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arXiv 2020年

作者： Kakas, Antonis Michael, Loizos Department of Computer Science University of Cyprus Cyprus Open University of Cyprus Research Center on Interactive Media Smart Systems and Emerging Technologies Giannou Kranidioti 33 Latsia2220 Cyprus

This paper presents Abduction and Argumentation as two principled forms for reasoning, and fleshes out the fundamental role that they can play within Machine Learning. It reviews the state-of-the-art work over the past few decades on the link of these two reasoning forms with machine learning work, and from this it elaborates on how the explanation-generating role of Abduction and Argumentation makes them naturally-fitting mechanisms for the development of Explainable Machine Learning and AI systems. Abduction contributes towards this goal by facilitating learning through the transformation, preparation, and homogenization of data. Argumentation, as a conservative extension of classical deductive reasoning, offers a flexible prediction and coverage mechanism for learning — an associated target language for learned knowledge — that explicitly acknowledges the need to deal, in the context of learning, with uncertain, incomplete and inconsistent data that are incompatible with any classically-represented logical theory. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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Deep learning of preconditioners for conjugate gradient solvers in urban water related problems

arXiv

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arXiv 2019年

作者： Sappl, Johannes Seiler, Laurent Harders, Matthias Rauch, Wolfgang Unit of Environmental Engineering Faculty of Engineering Sciences Universität Innsbruck Technikerstraße 13 Innsbruck6020 Austria Interactive Graphics and Simulation Group Department of Computer Science Universität Innsbruck Technikerstraße 21 A Innsbruck6020 Austria

Solving systems of linear equations is a problem occuring frequently in water engineering applications. Usually the size of the problem is too large to be solved via direct factorization. One can resort to iterative approaches, in particular the conjugate gradients method if the matrix is symmetric positive definite. Preconditioners further enhance the rate of convergence but hitherto only handcrafted ones requiring expert knowledge have been used. We propose an innovative approach employing Machine Learning, in particular a Convolutional Neural Network, to unassistedly design preconditioning matrices specifically for the problem at hand. Based on an in-depth case study in fluid simulation we are able to show that our learned preconditioner is able to improve the convergence rate even beyond well established methods like incomplete Cholesky factorization or Algebraic MultiGrid. Copyright © 2019, The Authors. All rights reserved.

关键词： Conjugate gradient method

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Engineering course specially designed to face retention issue 45

Engineering course specially designed to face retention issu...

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45th Annual Conference of the European Society for Engineering Education, SEFI 2017

作者： Brito, C.R. Ciampi, M.M. Vasconcelos, R.M. Amaral, L.A. Santos, H.D. Barros, V.A. Science and Education Research Council - COPEC São Paulo Brazil World Council on Systems Engineering and Information Technology IEEE Education Society São Paulo Brazil Pedagogical Council School of Engineering University of Minho Guimarães Portugal Computer Graphics Center - CCG Information Systems Department University of Minho Guimarães Portugal Science and Education Research Council - COPEC Information Systems Department University of Minho Jataí Portugal

ISBN: (纸本)9789899887572

Many engineering colleges have been facing the retention problem in engineering programs due to difficulties and lack of knowledge about what really means being an engineer. It is mainly due to the fact that the first 3 years are particularly intense and hard. A possible way to overcome this period is to implement a new kind of course more attractive and dynamic, which is the idea of COPEC's engineering education research team, to embed a course with a more interesting activity for students in the first year. It is a short-term workshop in order to show the students the possibilities of performing as engineers in a global environment - a project developed for a private university in order to augment the retention rate in their engineering courses.

关键词： Curricula

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A reliable neural network framework for the Zika system based reservoirs and human movement

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Knowledge-Based Systems 2024年 292卷

作者： Sabir, Zulqurnain Khan, Sundas Naqeeb Raja, Muhammad Asif Zahoor Babatin, M.M. Hashem, Atef F. Abdelkawy, M.A. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Riyadh11566 Saudi Arabia Department of Mathematics and Information Science Faculty of Science Beni-Suef University Egypt

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme. © 2024 Elsevier B.V.

关键词： Neural networks

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Learning a nonlinear color distance metric for the identification of skin immunohistochemical staining

Learning a nonlinear color distance metric for the identific...

