2,218 research outputs found
"It's not a career": Platform work among young people aged 16-19
In the online gig economy, or platform work as it is sometimes known, work can be organised through websites and smartphone apps. People can drive for Uber or Deliveroo, sell items on eBay or Etsy, or rent their properties on Airbnb.
This research examines the views of young people between the ages of 16 and 19 in the United Kingdom to see whether they knew about the online gig economy, whether they were using it already to earn money, and whether they expected to use it for their careers. It discovers careers professionalsâ levels of knowledge, and their ability (and desire) to include the gig economy in their professional practice.
This research contributes to discussions about what constitutes decent work, and whether it can be found within the online gig economy. The results point to ways in which careers practice could include platform work as a means of extending young peopleâs knowledge about alternative forms of work. This study also makes a theoretical contribution to literature, bringing together elements of careership, cognitive schema theory, and motivational theory and psychology of working theory, in a novel combination, to explain how young people were thinking about platform work in the context of their careers
Conversations on Empathy
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 â 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" â 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
Method versatility in analysing human attitudes towards technology
Various research domains are facing new challenges brought about by growing volumes of data. To make optimal use of them, and to increase the reproducibility of research findings, method versatility is required. Method versatility is the ability to flexibly apply widely varying data analytic methods depending on the study goal and the dataset characteristics.
Method versatility is an essential characteristic of data science, but in other areas of research, such as educational science or psychology, its importance is yet to be fully accepted. Versatile methods can enrich the repertoire of specialists who validate psychometric instruments, conduct data analysis of large-scale educational surveys, and communicate their findings to the academic community, which corresponds to three stages of the research cycle: measurement, research per se, and communication. In this thesis, studies related to these stages have a common theme of human attitudes towards technology, as this topic becomes vitally important in our age of ever-increasing digitization.
The thesis is based on four studies, in which method versatility is introduced in four different ways: the consecutive use of methods, the toolbox choice, the simultaneous use, and the range extension. In the first study, different methods of psychometric analysis are used consecutively to reassess psychometric properties of a recently developed scale measuring affinity for technology interaction. In the second, the random forest algorithm and hierarchical linear modeling, as tools from machine learning and statistical toolboxes, are applied to data analysis of a large-scale educational survey related to studentsâ attitudes to information and communication technology. In the third, the challenge of selecting the number of clusters in model-based clustering is addressed by the simultaneous use of model fit, cluster separation, and the stability of partition criteria, so that generalizable separable clusters can be selected in the data related to teachersâ attitudes towards technology. The fourth reports the development and evaluation of a scholarly knowledge graph-powered dashboard aimed at extending the range of scholarly communication means.
The findings of the thesis can be helpful for increasing method versatility in various research areas. They can also facilitate methodological advancement of academic training in data analysis and aid further development of scholarly communication in accordance with open science principles.Verschiedene Forschungsbereiche mĂŒssen sich durch steigende Datenmengen neuen Herausforderungen stellen. Der Umgang damit erfordert â auch in Hinblick auf die Reproduzierbarkeit von Forschungsergebnissen â Methodenvielfalt. Methodenvielfalt ist die FĂ€higkeit umfangreiche Analysemethoden unter BerĂŒcksichtigung von angestrebten Studienzielen und gegebenen Eigenschaften der DatensĂ€tze flexible anzuwenden.
Methodenvielfalt ist ein essentieller Bestandteil der Datenwissenschaft, der aber in seinem Umfang in verschiedenen Forschungsbereichen wie z. B. den Bildungswissenschaften oder der Psychologie noch nicht erfasst wird. Methodenvielfalt erweitert die Fachkenntnisse von Wissenschaftlern, die psychometrische Instrumente validieren, Datenanalysen von groĂ angelegten Umfragen im Bildungsbereich durchfĂŒhren und ihre Ergebnisse im akademischen Kontext prĂ€sentieren. Das entspricht den drei Phasen eines Forschungszyklus: Messung, Forschung per se und Kommunikation. In dieser Doktorarbeit werden Studien, die sich auf diese Phasen konzentrieren, durch das gemeinsame Thema der Einstellung zu Technologien verbunden. Dieses Thema ist im Zeitalter zunehmender Digitalisierung von entscheidender Bedeutung.
