No abstract

This study develops integration of the WoS and Scopus articles of Vietnamese authors into the Vietnam Citation Gateway system of Vietnam National University Hanoi; sets up a merging tool to filter the duplication for these two databases and establishes their bibliometric and citation index. Based on this integrated database, the reality of international publications of Vietnam and Vietnamese universities in the period of 2014-2018 was analyzed, benchmarked and discussed. For the first time, a completed data on WoS & Scopus publications has been established. Accordingly, in 2018, the annual productivity of international publications in Vietnam has almost reached the number of 10,000 articles, of which higher education institutions contribute up to 70%. The rate of increase in WoS & Scopus articles has strongly increased every year (34.7% for the Vietnam, in general, and 41.6% for higher education institutions, in particular). The growth of research productivity was also described for young universities such as Ton Duc Thang University and Duy Tan University. In terms of research quality, Vietnam has an average citation index of 9.2, which is comparable to that of Asian higher education institutions. This analysis allows classifying research-oriented universities and high impact research fields for Vietnam. Specially, this work also emphasizes the relation between university autonomy and the improvement of research productivity and quality.

Automated user  interaction  testing  of  Web applications has  been  received  great  attentions  from the  research  community  and  industry.  Currently, several  available  tools are proposed to partly deal withthe problem.  However, how to perform the  automated user  interaction  testing  of  whole  Web  applications effectively  is  still  an  open  problem.   This  research proposes  a  method  and  develops  a  tool  supporting automated  user  interaction  testing  of  whole  Web applications.  In  this  method, the  model  of  each  Web page  of  the  Web  application  under  testing  which describes the user interaction (UI)  is  represented  by  a finite state  automaton. The whole model that describes the  behaviors  of  the  whole  Web  application  then  is constructed by composing the models  of all  Web pages. After  that,  test  paths  are generated automatically based  on  the  compositional  model  of  the  Web application  so  that  these  test  paths  cover  all  possible user interactions  of the application.  A tool supporting the proposed method has  been developed and  applied to  test  on  some  simple  Web  applications.    The experimental results show the potential  application  of this tool for automated user interaction  testing of Webapplications in practice

Service  composition  is  a  process  of combining existing atomic services to perform a complex task. To choose atomic services for a composite service, usually  both  functionality  and  quality  of  service  (QoS) are  considered.  The  QoS  of  the  composite  service obviously  depends  on  QoS  of  atomic  services.  In  this research,  we  propose  a  simple  approach  for  the composite  service  and  atomic  services  to  establishing suitable  QoS  values.  Experiment  shows  that  our approach  is  feasible  and  significantly  better  than  the exhaustive approach in performance aspect.

Today, bibliometric databases are indispensable sources for researchers and research institutions. The main role of these databases is to find research articles and estimate the performance of researchers and institutions. Regarding the evaluation of the research performance of an organization, the accuracy in determining institutions of authors of articles is decisive. However, current popular bibliometric databases such as Scopus and Web of Science have not addressed this point efficiently. To this end, we propose an approach to revise the authors’ affiliation information of articles in bibliometric databases. We build a model to classify articles to institutions with high accuracy by assembling the bag of words and n-grams techniques for extracting features of affiliation strings. After that, these features are weighted to determine their importance to each institution. Affiliation strings of articles are transformed into the new feature space by integrating weights of features and local characteristics of words and phrases contributing to the sequences. Finally, on the feature space, the support vector classifier method is applied to learn a predictive model. Our experimental result shows that the proposed model’s accuracy is about 99.1%. Keywords:Affiliation, Disambiguation, Data cleaning, Classification, Supervised learning, if-iif, Support vector machine, Support vector classifier References[1] B. Shereen Hanafi, Discover the data behind the times higher education world university rankings, Elsevier Connect.[2] Dobrota, M. Bulajic, L. Bornmann, V. Jeremic, A new approach to the qs university ranking using the composite i-distance indicator: Uncertainty and sensitivity analyses, JASIST 67 (2016) 200-211.[3] -P. Pavel,  Global  university  rankings  -  a comparative analysis, Procedia Economics and Finance 26 (2015) 54-63. https://doi.org/10.1016/S2212-5671(15)00838-2.[4] Web of science databases, Clarivate Analytics.[5] F. Burnham, Scopus database: a review, Biomedical Digital Libraries 3. http://doi.org/10.1186/1742-5581-3-1.[6] Franceschini, D. Maisano, L. Mastrogiacomo, A novel approach for estimating the omitted-citation rate of bibliometric databases with an application to the field of bibliometrics, Journal of the american society for information science and technology 64 (2013) 2149-2156. https://doi.org/10.1002/asi.22898.[7] Franceschini, D. Maisano, L. Mastrogiacomo, Scientific journal publishers and omitted citations in bibliometric databases: Any relationship?, Journal of Informetrics 8(3) (2014) 751 - 765. https://doi.org/10.1016/j.joi.2014.07.003.[8] Buchanan, Accuracy of cited references: The role of citation databases, College Research Libraries 67. http://doi.org/10.5860/crl.67.4.292.[9] Valderrama-Zurián, R. Aguilar-Moya, D. Melero-Fuentes, R. Aleixandre-Benavent, A systematic analysis of  duplicate  records  in scopus, Journal of Informetrics 9 (2015) 570–576. http://doi.org/ 10.1016/j.joi.2015.05.002.[10] Zhu, G. Hu, W. Liu, Doi errors and possible solutions for web of science, Scientometrics 118(2) (2019) 709-718. http://doi.org/10.1007/s11192-018-2980-7.[11] Xu, L. Hao, X. An, D. Zhai, H. Pang, Types of doi errors of cited references in web of science with a cleaning method, Scientometrics 120(3) (2019) 1427-1437. http://doi.org/ 10.1007/s11192-019-03162-4.[12] Krauskopf, Missing documents in scopus: the case of the journal enfermeria nefrologica, Scientometrics 119(1) (2019) 543-547. https://doi.org/10.1007/ s11192-019-03040-z.[13] Liu, G. Hu, L. Tang, Missing author address information in web of science-an explorative study, Journal of Informetrics 12(3) (2018) 985-997. https://doi.org/10.1016/j.joi.2018.07.008.[14] Krauskopf, Standardization of the institutional address, Scientometrics 94(3) (2013) 1313-1315. http://doi.org/10.1007/s11192-012-0852-0.[15] Krauskopf, Call for caution in the use of bibliometric data, J. Assoc. Inf. Sci. Technol. 68(8) (2017) 2029-2032. http://doi.org/10.1002/asi.23809.[16] Awad, R. Khanna, Support Vector Machines for Classification, Apress, Berkeley, CA, 2015, pp. 39-66. http://doi:10.1007/978-1-4302-5990-9-3.[17] Breiman, Random forests, Machine Learning 45(1) (2001) 5-32. https://doi.org/10.1023/A:1010933404324.[18] Cover, P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theor. 13(1) (2006) 21-27. http://doi.org/10.1109/TIT.1967.1053964.[19] J.-C.B. Cuxac, P., Efficient supervised and semi-supervised approaches for affiliations disambiguation, Scientometrics 97(1) (2013) 47-58.