Methodology and applications of eyetracking

Rajs Arkadiusz, Aleksiewicz Mariusz, Banaszak-Piechowska Agnieszka, Gospodarczyk Jacek. Methodology and applications of eyetracking. Journal of Education, Health and Sport. 2016;6(4):115-121. eISSN 2391-8306. DOI http://dx.doi.org/10.5281/zenodo.49872http://ojs.ukw.edu.pl/index.php/johs/article/view/3455 The journal has had 7 points in Ministry of Science and Higher Education parametric evaluation. Part B item 755 (23.12.2015).755 Journal of Education, Health and Sport eISSN 2391-8306 7© The Author (s) 2016;This article is published with open access at Licensee Open Journal Systems of Kazimierz Wielki University in Bydgoszcz, PolandOpen Access. This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium,provided the original author(s) and source are credited. This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License(http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted, non commercial use, distribution and reproduction in any medium, provided the work is properly cited.This is an open access article licensed under the terms of the Creative Commons Attribution Non Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted, non commercialuse, distribution and reproduction in any medium, provided the work is properly cited.The authors declare that there is no conflict of interests regarding the publication of this paper.Received: 05.03.2016. Revised 10.04.2016. Accepted: 10.04.2016.  METHODOLOGY AND APPLICATIONS OF EYETRACKING dr inż. Arkadiusz RajsInstitute of Telecommunications and Computer Science, University of Science andTechnology, Al. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz, [email protected] inż. Mariusz AleksiewiczInstitute of Telecommunications and Computer Science, University of Science and Technology, Al. Prof. S. Kaliskiego 7, 85-796 Bydgoszcz,, [email protected] Agnieszka Banaszak-PiechowskaInstitute of Physics, Faculty of Mathematics, Physics and Technique Science, Kazimierz Wielki University,85-052 Bydgoszcz, Powstanców Wlkp. 2, [email protected] inż. Jacek GospodarczykInstitute of Computer Science and Mechatronics, University of Economy,Garbary 2,85-229 Bydgoszcz, [email protected] SummaryEyetracking gives great capability of computer’s systems control and study of usability applications. In this paper we show construction of eyetracker and range of applications. Key words: eyetracker, computer vision

An Interactive Zoo Guide: A Case Study of Collaborative Learning

Real Industry Projects and team work can have a great impact on student learning but providing these activities requires significant commitment from academics. It requires several years planning implementing to create a collaborative learning environment that mimics the real world ICT (Information and Communication Technology) industry workplace. In this project, staff from all the three faculties, namely the Faculty of Health, Engineering and Science, Faculty of Arts, Education and Human Development, and Faculty of Business and Law in higher education work together to establish a detailed project management plan and to develop the unit guidelines for participating students. The proposed project brings together students from business, multimedia and computer science degrees studying their three project-based units within each faculty to work on a relatively large IT project with our industry partner, Melbourne Zoo. This paper presents one multimedia software project accomplished by one of the multi-discipline student project teams. The project was called 'Interactive ZooOz Guide' and developed on a GPS-enabled PDA device in 2007. The developed program allows its users to navigate through the Zoo via an interactive map and provides multimedia information of animals on hotspots at the 'Big Cats' section of the Zoo so that it enriches user experience at the Zoo. A recent development in zoo applications is also reviewed. This paper is also intended to encourage academia to break boundaries to enhance students' learning beyond classroom.Comment: 11 Page

