1,252 research outputs found
Mobile heritage practices. Implications for scholarly research, user experience design, and evaluation methods using mobile apps.
Mobile heritage apps have become one of the most popular means for audience
engagement and curation of museum collections and heritage contexts. This
raises practical and ethical questions for both researchers and practitioners, such
as: what kind of audience engagement can be built using mobile apps? what are
the current approaches? how can audience engagement with these experience
be evaluated? how can those experiences be made more resilient, and in turn
sustainable? In this thesis I explore experience design scholarships together with
personal professional insights to analyse digital heritage practices with a view to
accelerating thinking about and critique of mobile apps in particular. As a result,
the chapters that follow here look at the evolution of digital heritage practices,
examining the cultural, societal, and technological contexts in which mobile
heritage apps are developed by the creative media industry, the academic
institutions, and how these forces are shaping the user experience design
methods. Drawing from studies in digital (critical) heritage, Human-Computer
Interaction (HCI), and design thinking, this thesis provides a critical analysis of
the development and use of mobile practices for the heritage. Furthermore,
through an empirical and embedded approach to research, the thesis also
presents auto-ethnographic case studies in order to show evidence that mobile
experiences conceptualised by more organic design approaches, can result in
more resilient and sustainable heritage practices. By doing so, this thesis
encourages a renewed understanding of the pivotal role of these practices in the
broader sociocultural, political and environmental changes.AHRC REAC
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
2023-2024 Catalog
The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation
Digital Traces of the Mind::Using Smartphones to Capture Signals of Well-Being in Individuals
General context and questions Adolescents and young adults typically use their smartphone several hours a day. Although there are concerns about how such behaviour might affect their well-being, the popularity of these powerful devices also opens novel opportunities for monitoring well-being in daily life. If successful, monitoring well-being in daily life provides novel opportunities to develop future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). Taking an interdisciplinary approach with insights from communication, computational, and psychological science, this dissertation investigated the relation between smartphone app use and well-being and developed machine learning models to estimate an individual’s well-being based on how they interact with their smartphone. To elucidate the relation between smartphone trace data and well-being and to contribute to the development of technologies for monitoring well-being in future clinical practice, this dissertation addressed two overarching questions:RQ1: Can we find empirical support for theoretically motivated relations between smartphone trace data and well-being in individuals? RQ2: Can we use smartphone trace data to monitor well-being in individuals?Aims The first aim of this dissertation was to quantify the relation between the collected smartphone trace data and momentary well-being at the sample level, but also for each individual, following recent conceptual insights and empirical findings in psychological, communication, and computational science. A strength of this personalized (or idiographic) approach is that it allows us to capture how individuals might differ in how smartphone app use is related to their well-being. Considering such interindividual differences is important to determine if some individuals might potentially benefit from spending more time on their smartphone apps whereas others do not or even experience adverse effects. The second aim of this dissertation was to develop models for monitoring well-being in daily life. The present work pursued this transdisciplinary aim by taking a machine learning approach and evaluating to what extent we might estimate an individual’s well-being based on their smartphone trace data. If such traces can be used for this purpose by helping to pinpoint when individuals are unwell, they might be a useful data source for developing future interventions that provide personalized support to individuals at the moment they require it (just-in-time adaptive interventions). With this aim, the dissertation follows current developments in psychoinformatics and psychiatry, where much research resources are invested in using smartphone traces and similar data (obtained with smartphone sensors and wearables) to develop technologies for detecting whether an individual is currently unwell or will be in the future. Data collection and analysis This work combined novel data collection techniques (digital phenotyping and experience sampling methodology) for measuring smartphone use and well-being in the daily lives of 247 student participants. For a period up to four months, a dedicated application installed on participants’ smartphones collected smartphone trace data. In the same time period, participants completed a brief smartphone-based well-being survey five times a day (for 30 days in the first month and 30 days in the fourth month; up to 300 assessments in total). At each measurement, this survey comprised questions about the participants’ momentary level of procrastination, stress, and fatigue, while sleep duration was measured in the morning. Taking a time-series and machine learning approach to analysing these data, I provide the following contributions: Chapter 2 investigates the person-specific relation between passively logged usage of different application types and momentary subjective procrastination, Chapter 3 develops machine learning methodology to estimate sleep duration using smartphone trace data, Chapter 4 combines machine learning and explainable artificial intelligence to discover smartphone-tracked digital markers of momentary subjective stress, Chapter 5 uses a personalized machine learning approach to evaluate if smartphone trace data contains behavioral signs of fatigue. Collectively, these empirical studies provide preliminary answers to the overarching questions of this dissertation.Summary of results With respect to the theoretically motivated relations between smartphone trace data and wellbeing (RQ1), we found that different patterns in smartphone trace data, from time spent on social network, messenger, video, and game applications to smartphone-tracked sleep proxies, are related to well-being in individuals. The strength and nature of this relation depends on the individual and app usage pattern under consideration. The relation between smartphone app use patterns and well-being is limited in most individuals, but relatively strong in a minority. Whereas some individuals might benefit from using specific app types, others might experience decreases in well-being when spending more time on these apps. With respect to the question whether we might use smartphone trace data to monitor well-being in individuals (RQ2), we found that smartphone trace data might be useful for this purpose in some individuals and to some extent. They appear most relevant in the context of sleep monitoring (Chapter 3) and have the potential to be included as one of several data sources for monitoring momentary procrastination (Chapter 2), stress (Chapter 4), and fatigue (Chapter 5) in daily life. Outlook Future interdisciplinary research is needed to investigate whether the relationship between smartphone use and well-being depends on the nature of the activities performed on these devices, the content they present, and the context in which they are used. Answering these questions is essential to unravel the complex puzzle of developing technologies for monitoring well-being in daily life.<br/
Reshaping Higher Education for a Post-COVID-19 World: Lessons Learned and Moving Forward
No abstract available
Evaluating Feature-Specific Similarity Metrics using Human Judgments for Norwegian News
Masteroppgave i informasjonsvitenskapINFO390MASV-INF
Producing Affection : Affect and Mediated Intimacy in Pokémon
Pokémon is a global multimedia franchise formed around a core series of videogames and a variety of characters to collect, learn about, and play with. Throughout its decades of development, Pokémon has grown into a media mix comprising of digital and analog games, animations, comics, toys, and a plethora of branded merchandize, all centering on the Pokémon characters and the audience’s relationship with them.
