363 research outputs found

    Listening to Community: Towards Best Research Practices in Pond Inlet, Nunavut

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    What are the specific conditions and circumstances that can either prevent or facilitate an ethical, meaningful, productive, and respectful collaboration between Settler researchers and Indigenous People engaged in community or regional monitoring programs? How can I bring Settler research and Indigenous knowledge systems together to facilitate more equitable and proactive environmental monitoring programs? My research examines the connections between community-based environmental monitoring, research ethics, and the role of social science in climate change adaptation programs. In this dissertation, I examine the context, community concerns and recommendations for research that emerged during my fieldwork, interviews, and workshops conducted in Pond Inlet and Cambridge Bay, Nunavut, and Calgary, Alberta. It is widely recognized that over the last few decades, the planet has been undergoing rapid climate change, particularly in the Arctic. Climate change has led to a discussion about the role of Settler research and Indigenous knowledge in understanding and addressing environmental changes and community and regional priorities. In the North of Canada and other Arctic regions, the role of Settler researchers facilitating ecological monitoring, environmental changes, and local and regional policy changes has been largely overlooked. As more Indigenous organizations and communities continue to advocate and demonstrate the validity of their knowledge systems, levels of government and research institutions seek to facilitate and embrace the co-integration Indigenous Knowledge (IK) and Settler research. At an individual level, the co-integration of IK with Settler research will build skills and promote community resilience brought on by climate change. At a societal level, the benefits and potential of integrating IK with Settler research are a resource that needs to be investigated. It can add new and essential aspects to climate change adaptation strategies. However, it can also be problematic and reproduce already existing colonial dynamics. In this dissertation, I provide an overview and discussion of the potential role for Settler researchers in climate change research related to adaptation measures for Indigenous communities across the North of Canada and case study results. The outcomes of my research indicate that: 1) there needs to be a significant increase in the number of climate change adaptation projects that incorporate Inuit Knowledge (IK); 2) social science could play a role in the success and sustainability of climate change program development and deployment, and 3) the measurable and tangible ways communities may evaluate the success of adaptation programs. My research also outlines the concerns related to Settler researcher behaviors and practices that a group of Inuit from Pond Inlet and Cambridge Bay, Nunavut, experienced while working on university-based research projects and reports a series of recommendations they provided. My study also presents the concerns and recommendations of Inuit community members about the need to decolonize university ethics boards and research. The objectives of the workshop were to 1) get a sense of Settler research behavior community members saw as unethical, 2) synthesize the recommendations made by various Indigenous organizations related to ethical engagement and a decolonized research approach, and 3) develop a framework for an ethics workshop aimed at decolonizing university research ethics processes, which Indigenous peoples lead, and research in general. The findings indicate the great need for: (1) the inclusion of Indigenous epistemologies into university ethics training and certification processes equal to Settler science; 2) improved understandings of how academic disciplines should consult and work with Indigenous communities; 3) protocols and procedures for Settler research to be integrated with Indigenous Knowledge to be established. Each university, Settler researcher, and Indigenous community has specific circumstances, limitations, obstacles, research priorities, and capacities that need to be understood. The conclusions of my study are: 1) there is a need for Settler researchers to be aware of and recognize different epistemological orientations; 2) universities and researchers must make a concerted effort to spend more time supporting Indigenous-led research, and co-designing and implementing research projects collegially with Indigenous communities; 3) the relevance of Settler research projects needs to be clearly articulated with community members, and the research results need to be presented to the community in a variety of ways, such as through social media, town halls, plain language reports, etc.; 4) Settler researchers can make efforts to document community-level concerns in order for the community to be able to collaboration with Settler researchers on specific concerns

    Dataset And Deep Neural Network Based Approach To Audio Question Answering

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    Audio question answering (AQA) is a multimodal task in which a system analyzes an audio signal and a question in natural language, to produce a desirable answer in natural language. In this thesis, a new dataset for audio question answering, Clotho-AQA, consisting of 1991 audio files each between 15 to 30 seconds in duration is presented. For each audio file in the dataset, six different questions and their corresponding answers were crowdsourced using Amazon Mechanical Turk (AMT). The questions and their corresponding answers were created by different annotators. Out of the six questions for each audio, two questions each were designed to have ‘yes’ and ‘no’ as answers respectively, while the remaining two questions have other single-word answers. For every question, answers from three independent annotators were collected. In this thesis, two baseline experiments are presented to portray the usage of the Clotho-AQA dataset - a multimodal binary classifier for ‘yes’ or ‘no’ answers and a multimodal multi-class classifier for single-word answers both based on long short-term memory (LSTM) layers. The binary classifier achieved an accuracy of 62.7% and the multi-class classifier achieved a top-1 accuracy of 54.2% and a top-5 accuracy of 93.7%. Further, an attention-based model was proposed, which increased the binary classifier accuracy to 66.2% and the top-1 and top-5 multiclass classifier accuracy to 57.5% and 99.8% respectively. Some drawbacks of the Clotho-AQA dataset such as the presence of the same answer words in different tenses, singular-plural forms, etc., that are considered as different classes for the classification problem were addressed and a refined version called Clotho-AQA_v2 is also presented. The multimodal baseline model achieved a top-1 and top-5 accuracy of 59.8% and 96.6% respectively while the attention-based model achieved a top-1 and top-5 accuracy of 61.3% and 99.6% respectively on this refined dataset

    Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

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    This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

    Machine Learning Algorithm for the Scansion of Old Saxon Poetry

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    Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input verses

