Temporal data mining is an advanced analytical field that focuses on the extraction of interpretable patterns, correlations, and trends over time within data streams. this paper presents a comprehensive framework for ...
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the paper describes the effect of the local surface modelling methods known as plane, quadric and triangulation on the generation of the 3D mesh models of the historical objects with rock-based building structures usi...
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Contrasting spatial co-location pattern discovery aims to find subsets of spatial features whose prevalences are substantially different in two spatial domains. this problem is important for generating hypotheses in m...
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ISBN:
(纸本)9781450395311
Contrasting spatial co-location pattern discovery aims to find subsets of spatial features whose prevalences are substantially different in two spatial domains. this problem is important for generating hypotheses in many spatial applications, including oncology, regional economics, ecology, and epidemiology. In oncology, for example, this problem is important in developing immune-checkpoint inhibitor therapy for cancer treatment. this problem is challenging due to the large number of potential patterns that are exponentially related to the number of input spatial features. Traditional methods of co-location pattern detection require multiple runs, making computationally expensive and do not scale to large datasets. To address these limitations, we propose a Contrasting Spatial Co-location Discovery (CSCD) framework and contribute two filter-refine algorithmsthat exploit a novel interest measure;the participation index distribution difference (PIDD). Experiments on multiple cancer datasets (e.g., MxIF) show that the proposed algorithm yields substantial computational time savings compared with a baseline algorithm. A real-world case study demonstrates that the proposed work discovers patterns that are missed by the related work and have the potential to inspire new scientific discovery.
Understanding and debugging of datastructures and algorithms (DSA) is one of the most common tasks in computer science. DSA tests have also become a standard threshold that software developers have to cross to "...
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ISBN:
(数字)9781665480925
ISBN:
(纸本)9781665480925
Understanding and debugging of datastructures and algorithms (DSA) is one of the most common tasks in computer science. DSA tests have also become a standard threshold that software developers have to cross to "get the job". One major challenge in comprehending and debugging DSA implementations lies in establishing and maintaining mental models of the quintessentially complex and twisted networks of events that make up their dynamic runtime behavior. Despite the high level of difficulty of this crucial task, general purpose tools to help users understand or reason about DSA implementations still have very limited capabilities. In this work we present Dbux-PDG, a dynamic Program Dependency Graph extension for the Dbux omniscient debugger. It captures data and control flow, as well as data dependencies of a program's execution for visualization and user interaction. To deal withthe immense complexity of non-trivial programs, it offers multiple layers of summarization, that allow the user to explore either the graph as a whole or in parts, one step at a time, as they see fit. We present our findings from applying Dbux-PDG to 94 diverse algorithms and explore its utility in several case studies. All visual results are made available in an online gallery. Dbux-PDG is open source and one-click installable, making it a powerful, easy-to-use tool prototype for DSA comprehension. Video URL: https://***/dgXj3VoQJZQ
the rendering and printing of 3D models of anatomical structures offers a wide range of promising applications in multiple medical disciplines, in the training of medical students, as well as in patient education. the...
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Network-valued data are encountered in a wide range of applications, and pose challenges in learning due to their complex structure and absence of vertex correspondence. Typical examples of such problems include class...
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We report on a new course Verified Functional datastructures and algorithms taught at the Technical University of Munich. the course first introduces students to interactive theorem proving withthe Isabelle proof as...
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ISBN:
(纸本)9781450382991
We report on a new course Verified Functional datastructures and algorithms taught at the Technical University of Munich. the course first introduces students to interactive theorem proving withthe Isabelle proof assistant. then it covers a range of standard datastructures, in particular search trees and priority queues: it is shown how to express these datastructures functionally and how to reason about their correctness and running time in Isabelle.
the proceedings contain 99 papers. the special focus in this conference is on Interplay Between Natural and Artificial Computation. the topics include: Improved Surface Defect Classification from a Simple Co...
