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International Conference on Information Technologies in engineering Education (Inforino)

作者： D.G Arseniev V.V. Borisov A.E. Misnik E.A Gomzhina M.A. Shalukhova Scientific Innovation Institute “Mathematical Modeling and Intelligent Control Systems” Peter the Great St. Petersburg Polytechnic University St. Petersburg Russian Federation Department of Computer Engineering National Research University “MPEI” Branch in Smolensk Smolensk Russia Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University” Mogilev Belarus Early Medical Rehabilitation FSBI “FCTOE” of the Ministry of Health of the Russian Federation Smolensk Russian Federation Information Technology Software Inter-State Educational Institution of Higher Education ‘Belarusian-Russian University Peter the Great St. Petersburg Polytechnic University Mogilev Belarus

ISBN: (数字)9798350371369

ISBN: (纸本)9798350371376

The article presents an innovative approach to the rehabilitation process for patients with joint endoprosthetics, focusing on the integration of a specialized software module based on computer vision technologies and the ontological approach. Developed for the Federal Centre for Traumatology, Orthopaedics, and Endoprosthesis in Smolensk, Russia, this system is designed to improve the quality of life for patients by offering personalized rehabilitation plans. The core of the study lies in the application of ontological models and meta-associative graphs for synthesizing individual rehabilitation plans, which streamline the recovery process and reduce the workload on healthcare professionals. This method allows for the processing of large amounts of information, providing detailed conclusions and recommendations for functional correction of the musculoskeletal system. The software utilizes computer vision to analyze patient movement and develop tailored rehabilitation strategies, overcoming the limitations of traditional diagnostic tools like MRI, which may not be suitable for all patients. By incorporating artificial intelligence and augmented reality, the system enhances the accuracy of patient assessments and the effectiveness of rehabilitation plans. Furthermore, the article discusses the application of this technology in the educational process at the Belarusian-Russian University, where students are introduced to cutting-edge software tools that assist in creating personalized rehabilitation plans. This educational integration not only enriches the students’ learning experience but also fosters innovation in the development of new applications for computer vision in medical rehabilitation. The ontological approach, combined with modern technology, represents a significant advancement in the management of musculoskeletal system diseases, offering a more efficient, personalized approach to rehabilitation and recovery.

关键词： computer vision Musculoskeletal system Technological innovation Computational modeling Medical services Propulsion Trajectory

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Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2004年 2981卷 V-VI页

作者： Müller-Schloer, Christian Ungerer, Theo Bauer, Bernhard University of Hannover Institute of Systems Engineering System and Computer Architecture - SRA Appelstr. 4 Hannover Germany University of Augsburg Institute of Informatics Augsburg Germany University of Augsburg Department of Software Engineering and Programming Languages Augsburg Germany

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DEVELOPMENT OF AGENT-BASED E-COMMERCE systems USING SEMIOTIC APPROACH AND DEMO TRANSACTION CONCEPT

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International Journal of Information Technology & Decision Making 2002年第3期1卷 491-510页

作者： JOSEPH BARJIS SAMUEL CHONG JAN L. G. DIETZ KECHENG LIU Department of Computer Science The University of Reading P.O. Box 225 Whiteknights Reading RG6 6AY United Kingdom Tel: +44 118 931 6024 Fax: +44 118 975 1822. School of Computing Staffordshire University Stafford Beaconside ST18 0AD England UK Tel: +44 1785 353509 Fax: +44 1785 353497. Department of Information Systems & Software Engineering Delft University of Technology P.O. Box 356 2600 AJ Delft The Netherlands Tel.: +31 15 2785827 Fax: +31 15 2786632.

As software agents get more sophisticated, it becomes difficult to understand and model such systems. This paper contends that all developers bring to the task of development some implicit or explicit assumptions of the agent communication pattern. This issue is not readily addressed in current literature and represents a gap in knowledge. For this purpose, a generic pattern of inter-agent communication is introduced and discussed in this paper. For better understanding and modelling of agent-based e-commerce systems, the semiotic approach and the DEMO transaction concept are briefly introduced. It is shown that the semiotic approach offers a unifying framework for identifying the roles of agents, the responsible human agents and the right/constraints associated with each role. The DEMO transaction concept is applied to model the communicative interaction between agents.

关键词： software agent agent communication Semiotics DEMO methodology

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Correction to: Next-generation UWB antennas gadgets for human health care using SAR

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EURASIP Journal on Wireless Communications and Networking 2021年第1期2021卷 1-1页

作者： Ali, Aysha Maryam Al Ghamdi, Mohammed A. Iqbal, Muhammad Munwar Khalid, Shehzad Aldabbas, Hamza Saeed, Saqib University of Engineering and Technology Taxila Pakistan Computer Science Department Umm Al-Qura University Makkah City Saudi Arabia Computer Engineering Department Bahria University Islamabad Pakistan Software Engineering Department Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Kingdom of Saudi Arabia

An amendment to this paper has been published and can be accessed via the original article.