    Geo-Information Harvesting from Social Media Data

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    As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multiperspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysisready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data

    Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

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    Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

    Information Refinement Technologies for Crisis Informatics: User Expectations and Design Implications for Social Media and Mobile Apps in Crises

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    In the past 20 years, mobile technologies and social media have not only been established in everyday life, but also in crises, disasters, and emergencies. Especially large-scale events, such as 2012 Hurricane Sandy or the 2013 European Floods, showed that citizens are not passive victims but active participants utilizing mobile and social information and communication technologies (ICT) for crisis response (Reuter, Hughes, et al., 2018). Accordingly, the research field of crisis informatics emerged as a multidisciplinary field which combines computing and social science knowledge of disasters and is rooted in disciplines such as human-computer interaction (HCI), computer science (CS), computer supported cooperative work (CSCW), and information systems (IS). While citizens use personal ICT to respond to a disaster to cope with uncertainty, emergency services such as fire and police departments started using available online data to increase situational awareness and improve decision making for a better crisis response (Palen & Anderson, 2016). When looking at even larger crises, such as the ongoing COVID-19 pandemic, it becomes apparent the challenges of crisis informatics are amplified (Xie et al., 2020). Notably, information is often not available in perfect shape to assist crisis response: the dissemination of high-volume, heterogeneous and highly semantic data by citizens, often referred to as big social data (Olshannikova et al., 2017), poses challenges for emergency services in terms of access, quality and quantity of information. In order to achieve situational awareness or even actionable information, meaning the right information for the right person at the right time (Zade et al., 2018), information must be refined according to event-based factors, organizational requirements, societal boundary conditions and technical feasibility. In order to research the topic of information refinement, this dissertation combines the methodological framework of design case studies (Wulf et al., 2011) with principles of design science research (Hevner et al., 2004). These extended design case studies consist of four phases, each contributing to research with distinct results. This thesis first reviews existing research on use, role, and perception patterns in crisis informatics, emphasizing the increasing potentials of public participation in crisis response using social media. Then, empirical studies conducted with the German population reveal positive attitudes and increasing use of mobile and social technologies during crises, but also highlight barriers of use and expectations towards emergency services to monitor and interact in media. The findings led to the design of innovative ICT artefacts, including visual guidelines for citizens’ use of social media in emergencies (SMG), an emergency service web interface for aggregating mobile and social data (ESI), an efficient algorithm for detecting relevant information in social media (SMO), and a mobile app for bidirectional communication between emergency services and citizens (112.social). The evaluation of artefacts involved the participation of end-users in the application field of crisis management, pointing out potentials for future improvements and research potentials. The thesis concludes with a framework on information refinement for crisis informatics, integrating event-based, organizational, societal, and technological perspectives

    Tirtha -- An Automated Platform to Crowdsource Images and Create 3D Models of Heritage Sites

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    Digital preservation of Cultural Heritage (CH) sites is crucial to protect them against damage from natural disasters or human activities. Creating 3D models of CH sites has become a popular method of digital preservation thanks to advancements in computer vision and photogrammetry. However, the process is time-consuming, expensive, and typically requires specialized equipment and expertise, posing challenges in resource-limited developing countries. Additionally, the lack of an open repository for 3D models hinders research and public engagement with their heritage. To address these issues, we propose Tirtha, a web platform for crowdsourcing images of CH sites and creating their 3D models. Tirtha utilizes state-of-the-art Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques. It is modular, extensible and cost-effective, allowing for the incorporation of new techniques as photogrammetry advances. Tirtha is accessible through a web interface at https://tirtha.niser.ac.in and can be deployed on-premise or in a cloud environment. In our case studies, we demonstrate the pipeline's effectiveness by creating 3D models of temples in Odisha, India, using crowdsourced images. These models are available for viewing, interaction, and download on the Tirtha website. Our work aims to provide a dataset of crowdsourced images and 3D reconstructions for research in computer vision, heritage conservation, and related domains. Overall, Tirtha is a step towards democratizing digital preservation, primarily in resource-limited developing countries.Comment: Accepted at The 28th International ACM Conference on 3D Web Technology (Web3D 2023

    Scholarly Communication Librarianship and Open Knowledge

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    The intersection of scholarly communication librarianship and open education offers a unique opportunity to expand knowledge of scholarly communication topics in both education and practice. Open resources can address the gap in teaching timely and critical scholarly communication topics—copyright in teaching and research environments, academic publishing, emerging modes of scholarship, impact measurement—while increasing access to resources and equitable participation in education and scholarly communication. Scholarly Communication Librarianship and Open Knowledge is an open textbook and practitioner’s guide that collects theory, practice, and case studies from nearly 80 experts in scholarly communication and open education. Divided into three parts: *What is Scholarly Communication? *Scholarly Communication and Open Culture *Voices from the Field: Perspectives, Intersections, and Case Studies The book delves into the economic, social, policy, and legal aspects of scholarly communication as well as open access, open data, open education, and open science and infrastructure. Practitioners provide insight into the relationship between university presses and academic libraries, defining collection development as operational scholarly communication, and promotion and tenure and the challenge for open access. Scholarly Communication Librarianship and Open Knowledge is a thorough guide meant to increase instruction on scholarly communication and open education issues and practices so library workers can continue to meet the changing needs of students and faculty. It is also a political statement about the future to which we aspire and a challenge to the industrial, commercial, capitalistic tendencies encroaching on higher education. Students, readers, educators, and adaptors of this resource can find and embrace these themes throughout the text and embody them in their work
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