Die Doktorarbeit basiert auf vier Studien, die Methodenvielfalt auf vier verschiedenen Arten vorstellt: die konsekutive Anwendung von Methoden, die Toolbox-Auswahl, die simultane Anwendung von Methoden sowie die Erweiterung der Bandbreite. In der ersten Studie werden verschiedene psychometrische Analysemethoden konsekutiv angewandt, um die psychometrischen Eigenschaften einer entwickelten Skala zur Messung der AffinitĂ€t von Interaktion mit Technologien zu ĂŒberprĂŒfen. In der zweiten Studie werden der Random-Forest-Algorithmus und die hierarchische lineare Modellierung als Methoden des Machine Learnings und der Statistik zur Datenanalyse einer groĂ angelegten Umfrage ĂŒber die Einstellung von SchĂŒlern zur Informations- und Kommunikationstechnologie herangezogen. In der dritten Studie wird die Auswahl der Anzahl von Clustern im modellbasierten Clustering bei gleichzeitiger Verwendung von Kriterien fĂŒr die Modellanpassung, der Clustertrennung und der StabilitĂ€t beleuchtet, so dass generalisierbare trennbare Cluster in den Daten zu den Einstellungen von Lehrern zu Technologien ausgewĂ€hlt werden können. Die vierte Studie berichtet ĂŒber die Entwicklung und Evaluierung eines wissenschaftlichen wissensgraphbasierten Dashboards, das die Bandbreite wissenschaftlicher Kommunikationsmittel erweitert.
Die Ergebnisse der Doktorarbeit tragen dazu bei, die Anwendung von vielfĂ€ltigen Methoden in verschiedenen Forschungsbereichen zu erhöhen. AuĂerdem fördern sie die methodische Ausbildung in der Datenanalyse und unterstĂŒtzen die Weiterentwicklung der wissenschaftlichen Kommunikation im Rahmen von Open Science
Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5
This ïŹfth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different ïŹelds 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 modiïŹed Proportional ConïŹict 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 classiïŹers, 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, identiïŹcation of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classiïŹcation.
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 classiïŹcation, and hybrid techniques mixing deep learning with belief functions as well
A Process Model for Continuous Public Service Improvement: Demonstrated in Local Government Context for Smart Cities.
The new era of the smart city is accompanied by Information and Communication Technology (ICT) and many other technologies to improve the quality of life for the citizen of the modern city, that in turn, has brought immense opportunities as well as challenges for government and organizations. Local authorities of the cities provide multiple services across different domains to the citizens (e.g. transport, health, environment, housing, etc.). Citizens are involved during different stages of smart city services and provide their feedback across those domains. Existing smart city initiatives provide various technological platforms for gathering citizensâ feedback to provide improved quality of services to them. Even though technological developments have resulted in a higher degree of digitalization, there is a need for improvement in the services provided by municipalities. There are multiple engagement platforms to obtain citizensâ feedback for the improvement of smart city services and to transform public services. However, limited studies consider the challenges faced by practitioners at the local level during the incorporation of those feedback for further service improvement. As a result, city services fail to fulfil the need of citizens and do not meet the goals set by existing engagement platforms. Technology-oriented solutions in the public sector domain require a logical and structured approach for the transformation of public services and digitalization. Enterprise Architecture (EA) can provide this structured approach to transform public services by providing a medium to manage change, and to respond to the need of multiple stakeholders including citizens. Thus, this research proposes a process model based on the guidelines of EA and the collaboration with practitioners that would assist local authorities to provide improved services to the citizens and fulfil their needs
The relationship between the interactive behavior of industryâuniversityâresearch subjects and the cooperative innovation performance: The mediating role of knowledge absorptive capacity
IntroductionIndustryâuniversityâresearch cooperation innovation, which is often characterized by resource complementarity and the sharing technology, has become one of the most preferred innovation cooperation methods for enterprises. However, various problems still occur in the process of industryâuniversityâresearch cooperations, such as poor innovation performance and difficulty in sustaining cooperation. Existing studies mostly focus on the macroscopic perspectives of geographic location, cooperation scale, concentration, and diversification of industryâuniversityâresearch cooperation subjects, and fail to explore the microscopic behavioral mechanisms.MethodsTherefore, this paper establishes the interactive behavior of industryâuniversityâresearch subjects and defines its concepts and dimensions in an attempt to provide a mechanism for improving the cooperative innovation performance of industryâuniversityâresearch from the micro-behavioral perspective. On the basis of theoretical analysis, this paper develops a model of the relationship between cooperative trust, cooperative communication, and cooperative innovation performance for interactive behavior, while exploring the mediating role of knowledge absorptive capacity. The model was validated by stepwise regression using data from 325 questionnaires.ResultsThe paper found that cooperative trust and cooperative communication in the cooperative interactive behavior of industryâuniversityâresearch positively contribute to the improvement of cooperative innovation performance. Knowledge absorptive capacity plays a partially mediating role between the interactive behaviors and cooperative innovation performance. More specifically, knowledge absorptive capacity partially mediates cooperative communication in cooperative innovation performance and completely mediates cooperative trust in cooperative innovation performance. The results are largely consistent with the results of the heterogeneity analysis of the sample.DiscussionThis paper not only explains why the cooperative innovation performance of industryâuniversityâresearch is poor from the perspective of interactive behavior, but also enriches the research perspective of industryâuniversityâresearch and provides theoretical support for enterprises to optimize the relationship between industry, university, and research institutes
