The problem of segmenting aerial photographs can generally not be solved in a reasonable manner by use of the information in the image alone. In this paper we present a structured approach to the problem, which in add...
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The problem of choosing tickmarks for nonlinear scales is of interest for some kindsof diagrams, e.g., nomograms and contour plots. A method for getting “nice”values at the tickmarks is discussed based on a database...
Die Lösungen des Coulombschen Reibungsproblems für starre Körper in zwei Dimensionen werden analysiert. Das bestimmende System von gewöhnlichen Differentialgleichungen und Ungleichungen wird aufges...
Die Lösungen des Coulombschen Reibungsproblems für starre Körper in zwei Dimensionen werden analysiert. Das bestimmende System von gewöhnlichen Differentialgleichungen und Ungleichungen wird aufgestellt. Beispiele werden vorgelegt, die einige ungewünschte Eigenschaften dieses speziellen Reibungsgesetzes nachweisen. Hinreichende Bedingungen für Existenz und Eindeutigkeit werden mit Hilfe der Theorie der linearen Komplementarität hergeleitet. The solutions to the Coulomb friction problem for rigid bodies in two dimensions are analyzed. The governing system of ordinary differential equations and inequalities is derived. Examples are presented demonstrating undesirable properties of this particular law of friction. Sufficient conditions for existence and uniqueness are given using the theory of linear complementarity.
A design process for a query language based on set algebra is described. Key principles used in the design are: make explicit assumptions about the end users background, delimit the scope of the language, and make it ...
A design process for a query language based on set algebra is described. Key principles used in the design are: make explicit assumptions about the end users background, delimit the scope of the language, and make it simple by omitting all features that have not been found necessary. The language closely mirrors concepts well known from algebra and set theory: it contains no join or relational division, and it has a high expressive power.
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential ...
A technique for estimating and iteratively correct for the smooth errors of discretization algorithms is presented. The theoretical foundation is given as a number of theorems. Some problems for ordinary differential equations are used as illustrative examples.
EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from r...
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EEG data classification plays a pivotal role in understanding brain activity and its applications in various domains. Deep learning has emerged as a powerful paradigm for automatically learning complex patterns from raw data, eliminating the need for manual feature extraction. However, in the context of medical data, and in particular for EEG analysis, the use of deep learning approaching while having been very successful is not being included in medical diagnosis routines, yet. The aim of this survey is twofold. On one side, it provides a comprehensive overview of the current state-of-the-art in EEG data classification, with a specific focus on the use of deep learning techniques. On the other side, it also addresses the clinician community, explaining the power and trustfulness of such new approaches. The survey begins with an introduction highlighting the limitations of traditional model-based approaches and the potential of deep learning in EEG data classification. The fundamental principles and architectures of deep learning models are presented, including convolutional neural networks (CNNs), recurrent neural networks (RNNs) and Graph Convolution Neural Network (GCNNs) that have been successfully applied to EEG data classification tasks. A detailed review and analysis of existing literature on deep learning-based EEG data classification are provided, categorizing the studies based on the type of the input data, e.g., sequences, images, graphs or multi-modalities. We also discuss about the existing tools and technologies for EEG data classification and highlights the challenges and limitations associated with deep learning in EEG data classification, including limited data availability, interpretability of deep models and bias mitigation. Potential solutions and ongoing research efforts to overcome these challenges are explored, providing insights into the future directions of this field. This survey serves as a valuable resource for researchers, practitioners
Computer Supported Cooperative Work (CSCW) is an interdisciplinary research area devoted to exploring the issues of designing computer-based systems that enhance the abilities to cooperate and integrate activities ...
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ISBN:
(数字)9789401103497
ISBN:
(纸本)9780792336976;9789401041553
Computer Supported Cooperative Work (CSCW) is an interdisciplinary research area devoted to exploring the issues of designing computer-based systems that enhance the abilities to cooperate and integrate activities in an efficient and flexible manner for people in cooperative work situations.;This volume is a rigorous selection of papers that represent both practical and theoretical approaches to CSCW from many leading researchers in the field. As an interdisciplinary area of research, CSCW brings together widely disparate research traditions and perspectives from computer, human, organisational and design sciences. The papers selected reflect a variety of approaches and cultures in the field.;Of interest to a wide audience because of the huge practical impact of the issues and the interdisciplinary nature of the problems and solutions proposed. In particular: researchers and professionals in computing, sociology, cognitive science, human factors, and system design.
This paper offers an extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence (AI) in healthcare, namely uncertainty, focusing on the large panoply of recent studies address...
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This paper offers an extensive survey of one of the fundamental aspects of the trustworthiness of Artificial Intelligence (AI) in healthcare, namely uncertainty, focusing on the large panoply of recent studies addressing the connection between uncertainty, AI, and healthcare. The concept of uncertainty is a recurring theme across multiple disciplines, with varying focuses and approaches. Here, we focus on the diverse nature of uncertainty in medical applications, emphasizing the importance of quantifying uncertainty in model predictions and its advantages in specific clinical settings. Questions that emerge in this context range from the guidelines for AI integration in the healthcare domain to the ethical deliberations and their compatibility with cutting-edge AI research. Together with a description of the main specific works in this context, we also discuss that, as medicine evolves and introduces novel sources of uncertainty, there is a need for more versatile uncertainty quantification methods to be developed collaboratively by researchers and healthcare professionals. Finally, we acknowledge the limitations of current uncertainty quantification methods in addressing the different facets of uncertainty within the medical domain. In particular, we identify from this survey a relative paucity of approaches that focus on the user’s perception of uncertainty and accordingly of trustworthiness.
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