12,456 research outputs found

    Economia colaborativa

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    A importância de se proceder à análise dos principais desafios jurídicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutível, correspondendo à necessidade de se fomentar a segurança jurídica destas práticas, potenciadoras de crescimento económico e bem-estar social. O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domínio, com o objetivo de identificar os problemas jurídicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação. O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202

    A Design Science Research Approach to Smart and Collaborative Urban Supply Networks

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    Urban supply networks are facing increasing demands and challenges and thus constitute a relevant field for research and practical development. Supply chain management holds enormous potential and relevance for society and everyday life as the flow of goods and information are important economic functions. Being a heterogeneous field, the literature base of supply chain management research is difficult to manage and navigate. Disruptive digital technologies and the implementation of cross-network information analysis and sharing drive the need for new organisational and technological approaches. Practical issues are manifold and include mega trends such as digital transformation, urbanisation, and environmental awareness. A promising approach to solving these problems is the realisation of smart and collaborative supply networks. The growth of artificial intelligence applications in recent years has led to a wide range of applications in a variety of domains. However, the potential of artificial intelligence utilisation in supply chain management has not yet been fully exploited. Similarly, value creation increasingly takes place in networked value creation cycles that have become continuously more collaborative, complex, and dynamic as interactions in business processes involving information technologies have become more intense. Following a design science research approach this cumulative thesis comprises the development and discussion of four artefacts for the analysis and advancement of smart and collaborative urban supply networks. This thesis aims to highlight the potential of artificial intelligence-based supply networks, to advance data-driven inter-organisational collaboration, and to improve last mile supply network sustainability. Based on thorough machine learning and systematic literature reviews, reference and system dynamics modelling, simulation, and qualitative empirical research, the artefacts provide a valuable contribution to research and practice

    A qualitative study about first year students’ experiences of transitioning to higher education and available academic support resources

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    Successfully transitioning students to higher education is a complex problem that challenges institutions internationally. Unsuccessful transitions have wide ranging implications that include both social and financial impacts for students and the universities. There appears to be a paucity in the literature that represents student perspectives on their transition experiences. This research study aimed to do two things: first to better understand the transition experience and use of academic support services from the student perspective and second to provide strategies for facilitating a more effective transition experience based on student discussions. This research explores the experiences of primarily non-traditional students at one institution in Australia. Data collection involved two phases using a yarning circle approach. The first involved participants in small unstructured yarning circles where they were given the opportunity to speak freely about their transition experience and their use of academic support services. This was then followed by a larger yarning circle that was semi-structured to explore some of the themes from the small yarning circles more fully. The yarning circle data was analysed using Braun and Clarke’s (2006) six-steps of thematic analysis. The analysis indicated that participants felt that the available academic support services did not meet their needs. It also provided insight into how the students approach higher education and what they are seeking from their institution by means of support. One major finding that has the potential to impact transition programs around the world is that older non-traditional students appear to approach higher education as they would a new job. This shifts the lens away from the traditional transition program of social integration to one that uses workplace induction strategies as a form of integration. The recommendations from this study also include recognising and accepting the emotions associated with transitioning to higher education, reworking the transition strategies for non-traditional students and facilitating opportunities for engagement as opposed to providing them directly

    Network Transmission Flags Data Affinity-based Classification by K-Nearest Neighbor

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    Abstract—This research is concerned with the data generated during a network transmission session to understand how to extract value from the data generated and be able to conduct tasks. Instead of comparing all of the transmission flags for a transmission session at the same time to conduct any analysis, this paper conceptualized the influence of each transmission flag on network-aware applications by comparing the flags one by one on their impact to the application during the transmission session, rather than comparing all of the transmission flags at the same time. The K-nearest neighbor (KNN) type classification was used becauseit is a simple distance-based learning algorithm that remembers earlier training samples and is suitable for taking various flags withtheir effect on application protocols by comparing each new sample with the K-nearest points to make a decision. We used transmission session datasets received from Kaggle for IP flow with 87 features and 3.577.296 instances. We picked 13 features from the datasets and ran them through KNN. RapidMiner was used for the study, and the results of the experiments revealed that the KNN-based model was not only significantly more accurate in categorizing data, but it was also significantly more efficient due to the decreased processing costs

