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Russia and Pacific Conference on Computer Technology and Applications (RPC)

作者： Dinar Miftakhutdinov Ildus Rizaev Michael Shleymovich Department of Automated Systems for Information Processing and Control KNITU-KAI Kazan Russia

ISBN: (纸本)9781538675328

The basic principles of an algorithm of Detection of objects in the onboard systems of unmanned aerial vehicles using information about changes in the image of the terrain is considered in the paper. The possibility of obtaining additional information about the objects determines the prospects of the technology for allocating changes for use in high-resolution aviation surveillance systems.

关键词： Detectors Feature extraction Unmanned aerial vehicles Surveillance Classification algorithms data mining Task analysis

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Education 4.0 in Teaching/Learning Strategies

Education 4.0 in Teaching/Learning Strategies

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International Conference on Developments in esystems Engineering (DESE)

作者： Irina N. Golitsyna Farid I. Eminov Bulat F. Eminov High School of Information Technologies and Intelligent Systems Kazan (Volga) Federal University Kazan Russian Federation Automated Information Processing Systems and Control department Kazan National Research Technical University Kazan Russian Federation Department of Computer Systems Kazan National Research Technical University Kazan Russian Federation

ISBN: (数字)9781728130217

ISBN: (纸本)9781728130224

This paper considers training of IT-specialists in a context of the concept of Education 4.0. Based on our experience paper reviews approaches to educational content development and to organization of teaching/learning process of IT-students. The paper discusses organization of interdisciplinary educational content based on infocommunication infrastructure of an enterprise. Formation of a students' personal electronic information-educational environment (EIEE) as well as introduction of elements of informal education into traditional educational process are also considered.

关键词： Training Organizations Collaboration Cloud computing Task analysis

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An Early Stage Researcher's Primer on systems Medicine Terminology

Network and Systems Medicine

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Network and systems Medicine 2021年第1期4卷 2-50页

作者： Zanin, Massimiliano Aitya, Nadim A.A. Basilio, José Baumbach, Jan Benis, Arriel Behera, Chandan K. Bucholc, Magda Castiglione, Filippo Chouvarda, Ioanna Comte, Blandine Dao, Tien-Tuan Ding, Xuemei Pujos-Guillot, Estelle Filipovic, Nenad Finn, David P. Glass, David H. Harel, Nissim Iesmantas, Tomas Ivanoska, Ilinka Joshi, Alok Boudjeltia, Karim Zouaoui Kaoui, Badr Kaur, Daman Maguire, Liam P. McClean, Paula L. McCombe, Niamh De Miranda, João Luís Moisescu, Mihnea Alexandru Pappalardo, Francesco Polster, Annikka Prasad, Girijesh Rozman, Damjana Sacala, Ioan Sanchez-Bornot, Jose M. Schmid, Johannes A. Sharp, Trevor Solé-Casals, Jordi Spiwok, Vojtěch Spyrou, George M. Stalidzans, Egils Stres, Blaa Sustersic, Tijana Symeonidis, Ioannis Tieri, Paolo Todd, Stephen Van Steen, Kristel Veneva, Milena Wang, Da-Hui Wang, Haiying Wang, Hui Watterson, Steven Wong-Lin, Kongfatt Yang, Su Zou, Xin Schmidt, Harald H.H.W. Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Spain Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Ulster United Kingdom Center for Physiology and Pharmacology Institute of Vascular Biology and Thrombosis Research Medical University of Vienna Vienna Austria TUM School of Life Sciences Weihenstephan Technical University of Munich Freising Germany Holon Israel CNR National Research Council IAC Institute for Applied Computing Rome Italy Lab of Computing Medical Informatics and Biomedical Imaging Technologies School of Medicine Aristotle University of Thessaloniki Thessaloniki Greece Université Clermont Auvergne INRAE UNH Plateforme d'Exploration du Métabolisme MetaboHUB Clermont Clermont-Ferrand France Université de Technologie de Compiègne Compiègne France Labex MS2T Control of Technological Systems-of-Systems CNRS and Université de Technologie de Compiègne Compiègne France Faculty of Engineering University of Kragujevac Kragujevac Serbia Kragujevac Serbia Steinbeis Advanced Risk Technologies Institute Doo Kragujevac Kragujevac Serbia Pharmacology and Therapeutics School of Medicine Galway Neuroscience Centre National University of Ireland Galway Ireland School of Computing Ulster University Ulster United Kingdom Holon Israel Department of Mathematics and Natural Sciences Kaunas University of Technology Kaunas Lithuania Faculty of Computer Science and Engineering Ss. Cyril and Methodius University Skopje Macedonia Medicine Faculty Université Libre de Bruxelles CHU de Charleroi Charleroi Belgium Northern Ireland Centre for Stratified Medicine Biomedical Sciences Research Institute Ulster University Ulster United Kingdom Escola Superior de Tecnologia e Gestão Instituto Politécnico de Portalegre Portalegre Portugal Instituto Superior Técnico Universidade de Lisboa Lisboa Portugal Faculty of Automatic Control and Computers University Politehnica of B

