dataesr/scientific-paragraphs-categorization huggingface dataset:
468 ± 0. 344 97. 99 ± 1. 265 0. 014 ± 5:036 Â 10 À4 Fisher score?ReliefF 7000 0. 510 ± 0. 393 97. 66 ± 1. 309 0. 009 ± 5:564 Â 10 À4 Fisher score?WPCA 410 0. 468 ± 0. 359 97. 94 ± 1. 383 0. 001 ± 5:921 Â 10 À4 Note: Time= CPU time needed to classify one palmprint image for the CASIA database Best results are shown in bold.
We augment these quantitative methods with a nuanced qualitative analysis to describe the content of the foundational and popular papers, as well as thematic trends  [13]. The present study is organized as follows. The next section provides basic understanding of the concept of network and approaches to network analysis, with a particular focus on the applications in educational research. The Related work section describes previous studies reviewing research on networks in education, concluding with the aim of this research review. The Method section details dataset selection and data analysis. Next, we present the review results, finishing with the Discussion and Conclusion where we synthesize observed findings, discuss implications, and note limitations of our work.
In their review, they further highlighted that research work that treats university as a complex system, with diverse network actors has been largely underdeveloped. With the increasing of adoption of technology, several literature reviews focused on summarizing studies in technology-mediated environments. Much of the focus of these reviews is on the methods and types of applications, rather than thematic overview of the field. For instance, Sie et al. [46]  presented a "primer" analysis on the applications of SNA in technology-enhanced learning settings. Using a dataset selected via a flexible search strategy with keywords that included -inter alia-"social science", "psychology", "computer science", and "information science", the authors summarized data collection methods (e. g. , survey methods) and offered an overview of the used network metrics (e.
A. SEARCH QUERY AND DATA SELECTION The search processes to identify dataset of studies followed PRISMA-S (Preferred Reporting Items for Systematic reviews and Meta-Analyses literature search extension;  [51]. The search was performed on the Scopus database, which besides including almost all journals in the Web of Science database, offers a broader coverage of social sciences journals and conferences relevant to our study  [52]. Scopus offers a robust database with well-maintained metadata as well as a rigorous quality assurance procedure for the selection and curation of scientific journals or conferences  [53] ,  [54]. Several pilot iterations of the search query were performed to ensure the quality of the search. Based on these iterations, we selected two search queries that resulted in the most comprehensive collection of articles covering networks and education.
B. DATA PRE-PROCESSING A total of 1,791 articles selected for analysis were preprocessed and cleaned to improve the accuracy of the reported results. Author names were scrutinized manually for misspelling, duplicate names of the same author or name changes. Similar keywords were manually combined (e. g. , SNA, social network analysis, social network analytics were combined). Furthermore, given the interdisciplinary and diverse nature of research on the intersection of networks and education, we had to further combine some of the keywords for the papers selected in the dataset. Three authors preprocessed the keywords making decisions on how some of the most granular level keywords were merged into larger categories. For compound keywords, such as teacher social capital, the keyword was split into two separate keywords, i.
C. ANALYSIS SOFTWARE The R statistical language with the Bibliometrix package  [55] ,  [56]  were used for the analysis. Bibliometrix offers extensive tools for extracting, processing, and analyzing bibliometric metadata (e. g. , authors, keywords, citations, and countries). Networks were plotted using Gephi, the open source network analysis software  [57]. Frequencies, plots, and trends were computed and plotted using R statistical language  [56].
VI. RESULTS Our study spanned over five decades from 1969 to 2020 with a total of 1791 documents (1024 journal articles, 698 conference papers and 69 book chapters). The average number of years since publication in the dataset was 6. 2 years, indicating that most studies were published recently. The dataset included 3,821 unique authors. Most of the papers have been produced collaboratively with an average of 2. 13 authors per document and only 13. 8% of the dataset (248) were single author studies. Articles in our dataset were relatively highly cited with an average of 12. 3 citations per article. During the last two decades, the average annual growth rate (average percentage change from year to year) of publications was 20. 1%, indicating a significant yearly increase in the number of articles.
A. KNOWLEDGE CREATORS Authors are the primary creators of knowledge whose work can shape the development of a field, drive its advancement, and influence future generations. Understanding author networks help map the extent of collaboration, the breadth of influence, and the links between research groups. Therefore, to conduct our analysis of authors we considered authors productivity, co-authorship networks and their countries of affiliation. Authors. The top authors in our sample of articles represent diverse groups from education technology, learning analytics, SNA tools, social sciences, and interdisciplinary researchers. Figure  2  shows the top authors' timeline of productivity and citations. They were predominantly from developed countries. The list of most authors with the highest number of publications in our dataset includes Alan J.
