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Journal of Network Intelligence 2020年第4期5卷 218-225页

作者： Ye, Li Meng, Xiaojing Hang, Yongxin Tayeb, Shahab School of Architecture and Design China University of Mining and Technology No1 Daxue Road XuzhouJiangsu China School of Medical Information and Engineering Xuzhou Medical University No 209 Tongshan Road XuzhouJiangsu China China University of Mining and Technology No1 Daxue Road XuzhouJiangsu China Department of Electrical and Computer Engineering California State University Fresno United States

In order to improve the quality of student online education associated to the area of art literacy, it is necessary to track and identify their browsing data processes in real time. A visual tracking method for such activity, based on Hilbert’s spectrum anal-ysis, is proposed. A data mining model, a platform for online education that supports art literacy, has been constructed. The approach utilizes the spectral feature extraction method for data visualization pattern recognition, and Hilbert’s spectrum feature extraction algorithm. By using information fusion and feature matching, students in the same time period are scanned for spectral feature fusion and adaptive matching, and the fea-tures of Hilbert’s spectrum are extracted by segmentation. The extracted information is processed by visual spectral feature recognition, information recovery is realized, and visual tracking of browsing data for the cohort completed. The simulation results show that this method can effectively realize visual tracking and recognition of the students’ browsing data and improve the ability of data mining and information analysis. The research aims to show that the quality of online education of artistic literacy is improved through such practice. © 2020, Taiwan Ubiquitous Information. All rights reserved.

关键词： Data mining

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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A Fast and Compact Invertible Sketch for Network-Wide Heavy Flow Detection

arXiv

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arXiv 2019年

作者： Tang, Lu Huang, Qun Lee, Patrick P.C. Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong State Key Lab of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences

Fast detection of heavy flows (e.g., heavy hitters and heavy changers) in massive network traffic is challenging due to the stringent requirements of fast packet processing and limited resource availability. Invertible sketches are summary data structures that can recover heavy flows with small memory footprints and bounded errors, yet existing invertible sketches incur high memory access overhead that leads to performance degradation. We present MV-Sketch, a fast and compact invertible sketch that supports heavy flow detection with small and static memory allocation. MV-Sketch tracks candidate heavy flows inside the sketch data structure via the idea of majority voting, such that it incurs small memory access overhead in both update and query operations, while achieving high detection accuracy. We present theoretical analysis on the memory usage, performance, and accuracy of MV-Sketch in both local and network-wide scenarios. We further show how MV-Sketch can be implemented and deployed on P4-based programmable switches subject to hardware deployment constraints. We conduct evaluation in both software and hardware environments. Trace-driven evaluation in software shows that MV-Sketch achieves higher accuracy than existing invertible sketches, with up to 3.38× throughput gain. We also show how to boost the performance of MV-Sketch with SIMD instructions. Furthermore, we evaluate MV-Sketch on a Barefoot Tofino switch and show how MV-Sketch achieves line-rate measurement with limited hardware resource overhead. Copyright © 2019, The Authors. All rights reserved.

关键词： Memory architecture

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Two-Dimensional Error Estimation in Point Positioning with Fuzzy Logic

Two-Dimensional Error Estimation in Point Positioning with F...

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2018 International Conference on Artificial Intelligence and Data Processing, IDAP 2018

作者： Bülbül, Mehmet Akif Öztürk, Celal Ilçi, Veli Ozulu, Ibrahim Murat Computer Technology Department Hitit University Çorum Turkey Computer Engineering Department Erciyes University Kayseri Turkey Architecture and City Planning Department Hitit University Çorum Turkey

ISBN: (纸本)9781538668788

Developments in technology and computer aims to facilitate the human life. Every new technology developed by computer science is tried to integrate into systems. Rapidly developing satellite, navigation and geographic information systems also use computer science. The position information that these systems provide to people has had too much importance in recent years. The low margin of error in point position estimation has become a criterion for developed technologies. In this study, a model based on fuzzy logic is developed for estimation of point position error. With this derived model, point position error prediction has been determined and the accuracy of estimates of the system has been measured. © 2018 IEEE.

