11 research outputs found

    Novel Methods for Designing Tasks in Crowdsourcing

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    Crowdsourcing is becoming more popular as a means for scalable data processing that requires human intelligence. The involvement of groups of people to accomplish tasks could be an effective success factor for data-driven businesses. Unlike in other technical systems, the quality of the results depends on human factors and how well crowd workers understand the requirements of the task, to produce high-quality results. Looking at previous studies in this area, we found that one of the main factors that affect workers’ performance is the design of the crowdsourcing tasks. Previous studies of crowdsourcing task design covered a limited set of factors. The main contribution of this research is the focus on some of the less-studied technical factors, such as examining the effect of task ordering and class balance and measuring the consistency of the same task design over time and on different crowdsourcing platforms. Furthermore, this study ambitiously extends work towards understanding workers’ point of view in terms of the quality of the task and the payment aspect by performing a qualitative study with crowd workers and shedding light on some of the ethical issues around payments for crowdsourcing tasks. To achieve our goal, we performed several crowdsourcing experiments on specific platforms and measured the factors that influenced the quality of the overall result

    ECSCW 2013 Adjunct Proceedings The 13th European Conference on Computer Supported Cooperative Work 21 - 25. September 2013, Paphos, Cyprus

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    This volume presents the adjunct proceedings of ECSCW 2013.While the proceedings published by Springer Verlag contains the core of the technical program, namely the full papers, the adjunct proceedings includes contributions on work in progress, workshops and master classes, demos and videos, the doctoral colloquium, and keynotes, thus indicating what our field may become in the future

    Collaborative video editing

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    Samarbeid i videoredigering Denne avhandlingen tar opp følgende spørsmål: Hvordan kan vi støtte samarbeid i videoredigering? I ulike anvendelsesområder, som skriving og design, er bruk av samarbeidsverktøy utbredt. Likevel er programvare for videoredigering i hovedsak utviklet for individuell bruk. Videoredigering bør forstås som en sosial aktivitet og blir i profesjonelle sammenhenger ofte utført som et samarbeid mellom ulike aktører. Basert på intervjuer og designverksteder, undersøker denne avhandlingen hvordan videoredigerere samarbeider og utforsker mulighetsrommet for å støtte samarbeid i videoredigering gjennom design av nye løsninger. I tre studier undersøker denne avhandlingen videoredigering fra tre perspektiver. Først undersøker den samarbeidspraksiser blant profesjonelle videoredigerere og identifiserer ulike strategier og sosiale mekanismer som brukes for å oppnå enighet mellom aktørene som er involvert i videoproduksjon. Denne første studien identifiserer ni temaer som beskriver hvordan videoredigerere håndterer usikkerhet og oppnår enighet, spesielt gjennom organisatoriske mekanismer, dokumentasjon og ikoniske referanser. Studien foreslår også tre ulike retninger for design av nye løsninger for å støtte samarbeid i videoredigering. Det andre studiet undersøker videoproduksjon fra et organisatorisk perspektiv, med fokus på en pågående overgang til distribuert arbeid og dets innvirkning på videoproduksjon. Den andre studien skisserer de kortsiktige og langsiktige implikasjonene av å innføre distribuerte arbeidsformer i TV-produksjonsorganisasjoner under COVID-19-pandemien. Den siste studien ser på samarbeid i videoredigering som et designproblem og presenterer designideer for hvordan man kan støtte et slikt samarbeid. I tillegg peker denne studien på utfordringer som kan være til hinder for innføringen av nye videoredigeringsverktøy som skal støtte samarbeid. Ved å sammenstille resultatene fra de tre studiene, samt analysere tidligere forskning og eksisterende videoredigeringsverktøy, identifiserer avhandlingen tre designtilnærminger for å støtte samarbeid i videoredigeringsprogramvare: holistisk, skreddersydd og konfigurerbar. Selv om disse tilnærmingene diskuteres med tanke på samarbeid i videoredigeringspraksiser, kan de tilby et bredere analytisk rammeverk for å vurdere utformingen av samarbeidsverktøy også for andre anvendelsesområder.This thesis addresses the following question: how can collaboration be supported in video editing? In many domains, such as writing and design, collaborative tools have become common and widespread. However, video-editing software is still predominantly designed for solo users. Nevertheless, video editing is a social activity that, in a professional setting, often involves various people working together. Based on interviews and design workshops, this thesis investigates the collaborative practices of video editors and explores the design space of collaborative video editing. In three studies, this thesis looks at video editing from three angles. First, it investigates the collaborative practices of video editors and identifies the strategies and social mechanisms they employ to reach agreements with various parties involved in the videoproduction process. The first study identifies nine themes that characterise the ways video editors manage uncertainties and reach agreements, particularly through organisational mechanisms, documentation, and iconic referencing. The study also suggests three design paths to explore further. Second, it examines video production from an organisational point of view, focusing on the recent shift towards remote work and its impact on video production. The second study delineates the short-term and long-term implications of adopting remote work in TV production organisations during the COVID-19 pandemic. Third, it approaches collaborative video editing as a design problem and offers design ideas to enhance collaboration. Additionally, it uncovers challenges that might impede the adoption of new collaborative video-editing tools. In synthesising the results of the three studies, as well as analysing previous research and existing video-editing tools, this thesis identifies three design approaches for supporting collaboration in video-editing software: holistic, tailored, and configurable. While discussed in the context of collaborative video editing, these approaches offer a broader analytical framework for considering the design of collaborative production tools.Doktorgradsavhandlin

