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2018 International Conference on Smart Materials, Intelligent Manufacturing and Automation, SMIMA 2018

作者： Lian, Xin Zhang, Tianyu Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing400065 China Chongqing Engineering Research Center of Mobile Internet Data Application Chongqing400065 China

Spark needs to use lots of memory resources, network resources and disk I/O resources when Spark SQL execute Join operation. The Join operation will greatly affect the performance of Spark SQL. How to improve the Join operation performance become an urgent problem. Spark SQL use Catalyst as query optimizer in the latest release. Catalyst query optimizer both implement the rule-based optimize strategy (RBO) and cost-based optimize strategy (CBO). There are some problems with the Catalyst CBO module. In the first place, the characteristic of In-memory computing in Spark was not fully considered. In the second place, the cost estimation of network transfer and disk I/O is insufficient. To solve these problems and improve the performance of Spark SQL. In this study, we proposed a cost estimation model for Join operator which take the cost from four aspects: Time complexity, space complexity, network transfer and disk I/O. Then, the most cost-efficiency plan could be selected by using hierarchical analysis method from the equivalence physical plans which generated by Spark SQL. The experimental results show that the total amount of network transmission is reduced and the usage of processor is increased. Thus the performance of Spark SQL has improved. © The Authors, published by EDP sciences, 2018.

关键词： Cost estimating

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A Green Paradigm for Internet of Things: Ambient Backscatter Communications

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China Communications 2019年第7期16卷 109-119页

作者： Wei Zhang Yao Qin Wenjing Zhao Minzheng Jia Qiang Liu Ruisi He Bo ai Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology and also with Institute of Telecommunication Satellites China Academy of Space Technology (CAST) Beijing Key Lab of Transportation Data Analysis and Mining Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Department of Information Engineering Beijing Polytechnic College Beijing 100042 China State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing 100044 China

Internet of Things (IoT) has attracted extensive interest from both academia and industries, and is recognized as an ultimate infrastructure to connect everything at anytime and anywhere. The implementation of IoT generally faces the challenges from energy constraint and implementation cost. In this paper, we will introduce a new green communication paradigm, the ambient backscatter (AmBC), that could utilize the environmental wireless signals for both powering a tiny-cost device and backscattering the information symbols. Specifically, we will present the basic principles of AmBC, analyze its features and advantages, suggest its open problems, and predict its potential applications for our future IoT.

关键词： ambient backscatter (AmBC) RF-powered device Internet of Things (IoT) battery-free tag wire-less sensor

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A Regularization-adaptive Non-negative Latent Factor Analysis-based Model For Recommender Systems

A Regularization-adaptive Non-negative Latent Factor Analysi...

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IEEE International Conference on Human-Machine Systems

作者： Jiufang Chen Xin Luo MengChu Zhou School of Computer Science China West Normal University Nanchong Sichuan China Chinese Academy of Sciences Chongqing Inst. of Green and Intelligent Tech Chongqing China Department of Big Data Analyses Techniques Hengrui (Chongqing) Artificial Intelligence Research Center Cloudwalk Chongqing China Department of Electrical and Computer Engineering New Jersey Inst. of Tech Newark NJ USA

ISBN: (数字)9781728158716

ISBN: (纸本)9781728158723

Non-negative latent factor analysis (NLFA) can high-efficiently extract useful information from high dimensional and sparse (HiDS) matrices often encountered in recommender systems (RSs). However, an NLFA-based model requires careful tuning of regularization coefficients, which is highly expensive in both time and computation. To address this issue, this study proposes an adaptive NLFA-based model whose regularization coefficients become self-adaptive via particle swarm optimization. Experimental results on two HiDS matrices indicate that owing to such self-adaptation, it outperforms an NLFA model in terms of both convergence rate and prediction accuracy for missing data estimation.

