for scientists, developers and investors, so that they can understand the research landscape of the “Energy Engineering and Power Technology” subject.


Author(s):  
Răzvan Andonie ◽  
Ioan Dzitac

The academic world has come to place enormous weight on bibliometric measures to assess the value of scientific publications. Our paper has two major goals. First, we discuss the limits of numerical assessment tools as applied to computer science publications. Second, we give guidelines on how to write a good paper, where to submit the manuscript, and how to deal with the reviewing process. We report our experience as editors of International Journal of Computers Communications & Control (IJCCC). We analyze two important aspects of publishing: plagiarism and peer reviewing. As an example, we discuss the promotion assessment criteria used in the Romanian academic system. We express openly our concerns about how our work is evaluated, especially by the existent bibliometric products. Our conclusion is that we should combine bibliometric measures with human interpretation.


2015 ◽  
Vol 11 (1) ◽  
Author(s):  
Sumarsih C Purbarani ◽  
Hanif Wisesa ◽  
Ari Wibisono

On the contribution to scientific publications, The Faculty of Computer Science Universitas Indonesia (Fasilkom UI) holds an annual international conference entitled International Conference on Advance Computer Science and Information Systems (ICACSIS). This international conference is aimed to be a platform for scientists all over the world to present their findings to other colleagues of the same interest. The process of collecting papers from these particular countries is not easy. Thus, a system that can automatically manage the paper submission process is needed. There are numerous webbased conference management systems on the Internet. Despite the basic common features, many of them offer various features that distinguish them from the others. This paper presents various features that are available in the market, analyzes the functions of features that meets the ICACSIS paper management’s requirement and integrating both the former and the later.


Author(s):  
Shintaro Yamamoto ◽  
Anne Lauscher ◽  
Simone Paolo Ponzetto ◽  
Goran Glavaš ◽  
Shigeo Morishima

The exponential growth of scientific literature yields the need to support users to both effectively and efficiently analyze and understand the some body of research work. This exploratory process can be facilitated by providing graphical abstracts–a visual summary of a scientific publication. Accordingly, previous work recently presented an initial study on automatic identification of a central figure in a scientific publication, to be used as the publication’s visual summary. This study, however, have been limited only to a single (biomedical) domain. This is primarily because the current state-of-the-art relies on supervised machine learning, typically relying on the existence of large amounts of labeled data: the only existing annotated data set until now covered only the biomedical publications. In this work, we build a novel benchmark data set for visual summary identification from scientific publications, which consists of papers presented at conferences from several areas of computer science. We couple this contribution with a new self-supervised learning approach to learn a heuristic matching of in-text references to figures with figure captions. Our self-supervised pre-training, executed on a large unlabeled collection of publications, attenuates the need for large annotated data sets for visual summary identification and facilitates domain transfer for this task. We evaluate our self-supervised pretraining for visual summary identification on both the existing biomedical and our newly presented computer science data set. The experimental results suggest that the proposed method is able to outperform the previous state-of-the-art without any task-specific annotations.


1997 ◽  
Vol 42 (11) ◽  
pp. 1007-1008
Author(s):  
Rodney L. Lowman

Sign in / Sign up

Export Citation Format

Share Document