关键词： Fuzzy logic

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Disability in space - What ESA should have aimed for

arXiv

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arXiv 2021年

作者： Heinicke, Christiane Kaczmarzyk, Marcin Tannert, Benjamin Wasniowski, Aleksander Perycz, Malgorzata Schöning, Johannes Am Fallturm 2 Bremen28359 Germany Rzeszow University of Technology The Faculty of Civil and Environmental Engineering and Architecture Department of Building Engineering Poznańska Street 2 Rzeszow35-084 Poland Hochschule Bremen - City University of Applied Science Applied Media Informatics Flughafenallee 10 Bremen28199 Germany Space Is More ul. Mikulskiego 17 Wroclaw52-420 Poland Institute of Computer Science Polish Academy of Sciences Department of Artificial Intelligence Lab of Computational Biology Jana Kazimierza 5 st. Warsaw01-248 Poland University of Bremen Human-Computer Interaction Bibliothekstraße 5 Bremen28359 Germany

With announcing their "Parastronaut Feasibility Project", the European Space Agency ESA promises to make every reasonable effort to send astronauts with disability to space. This could spark hope in the 15% of all people who live with some form of disability. However, the fine-print of the announcement sets overly tight boundaries to "acceptable" disabilities, allowing only malformations that are expected to have a positive impact on the parastronaut's performance in space. By doing so, ESA is following decade-old traditions in space flight, with some inclusion for the sake of inclusion and public opinion. Instead of accommodating people with disabilities, ESA should send a strong signal for inclusion and truly challenge people with disabilities so they can and will tease out the best of their qualities. © 2021, CC BY.

关键词： Manned space flight

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Localized resolution of identity approach to the analytical gradients of random-phase approximation ground-state energy: Algorithm and benchmarks

arXiv

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arXiv 2021年

作者： Tahir, Muhammad N. Li, Jia Zhu, Tong Shang, Honghui Blum, Volker Ren, Xinguo Guangdong Provincial Key Laboratory of Thermal Management Engineering and Materials Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Chemistry Duke University DurhamNC27708 United States State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China

We develop and implement a formalism which enables calculating the analytical gradients of particle-hole random-phase approximation (RPA) ground-state energy with respect to the atomic positions within the atomic orbital basis set framework. Our approach is based on a localized resolution of identity (LRI) approximation for evaluating the two-electron Coulomb integrals and their derivatives, and the density functional perturbation theory for computing the first-order derivatives of the Kohn-Sham (KS) orbitals and orbital energies. Our implementation allows one to relax molecular structures at the RPA level using both Gaussian-type orbitals (GTOs) and numerical atomic orbitals (NAOs). Benchmark calculations show that our approach delivers high numerical precision compared to previous implementations. A careful assessment of the quality of RPA geometries for small molecules reveals that post-KS RPA systematically overestimates the bond lengths. We furthermore optimized the geometries of the four low-lying water hexamers – cage, prism, cyclic and book isomers, and determined the energy hierarchy of these four isomers using RPA. The obtained RPA energy ordering is in good agreement with that yielded by the coupled cluster method with single, double and perturbative triple excitations, despite that the dissociation energies themselves are appreciably underestimated. The underestimation of the dissociation energies by RPA is well corrected by the renormalized single excitation correction. © 2021, CC BY.

关键词： Approximation algorithms

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Preface

Smart Innovation, Systems and Technologies

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Smart Innovation, Systems and Technologies 2021年 197卷 vii-viii页

作者： Mealha, Óscar Rehm, Matthias Rebedea, Traian Department of Communication and Art Digital Media and Interaction Research Center University of Aveiro Aveiro Portugal Department of Computer Science University Politehnica of Bucharest Bucharest Romania Department of Architecture Design and Media Technology Aalborg University Aalborg Denmark

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Transformer-Based Deep Learning for Mesoscale Eddy Detection in Sea Surface Temperature Maps

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年

作者： Ji, Chen Xu, Wenyang Zheng, Xiangtian Ahmed, Yasmeen Jamal, Saad Ahmed Imam, Fakhar Ali, Mohammed Saleh Muthanna, Maha Ibrahim Ullah, Sajid Kucher, Dmitry E. Nanjing University School of Geography and Ocean Science Nanjing210046 China Nanjing Institute of Technology Nanjing211167 China Mississippi State University Department of Building Construction Science College of Architecture Art & Design MS39762 United States Paris Lodron Universitat Salzburg Institute for Research and Advanced Studies Universidade de Evora Department of Computer Science University of South Brittany Department of Geoinformatics PortugalZ_GIS France Austria University of the Punjab Lahore Pakistan Tashkent State University of Economics Department of International Business Management Tashkent Uzbekistan Nangarhar University Department of Water Resources and Environmental Engineering Nangarhar 2600 Afghanistan RUDN University Department of Environmental Management Institute of Environmental Engineering Moscow117198 Russia