关键词： Computational modeling Adaptation models Sparse matrices Analytical models Tuning Particle swarm optimization Big data

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Knowledge-Based Expert System for Orthopedic Injury Management With Integrated Transcutaneous Electrical Nerve Simulation Therapy

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International Journal of Telemedicine and applications 2025年第1期2025卷

作者： Haneen Banjar Shahad Almalki Lama Almehmadi Amjad Alshahrani Ali Chaudhary Reda Ghoname Computer Science Department Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Center of Research Excellence in Artificial Intelligence and Data Science King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Institute of Genomic Medicine Sciences King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa Centre of Artificial Intelligence in Precision Medicines King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa John Hopkins Aramco Healthcare Dhahran Saudi Arabia *** Computer and Electrical Engineering Department King Abdulaziz University Jeddah Saudi Arabia kau.edu.sa

Orthotic treatment, particularly for ankle injuries, often relies on devices such as ankle–foot orthoses (AFOs) to support and stabilize the foot and ankle. Given the high prevalence of lower extremity injuries, especially in sports, and the diagnostic challenges faced by clinicians, there is a pressing need for intelligent systems that aid in accurate assessment and personalized management. Misdiagnosis and suboptimal treatment of ankle conditions, including fractures and sprains, can lead to long-term complications and hinder recovery. This study presents TeleOrtho , a knowledge-based expert system designed to provide personalized diagnosis, treatment recommendations, and recovery plans for ankle injuries, integrating transcutaneous electrical nerve stimulation (TENS) therapy to enhance rehabilitation. The system was implemented using Flutter for cross-platform deployment and Python for expert rule modeling, and it features a user-friendly interface for both clinicians and patients. Diagnostic accuracy and treatment pathways were validated through two published case scenarios, confirming the system’s alignment with medical guidelines. Static and dynamic testing demonstrated consistent functionality, and initial usability assessment indicated high user satisfaction (average SUS score: 85.3/100). The expert system reliably identified injury types based on structured symptom input and suggested individualized recovery plans. These results affirm the potential of TeleOrtho to reduce diagnostic errors, improve therapy adherence, and enhance clinical decision-making. Future work will focus on integrating imaging analysis, expanding the knowledge base to include additional orthopedic conditions, and conducting clinical trials for broader validation.

关键词： ankle injuries ankle–foot orthoses (AFOs) knowledge-based expert system personalized treatment TENS therapy

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A Survey on Edge Detection based recent Marine Horizon Line Detection Methods and their applications

A Survey on Edge Detection based recent Marine Horizon Line ...

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International Conference on Computing, Mathematics and engineering Technologies (iCoMET)

作者： Manzoor Ahmed Hashmani Muhammad Umair Syed Sajjad Hussain Rizvi Abdul Rehman Gilal High-Performance Cloud Computing Center (HPC3) Center for Research in Data Science (CERDAS) Universiti Teknologi PETRONAS Seri Iskandar Malaysia High-Performance Cloud Computing Center (HPC3) Universiti Teknologi PETRONAS Seri Iskandar Malaysia Faculty of Engineering Sciences and Technology Hamdard University Karachi Pakistan Department of Computer Science Sukkur IBA University Sukkur Pakistan

ISBN: (数字)9781728149707

ISBN: (纸本)9781728149714

Sea and sky boundary identification (i.e. marine horizon line detection) from a marine image is a problem of great interest for reasons such as, unmanned surface or aerial vehicle navigation, surveillance by object detection and tracking, and determining the spatial orientation of the ship. Due to the complexity of the marine environment, the problem poses its own unique challenges. In recent years, different methods have been proposed by the researchers to solve the problem. Those methods can be grouped into two categories; (i) edge detection based horizon detection, and (ii) machine learning-based horizon detection. In this paper, we present a survey on edge detection based recent marine horizon line detection methods and their applications. We have selected studies from the previous three years and discussed each study's approach to marine horizon line detection issue, the datasets used for testing purposes and its results. The authors' observations for each study are presented with a recommendation for their suitability for a specific application in the marine environment. Findings of the survey and future research directions for the researchers are also identified and presented. We hope that this survey paper provides a comprehensive overview of edge detection based recent marine horizon line detection methods and help the researchers in exploring new solutions to this challenging problem.