Eagan. The paper combines SNA and ENA to study student ties to predict academic performance  [62]. Dragan Gašević, Professor of Learning Analytics at the Faculty of Education, University of Monash, is a proliferative learning analytics researcher working with a diverse range of methods and topics in education technology and learning analytics. His earliest work and most cited paper in our dataset explored the association between students' social capital and their academic performance  [63]. Heinz Ulrich Hoppe, Professor of Cooperative and Learning Support Systems at the University of Duisburg-Essen, Germany, has worked on a wide range of SNA applications with a focus of learning design  [31]  and analysis of bipartite networks in computer-supported collaborative learning  [64]. Mihai Dascalu, Professor at the Department of Computers, University Politehnica of Bucharest, is known for his work on ReaderBench, a tool that aims at supporting students' and teachers through cohesion-based analysis, identification of reading strategies identification and evaluation of textual complexity  [65].
B. KNOWLEDGE DISSEMINATION The manuscripts in our dataset were distributed over a wide variety of disciplines including education, computer science, social sciences, and interdisciplinary venues making 857 unique venues in total (counting different conference editions as a single venue). The majority of publication venues were not specialized in networked approaches, i. e. , some 624 (73%) publication outlets published a single article in this area, and some 116 (13. 5%) published two articles. In other words, these publication venues included an occasional study on networks in education. Most of the articles in our collection were published in journals (57%), followed by conferences (39%) and the rest in book chapters; which is in contrast to most Computer Science sub-domains -where most of the articles in our datasets were publishedin which conferences represent the main outlet for research dissemination  [69].
C. MILESTONES AND SEMINAL MANUSCRIPTS A full picture of the field of networks in education can only be obtained by considering the historical papers that may have shaped the field today. In this section, we briefly describe all the articles in our dataset published prior to 2000 as well as highly cited seminal papers that have shaped this research area. Historical papers before 2000. The oldest article in our dataset dated back to the late sixties  [9]. The authors described the process of network analysis at that time as "may not be easy in face of the time and intellectual demands required". The authors pointed out the "possible economic, social or intellectual gains'' (p. 109), and summarized such gains as "flexible planning, scheduling and control of programmes, operations, procedures and projects with a forecast of time required and a guide where time could be reduced at minimum cost.
" (p. 117). Other gains were summarized by the authors as: organizing courses, simulation of "educational operations before being committed" or making better use of resources. The second oldest article in our dataset used network analysis to analyze the syllabus of a course in librarianship and demonstrated how network analysis can help map the connections between learning objectives  [70]. The third oldest article  [71]  used network analysis to help students conceptualize the behavior of organizations. Students were also given exercises to practice network analysis by constructing their own networks. Similarly, Raghavan & Glaser  [72]  used networks to help students engage in scientific reasoning by using scientific concepts as a network, offering one of the early examples of concept map representations  [73].
By the late nineties, computers became more prevalent, and so did network analysis software giving rise to a wave of SNA research, e. g. , studying how students' networks form, evolve and explain their differences  [27] , as well as their preferences for different contexts for learning science  [74]  or hypermedia systems  [75]. Seminal papers. Review of papers that have the highest number of citations (Table  S1 ) describes the themes of research that have influenced current research directions. The most cited manuscripts in the dataset covered a broad range of applications of network analysis that are common in educational settings, e. g. , online communication networks and face-to-face social networks in primary and secondary school settings  [76] ,  [77] ; teacher and principal networks  [8] , university student interaction networks in online settings  [78] ,  [79] , adult learner networks in MOOCs  [80] , as well as networks with nodes that are non-human, such as brain regions  [3].
[103]. These references enable researchers to build on the notions of community, communication, and network ties within the context of online learning. This cluster also contains early applications of network analysis to online environments. These foundational cases include de Laat's  [82]  early framework combining content analysis of online discussions and network analysis, Dawson's  [78]  early work on applying network analysis to LMS discussion data, research by Aviv et al. [90]  focused on knowledge construction in online settings, and attempts by De Wever et al. [104]  to categorize types of online communication for learning. Finally, this cluster contains references to software and methodological texts most often used within research on networks in learning and classroom settings, by including citations of network analysis software such as Gephi and Igraph, as well as to the seminal methodological  [20] ,  [25].
