Pub Date : 2024-03-05DOI: 10.3103/s014768822304007x
Farshid Danesh, Somayeh GhaviDel
Abstract
The present study investigates the consolidation or disruption of the global publication of information creation (IC) and the continuity of citations and bibliographic coupling of focal papers. This investigation is applied research performed using citation analysis, bibliographic coupling, and triple DI’s (DI, ({text{DI*}}), and ({text{D}}{{{text{I}}}^{# }})) indicators. The present study is an applied research performed using scientometric techniques and indicators. Citation analyzes, bibliographic coupling techniques, and disruption index were used in the present study to highlight the continuity, consolidation, or disruption in the co-citation network of IC papers. Srinivasan and Swink (2018) (({text{DI*}}) = 0.596) caused significant interference to citation continuity in IC publications. Carter et al., (2017) ranked the highest DI to be 0.720, and Farooq, Zhu, and QY (2018) ranked the highest ({text{D}}{{{text{I}}}^{# }}) to be 0.769. The USA, University of London, Journal of Physics conference series, and National Natural Science Foundation of China were the top country, university, publication, and funding agency in the field of IC. The use of improved indicators (in terms of performance and efficiency) led to the presentation of valuable results and knowledge in the focal points of knowledge creation in library and information science research. In the co-citation network of the IC domain, some articles are always cited and lead to the formation of “consolidation” and “continuity”. Disruption indexes (DI) are new multidimensional indicators measuring the impact of focal papers (FPs) by examining the number of citations received and the references cited in publications. Citation analyses often focus on counting the number of citations to a focal paper (FP). Therefore, the analysis of co-citation networks provides valuable knowledge for policy-making.
{"title":"Measuring Consolidation and Disruption Indexes in Global Knowledge and Information Creation Publications","authors":"Farshid Danesh, Somayeh GhaviDel","doi":"10.3103/s014768822304007x","DOIUrl":"https://doi.org/10.3103/s014768822304007x","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The present study investigates the consolidation or disruption of the global publication of information creation (IC) and the continuity of citations and bibliographic coupling of focal papers. This investigation is applied research performed using citation analysis, bibliographic coupling, and triple DI’s (DI, <span>({text{DI*}})</span>, and <span>({text{D}}{{{text{I}}}^{# }})</span>) indicators. The present study is an applied research performed using scientometric techniques and indicators. Citation analyzes, bibliographic coupling techniques, and disruption index were used in the present study to highlight the continuity, consolidation, or disruption in the co-citation network of IC papers. Srinivasan and Swink (2018) (<span>({text{DI*}})</span> = 0.596) caused significant interference to citation continuity in IC publications. Carter et al., (2017) ranked the highest DI to be 0.720, and Farooq, Zhu, and QY (2018) ranked the highest <span>({text{D}}{{{text{I}}}^{# }})</span> to be 0.769. The USA, University of London, Journal of Physics conference series, and National Natural Science Foundation of China were the top country, university, publication, and funding agency in the field of IC. The use of improved indicators (in terms of performance and efficiency) led to the presentation of valuable results and knowledge in the focal points of knowledge creation in library and information science research. In the co-citation network of the IC domain, some articles are always cited and lead to the formation of “consolidation” and “continuity”. Disruption indexes (DI) are new multidimensional indicators measuring the impact of focal papers (FPs) by examining the number of citations received and the references cited in publications. Citation analyses often focus on counting the number of citations to a focal paper (FP). Therefore, the analysis of co-citation networks provides valuable knowledge for policy-making.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"17 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.3103/s0147688223040147
A. S. Krymskaya
Abstract—
The author discusses the growth of bibliometric and scientometric studies in various fields of knowledge. The patterns of its development are similar to the flow of bibliographies that at one time led to the emergence of the bibliography of bibliographies. It is noted that similar features allow us to speak about the emergence of the bibliometrics of bibliometrics as a new area of research.
{"title":"The Bibliometrics of Bibliometrics as a New Area of Research","authors":"A. S. Krymskaya","doi":"10.3103/s0147688223040147","DOIUrl":"https://doi.org/10.3103/s0147688223040147","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract—</h3><p>The author discusses the growth of bibliometric and scientometric studies in various fields of knowledge. The patterns of its development are similar to the flow of bibliographies that at one time led to the emergence of the bibliography of bibliographies. It is noted that similar features allow us to speak about the emergence of the bibliometrics of bibliometrics as a new area of research.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"18 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140036323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.3103/s0147688223050143
S. S. Volkova
Abstract—
This paper proposes a method of countering spoofing attacks by improving the resilience of face-based biometric authentication systems to digital face manipulation attacks on the biometric input module. The proposed method of digital face manipulation detection (deepfake detection) is based on a convolutional neural network trained on a large dataset containing various types of manipulations, images of different quality, and a large number of identities and as a result achieves an accuracy of at least 99%. Experiment results also indicate high performance of the proposed approach compared to other available methods tested on the same dataset. The method can be used to improve the security of biometric authentication systems by reducing the risk of unauthorized access.