Mesoscale eddies are dynamic oceanic phenomena significantly influencing marine ecosystems' energy transfer, nutrients, and biogeochemical cycles. These eddies' precise identification and categorization can improve climate modeling, ocean circulation research, and environmental surveillance. This study presents an innovative methodology for mesoscale eddy detection utilizing Transformer-based deep learning models, namely Swin Transformer U-Net (Swin-Unet) and SegFormer, to categorize ocean eddies from Sea Surface Temperature (SST) maps sourced from the Copernicus Marine Environment Monitoring Service (CMEMS). In contrast to traditional convolutional neural networks (CNNs) that have prevailed in the domain, Transformer-based models provide superior global attention mechanisms, facilitating greater feature extraction and segmentation precision. The models are trained on labeled SST datasets and assessed using Intersection over Union (IoU), Dice coefficient, precision, recall, and F1-score. Experimental findings demonstrate that Transformer-based designs surpass conventional CNN-based techniques, yielding enhanced generalization and superior accuracy in classifying cyclonic and anticyclonic eddies. This study illustrates the efficacy of attention based segmentation algorithms for resilient oceanographic applications. © 2008-2012 IEEE.

关键词： Semantic Segmentation

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Monitoring System for Prediction and Detection of Epilepsy Seizure

Monitoring System for Prediction and Detection of Epilepsy S...

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International Conference on Advances in Computational Tools for Engineering Applications, ACTEA

作者： Nashaat el Halabi Roy Abi Zeid Daou Roger Achkar Ali Hayek Josef Börcsök Department of Computer and Communications Engineering American University of Science and Technology Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon Department of Computer Architecture and System Programming University of Kassel Germany

Epilepsy is a neurological disorder associated with abnormal electrical activity in the brain, which causes seizures. The occurrence of seizure is not predictable; the duration between seizures, as well as the symptoms, varies from patient to another. Since the seizures are not predictable, and most of epileptic patients suffer from physical risky symptoms during the seizure, such patients are not able to perform daily work activities. The objective of this project is to design and implement a monitoring system for epileptic patients; the system should continuously check some vital signs, analyze the measurements, and decide whether the patient is nearly to have a seizure or not. Whenever a seizure is predicted, the system initiates an alarm. In addition, a notification should be sent to the health care responsible, as well as one preferred contact. By implementing the monitoring system, people who suffer from epilepsy will have more chance to work and live a normal life. Thus, this paper presents the concept of the overall system and shows results of the implemented systems: EEG, ECG and Fall Detection system. Results have shown that the fall detection accuracy reached 99.89% whereas the accuracy of the prediction using the ANN was about 97.34%.

关键词： Electroencephalography Epilepsy Feature extraction Tools Monitoring Artificial neural networks Support vector machines

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Harnessing artificial intelligence to fill global shortfalls in biodiversity knowledge

Nature Reviews Biodiversity

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Nature Reviews Biodiversity 2025年第3期1卷 166-182页

作者： Laura J. Pollock David Rolnick Justin Kitzes Sara Beery Kaitlyn M. Gaynor Marta A. Jarzyna Tanya Berger-Wolf Oisin Mac Aodha Bernd Meyer Graham W. Taylor Devis Tuia Department of Biology McGill University Montréal Quebec Canada Mila—Quebec AI Institute Montréal Quebec Canada School of Computer Science McGill University Montréal Quebec Canada Department of Biological Sciences University of Pittsburgh Pittsburgh PA USA Faculty of AI and Decision Making Massachusetts Institute of Technology Cambridge MA USA Department of Botany University of British Columbia Vancouver British Columbia Canada Department of Zoology University of British Columbia Vancouver British Columbia Canada Department of Evolution Ecology and Organismal Biology The Ohio State University Columbus OH USA Translational Data Analytics Institute The Ohio State University Columbus OH USA Department of Computer Science and Engineering The Ohio State University Columbus OH USA Department of Electrical & Computer Engineering The Ohio State University Columbus OH USA School of Informatics University of Edinburgh Edinburgh UK Department of Data Science and AI Monash University Clayton Victoria Australia School of Engineering University of Guelph Guelph Ontario Canada Vector Institute Toronto Ontario Canada School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland

Large, well described gaps exist in both what we know and what we need to know to address the biodiversity crisis. Artificial intelligence (AI) offers new potential for filling these knowledge gaps, but where the biggest and most influential gains could be made remains unclear. To date, biodiversity-related uses of AI have largely focused on tracking and monitoring of wildlife populations. Rapid progress is being made in the use of AI to build phylogenetic trees and species distribution models. However, AI also has considerable unrealized potential in the re-evaluation of important ecological questions, especially those that require the integration of disparate and inherently complex data types, such as images, video, text, audio and DNA. This Review describes the current and potential future use of AI to address seven clearly defined shortfalls in biodiversity knowledge. Recommended steps for AI-based improvements include the re-use of existing image data and the development of novel paradigms, including the collaborative generation of new testable hypotheses. The resulting expansion of biodiversity knowledge could lead to science spanning from genes to ecosystems — advances that might represent our best hope for meeting the rapidly approaching 2030 targets of the Global Biodiversity Framework.

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