Complex systems science: expert consultation report

Executive SummaryA new programme of research in Complex Systems Science must be initiated by FETThe science of complex systems (CS) is essential to establish rigorous scientific principles on which to develop the future ICT systems that are critical to the well-being, safety and prosperity of Europe and its citizens. As the “ICT incubator and pathfinder for new ideas and themes for long-term research in the area of information and communication technologies” FET must initiate a significant new programme of research in complex systems science to underpin research and development in ICT. Complex Systems Science is a “blue sky” research laboratory for R&D in ICT and their applications. In July 2009, ASSYST was given a set of probing questions concerning FET funding for ICT-related complex systems research. This document is based on the CS community’s response.Complex systems research has made considerable progress and is delivering new scienceSince FET began supporting CS research, considerable progress has been made. Building on previous understanding of concepts such as emergence from interactions, far-from-equilibrium systems, border of chaos and self-organised criticality, recent CS research is now delivering rigorous theory through methods of statistical physics, network theory, and computer simulation. CS research increasingly demands high-throughput data streams and new ICT-based methods of observing and reconstructing, i.e. modelling, the dynamics from those data in areas as diverse as embryogenesis, neuroscience, transport, epidemics, linguistics, meteorology, and robotics. CS research is also beginning to address the problem of engineering robust systems of systems of systems that can adapt to changing environments, including the perplexing problem that ICT systems are too often fragile and non-adaptive.Recommendation: A Programme of Research in Complex Systems Science to Support ICTFundamental theory in Complex Systems Science is needed, but this can only be achieved through real-world applications involving large, heterogeneous, and messy data sets, including people and organisations. A long-term vision is needed. Realistic targets can be set. Fundamental research can be ensured by requiring that teams include mathematicians, computer scientists, physicists and computational social scientists.One research priority is to develop a formalism for multilevel systems of systems of systems, applicable to all areas including biology, economics, security, transportation, robotics, health, agriculture, ecology, and climate change. Another related research priority is a scientific perspective on the integration of the new science with policy and its implementation, including ethical problems related to privacy and equality.A further priority is the need for education in complex systems science. Conventional education continues to be domain-dominated, producing scientists who are for the most part still lacking fundamental knowledge in core areas of mathematics, computation, statistical physics, and social systems. Therefore:1. We recommend that FET fund a new programme of work in complex systems science as essential research for progress in the development of new kinds of ICT systems.2. We have identified the dynamics of multilevel systems as the area in complex systems science requiring a major paradigm shift, beyond which significant scientific progress cannot be made.3. We propose a call requiring: fundamental research in complex systems science; new mathematical and computational formalisms to be developed; involving a large ‘guinea pig’ organisation; research into policy and its meta-level information dynamics; and that all research staff have interdisciplinary knowledge through an education programme.Tangible outcomes, potential users of the new science, its impact and measures of successUsers include (i) the private and public sectors using ICT to manage complex systems and (ii) researchers in ICT, CSS, and all complex domains. The tangible output of a call will be new knowledge on the nature of complex systems in general, new knowledge of the particular complex system(s) studied, and new knowledge of the fundamental role played by ICT in the research and implementation to create real systems addressing real-world problems. The impact of the call will be seen through new high added-value opportunities in the public and private sectors, new high added-value ICT technologies, and new high added-value science to support innovation in ICT research and development. The measure of success will be through the delivery of these high added-value outcomes, and new science to better understand failures

Opportunities for use of blockchain technology in medicine

Blockchain technology is a decentralized database that stores a registry of assets and transactions across a peer-to-peer computer network, which is secured through cryptography, and over time, its history gets locked in blocks of data that are cryptographically linked together and secured. So far, there have been use cases of this technology for cryptocurrencies, digital contracts, financial and public records, and property ownership. It is expected that future uses will expand into medicine, science, education, intellectual property, and supply chain management. Likely applications in the field of medicine could include electronic health records, health insurance, biomedical research, drug supply and procurement processes, and medical education. Utilization of blockchain is not without its weaknesses and currently, this technology is extremely immature and lacks public or even expert knowledge, making it hard to have a clear strategic vision of its true future potential. Presently, there are issues with scalability, security of smart contracts, and user adoption. Nevertheless, with capital investments into blockchain technology projected to reach US$400 million in 2019, health professionals and decision makers should be aware of the transformative potential that blockchain technology offers for healthcare organizations and medical practice

Artificial intelligence in the education of health professions: a descriptive analysis through bibliometrics