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2009 22nd IEEE International Symposium on computer-Based Medical Systems, CBMS 2009

作者： Sobieranski, Antonio Carlos Neto, Sylvio Luiz Mantelli Coser, Leandro Comunello, Eros Von Wangenheim, Aldo Cargnin-Ferreira, Eduardo Di Giunta, Gabriella LAPIX Laboratory of Image Processing and Computer Graphics UFSC Federal University of Santa Catarina Brazil CPTEC Center for Weather Forecasts and Environment Studies INPE - Brazilian Institute for Space Research Brazil EGC Knowledge Engineering Department UFSC - Federal University of Santa Catarina Brazil MCA Graduate Program in Applied Computer Science UNIVALI - Itajai Valley University Brazil Laboratory of Histological Markers Department of Biology Embriology and Genetics UFSC - Federal University of Santa Catarina Brazil Pathology Service UFSC - Federal University of Santa Catarina Brazil

ISBN: (纸本)9781424448784

This paper presents a semiautomatic method for the identification of immunohistochemical (IHC) staining in digitized samples. The user trains the system by selecting on a sample image some typical positive stained regions that will be used as a reference for the construction of a distance metric. In this learning process, the global optimum is obtained by induction employing higher polynomial terms of the Mahalanobis distance, extracting nonlinear features of the IHC pattern distributions. The results of the proposed method showed a high correlation to a pathologist's manual analysis, which was used as a golden standard, presenting a more robust discrimination between stained and non-stained areas with little bias. © 2009 IEEE.

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IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS 2023)

IEEE VR 2023 Workshops: Workshop: 3D Reconstruction, Digital...

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2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023

作者： Cannavò, Alberto Kapralos, Bill Seinfeld, Sofia Pratticò, Filippo Gabriele Zhang, Congyi Politecnico di Torino Turin Italy Game Development and Interactive Media Program and MaxSIMhealth Lab Ontario Tech University Oshawa Canada Image Processing and Multimedia Technology Center Universitat Politècnica de Catalunya-Barcelona Tech Terrassa Spain Politecnico di Torino Turin Italy Department of Computer Science The University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798350348392

Today, Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) technologies are becoming fundamental across a wide variety of application domains. All these technologies fall under the Extended Reality (XR) spectrum. Areas in which XR technologies and, in particular, immersive experiences have been successfully introduced are expanding daily, from traditional areas, such as entertainment and video game production, to novel scenarios, such as industry, healthcare, smart cities, and autonomous vehicles, to mention a few. The spread of XR and VR is also driven by technological advancements that are making new ways of operating within virtual experiences easier, and sometimes even possible. In particular, the availability of new hardware and software tools make the use of '3D reconstructions', 'digital twins' and 'simulations' commonplace in many XR and VR applications. For example, the possibility to leverage networks with higher bandwidth than never before and lower latency makes it possible to transmit the amount of data needed for massive, real-time digital twins, whereas high-performance hardware enables the reconstruction of high-fidelity digital objects and environments, while lowering the time needed for high-demanding simulations. The aim of the 3D Reconstruction, Digital Twinning, and Simulation for Virtual Experiences (ReDigiTS) workshop is to bring together researchers, practitioners, educators, and students to share ideas and promote further work in this growing area of 3D reconstruction, digital twinning, and simulation in VR and related technologies. © 2023 IEEE.

关键词： Augmented reality

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Enhancing Cyclone center Identification in Radar Images through Deep Learning and Match Recognition

Enhancing Cyclone Center Identification in Radar Images thro...

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International Conference on Computing and Networking Technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana Senthil Kumar Thangavel College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone’s center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network’s capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from $86 \%$ to $92 \%$ or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting.

关键词： Deep learning YOLO Training Adaptation models Accuracy Machine learning algorithms Transfer learning Radar Radar imaging Cyclones

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MIT educators share success

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computer graphics (ACM) 2001年第1期35卷 20-23页

作者： Wolfe, Rosalee Belcher, John W. Mark Bessette, R. Massachusetts Institute of Technology Astrophysics Division MIT Physics Department Studio/TEAL Project MIT New England 3D Studio Max Users Group Physics Demonstration Group MIT Department of Physics Center for Space Research Massachusetts Institute of Technology Center for Educational Computing Initiatives Massachusetts Institute of Technology Division of Graphics and Human-Computer Interaction School of Computer Science Telecommunications and Information Systems DePaul University School of Computer Science DePaul University Chicago IL 60604

Professor John Belcher developed a series of short videos with animation and text of selected experiments to properly demonstrate the phenomena of electromagnetism. Such 3D animations are visually compelling, instilling in the student both a sense of wonder about the phenomena and a mental model of why and how it works. The products of these approach includes video clips of demonstrations, 3D animations of these demonstration which display time changing field-lines as a guide to understanding their dynamical effects and Java applets which allow the student to actively construct and animate 2D field lines for varying configurations of sources. These technologies are also implemented to develop animations for Faraday's insights, using video clips.

