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The objective of the study is to describe the world scientific production on gamification in the Scopus and Web of Science databases between the months of January 2020 and March 2022. We worked with a universe consisting of 754 documents from the database Web of Science and 1443 from Scopus. The results indicate that, in terms of the number of authors, Scopus has 1,336 and Web of Science, 2,223. The rate of collaboration between authors is slightly higher in Web of Science (3.18) compared to 3.05 in Scopus. Regarding the author with the highest production on the subject, Juho Hamari stood out, while the Lecture notes in computer science magazines in Scopus and the Sustainability magazine in WoS stood out as the media with the highest production of articles on gamification; On the other hand, Spain was the main country producing scientific evidence, and the type of scientific production that stood out was the original articles. The growth of scientific production on gamification is corroborated and further growth is forecast for the coming years. [ABSTRACT FROM AUTHOR]

Shortest distance computation is one of the widely researched areas in theoretical computer science and graph databases. Distance labeling are well-known for improving the performance of shortest distance queries. One of the best distance labeling approaches is Pruned Landmark Labeling (PLL). PLL is a 2-hop distance labeling which prunes a lot of unnecessary labels while doing breadth-first-search. Another well-known 2-hop labeling is Pruned Highway Labeling (PHL) which is designed for undirected road networks. Both PLL and PHL suffer from the problem of large index size. In this paper, we propose two approaches to address the problem, one is to compress the PLL index as well as the graph for directed graphs; the other is to compress undirected road networks using linear sets, which are essentially maximal-length non-branching paths. Our aim is to reduce the index size and index construction time without significantly compromising query performance. Extensive experiments with real world datasets confirm the effectiveness of our approaches. [ABSTRACT FROM AUTHOR]

Quantum Machine Learning (QML) is one of the core research fields in the larger paradigm of Quantum Computing (also known alternatively as Quantum Information). In recent years, researchers have taken deep interest in QML, given the potential time and cost advantages that solutions to real-life problems using QML algorithms provide, in comparison to their classical (or digital) machine learning equivalents. This is still a very new and exciting area of research with new algorithms and their uses being developed almost every other day. Deep research interest in this area has picked up only in the past 5–6 years. Given the background, this paper focuses on studying Scopus and Web of Science databases for the past 6 years (2014–2019) to identify various publication trends in the areas of Quantum Machine Learning. The authors have done an in-depth study of the Scopus and Web of Science publication data pertaining to this area and have come up with interesting insights. The survey covers 276 publications in Scopus and 154 publications in Web of Science. From the Scopus database, it is found that there has been a consistent growth in the number of publications in this period. Four research areas, namely, Physics, Astronomy, Computer Science, and Mathematics, have contributed 68.1% of the research publications. The USA leads the top 10 countries with nearly half (49.2%) of the research publications. A total of 148 patents have been published with 94 of these being published in the last four years (2016–2019). This essentially translates to one patent for every two publications. The Web of Science database, though bringing out 154 publications in the period, shows similar trends across the metrics. We have carried out a comparative study of some of the metrics in Scopus and Web of Science databases. Overall the study identifies the top 10 Institutions, authors, and research journals. [ABSTRACT FROM AUTHOR]

The aim of the present study is to visualize the dynamics of conceptual clusters, hidden patterns and emerging knowledge of "cybernetics in Middle Eastern countries" subject area in a global scale based on the co-word analysis of scientific outcomes indexed in the Web of Science database. This research is an applied one, performed with the co-word analysis method and analytical approach. The population includes 2280 keywords from 705 records in the cybernetics subject area in Middle Eastern countries, published in 7 journals indexed in the Web of Science database until 2018. To have a coherence set of data and create the co-occurrence matrix, RavarPreMap software applied. Then, using SPSS 16 and VOSViewer, the subjects were clustered and mapped. Specifications of co-word networks such as centrality and density illustrated using UCInet software in the form of a strategic diagram. Thereby, the situation and evolutionary branches of the cybernetics subject area revealed in Middle Eastern countries. The trend of scientific productions in cybernetics in Middle Eastern counties is incremental. Computer science, with 444 records, is the core subject in the mentioned studies. Researchers' intellectual structures are identifiable in 12 clusters. The most significant cluster is the twelfth one, named master data mining algorithms and including 20 keywords. The distribution of clusters in the strategic diagram shows that Clusters 1, 2, and 4 are core issues in the subject area. Furthermore, they are mature and coherent, being studied at the core of the research area, having a core and developable role. The coword analysis is a proper and powerful method in discovering and visualizing science and knowledge, tracking sciences, studying the trend of conceptual dynamicity, identifying and analyzing researchable fields in the subject area of cybernetics. This strategic approach can drastically help research planners and policy-makers as well as governmental authorities. [ABSTRACT FROM AUTHOR]