Introduction and Objectives: Artificial intelligence (AI) refers to a branch of computer science that focuses on creating machines and software programs that can perform tasks that typically require human-like intelligence, such as learning, problem-solving, decision making, and language understanding. AI technologies include machine learning, deep learning, natural language processing, and computer vision, among others. AI has applications in various fields, including healthcare, finance, education, among others, and has the potential to transform how we live, work, and interact with technology. AI has the potential to revolutionize the education of healthcare professionals by providing new tools and resources for teaching and training, such as personalized learning, intelligent tutoring, virtual simulation, and automated grading. The use of AI in healthcare education is growing, and it has the potential to impact research as well. However, the vast amount of scientific literature in this field makes it challenging to understand its scientific structure and development. Visualization techniques based on bibliometric data can be helpful in comprehending scientific fields. Material and Methods: This is a bibliometric, descriptive, and retrospective study. The author identified publications from the Pubmed database from 1990 till 2023 related to the use of Artificial Intelligence in Health Professions Education using this search string (AI OR "Artificial Intelligence"[Mesh]) AND "Education"[Mesh] AND "Health Personnel"[Mesh]. From the titles and abstracts of these publications, was selected the main terms related to the field, extracted by VOSviewer software, to create a visualization of the most important trends referred to in the literature. Results: The researchers identified a total of 576 relevant references, including 36 clinical trials and randomized controlled trials, as well as 57 meta-analyses and systematic reviews. Upon examining the co-occurrence of Mesh terms associated with AI and healthcare professionals' education, it was found that the most common usage of this approach was in various medical fields and educational levels, followed by allied health personnel. Another noteworthy observation was the emergence of the use of AI in healthcare education in surgery, which began to gain traction after 2018. Conclusions: Overall, as shown by published research, the interest in AI has grown exponentially, influencing all aspects related to the use of this approach in the education and training of healthcare professions. The use of AI in healthcare education has the potential to enhance the learning experience for students, improve their clinical skills and decision-making abilities, and ultimately lead to better patient outcomes. However, it is important to ensure that these technologies are designed and implemented in an ethical and responsible manner, with appropriate consideration given to issues such as bias, privacy, and transparency.N/

Artificial intelligence in the education of health professions: a descriptive analysis through bibliometrics

Introduction and Objectives: Artificial intelligence (AI) refers to a branch of computer science that focuses on creating machines and software programs that can perform tasks that typically require human-like intelligence, such as learning, problem-solving, decision making, and language understanding. AI technologies include machine learning, deep learning, natural language processing, and computer vision, among others. AI has applications in various fields, including healthcare, finance, education, among others, and has the potential to transform how we live, work, and interact with technology. AI has the potential to revolutionize the education of healthcare professionals by providing new tools and resources for teaching and training, such as personalized learning, intelligent tutoring, virtual simulation, and automated grading. The use of AI in healthcare education is growing, and it has the potential to impact research as well. However, the vast amount of scientific literature in this field makes it challenging to understand its scientific structure and development. Visualization techniques based on bibliometric data can be helpful in comprehending scientific fields. Material and Methods: This is a bibliometric, descriptive, and retrospective study. The author identified publications from the Pubmed database from 1990 till 2023 related to the use of Artificial Intelligence in Health Professions Education using this search string (AI OR "Artificial Intelligence"[Mesh]) AND "Education"[Mesh] AND "Health Personnel"[Mesh]. From the titles and abstracts of these publications, was selected the main terms related to the field, extracted by VOSviewer software, to create a visualization of the most important trends referred to in the literature. Results: The researchers identified a total of 576 relevant references, including 36 clinical trials and randomized controlled trials, as well as 57 meta-analyses and systematic reviews. Upon examining the co-occurrence of Mesh terms associated with AI and healthcare professionals' education, it was found that the most common usage of this approach was in various medical fields and educational levels, followed by allied health personnel. Another noteworthy observation was the emergence of the use of AI in healthcare education in surgery, which began to gain traction after 2018. Conclusions: Overall, as shown by published research, the interest in AI has grown exponentially, influencing all aspects related to the use of this approach in the education and training of healthcare professions. The use of AI in healthcare education has the potential to enhance the learning experience for students, improve their clinical skills and decision-making abilities, and ultimately lead to better patient outcomes. However, it is important to ensure that these technologies are designed and implemented in an ethical and responsible manner, with appropriate consideration given to issues such as bias, privacy, and transparency.info:eu-repo/semantics/publishedVersio

Sentiment Analysis in the Era of Web 2.0: Applications, Implementation Tools and Approaches for the Novice Researcher