关键词： computer graphics

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RVNoC: A Framework for Generating RISC-V NoC-based MPSoC

RVNoC: A Framework for Generating RISC-V NoC-based MPSoC

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Euromicro International Conference on Parallel, Distributed and Network-Based Processing

作者： Mahmoud A. Elmohr Ahmed S. Eissa Moamen Ibrahim Mostafa Khamis Sameh El-Ashry Ahmed Shalaby Mohamed AbdElsalam M. Watheq El-Kharashi Department of Communications and Electronics Alexandria University Alexandria Egypt Center for Ubiquitous Computing University of Oulu Oulu Finland Mentor Graphics Cairo Egypt Department of Computer and Systems Engineering Ain Shams University Cairo Egypt Department of Computer Science Benha University Egypt Ain Shams University Cairo EG

With the increase in the number of cores embedded on a chip;The main challenge for Multiprocessor System-on-Chip (MPSoC) platforms is the interconnection between that massive number of cores. Networks-on-Chip (NoC) was introduced to solve that challenge, by providing a scalable and modular solution for communication between the cores. In this paper, we introduce a configurable MPSoC framework called RVNoC that generates synthesizable RTL that can be used in both ASIC and FPGA implementations. The proposed framework is based on the open source RISC-V Instruction Set Architecture (ISA) and an open source configurable flit-based router for interconnection between cores, with a core network interface of our design to connect each core with its designated router. A benchmarking environment is developed to evaluate variant parameters of the generated MPSoC. Synthesis of a single building block containing a single core without any peripherals, a router, and a core network interface, using 45nm technology, shows an area of 102.34 kilo Gate Equivalents (kGE), a maximum frequency of 250 MHz, and a 9.9 μW/MHz power consumption.

关键词： Multiprocessor System-on-Chip (MPSoC) Network-on-Chip (NoC) RISC-V System-on-Chip (SoC)

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CLASSIFICATION OF LUNG CANCER SUBTYPES ON CT IMAGES WITH SYNTHETIC PATHOLOGICAL PRIORS

arXiv

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arXiv 2023年

作者： Zhu, Wentao Jin, Yuan Ma, Gege Chen, Geng Egger, Jan Zhang, Shaoting Metaxas, Dimitris N. Research Center for Healthcare Data Science Zhejiang Lab Hangzhou311121 China School of Computer Science and Engineering Northwestern Polytechnical University Shaanxi Xi’an710072 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Shanghai Artificial Intelligence Laboratory Shanghai200120 China Department of Computer Science Rutgers University PiscatawayNJ08854 United States

The accurate diagnosis on pathological subtypes for lung cancer is of significant importance for the follow-up treatments and prognosis managements. In this paper, we propose self-generating hybrid feature network (SGHF-Net) for accurately classifying lung cancer subtypes on computed tomography (CT) images. Inspired by studies stating that cross-scale associations exist in the image patterns between the same case’s CT images and its pathological images, we innovatively developed a pathological feature synthetic module (PFSM), which quantitatively maps cross-modality associations through deep neural networks, to derive the "gold standard" information contained in the corresponding pathological images from CT images. Additionally, we designed a radiological feature extraction module (RFEM) to directly acquire CT image information and integrated it with the pathological priors under an effective feature fusion framework, enabling the entire classification model to generate more indicative and specific pathologically related features and eventually output more accurate predictions. The superiority of the proposed model lies in its ability to self-generate hybrid features that contain multi-modality image information based on a single-modality input. To evaluate the effectiveness, adaptability, and generalization ability of our model, we performed extensive experiments on a large-scale multi-center dataset (i.e., 829 cases from three hospitals) to compare our model and a series of state-of-the-art (SOTA) classification models. The experimental results demonstrated the superiority of our model for lung cancer subtypes classification with significant accuracy improvements in terms of accuracy (ACC), area under the curve (AUC), and F1 score. © 2023, CC0.

关键词： Classification (of information)

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