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The volume and diversity of scientific literature are escalating every day and millions of new scientific articles are published every year. Researchers work and publish in their respective fields of interest. A major portion of the scientific community publishing in the same field of interest forms a trend in the field which could be deemed as relatively more popular than other trends. A pioneering researcher picks a field of interest by depending upon its popularity. This may have a positive impact on the acceptance of a study or high count of citations in future. This study identifies how significant it is to follow a research trend and the impact of the field of study (FoS) trend on research paper citations. For this purpose, we have chosen the field of Computer Science and Microsoft Academic Graph dataset from the 2007-2015 time period. In the dataset, every paper has a list of fields of study. The FoS provided in MAG is systematized hierarchically into 4 levels; level-0 - level-3. In this study, we apply the clustering technique to the FoS and citations pattern separately. Likewise, we also analyze how papers following a FoS trend, gain citations over the time. We also introduce a novel method Field of Study Multigraph (FoM) using graph centrality measures degree, betweenness and closeness to analyze the FoS trend, citation trend, and the relation between research areas in scientific articles from the domain of Computer Science. The experimental results show that the FoS has a certain impact on citation count. Furthermore, the results depicts that if papers belong to the same FoS, then there are 66% of the chances of having a similar citation pattern and that they have the same citation trend as they also achieved a high correlation value. This proves that a FoS has a certain impact on the citation count of a paper and researchers should contemplate the FoS trend before selecting a particular research area.

Computational experiments have been important since the 70s to find solutions to problems using computers, and even more now with the advancement of technology, especially artificial intelligence. The aim of this study is to know the level of interest on the part of researchers toward computational experiments in Computer Science. To find out this information, a Systematic Mapping Study has been carried out in IEEE, DBLP, Scopus, ACM and Web of Science databases. A total of 226 papers were selected as primary studies and 22 types of measurements were found, it was determined that the three most used measurements in computational experiments are performance at 51.33%, efficiency at 15.49%, and effectiveness at 15.04%. A total of 25 frameworks used for the computational experiments were determined, however 74.3% of the reports did not specify any framework used, 11.5% used a custom framework and the rest used a specific framework. It is concluded that performance is the most important measurement in computational experiments and that it is necessary for researchers to rely on a framework so that these experiments can be replicated or refined.

Software engineering (SE) and computer science (CS) programs in universities worldwide are marked by a gender gap, which subsequently translates into a gender gap at the industry level. However, there are positive activities that can help attract more women to these male-dominant professions. This study maps the literature related to the achievement of gender balance in SE and CS university-level education and identifies future research directions. More specifically, this article reports on a systematic mapping study of female-inclusive SE and CS tertiary education programs. The authors collected 882 publications between 2015 and 2022 from five databases (ACM, IEEE, Scopus, Web of Science, and Science Direct), selecting 143 peer-reviewed papers for further analysis. The results showed that the main academic contributors were researchers from the USA. The majority of the publications contained observations and explanations regarding the gender gap in computing education. However, an important part of the literature considered proposals and practical activities for achieving gender balance in SE and CS programs. Finally, the authors classified the literature related to female-inclusive SE and CS tertiary education programs, identified the main research focuses and regional distribution, and considered ideas for future research.

Feld's friendship paradox is a widely accepted observation that states that the friends of any particular individual tend to have more friends on average than the individual has. Due to its implications for information transmission, the friendship paradox has become a generalized paradigm in many disciplines. However, how many of an individual's friends have more friends than the individual does is still unknown, yet interesting. In this paper, we revisited the Feld's friendship paradox and we found that only a limited number of a person's friends have more friends than the person herself has. This conclusion was reached from empirical studies using real-world networks and is true regardless of the number of one's neighbours, which contradicts the intuitive deduction from the friendship paradox. For one thing, if a person is unpopular, the number of her friends that have more friends than she does grows with the number of friends that she makes. This observation crystallizes the tenable margin of the friendship paradox. In another case, if a person is popular, as she acquires more friends, fewer of her friends have more friends than she does. This finding suggests an observation bias in the friendship paradox, which makes individuals feel less popular than their friends. Besides enriching our knowledge of the friendship paradox in psychological science, the findings reported here are also beneficial for technical areas such as large-scale triangle discovery. Although the friendship paradox was proposed from the perspective of the number of neighbours, the findings reported here can shed light on theories and applications from different disciplines like information, happiness, obesity, and extroversion, to name a few.