Background: systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic. © Massimiliano Zanin et al., 2021;Published by Mary Ann Liebert, Inc. 2021.

关键词： Semantics

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Augmenting reality in the tasks of classifying objects in aerospace images

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Journal of Physics: Conference Series 2020年第1期1582卷

作者： A V Kuchuganov D R Kasimov D A Khvorenkov O B Lebedev A A Zhiglaty Automated Data Processing and Control Systems Kalashnikov Izhevsk State Technical University 7 Studencheskaya str. Izhevsk 426069 Russia Department of Computer Aided Design Southern Federal University 44 Nekrasovsky lane Taganrog 347928 Russia

The problem of increasing the reliability of object recognition in aerospace images consists in the insufficient quality of image segmentation based on pixel colour characteristics and boundary shape descriptors. The paper proposes an approach based on description logic with an extension on graphs. The essence of the approach is the searching for relevant cases in the ontological base by calculating the degree of similarity and identifying differences of attributed graphs of cases. With the help of retrieved cases, the object structure and the boundary shape are refined. After that, objects can be augmented with subject domain information stored in the ontological base. Representation of the structure of cases in the form of attributed relational graphs allows avoiding the rapid growth of the case base, since the similarity of connected subgraphs is generally sufficient. The experiments are conducted on urban area aerospace images.

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Convergence rate of mean-square error of kernel estimators of non-homogeneous Poisson process intensity function

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Journal of Physics: Conference Series 2020年第1期1680卷

作者： A V Kitaeva M V Kolupaev N V Stepanova O I Zhukovskiy Department of Computer Science Tomsk State University Tomsk Russia Facebook Menlo Park CA USA Institution of Control Sciences of Russian Academy of Sciences Moscow Department of Data Processing Automation Tomsk State University of Control Systems and Radioelectronics Tomsk Russia

The estimator has been constructed similar to the Parzen-Rosenblatt window method using a single realization of a Poisson process at a fixed time interval. The intensity function of a non-homogeneous Poisson process is estimated up to a multiplicative constant. The convergence rate of the mean square error was found in the series of schemes with an unlimited increase of the intensity.

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Effect of Multiple Additive Misinformation Noise on Immunity of Associative Stegoprotection

Effect of Multiple Additive Misinformation Noise on Immunity...

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International Russian Automation Conference (RusAutoCon)

作者： Igor Vershinin Stella Lyasheva Mikhail Shleymovich Department for Computers Systems Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russia Department for Applied Mathematics and Informatics Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russia Department for Automated Systems for Information Processing and Control Kazan National Research Technical University named after A.N. Tupolev-KAI Kazan Russia

Associative steganography is a special case of the historical stencil method of steganography, when a hidden message is stencilled onto a blank sheet, after which a meaningful text is formed with such an insert. The difference is that in this case the message is embedded in the noise picture. To form a message, a two-dimensional associative masking mechanism is used. On the basis of previous studies, it was shown that the use of multiple sets of masks (stego-keys) significantly increases the noise immunity of the stored and transmitted data under the action of random and purposive noise. This paper presents the results of experimental studies to assess noise immunity using the method of forming intentional additive noise with redundancy. It also gives practical recommendations for noise control.

