Analysis of Computational Science Papers from ICCS 2001-2016 using Topic Modeling and Graph Theory

This paper presents results of topic modeling and network models of topics using the International Conference on Computational Science corpus, which contains domain-specific (computational science) papers over sixteen years (a total of 5695 papers). We discuss topical structures of International Conference on Computational Science, how these topics evolve over time in response to the topicality of various problems, technologies and methods, and how all these topics relate to one another. This analysis illustrates multidisciplinary research and collaborations among scientific communities, by constructing static and dynamic networks from the topic modeling results and the keywords of authors. The results of this study give insights about the past and future trends of core discussion topics in computational science. We used the Non-negative Matrix Factorization topic modeling algorithm to discover topics and labeled and grouped results hierarchically.Comment: Accepted by International Conference on Computational Science (ICCS) 2017 which will be held in Zurich, Switzerland from June 11-June 1

A literature review

Rosário, A., Moniz, L. B., & Cruz, R. (2021). Data science applied to marketing: A literature review. Journal of Information Science and Engineering, 37(5), 1067-1081. https://doi.org/10.6688/JISE.202109_37(5).0006 -------------------------------------------------------------- Funding Information: We would like to express our gratitude to the Editor and the Referees. They offered extremely valuable suggestions or improvements. The authors were supported by the GOVCOPP Research Unit of Universidade de Aveiro and UNIDCOM/IADE research unit of Universidade Europeia. Publisher Copyright: © 2021 Institute of Information Science. All rights reserved.Data Science applied to Marketing has been a research interest due to competitive advantages in business. We have applied a systematic literature review between 2010 and 2020, reaching a total of 19 valid articles. After a deeper segmentation, 13 articles were selected for inclusion in the review comprising the period 2013-2020. On scientific production, the topic Data Science Applied to Marketing, in 2020, has a new subject of interest. The number of citations has been growing since 2015 and the findings revealed that marketing is recurring of a variety of data science methods, from micro-segmentation and realtime application to natural language processing. The impact is evident in digital advertising, micro-segmentation and micro-targeting, speed and performance, and real-time experimentation. The use cases of data analytics in marketing have used four methods with the highest potential to impact marketing approaches: Internet-of-Things, big data, artificial intelligence, and machine learning.publishersversionpublishe

INVALSI data: assessments on teaching and methodologies

The school system has always aimed to achieve quality teaching, which is able, on the one hand, to give adequate responses to the expectations of all the stakeholders and, on the other, to introduce tools, actions, and checks through which the training offer can be constantly improved. This process is undoubtedly linked to scientific research. Researchers and Academics start from the data available to them or collect new ones, to discover and/or interpret facts and to find answers and new cues of reflection. A favorable environment for this work was the Seminar “INVALSI data: a research and educational teaching tool”, in its fourth edition in November 2019. The volume consists of six chapters, which are arise within the aforementioned Seminar context and, while dealing with heterogeneous topics, offer important examples of research both on teaching and on the methodologies applied to it. As a Statistical Service, which for years has taken care of the collection and dissemination of data, we hope that in this, as in the other volumes of the series, the reader will find confirmation of the importance that data play, both in scientific research and in practice in classroom

INVALSI data: assessments on teaching and methodologies

The school system has always aimed to achieve quality teaching, which is able, on the one hand, to give adequate responses to the expectations of all the stakeholders and, on the other, to introduce tools, actions, and checks through which the training offer can be constantly improved. This process is undoubtedly linked to scientific research. Researchers and Academics start from the data available to them or collect new ones, to discover and/or interpret facts and to find answers and new cues of reflection. A favorable environment for this work was the Seminar “INVALSI data: a research and educational teaching tool”, in its fourth edition in November 2019. The volume consists of six chapters, which are arise within the aforementioned Seminar context and, while dealing with heterogeneous topics, offer important examples of research both on teaching and on the methodologies applied to it. As a Statistical Service, which for years has taken care of the collection and dissemination of data, we hope that in this, as in the other volumes of the series, the reader will find confirmation of the importance that data play, both in scientific research and in practice in classroom