The automatic classification of abstract sentences into its main elements (background, objectives, methods, results, conclusions) is a key tool to support scientific database querying, to summarize relevant literature works and to assist in the writing of new abstracts. In this paper, we propose a novel deep learning approach based on a convolutional layer and a bidirectional gated recurrent unit to classify sentences of abstracts. First, the proposed neural network was tested on a publicly available repository containing 20 thousand abstracts from the biomedical domain. Competitive results were achieved, with weight-averaged Precision, Recall and F1-score values around 91%, and an area under the ROC curve (AUC) of 99%, which are higher when compared to a state-of-the-art neural network. Then, a crowdsourcing approach using gamification was adopted to create a new comprehensive set of 4111 classified sentences from the computer science domain, focused on social media abstracts. The results of applying the same deep learning modeling technique trained with 3287 (80%) of the available sentences were below the ones obtained for the larger biomedical dataset, with weight-averaged Precision, Recall and F1-score values between 73 and 76%, and an AUC of 91%. Considering the dataset dimension as a likely important factor for such performance decrease, a data augmentation approach was further applied. This involved the use of text mining to translate sentences of the computer science abstract corpus while retaining the same meaning. Such approach resulted in slight improvements (around 2 percentage points) for the weight-averaged Recall and F1-score values. [ABSTRACT FROM AUTHOR]

The aim of the present study is to visualize the dynamics of conceptual clusters, hidden patterns and emerging knowledge of "cybernetics in Middle Eastern countries" subject area in a global scale based on the co-word analysis of scientific outcomes indexed in the Web of Science database. This research is an applied one, performed with the co-word analysis method and analytical approach. The population includes 2280 keywords from 705 records in the cybernetics subject area in Middle Eastern countries, published in 7 journals indexed in the Web of Science database until 2018. To have a coherence set of data and create the co-occurrence matrix, RavarPreMap software applied. Then, using SPSS 16 and VOSViewer, the subjects were clustered and mapped. Specifications of co-word networks such as centrality and density illustrated using UCInet software in the form of a strategic diagram. Thereby, the situation and evolutionary branches of the cybernetics subject area revealed in Middle Eastern countries. The trend of scientific productions in cybernetics in Middle Eastern counties is incremental. Computer science, with 444 records, is the core subject in the mentioned studies. Researchers' intellectual structures are identifiable in 12 clusters. The most significant cluster is the twelfth one, named master data mining algorithms and including 20 keywords. The distribution of clusters in the strategic diagram shows that Clusters 1, 2, and 4 are core issues in the subject area. Furthermore, they are mature and coherent, being studied at the core of the research area, having a core and developable role. The co-word analysis is a proper and powerful method in discovering and visualizing science and knowledge, tracking sciences, studying the trend of conceptual dynamicity, identifying and analyzing researchable fields in the subject area of cybernetics. This strategic approach can drastically help research planners and policy-makers as well as governmental authorities. [ABSTRACT FROM AUTHOR]

Background: Web-based, free-text documents on science and technology have been increasing growing on the web. However, most of these documents are not immediately processable by computers slowing down the acquisition of useful information. Computational ontologies might represent a possible solution by enabling semantically machine readable data sets. But, the process of ontology creation, instantiation and maintenance is still based on manual methodologies and thus time and cost intensive. Method: We focused on a large corpus containing information on researchers, research fields, and institutions. We based our strategy on traditional entity recognition, social computing and correlation. We devised a semi automatic approach for the recognition, correlation and extraction of named entities and relations from textual documents which are then used to create, instantiate, and maintain an ontology. Results: We present a prototype demonstrating the applicability of the proposed strategy, along with a case study describing how direct and indirect relations can be extracted from academic and professional activities registered in a database of curriculum vitae in free-text format. We present evidence that this system can identify entities to assist in the process of knowledge extraction and representation to support ontology maintenance. We also demonstrate the extraction of relationships among ontology classes and their instances. Conclusion: We have demonstrated that our system can be used for the conversion of research information in free text format into database with a semantic structure. Future studies should test this system using the growing number of free-text information available at the institutional and national levels. [ABSTRACT FROM AUTHOR]

Timelines have been used for centuries and have become more and more widely used with the development of social media in recent years. Every day, various smart phones and other instruments on the internet of things generate massive data related to time. Most of these data can be managed in the way of timelines. However, it is still a challenge to effectively and efficiently store, query, and process big timeline data, especially the instant recommendation based on timeline similarities. Most existing studies have focused on indexing spatial and interval datasets rather than the timeline dataset. In addition, many of them are designed for a centralized system. A timeline index structure adapting to parallel and distributed computation framework is in urgent need. In this research, we have defined the timeline similarity query and developed a novel timeline index in the distributed system, called the Distributed Triangle Increment Tree (DTI-Tree), to support the similarity query. The DTI-Tree consists of one T-Tree and one or more TI-Trees based on a triangle increment partition strategy with the Apache Spark. Furthermore, we have provided an open source timeline benchmark data generator, named TimelineGenerator, to generate various timeline test datasets for different conditions. The experiments for DTI-Tree’s construction, insertion, deletion, and similarity queries have been executed on a cluster with two benchmark datasets that are generated by TimelineGenerator. The experimental results show that the DTI-tree provides an effective and efficient distributed index solution to big timeline data. [ABSTRACT FROM AUTHOR]