1,114 research outputs found

    1st Design Factory Global Network Research Conference ‘Designing the Future’ 5-6 October 2022

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    DFGN.R 2022 -Designing the Future - is the first research conference organised by the Design Factory Global Network. The open event offers the opportunity for all like-minded educators, designers and researchers to share their insights and inspire others on education, methods, practices and ecosystems of co-creation and innovation. The DFGN.R conference is a two-day event hosted on-site in Leeuwarden, the Netherlands. The conference is organized alongside International Design Factory Week 2022, the annual gathering of DFGN members. This year's conference is organized in collaboration with Aalto University from Helsinki Finland and hosted by the NHL Stenden University of Applied Sciences

    Conversations on Empathy

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    In the aftermath of a global pandemic, amidst new and ongoing wars, genocide, inequality, and staggering ecological collapse, some in the public and political arena have argued that we are in desperate need of greater empathy — be this with our neighbours, refugees, war victims, the vulnerable or disappearing animal and plant species. This interdisciplinary volume asks the crucial questions: How does a better understanding of empathy contribute, if at all, to our understanding of others? How is it implicated in the ways we perceive, understand and constitute others as subjects? Conversations on Empathy examines how empathy might be enacted and experienced either as a way to highlight forms of otherness or, instead, to overcome what might otherwise appear to be irreducible differences. It explores the ways in which empathy enables us to understand, imagine and create sameness and otherness in our everyday intersubjective encounters focusing on a varied range of "radical others" – others who are perceived as being dramatically different from oneself. With a focus on the importance of empathy to understand difference, the book contends that the role of empathy is critical, now more than ever, for thinking about local and global challenges of interconnectedness, care and justice

    CiteSee: Augmenting Citations in Scientific Papers with Persistent and Personalized Historical Context

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    When reading a scholarly article, inline citations help researchers contextualize the current article and discover relevant prior work. However, it can be challenging to prioritize and make sense of the hundreds of citations encountered during literature reviews. This paper introduces CiteSee, a paper reading tool that leverages a user's publishing, reading, and saving activities to provide personalized visual augmentations and context around citations. First, CiteSee connects the current paper to familiar contexts by surfacing known citations a user had cited or opened. Second, CiteSee helps users prioritize their exploration by highlighting relevant but unknown citations based on saving and reading history. We conducted a lab study that suggests CiteSee is significantly more effective for paper discovery than three baselines. A field deployment study shows CiteSee helps participants keep track of their explorations and leads to better situational awareness and increased paper discovery via inline citation when conducting real-world literature reviews

    Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts

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    Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support

    Nerf This! Navigating the Accessibility and Inclusivity of Video Games Through Expressive Arts Therapies: A Literature Review

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    This thesis explores the accessibility of video games to populations with disabilities, as well as the inclusivity of video game design, and how the application of expressive arts therapy (ExAT) can benefit in processing and navigating difficult feelings that may arise for a gamer in the video game community. Video games have evolved immensely since first being introduced in the 1970-s and come a long way to accommodate a diverse set of people, yet video games are still marketed toward and for ableist populations. This paper reviews literature on the limitations of video games for the player as well as the toxic nature of the gaming community and the benefits of video games on mental wellbeing. It also examines shared personal experiences with colleagues and my own lived experience of physical and mental limitations with video games. Findings suggest that video games can offer positive benefits on social skills and mental health and if they were more accessible to a wider audience, more individuals would be able to experience these benefits. Video games can be used as a therapeutic tool to explore worlds and landscapes that may otherwise be impossible, offer the player to use their senses to explore, play, and find meaning through use of the ETC, and help connect and reconnect relationships

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Hybrid human-AI driven open personalized education

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    Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer. In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer). All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result

    PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

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    Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

    Towards a Digital Capability Maturity Framework for Tertiary Institutions

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    Background: The Digital Capability (DC) of an Institution is the extent to which the institution's culture, policies, and infrastructure enable and support digital practices (Killen et al., 2017), and maturity is the continuous improvement of those capabilities. As technology continues to evolve, it is likely to give rise to constant changes in teaching and learning, potentially disrupting Tertiary Education Institutions (TEIs) and making existing organisational models less effective. An institution’s ability to adapt to continuously changing technology depends on the change in culture and leadership decisions within the individual institutions. Change without structure leads to inefficiencies, evident across the Nigerian TEI landscape. These inefficiencies can be attributed mainly to a lack of clarity and agreement on a development structure. Objectives: This research aims to design a structure with a pathway to maturity, to support the continuous improvement of DC in TEIs in Nigeria and consequently improve the success of digital education programmes. Methods: I started by conducting a Systematic Literature Review (SLR) investigating the body of knowledge on DC, its composition, the relationship between its elements and their respective impact on the Maturity of TEIs. Findings from the review led me to investigate further the key roles instrumental in developing Digital Capability Maturity in Tertiary Institutions (DCMiTI). The results of these investigations formed the initial ideas and constructs upon which the proposed structure was built. I then explored a combination of quantitative and qualitative methods to substantiate the initial constructs and gain a deeper understanding of the relationships between elements/sub-elements. Next, I used triangulation as a vehicle to expand the validity of the findings by replicating the methods in a case study of TEIs in Nigeria. Finally, after using the validated constructs and knowledge base to propose a structure based on CMMI concepts, I conducted an expert panel workshop to test the model’s validity. Results: I consolidated the body of knowledge from the SLR into a universal classification of 10 elements, each comprising sub-elements. I also went on to propose a classification for DCMiTI. The elements/sub-elements in the classification indicate the success factors for digital maturity, which were also found to positively impact the ability to design, deploy and sustain digital education. These findings were confirmed in a UK University and triangulated in a case study of Northwest Nigeria. The case study confirmed the literature findings on the status of DCMiTI in Nigeria and provided sufficient evidence to suggest that a maturity structure would be a well-suited solution to supporting DCM in the region. I thus scoped, designed, and populated a domain-specific framework for DCMiTI, configured to support the educational landscape in Northwest Nigeria. Conclusion: The proposed DCMiTI framework enables TEIs to assess their maturity level across the various capability elements and reports on DCM as a whole. It provides guidance on the criteria that must be satisfied to achieve higher levels of digital maturity. The framework received expert validation, as domain experts agreed that the proposed Framework was well applicable to developing DCMiTI and would be a valuable tool to support TEIs in delivering successful digital education. Recommendations were made to engage in further iterations of testing by deploying the proposed framework for use in TEI to confirm the extent of its generalisability and acceptability
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