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2021 Ural Symposium on Biomedical engineering, Radioelectronics and Information Technology, USBEREIT 2021

作者： Sozykin, Andrey Koshelev, Anton Bersenev, Alexander Shadrin, Denis Aksenov, Alexander Kuklin, Evgeniy N.N. Krasovskii Institute of Mathematics and Mechanics Department of Computing Systems Ekaterinburg Russia Engineering School of IT Telecom. and Control Systems Ural Federal University Ekaterinburg Russia Institute of Natural Sciences and Mathematics Ural Federal University Ekaterinburg Russia

ISBN: (纸本)9781728176918

The paper presents an approach to developing new educational programs for information technologies (IT) specialists using job market analysis. We developed a set of innovative software tools for collecting data from job sites and analyzing the data using machine learning algorithms. The tools were used to create a dataset of job advertisements from popular Russian job site HeadHunter for a period from 2006 until 2020. Both tools and dataset are freely available on the Internet. In order to develop the educational programs, we analyzed the demand for IT professions and programming languages, as well as joint occurrence of skills in job descriptions. To demonstrate the possibilities of our approach, two educational programs were created: A JavaScript-based program for Web Development and a Python-based program for Data Science. Developing the educational programs based on the requirements of the job market allows graduates to increase their employability. During the programs, students not only study the foundation of the profession such as programming languages, but also acquire a complete set of skills required to work in demanded positions. © 2021 IEEE.

关键词： Employment

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Privacy-Aware Joint DNN Model Deployment and Partition Optimization for Delay-Efficient Collaborative Edge Inference

arXiv

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arXiv 2025年

作者： Cheng, Zhipeng Xia, Xiaoyu Wang, Hong Liwang, Minghui Chen, Ning Fan, Xuwei Wang, Xianbin School of Future Science and Engineering Soochow University Suzhou215006 China School of Computing Technologies RMIT University MelbourneVIC Australia Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai201804 China Qingdao China College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou350002 China Department of Electrical and Computer Engineering Western University ON Canada

Edge inference (EI) is a key solution to address the growing challenges of delayed response times, limited scalability, and privacy concerns in cloud-based Deep Neural Network (DNN) inference. However, deploying DNN models on resource-constrained edge devices faces more severe challenges, such as model storage limitations, dynamic service requests, and privacy risks. This paper proposes a novel framework for privacy-aware joint DNN model deployment and partition optimization to minimize long-term average inference delay under resource and privacy constraints. Specifically, the problem is formulated as a complex optimization problem considering model deployment, user-server association, and model partition strategies. To handle the NP-hardness and future uncertainties, a Lyapunov-based approach is introduced to transform the long-term optimization into a single-time-slot problem, ensuring system performance. Additionally, a coalition formation game model is proposed for edge server association, and a greedy-based algorithm is developed for model deployment within each coalition to efficiently solve the problem. Extensive simulations show that the proposed algorithms effectively reduce inference delay while satisfying privacy constraints, outperforming baseline approaches in various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Computationally Expensive High-Dimensional Multiobjective Optimization via Surrogate-Assisted Reformulation and Decomposition

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Pan, Linqiang Lin, Jianqing Wang, Handing He, Cheng Tan, Kay Chen Jin, Yaochu School of Artificial Intelligence and Automation Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Huazhong University of Science and Technology Wuhan China School of Artificial Intelligence Xidian University Xi’an China School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Engineering Trustworth and General Artificial Intelligence Laboratory Westlake University Hangzhou China

