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International Meeting on High-Dimensional Data-Driven Science, HD3 2017

作者： Fujishiro, Issei Sawada, Naoko Nakayama, Masanori Wu, Hsiang-Yun Watanabe, Kazuho Takahashi, Shigeo Uemura, Makoto Center for Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Institute of Computer Graphics and Algorithms TU Wien Favoritenstr. 9 / 5. Stock ViennaA-1040 Austria Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Toyohashi441-8580 Japan School of Computer Science and Engineering University of Aizu Aizu Wakamatsu Fukushima965-8580 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima739-8526 Japan

Blazars are attractive objects for astronomers to observe in order to burrow into the magnetic field in the relativistic jet. This paper presents TimeTubes as a novel visualization scheme that allows astronomers to interactively explore characteristic temporal variation patterns in observed blazar datasets. In the TimeTubes spatialization, the two Stokes parameters and their errors with a common timestamp are transformed into an ellipse. A series of such ellipses are aligned in parallel along the timeline to form a 3D volumetric tube. The resulting tube is then colorized by the observed intensities and colors of the blazar, and finally volume-rendered. A designated user interface is provided with visual exploration functions according to Shneiderman's Visual Information Seeking Mantra. In the latest version, an auxiliary mechanism, called visual data fusion, was incorporated to ameliorate data- and mapping-inherent uncertainties for more efficient and effective visual exploration. © Published under licence by IOP Publishing Ltd.

关键词： Data fusion

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CAFE-the MIT computer aided fabrication environment

CAFE-the MIT computer aided fabrication environment

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IEEE/CHMT(CPMT) International Electronics Manufacturing Technology Symposium

作者： M.B. McIlrath D.E. Troxel D.S. Boning M.L. Heytens P. Penfield R. Jayavant Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Graphics Technology Division Hewlett Packard Company Fort Collins CO USA

The computer-aided fabrication environment (CAFE) is a software system being developed at MIT for use in the manufacture of integrated circuits. CAFE is intended to be used in all phases of process design, development, planning, and manufacturing of integrated-circuit wafers. The CAFE architectural framework supports a wide variety of software modules, including both development tools and online applications. The key components of the CAFE architecture are the data model and database schema, the process flow and wafer representations, the user interface, and the application programming and database interfaces. All CAFE application modules store and retrieve persistent data through a common database interface layer. Interface wrappers provide seamless, transparent integration of external tools and packages which have their own internal data formats.< >

关键词： Fabrication Application software Databases computer aided manufacturing Software systems Integrated circuit manufacture computer integrated manufacturing Process design Process planning Manufacturing processes

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A conceptual model for collaborative learning activities design

A conceptual model for collaborative learning activities des...

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IEEE Education engineering (EDUCON)

作者： Dulce Mota Carlos Vaz de Carvalho Luis Paulo Reis GILT Graphics Interaction and Learning Technologies Institute of Engineering Polytechnic of Porto Porto Portugal FEUP Faculty of Engineering LIAAC Artificial Intelligence and Computer Science Lab University of Porta Porto Portugal

This paper presents the Advanced Collaborative Educational Model (ACEM) for conception of collaborative learning activities. It is based on collaboration between teachers through virtual interactions, reutilization, interoperability and adaptability of learning activities. The definition of learning activity is oriented to a constructivist approach. Tagging collaboration and ontologies are also considered in order to facilitate sharing knowledge in a more effective and personalized way. The main goal of this research is to describe teachers' procedures when interacting with the collaborative environment and to develop mechanisms to support teachers in their tasks, mainly in the creation of learning activities to be used in an e-learning context. The model will be implemented through a high-level graphical tool that facilitates synchronous and asynchronous communication.

关键词： Collaboration Collaborative work engineering education Electronic learning computers Computational modeling

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SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

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12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

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Convex-concave backtracking for inertial bregman proximal gradient algorithms in non-convex optimization

arXiv

引用

arXiv 2019年

作者： Mukkamala, Mahesh Chandra Ochs, Peter Pock, Thomas Sabach, Shoham Faculty of Mathematics and Computer Science Saarland University Saarbrucken66123 Germany Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Faculty of Industrial Engineering Technion Haifa3200003 Israel

Backtracking line-search is an old yet powerful strategy for finding a better step sizes to be used in proximal gradient algorithms. The main principle is to locally find a simple convex upper bound of the objective function, which in turn controls the step size that is used. In case of inertial proximal gradient algorithms, the situation becomes much more difficult and usually leads to very restrictive rules on the extrapolation parameter. In this paper, we show that the extrapolation parameter can be controlled by locally finding also a simple concave lower bound of the objective function. This gives rise to a double convex-concave backtracking procedure which allows for an adaptive choice of both the step size and extrapolation parameters. We apply this procedure to the class of inertial Bregman proximal gradient methods, and prove that any sequence generated by these algorithms converges globally to a critical point of the function at hand. Numerical experiments on a number of challenging non-convex problems in image processing and machine learning were conducted and show the power of combining inertial step and double backtracking strategy in achieving improved *** Codes 90C25, 26B25, 49M27, 52A41, 65K05 Copyright © 2019, The Authors. All rights reserved.