ISBN:
(纸本)9783031611391
the proceedings contain 99 papers. the special focus in this conference is on Interplay Between Natural and Artificial Computation. the topics include: Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and data Augmentation;prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis;enhanced Cellular Detection Using Convolutional Neural Networks and Sliding Window Super-Resolution Inference;exploring Text-Driven Approaches for Online Action Detection;deep Learning for Assistive Decision-Making in Robot-Aided Rehabilitation therapy;text-Driven data Augmentation Tool for Synthetic Bird Behavioural Generation;deep Learning for Enhanced Risk Assessment in Home Environments;Lightweight CNNs for Advanced Bird Species Recognition on the Edge;learning Adaptable Utility Models for Morphological Diversity;deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques;Refinement of Protein structures with a Memetic Algorithm. Examples with SARS-CoV-2 Proteins;evolutionary algorithms for Bin Packing Problem with Maximum Lateness and Waste Minimization;Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19;private Inference on Layered Spiking Neural P Systems;cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling;a Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty;AI Emmbedded in Drone Control;dual-System Recommendation Architecture for Adaptive Reading Intervention Platform for Dyslexic Learners;Accurate LiDAR-Based Semantic Classification for Powerline Inspection;RESISTO Project: Automatic Detection of Operation Temperature Anomalies for Power Electric Transformers Using thermal Imaging;RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena;Multi-UAV System for Power-Line Failure Detection Within the RESISTO Project;machin
In this study, machine learning algorithms are trained and compared to identify and characterise impacts effects on typical aerospace panels with different geometries. Experiments are conducted to create a suitable im...
In this study, machine learning algorithms are trained and compared to identify and characterise impacts effects on typical aerospace panels with different geometries. Experiments are conducted to create a suitable impact dataset. Polynomial regression algorithms and shallow neural networks are applied to panels without stringers and optimised to test their ability to identify the impacts. the algorithms are then applied to panels reinforced with stringers, which represents a significant increase in complexity in terms of the dynamic characteristics of the system under test. the focus is not only on the detection of the impact position, but also on the severity of the event. the aim of the work is to demonstrate the validity of the application of machine learning to impact localization on realistic structures and to demonstrate the simplicity and efficiency of the computations despite the complexity of the test specimens.
the proceedings contain 99 papers. the special focus in this conference is on Interplay Between Natural and Artificial Computation. the topics include: Improved Surface Defect Classification from a Simple Co...
ISBN:
(纸本)9783031611360
the proceedings contain 99 papers. the special focus in this conference is on Interplay Between Natural and Artificial Computation. the topics include: Improved Surface Defect Classification from a Simple Convolutional Neural Network by Image Preprocessing and data Augmentation;prediction of Optimal Locations for 5G Base Stations in Urban Environments Using Neural Networks and Satellite Image Analysis;enhanced Cellular Detection Using Convolutional Neural Networks and Sliding Window Super-Resolution Inference;exploring Text-Driven Approaches for Online Action Detection;deep Learning for Assistive Decision-Making in Robot-Aided Rehabilitation therapy;text-Driven data Augmentation Tool for Synthetic Bird Behavioural Generation;deep Learning for Enhanced Risk Assessment in Home Environments;Lightweight CNNs for Advanced Bird Species Recognition on the Edge;learning Adaptable Utility Models for Morphological Diversity;deep Learning-Based Classification of Invasive Coronary Angiographies with Different Patch-Generation Techniques;Refinement of Protein structures with a Memetic Algorithm. Examples with SARS-CoV-2 Proteins;evolutionary algorithms for Bin Packing Problem with Maximum Lateness and Waste Minimization;Stationary Wavelet Entropy and Cat Swarm Optimization to Detect COVID-19;private Inference on Layered Spiking Neural P Systems;cooperative Multi-fitness Evolutionary Algorithm for Scientific Workflows Scheduling;a Genetic Approach to Green Flexible Job Shop Problem Under Uncertainty;AI Emmbedded in Drone Control;dual-System Recommendation Architecture for Adaptive Reading Intervention Platform for Dyslexic Learners;Accurate LiDAR-Based Semantic Classification for Powerline Inspection;RESISTO Project: Automatic Detection of Operation Temperature Anomalies for Power Electric Transformers Using thermal Imaging;RESISTO Project: Safeguarding the Power Grid from Meteorological Phenomena;Multi-UAV System for Power-Line Failure Detection Within the RESISTO Project;machin
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