857 research outputs found
Accessibility at Film Festivals: Guidelines for Inclusive Subtitling
In today's media-dominated world, the imperative for accessibility has never been greater, and ensuring that audiovisual experiences cater to individuals with sensory disabilities has become a pressing concern. One of the key initiatives in this endeavour is inclusive subtitling (IS), a practice rooted in the broader contexts of subtitling for the deaf and hard of hearing (SDH/CC), audiovisual translation studies (AVTS), media accessibility studies (MAS), and the evolving field of Deaf studies (DS). This study aims to offer a comprehensive exploration of how inclusive subtitling contributes to fostering accessible and inclusive audiovisual experiences, with a particular focus on its implications within the unique environment of film festivals. To gain a holistic perspective of inclusive subtitling, it is essential to examine its lineage in relation to analogous practices, which is the focus of the first chapter. Inclusive subtitling is an extension of SDH/CC, designed for individuals with hearing impairments, and SDH/CC, in turn, is a nuanced variation of traditional subtitling extensively explored within the realm of AVTS. To encapsulate the diverse techniques and modalities aimed at making audiovisual content universally accessible, the study recognises the term "Audiovisual Accessibility" (AVA). The second chapter explores the interconnection of accessibility studies (AS), AVTS, and MAS, highlighting their symbiotic relationship and their role in framing inclusive subtitles within these fields. These interconnections are pivotal in shaping a framework for the practice of inclusive subtitling, enabling a comprehensive examination of its applicability and research implications. The third chapter delves into Deaf studies and the evolution of Deafhood, which hinges on the history and culture of Deaf individuals. This chapter elucidates the distinction between ‘deafness’ as a medical construct and ‘Deafhood’ as a cultural identity, crucial to the understanding of audiovisual accessibility and its intersection with the Deaf community's perspectives. In the fourth chapter, the focus turns to the exploration of film festivals, with a specific emphasis on the crucial role of subtitles in enhancing accessibility, particularly when films are presented in their original languages. The chapter marks a critical point, highlighting the inherent connection between subtitles and the immersive nature of film festivals that aspire to promote inclusivity in the cinematic experience. The emphasis on inclusivity extends to the evolution of film festivals, giving rise to more advanced forms, including accessible film festivals and Deaf film festivals. At the core of the chapter is a thorough examination of the corpus, specifically, the SDH/CC of films spanning the editions from 2020 to 2023 of two highly significant film festivals, namely BFI Flare and the London Film Festival. The corpus serves as the foundation upon which my research unfolds, providing a nuanced understanding of the role subtitles play in film festival contexts. The main chapter, chapter five, thoroughly analyses the technical and linguistic aspects of inclusive subtitling, drawing insights from the Inclusive Subtitling Guidelines - a two version document devised by myself - and offering real-world applications supported by a case study at an Italian film festival and another case study of the short film Pure, with the relevant inclusive subtitles file annexed. In conclusion, the research sets the stage for a comprehensive exploration of inclusive subtitling's role in ensuring accessible and inclusive audiovisual experiences, particularly within film festivals. It underscores the importance of accessibility in the world of audiovisual media and highlights the need for inclusive practices to cater to diverse audiences
Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts
Background: Personal recovery, ‘living a satisfying, hopeful and contributing lifeeven with the limitations caused by the illness’ (Anthony, 1993) is of particular value in bipolar disorder where symptoms often persist despite treatment. So far, personal recovery has only been studied in researcher-constructed environments (interviews, focus groups). Support forum posts can serve as a complementary naturalistic data source. Objective: The overarching aim of this thesis was to study personal recovery experiences that people living with bipolar disorder have shared in online support forums through integrating health research, NLP, and corpus linguistics in a mixed methods approach within a pragmatic research paradigm, while considering ethical issues and involving people with lived experience. Methods: This mixed-methods study analysed: 1) previous qualitative evidence on personal recovery in bipolar disorder from interviews and focus groups 2) who self-reports a bipolar disorder diagnosis on the online discussion platform Reddit 3) the relationship of mood and posting in mental health-specific Reddit forums (subreddits) 4) discussions of personal recovery in bipolar disorder subreddits. Results: A systematic review of qualitative evidence resulted in the first framework for personal recovery in bipolar disorder, POETIC (Purpose & meaning, Optimism & hope, Empowerment, Tensions, Identity, Connectedness). Mainly young or middle-aged US-based adults self-report a bipolar disorder diagnosis on Reddit. Of these, those experiencing more intense emotions appear to be more likely to post in mental health support subreddits. Their personal recovery-related discussions in bipolar disorder subreddits primarily focussed on three domains: Purpose & meaning (particularly reproductive decisions, work), Connectedness (romantic relationships, social support), Empowerment (self-management, personal responsibility). Support forum data highlighted personal recovery issues that exclusively or more frequently came up online compared to previous evidence from interviews and focus groups. Conclusion: This project is the first to analyse non-reactive data on personal recovery in bipolar disorder. Indicating the key areas that people focus on in personal recovery when posting freely and the language they use provides a helpful starting point for formal and informal carers to understand the concerns of people diagnosed with bipolar disorder and to consider how best to offer support
Mining Butterflies in Streaming Graphs
This thesis introduces two main-memory systems sGrapp and sGradd for performing the fundamental analytic tasks of biclique counting and concept drift detection over a streaming graph. A data-driven heuristic is used to architect the systems. To this end, initially, the growth patterns of bipartite streaming graphs are mined and the emergence principles of streaming motifs are discovered. Next, the discovered principles are (a) explained by a graph generator called sGrow; and (b) utilized to establish the requirements for efficient, effective, explainable, and interpretable management and processing of streams. sGrow is used to benchmark stream analytics, particularly in the case of concept drift detection.
