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In an era where inclusivity and gender equality are at the centre of our societal preoccupations, this thesis investigates, through the prism of identity dilemmas, how female gamers manage their identity(ies) in a hostile and gendered gaming community: League of Legends. Accordingly, this research fills a gap in the literature by combining the themes of online communities, female gamers, and identity creation in relation to aggressive behaviours, namely gender-based harassment and stigmatisations. Additionally, this research, by investigating such a challenging topic for the industry, brings food for thought to the gaming sector, namely game developers. As a result, various findings could be drawn from this research relying on in-depth-interviews of 20 female League of Legends players.
First, the focus of this research is based on the definitions of the gaming communities and the notion of femininity accepted by both our society and respondents. I argue that several women and gamer identities must be considered to analyse such an identity process. Then, the sources of identity dilemmas are explored which leads to the conclusion that stigmatisations are the trigger of the studied identity dilemmas, complemented by gender-based harassment and incongruity between identities. Later, this research allows the identification of four types of resolutions to identity dilemmas, also called identity management styles in this thesis. In addition, factors which might influence the identity management styles developed by female gamers are addressed. Thus, the influence of game rank is denied while the influence of exposure to stigmatisations outside the community is assumed. Finally, recommendations for future research are formulated, as well as the identified limitations of this thesis.

Identity creation --- Identity management --- Female gamers --- Gaming communities --- Identity dilemma --- Blended identity --- Defensive othering --- Stigmatisations --- Gender-based harassment --- League of Legends --- Sciences économiques & de gestion > Marketing

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This book is published open access under a CC BY 4.0 licence.The book offers a concise guide for librarians, helping them understand the challenges, processes and technologies involved in managing access to online resources. After an introduction the book presents cases of general authentication and authorisation. It helps readers understand web based authentication and provides the fundamentals of IP address recognition in an easy to understand manner. A special chapter is dedicated to Security Assertion Markup Language (SAML), followed by an overview of the key concepts of OpenID Connect. The book concludes with basic troubleshooting guidelines and recommendations for further assistance. Librarians will benefit from this quick and easy read, which demystifies the technologies used, features real-life scenarios, and explains how to competently employ authentication and access management.

online --- digitale --- elektroniske ressurser --- nettressurser --- bibliotek --- tilgangskontroll --- digitale medier --- digitale bøker --- e-bøker --- informasjonsressurser --- administrasjon --- Culture --- Library science. --- Industrial management. --- Management information systems. --- Educational technology. --- Cultural and Media Studies. --- Library Science. --- Business Information Systems. --- Information Systems Applications (incl. Internet). --- Technology and Digital Education. --- Media Management. --- Study and teaching. --- Instructional technology --- Technology in education --- Technology --- Educational innovations --- Instructional systems --- Teaching --- Computer-based information systems --- EIS (Information systems) --- Executive information systems --- MIS (Information systems) --- Sociotechnical systems --- Information resources management --- Management --- Business administration --- Business enterprises --- Business management --- Corporate management --- Corporations --- Industrial administration --- Management, Industrial --- Rationalization of industry --- Scientific management --- Business --- Industrial organization --- Librarianship --- Library economy --- Bibliography --- Documentation --- Information science --- Cultural studies --- Aids and devices --- Communication systems --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- access management --- online resources --- library technology --- access protocols --- identity management --- authentication --- authorisation --- troubleshooting --- SAML

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Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing

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Over the past decade, computational methods, including machine learning (ML) and deep learning (DL), have been exponentially growing in their development of solutions in various domains, especially medicine, cybersecurity, finance, and education. While these applications of machine learning algorithms have been proven beneficial in various fields, many shortcomings have also been highlighted, such as the lack of benchmark datasets, the inability to learn from small datasets, the cost of architecture, adversarial attacks, and imbalanced datasets. On the other hand, new and emerging algorithms, such as deep learning, one-shot learning, continuous learning, and generative adversarial networks, have successfully solved various tasks in these fields. Therefore, applying these new methods to life-critical missions is crucial, as is measuring these less-traditional algorithms' success when used in these fields.

fintech --- financial technology --- blockchain --- deep learning --- regtech --- environment --- social sciences --- machine learning --- learning analytics --- student field forecasting --- imbalanced datasets --- explainable machine learning --- intelligent tutoring system --- adversarial machine learning --- transfer learning --- cognitive bias --- stock market --- behavioural finance --- investor’s profile --- Teheran Stock Exchange --- unsupervised learning --- clustering --- big data frameworks --- fault tolerance --- stream processing systems --- distributed frameworks --- Spark --- Hadoop --- Storm --- Samza --- Flink --- comparative analysis --- a survey --- data science --- educational data mining --- supervised learning --- secondary education --- academic performance --- text-to-SQL --- natural language processing --- database --- machine translation --- medical image segmentation --- convolutional neural networks --- SE block --- U-net --- DeepLabV3plus --- cyber-security --- medical services --- cyber-attacks --- data communication --- distributed ledger --- identity management --- RAFT --- HL7 --- electronic health record --- Hyperledger Composer --- cybersecurity --- password security --- browser security --- social media --- ANOVA --- SPSS --- internet of things --- cloud computing --- computational models --- metaheuristics --- phishing detection --- website phishing

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Cybercrime is increasingly exposing the health domain to growing risk. The push towards a strong connection of citizens to health services, through digitalization, has undisputed advantages. Digital health allows remote care, the use of medical devices with a high mechatronic and IT content with strong automation, and a large interconnection of hospital networks with an increasingly effective exchange of data. However, all this requires a great cybersecurity commitment—a commitment that must start with scholars in research and then reach the stakeholders. New devices and technological solutions are increasingly breaking into healthcare, and are able to change the processes of interaction in the health domain. This requires cybersecurity to become a vital part of patient safety through changes in human behaviour, technology, and processes, as part of a complete solution. All professionals involved in cybersecurity in the health domain were invited to contribute with their experiences. This book contains contributions from various experts and different fields. Aspects of cybersecurity in healthcare relating to technological advance and emerging risks were addressed. The new boundaries of this field and the impact of COVID-19 on some sectors, such as mhealth, have also been addressed. We dedicate the book to all those with different roles involved in cybersecurity in the health domain.

Medical equipment & techniques --- personal health record --- emergency access --- access control --- blockchain --- hyperledger fabric --- hyperledger composer --- auditability --- privacy &amp --- security --- healthcare --- review --- electronic medical record --- cloud --- internet of things --- technology convergence --- eHealth --- medical devices --- digital health --- mHealth --- cyber-risk --- pacemaker --- artificial pancreas --- app --- regulation --- wearable device --- digital identity --- decentralized identity --- identity management --- smart contract --- Ethereum --- sexism --- social networks --- adolescence --- digital gender gap --- emotional well-being --- healthcare service --- body area network --- privacy --- authentication --- security protocol --- cybersecurity culture --- COVID-19 --- security assessment --- phishing --- health domain --- cybersecurity --- fuzzy cognitive maps --- telehealth --- scenario analysis --- planning --- contact tracing --- pandemic --- fall detection --- fall prediction --- fall prevention --- fall risk factors --- gait assessment --- 5G networks --- key performance indicators --- wireless communication --- awareness --- healthcare domain