In recent decades, various surrogate-assisted evolutionary algorithms (SAEAs) have been proposed to solve computationally expensive multiobjective optimization problems (EMOPs). Nevertheless, designing an SAEA to handle high-dimensional EMOPs and balance convergence, diversity, and computational complexity remains challenging. Here, we propose a two-phase SAEA (TP-SAEA), which follows the idea of convergence first and diversity second, for solving high-dimensional EMOPs. In Phase I, a surrogate-assisted problem reformulation method is proposed to fast-track the Pareto optimal set in association with some reference solutions. Specifically, the high-dimensional EMOP is reformulated into an expensive single-objective one with low-dimensional decision space. Then, the surrogate-assisted optimization is utilized to obtain well-converged solutions. In Phase II, the high-dimensional EMOP is decomposed into two subproblems to explore subregions of the decision space that can effectively promote the diversity of the solutions. The two subproblems are optimized independently via surrogate-assisted optimization, aiming to push the population towards different regions of the Pareto optimal front. Experiments are conducted on EMOPs with 100 to 500 decision variables compared with four state-of-the-art SAEAs. The proposed TP-SAEA obtains well-converged and diverse solutions with only 509 real function evaluations. Moreover, its superiority is examined in six real-world instances with up to 12,000 decision variables. IEEE

关键词： Iron

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An Improved Machine Learning Technique with Effective Heart Disease Prediction System

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Computers, Materials & Continua 2021年第12期69卷 4169-4181页

作者： Mohammad Tabrez Quasim Saad Alhuwaimel Asadullah Shaikh Yousef Asiri Khairan Rajab Rihem Farkh Khaled Al Jaloud College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology P.O.Box 6086Riyadh11442Saudi Arabia College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia College of Engineering Muzahimiyah BranchKing Saud UniversityRiyadh11451Saudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar University1002Tunisia

Heart disease is the leading cause of death *** heart disease is challenging because it requires substantial experience and *** research studies have found that the diagnostic accuracy of heart disease is *** coronary heart disorder determines the state that influences the heart valves,causing heart *** indications of coronary heart disorder are strep throat with a red persistent skin rash,and a sore throat covered by tonsils or strep *** work focuses on a hybrid machine learning algorithm that helps predict heart attacks and arterial *** first,we achieved the component perception measured by using a hybrid cuckoo search particle swarm optimization(CSPSO)*** this perception measure,characterization and accuracy were improved,while the execution time of the proposed model was *** CSPSO-deep recurrent neural network algorithm resolved issues that state-of-the-art methods *** proposed method offers an illustrative framework that helps predict heart attacks with high *** proposed technique demonstrates the model accuracy,which reached 0.97 with the applied dataset.

关键词： Machine learning deep recurrent neural network effective heart disease prediction framework

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3D Face Capture for Rehabilitation Progress Assessment After Brain Surgery 1

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4th Computational Methods in Systems and Software, CoMeSySo 2020

作者： Tomeš, Jakub Kohout, Jan Mareš, Jan Department of Computing and Control Engineering University of Chemistry and Technology in Prague Technická 1905/5 166 28 Praha 6 Prague Czech Republic

ISBN: (数字)9783030633226

ISBN: (纸本)9783030633219

The aim of this article is to show how modern sensors can be used in rehabilitation monitoring after a specific brain surgery that affects the facial muscles. Rehabilitation takes several months, it is important for both doctors and patients to be able to monitor progress quantitatively. To evaluate a patient’s facial disorders, the stereo-vision camera is used to detect and capture precise facial movements, which the subject is asked to perform. From these videos, specific patterns can be extracted for the rehabilitation process description. In this paper, we focused on a 3D analysis app for mobile phones. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stereo image processing

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ROCFL: A Robust Clustered Federated Learning Framework towards Heterogeneous Data

ROCFL: A Robust Clustered Federated Learning Framework towar...