    Limit theorems for non-Markovian and fractional processes

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    This thesis examines various non-Markovian and fractional processes---rough volatility models, stochastic Volterra equations, Wiener chaos expansions---through the prism of asymptotic analysis. Stochastic Volterra systems serve as a conducive framework encompassing most rough volatility models used in mathematical finance. In Chapter 2, we provide a unified treatment of pathwise large and moderate deviations principles for a general class of multidimensional stochastic Volterra equations with singular kernels, not necessarily of convolution form. Our methodology is based on the weak convergence approach by Budhiraja, Dupuis and Ellis. This powerful approach also enables us to investigate the pathwise large deviations of families of white noise functionals characterised by their Wiener chaos expansion as~Xε=n=0εnIn(fnε).X^\varepsilon = \sum_{n=0}^{\infty} \varepsilon^n I_n \big(f_n^{\varepsilon} \big). In Chapter 3, we provide sufficient conditions for the large deviations principle to hold in path space, thereby refreshing a problem left open By Pérez-Abreu (1993). Hinging on analysis on Wiener space, the proof involves describing, controlling and identifying the limit of perturbed multiple stochastic integrals. In Chapter 4, we come back to mathematical finance via the route of Malliavin calculus. We present explicit small-time formulae for the at-the-money implied volatility, skew and curvature in a large class of models, including rough volatility models and their multi-factor versions. Our general setup encompasses both European options on a stock and VIX options. In particular, we develop a detailed analysis of the two-factor rough Bergomi model. Finally, in Chapter 5, we consider the large-time behaviour of affine stochastic Volterra equations, an under-developed area in the absence of Markovianity. We leverage on a measure-valued Markovian lift introduced by Cuchiero and Teichmann and the associated notion of generalised Feller property. This setting allows us to prove the existence of an invariant measure for the lift and hence of a stationary distribution for the affine Volterra process, featuring in the rough Heston model.Open Acces

    Post-Millennial Queer Sensibility: Collaborative Authorship as Disidentification in Queer Intertextual Commodities

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    This dissertation is examining LGBTQ+ audiences and creatives collaborating in the creation of new media texts like web shows, podcasts, and video games. The study focuses on three main objects or media texts: Carmilla (web series), Welcome to Night Vale (podcast), and Undertale (video game). These texts are transmedia objects or intertextual commodities. I argue that by using queer gestures of collaborative authorship that reaches out to the audience for canonical contribution create an emerging queer production culture that disidentifies with capitalism even as it negotiates capitalistic structures. The post-millennial queer sensibility is a constellation of aesthetics, self-representation, alternative financing, and interactivity that prioritizes community, trust, and authenticity using new technologies for co-creation. Within my study, there are four key tactics or queer gestures being explored: remediation, radical ambiguity and multi-forms as queer aesthetics, audience self-representation, alternative financing like micropatronage & licensed fan-made merchandise, and interactivity as performance. The goal of this project is to better understand the changing conceptions of authorship/ownership, canon/fanon (official text/fan created extensions), and community/capitalism in queer subcultures as an indicator of the potential change in more mainstream cultural attitudes. The project takes into consideration a variety of intersecting identities including gender, race, class, and of course sexual orientation in its analysis. By examining the legal discourse around collaborative authorship, the real-life production practices, and audience-creator interactions and attitudes, this study provides insight into how media creatives work with audiences to co-create self-representative media, the motivations, and rewards for creative, audiences, and owners. This study aims to contribute towards a fuller understanding of queer production cultures and audience reception of these media texts, of which there is relatively little academic information. Specifically, the study mines for insights into the changing attitudes towards authorship, ownership, and collaboration within queer indie media projects, especially as these objects are relying on the self-representation of both audiences and creatives in the formation of the text

    A Case Study Examining Japanese University Students' Digital Literacy and Perceptions of Digital Tools for Academic English learning

