1,664 research outputs found
La traduzione specializzata all’opera per una piccola impresa in espansione: la mia esperienza di internazionalizzazione in cinese di Bioretics© S.r.l.
Global markets are currently immersed in two all-encompassing and unstoppable processes: internationalization and globalization. While the former pushes companies to look beyond the borders of their country of origin to forge relationships with foreign trading partners, the latter fosters the standardization in all countries, by reducing spatiotemporal distances and breaking down geographical, political, economic and socio-cultural barriers. In recent decades, another domain has appeared to propel these unifying drives: Artificial Intelligence, together with its high technologies aiming to implement human cognitive abilities in machinery. The “Language Toolkit – Le lingue straniere al servizio dell’internazionalizzazione dell’impresa” project, promoted by the Department of Interpreting and Translation (Forlì Campus) in collaboration with the Romagna Chamber of Commerce (Forlì-Cesena and Rimini), seeks to help Italian SMEs make their way into the global market. It is precisely within this project that this dissertation has been conceived. Indeed, its purpose is to present the translation and localization project from English into Chinese of a series of texts produced by Bioretics© S.r.l.: an investor deck, the company website and part of the installation and use manual of the Aliquis© framework software, its flagship product. This dissertation is structured as follows: Chapter 1 presents the project and the company in detail; Chapter 2 outlines the internationalization and globalization processes and the Artificial Intelligence market both in Italy and in China; Chapter 3 provides the theoretical foundations for every aspect related to Specialized Translation, including website localization; Chapter 4 describes the resources and tools used to perform the translations; Chapter 5 proposes an analysis of the source texts; Chapter 6 is a commentary on translation strategies and choices
Large language models shape and are shaped by society: A survey of arXiv publication patterns
There has been a steep recent increase in the number of large language model
(LLM) papers, producing a dramatic shift in the scientific landscape which
remains largely undocumented through bibliometric analysis. Here, we analyze
388K papers posted on the CS and Stat arXivs, focusing on changes in
publication patterns in 2023 vs. 2018-2022. We analyze how the proportion of
LLM papers is increasing; the LLM-related topics receiving the most attention;
the authors writing LLM papers; how authors' research topics correlate with
their backgrounds; the factors distinguishing highly cited LLM papers; and the
patterns of international collaboration. We show that LLM research increasingly
focuses on societal impacts: there has been an 18x increase in the proportion
of LLM-related papers on the Computers and Society sub-arXiv, and authors newly
publishing on LLMs are more likely to focus on applications and societal
impacts than more experienced authors. LLM research is also shaped by social
dynamics: we document gender and academic/industry disparities in the topics
LLM authors focus on, and a US/China schism in the collaboration network.
Overall, our analysis documents the profound ways in which LLM research both
shapes and is shaped by society, attesting to the necessity of sociotechnical
lenses.Comment: Working pape
Literature Reviews in HCI: A Review of Reviews
This paper analyses Human-Computer Interaction (HCI) literature reviews to provide a clear conceptual basis for authors, reviewers, and readers. HCI is multidisciplinary and various types of literature reviews exist, from systematic to critical reviews in the style of essays. Yet, there is insufficient consensus of what to expect of literature reviews in HCI. Thus, a shared understanding of literature reviews and clear terminology is needed to plan, evaluate, and use literature reviews, and to further improve review methodology. We analysed 189 literature reviews published at all SIGCHI conferences and ACM Transactions on Computer-Human Interaction (TOCHI) up until August 2022. We report on the main dimensions of variation: (i) contribution types and topics; and (ii) structure and methodologies applied. We identify gaps and trends to inform future meta work in HCI and provide a starting point on how to move towards a more comprehensive terminology system of literature reviews in HCI
Doing Research. Wissenschaftspraktiken zwischen Positionierung und Suchanfrage
Forschung wird zunehmend aus Sicht ihrer Ergebnisse gedacht - nicht zuletzt aufgrund der Umwälzungen im System Wissensschaft. Der Band lenkt den Fokus jedoch auf diejenigen Prozesse, die Forschungsergebnisse erst ermöglichen und Wissenschaft konturieren. Dabei ist der Titel Doing Research als Verweis darauf zu verstehen, dass forschendes Handeln von spezifischen Positionierungen, partiellen Perspektiven und Suchbewegungen geformt ist. So knüpfen alle Beitragenden auf reflexive Weise an ihre jeweiligen Forschungspraktiken an. Ausgangspunkt sind Abkürzungen - die vermeintlich kleinsten Einheiten wissenschaftlicher Aushandlung und Verständigung. Der in den Erziehungs-, Sozial-, Medien- und Kunstwissenschaften verankerte Band zeichnet ein vieldimensionales Bild gegenwärtigen Forschens mit transdisziplinären Anknüpfungspunkten zwischen Digitalität und Bildung. (DIPF/Orig.
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/
Facilitating prosociality through technology: Design to promote digital volunteerism
Volunteerism covers many activities involving no financial rewards for volunteers but which contribute
to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve
performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism
in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research
involved volunteers and others in the volunteering context to generate recommendations for a
volunteer-centric design for digital volunteerism. The thesis has three aims:
1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences
2. To identify digital platform attributes linked to volunteer wellbeing
3. To create guidelines for effectively supporting volunteer engagement in digital volunteering
platforms
In the first case study I investigate the design of a chat widget for volunteers working in an
organisation with a view to develop a design that improves their workflow and wellbeing. The second
case study investigates the needs, motivations, and wellbeing of volunteers who help medical
students improve their medical communication skills. An initial mixed-methods study was followed by
an experiment comparing two design strategies to improve volunteer relatedness; an important
indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and
wellbeing with a focus on volunteer identity and meaning-making on a science-based research
platform. I then analyse my findings from these case studies using the lens of care ethics to derive
critical insights for design.
The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric
design framework to enhance the motivation, wellbeing and engagement of digital volunteers
ComLittee: Literature Discovery with Personal Elected Author Committees
In order to help scholars understand and follow a research topic, significant
research has been devoted to creating systems that help scholars discover
relevant papers and authors. Recent approaches have shown the usefulness of
highlighting relevant authors while scholars engage in paper discovery.
However, these systems do not capture and utilize users' evolving knowledge of
authors. We reflect on the design space and introduce ComLittee, a literature
discovery system that supports author-centric exploration. In contrast to
paper-centric interaction in prior systems, ComLittee's author-centric
interaction supports curation of research threads from individual authors,
finding new authors and papers with combined signals from a paper recommender
and the curated authors' authorship graphs, and understanding them in the
context of those signals. In a within-subjects experiment that compares to an
author-highlighting approach, we demonstrate how ComLittee leads to a higher
efficiency, quality, and novelty in author discovery that also improves paper
discovery
Chatbots for Modelling, Modelling of Chatbots
Tesis Doctoral inĂ©dita leĂda en la Universidad AutĂłnoma de Madrid, Escuela PolitĂ©cnica Superior, Departamento de IngenierĂa Informática. Fecha de Lectura: 28-03-202
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
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