引用

Intelligent Communication and Networking (ICN), International Conference on

作者： Zihan Li Zhitao Guan Shuai Yuan Ningyu An Xiao Liang School of Control and Computer Engineering North China Electric Power University Beijing China Department of Finance Operations and Information Systems (FOIS) Brock University Canada State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Beijing China

As a decentralized machine learning approach, federated learning has become a key solution for numerous applications. However, one primary challenge lies in the data heterogeneity issue when developing effective federated learning algorithms. While many existing frameworks address the concern of non-independent and identically distributed data(non-IID), they have not considered heterogeneity of data quality. Therefore, we propose ROCFL, a robust clustered federated learning method in this study, which can amplify the disparity in weight allocation between models trained on different quality data. We first develop an optimal clustering matching mechanism that groups clients with similar data distributions. This allows the optimal clustering model to be derived without specifying a predetermined number of clusters. Next, we introduce a personalized weight allocation strategy, which assigns a weight benchmark to each cluster based on its cluster importance index. This strategy mitigates the negative impacts of low-quality data during model aggregation. Finally, we design a federated aggregation strategy grounded in a sampling approach. It not only ensures unbiased sampling but also significantly reduces both computational and communication overheads. Experiments have been carried out and the results demonstrate that higher accuracy and robustness can be achieved by ROCFL in scenarios wherein data distribution and quality heterogeneity coexist, in comparison with the benchmarks.

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Recent advancement in MXene-based nanomaterials for flame retardant polymers and composites

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Advanced Industrial and engineering Polymer Research 2025年

作者： Alli, Yakubu Adekunle Bamisaye, Abayomi Ejeromedoghene, Onome Jimoh, Olusegun Oluwaseun Oni, Samuel Oluwadadepo Ezeamii, Gerald Chekwube Ozoemezim, Chukwurimazu Ogunlaja, Adeniyi Sunday Abdul Rashid, Suraya Kandola, Baljinder K. Department of Chemistry Nelson Mandela University Port Elizabeth South Africa Department of Chemistry Faculty of Natural and Applied Sciences Lead City University Oyo State Ibadan Nigeria College of Chemistry Chemical Engineering and Materials Science Soochow University 199 Renai Road Jiangsu Province Suzhou215123 China Department of Pharmacology Southern Illinois University Carbondale62702 United States Department of Physics Georgia Southern University Statesboro30458 United States Department of Applied Instrumentation and Control School of Computing Engineering and Built Environment Glasgow Caledonian University United Kingdom Department of Electrical and Computer Engineering University of Florida United States Institute of Nanoscience & Nanotechnology Universiti Putra Malaysia Malaysia Institute for Materials Research and Innovation University of Bolton BoltonBL3 5AB United Kingdom

This review explores the advancements in MXene-based nanomaterials as flame-retardant additives for polymers and composites, driven by increasing fire safety demands across industries. It highlights the critical role of flame-retardant materials in mitigating fire hazards in structures, electronics, transportation, and textiles, emphasizing the need for innovative solutions due to stricter safety regulations. MXenes, a class of two-dimensional nanomaterials with unique structural properties such as high surface area, tunable composition, and superior thermal stability, are presented as promising candidates. The review discusses various synthesis and incorporation techniques for MXenes in polymer matrices, showcasing improvements in flame retardancy, mechanical properties, and thermal stability. Additionally, it emphasizes the multifunctionality of MXenes, which offer conductivity, electromagnetic shielding, and mechanical reinforcement alongside flame suppression. In conclusion, the review underscores MXenes' potential to address challenges in flame-retardant materials, advocating for further research to optimize their applications and explore synergies with other agents to enhance safety and sustainability in engineering materials. © 2025 Kingfa Scientific and Technological Co. Ltd.

关键词： Nanocomposites

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Distributed Load Shedding via Differentially Private Average Consensus Algorithm

Distributed Load Shedding via Differentially Private Average...