关键词： Image edge detection Transforms Gray-scale Feature extraction Real-time systems Computational efficiency Image color analysis

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Metrics Reloaded: Recommendations for image analysis validation

arXiv

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arXiv 2022年

作者： Maier-Hein, Lena Reinke, Annika Godau, Patrick Tizabi, Minu D. Buettner, Florian Christodoulou, Evangelia Glocker, Ben Isensee, Fabian Kleesiek, Jens Kozubek, Michal Reyes, Mauricio Riegler, Michael A. Wiesenfarth, Manuel Emre Kavur, A. Sudre, Carole H. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Rädsch, Tim Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Jorge Cardoso, M. Cheplygina, Veronika Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian Ginneken, Bram Van Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kofler, Florian Kopp-Schneider, Annette Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rajpoot, Nasir Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Smeden, Maarten Van Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. Calster, Ben Van Varoquaux, Gaël Jäger, Paul F. Heidelberg Division of Intelligent Medical Systems and HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg a partnership between DKFZ University Medical Center Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt-Marburg Germany Heidelberg Goethe University Frankfurt Germany Department of Medicine Goethe University Frankfurt Germany Department of Informatics Frankfurt Cancer Insititute Germany Department of Computing Imperial College London London United Kingdom Heidelberg Division of Medical Image Computing and HI Applied Computer Vision Lab Germany Institute for AI in Medicine University Medicine Essen Essen Germany Centre for Biomedical Image Analysis Faculty of Informatics Masaryk University Brno Czech Republic ARTORG Center for Biomedical Engineering Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Bern Switzerland Simula Metropolitan Center for Digital Engineering Oslo Norway UiT The Arctic University of Norway Romsø Norway Heidelberg Division of Biostatistics Germany Heidelberg Division of Intelligent Medical Systems Division of Medical Image Computing HI Applied Computer Vision Lab Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Heidelberg Division of Medical Image Computing Germany Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montréal

Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, we created Metrics Reloaded, a comprehensive framework guiding researchers in the problem-aware selection of metrics. It was developed by a large international consortium in a multi-stage Delphi process and is based on the novel concept of a problem fingerprint – a structured representation of the given problem that captures all aspects that are relevant for metric selection, from the domain interest to the properties of the target structure(s), data set and algorithm output. Based on the problem fingerprint, users are guided through the process of choosing and applying appropriate validation metrics while being made aware of potential pitfalls. Metrics Reloaded targets image analysis problems that can be interpreted as a classification task at image, object or pixel level, namely image-level classification, object detection, semantic segmentation, and instance segmentation tasks. To improve the user experience, we implemented the framework in the Metrics Reloaded online tool. Following the convergence of ML methodology across application domains, Metrics Reloaded fosters the convergence of validation methodology. Its applicability is demonstrated for various biomedical use cases. © 2022, CC BY.

关键词： Semantic Segmentation

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TCIM: Triangle counting acceleration with processing-in-MRAM architecture

arXiv

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arXiv 2020年

作者： Wang, Xueyan Yang, Jianlei Zhao, Yinglin Qi, Yingjie Liu, Meichen Cheng, Xingzhou Jia, Xiaotao Chen, Xiaoming Qu, Gang Zhao, Weisheng Fert Beijing Research Institute School of Microelectronics Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering University of Maryland College ParkMD United States

Triangle counting (TC) is a fundamental problem in graph analysis and has found numerous applications, which motivates many TC acceleration solutions in the traditional computing platforms like GPU and FPGA. However, these approaches suffer from the bandwidth bottleneck because TC calculation involves a large amount of data transfers. In this paper, we propose to overcome this challenge by designing a TC accelerator utilizing the emerging processing-in-MRAM (PIM) architecture. The true innovation behind our approach is a novel method to perform TC with bitwise logic operations (such as AND), instead of the traditional approaches such as matrix computations. This enables the efficient in-memory implementations of TC computation, which we demonstrate in this paper with computational Spin-Transfer Torque Magnetic RAM (STT-MRAM) arrays. Furthermore, we develop customized graph slicing and mapping techniques to speed up the computation and reduce the energy consumption. We use a device-to-architecture co-simulation framework to validate our proposed TC accelerator. The results show that our data mapping strategy could reduce 99.99% of the computation and 72% of the memory WRITE operations. Compared with the existing GPU or FPGA accelerators, our in-memory accelerator achieves speedups of 9× and 23.4×, respectively, and a 20.6× energy efficiency improvement over the FPGA accelerator. Copyright © 2020, The Authors. All rights reserved.