{"title":"A Method for Deepfake Detection Using Convolutional Neural Networks","authors":"S. S. Volkova","doi":"10.3103/s0147688223050143","DOIUrl":"https://doi.org/10.3103/s0147688223050143","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract—</h3><p>This paper proposes a method of countering spoofing attacks by improving the resilience of face-based biometric authentication systems to digital face manipulation attacks on the biometric input module. The proposed method of digital face manipulation detection (deepfake detection) is based on a convolutional neural network trained on a large dataset containing various types of manipulations, images of different quality, and a large number of identities and as a result achieves an accuracy of at least 99%. Experiment results also indicate high performance of the proposed approach compared to other available methods tested on the same dataset. The method can be used to improve the security of biometric authentication systems by reducing the risk of unauthorized access.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"27 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140045982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.3103/s0147688223050106
P. A. Lobanova, I. F. Kuzminov, E. Yu. Karatetskaia, E. A. Sabidaeva, V. V. Anpilogov
Abstract
The purpose of this article is to present the principles of a developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers to be implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested for dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; and provides examples of two key visualizations (semantic, trend maps) that outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.
{"title":"Trend Detection Using NLP as a Mechanism of Decision Support","authors":"P. A. Lobanova, I. F. Kuzminov, E. Yu. Karatetskaia, E. A. Sabidaeva, V. V. Anpilogov","doi":"10.3103/s0147688223050106","DOIUrl":"https://doi.org/10.3103/s0147688223050106","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The purpose of this article is to present the principles of a developed algorithm for identifying trends based on the analysis of big text data and presenting the result in formats that are convenient for decision makers to be implemented in the iFORA Big Data Mining System. The paper provides an overview of existing text analytics algorithms; outlines the mathematical basis for identifying terms that mean trends, which is proposed and tested for dozens of implemented projects; describes approaches to clustering terms based on their vectors in the Word2vec space; and provides examples of two key visualizations (semantic, trend maps) that outline the range of topics and trends that characterize a particular area of study, as a way to adapt the results of the analysis to the tasks of decision makers. The limitations and advantages of using the proposed approach for decision support are discussed, and directions for future research are suggested.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"99 1 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140046139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-03-05DOI: 10.3103/s0147688223050179
A. A. Zinina, L. Ya. Zaidelman, A. A. Kotov, B. M. Velichovsky
Abstract
The control system of the F-2 companion robot implements a competitive system of rules (scenarios) to model the robot’s reactions to a wide range of events. The system is designed in such a way as to provide balanced responses by the robot to speech utterances and other events recognized by the computer vision system (orientation of the user’s face and gaze, events in the tangram game), as well as to the user’s touches. In this experiment, we apply this system to evaluate two robots that are able to determine the orientation of a person’s face and the direction of the gaze and respond differently to his attention. The implicit reactions of a person to the robot’s gaze and the problems of differences between reflexive and reflex behavior in eye movements in comparison with other communicative actions are considered.
{"title":"Reflex or Reflection? The Oculomotor Behavior of the Companion Robot, Creating the Impression of Communicating with an Emotional Being","authors":"A. A. Zinina, L. Ya. Zaidelman, A. A. Kotov, B. M. Velichovsky","doi":"10.3103/s0147688223050179","DOIUrl":"https://doi.org/10.3103/s0147688223050179","url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The control system of the F-2 companion robot implements a competitive system of rules (scenarios) to model the robot’s reactions to a wide range of events. The system is designed in such a way as to provide balanced responses by the robot to speech utterances and other events recognized by the computer vision system (orientation of the user’s face and gaze, events in the tangram game), as well as to the user’s touches. In this experiment, we apply this system to evaluate two robots that are able to determine the orientation of a person’s face and the direction of the gaze and respond differently to his attention. The implicit reactions of a person to the robot’s gaze and the problems of differences between reflexive and reflex behavior in eye movements in comparison with other communicative actions are considered.</p>","PeriodicalId":43962,"journal":{"name":"Scientific and Technical Information Processing","volume":"12 1","pages":""},"PeriodicalIF":0.7,"publicationDate":"2024-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140889727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}