1,495 research outputs found

    Improving Cross-Lingual Transfer Learning for Event Detection

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    The widespread adoption of applications powered by Artificial Intelligence (AI) backbones has unquestionably changed the way we interact with the world around us. Applications such as automated personal assistants, automatic question answering, and machine-based translation systems have become mainstays of modern culture thanks to the recent considerable advances in Natural Language Processing (NLP) research. Nonetheless, with over 7000 spoken languages in the world, there still remain a considerable number of marginalized communities that are unable to benefit from these technological advancements largely due to the language they speak. Cross-Lingual Learning (CLL) looks to address this issue by transferring the knowledge acquired from a popular, high-resource source language (e.g., English, Chinese, or Spanish) to a less favored, lower-resourced target language (e.g., Urdu or Swahili). This dissertation leverages the Event Detection (ED) sub-task of Information Extraction (IE) as a testbed and presents three novel approaches that improve cross-lingual transfer learning from distinct perspectives: (1) direct knowledge transfer, (2) hybrid knowledge transfer, and (3) few-shot learning

    EMID: An Emotional Aligned Dataset in Audio-Visual Modality

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    In this paper, we propose Emotionally paired Music and Image Dataset (EMID), a novel dataset designed for the emotional matching of music and images, to facilitate auditory-visual cross-modal tasks such as generation and retrieval. Unlike existing approaches that primarily focus on semantic correlations or roughly divided emotional relations, EMID emphasizes the significance of emotional consistency between music and images using an advanced 13-dimension emotional model. By incorporating emotional alignment into the dataset, it aims to establish pairs that closely align with human perceptual understanding, thereby raising the performance of auditory-visual cross-modal tasks. We also design a supplemental module named EMI-Adapter to optimize existing cross-modal alignment methods. To validate the effectiveness of the EMID, we conduct a psychological experiment, which has demonstrated that considering the emotional relationship between the two modalities effectively improves the accuracy of matching in abstract perspective. This research lays the foundation for future cross-modal research in domains such as psychotherapy and contributes to advancing the understanding and utilization of emotions in cross-modal alignment. The EMID dataset is available at https://github.com/ecnu-aigc/EMID

    Talking about personal recovery in bipolar disorder: Integrating health research, natural language processing, and corpus linguistics to analyse peer online support forum posts

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    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

    Theologische Zugänge zu Technik und Künstlicher Intelligenz

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    The publication of this work was supported by the Open Access Publication Fund of Humboldt-Universität zu Berlin.Technik und Künstliche Intelligenz gehören zu den brisanten Themen der gegenwärtigen Theologie. Wie kann Theologie zu Technik und KI beitragen? Der Technikdiskurs ist aufgeladen mit religiösen Motiven, und Technologien wie Roboter fordern die Theologie, z. B. das Menschenbild, die Ethik und die religiöse Praxis, neu heraus. Der Sammelband erforscht aus theologischer Perspektive die drängenden Themen unserer Zeit. Dazu begibt sich die Theologie in Dialog mit den Technikwissenschaften. Untersucht werden die Veränderungen des Menschenbildes durch Roboter, Religiöse Roboter, Optimierung des Körpers, medizinische Technologien, Autoregulative Waffensysteme und wie die Theologie durch die Technologisierung transformiert wird. Aus interdisziplinärer Perspektive werden neue Forschungsergebnisse aus dem internationalen Raum vorgestellt und neue Wege beschritten

    Workshop Proceedings of the 12th edition of the KONVENS conference

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    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

    Measuring the impact of COVID-19 on hospital care pathways

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    Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted

    Computer audition for emotional wellbeing

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    This thesis is focused on the application of computer audition (i. e., machine listening) methodologies for monitoring states of emotional wellbeing. Computer audition is a growing field and has been successfully applied to an array of use cases in recent years. There are several advantages to audio-based computational analysis; for example, audio can be recorded non-invasively, stored economically, and can capture rich information on happenings in a given environment, e. g., human behaviour. With this in mind, maintaining emotional wellbeing is a challenge for humans and emotion-altering conditions, including stress and anxiety, have become increasingly common in recent years. Such conditions manifest in the body, inherently changing how we express ourselves. Research shows these alterations are perceivable within vocalisation, suggesting that speech-based audio monitoring may be valuable for developing artificially intelligent systems that target improved wellbeing. Furthermore, computer audition applies machine learning and other computational techniques to audio understanding, and so by combining computer audition with applications in the domain of computational paralinguistics and emotional wellbeing, this research concerns the broader field of empathy for Artificial Intelligence (AI). To this end, speech-based audio modelling that incorporates and understands paralinguistic wellbeing-related states may be a vital cornerstone for improving the degree of empathy that an artificial intelligence has. To summarise, this thesis investigates the extent to which speech-based computer audition methodologies can be utilised to understand human emotional wellbeing. A fundamental background on the fields in question as they pertain to emotional wellbeing is first presented, followed by an outline of the applied audio-based methodologies. Next, detail is provided for several machine learning experiments focused on emotional wellbeing applications, including analysis and recognition of under-researched phenomena in speech, e. g., anxiety, and markers of stress. Core contributions from this thesis include the collection of several related datasets, hybrid fusion strategies for an emotional gold standard, novel machine learning strategies for data interpretation, and an in-depth acoustic-based computational evaluation of several human states. All of these contributions focus on ascertaining the advantage of audio in the context of modelling emotional wellbeing. Given the sensitive nature of human wellbeing, the ethical implications involved with developing and applying such systems are discussed throughout

    Design of an E-learning system using semantic information and cloud computing technologies

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    Humanity is currently suffering from many difficult problems that threaten the life and survival of the human race. It is very easy for all mankind to be affected, directly or indirectly, by these problems. Education is a key solution for most of them. In our thesis we tried to make use of current technologies to enhance and ease the learning process. We have designed an e-learning system based on semantic information and cloud computing, in addition to many other technologies that contribute to improving the educational process and raising the level of students. The design was built after much research on useful technology, its types, and examples of actual systems that were previously discussed by other researchers. In addition to the proposed design, an algorithm was implemented to identify topics found in large textual educational resources. It was tested and proved to be efficient against other methods. The algorithm has the ability of extracting the main topics from textual learning resources, linking related resources and generating interactive dynamic knowledge graphs. This algorithm accurately and efficiently accomplishes those tasks even for bigger books. We used Wikipedia Miner, TextRank, and Gensim within our algorithm. Our algorithm‘s accuracy was evaluated against Gensim, largely improving its accuracy. Augmenting the system design with the implemented algorithm will produce many useful services for improving the learning process such as: identifying main topics of big textual learning resources automatically and connecting them to other well defined concepts from Wikipedia, enriching current learning resources with semantic information from external sources, providing student with browsable dynamic interactive knowledge graphs, and making use of learning groups to encourage students to share their learning experiences and feedback with other learners.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Luis Sánchez Fernández.- Secretario: Luis de la Fuente Valentín.- Vocal: Norberto Fernández Garcí

    A Routine and Post-disaster Road Corridor Monitoring Framework for the Increased Resilience of Road Infrastructures

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