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2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Xiao, Huaxin Peng, Yang Tan, Hanlin Li, Ping Department of Systems Engineering National University of Defense Technology China School of Computer Science and Technology Hangzhou Dianzi Univeristy China

ISBN: (纸本)9781665438643

Building-based damage assessment usually involves two different tasks, i.e., localizing buildings before disasters and scoring the damage levels after disasters. Recently, Convolutional Neural Network based machine learning methods achieve impressive performance by directly fusing the feature from pre- and post-disaster satellite imageries, which are collected at totally different dates and climatic conditions. The discrepancy of input sources may disturb the learning process, especially when simultaneously detecting buildings and assessing the damage levels by a unified model become a new trend. In this paper, we propose a Dynamic Cross Fusion Network (DCFNet) that makes each task bidirectionally access to multi-levels features from a shared backbone network. Moreover, a task-shared head is designed to adaptively exchange information cross tasks and avoid under-fitting for one specific task. Without bells and whistles, the proposed DCSNet achieves state-of-the-art single-model performance in the xView2 Challenge, which provides a new and large-scale damage assessment dataset xBD. © 2021 IEEE

关键词： Disasters

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A review of smartphones-based indoor positioning:Challenges and applications

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IET Cyber-systems and Robotics 2021年第1期3卷 1-30页

作者： Khuong An Nguyen Zhiyuan Luo Guang Li Chris Watkins School of Computing Engineering and MathematicsUniversity of BrightonBrightonUK Computer Science Department Royal HollowayUniversity of LondonEghamUK Institute of Cyber-Systems and Control Zhejiang UniversityHangzhouChina

The continual proliferation of mobile devices has encouraged much effort in using the smartphones for indoor *** article is dedicated to review the most recent and interesting smartphones-based indoor navigation systems,ranging from electromagnetic to inertia to visible light ones,with an emphasis on their unique challenges and potential realworld applications.A taxonomy of smartphone sensors will be introduced,which serves as the basis to categorise different positioning systems for reviewing.A set of criteria to be used for the evaluation purpose will be *** each sensor category,the most recent,interesting,and practical systems will be examined,with detailed discussion on the open research questions for the academics,and the practicality for the potential clients.

关键词： client smart taxonomy

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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Congress on Evolutionary Computation

作者： Wenjing Hong Cheng Chen Zexuan Zhu Ke Tang National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design.

关键词： Measurement Design methodology Evolutionary computation Search problems Linear programming Stability analysis Vaccines

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IntSHU: A Security-Enabled Intelligent Soft Handover Approach for UAV-Aided 5G and Beyond

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Karmakar, Raja Kaddoum, Georges Akhrif, Ouassima Heritage Institute of Technology Department of Computer Science and Engineering IoT and Cyber Security including Blockchain Technology Kolkata700107 India University of Quebec Department of Electrical Engineering ETS Montreal Canada Lebanese American University Cyber Security Systems and Applied AI Research Center Beirut Lebanon

Unmanned aerial vehicles (UAVs) play a significant role in serving as gNodeBs (gNBs), or user devices (UDs). However, considering the high data rates and low latency required by fifth generation (5G) and beyond, it is essential to maintain a reliable connection at UAVs, particularly due to frequent handovers. To minimize handover failures, the hysteresis margin and time-to-trigger (TTT) should be dynamically adjusted. Furthermore, to prevent vulnerabilities to threats, UAV and gNB authentication and secure communication should be addressed during handovers. However, available literature uses a learning-based approach to handover management in UAV-aided 5G networks, thereby neglecting security, or offers a handover authentication mechanism, but lacks a novel handover strategy. To fill this gap in the literature, in this study, we design an Intelligent Soft Handover for UAV-enabled cellular networks (IntSHU), a generative adversarial network (GAN)-based scheme designed for intelligent soft handovers in UAV-enabled 5G and beyond. IntSHU uses adapted hysteresis margins and TTT values, dynamically generated through an -greedy policy. In addition, we implement a lightweight, physically unclonable function (PUF)-based message encryption and authentication scheme via blockchain for streamlined access control during handovers. The results of our simulation indicate that IntSHU significantly enhances network performance by facilitating reliable soft handovers. © 2015 IEEE.