In this thesis, I explore how affection is formed and distributed in Pokémon. I view the relationship with Pokémon characters as a form of mediated intimacy, theorizing it as feelings of affection and closeness expressed through and aimed at technology. Through this, I discuss how technological and fantastical bodies wield agency and actively participate in the formation of everyday affects. By drawing primarily on game studies and affect studies, I develop an interdisciplinary method for playing and reading media texts for their affects and use it to analyze the media mix of Pokémon and the affective relations therein.
I focus primarily on the Pokémon videogames that serve as the core product of the entire media mix. I examine what it means to construct an entire media mix based on videogames and play and suggest this as a key interpretive arrangement for understanding the mediated intimacy of Pokémon.
This study presents the mediated intimacy of Pokémon as the result of the ludic and technological foundations of the Pokémon media mix, at the heart of which is the role-playing form of the original videogames and the way they have positioned audiences as participants and characters in the world of Pokémon. In this playful environment that overlaps fiction and everyday reality, the media mix guides its players to conduct a form of affective labor to access and traverse the textual whole of Pokémon and furthermore aligns this effort with the diegetic theme of caretaking as captured on the transmedia bodies of Pokémon.
Additionally, this work contributes to the theorization and rethinking of intimacies by exploring affection in human and non-human networks as an entanglement of biological and technological actors.Tuotettua kiintymystä. Pokémonin affekti ja medioitu intiimiys
Pokémon on globaali monimediakokonaisuus. Sen keskiössä on joukko videopelejä sekä niiden hahmoja, joita kerätään, joista opitaan ja joiden kanssa leikitään. Pokémonista on vuosikymmenten mittaan kasvanut mediatuotteiden rypäs, media mix: monista tuote- ja julkaisukanavista koostuva kokonaisuus, joka sisältää digitaalisia ja analogisia pelejä, animaatioita, sarjakuvia, leluja ja brändituotteita, joissa kaikissa korostuvat Pokémon-hahmot sekä yleisön suhde niihin.
Väitöskirjassani tarkastelen, miten kiintymystä rakennetaan ja levitetään Pokémonissa. Tutkin Pokémon-hahmoihin muodostettuja suhteita medioidun intiimiyden käsitteen kautta. Tutkimuksessani suhteet näyttäytyvät kiintymyksellisten tunteiden tiivistyminä sekä läheisyytenä, jota ilmaistaan teknologian avulla ja sitä kohtaan. Näin tarkastelen, miten teknologisten sekä fantastisten kehojen toimijuus näkyy arkipäiväisten affektien muodostumisessa. Ammentamalla pelitutkimuksesta ja affektitutkimuksesta kehitän monitieteisen metodin mediatekstien pelaamiseen ja lukemiseen, ja käytän sitä Pokémonin media mixin, sen affektien ja sen piirissä muodostettujen kiintymyssuhteiden analysointiin.
Keskityn erityisesti Pokémon-videopeleihin, jotka toimivat koko media mixin ydintuotteena. Tutkin, miten Pokémonin media mix on rakennettu ensisijaisesti pelilliselle ja leikilliselle pohjalle, ja ehdotan tätä tulkintamallia keskeiseksi Pokémonin medioidun intiimiyden ymmärtämiselle.
Tutkimuksen tuloksena esitän Pokémonin medioidun intiimiyden muodostuvan Pokémonin media mixin leikillisistä ja teknologisista juurista, joiden perustana on alkuperäisten Pokémon-videopelien roolipelillinen rakenne sekä se, miten sen avulla pelaajat on asemoitu hahmoiksi Pokémonin maailmaan. Fiktiota ja todellisuutta sekoittavassa leikillisessä ympäristössä Pokémonin media mix ohjaa pelaajia hoivan ja huolenpidon teemojen kautta tekemään tunnetyötä tuoteperheen mediatekstien parissa ja piirtää tämän työn tulokset Pokémon-hahmojen monimediakehoille.
Lisäksi väitöstutkimukseni osallistuu intiimiyden laajempaan teoretisointiin ja uudelleenmäärittelyyn tarkastelemalla elollisten ja leikillisesti elävien toimijoiden suhteita biologisena ja teknologisena yhteenliittymän
Knowledge on the Move: Studies on Mobile Social Education
This book draws on work undertaken by colleagues involved with the Erasmus+ project called SoMoveED, or Social Education on the Move. The broader aim of the project is to develop, implement, and disseminate innovation in the form of a model of mobile social education in higher education, of which this book makes up one small part.The project draws together institutions and organizations from ten European countries (Croatia, the Czech Republic, France, Italy, the Netherlands, Poland, Portugal, Romania, Turkey, and the United Kingdom), including eight universities, two non-governmental organizations and one social enterprise. Approximately 40 people are working on the project, including academic teachers and researchers, entrepreneurs, and social activists. The project’s main objective is to explore and develop ways in which the teaching process can be organized in motion, outside the university walls, with the participation of stakeholders from outside the academic community (citizens, representatives of institutions and organizations, activists, people at risk of marginalization). This model incorporates three important features into the educational process: (1) mobility; (2) participation; and (3) inclusion
WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM
Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments
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