3,172 research outputs found

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    Protecting Privacy in Indian Schools: Regulating AI-based Technologies' Design, Development and Deployment

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    Education is one of the priority areas for the Indian government, where Artificial Intelligence (AI) technologies are touted to bring digital transformation. Several Indian states have also started deploying facial recognition-enabled CCTV cameras, emotion recognition technologies, fingerprint scanners, and Radio frequency identification tags in their schools to provide personalised recommendations, ensure student security, and predict the drop-out rate of students but also provide 360-degree information of a student. Further, Integrating Aadhaar (digital identity card that works on biometric data) across AI technologies and learning and management systems (LMS) renders schools a ‘panopticon’. Certain technologies or systems like Aadhaar, CCTV cameras, GPS Systems, RFID tags, and learning management systems are used primarily for continuous data collection, storage, and retention purposes. Though they cannot be termed AI technologies per se, they are fundamental for designing and developing AI systems like facial, fingerprint, and emotion recognition technologies. The large amount of student data collected speedily through the former technologies is used to create an algorithm for the latter-stated AI systems. Once algorithms are processed using machine learning (ML) techniques, they learn correlations between multiple datasets predicting each student’s identity, decisions, grades, learning growth, tendency to drop out, and other behavioural characteristics. Such autonomous and repetitive collection, processing, storage, and retention of student data without effective data protection legislation endangers student privacy. The algorithmic predictions by AI technologies are an avatar of the data fed into the system. An AI technology is as good as the person collecting the data, processing it for a relevant and valuable output, and regularly evaluating the inputs going inside an AI model. An AI model can produce inaccurate predictions if the person overlooks any relevant data. However, the state, school administrations and parents’ belief in AI technologies as a panacea to student security and educational development overlooks the context in which ‘data practices’ are conducted. A right to privacy in an AI age is inextricably connected to data practices where data gets ‘cooked’. Thus, data protection legislation operating without understanding and regulating such data practices will remain ineffective in safeguarding privacy. The thesis undergoes interdisciplinary research that enables a better understanding of the interplay of data practices of AI technologies with social practices of an Indian school, which the present Indian data protection legislation overlooks, endangering students’ privacy from designing and developing to deploying stages of an AI model. The thesis recommends the Indian legislature frame better legislation equipped for the AI/ML age and the Indian judiciary on evaluating the legality and reasonability of designing, developing, and deploying such technologies in schools

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    Learning Situation Hyper-Graphs for Video Question Answering

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    Answering questions about complex situations in videos requires not only capturing the presence of actors, objects, and their relations but also the evolution of these relationships over time. A situation hyper-graph is a representation that describes situations as scene sub-graphs for video frames and hyper-edges for connected sub-graphs and has been proposed to capture all such information in a compact structured form. In this work, we propose an architecture for Video Question Answering (VQA) that enables answering questions related to video content by predicting situation hyper-graphs, coined Situation Hyper-Graph based Video Question Answering (SHG-VQA). To this end, we train a situation hyper-graph decoder to implicitly identify graph representations with actions and object/human-object relationships from the input video clip. and to use cross-attention between the predicted situation hyper-graphs and the question embedding to predict the correct answer. The proposed method is trained in an end-to-end manner and optimized by a VQA loss with the cross-entropy function and a Hungarian matching loss for the situation graph prediction. The effectiveness of the proposed architecture is extensively evaluated on two challenging benchmarks: AGQA and STAR. Our results show that learning the underlying situation hyper-graphs helps the system to significantly improve its performance for novel challenges of video question-answering tasks

    NEMISA Digital Skills Conference (Colloquium) 2023

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    The purpose of the colloquium and events centred around the central role that data plays today as a desirable commodity that must become an important part of massifying digital skilling efforts. Governments amass even more critical data that, if leveraged, could change the way public services are delivered, and even change the social and economic fortunes of any country. Therefore, smart governments and organisations increasingly require data skills to gain insights and foresight, to secure themselves, and for improved decision making and efficiency. However, data skills are scarce, and even more challenging is the inconsistency of the associated training programs with most curated for the Science, Technology, Engineering, and Mathematics (STEM) disciplines. Nonetheless, the interdisciplinary yet agnostic nature of data means that there is opportunity to expand data skills into the non-STEM disciplines as well.College of Engineering, Science and Technolog

