2,904 research outputs found

    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

    Quality of experience and access network traffic management of HTTP adaptive video streaming

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    The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    20th SC@RUG 2023 proceedings 2022-2023

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    GeoYCSB: A Benchmark Framework for the Performance and Scalability Evaluation of Geospatial NoSQL Databases

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    The proliferation of geospatial applications has tremendously increased the variety, velocity, and volume of spatial data that data stores have to manage. Traditional relational databases reveal limitations in handling such big geospatial data, mainly due to their rigid schema requirements and limited scalability. Numerous NoSQL databases have emerged and actively serve as alternative data stores for big spatial data. This study presents a framework, called GeoYCSB, developed for benchmarking NoSQL databases with geospatial workloads. To develop GeoYCSB, we extend YCSB, a de facto benchmark framework for NoSQL systems, by integrating into its design architecture the new components necessary to support geospatial workloads. GeoYCSB supports both microbenchmarks and macrobenchmarks and facilitates the use of real datasets in both. It is extensible to evaluate any NoSQL database, provided they support spatial queries, using geospatial workloads performed on datasets of any geometric complexity. We use GeoYCSB to benchmark two leading document stores, MongoDB and Couchbase, and present the experimental results and analysis. Finally, we demonstrate the extensibility of GeoYCSB by including a new dataset consisting of complex geometries and using it to benchmark a system with a wide variety of geospatial queries: Apache Accumulo, a wide-column store, with the GeoMesa framework applied on top

    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

    Marine Data Fusion for Analyzing Spatio-Temporal Ocean Region Connectivity

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    This thesis develops methods to automate and objectify the connectivity analysis between ocean regions. Existing methods for connectivity analysis often rely on manual integration of expert knowledge, which renders the processing of large amounts of data tedious. This thesis presents a new framework for Data Fusion that provides several approaches for automation and objectification of the entire analysis process. It identifies different complexities of connectivity analysis and shows how the Data Fusion framework can be applied and adapted to them. The framework is used in this thesis to analyze geo-referenced trajectories of fish larvae in the western Mediterranean Sea, to trace the spreading pathways of newly formed water in the subpolar North Atlantic based on their hydrographic properties, and to gauge their temporal change. These examples introduce a new, and highly relevant field of application for the established Data Science methods that were used and innovatively combined in the framework. New directions for further development of these methods are opened up which go beyond optimization of existing methods. The Marine Science, more precisely Physical Oceanography, benefits from the new possibilities to analyze large amounts of data quickly and objectively for its exact research questions. This thesis is a foray into the new field of Marine Data Science. It practically and theoretically explores the possibilities of combining Data Science and the Marine Sciences advantageously for both sides. The example of automating and objectifying connectivity analysis between marine regions in this thesis shows the added value of combining Data Science and Marine Science. This thesis also presents initial insights and ideas on how researchers from both disciplines can position themselves to thrive as Marine Data Scientists and simultaneously advance our understanding of the ocean

    Latent Spaces for Antimicrobial Peptide Design

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    Current antibacterial treatments cannot overcome the growing resistance of bacteria to antibiotic drugs, and novel treatment methods are required. One option is the development of new antimicrobial peptides (AMPs), to which bacterial resistance build-up is comparatively slow. Deep generative models have emerged as a powerful method for generating novel therapeutic candidates from existing datasets; however, there has been less research focused on evaluating the search spaces associated with these generators. In this research I employ five deep learning model architectures for de novo generation of antimicrobial peptide sequences and assess the properties of their associated latent spaces. I train a RNN, RNN with attention, WAE, AAE and Transformer model and compare their abilities to construct desirable latent spaces in 32, 64, and 128 dimensions. I assess reconstruction accuracy, generative capability, and model interpretability and demonstrate that while most models are able to create a partitioning in their latent spaces into regions of low and high AMP sampling probability, they do so in different manners and by appealing to different underlying physicochemical properties. In this way I demonstrate several benchmarks that must be considered for such models and suggest that for optimization of search space properties, an ensemble methodology is most appropriate for design of new AMPs. I design an AMP discovery pipeline and present candidate sequences and properties from three models that achieved high benchmark scores. Overall, by tuning models and their accompanying latent spaces, targeted sampling of anti-microbial peptides with ideal characteristics is achievable
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