VII. DISCUSSION AND CONCLUSION There is a long and rich history of research on networks in education. Recently, an accelerating pace of network research (with an average growth rate of 20%) has led network scientific production to double in volume in the last five years. Platts & Wyant-authors of the oldest article in our dataset-had high hopes that using networks would bring excellence to education by, e. g. , better planning, effective implementations, and efficient usage of resources  [9]. Having reviewed this large corpus of research, we see a field that has grown far more diverse in applications and research traditions yet falls short of Platts and Wyant's aspirations. To provide an overview of research located on the intersection of networks and education, we combined a scientometric approach with qualitative insights.
We analyzed relevant studies in relation to major knowledge producers, namely authors and publication venues where research on networks in education can be found. We further analyzed most cited papers to present important research directions, as well as most co-cited papers to describe foundational research work. Finally, we studied the keywords to describe research themes and their evolution over time. Our findings indicate that network research in education is diverse, widely used, and interdisciplinary. In our dataset, the majority of articles had a collaborative nature, authored by multiple researchers. Publication venues were spread over a large number of journals and conferences in different domains. Despite this seeming disciplinary diversity, this research is mostly authored in developed countries.
The affiliation country networks were -unsurprisinglybased on geographic proximity. Therefore, our knowledge about two thirds of the world is thin  [110]. Given that social traditions, relationships, and interactions vary across cultures and societies, it remains unclear if today's network research applies beyond its narrow origins  [111]. Diversity, reach and extending networks of collaboration could help us gain a better understanding of the full breadth of human behavior  [110] ,  [111]. Our findings further suggest fragmentation of research focus on networks and education. Most of the prolific and highly cited researchers in our dataset were not exclusively devoted to network research, few affiliated with network laboratories or specialized centers. The sparse authorship landscape with few devoted researchers could lead to the situation where the few proliferative researchers have an outsized influence on the field.
In fact, the analysis of keywords and seminal papers have shown that this may be partially true where most research themes were reflections of top authors' interests. This is not necessarily bad in its own right; however, a research field always advances with diversity of perspectives and novel ideas  [111] ,  [112]. The analysis of research venues shows a similar picture: a highly fragmented dissemination landscape with no specialized venue or research outlet. The publication venues in our dataset belonged to 81 different fields according to Scopus, dominated by computer science and its subcategories (57%), education (41%), social sciences, psychology, e-learning, and human-computer interaction 1. Whereas the top domains belonged to computer science in which conferences take the upper hand, network research was more likely to be published in journals  [69].
It remains to be seen whether that downtrend of MOOCs continues, if this is a temporary phenomenon, or a trend that simply reflects declining interest from committed proliferative researchers in our dataset. Despite the increasing number of articles from most keywords, ratios in most of them are decreasing, which is indicative of a growing diversity of themes. Keywords such as LA/EDM, academic achievement, higher education, and centrality were strongly interlinked showing the growing interest in capitalizing on network metrics to understand and possibly help predict academic achievement  [32] ,  [80] ,  [81]. Analysis also shows 1 Scopus assigns multiple categories to the same journal such as medicine and education for a medical education journal growing interest in teacher-related research as well as social media which could be partially attributed to interested sub communities of researchers (see our review of authors) and the use of social media for professional development in communities of practice.
Equally important are the keywords that -against our expectations-showed limited presence in our dataset, despite their overall popularity in other network scientific domains. Appearances of keywords reflecting network inference methods (e. g. , Exponential Random Graphs or Stochastic Actor Oriented Models) was lower than one per cent. Network inference methods go beyond describing the network and offer a powerful analytical framework for causal inference and theory formulation that can help understand the processes behind network generation while accounting for the complex dependencies between network elements  [106] ,  [113]. In doing so, it allows us to explain why a phenomenon occurs, i. e. , why a student chose to engage in an interaction, why and how a collaborative group formed, or why there is an association between an element of discourse and another  [37].
Given the complexity, the relational nature, and the multiple dependencies between learners and learning processes, network inference methods seem to offer a muchneeded solution that could advance our understanding of learning  [114]. Similarly, our data has shown rare occurrences of some of the latest trends in network research, such as psychological networks and graphical gaussian models (less than 0. 2%). Temporality has witnessed an increased attention in the last decade, however, "temporal" as a keyword has appeared in fewer than one per cent of the articles in our dataset, and the appearance of temporal networks is lower than 0. 3%. In the same way, bipartite, twomode, and multi-layer network analysis, and link prediction have all appeared even less, despite being a rising area in network science.
Finally, although network science has been around for two decades, fewer than one per cent of the articles in our dataset identified as network science  [1]. This may indicate either that those educational researchers do not subscribe to the epistemologies associated with network science, or rather that the techniques and approaches used in network science require more advanced disciplinary knowledge and require specialized expertise from educational researchers. Centrality measures have appeared frequently within our reviewed papers -keywords and abstracts-with strong connection to data-driven applications, friendships, and social capital. However, the reported centralities were limited to the traditional measures, e. g. , degree, closeness and betweenness centralities with rare appearance of Katz centrality as well as some others.