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A Unified Graph-Theoretic Framework for Free-Fermion Solvability

arXiv

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arXiv 2023年

作者： Chapman, Adrian Elman, Samuel J. Mann, Ryan L. Department of Materials University of Oxford Parks Road OxfordOX1 3PH United Kingdom Centre for Quantum Software and Information School of Computer Science Faculty of Engineering & Information Technology University of Technology Sydney NSW 2007 Australia Centre for Engineered Quantum Systems School of Physics The University of Sydney Sydney Australia Centre for Quantum Computation and Communication Technology Australia School of Mathematics University of Bristol BristolBS8 1UG United Kingdom

We show that a quantum spin system has an exact description by non-interacting fermions if its frustration graph is claw-free and contains a simplicial clique. The frustration graph of a spin model captures the pairwise anticommutation relations between Pauli terms of its Hamiltonian in a given basis. This result captures a vast family of known free-fermion solutions. In previous work, it was shown that a free-fermion solution exists if the frustration graph is either a line graph, or (even-hole, claw)-free. The former case generalizes the celebrated Jordan-Wigner transformation and includes the exact solution to the Kitaev honeycomb model. The latter case generalizes a non-local solution to the four-fermion model given by Fendley. Our characterization unifies these two approaches, extending generalized Jordan-Wigner solutions to the non-local setting and generalizing the four-fermion solution to models of arbitrary spatial dimension. Our key technical insight is the identification of a class of cycle symmetries for all models with claw-free frustration graphs. We prove that these symmetries commute, and this allows us to apply Fendley's solution method to each symmetric subspace independently. Finally, we give a physical description of the fermion modes in terms of operators generated by repeated commutation with the Hamiltonian. This connects our framework to the developing body of work on operator Krylov subspaces. Our results deepen the connection between many-body physics and the mathematical theory of claw-free graphs. Copyright © 2023, The Authors. All rights reserved.

关键词： Hamiltonians

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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A Method based on Sentence Embeddings for the Sub-Topics Detection

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Journal of Physics: Conference Series 2019年第5期1168卷

作者： Yu Xie Bin Zhou Yang Ou National University of Defense Technology Department of Computer Hunan 410005 China Laboratory of Software Engineering for Complex Systems Hunan 410005 China China Satellite Launch and Tracking Control General Beijing 100720 China

Weibo topics are characterized by diversity and complexity, and often contain different sub-topics even for the same topic. Therefore, how to classify sub-topics effectively and accurately is of great significance. Due to the strong similarity between sub-topics belonging to the same topic, most existing methods cannot be directly applied to the task of sub-topic discovery. In this paper, a new technique based on sentence embeddings for detecting sub-topics of weibo is proposed. The information of blogs is represented by sentence embeddings containing semantic information and the results are verified by clustering.

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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Secure and Privacy-Preserving Automated Machine Learning Operations into End-to-End Integrated IoT-Edge-Artificial Intelligence-Blockchain Monitoring System for Diabetes Mellitus Prediction

arXiv

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arXiv 2022年

作者： Hennebelle, Alain Ismail, Leila Materwala, Huned Kaabi, Juma Al Ranjan, Priya Janardhanan, Rajiv School of Computing and Information Systems The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates College of Health Sciences Department of Internal Medicine United Arab Emirates University United Arab Emirates Mediclinic Al Ain Abu Dhabi United Arab Emirates Bhubaneswar Institute of Technology India Faculty of Medical & Health Sciences SRM Institute of Science & Technology India

Diabetes Mellitus, one of the leading causes of death worldwide, has no cure to date and can lead to severe health complications, such as retinopathy, limb amputation, cardiovascular diseases, and neuronal disease, if left untreated. Consequently, it becomes crucial to take precautionary measures to avoid/predict the occurrence of diabetes. Machine learning approaches have been proposed and evaluated in the literature for diabetes prediction. This paper proposes an IoT-edge-Artificial Intelligence (AI)-blockchain system for diabetes prediction based on risk factors. The proposed system is underpinned by the blockchain to obtain a cohesive view of the risk factors data from patients across different hospitals and to ensure security and privacy of the user’s data. Furthermore, we provide a comparative analysis of different medical sensors, devices, and methods to measure and collect the risk factors values in the system. Numerical experiments and comparative analysis were carried out between our proposed system, using the most accurate random forest (RF) model, and the two most used state-of-the-art machine learning approaches, Logistic Regression (LR) and Support Vector Machine (SVM), using three real-life diabetes datasets. The results show that the proposed system using RF predicts diabetes with 4.57% more accuracy on average compared to LR and SVM, with 2.87 times more execution time. Data balancing without feature selection does not show significant improvement. The performance is improved by 1.14% and 0.02% after feature selection for PIMA Indian and Sylhet datasets respectively, while it reduces by 0.89% for MIMIC III. © 2022, CC BY.

关键词： Logistic regression

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Corrigendum to “Identification of significant features and data mining techniques in predicting heart disease” [Telematics Informatics 36 (2019) 82–93]

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Telematics and Informatics 2022年 70卷

作者： Mohammad Shafenoor Amin Yin Kia Chiam Kasturi Dewi Varathan Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia BRAC University 66 Mohakhali Dhaka 1212 Bangladesh Department of Information Systems Faculty of Computer Science and Information Technology University of Malaya 50603 Kuala Lumpur Malaysia

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