354 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

    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

    2023-2024 Catalog

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    The 2023-2024 Governors State University Undergraduate and Graduate Catalog is a comprehensive listing of current information regarding:Degree RequirementsCourse OfferingsUndergraduate and Graduate Rules and Regulation

    20th SC@RUG 2023 proceedings 2022-2023

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    Testing the Limits: Unusual Text Inputs Generation for Mobile App Crash Detection with Large Language Model

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    Mobile applications have become a ubiquitous part of our daily life, providing users with access to various services and utilities. Text input, as an important interaction channel between users and applications, plays an important role in core functionality such as search queries, authentication, messaging, etc. However, certain special text (e.g., -18 for Font Size) can cause the app to crash, and generating diversified unusual inputs for fully testing the app is highly demanded. Nevertheless, this is also challenging due to the combination of explosion dilemma, high context sensitivity, and complex constraint relations. This paper proposes InputBlaster which leverages the LLM to automatically generate unusual text inputs for mobile app crash detection. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. In detail, InputBlaster leverages LLM to produce the test generators together with the mutation rules serving as the reasoning chain, and utilizes the in-context learning schema to demonstrate the LLM with examples for boosting the performance. InputBlaster is evaluated on 36 text input widgets with cash bugs involving 31 popular Android apps, and results show that it achieves 78% bug detection rate, with 136% higher than the best baseline. Besides, we integrate it with the automated GUI testing tool and detect 37 unseen crashes in real-world apps from Google Play.Comment: Accepted by IEEE/ACM International Conference on Software Engineering 2024 (ICSE 2024

    Unveiling Energy Efficiency in Deep Learning: Measurement, Prediction, and Scoring across Edge Devices

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    Today, deep learning optimization is primarily driven by research focused on achieving high inference accuracy and reducing latency. However, the energy efficiency aspect is often overlooked, possibly due to a lack of sustainability mindset in the field and the absence of a holistic energy dataset. In this paper, we conduct a threefold study, including energy measurement, prediction, and efficiency scoring, with an objective to foster transparency in power and energy consumption within deep learning across various edge devices. Firstly, we present a detailed, first-of-its-kind measurement study that uncovers the energy consumption characteristics of on-device deep learning. This study results in the creation of three extensive energy datasets for edge devices, covering a wide range of kernels, state-of-the-art DNN models, and popular AI applications. Secondly, we design and implement the first kernel-level energy predictors for edge devices based on our kernel-level energy dataset. Evaluation results demonstrate the ability of our predictors to provide consistent and accurate energy estimations on unseen DNN models. Lastly, we introduce two scoring metrics, PCS and IECS, developed to convert complex power and energy consumption data of an edge device into an easily understandable manner for edge device end-users. We hope our work can help shift the mindset of both end-users and the research community towards sustainability in edge computing, a principle that drives our research. Find data, code, and more up-to-date information at https://amai-gsu.github.io/DeepEn2023.Comment: This paper has been accepted by ACM/IEEE Symposium on Edge Computing (SEC '23

    20th SC@RUG 2023 proceedings 2022-2023

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    Copyright as a constraint on creating technological value

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    Defence date: 8 January 2019Examining Board: Giovanni Sartor, EUI; Peter Drahos, EUI; Jane C. Ginsburg, Columbia Law School; Raquel Xalabarder, Universitat Oberta de Catalunya.How do we legislate for the unknown? This work tackles the question from the perspective of copyright, analysing the judicial practice emerging from case law on new uses of intellectual property resulting from technological change. Starting off by comparing results of actual innovation-related cases decided in jurisdictions with and without the fair use defence available, it delves deeper into the pathways of judicial reasoning and doctrinal debate arising in the two copyright realities, describing the dark sides of legal flexibility, the attempts to ‘bring order into chaos’ on one side and, on the other, the effort of judges actively looking for ways not to close the door on valuable innovation where inflexible legislation was about to become an impassable choke point. The analysis then moves away from the high-budget, large-scale innovation projects financed by the giants of the Internet era. Instead, building upon the findings of Yochai Benkler on the subject of networked creativity, it brings forth a type of innovation that brings together networked individuals, sharing and building upon each other’s results instead of competing, while often working for non-economic motivations. It is seemingly the same type of innovation, deeply rooted in the so-called ‘nerd culture’, that powered the early years of the 20th century digital revolution. As this culture was put on trial when Oracle famously sued Google for reuse of Java in the Android mobile operating system, the commentary emerging from the surrounding debate allowed to draw more general conclusions about what powers the digital evolution in a networked environment. Lastly, analysing the current trends in European cases, the analysis concludes by offering a rationale as to why a transformative use exception would allow courts to openly engage in the types of reasoning that seem to have become a necessity in cases on the fringes of copyright
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