    When Deep Learning Meets Polyhedral Theory: A Survey

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    In the past decade, deep learning became the prevalent methodology for predictive modeling thanks to the remarkable accuracy of deep neural networks in tasks such as computer vision and natural language processing. Meanwhile, the structure of neural networks converged back to simpler representations based on piecewise constant and piecewise linear functions such as the Rectified Linear Unit (ReLU), which became the most commonly used type of activation function in neural networks. That made certain types of network structure \unicode{x2014}such as the typical fully-connected feedforward neural network\unicode{x2014} amenable to analysis through polyhedral theory and to the application of methodologies such as Linear Programming (LP) and Mixed-Integer Linear Programming (MILP) for a variety of purposes. In this paper, we survey the main topics emerging from this fast-paced area of work, which bring a fresh perspective to understanding neural networks in more detail as well as to applying linear optimization techniques to train, verify, and reduce the size of such networks

    Fine Tuning Transformer Models for Domain Specific Feature Extraction

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    La naturalesa del processament de llengües naturals ha canviat dràsticament en els últims anys. La implementació de Large Language Models pre-entrenat en milers de dades sense etiquetar ha obert la porta a una nova capa de comprensió del processament de text. Això ha desplaçat la investigació a la zona per explotar aquests grans models per obtenir millors resultats per a les tasques més petites. D'aquesta manera, el processament de llengües naturals està adquirint una importància cada vegada major. Afinant els diferents models de llenguatge gran amb dades específiques de context i de tasques, aquests models ràpidament aprenen a seguir patrons i generalitzar-los a nous conceptes. Entenen el llenguatge natural en gran mesura i poden generar relacions en paraules, frases i paràgrafs. La sintonització fina neuronal s'ha convertit en una tasca cada vegada més important per simplificar l'ús de solucions d'aprenentatge automàtic amb pocs recursos. L'augment dels models de transformadors pre-entrenats per al processament del llenguatge natural ha complicat la selecció i l'experimentació d'aquests models, augmentant el temps de recerca i experimentació. Aquest estudi passa per l'estat actual de l'art dels models transformadors i intenta estudiar l'abast i l'aplicabilitat d'aquests models. A partir d'aquest treball inicial, el document produeix un gasoducte complet d'ajust fi del model que permet a l'usuari obtenir fàcilment un model llest per a utilitzar per a una tasca de llenguatge natural. Per provar aquest model, la canonada es prova i s'avalua per a l'extracció automàtica de característiques (és a dir, funcionalitats) des d'aplicacions mòbils utilitzant documents de llenguatge natural disponibles, com ara descripcions.The nature of Natural Language Processing has drastically changed in the past years. The implementation of Large Language Models pre-trained on thousands of unlabelled data has opened the door to a new layer of comprehension of text processing. This has shifted research in the area to exploit these large models to obtain better results for smaller tasks. In this way, fine-tuning Natural Language Processing is becoming increasingly important. By fine-tuning the different large language models with context and task-specific data, these models quickly learn to track patterns and generalize to new concepts. They understand natural language to a great extent and can generate relationships in words, phrases, and paragraphs. Fine Tuning has become an increasingly important task to simplify the use of machine learning solutions with low resources. The increase in pre-trained transformer models for Natural Language Processing has complicated the selection and experimentation of these models, increasing research and experimentation time. This study goes through the current state of the art of transformer models and attempts to study the scope and applicability of these models. From this initial work, the paper produces a compre- hensive pipeline of model fine-tuning that allows the user to easily obtain a ready-to-use model for a natural language task. To test this model, the pipeline is tested and evaluated for the automatic extraction of features (i.e. functionalities) from mobile applications using available natural language documents, such as descriptions
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