    Description of six scenarios and of the results of six validated trials

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    Description of six scenarios and of the results of six validated trialsThis deliverable aims at presenting and analysing the processes of elaboration and validation of the PALETTE scenarios. After having defined these two processes and situated them into the PALETTE methodology, the scenarios are presented. For each scenario, the specific methodology of elaboration and validation is described with a special focus on the participation of the concerned Communities of Pratcice (CoPs). Then the results of the validation are presented as well as the reports of their technical feasability and the usability of PALETTE services from a user perspective. Finally we reflect on and we discuss about the whole process of validation of the scenarios and we describe the next steps towards the development of the scenarios and their trilas with the CoPs

    Personalised video retrieval: application of implicit feedback and semantic user profiles

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    A challenging problem in the user profiling domain is to create profiles of users of retrieval systems. This problem even exacerbates in the multimedia domain. Due to the Semantic Gap, the difference between low-level data representation of videos and the higher concepts users associate with videos, it is not trivial to understand the content of multimedia documents and to find other documents that the users might be interested in. A promising approach to ease this problem is to set multimedia documents into their semantic contexts. The semantic context can lead to a better understanding of the personal interests. Knowing the context of a video is useful for recommending users videos that match their information need. By exploiting these contexts, videos can also be linked to other, contextually related videos. From a user profiling point of view, these links can be of high value to recommend semantically related videos, hence creating a semantic-based user profile. This thesis introduces a semantic user profiling approach for news video retrieval, which exploits a generic ontology to put news stories into its context. Major challenges which inhibit the creation of such semantic user profiles are the identification of user's long-term interests and the adaptation of retrieval results based on these personal interests. Most personalisation services rely on users explicitly specifying preferences, a common approach in the text retrieval domain. By giving explicit feedback, users are forced to update their need, which can be problematic when their information need is vague. Furthermore, users tend not to provide enough feedback on which to base an adaptive retrieval algorithm. Deviating from the method of explicitly asking the user to rate the relevance of retrieval results, the use of implicit feedback techniques helps by learning user interests unobtrusively. The main advantage is that users are relieved from providing feedback. A disadvantage is that information gathered using implicit techniques is less accurate than information based on explicit feedback. In this thesis, we focus on three main research questions. First of all, we study whether implicit relevance feedback, which is provided while interacting with a video retrieval system, can be employed to bridge the Semantic Gap. We therefore first identify implicit indicators of relevance by analysing representative video retrieval interfaces. Studying whether these indicators can be exploited as implicit feedback within short retrieval sessions, we recommend video documents based on implicit actions performed by a community of users. Secondly, implicit relevance feedback is studied as potential source to build user profiles and hence to identify users' long-term interests in specific topics. This includes studying the identification of different aspects of interests and storing these interests in dynamic user profiles. Finally, we study how this feedback can be exploited to adapt retrieval results or to recommend related videos that match the users' interests. We analyse our research questions by performing both simulation-based and user-centred evaluation studies. The results suggest that implicit relevance feedback can be employed in the video domain and that semantic-based user profiles have the potential to improve video exploration

    CLARIN

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    The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

    CLARIN. The infrastructure for language resources

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    CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)

    CLARIN

    Get PDF
    The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

    WSN based sensing model for smart crowd movement with identification: a conceptual model

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    With the advancement of IT and increase in world population rate, Crowd Management (CM) has become a subject undergoing intense study among researchers. Technology provides fast and easily available means of transport and, up-to-date information access to the people that causes crowd at public places. This imposes a big challenge for crowd safety and security at public places such as airports, railway stations and check points. For example, the crowd of pilgrims during Hajj and Ummrah while crossing the borders of Makkah, Kingdom of Saudi Arabia. To minimize the risk of such crowd safety and security identification and verification of people is necessary which causes unwanted increment in processing time. It is observed that managing crowd during specific time period (Hajj and Ummrah) with identification and verification is a challenge. At present, many advanced technologies such as Internet of Things (IoT) are being used to solve the crowed management problem with minimal processing time. In this paper, we have presented a Wireless Sensor Network (WSN) based conceptual model for smart crowd movement with minimal processing time for people identification. This handles the crowd by forming groups and provides proactive support to handle them in organized manner. As a result, crowd can be managed to move safely from one place to another with group identification. The group identification minimizes the processing time and move the crowd in smart way
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