208 research outputs found

    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

    Aspect and Meaning in the Russian Future Tense: Corpus and Experimental Investigations

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    This dissertation is a study of the Russian future tense within the framework of cognitive linguistics. In this dissertation I focus on the distribution of the perfective and imperfective future forms, their future and non-future meanings, and the use of the future tense verb forms by both native and non-native speakers. In the Russian tense-aspect system, it is reasonable to operate with markedness on a local level of tense, rather than the level of the verb. Via local markedness it is possible to see that the perfective future is the unmarked member of the opposition, and the imperfective future is the marked one. The perfective future tense forms are approximately fourteen times more frequent than imperfective future tense forms in the Russian National Corpus. Both perfective and imperfective future tense forms express not only future meanings but also gnomic, directive etc. The (non-)future meanings form a radial category with the future meaning as a prototype and other meanings as extensions. Native speakers operate with frequency when they use future tense forms. Non-native speakers are not sensitive to frequency, and instruction in the use of the future tense forms in Russian could be improved

    ATHENA Research Book, Volume 2

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    ATHENA European University is an association of nine higher education institutions with the mission of promoting excellence in research and innovation by enabling international cooperation. The acronym ATHENA stands for Association of Advanced Technologies in Higher Education. Partner institutions are from France, Germany, Greece, Italy, Lithuania, Portugal and Slovenia: University of Orléans, University of Siegen, Hellenic Mediterranean University, Niccolò Cusano University, Vilnius Gediminas Technical University, Polytechnic Institute of Porto and University of Maribor. In 2022, two institutions joined the alliance: the Maria Curie-Skłodowska University from Poland and the University of Vigo from Spain. Also in 2022, an institution from Austria joined the alliance as an associate member: Carinthia University of Applied Sciences. This research book presents a selection of the research activities of ATHENA University's partners. It contains an overview of the research activities of individual members, a selection of the most important bibliographic works of members, peer-reviewed student theses, a descriptive list of ATHENA lectures and reports from individual working sections of the ATHENA project. The ATHENA Research Book provides a platform that encourages collaborative and interdisciplinary research projects by advanced and early career researchers

    CLARIN

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    The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

    ICE-MILK: Intelligent Crowd Engineering using Machine-based Internet of Things Learning and Knowledge Building

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    Title from PDF of title page viewed June 1, 2022Dissertation advisor: Sejun SongVitaIncludes bibliographical references (pages 136-159)Thesis (Ph.D.)--School of Computing and Engineering. University of Missouri--Kansas City, 2022The lack of proper crowd safety control and management often leads to spreading human casualties and infectious diseases (e.g., COVID-19). Many Machine Learning (ML) technologies inspired by computer vision and video surveillance systems have been developed for crowd counting and density estimation to prevent potential personal injuries and deaths at densely crowded political, entertaining, and religious events. However, existing crowd safety management systems have significant challenges and limitations on their accuracy, scalability, and capacity to identify crowd characterization among people in crowds in real-time, such as a group characterization, impact of occlusions, mobility and contact tracing, and distancing. In this dissertation, we propose an Intelligent Crowd Engineering platform using Machine-based Internet of Things Learning, and Knowledge Building approaches (ICE-MILK) to enhance the accuracy, scalability, and crowd safety management capacity in real-time. Specifically, we design an ICE-MILK structure with three critical layers: IoT-based mobility characterization, ML-based video surveillance, and semantic information-based application layers. We built an IoT-based mobility characterization system by predicting and preventing potential disasters through real-time Radio Frequency (RF) data characterization and analytics. We tackle object group identification, speed, direction detection, and density for the mobile group among the many crowd mobility characteristics. Also, we tackled an ML-based video surveillance approach for effective dense crowd counting by characterizing scattered occlusions, named CSONet. CSONet recognizes the implications of event-induced, scene-embedded, and multitudinous obstacles such as umbrellas and picket signs to achieve an accurate crowd analysis result. Finally, we developed a couple of group semantics to track and prevent crowd-caused infectious diseases. We introduce a novel COVID-19 tracing application named Crowd-based Alert and Tracing Services (CATS) and a novel face masking and social distancing monitoring system for Modeling Safety Index in Crowd (MOSAIC). CATS and MOSAIC apply privacy-aware contact tracing, social distancing, and calculate spatiotemporal Safety Index (SI) values for the individual community to provide higher privacy protection, efficient penetration of technology, greater accuracy, and effective practical policy assistance.Introduction -- Literature review -- IoT-based mobility characterization -- ML-based video/image surveillance -- Semantic knowledge information-based tracing application -- Conclusions and future directions -- Appendi

