The future of urban models in the Big Data and AI era: a bibliometric analysis (2000-2019)

This article questions the effects on urban research dynamics of the Big Data and AI turn in urban management. To identify these effects, we use two complementary materials: bibliometric data and interviews. We consider two areas in urban research: one, covering the academic research dealing with transportation systems and the other, with water systems. First, we measure the evolution of AI and Big Data keywords in these two areas. Second, we measure the evolution of the share of publications published in computer science journals about urban traffic and water quality. To guide these bibliometric analyses, we rely on the content of interviews conducted with academics and higher education officials in Paris and Edinburgh at the beginning of 2018

Data-Driven Modeling of Engagement Analytics for Quality Blended Learning

Engagement analytics is a branch of learning analytics (LA) that focuses on student engagement, with the majority of studies conducted by computer scientists.Thus, rather than focusing on learning, research in this field usually treats education as a scenario for algorithms optimization and it rarely concludes with implications for practice. While LA as a research field is reaching ten years, its contribution to our understanding of teaching and learning and its impact on learning enhancement are still underdeveloped. This paper argues that data-driven modeling of engagement analytics is helpful to assess student engagement and to promote reflections on the quality of teaching and learning. In this article, the authors a) introduce four key constructs (student engagement, learning analytics, engagement analytics, modeling and data-driven modeling); b) explain why data-driven modeling is chosen for engagement analytics and the limitations of using a predefined framework; c) discuss how to use engagement analytics to promote pedagogical reflection using a pilot study as a demonstration. As a final remark, the authors see the need of interdisciplinary collaboration on engagement analytics between computer science and educational science. In fact, this collaboration should enhance the use of machine learning and data mining methods to explore big data in education as a means to provide effective insights for quality educational practice.Peer reviewe

Preface

DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018.DAMSS-2018 is the jubilee 10th international workshop on data analysis methods for software systems, organized in Druskininkai, Lithuania, at the end of the year. The same place and the same time every year. Ten years passed from the first workshop. History of the workshop starts from 2009 with 16 presentations. The idea of such workshop came up at the Institute of Mathematics and Informatics. Lithuanian Academy of Sciences and the Lithuanian Computer Society supported this idea. This idea got approval both in the Lithuanian research community and abroad. The number of this year presentations is 81. The number of registered participants is 113 from 13 countries. In 2010, the Institute of Mathematics and Informatics became a member of Vilnius University, the largest university of Lithuania. In 2017, the institute changes its name into the Institute of Data Science and Digital Technologies. This name reflects recent activities of the institute. The renewed institute has eight research groups: Cognitive Computing, Image and Signal Analysis, Cyber-Social Systems Engineering, Statistics and Probability, Global Optimization, Intelligent Technologies, Education Systems, Blockchain Technologies. The main goal of the workshop is to introduce the research undertaken at Lithuanian and foreign universities in the fields of data science and software engineering. Annual organization of the workshop allows the fast interchanging of new ideas among the research community. Even 11 companies supported the workshop this year. This means that the topics of the workshop are actual for business, too. Topics of the workshop cover big data, bioinformatics, data science, blockchain technologies, deep learning, digital technologies, high-performance computing, visualization methods for multidimensional data, machine learning, medical informatics, ontological engineering, optimization in data science, business rules, and software engineering. Seeking to facilitate relations between science and business, a special session and panel discussion is organized this year about topical business problems that may be solved together with the research community. This book gives an overview of all presentations of DAMSS-2018

Constructing Knowledge Graph for Cybersecurity Education

abstract: There currently exist various challenges in learning cybersecuirty knowledge, along with a shortage of experts in the related areas, while the demand for such talents keeps growing. Unlike other topics related to the computer system such as computer architecture and computer network, cybersecurity is a multidisciplinary topic involving scattered technologies, which yet remains blurry for its future direction. Constructing a knowledge graph (KG) in cybersecurity education is a first step to address the challenges and improve the academic learning efficiency. With the advancement of big data and Natural Language Processing (NLP) technologies, constructing large KGs and mining concepts, from unstructured text by using learning methodologies, become possible. The NLP-based KG with the semantic similarity between concepts has brought inspiration to different industrial applications, yet far from completeness in the domain expertise, including education in computer science related fields. In this research work, a KG in cybersecurity area has been constructed using machine-learning-based word embedding (i.e., mapping a word or phrase onto a vector of low dimensions) and hyperlink-based concept mining from the full dataset of words available using the latest Wikipedia dump. The different approaches in corpus training are compared and the performance based on different similarity tasks is evaluated. As a result, the best performance of trained word vectors has been applied, which is obtained by using Skip-Gram model of Word2Vec, to construct the needed KG. In order to improve the efficiency of knowledge learning, a web-based front-end is constructed to visualize the KG, which provides the convenience in browsing related materials and searching for cybersecurity-related concepts and independence relations.Dissertation/ThesisMasters Thesis Computer Science 201