关键词： 5G mobile communication systems

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An Evolutionary Task Scheduling Algorithm Using Fuzzy Fitness Evaluation Method for Communication Satellite Network

arXiv

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arXiv 2024年

作者： Jiang, Xuemei Guo, Yangyang Zhang, Yue Song, Yanjie Pedrycz, Witold Xing, Lining School of Electronic Engineering Xidian University Xi’an China School of Systems Science Beijing Jiaotong University Beijing China School of Reliability and Systems Engineering Beihang University Beijing China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

Communications satellite network (CSN), as an integral component of the next generation of communication systems, has the capability to offer services globally. Data transmission in this network primarily relies on two modes: inter-satellite communication and satellite-to-ground station communication. The latter directly impacts the successful reception of data by users. However, due to resource and task limitations, finding a satisfactory solution poses a significant challenge. The communication satellite-ground station network scheduling problem (CS-GSNSP) aims to optimize CSN effectiveness by devising a plan that maximizes link construction time while considering constraints associated with satellite operation modes. The large number of tasks and numerous constraints in the problem result in a time-consuming evaluation of fitness function values. To address this issue, we propose a fuzzy fitness evaluation method (FFEM) that employs fuzzy or real evaluation methods based on individual similarity degrees. Additionally, we introduce an evolutionary algorithm based on FFEM, called evolutionary algorithm based on FFEM (FFEEA), for iteratively searching high-quality network construction schemes. In FFEEA, an adaptive crossover approach is used for efficient population search. Finally, extensive experiments are conducted to demonstrate that our proposed fuzzy fitness evaluation method and other improvement strategies significantly enhance satellite network service time. The study introduces a novel approach to enhance the efficiency of solving combinatorial optimization problems, such as CS-GSNSP, by mitigating the complexity associated with fitness evaluation. © 2024, CC BY-NC-ND.

关键词： Combinatorial optimization

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Application of Artificial Intelligence in Malignant Tumors: A science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence Bioinformatics Cancer Genetic algorithms

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Automated Food Intake Monitoring System to Prevent Malnutrition Using the Tiago Robot Camera

Automated Food Intake Monitoring System to Prevent Malnutrit...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Konstantakopoulos, Fotios S. Plati, Daphni K. Pliakou, Labrina A. Di Luzio, Francesco Scotto Tagliamonte, Nevio Luigi Zollo, Loredana Tsiknakis, Manolis Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department Greece Forth Biomedical Research Institute IoanninaGR 45110 Greece Campus Bio-Medico University Research Unit of Advanced Robotics and Human-Centred Technologies Rome Italy Forth Institute of Computer Science Crete HeraklionGR 70013 Greece

ISBN: (纸本)9798350383386

The management of a daily diet is a significant concern among individuals in modern culture. The utilization of dietary assessment systems has significantly contributed to the efficient management of malnutrition and dietary habits over a period of time. In order to determine the nutritional value of the food that individuals consume, this study introduces a novel food monitoring system and its architecture. By creating a dataset of food images in a tray and using image processing, machine learning, and computer vision techniques, the nutrients and calories consumed by a patient are calculated. The proposed system achieves a 95.6% Intersection over Union score in the segmentation task, 92.3% top-1 accuracy in the classification task, and 4.6 g mean weight absolute error in the weight estimation *** Relevance: The proposed system has the ability to calculate the nutritional composition, encompassing calories, proteins, fats, and carbs, of food consumed by a patient, making it suitable for nutritional monitoring applications and healthcare systems that aim to monitor malnutrition in hospital patients. © 2023 IEEE.