    Transforming our World through Universal Design for Human Development

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    An environment, or any building product or service in it, should ideally be designed to meet the needs of all those who wish to use it. Universal Design is the design and composition of environments, products, and services so that they can be accessed, understood and used to the greatest extent possible by all people, regardless of their age, size, ability or disability. It creates products, services and environments that meet people’s needs. In short, Universal Design is good design. This book presents the proceedings of UD2022, the 6th International Conference on Universal Design, held from 7 - 9 September 2022 in Brescia, Italy.The conference is targeted at professionals and academics interested in the theme of universal design as related to the built environment and the wellbeing of users, but also covers mobility and urban environments, knowledge, and information transfer, bringing together research knowledge and best practice from all over the world. The book contains 72 papers from 13 countries, grouped into 8 sections and covering topics including the design of inclusive natural environments and urban spaces, communities, neighborhoods and cities; housing; healthcare; mobility and transport systems; and universally- designed learning environments, work places, cultural and recreational spaces. One section is devoted to universal design and cultural heritage, which had a particular focus at this edition of the conference. The book reflects the professional and disciplinary diversity represented in the UD movement, and will be of interest to all those whose work involves inclusive design

    Learning with Limited Data and Supervision

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    Deep neural networks have been the main driving force of recent successes in machine learning leading to the deployment of these models in a wide range of industries such as healthcare, autonomous driving, and fintech. Despite the great success, these models are known as data-hungry models requiring many labelled training examples and costly computational resources to solve a pre-determined task. Several obstacles limit the applicability of deep learning models in real-world scenarios. First, annotating large-scale training data in tasks such as object localization or segmentation is cumbersome and demands huge time and labor. Second, in real-world scenarios and applications such as field robotics, the models may be required to learn new classes in an ever-changing environment. However, accessing abundant fully labelled training data for novel classes may be infeasible. Therefore, a model needs to adapt to learn novel classes given only a few examples with simple (weak) annotations. Finally, it is known that most modern deep convolutional networks do not have calibrated confidence scores, meaning that the confidence scores they assign to the outcomes do not match the true frequency of those events. These models are of utmost importance to output calibrated prediction scores that the downstream applications can rely upon, especially in safety-critical applications. This thesis focuses on tackling these limitations in deep learning models with applications in Computer Vision. We investigate the task of finding common objects in small image collections and propose an efficient graphical model inference algorithm that utilizes the structure of the problem to reduce the computational time compared to traditional inference algorithms significantly. We also propose a probabilistic approach to solve the few-shot common object localization problem based on a parametric distribution of each class on a unit sphere. We further extend our model to localize objects of novel classes in unseen images. In the next step, we study pairwise similarity knowledge transfer for weakly supervised object localization to reduce the cost of labor and time in annotating large-scale object detection datasets for novel classes. We learn the similarity functions and the assignment of proposals to different novel classes jointly using alternating optimization and show that the assignment problem becomes an integer linear program for a certain type of loss function. Furthermore, we propose an efficient inference algorithm to overcome the difficulty of computing all pairwise similarities. Finally, to overcome pre-trained models' accuracy degradation in learning expressive probability calibration functions using small calibration data, we introduce and formalize the notion of order-preserving functions. We also present two sub-families of order-preserving functions that benefit from parameter sharing across different classes in classification problems

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

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    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

    Multimedia Forensics

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    This book is open access. Media forensics has never been more relevant to societal life. Not only media content represents an ever-increasing share of the data traveling on the net and the preferred communications means for most users, it has also become integral part of most innovative applications in the digital information ecosystem that serves various sectors of society, from the entertainment, to journalism, to politics. Undoubtedly, the advances in deep learning and computational imaging contributed significantly to this outcome. The underlying technologies that drive this trend, however, also pose a profound challenge in establishing trust in what we see, hear, and read, and make media content the preferred target of malicious attacks. In this new threat landscape powered by innovative imaging technologies and sophisticated tools, based on autoencoders and generative adversarial networks, this book fills an important gap. It presents a comprehensive review of state-of-the-art forensics capabilities that relate to media attribution, integrity and authenticity verification, and counter forensics. Its content is developed to provide practitioners, researchers, photo and video enthusiasts, and students a holistic view of the field
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