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16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019

作者： Cappelli, Eleonora Weitschek, Emanuel Cumbo, Fabio Department of Engineering University of Roma Tre Via della Vasca Navale 79/81 Rome00146 Italy Department of Engineering Uninettuno University Rome00186 Italy Institute for System Analysis and Computer Science "Antonio Ruberti" National Research Council of Italy Via dei Taurini 19 Rome00185 Italy University of Trento Via Sommarive 9 Povo Trento38123 Italy

ISBN: (纸本)9783030630607

The advent of Next Generation Sequencing (NGS) technologies and the reduction of sequencing costs, characterized the last decades by a massive production of experimental data. These data cover a wide range of biological experiments derived from several sequencing strategies, producing a big amount of heterogeneous data. They are often linked to a set of related metadata that are essential to describe experiments and the analyzed samples, with also information about patients from which samples have been collected. Nowadays, browsing all these data and retrieving significant insights from them is a big challenge that has been already faced with different techniques in order to facilitate their accessibility and interoperability. In this work, we focus on genomic data of cancer and related metadata exploiting domain-specific ontologies in order to allow executing taxonomy-based relaxed queries. In particular, we apply the upward and downward query extension methods to obtain a finer or coarser granularity of the requested information. We define diverse use cases with which a user can perform a query specifying particular attributes related to metadata or genomic data, even if they are not available in the considered repository. Thus, we are able to extract the requested data through the use of domain-specific ontologies of The Open Biological and Biomedical Ontology (OBO) Foundry. Finally, we propose a new ontological software layer, which allows users to interact with experimental data and metadata without knowledge about their representation schema. © Springer Nature Switzerland AG 2020.

关键词： Metadata

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Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

arXiv

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arXiv 2022年

作者： Chen, Yali Ai, Bo Niu, Yong Zhang, Hongliang Han, Zhu The State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University Beijing100044 China Henan Joint International Research Laboratory of Intelligent Networking and Data Analysis Zhengzhou University Zhengzhou450001 China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China The National Mobile Communications Research Laboratory Southeast University Nanjing211189 China The Department of Electrical Engineering Princeton University PrincetonNJ08544 United States The Department of Electrical and Computer Engineering The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system overhead or higher task drop rate may occur, which severely damages the system robustness. To address this issue, we characterize the uncertainty with a data-driven approach, and formulate a distributionally robust optimization problem to minimize the expected energy-constrained system latency under the worst-case probability distribution. Furthermore, the distributionally robust latency optimization algorithm is proposed to reach the suboptimal solution. Finally, we perform simulations on the realworld data set, and compare with other benchmark schemes to verify the efficiency and robustness of our proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Probability distributions

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Low-temperature tunneling of CH3 quantum rotor in van der Waals solids

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Fizika Nizkikh Temperatur 2019年第4期45卷 495-510页

作者： Benetis, Nikolas P. Zelenetckii, Ilia A. Dmitriev, Yurij A. Kila Kozani50 100 Greece Department of System Analysis and Control Institute of Computer Science and Technology Peter the Great St. Petersburg Polytechnic University 29 Politekhnicheskaya ul. St. Petersburg195251 Russia Ioffe Institute 26 Politekhnicheskaya ul. St. Petersburg194021 Russia

Motional quantum effects of tunneling methyl radical isolated in solid gases as they appear on experimental electron paramagnetic resonance (EPR) spectra are examined. Obtained analytical expressions of the tunneling frequency for methyl rotor/torsional-oscillator utilizing localized Hermite polynomials are compared to full numerical computations and tested against experimental EPR lineshape simulations. In particular, the X-band of methyl radical was displaying partial anisotropy averaging even at lowest temperatures. EPR lineshape simulations involving rotational dynamics were applied for the accurate determination of the potential barrier and the tunneling frequency. Tunneling frequency, as the splitting between the A and E torsional levels by the presence of a periodic C3 model potential with periodic boundary conditions, was computed and related to the EPR-lineshape alteration. The corresponding C2 rotary tunneling about the in-plane axes of methyl was also studied while both the C2 and C3 rotations were compared with the rotation of deuteriated methyl radical. © Nikolas P. Benetis, Ilia A. Zelenetckii, and Yurij A. Dmitriev, 2019.