关键词： Extrapolation

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Modeling of Influence of Shupe Effect on Fiber-Optic Goniometer with different coil winding types 3

Modeling of Influence of Shupe Effect on Fiber-Optic Goniome...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Ivanov, Sergiy Oliinyk, Pavlo Virchenko, Gennadii Vanin, Volodymyr Tereschuk, Mykola Research and Development Institute of Telecommunications National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Descriptive Geometry Engineering and Computer Graphics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Faculty of Physics and Mathematics Kyiv Ukraine Kyiv National University of Construction and Architecture Department of Architectural Constructions Kyiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

One of key errors of the classic interference-type fiber-optic goniometer is the thermo-optical zero shift. Its caused by the non-uniform change of temperature of different fiber sections in the loop under the influence of temperature;as a result of that, counter-rotating waves get different phase incursions during stretching of fiber in the loop, thus creating thermo-optical zero shift (Shupe effect). Difference of the phase incursions of counter-rotating waves is the bigger the higher speed of fiber length change is, and the higher difference of delay time of those waves. The current study is focused on the modeling of influence of the Shupe effect on on the precision of the fiber optic goniometer. The key features of the goniometer mentioned are use of the copper coil carcass and compound with high thermal conductivity (use of nanotubes is desired). Different coil winding types (simple, dipole quadrupole, modified quadrupole, bifilar, octupole) are considered from the point of view of minimizing the thermo-optical zero shift under self-heating and influence of the ambient temperature changes. Analysis is based on the transient thermal analysis of finite element model of the goniometer performed in ANSYS environment with subsequent data import and integration of phase incursion, calculated based on temperature data, along the fiber length. Study results show that best results during self-heating gives the use of quadrupole winding, at that goniometer's zero shift changes insignificantly (about 0.08°, compared to 1,35° for the simple winding). Change of ambient temperature for a given design has almost no influence on the zero shift, as device is heated uniformly. One should note that as during modeling not all factors could be taken into account, the result obtained should be checked and qualified during FOG tests. © 2022 IEEE.

关键词： 3D modeling

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Application of binary classification of magnetic resonance therapy images to detect the threat of the disease due to COVID-19 3

Application of binary classification of magnetic resonance t...

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3rd IEEE KhPI Week on Advanced Technology, KhPI Week 2022

作者： Zalevska, Olga Vanin, Volodymyr Sydogenko, Julia Finogenov, Oleksii Demydenko, Olga Yablonskyi, Petro Ladogubets, Tetyana Oliinyk, Yurii Miroshnichenko, Ivan National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Automation of Power Processes and Systems Engineering Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Descriptive Geometry Engineering and Computer Graphics Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Department of Computer-Aided Science and Data Processing Systems Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' The Department of Theory Practice and Translation of English Kyiv Ukraine National Technical University of Ukraine 'Igor Sikorsky Kyiv Polytechnic Institute' Deprtament of Applied Mathematics Kyiv Ukraine

ISBN: (数字)9798350399202

ISBN: (纸本)9798350399202

The paper considers the use of a neural network for the binary classification of magnetic resonance imaging images to establish a possible disease of COVID-19. Processing of input data and their reduction to one format is provided, and neural network software for binary classification is developed and its training based on about 60,000 open patient images. The using of developed software is universal for medical doctors or ordinary Internet users and does not require additional training. © 2022 IEEE.