关键词： Geographic information systems Automation Redundancy Standards Distortion Additives Computers

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Privacy-preserving medical treatment system through nondeterministic finite automata

arXiv

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arXiv 2020年

作者： Yang, Yang Deng, Robert H. Liu, Ximeng Wu, Yongdong Weng, Jian Zheng, Xianghan Rong, Chunming School of Information Systems Singapore Management University Singapore College of Mathematics and Computer Science Fuzhou University Fujian China State Key Laboratory of Integrated Services Networks Xidian University Guangdong Provincial Key Laboratory of Data Security and Privacy Protection Guangzhou China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China School of Information Systems Singapore Management University Singapore Singapore Department of Computer Science Jinan University Guangdong China College of Mathematics and Computer Science Fuzhou University Fujian China Department of Electronic Engineering and Computer Science University of Stavanger Norway MingByte Technology Qingdao China

In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the nondeterministic transition characteristic of NFA to flexibly represent the medical model, which includes illness states, treatment methods and state transitions caused by exerting different treatment methods. A medical model is encrypted and outsourced to the cloud to deliver telemedicine services. Using P-Med, patient-centric diagnosis and treatment can be made on-the-fly while protecting the confidentiality of a patient's illness states and treatment recommendation results. Moreover, a new privacy-preserving NFA evaluation method is given in P-Med to get a confidential match result for the evaluation of an encrypted NFA and an encrypted data set, which avoids the cumbersome inner state transition determination. We demonstrate that P-Med realizes treatment procedure recommendation without privacy leakage to unauthorized parties. We conduct extensive experiments and analyses to evaluate efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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Neural Networks for the Moving Objects control

Neural Networks for the Moving Objects Control

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Russia and Pacific Conference on Computer Technology and Applications (RPC)

作者： Elza Takhavova Galina Kosjukhina Automated Systems for Information Processing and Control department Kazan National Research Technical University named after A.N.Tupolev - KAI Kazan Russia

ISBN: (纸本)9781538675328

This research describes the methods of neural networks with the aim of generating recommendations for the optimal choice of the learning method for a neural network construction. Two-wheeled Lego robot Mindshtorms nxt 2.0 is considered as an example of a moving object. Back propagation and genetic algorithm methods are considered and compared as methods to train a neural network. A comparative analysis of experimental results was done, which was resulted in the optimal method of back propagation learning algorithm. The optimal network architecture for this result was defined. The results of the training of the neural network can be used in the development of the control systems for moving objects that are currently being used to solve a huge range of tasks. Further results can be improved by selecting the optimal trajectory of the robot, and the use of neuro-fuzzy learning methods, which should provide a more effective training of a neural network.

关键词： Training Biological neural networks Robot kinematics Genetic algorithms Neurons

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Principles of organizing the semantic ontology of programming

Principles of organizing the semantic ontology of programmin...

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2017 IEEE International Conference on Cyber Conflict U.S., CyCon U.S. 2017

作者： Kuchuganov, V.N. Kasimov, D.R. Kuchuganov, M.V. Department of Automated Data Processing and Control Systems Kalashnikov Izhevsk State Technical University Izhevsk Russia Parallel Computation Lab Udmurt State University Izhevsk Russia

ISBN: (纸本)9781538612064

A promising way to improve the programming process is increasing the declarativeness of programming, approximation to natural language on the base of accumulating and actively using knowledge. The essence of the proposed approach is representing the semantics of a program in the form of a set of concepts about actions, participants, resources and relations between them, accumulation and classification of machine-readable knowledge of computer programs in order to increase the degree of automation of programming. The novelty of the presented work is retrieving relevant precedents by the given description of resources and actions under them. The paper describes the ontological model of a program, considers the technology of programming using ontologies, gives an example of definition of the semantics of a problem, and discusses the positive aspects of the proposed approach. © 2017 IEEE.

关键词： Semantics

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