关键词： Nutrition

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Communication-Efficient Stochastic Distributed Learning

arXiv

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arXiv 2025年

作者： Ren, Xiaoxing Bastianello, Nicola Johansson, Karl H. Parisini, Thomas Department of Electrical and Electronic Engineering Imperial College London London United Kingdom School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Trieste Italy

We address distributed learning problems, both nonconvex and convex, over undirected networks. In particular, we design a novel algorithm based on the distributed Alternating Direction Method of Multipliers (ADMM) to address the challenges of high communication costs, and large datasets. Our design tackles these challenges i) by enabling the agents to perform multiple local training steps between each round of communications;and ii) by allowing the agents to employ stochastic gradients while carrying out local computations. We show that the proposed algorithm converges to a neighborhood of a stationary point, for nonconvex problems, and of an optimal point, for convex problems. We also propose a variant of the algorithm to incorporate variance reduction thus achieving exact convergence. We show that the resulting algorithm indeed converges to a stationary (or optimal) point, and moreover that local training accelerates convergence. We thoroughly compare the proposed algorithms with the state of the art, both theoretically and through numerical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Federated learning

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Incremental Composition of Learned Control Barrier Functions in Unknown Environments

arXiv

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arXiv 2024年

作者： Lutkus, Paul Anantharaman, Deepika Tu, Stephen Lindemann, Lars Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States

We consider the problem of safely exploring a static and unknown environment while learning valid control barrier functions (CBFs) from sensor data. Existing works either assume known environments, target specific dynamics models, or use a-priori valid CBFs, and thus provide limited safety guarantees for general control-affine systems during exploration. We present a method for safely exploring by incrementally composing a global CBF from local CBFs. The challenge here is that local CBFs may not have well-defined end behavior outside their training domain, i.e. local CBFs may be positive (indicating safety) in regions where no training data is available. We show that well-defined end behavior can be obtained when local CBFs are parameterized by compactly-supported radial basis functions. For learning local CBFs, we collect sensor data, e.g. LiDAR capturing obstacles in the environment, and augment it with simulated data from a safe oracle controller. Our work complements recent efforts to learn CBFs from safe demonstrations, where learned safe sets are limited to their training domains, by demonstrating how to grow the safe set over time as more data becomes available. We evaluate our approach on two simulated systems, where our method successfully explores an unknown environment while maintaining safety throughout the entire execution. Copyright © 2024, The Authors. All rights reserved.

关键词： Radial basis function networks

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Using Blockchain Technology to Achieve Sustainability in the Hospitality Industry by Reducing Food Waste

SSRN

引用

SSRN 2023年

作者： Omar, Ilhaam A. Hasan, Haya Jayaraman, Raja Salah, Khaled Omar, Mohammad Department of Industrial and Systems Engineering Khalifa University Abu Dhabi United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

The hospitality industry generates significant food waste leading to adverse environmental and economic impacts, social and ethical concerns, legal and regulatory issues, and operational challenges. In particular, the lack of traceability and trust among stakeholders in the hospitality industry makes it expensive and difficult to manage food waste. Blockchain technology can revolutionize how food waste is managed within the hospitality industry. This work proposes a blockchain-based solution that encourages a sustainable food ecosystem by minimizing food waste generated in hospitality outlets. With the proposed solution, businesses can track and verify the origin of food items, movement, and disposal in real-time, allowing them to plan and purchase inventory, reduce waste, and increase efficiency. Additionally, our proposed system can provide transparency and accountability, ensuring food waste is properly disposed of. Using a permissioned Ethereum network, our solution connects food and beverage outlets with food waste organizations. We present a generalized system for securely sharing information, including algorithms to capture stakeholder interactions to enhance trust among participating stakeholders. Additionally, we analyze the solution for various security vulnerabilities. The proposed blockchain-based solution is commercially viable, the hospitality industry can operate with enhanced efficiency and provides better information connectivity for various stakeholders in the food supply chain. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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