关键词： Magnetic resonance imaging

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Synthetic Data for Face Recognition: Current State and Future Prospects

arXiv

引用

arXiv 2023年

作者： Boutros, Fadi Struc, Vitomir Fierrez, Julian Damer, Naser Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Faculty of Electrical Engineering University of Ljubljana Ljubljana Slovenia School of Engineering Universidad Autonoma de Madrid Madrid Spain Department of Computer Science TU Darmstadt Darmstadt Germany

Over the past years, deep learning capabilities and the availability of large-scale training datasets advanced rapidly, leading to breakthroughs in face recognition accuracy. However, these technologies are foreseen to face a major challenge in the next years due to the legal and ethical concerns about using authentic biometric data in AI model training and evaluation along with increasingly utilizing data-hungry state-of-the-art deep learning models. With the recent advances in deep generative models and their success in generating realistic and high-resolution synthetic image data, privacy-friendly synthetic data has been recently proposed as an alternative to privacy-sensitive authentic data to overcome the challenges of using authentic data in face recognition development. This work aims at providing a clear and structured picture of the use-cases taxonomy of synthetic face data in face recognition along with the recent emerging advances of face recognition models developed on the bases of synthetic data. We also discuss the challenges facing the use of synthetic data in face recognition development and several future prospects of synthetic data in the domain of face recognition. Copyright © 2023, The Authors. All rights reserved.

关键词： Face recognition

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IEEE BigData 2023 Keystroke Verification Challenge (KVC)

IEEE BigData 2023 Keystroke Verification Challenge (KVC)

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Stragapede, Giuseppe Vera-Rodriguez, Ruben Tolosana, Ruben Morales, Aythami Deandres-Tame, Ivan Damer, Naser Fierrez, Julian Garcia, Javier-Ortega Gonzalez, Nahuel Shadrikov, Andrei Gordin, Dmitrii Schmitt, Leon Wimmer, Daniel Grosmann, Christoph Krieger, Joerdis Heinz, Florian Krestel, Ron Mayer, Christoffer Haberl, Simon Gschrey, Helena Yamagishi, Yosuke Saha, Sanjay Rasnayaka, Sanka Wickramanayake, Sandareka Sim, Terence Gutfeter, Weronika Baran, Adam Krzyszton, Mateusz Jaskola, Przemyslaw Lab Spain Fraunhofer Institute for Computer Graphics Research Igd Darmstadt Germany Argentina Verigram Llc Singapore Citix Almaty Kazakhstan Regensburg University of Applied Sciences Germany The University of Tokyo Division of Radiology and Biomedical Engineering Japan National University of Singapore Singapore University of Moratuwa Sri Lanka Nask - National Research Warsaw Poland

ISBN: (纸本)9798350324457

institute, Warsaw, Poland This paper describes the results of the IEEE BigData 2023 Keystroke Verification Challenge 1 (KVC), that considers the biometric verification performance of Keystroke Dynamics (KD), captured as tweet-long sequences of variable transcript text from over 185,000 subjects. The data are obtained from two of the largest public databases of KD up to date, the Aalto Desktop and Mobile Keystroke Databases, guaranteeing a minimum amount of data per subject, age and gender annotations, absence of corrupted data, and avoiding excessively unbalanced subject distributions with respect to the considered demographic attributes. Several neural architectures were proposed by the participants, leading to global Equal Error Rates (EERs) as low as 3.33% and 3.61% achieved by the best team respectively in the desktop and mobile scenario, outperforming the current state of the art biometric verification performance for KD. Hosted on CodaLab2, the KVC will be made ongoing to represent a useful tool for the research community to compare different approaches under the same experimental conditions and to deepen the knowledge of the field. © 2023 IEEE.

关键词： behavioral biometrics biometric verification challenge Keystroke dynamics KVC

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Exploit fully automatic low-level segmented PET data for training high-level deep learning algorithms for the corresponding CT data

arXiv

引用

arXiv 2019年

作者： Gsaxner, Christina Roth, Peter M. Wallner, Jürgen Egger, Jan Institute for Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral & Maxillofacial Surgery Medical University of Graz Auenbruggerplatz Styria Austria

We present an approach for fully automatic urinary bladder segmentation in CT images with artificial neural networks in this study. Automatic medical image analysis has become an invaluable tool in the different treatment stages of diseases. Especially medical image segmentation plays a vital role, since segmentation is often the initial step in an image analysis pipeline. Since deep neural networks have made a large impact on the field of image processing in the past years, we use two different deep learning architectures to segment the urinary bladder. Both of these architectures are based on pre-trained classification networks that are adapted to perform semantic segmentation. Since deep neural networks require a large amount of training data, specifically images and corresponding ground truth labels, we furthermore propose a method to generate such a suitable training data set from Positron Emission Tomography/Computed Tomography image data. This is done by applying thresholding to the Positron Emission Tomography data for obtaining a ground truth and by utilizing data augmentation to enlarge the dataset. In this study, we discuss the influence of data augmentation on the segmentation results, and compare and evaluate the proposed architectures in terms of qualitative and quantitative segmentation performance. The results presented in this study allow concluding that deep neural networks can be considered a promising approach to segment the urinary bladder in CT images. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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