A Quadruple-Based Text Analysis System for History and Philosophy of Science

abstract: Computational tools in the digital humanities often either work on the macro-scale, enabling researchers to analyze huge amounts of data, or on the micro-scale, supporting scholars in the interpretation and analysis of individual documents. The proposed research system that was developed in the context of this dissertation ("Quadriga System") works to bridge these two extremes by offering tools to support close reading and interpretation of texts, while at the same time providing a means for collaboration and data collection that could lead to analyses based on big datasets. In the field of history of science, researchers usually use unstructured data such as texts or images. To computationally analyze such data, it first has to be transformed into a machine-understandable format. The Quadriga System is based on the idea to represent texts as graphs of contextualized triples (or quadruples). Those graphs (or networks) can then be mathematically analyzed and visualized. This dissertation describes two projects that use the Quadriga System for the analysis and exploration of texts and the creation of social networks. Furthermore, a model for digital humanities education is proposed that brings together students from the humanities and computer science in order to develop user-oriented, innovative tools, methods, and infrastructures.Dissertation/ThesisDoctoral Dissertation Biology 201

Decision Support System (DSS) for the Determination of Percentage of Scholarship Quantity Based Fuzzy Tahani

To ensure quality education, the Government must provide services and conveniences to education without any descriminations. To conduct quality education needed some big enough fee therefore for every learner at any unit of education are entitled to tuition fees for those whose parents could not afford to finance their education, and the right to obtain a scholarship for those who has achievment. (Putra & Hardiyanti, 2011).By taking the results of previous research, entitled the application of Fuzzy Inference System Mamdani\u27s method for the determination of quantity percentage of scholarship (Research Lecturer Beginners by 2013), on research this time the researcher do application design with combination of algortitma Fuzzy Inference System Mamdani Method and using the Fuzzy Method for designing Databases Tahani decision support system (SPK).Fuzzy tahani is one method of Fuzzy that uses the database standard. the data are classified based on how the data are seen by the user. So that all the results of the algorithms of previous research can be used to create applications by using Fuzzy method Tahani Database.The type of This research is a kind of applied research, it is a bridge from basic research between experimental research. In the design and development of decision support system based on Desktop Application is a system development methodology used is a structured methodology with the model development SDLC (System Development Life Cycle) that serves to illustrate the main stages and steps of each stage are generally divided into three main activity analysis, design and implementation of data collection Techniques as well as performed there are 3 methods of interview techniques, Documentation and Observation. The Data obtained from the marketing of Computer Science Faculty -Bandar Lampung UniversityThe purpose of this research is Designing Applications by using the Fuzzy Database method\u27s Tahani with available the desktop application so that the eligibility determination process for new student scholarships being accepted right on the target, fast and objective

Os profissionais da informação: avaliação de currículo Lattes no domínio da organização do conhecimento na era do big data

The Big Data phenomenon evidences a context in constant cultural, social, political and economic changes and also in Information Science with a concern in the treatment of data, to guarantee the adequate and efficient recovery, in digital environments. In this way, new horizons for information professionals are opened up. The present research presents as problematic the formation and performance of the information professional for data processing in the Big Data Age, in which the SOCs have the tools to assist in the recovery of digital knowledge. Within this context, we mean the contributions of knowledge organization systems (KOS) supported by technologies and contributions from other fields, such as Computer Science and Linguistics. Therefore, studies and research are needed to better understand the dimensions of how the information professional is preparing and dealing with the systems of knowledge organization in relation to the big data era. In the present research, the training and performance of the information professionals working with knowledge organization systems together with the big data were analyzed. For this, a curricular analysis was carried out by the IC professionals who approach the proposed theme. In this way, it is a general objective to investigate the profile of the IC professional in the KOS theme along with Big Data, to contribute to the development of the area. Specific objectives are to identify: I) what are the university degrees of these professionals, II) if these professionals have a postgraduate degree and in which area, III) the area where the information professionals are performing their duties. In this way, the curricula were retrieved on the Lattes platform, which is a database of the Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq - Plataforma Lattes do Brasil, using the filter "Area of Information Science" and search terms used were "big data and knowledge organization systems" in all fields offered by the database. Once the quantitative survey was carried out, the university training of these professionals and their professional performance were analyzed, and the professional skills and competences of the information professional in the Big Data era were discussed. their contributions. We recovered 71 curricula that work with KOS and big data, in which all these professionals concluded their doctorate, 70 of these professionals act as university professor and only 1 is not professor and is inserted in the administrative branch. It was verified that Blioteconomia is the graduation that most form information professionals working in the area of knowledge organization systems and big data together. All the curricula analyzed have some type of training in higher education, the post graduate that stood out most was in the area of information technology