Ethnographies of Collaborative Economies across Europe: Understanding Sharing and Caring
"Sharing economy" and "collaborative economy" refer to a proliferation of initiatives, business models, digital platforms and forms of work that characterise contemporary life: from community-led initiatives and activist campaigns, to the impact of global sharing platforms in contexts such as network hospitality, transportation, etc. Sharing the common lens of ethnographic methods, this book presents in-depth examinations of collaborative economy phenomena. The book combines qualitative research and ethnographic methodology with a range of different collaborative economy case studies and topics across Europe. It uniquely offers a truly interdisciplinary approach. It emerges from a unique, long-term, multinational, cross-European collaboration between researchers from various disciplines (e.g., sociology, anthropology, geography, business studies, law, computing, information systems), career stages, and epistemological backgrounds, brought together by a shared research interest in the collaborative economy. This book is a further contribution to the in-depth qualitative understanding of the complexities of the collaborative economy phenomenon. These rich accounts contribute to the painting of a complex landscape that spans several countries and regions, and diverse political, cultural, and organisational backdrops. This book also offers important reflections on the role of ethnographic researchers, and on their stance and outlook, that are of paramount interest across the disciplines involved in collaborative economy research
Towards addressing training data scarcity challenge in emerging radio access networks: a survey and framework
The future of cellular networks is contingent on artificial intelligence (AI) based automation, particularly for radio access network (RAN) operation, optimization, and troubleshooting. To achieve such zero-touch automation, a myriad of AI-based solutions are being proposed in literature to leverage AI for modeling and optimizing network behavior to achieve the zero-touch automation goal. However, to work reliably, AI based automation, requires a deluge of training data. Consequently, the success of the proposed AI solutions is limited by a fundamental challenge faced by cellular network research community: scarcity of the training data. In this paper, we present an extensive review of classic and emerging techniques to address this challenge. We first identify the common data types in RAN and their known use-cases. We then present a taxonomized survey of techniques used in literature to address training data scarcity for various data types. This is followed by a framework to address the training data scarcity. The proposed framework builds on available information and combination of techniques including interpolation, domain-knowledge based, generative adversarial neural networks, transfer learning, autoencoders, fewshot learning, simulators and testbeds. Potential new techniques to enrich scarce data in cellular networks are also proposed, such as by matrix completion theory, and domain knowledge-based techniques leveraging different types of network geometries and network parameters. In addition, an overview of state-of-the art simulators and testbeds is also presented to make readers aware of current and emerging platforms to access real data in order to overcome the data scarcity challenge. The extensive survey of training data scarcity addressing techniques combined with proposed framework to select a suitable technique for given type of data, can assist researchers and network operators in choosing the appropriate methods to overcome the data scarcity challenge in leveraging AI to radio access network automation
Rule learning of the Atomic dataset using Transformers
Models used for machine learning are used for a multitude of tasks that require some type of reasoning. Language models have been very capable of capturing patterns and regularities found in natural language, but their ability to perform logical reasoning has come under scrutiny. In contrast, systems for automated reasoning are well-versed in logic-based reasoning but require their input to be in logical rules to do so. The issue is that the conception of such systems, and the production of adequate rules are time-consuming processes that few have the skill set to perform. Thus, we investigate the Transformer architecture's ability to translate natural language sentences into logical rules. We perform experiments of neural machine translation on the DKET dataset from the literature consisting of definitory sentences, and we create a dataset of if-then statements from the Atomic knowledge bank by using an algorithm we have created that we also perform experiments on.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN
Gaps and requirements for applying automatic architectural design to building renovation
The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency
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