关键词： Paramagnetic resonance

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E-trade Management system Architecture

E-trade Management System Architecture

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2019 IEEE International Conference on Advanced Trends in Information Theory, ATIT 2019

作者： Pursky, Oleg Selivanova, Anna Kharchenko, Oleksander Demidov, Pavlo Kulazhenko, Volodymyr Kyiv National University of Trade and Economics Department of Computer Science Kyiv Ukraine Kyiv National University of Trade and Economics Department of Software Engineering and Cyber Security Kyiv Ukraine Kyiv National University of Trade and Economics Department of Cybernetics and System Analysis Kyiv Ukraine

ISBN: (纸本)9781728161440

In this work we present the model for construction of e-trade management system architecture which consists of two basic blocks: the landing Web-pages management unit of the online store is responsible for interacting with the user, receiving orders and collecting primary information and Web-enterprise management unit, responsible for processing orders, their delivery, and all enterprise's business processes realization. We have focused our attention to developing an architecture model for e-trady management system. A service-oriented architecture (SOA) using enterprise service bus (ESB) concept is offered for architectural implementation. In developed e-trade management system architecture we used the method of integrating complex information systems within the information environment based on SOA and ESB technologies, which represents an independent, integrative subsystem for relatively integrated information systems. Typical information systems that belong to the e-trade system are used as services. These web-based services should be developed and used to enhance ESB efficiency. Such web-services implement SOAP and use WSDL (Web Services Description Language), as well as UDDI specification (Universal Description, Discovery and Integration). At that, each typical information system is simultaneously data supplier and data consumer. Each information system handles its own data class, while being able to both transmit and receive, adjust and modify data according to operational information from other information systems within main business processes. © 2019 IEEE.

关键词： Information systems

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Library pruning for power saving during timing and electrical design rules optimization

Library pruning for power saving during timing and electrica...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2018

作者： Chentouf, Mohamed Cherif, Lekbir El Abidine Alaoui Ismaili, Zine ICDS Department Mentor Graphics Rabat Morocco Laboratory of Systems Engineering ENSA Ibn Tofail University Kénitra Morocco Team National Superior School of Computer Science and System Analysis University Mohammed V Rabat Morocco

ISBN: (纸本)9783030120641

Timing optimization techniques are widely used to meet the frequency and electrical design rules requirement of integrated circuits, they use logical and physical transformation to speed up the problematic signals and to close the design setup and hold constraints. On the other side, each technique induces a power increase as a cost for signal speed up. In this paper, we propose a standard cell library tuning methodology to reduce the timing optimization impact on power increase. We divide each optimization step of the place and route process into two sub-steps, the first one uses only low power standard library cells and try to correct the maximum number of violations, and the second uses all the available cells in the library to close the remaining violations. Experimental results on 45 industrial designs of different processes show that the proposed methodology provides a leakage power reduction of 5%, a total power reduction of 1.3% and a timing improvement of 55.8% in Total Negative Slack and 37.5% in Worst Negative Slack. © Springer Nature Switzerland AG 2019.

关键词： system-on-chip

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UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection

arXiv

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arXiv 2021年

作者： Abdar, Moloud Salari, Soorena Qahremani, Sina Lam, Hak-Keung Karray, Fakhri Hussain, Sadiq Khosravi, Abbas Acharya, U. Rajendra Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia The Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States The Department of Electrical Engineering Sharif University of Technology Tehran Iran The Centre for Robotics Research Department of Engineering King’s College London London United Kingdom The Centre for Pattern Analysis and Machine Intelligence Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada The Department of Machine Learning Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates The System Administrator Dibrugarh University Dibrugarh India The Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore The Department of Computer Science University of Quebec in Montreal MontrealQC Canada