关键词： data transfer

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Weeds e-Catalog as a Tool for Identification of Weeds in Plantation

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IOP Conference Series: Earth and Environmental science 2021年第1期794卷

作者： Hangger Gahara Mawandha Teddy Suparyanto Bens Pardamean Agrotechnology Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Agricultural Engineering Department Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Bioinformatics and Data Science Research Center Universitas Bina Nusantara Jakarta Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta

Weeds are one of the organisms that interfere with plant growth. Information about the identity of weeds becomes very important on plantations. Although weeds data digitization has been done a lot, currently there is still not much weeds data information system can be accessed online. Weeds management is often dealt with weeds herbarium or weeds photographs. In an effort to provide a good information system for farmers, this research aims to create a database of various types of weeds and an information system that can be accessed online. The methodology for developing a weed catalog information system uses the Software Development Life Cycle (SDLC). Weed samples in database were collected through systematic random sampling in the form of images and text. The result of this study is a Weeds Electronic Catalog or Weeds e-Catalog that can facilitate the information of weeds identity such as names, classifications, morphology, life cycles, and habitats of various types of weeds that grow on plantation land. Weeds e-Catalog can be used by plantation practitioners and farmers to make decisions in controlling weeds.

关键词：

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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Preliminary Effectiveness of a Postnatal mHealth and Virtual Social Support Intervention on Newborn and Infant Health and Feeding Practices in Punjab, India: Quasi-Experimental Pre-Post Pilot Study

JMIR Pediatrics and Parenting

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JMIR Pediatrics and Parenting 2025年 8卷

作者： Garima Singh Verma Lakshmi Gopalakrishnan Alison El Ayadi Nadia Diamond-Smith Rashmi Bagga Shashi Kant Dhir Pushpendra Singh Navneet Gill Vaibhav Miglani Naveen Mutyala Ankita Kankaria Jasmeet Kaur Alka Ahuja Vijay Kumar Mona Duggal Department of Obstetrics and Gynecology Postgraduate Institute of Medical Education and Research Chandigarh India Institute for Global Health Sciences University of California San Francisco San Francisco CA United States Bixby Center for Global Reproductive Health Department of Obstetrics Gynecology and Reproductive Sciences University of California San Francisco San Francisco CA United States Department of Epidemiology and Biostatistics University of California San Francisco San Francisco CA United States Department of Pediatrics Guru Gobind Singh Medical College and Hospital Faridkot India Department of Computer Science and Engineering Indraprastha Institute of Information Technology Delhi India Department of Hematology Postgraduate Institute of Medical Education and Research Chandigarh India Department of Community Medicine and School of Public Health Postgraduate Institute of Medical Education and Research Chandigarh India SWACH – Foundation for Survival of Women and Children Panchkula India National Institute for Research in Digital Health and Data Science Indian Council of Medical Research Ansari Nagar East New Delhi India

Background We evaluated a pilot mobile health (mHealth) intervention aimed at improving postnatal maternal and infant health. The intervention featured provider-led group sessions for education, health care communication, in-person care referrals, and virtual mHealth support for postpartum mothers through weekly calls, texts, interactive voice response (IVR), and a phone app. Objective We aimed to assess the preliminary effectiveness of the pilot mHealth intervention, MeSSSSage ( Maa Shishu Swasthya Sahayak Samooh , which means maternal and child health support group), on infant health knowledge, behaviors, and outcomes at 6 months post partum. We focus on maternal knowledge of infant danger signs and optimal young child feeding practices at 6 months post partum and also evaluate maternal care-seeking behaviors for infants, adherence to age-appropriate immunization, and infant and young child feeding practices such as early initiation of breastfeeding and complementary feeding. Methods We evaluated the preliminary effectiveness of an intervention on maternal health knowledge among 135 participants in Punjab, India, who completed pre- and postintervention surveys. The intervention, led by research personnel with backgrounds similar to community health officers, aimed to empower society and support universal health coverage if successful. We assessed changes in knowledge of maternal danger signs and the appropriate age for introducing different food groups over 6 months post partum. Additionally, we examined postintervention differences in health-seeking behavior for infants, adherence to age-appropriate immunizations, and adoption of breastfeeding and complementary feeding practices among women in the synchronous (group call), asynchronous (IVR and app), and control arms. Results Of 12 infant risk factors, maternal knowledge of infant danger signs remained low (mean range: 1.85-2.31 preintervention and 1.81-2.22 postintervention). Participants in the synchronous arm

关键词： digital health mHealth mobile health app postpartum health education social support India infant morbidity care seeking postnatal maternal mother text message virtual support online support pediatric infant baby neonate newborn breastfeeding feeding

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