1,094 research outputs found

    Literature review on the smart city resources analysis with big data methodologies

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    This article provides a systematic literature review on applying different algorithms to municipal data processing, aiming to understand how the data were collected, stored, pre-processed, and analyzed, to compare various methods, and to select feasible solutions for further research. Several algorithms and data types are considered, finding that clustering, classification, correlation, anomaly detection, and prediction algorithms are frequently used. As expected, the data is of several types, ranging from sensor data to images. It is a considerable challenge, although several algorithms work very well, such as Long Short-Term Memory (LSTM) for timeseries prediction and classification.Open access funding provided by FCT|FCCN (b-on).info:eu-repo/semantics/publishedVersio

    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

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    A review of technical factors to consider when designing neural networks for semantic segmentation of Earth Observation imagery

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    Semantic segmentation (classification) of Earth Observation imagery is a crucial task in remote sensing. This paper presents a comprehensive review of technical factors to consider when designing neural networks for this purpose. The review focuses on Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and transformer models, discussing prominent design patterns for these ANN families and their implications for semantic segmentation. Common pre-processing techniques for ensuring optimal data preparation are also covered. These include methods for image normalization and chipping, as well as strategies for addressing data imbalance in training samples, and techniques for overcoming limited data, including augmentation techniques, transfer learning, and domain adaptation. By encompassing both the technical aspects of neural network design and the data-related considerations, this review provides researchers and practitioners with a comprehensive and up-to-date understanding of the factors involved in designing effective neural networks for semantic segmentation of Earth Observation imagery.Comment: 145 pages with 32 figure

    Frivolous Floodgate Fears

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    When rejecting plaintiff-friendly liability standards, courts often cite a fear of opening the floodgates of litigation. Namely, courts point to either a desire to protect the docket of federal courts or a burden on the executive branch. But there is little empirical evidence exploring whether the adoption of a stricter standard can, in fact, decrease the filing of legal claims in this circumstance. This Article empirically analyzes and theoretically models the effect of adopting arguably stricter liability standards on litigation by investigating the context of one of the Supreme Court’s most recent reliances on this argument when adopting a stricter liability standard for causation in employment discrimination claims. In 2013, the Supreme Court held that a plaintiff proving retaliation under Title VII of the Civil Rights Act must prove that their participation in a protected activity was a but-for cause of the adverse employment action they experienced. Rejecting the arguably more plaintiff-friendly motivating-factor standard, the Court stated, “[L]essening the causation standard could also contribute to the filing of frivolous claims, which would siphon resources from efforts by employer[s], administrative agencies, and courts to combat workplace harassment.” Univ. of Tex. Sw. Med. Ctr. v. Nassar, 570 U.S. 338, 358 (2013). And over the past ten years, the Court has overturned the application of motivating-factor causation as applied to at least four different federal antidiscrimination statutes. Contrary to the Supreme Court’s concern that motivating-factor causation encourages frivolous charges, many employment law scholars worry that the heightened but-for standard will deter legitimate claims. This Article empirically explores these concerns, in part using data received from the Equal Employment Opportunity Commission (EEOC) through a Freedom of Information Act (FOIA) request. Specifically, it empirically tests whether the adoption of the but-for causation standard for claims filed under the Age Discrimination in Employment Act and by federal courts of appeals under the Americans with Disabilities Act has impacted the filing of discrimination claims and the outcome of those claims in federal court. Consistent with theory detailed in this Article, the empirical analysis provides evidence that the stricter standard may have increased the docket of the federal courts by decreasing settlement within the EEOC and during litigation. The empirical results weigh in on concerns surrounding the adoption of the but-for causation standard and provide evidence that the floodgates argument, when relied on to deter frivolous filings by changing liability standards, in fact, may do just the opposite by decreasing the likelihood of settlement in the short term, without impacting the filing of claims or other case outcomes

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    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

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Undergraduate and Graduate Course Descriptions, 2023 Spring

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    Wright State University undergraduate and graduate course descriptions from Spring 2023
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