The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate priority Such automatic systems are usually based on traditional machine learning or deep learning methods Differently from most of existing studies, which used either CT scan or X-ray images in COVID-19-case classification, we present a simple but efficient deep learning feature fusion model, called UncertaintyFuseNet, which is able to classify accurately large datasets of both of these types of images We argue that the uncertainty of the model’s predictions should be taken into account in the learning process, even though most of existing studies have overlooked it We quantify the prediction uncertainty in our feature fusion model using effective Ensemble MC Dropout (EMCD) technique A comprehensive simulation study has been conducted to compare the results of our new model to the existing approaches, evaluating the performance of competing models in terms of Precision, Recall, F-Measure, Accuracy and ROC curves The obtained results prove the efficiency of our model which provided the prediction accuracy of 99.08% and 96.35% for the considered CT scan and X-ray datasets, respectively Moreover, our UncertaintyFuseNet model was generally robust to noise and performed well with previously unseen data The source code of our implementation is freely available at: https://***/moloud1987/UncertaintyFuseNet-for-COVID-19-Classification. Copyright © 2021, The Authors. All rights reserved.

关键词： Coronavirus

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Cross-domain intelligent cooperative spectrum sensing algorithm based on Federated Learning and Swin-Transformer neural network

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Engineering Applications of Artificial Intelligence 2025年 157卷

作者： Lingwei Xu Zhihe Gao Yufang Li T. Aaron Gulliver Department of Information Science and Technology Qingdao University of Science & Technology Qingdao 266061 China Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Chongqing University of Posts and Telecommunications Chongqing 400065 China Hubei Key Laboratory of Power System Design and Test for Electrical Vehicle Hubei University of Arts and Science Xiangyang 441053 Hubei China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis(Guangxi Minzu University) Nanning 530006 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 2Y2 Canada

The deployment of 6th Generation (6G) mobile networks will bring revolutionary changes to organizations and users through higher speeds, lower latency, and more intelligent network management. This will require more efficient spectrum sharing to improve network performance. Cognitive Radio (CR) can be employed to help achieve this goal. However, data collection at the fusion center for cooperative spectrum sensing (CSS) creates a risk of sensing user (SU) data leakage. In this paper, an intelligent CSS algorithm for mobile communication is proposed based on Federated Learning with a Swin-Transformer. First, to achieve the cross-domain process, a Continuous Wavelet Transform (CWT) is used with the normalized time series data to obtain a time-frequency spectrum map with joint time-frequency features. Second, Federated Learning is employed to realize distributed CSS to reduce sensing overhead, improve the security of sensing data, and eliminate the SU data isolation with traditional distributed CSS. Then, a new Swin-Transformer spectrum sensing is proposed for fusion learning of the time-frequency spectrum map. The fusion of Federated Learning and Swin-Transformer neural network provides cooperation with multiple SUs to achieve efficient spectrum sensing using the K -rank fusion criterion. Compared with the Convolutional Neural Network (CNN), ViT-Transformer, Graph Neural Network (GNN) and EfficientNet algorithms, the detection probability of the proposed algorithm has increased by 45 %, and the false alarm probability has decreased by 13 %.

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Biometric authentication integrated with wireless communication malicious activity detection in a cyber physical system-based Fintech banking

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Optik 2023年 272卷

作者： Almuhannad Sulaiman Alorfi Saud Yonbawi Sultan Alahmari Abdullaev Abror Bozorboevich Mahendran Arumugam Pham Quang Huy Department of Information System Faculty of Computing and Information Technology King Abdulaziz University City Rabigh Saudi Arabia Department of Software Engineering College Of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia King Abdul Aziz City for Science and Technology Riyadh Saudi Arabia The Department of Acconting Economic Analysis and Audit Tashkent Institute of Finance Tashkent Uzbekistan Center for Transdisciplinary Research Saveetha Dental College Saveetha institute of Medical and Technical Science Chennai India University of Economics Ho Chi Minh City (UEH) Vietnam