引用

Chinese control and Decision Conference, CCDC

作者： Zixin Qiao Fanghong Guo Xiping Pan Yue Sun Lei Wang Shanghai Institute of Computing Technology Shanghai China Department of Automation Zhejiang University of Technology Hangzhou China Shandong Institute of Blockchain Jinan China College of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665478977

This paper presents a distributed load shedding strategy for islanded active distribution power system, where the average consensus algorithm acts as a key tool to discover the required global information for load shedding. For privacy concerns, we propose a new differentially private average consensus algorithm, where a distributed shuffling mechanism based on the Pailler cryptosystem is established to initialize the initial states for the subsequent stage of running standard average consensus. For the proposed algorithm, both rigorous provable differential privacy and accuracy guarantee are established, with an improved privacy/accuracy trade-off than the existing relevant frameworks. Particularly, it is shown that given any expected differential privacy levels, the achievable mean-square computation error is inversely proportional to the network size, in contrast with the optimal error achieved in the literature that is independent of the network size. A numerical example and an application to the distributed load shedding for an IEEE 33-bus power system are used to validate the effectiveness of the proposed approach.

关键词： Privacy Differential privacy Load shedding Consensus algorithm Power systems Standards

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UAV-miniUGV Hybrid System for Hidden Area Exploration and Manipulation

arXiv

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arXiv 2022年

作者： Pushp, Durgakant Kalhapure, Swapnil Das, Kaushik Liu, Lantao School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Automatic Control and Systems Engineering Department University of Sheffield Mappin St SheffieldS1 3JD United Kingdom TCS Research India

— We propose a novel hybrid system (both hardware and software) of an Unmanned Aerial Vehicle (UAV) carrying a miniature Unmanned Ground Vehicle (miniUGV) to perform a complex search and manipulation task. This system leverages heterogeneous robots to accomplish a task that cannot be done using a single robot system. It enables the UAV to explore a hidden space with a narrow opening through which the miniUGV can easily enter and escape. The hidden space is assumed to be navigable for the miniUGV. The miniUGV uses Infrared (IR) sensors and a monocular camera search for an object in the hidden space. The proposed system takes advantage of a wider field of view (fov) of the camera as well as the stochastic nature of the object detection algorithms to guide the miniUGV in the hidden space to find the object. Upon finding the object the miniUGV grabs it using visual servoing and then returns back to its start point from where the UAV retracts it back and transports the object to a safe place. In case there is no object found in the hidden space, UAV continues the aerial search. The tethered miniUGV gives the UAV an ability to act beyond its reach and perform a search and manipulation task which was not possible before for any of the robots individually. The system has a wide range of applications and we have demonstrated its feasibility through repetitive experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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Distributed Coverage Hole Prevention for Visual Environmental Monitoring with Quadcopters via Nonsmooth control Barrier Functions

arXiv

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arXiv 2022年

作者： Funada, Riku Santos, María Maniwa, Ryuichi Yamauchi, Junya Fujita, Masayuki Sampei, Mitsuji Egerstedt, Magnus The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8550 Japan The Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States The Department of Information Physics and Computing The University of Tokyo Tokyo113-8656 Japan The Department of Electrical Engineering and Computer Science University of California Irvine IrvineCA92697 United States

This paper proposes a distributed coverage control strategy for quadcopters equipped with downward-facing cameras that prevents the appearance of unmonitored areas in between the quadcopters' fields of view (FOVs). We derive a necessary and sufficient condition for eliminating any unsurveilled area that may arise in between the FOVs among a trio of quadcopters by utilizing a power diagram, i.e. a weighted Voronoi diagram defined by radii of FOVs. Because this condition can be described as logically combined constraints, we leverage nonsmooth control barrier functions (NCBFs) to prevent the appearance of unmonitored areas among a team's FOV. We then investigate the symmetric properties of the proposed NCBFs to develop a distributed algorithm. The proposed algorithm can support the switching of the NCBFs caused by changes of the quadcopters composing trios. The existence of the control input satisfying NCBF conditions is analyzed by employing the characteristics of the power diagram. The proposed framework is synthesized with a coverage control law that maximizes the monitoring quality while reducing overlaps of FOVs. The proposed method is demonstrated in simulation and experiment. Copyright © 2022, The Authors. All rights reserved.

关键词： Sensor networks

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