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摘要： Background: Conferences bring scientists together and provide one of the most timely means for disseminating new ideas and cutting-edge works. The importance of conferences in scientific areas is testified by quantitative indicators. In Computer Science, for instance, almost two out of three papers published on Scopus are conference papers. Objective/Purpose: The main goal of this paper is to investigate a novel research question: is there any correlation between the impact of a scientific conference and the venue where it took place? Approach: In order to measure the impact of conferences we conducted a large scale analysis on the bibliographic data extracted from 3,838 Computer Science conference series and over 2.5 million papers spanning more than 30 years of research. To quantify the"touristicity" of a venue we exploited some indicators such as the size of the Wikipedia page for the city hosting the venue and other indexes from reports of the World Economic Forum. Results/Findings: We found out that the two aspects are related, and the correlation with conference impact is stronger when considering country-wide touristic indicators, such as the Travel&Tourism Competitiveness Index. Moreover the almost linear correlation with the Tourist Service Infrastructure index attests the specific importance of tourist/accommodation facilities in a given country. DRAF such as allowing conference organizers and authors to estimate in advance the impact of conferences, thus supporting them in their decisions. © 2021, CC BY.

关键词： Performance measurement   Scholarly communication   Altmetrics   Comparative analysis   Research evaluation   Scientometrics  

摘要： Purpose Social interaction applications and reference tools are actively used by researchers to share and manage their research publications. Thus, this paper aims to determine the scholarly impact of selected Indian central universities. Design/methodology/approach This study analyzed 669 articles having both Dimensions citations and Altmetric attention scores published by 35 Indian central universities for 4 subfields of Computer Science using Altmetric Explorer. This paper determined each university's contribution in the studied subfields of Computer Science and the correlation among Altmetric attention score (aggregated and individual), Dimensions citation, and Mendeley readership counts for all 669 articles and stratified percentile sets of top 25%, and top 50% of the overall number of articles. Findings The findings showed that Jawaharlal Nehru University had the maximum Altmetric attention score, Banaras Hindu University received the maximum Dimensions citation, and University of Hyderabad (UoH) received the maximum number of Mendeley readers. Each central university was examined individually and then ranked based on their median values of Dimensions citations and Altmetric attention scores. Further, Twitter had the maximum Altmetric coverage, followed by Google+, Patent and Facebook for the retrieved articles. A significant strong positive correlation was observed between the Dimensions citation and Mendeley readership counts for all the three categories. Research limitations/implications Both Altmetric attention scores and Dimensions citations can help funding agencies to assess and evaluate the research productivity of these universities, thus, making important decisions such as increasing, decreasing, re-distributing their funds. Originality/value The current body of research is focused mostly on relationships between citations and individual Altmetric indicators predominantly. For most of the studies, the citations were retrieved from Scopus, Web of Scie

关键词： Computer science   Machine learning   Society  

摘要： Two books offer complementary insights into how artificial intelligence is shaping society.

关键词： Network models   Computer science  

摘要： The neural encoding of visual features in primary visual cortex (V1) is well understood, with strong correlates to low-level perception, making V1 a strong candidate for vision restoration through neuroprosthetics. However, the functional relevance of neural dynamics evoked through external stimulation directly imposed at the cortical level is poorly understood. Furthermore, protocols for designing cortical stimulation patterns that would induce a naturalistic perception of the encoded stimuli have not yet been established. Here, we demonstrate a proof of concept by solving these issues through a computational model, combining (1) a large-scale spiking neural network model of cat V1 and (2) a virtual prosthetic system transcoding the visual input into tailored light-stimulation patterns which drive in situ the optogenetically modified cortical tissue. Using such virtual experiments, we design a protocol for translating simple Fourier contrasted stimuli (gratings) into activation patterns of the optogenetic matrix stimulator. We then quantify the relationship between spatial configuration of the imposed light pattern and the induced cortical activity. Our simulations in the absence of visual drive (simulated blindness) show that optogenetic stimulation with a spatial resolution as low as 100 mu m, and light intensity as weak as 1016 photons/s/cm2 is sufficient to evoke activity patterns in V1 close to those evoked by normal vision.

关键词： Computer science   Information technology   Scientific data   Software   Statistics  

摘要： After the great success of the Women's World Cup in 2019, several platforms have started identifying the reasons for gender inequality in European football. Even though these inequalities emerge from a variety of key aspects in the modern sport, we focused on the game and evaluated the main differential features of European male and female football players in match actions data under the assumption of finding significant differences and established patterns between genders. A methodology for unbiased feature extraction and objective analysis is presented based on data integration and machine learning explainability algorithms. Female (n0=1511) and male (n1=2703) data points were collected from event data and categorized by game period and player position. We set up a supervised classification pipeline to predict the gender of each player by looking at their actions in the game. The comparison methodology did not include any qualitative enrichment or subjective analysis to prevent biased data enhancement or gender-related processing. The pipeline included three representative binary classification models;A logic-based Decision Trees, a probabilistic Logistic Regression and a multilevel perceptron Neural Network. Each model tried to draw the differences between male and female data points, and we extracted the results using machine learning explainability methods to understand the underlying mechanics of the models implemented. The study was able to determine pivotal factors that differentiate each gender performance as well as disseminate unique patterns by gender involving more than one indicator. Data enhancement and critical variables analysis are essential next steps to support this framework and serve as a baseline for further studies and training developments.

关键词： computational science   computer science   reproducibility   validation   verification   uncertainty quantification  

关键词： Engineering education  

摘要： Based on the Technological Pedagogical and Content Knowledge (TPACK) framework (Mishra & Koehler, 2006) and the Schmidt et al. (2009) instrument which explore TPACK, this study examines a national sample of 1032 secondary teachers of computer science and measures their knowledge with respect to technology, pedagogy, content knowledge and the combination of each of these areas. Findings indicate that content knowledge and technology knowledge rating are high (average 4.38 and 4.16 respectively) and it seems that secondary teachers are less confident with their pedagogical content knowledge and their technological content knowledge (average 3.51 and 3.68 respectively). © 2021, CC BY.

关键词： Computer science   Policy  

摘要： Proliferating military artificial intelligence will leave the world less safe — so we must focus on ethics and global cooperation.

关键词： Social networking (online)  

摘要： Scientific collaboration networks can present different views of researchers’ interaction. The analysis of these networks allows research transparency focusing on the understanding of how science evolves in different contexts. In this article, we present a scientific collaboration network analysis using social network metrics as average degree, connected components, betweenness and closeness centralities applied to five Brazilian Computer Science graduate programs. An online artifact built upon the design science research paradigm, namely SCI-synergy, was developed to ease the analysis involving publication data available on the Digital Bibliography & Library Project (DBLP) from the graduate program members allied to the Federal University of Minas Gerais (UFMG), the State University of São Paulo (USP), the Federal University of Rio Grande do Norte (UFRN), the Federal University of Amazonas (UFAM), and the University of Brasília (UnB). The objective is to analyze the scientific collaboration network of each program focusing on the researchers’ collaboration to understand patterns considering intra- and inter-program relationships. The analysis covers two database updates of 2019 and 2021. Results show that intra-program relationships are not always bigger among the highest-ranked programs in CAPES. Nevertheless, UFMG presents a greater total of cooperation among the five programs indicating research leadership and good inter-program collaboration patterns. We advocate that the collaboration network analysis using an online artifact can be useful to understand patterns of all the Brazilian Computer Science graduate programs to discover new research perspectives to be improved, avoided or even applied to other contexts. © 2021, CC BY.