sGrow displays robust realization of streaming growth patterns independent of initial conditions, scale and temporal characteristics, and model configurations. Extensive evaluations confirm the simultaneous effectiveness and efficiency of sGrapp and sGradd. sGrapp achieves mean absolute percentage error up to 0.05/0.14 for the cumulative butterfly count in streaming graphs with uniform/non-uniform temporal distribution and a processing throughput of 1.5 million data records per second. The throughput and estimation error of sGrapp are 160x higher and 0.02x lower than baselines. sGradd demonstrates an improving performance over time, achieves zero false detection rates when there is not any drift and when drift is already detected, and detects sequential drifts in zero to a few seconds after their occurrence regardless of drift intervals
Detecting Team Conflict From Multiparty Dialogue
The emergence of online collaboration platforms has dramatically changed the dynamics of human teamwork, creating a veritable army of virtual teams composed of workers in different physical locations. The global world requires a tremendous amount of collaborative problem solving, primarily virtual, making it an excellent domain for computer scientists and team cognition researchers who seek to understand the dynamics involved in collaborative tasks to provide a solution that can support effective collaboration. Mining and analyzing data from collaborative dialogues can yield insights into virtual teams\u27 thought processes and help develop virtual agents to support collaboration. Good communication is indubitably the foundation of effective collaboration. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomenon in which humans synchronize their linguistic choices. This dissertation presents several technical innovations in the usage of machine learning towards analyzing, monitoring, and predicting collaboration success from multiparty dialogue by successfully handling the problems of resource scarcity and natural distribution shifts. First, we examine the problem of predicting team performance from embeddings learned from multiparty dialogues such that teams with similar conflict scores lie close to one another in vector space. We extract the embeddings from three types of features: 1) dialogue acts 2) sentiment polarity 3) syntactic entrainment. Although all of these features can be used to predict team performance effectively, their utility varies by the teamwork phase. We separate the dialogues of players playing a cooperative game into stages: 1) early (knowledge building), 2) middle (problem-solving), and 3) late (culmination). Unlike syntactic entrainment, both dialogue act and sentiment embeddings effectively classify team performance, even during the initial phase. Second, we address the problem of learning generalizable models of collaboration. Machine learning models often suffer domain shifts; one advantage of encoding the semantic features is their adaptability across multiple domains. We evaluate the generalizability of different embeddings to other goal-oriented teamwork dialogues. Finally, in addition to identifying the features predictive of successful collaboration, we propose multi-feature embedding (MFeEmb) to improve the generalizability of collaborative task success prediction models under natural distribution shifts and resource scarcity. MFeEmb leverages the strengths of semantic, structural, and textual features of the dialogues by incorporating the most meaningful information from dialogue acts (DAs), sentiment polarities, and vocabulary of the dialogues. To further enhance the performance of MFeEmb under a resource-scarce scenario, we employ synthetic data generation and few-shot learning. We use the method proposed by Bailey and Chopra (2018) for few-shot learning from the FsText python library. We replaced the universal embedding with our proposed multi-feature embedding to compare the performance of the two. For data augmentation, we propose using synonym replacement from collaborative dialogue vocabulary instead of synonym replacement from WordNet. The research was conducted on several multiparty dialogue datasets, including ASIST, SwDA, Hate Speech, Diplomacy, Military, SAMSum, AMI, and GitHub. Results show that the proposed multi-feature embedding is an excellent choice for the meta-training stage of the few-shot learning, even if it learns from a small train set of size as small as 62 samples. Also, our proposed data augmentation method showed significant performance improvement. Our research has potential ramifications for the development of conversational agents that facilitate teaming as well as towards the creation of more effective social coding platforms to better support teamwork between software engineers
Elements, Government, and Licensing: Developments in phonology
Elements, Government, and Licensing brings together new theoretical and empirical developments in phonology. It covers three principal domains of phonological representation: melody and segmental structure; tone, prosody and prosodic structure; and phonological relations, empty categories, and vowel-zero alternations. Theoretical topics covered include the formalisation of Element Theory, the hotly debated topic of structural recursion in phonology, and the empirical status of government.