Nowadays, people find it easier to express opinions via social media-formally known as Web 2.0. Sentiment analysis is an essential field under natural language processing in Computer Science that deals with analyzing people's opinions on the subject matter and discovering the polarity they contain. These opinions could be processed in collective form (as a document) or segments or units as sentences or phrases. Sentiment analysis can be applied in education, research optimization, politics, business, education, health, science and so on, thus forming massive data that requires efficient tools and techniques for analysis. Furthermore, the standard tools currently used for data collection, such as online surveys, interviews, and student evaluation of teachers, limit respondents in expressing opinions to the researcher's surveys and could not generate huge data as Web 2.0 becomes bigger. Sentiment analysis techniques are classified into three (3): Machine learning algorithms, lexicon and hybrid. This study explores sentiment analysis of Web 2.0 for novice researchers to promote collaboration and suggest the best tools for sentiment data analysis and result efficiency. Studies show that machine learning approaches result in large data sets on document-level sentiment classification. In some studies, hybrid techniques that combine machine learning and lexicon-based performance are better than lexicon. Python and R programming are commonly used tools for sentiment analysis implementation, but SentimentAnalyzer and SentiWordnet are recommended for the novice. Keywords:   Sentiment Analysis; Web 2.0; Applications; Tools; Novic

Interdisciplinary perspectives on privacy awareness in lifelogging technology development

Population aging resulting from demographic changes requires some challenging decisions and necessary steps to be taken by different stakeholders to manage current and future demand for assistance and support. The consequences of population aging can be mitigated to some extent by assisting technologies that can support the autonomous living of older individuals and persons in need of care in their private environments as long as possible. A variety of technical solutions are already available on the market, but privacy protection is a serious, often neglected, issue when using such (assisting) technology. Thus, privacy needs to be thoroughly taken under consideration in this context. In a three-year project PAAL (‘Privacy-Aware and Acceptable Lifelogging Services for Older and Frail People’), researchers from different disciplines, such as law, rehabilitation, human-computer interaction, and computer science, investigated the phenomenon of privacy when using assistive lifelogging technologies. In concrete terms, the concept of Privacy by Design was realized using two exemplary lifelogging applications in private and professional environments. A user-centered empirical approach was applied to the lifelogging technologies, investigating the perceptions and attitudes of (older) users with different health-related and biographical profiles. The knowledge gained through the interdisciplinary collaboration can improve the implementation and optimization of assistive applications. In this paper, partners of the PAAL project present insights gained from their cross-national, interdisciplinary work regarding privacy-aware and acceptable lifelogging technologies.Open Access funding enabled and organized by Projekt DEAL. This work is part of the PAAL-project (“Privacy-Aware and Acceptable Lifelogging services for older and frail people”). The support of the Joint Programme Initiative “More Years, Better Lives” (award number: PAAL_JTC2017), the German Federal Ministry of Education and Research (grant no: 16SV7955), the Swedish Research Council for Health, Working Life, and Welfare (grant no: 2017–02302), the Spanish Agencia Estatal de Investigacion (PCIN-2017-114), the Italian Ministero dell’Istruzione dell’Universitá e della Ricerca, (CUP: I36G17000380001), and the Canadian Institutes of Health Research is gratefully acknowledged

The U.S. Science and Engineering Workforce: Recent, Current, and Projected Employment, Wages, and Unemployment

[Excerpt] As Congress develops policies and programs and makes appropriations to help address the nation’s needs for scientists and engineers, it may wish to consider past, current, and projected S&E workforce trends. In this regard, this report provides employment, wage, and unemployment information for the computer occupations, mathematical occupations, engineers, life scientists, physical scientists, and S&E management occupations, in three sections: “Current Employment, Wages, and Unemployment” provides a statistical snapshot of the S&E workforce in 2011 (the latest year for which data are available) with respect to occupational employment, wage, and unemployment data. “Recent Trends in Employment, Wages, and Unemployment” provides a perspective on how S&E employment, wages, and unemployment have changed during the 2008-2011 period. “Employment Projections, 2010-2020” provides an analysis of the Bureau of Labor Statistics’ occupational projections examining how the number employed in S&E occupations are expected to change during the 2010-2020 period, as well as how many openings will be created by workers exiting each occupation (replacement needs). A final section, “Concluding Observations,” provides various stakeholder perspectives that Congress may wish to consider as it seeks to ensure that the United States has an adequate S&E workforce to meet the demands of the 21st century