Method versatility in analysing human attitudes towards technology

Various research domains are facing new challenges brought about by growing volumes of data. To make optimal use of them, and to increase the reproducibility of research findings, method versatility is required. Method versatility is the ability to flexibly apply widely varying data analytic methods depending on the study goal and the dataset characteristics. Method versatility is an essential characteristic of data science, but in other areas of research, such as educational science or psychology, its importance is yet to be fully accepted. Versatile methods can enrich the repertoire of specialists who validate psychometric instruments, conduct data analysis of large-scale educational surveys, and communicate their findings to the academic community, which corresponds to three stages of the research cycle: measurement, research per se, and communication. In this thesis, studies related to these stages have a common theme of human attitudes towards technology, as this topic becomes vitally important in our age of ever-increasing digitization. The thesis is based on four studies, in which method versatility is introduced in four different ways: the consecutive use of methods, the toolbox choice, the simultaneous use, and the range extension. In the first study, different methods of psychometric analysis are used consecutively to reassess psychometric properties of a recently developed scale measuring affinity for technology interaction. In the second, the random forest algorithm and hierarchical linear modeling, as tools from machine learning and statistical toolboxes, are applied to data analysis of a large-scale educational survey related to students’ attitudes to information and communication technology. In the third, the challenge of selecting the number of clusters in model-based clustering is addressed by the simultaneous use of model fit, cluster separation, and the stability of partition criteria, so that generalizable separable clusters can be selected in the data related to teachers’ attitudes towards technology. The fourth reports the development and evaluation of a scholarly knowledge graph-powered dashboard aimed at extending the range of scholarly communication means. The findings of the thesis can be helpful for increasing method versatility in various research areas. They can also facilitate methodological advancement of academic training in data analysis and aid further development of scholarly communication in accordance with open science principles.Verschiedene Forschungsbereiche müssen sich durch steigende Datenmengen neuen Herausforderungen stellen. Der Umgang damit erfordert – auch in Hinblick auf die Reproduzierbarkeit von Forschungsergebnissen – Methodenvielfalt. Methodenvielfalt ist die Fähigkeit umfangreiche Analysemethoden unter Berücksichtigung von angestrebten Studienzielen und gegebenen Eigenschaften der Datensätze flexible anzuwenden. Methodenvielfalt ist ein essentieller Bestandteil der Datenwissenschaft, der aber in seinem Umfang in verschiedenen Forschungsbereichen wie z. B. den Bildungswissenschaften oder der Psychologie noch nicht erfasst wird. Methodenvielfalt erweitert die Fachkenntnisse von Wissenschaftlern, die psychometrische Instrumente validieren, Datenanalysen von groß angelegten Umfragen im Bildungsbereich durchführen und ihre Ergebnisse im akademischen Kontext präsentieren. Das entspricht den drei Phasen eines Forschungszyklus: Messung, Forschung per se und Kommunikation. In dieser Doktorarbeit werden Studien, die sich auf diese Phasen konzentrieren, durch das gemeinsame Thema der Einstellung zu Technologien verbunden. Dieses Thema ist im Zeitalter zunehmender Digitalisierung von entscheidender Bedeutung. Die Doktorarbeit basiert auf vier Studien, die Methodenvielfalt auf vier verschiedenen Arten vorstellt: die konsekutive Anwendung von Methoden, die Toolbox-Auswahl, die simultane Anwendung von Methoden sowie die Erweiterung der Bandbreite. In der ersten Studie werden verschiedene psychometrische Analysemethoden konsekutiv angewandt, um die psychometrischen Eigenschaften einer entwickelten Skala zur Messung der Affinität von Interaktion mit Technologien zu überprüfen. In der zweiten Studie werden der Random-Forest-Algorithmus und die hierarchische lineare Modellierung als Methoden des Machine Learnings und der Statistik zur Datenanalyse einer groß angelegten Umfrage über die Einstellung von Schülern zur Informations- und Kommunikationstechnologie herangezogen. In der dritten Studie wird die Auswahl der Anzahl von Clustern im modellbasierten Clustering bei gleichzeitiger Verwendung von Kriterien für die Modellanpassung, der Clustertrennung und der Stabilität beleuchtet, so dass generalisierbare trennbare Cluster in den Daten zu den Einstellungen von Lehrern zu Technologien ausgewählt werden können. Die vierte Studie berichtet über die Entwicklung und Evaluierung eines wissenschaftlichen wissensgraphbasierten Dashboards, das die Bandbreite wissenschaftlicher Kommunikationsmittel erweitert. Die Ergebnisse der Doktorarbeit tragen dazu bei, die Anwendung von vielfältigen Methoden in verschiedenen Forschungsbereichen zu erhöhen. Außerdem fördern sie die methodische Ausbildung in der Datenanalyse und unterstützen die Weiterentwicklung der wissenschaftlichen Kommunikation im Rahmen von Open Science

Effective Computation Resilience in High Performance and Distributed Environments

The work described in this paper aims at effective computation resilience for complex simulations in high performance and distributed environments. Computation resilience is a complicated and delicate area; it deals with many types of simulation cores, many types of data on various input levels and also with many types of end-users, which have different requirements and expectations. Predictions about system and computation behaviors must be done based on deep knowledge about underlying infrastructures, and simulations' mathematical and realization backgrounds. Our conceptual framework is intended to allow independent collaborations between domain experts as end-users and providers of the computational power by taking on all of the deployment troubles arising within a given computing environment. The goal of our work is to provide a generalized approach for effective scalable usage of the computing power and to help domain-experts, so that they could concentrate more intensive on their domain solutions without the need of investing efforts in learning and adapting to the new IT backbone technologies

Viability of Numerical Full-Wave Techniques in Telecommunication Channel Modelling

In telecommunication channel modelling the wavelength is small compared to the physical features of interest, therefore deterministic ray tracing techniques provide solutions that are more efficient, faster and still within time constraints than current numerical full-wave techniques. Solving fundamental Maxwell's equations is at the core of computational electrodynamics and best suited for modelling electrical field interactions with physical objects where characteristic dimensions of a computing domain is on the order of a few wavelengths in size. However, extreme communication speeds, wireless access points closer to the user and smaller pico and femto cells will require increased accuracy in predicting and planning wireless signals, testing the accuracy limits of the ray tracing methods. The increased computing capabilities and the demand for better characterization of communication channels that span smaller geographical areas make numerical full-wave techniques attractive alternative even for larger problems. The paper surveys ways of overcoming excessive time requirements of numerical full-wave techniques while providing acceptable channel modelling accuracy for the smallest radio cells and possibly wider. We identify several research paths that could lead to improved channel modelling, including numerical algorithm adaptations for large-scale problems, alternative finite-difference approaches, such as meshless methods, and dedicated parallel hardware, possibly as a realization of a dataflow machine