In addition, a wealth of new analyses and empirical evidence sheds new light on empty categories in phonology, the analysis of certain consonantal sequences, phonological and non-phonological alternation, the elemental composition of segments, and many more. Taking up long-standing empirical and theoretical issues informed by the Government Phonology and Element Theory, this book provides theoretical advances while also bringing to light new empirical evidence and analysis challenging previous generalisations.
The insights offered here will be equally exciting for phonologists working on related issues inside and outside the Principles & Parameters programme, such as researchers working in Optimality Theory or classical rule-based phonology
Workshop Proceedings of the 12th edition of the KONVENS conference
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years
Next Generation Business Ecosystems: Engineering Decentralized Markets, Self-Sovereign Identities and Tokenization
Digital transformation research increasingly shifts from studying information systems within organizations towards adopting an ecosystem perspective, where multiple actors co-create value. While digital platforms have become a ubiquitous phenomenon in consumer-facing industries, organizations remain cautious about fully embracing the ecosystem concept and sharing data with external partners. Concerns about the market power of platform orchestrators and ongoing discussions on privacy, individual empowerment, and digital sovereignty further complicate the widespread adoption of business ecosystems, particularly in the European Union.
In this context, technological innovations in Web3, including blockchain and other distributed ledger technologies, have emerged as potential catalysts for disrupting centralized gatekeepers and enabling a strategic shift towards user-centric, privacy-oriented next-generation business ecosystems. However, existing research efforts focus on decentralizing interactions through distributed network topologies and open protocols lack theoretical convergence, resulting in a fragmented and complex landscape that inadequately addresses the challenges organizations face when transitioning to an ecosystem strategy that harnesses the potential of disintermediation.
To address these gaps and successfully engineer next-generation business ecosystems, a comprehensive approach is needed that encompasses the technical design, economic models, and socio-technical dynamics. This dissertation aims to contribute to this endeavor by exploring the implications of Web3 technologies on digital innovation and transformation paths. Drawing on a combination of qualitative and quantitative research, it makes three overarching contributions:
First, a conceptual perspective on \u27tokenization\u27 in markets clarifies its ambiguity and provides a unified understanding of the role in ecosystems.
This perspective includes frameworks on: (a) technological; (b) economic; and (c) governance aspects of tokenization.
Second, a design perspective on \u27decentralized marketplaces\u27 highlights the need for an integrated understanding of micro-structures, business structures, and IT infrastructures in blockchain-enabled marketplaces. This perspective includes: (a) an explorative literature review on design factors; (b) case studies and insights from practitioners to develop requirements and design principles; and (c) a design science project with an interface design prototype of blockchain-enabled marketplaces.
Third, an economic perspective on \u27self-sovereign identities\u27 (SSI) as micro-structural elements of decentralized markets. This perspective includes: (a) value creation mechanisms and business aspects of strategic alliances governing SSI ecosystems; (b) business model characteristics adopted by organizations leveraging SSI; and (c) business model archetypes and a framework for SSI ecosystem engineering efforts.
The dissertation concludes by discussing limitations as well as outlining potential avenues for future research. These include, amongst others, exploring the challenges of ecosystem bootstrapping in the absence of intermediaries, examining the make-or-join decision in ecosystem emergence, addressing the multidimensional complexity of Web3-enabled ecosystems, investigating incentive mechanisms for inter-organizational collaboration, understanding the role of trust in decentralized environments, and exploring varying degrees of decentralization with potential transition pathways
Analyzing public opinions regarding virtual tourism in the context of COVID-19: Unidirectional vs. 360-degree videos
Over the last few years, more and more people have been using YouTube videos to experience virtual reality travel. Many individuals utilize comments to voice their ideas or criticize a subject on YouTube. The number of replies to 360-degree and unidirectional videos is enormous and might differ between the two kinds of videos. This presents the problem of efficiently evaluating user opinions with respect to which type of video will be more appealing to viewers, positive comments, or interest. This paper aims to study SentiStrength-SE and SenticNet7 techniques for sentiment analysis. The findings demonstrate that the sentiment analysis obtained from SenticNet7 outperforms that from SentiStrength-SE. It is revealed through the sentiment analysis that sentiment disparity among the viewers of 360-degree and unidirectional videos is low and insignificant. Furthermore, the study shows that unidirectional videos garnered the most traffic during COVID-19 induced global travel bans. The study elaborates on the capacity of unidirectional videos on travel and the implications for industry and academia. The second aim of this paper also employs a Convolutional Neural Network and Random Forest for sentiment analysis of YouTube viewers' comments, where the sentiment analysis output by SenticNet7 is used as actual values. Cross-validation with 10-folds is employed in the proposed models. The findings demonstrate that the max-voting technique outperforms compared with an individual fold.IGA/CebiaTech/2022/001TBU in Zlin [CZ.02.2.69/0.0/19_073/0016941]; Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2022/001
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