TECHNOLOGIE CHMUROWE W EDUKACJI: PRZEGLĄD BIBLIOGRAFICZNY

The paper considers the use of cloud technologies in education through the prism of bibliographic analysis. The article characterizes the current state of cloud technologies in education, summarizes the trends, and forecasts the directions of recent scientific research. The leading research methods were bibliographic (visual and quantitative) analysis of keyword networks and qualitative discussion. The bibliographic analysis is based on publications indexed by the scientometric database Web Of Science over the past 20 years. The sample for analysis was formed by searching for the words cloud technology, education, learning, and teaching. The results of the study showed: a significant increase in the popularity of cloud technologies in education in recent years; an increase in the number of studies related to various aspects of educational activities under the influence of Industry 4.0; a gradual increase in the number of studies on the virtualization of the educational process and the use of artificial intelligence in education; dissemination of research on the effectiveness of various types of training using cloud services and teaching methods based on artificial intelligence; the relevance of the trend of visualization of educational material and visual analysis in education. The qualitative discussion provided grounds to identify general trends regarding future research directions.: development of mass online courses and learning technologies (immersive, the use of virtual, augmented, and mixed reality, gaming learning technologies, BYOD approach); further virtualization of universities; development of inclusive education, educational analytics, and assessment (formative and adaptive computer assessment); early training of teachers to use cloud technologies and specialized services in subject learning; research related to visualization (big data, design, simulation, simulation of various processes, etc.) and the designing of relevant new academic disciplines; research of STEM and STEAM education.W artykule omówiono wykorzystanie technologii chmurowych w edukacji pod kątem analizy bibliograficznej. W artykule scharakteryzowano obecny stan wykorzystania technologii chmurowych w edukacji, podsumowano trendy i prognozy kierunków odpowiednich badań naukowych. Wiodącymi metodami badawczymi były wizualna i ilościowa analiza sieci słów kluczowych oraz dyskusja jakościowa. Analiza bibliograficzna została przeprowadzona na publikacjach indeksowanych przez scjentometryczną bazę Web Of Science przez ostatnie 20 lat. Próbkę do analizy tworzy się poprzez wyszukiwanie słów words cloud technology, education, learning, teaching. Wyniki analizy wykazały: znaczący wzrost popularności technologii chmurowych w edukacji w ostatnich latach; wzrost liczby badań związanych z różnymi aspektami działalności edukacyjnej pod wpływem Przemysłu 4.0; stopniowy wzrost liczby badań nad wirtualizacją procesu edukacyjnego i wykorzystaniem sztucznej inteligencji w edukacji; upowszechnianie badań nad efektywnością różnego rodzaju szkoleń z wykorzystaniem usług chmurowych oraz metod nauczania opartych na sztucznej inteligencji; znaczenie trendu wizualizacji materiałów edukacyjnych i analizy wizualnej w edukacji. Dyskusja jakościowa dała podstawy do przewidywania kierunków odpowiednich badań: rozwoju masowych kursów online i technologii uczenia się (immersyjne, wykorzystanie rzeczywistości wirtualnej, rozszerzonej i mieszanej, technologie uczenia się w grach, podejście BYOD); dalsza wirtualizacja uczelni; rozwój uczenia się włączającego, analityki edukacyjnej i oceny (formatywna i adaptacyjna ocena komputerowa); proaktywne szkolenie nauczycieli w zakresie korzystania z technologii chmurowych i specjalistycznych usług w zakresie uczenia się przedmiotów; badania związane z wizualizacją (big data, projektowanie, symulacja, symulacja różnych procesów itp.) oraz rozwój nowych dyscyplin akademickich do prezentacji różnych danych; badanie edukacji STEM i STEAM