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    Current Japanese youth are constantly connected to the Internet and using digital devices, but predominantly for social media and entertainment. According to literature on the Japanese digital native, tertiary students do not—and cannot—use technology with any reasonable fluency, but the likely reasons are rarely addressed. To fill the gap in the literature, this study, by employing a case study methodology, explores students’ experience with technology for English learning through the introduction of digital tools. First-year Japanese university students in an Academic English Program (AEP) were introduced to a variety of easily available digital tools. The instruction was administered online, and each tool was accompanied by a task directly related to classwork. Both quantitative and qualitative data were collected in the form of a pre-course Computer Literacy Survey, a post-course open-ended Reflection Activity survey, and interviews. The qualitative data was reviewed drawing on the Technology Acceptance Model (TAM) and its educational variants as an analytical framework. Educational, social, and cultural factors were also examined to help identify underlying factors that would influence students’ perceptions. The results suggest that the subjects’ lack of awareness of, and experience with, the use of technology for learning are the fundamental causes of their perceptions of initial difficulty. Based on these findings, this study proposes a possible technology integration model that enhances digital literacy for more effective language learning in the context of Japanese education

    Examining the Impact of Personal Social Media Use at Work on Workplace Outcomes

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    A noticable shift is underway in today’s multi-generational workforce. As younger employees propel digital workforce transformation and embrace technology adoption in the workplace, organisations need to show they are forward-thinking in their digital transformation strategies, and the emergent integration of social media in organisations is reshaping internal communication strategies, in a bid to improve corporate reputations and foster employee engagement. However, the impact of personal social media use on psychological and behavioural workplace outcomes is still debatebale with contrasting results in the literature identifying both positive and negative effects on workplace outcomes among organisational employees. This study seeks to examine this debate through the lens of social capital theory and study personal social media use at work using distinct variables of social use, cognitive use, and hedonic use. A quantitative analysis of data from 419 organisational employees in Jordan using SEM-PLS reveals that personal social media use at work is a double-edged sword as its impact differs by usage types. First, the social use of personal social media at work reduces job burnout, turnover intention, presenteeism, and absenteeism; it also increases job involvement and organisational citizen behaviour. Second, the cognitive use of personal social media at work increases job involvement, organisational citizen behaviour, employee adaptability, and decreases presenteeism and absenteeism; it also increases job burnout and turnover intention. Finally, the hedonic use of personal social media at work carries only negative effects by increasing job burnout and turnover intention. This study contributes to managerial understanding by showing the impact of different types of personal social media usage and recommends that organisations not limit employee access to personal social media within work time, but rather focus on raising awareness of the negative effects of excessive usage on employee well-being and encourage low to moderate use of personal social media at work and other personal and work-related online interaction associated with positive workplace outcomes. It also clarifies the need for further research in regions such as the Middle East with distinct cultural and socio-economic contexts

    Machine learning for managing structured and semi-structured data

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    As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

    Freelance subtitlers in a subtitle production network in the OTT industry in Thailand: a longitudinal study

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    The present study sets out to investigate a subtitle production network in the over-the-top (OTT) industry in Thailand through the perspective of freelance subtitlers. A qualitative longitudinal research design was adopted to gain insights into (1) the way the work practices of freelance subtitlers are influenced by both human and non-human actors in the network, (2) the evolution of the network, and (3) how the freelance subtitlers’ perception of quality is influenced by changes occurring in the network. Eleven subtitlers were interviewed every six months over a period of two years, contributing to over 60 hours of interview data. The data analysis was informed by selected concepts from Actor-Network Theory (ANT) (Law 1992, 2009; Latour 1996, 2005; Mol 2010), and complemented by the three-dimensional quality model proposed by Abdallah (2016, 2017). Reflexive thematic analysis (Braun and Clarke 2019a, 2020b) was used to generate themes and sub-themes which address the research questions and tell compelling stories about the actor-network. It was found that from July 2017 to September 2019, the subtitle production network, which was sustained by complex interrelationships between actors, underwent a number of changes. The changes affected the work practices of freelance subtitlers in a more negative than positive way, demonstrating their precarious position in an industry that has widely adopted the vendor model (Moorkens 2017). Moreover, as perceived by the research participants, under increasingly undesirable working conditions, it became more challenging to maintain a quality process and to produce quality subtitles. Finally, translation technology and tools, including machine translation, were found to be key non-human actors that catalyse the changes in the network under study
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