Schools, universities, and other educational entities are increasingly aware of the untapped potential of digital transformation, an essential process for increasing efficiency and collaboration, and reducing costs and errors in the management of at-scale training systems. In this context, the “Riconnessioni” project was promoted by the Compagnia di San Paolo in agreement with the Ministry of Education but planned, started and developed by the Foundation for the School. The digital transformation started with a defined strategy that leveraged opportunities presented by new technology while meeting the objectives of system stakeholders. Through several steps, that strategy was developed for education connecting everything to support tomorrow's digital world and creating strong strategic partnerships able to build an ecosystem connecting people, processes, and things into a powerful, secure, and smart communications network. This paper reports on the three-year Riconnessioni project, which is combining the energies of teachers, managers, administrative staff, students, among others, and experimenting with new learning models, taking advantage of opportunities that emerged from perceptions stemming from concerns and systemic issues. To date, more than 150 schools in Italy have been included in the project, together with 550 teachers selected to scale up the instructional process. Using a methodology called “cascade training”, the 550 selected teachers were able to spread the knowledge to more than 2,600 colleagues. The monitoring and evaluation activity performed in Riconnessioni aims at processing information on implementation and results, following three lines. First, it regularly evaluates project activities from a reporting standpoint. Second, it verifies the plan consistency against implementation achievements. Third, it identifies changes produced and focuses on teachers' and students' skills to evaluate the effects of the project. The assessment framework is also discussed in this work, reporting on results regarding feedback, follow-up, and effects gathered from the field. The evaluation highlighted that labs were indeed able to improve teachers' competence and underlined the added value of cascade training which spread digital domain knowledge and awareness into the group of involved schools.

As a common human behavior, interaction is everywhere in human life. With the rise of big data and human–computer interaction in the 21st century, more and more researchers from different industries and disciplines pay great attention to the human interactive behavior research. From the perspective of computer science, scholars try to use computer technology to make research more meaningful. To the best of our knowledge, this paper is the first study to investigate the potential rules of human interactive behavior in the view of computer science, based on 16 top-tier journals of human interactive behavior from Microsoft Academic Graph dataset. We put forward a topic extraction and clustering model based on word2vec to infer key topics, which can be widely used in different fields of research. We find that the growth of human interactive behavior is in an uptrend on the whole. Besides, the cooperative relationship between authors and countries/regions is closer over time. We also make the mensurable evolution analysis of topics by a statistical method. Some topics are hot all the time, while some are unpopular as time goes by. Finally, we do rankings in the field of human behavior research from a new perspective. All these findings help researchers observe potential patterns and the topic evolution in half a century, which may shed dazzling light on the exploration of human interactive behavior.

Matrix factorization is a widely used technique in recommender systems. However, its performance is often affected by the sparsity and the scalability. To address the above-mentioned problem, we propose an interpretable and scalable recommendation method based on network embedding (ISRM_NE) in this paper. First, a novel user-item co-occurrence network is presented, which reflects both the user's preferences and the co-occurrence relationship among items. Second, the conceptions of tightness and equivalence are given to describe the structural similarity in the network, which can explore four relationships in recommender system: user's preference, item co-occurrence relationship, user's potential preference, and similarity between users. Finally, two sampling strategies are combined to traverse the network so as to get the latent vector of items and users through network representation learning. Thus, the top-N recommendation can be achieved by vector computing. The proposed method called ISRM_NE improves the performance of the recommender system in terms of interpretability and scalability. Moreover, extensive experiments on two real-world datasets demonstrate that the ISRM_NE outperforms three popular methods.

Business platform models frequently require continuous adaptation and agility to allow new experiences to be created and delivered to customers. To understand user behavior in online systems, researchers have taken advantage of a combination of traditional and recently developed analysis techniques. Earlier studies have shown that user behavior monitoring data, as obtained by mouse tracking, can be utilized to improve user experience (UX). Many mouse-tracking solutions exist; however, the vast majority is proprietary, and open-source packages do not provide the resources and data needed to support UX research. Thus, this paper presents: 1) the development of an interaction monitoring application titled Artificial Intelligence and Mouse Tracking-based User eXperience Tool (AIMT-UXT); 2) the validation of the tool in a case study conducted on the Website of the Brazilian Federal Revenue Service (BFR); 3) the definition of a new relationship pattern of variables that determine user behavior; 4) the construction of a fuzzy inference system for measuring user performance using the defined variables and the data captured in the case study; and 5) the application of a clustering algorithm to complement the analysis. A comparison of the results of the applied quantitative methodologies indicates that the developed framework was able to infer UX scores similar to those reported by users in questionnaires.

The use of a graphics processing unit (GPU) together with a CPU, referred as GPU-accelerated computing, to accelerate tasks that requires extensive computations has been the trends for last a few years in high performance computing. In this paper, we propose a new paradigm of GPU-accelerated method to parallelize extraction of a set of features based on the gray-level co-occurrence matrix (GLCM), which may be the most widely, used method. The method is evaluated on various GPU devices and compared with its serial counterpart implemented and optimized in both Matlab and C on a single machine. A series of experimental tests focused on magnetic resonance (MR) brain images demonstrate that the proposed method is very efficient and superior to its serial counterpart, as it could achieve more than 25-105 folds of speedup for single precision and more than 15-85 folds of speedup for double precision on Geforce GTX 1080 along different size of ROIs.