Recently, biometric technology has been extensively embedded in mobile devices to enhance security of mobile devices. With rise of financial technology (FinTech) that employs mobile applications as well as devices as promotional platforms, biometrics has a significant role in strengthening the detection of this privacy application. This manuscript offers the design of salp swarm optimization with auto-encoder based biometric authentication (SSOAE-BMA) model for the recognition of abnormal activities in the Fintech banking applications based on wireless communication. The major aim of the SSOAE-BMA model lies in the proper authentication of persons via biometric matching process. Initially, the presented SSOAE-BMA model makes use of stacked ResNet-50 model for deriving feature vectors. Next, the SSOAE-BMA model utilizes AE for biometric verification and the performance of the AE model is adjusted using the Social Spider Optimization (SSO) Algorithm which in turn enhances the recognition outcomes. To demonstrate the improved performance of SSOAE-BMA model, a series of simulations were carried out. The experimental outcomes signified the enhancements of the SSOAE-BMA model over existing models.

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The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels (vol 400, pg 1619, 2022)

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LANCET 2022年第10365期400卷 1766-1766页

作者： Romanello, M. Di Napoli, C. Drummond, P. Institute for Global Health University College London London UK School of Agriculture Policy and Development University of Reading Reading UK Institute for Sustainable Resources University College London London UK Department of Global Health Centre for Health and the Global Environment University of Washington Seattle WA USA UCL Energy Institute University College London London UK Department of Health Sciences University of York York UK Department of Meteorology University of Reading Reading UK Institute for Risk and Disaster Reduction University College London London UK School of Earth and Environment University of Leeds Leeds UK Centre on Climate Change and Planetary Health London School of Hygiene & Tropical Medicine London UK School of Population Health University of Melbourne Melbourne VIC Australia Department of Earth System Science Tsinghua University Beijing China Mercator Research Institute on Global Commons and Climate Change Berlin Germany Department of Environment Climate Change and Health World Health Organization Geneva Switzerland Institute of Environmental Sciences University of Geneva Geneva Switzerland Cardiovascular Epidemiology Unit Department of Public Health & Primary Care University of Cambridge Cambridge UK School of Government University of Birmingham Birmingham UK Economic Analysis of Climate Impacts and Policy Division Centro Euro-Mediterraneo sui Cambiamenti Climatici Venice Italy Institute for Environmental Design and Engineering University College London London UK Natural Resources Institute University of Greenwich London UK Department of Environmental Health Sciences and Yale Center on Climate Change and Health Yale University New Haven CT USA Department of Civil and Environmental Engineering Northeastern University Boston MA USA Department of Fish and Wildlife Conservation Virginia Polytechnic Institute and State University Blacksburg VA USA Department of Geography University College London London UK NU

With advancements in the science of detection and attribution studies, the influence of climate change over many events has now been quantified. Because of the rapidly increasing temperatures, vulnerable populations (adults older than 65 years, and children younger than one year of age) were exposed to 3·7 billion more heatwave days in 2021 than annually in 1986–2005 (indicator 1.1.2), and heat-related deaths increased by 68% between 2000–04 and 2017–21 (indicator 1.1.5), a death toll that was significantly exacerbated by the confluence of the COVID-19 pandemic. Despite the local cooling and overall health benefits of urban greenspaces, only 277 (27%) of 1038 global urban centres were at least moderately green in 2021 (indicator 2.2.3), and the number of households with air conditioning increased by 66% from 2000 to 2020, a maladaptive response that worsens the energy crisis and further increases urban heat, air pollution, and greenhouse gas emissions. [...]millions of people do not have access to the energy needed to keep their homes at healthy temperatures, preserve food and medication, and meet the seventh Sustainable Development Goal (to ensure access to affordable, reliable, sustainable, and modern energy for all). Aggravating this situation even further, governments continue to incentivise fossil fuel production and consumption: 69 (80%) of 86 countries reviewed had net-negative carbon prices (ie, provided a net subsidy to fossil fuels) for a net total of US$400 billion

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