Yet, the conclusions of Dado et al. still apply to this larger dataset: descriptive network methods are go-to methods in educational research  [26]. The present study has a number of limitations. Bibliographic databases, where we draw metadata analyzed in the study, have deficiencies, inaccuracies, and missing data especially before 2000. To minimize the influence of such problems and improve the accuracy of our research we cleaned author names, the keywords, and the publication venues manually. We have also filtered articles manually to avoid non-relevant research. Bibliometric research is.
A strong trend in the software industry is to merge the activities of deployment and operationalization through the DevOps approach, which in the case of artificial intelligence is called Machine Learning Operations (MLOps). We present here a microservices architecture containing the whole pipeline (frontend, backend, data predictions) hosted in Docker containers which exposes a model implemented for opinion prediction in Twitter on the COVID vaccines. This is the first description in the literature of implementing a microservice architecture using TorchServe, a library for serving Pytorch models.
Results and Discussion As a proof of concept, we have implemented a simple web interface that allows the user to request the analysis of a sentence using our model. This request is then forwarded to Django via NGINX and uWSGI, then validated (format, authorization, etc. ) and finally sent to our TorchServe service for prediction. Finally, the result is presented to the user in the same web interface. This interface was implemented using Vue. js, an open source JavaScript framework for building user interface and single-page applications. The F-Score of the classifier was 0. 75 (precision: 0. 74; recall: 0. 75)  [1]. This work demonstrates the feasibility of integrating a deep learning model with other applications once it is served using the proposed architecture. Furthermore, it is quite flexible, and it can be modified to meet various requirements.
To our knowledge, this is the first description of a microservices architecture using TorchServe in the medical literature. This library presents two benefits: First, TorchServe keeps the deep learning model in memory and doesn't necessitate to reload it every time a new request arrives. It can also handle requests in parallels. Second, TorchServe can manage a preprocessing and a post-processing function that are defined in handlers. The main limitation of this work is that it does not take into account all the constraints related to production such as scaling, management of versions of the model, and verification of the stability of predictions over time. This preliminary work is a first step for a research program on best practices related to the deployment of deep learning algorithms using Machine Learning Operations (MLOps), their advantages and disadvantages.
Acknowledgements The work of the second author,  A. Gogatishvili  has been partially supported by Grant  18-00580S  of the  Czech Science Foundation  and also supported by  RVO-679 85840. The work of  Ales Nekvinda  was supported by the  European Regional Development Fund , project No. CZ 02. 1. 01/0. 0/0. 0/16_019/0000778.
Some notable DeepFake examples include: a DeepFake of the US president Donald Trump in which he urges Belgian politicians to pull out of the Paris climate agreement  2  , a DeepFake of Meta CEO Mark Zuckerberg in which he gives a sinister speech about the influence of Facebook on its users  3  , and a fake video of US president Barack Obama during which he insults Donald Trump  4. This has attracted the interest of the multimedia community for the development of methods to tackle this threat, and as a result, the generated research in the field has skyrocketed in the last recent years. Figure  1  shows the number of papers published that mention the term DeepFake since 2016. Furthermore, data availability has also seen such an increase in activity that in 2021 only, eight new DeepFake datasets have been released  [16, 22, 24, 29, 37, 39, 56, 82].
Despite these facts, the DeepFake problem remains challenging, especially in the case of novel manipulations that have not been included in the training set of DeepFake detection systems. We argue that it is in part due to two reasons. The first relates to the challenge of training Neural Networks that are robust to out-of-distribution samples. In this context, by out-of-distribution samples we refer to DeepFakes generated with different manipulation methods than those used for training. The second reason relates to the misalignment between the synthetic datasets, developed by researchers that exhibit a strong bias towards selecting trimmed videos containing only a single face, and DeepFakes on the Internet, where videos are longer and contain many shots with multiple faces of which one or more may have been manipulated.
The detected faces are submitted to an ensemble scheme of five DeepFake detection models. The outputs are aggregated to derive a single probability score indicating whether the input medium contains DeepFake faces. To provide a transparent documentation for our service, we have compiled a model card. We evaluate our service on three wellknown datasets and also assess the service robustness to adversarial attacks in the spirit of trustworthy AI. Finally, we document the practical challenges we faced when pivoting to a robust service API from the point of view of research code, hoping that our experience will be helpful to other academic or industry teams in the field.