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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Wang, Qianhui Zhu, Qingling Ji, Junkai College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

ISBN: (纸本)9798350308365

Recently, the application of transfer learning within dynamic multiobjective evolutionary algorithms (DMOEAs) has shown significant potential to solve dynamic multiobjective optimization problems (DMOPs). This approach utilizes the transfer learning technique which reuses information from previous environments to accelerate the search process in the new environment. However, the risk of negative transfer can lead to an erroneous search direction and inefficient use of computational resources. To address this issue, this paper proposes a novel multistage transfer learning DMOEA, named MSTL. The algorithm is designed to enhance the convergence and diversity of the population through the combination of the pre-transfer learning stage and the transfer learning and validation stage when environmental changes occur. In the pre-transfer learning stage, a suitable target domain, source domain, and validation classifier sample sets are generated guided by historical information. In the transfer learning and validation stage, the TrAdaboost technique to transfer knowledge from the target to the source domain, with the results verified by two validation classifiers to mitigate negative transfer. Experimental results demonstrate that our proposed algorithm outperforms four competing algorithms in terms of diversity and convergence on a widely recognized benchmark suite. © 2024 IEEE.

关键词： Dynamic multiobjective optimization evolutionary algorithm transfer learning

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Secret Sharing Based Key Agreement Protocol for Body Area Networks 18th

Secret Sharing Based Key Agreement Protocol for Body Area N...

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18th International Conference on Wireless Artificial Intelligent Computing Systems and Applications, WASA 2024

作者： Sheng, Weihong Cai, Bin Hu, Chunqiang Li, Ruinian School of Big Data and Software Engineering Chongqing University Chongqing China Computer Science Department Bowling Green State University Bowling Green United States

ISBN: (纸本)9783031714634

In order to provide more comprehensive medical services and personalized health monitoring according to individual needs, Body Area Networks (BANs) have been extensively studied by many researchers. As BANs involve the transmission of personal private data, the security of the communication is of utmost importance. Unfortunately, existing encryption techniques cannot be directly applied with the limited power or computation ability of the sensors in BANs. An alternative direction is to use physiological signals for key agreement. However, many of the current physiological signal-based key agreement schemes either have high overheads or are vulnerable to certain security issues. In this paper, we proposed a novel secret sharing and Bloom filter based key agreement scheme that balances overhead and security for BANs. Also, we use an approximate hash table to free ourselves from the problem of dependence on the ordering of features, which has been neglected by previous researchers. We validate the security of our scheme with based on real datasets and compare overheads with other solutions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Electronic health record

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Wearable-Based Human Emotion Inference System 2nd

Wearable-Based Human Emotion Inference System

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2nd EAI International Conference on Edge Computing and IoT: Systems, Management and Security, ICECI 2021

作者： Zhao, Zirui Zheng, Canlin College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9783031042300

Many emotion recognition methods which have been proposed today have different shortcomings. For example, some methods use expensive and cumbersome special-purpose hardware, such as EEG/ECG helmets, while others based on cameras and speech caused risk of privacy leakage. With the prosperous development and popularization of wearable devices, people tend to be equipped with multiple smart devices, which provides potential opportunities for lightweight emotional perception. Based on this, we take actions on developing universal portable system and multi-source wearable sensing technology devices. This paper designs an emotion recognition framework called MW-Emotion (Multi-source Wearable emotion recognition) featuring on low cost, universality, and portable commercial wearable devices to perceive multi-source sensing data and implement a system. It takes four basic emotions as the research object and implement an emotion recognition to explore deep context innovatively. The experimental results show that MW-Emotion has a recognition accuracy of 85.1% for person-dependent mode. The framework uses the method that different types of data are effectively fused through signal processing. We call it multimodal data fusion technology, which reduces the energy waste caused by data redundancy and effectively resists interference. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Emotion Recognition

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Convergence Track Based Adaptive Differential Evolution Algorithm(CTbADE)

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computers, Materials & Continua 2022年第7期72卷 1229-1250页

作者： Qamar Abbas Khalid Mahmood Malik Abdul Khader Jilani Saudagar Muhammad Badruddin Khan Mozaherul Hoque Abul Hasanat Abdullah AlTameem Mohammed AlKhathami Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan Department of Computer Science and Engineering Oakland UniversityRochesterMIUSA Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)RiyadhSaudi Arabia

One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima.A novel convergence track based adaptive differential evolution(CTbADE)algorithm is presented in this research *** crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima.A more diverse population improves the global searching capability and helps to escape from the local optima *** the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying *** adaptive powerful parameter-controlled sequences utilized learning period-based memory and following convergence track over time are introduced in this *** proposed algorithm will be helpful in maintaining the equilibrium between an algorithm’s exploration and exploitation capability.A comprehensive test suite of standard benchmark problems with different natures,i.e.,unimodal/multimodal and separable/non-separable,was used to test the convergence power of the proposed CTbADE *** results show the significant performance of the CTbADE algorithm in terms of average fitness,solution quality,and convergence speed when compared with standard differential evolution algorithms and a few other commonly used state-of-the-art algorithms,such as jDE,CoDE,and EPSDE *** algorithm will prove to be a significant addition to the literature in order to solve real time problems and to optimize computationalmodels with a high number of parameters to adjust during the problem-solving process.

关键词： Differential evolution function optimization convergence track parameter sequence adaptive control parameters

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Image-based Jointly Sparse Maximum Margin Criterion with Pairwise Constrain for Feature Extraction 2

Image-based Jointly Sparse Maximum Margin Criterion with Pai...

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2nd International Conference on computer science, Electronic Information engineering and Intelligent Control Technology, CEI 2022

作者： Zhou, Yifan The College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9781665476164

ISBN: (纸本)9781665476164

In this paper, we propose an image-based feature extraction framework. To our best knowledge, there are few methods to combine image feature extraction and pairwise constrain (PC) jointly and directly. We fill the gap between them. With the PC, we devise an objective function, taking the PC and image feature extraction consideration jointly. Besides, we introduce maximum margin criterion into the loss function, making all positive constrains and negative constrains take part in the process of feature extraction. Finally, the solution of the new framework can be deduced into two sperate individual problems. Comprehensive experiment results show our framework effective. © 2022 IEEE.

关键词： Feature extraction

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TSMS-InceptionNeXt:A Framework for Image-Based Combustion State Recognition in Counterflow Burners via Feature Extraction Optimization

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computers, Materials & Continua 2025年第6期83卷 4329-4352页

作者： Huiling Yu Xibei Jia Yongfeng Niu Yizhuo Zhang Software Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China Electrical Engineering Department of Computer ScienceChangzhou UniversityChangzhou213146China

The counterflow burner is a combustion device used for research on *** utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion *** existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and *** has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction ***,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction ***,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic ***,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical ***,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual ***,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input *** validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image datase

关键词： Counterflow burner combustion state recognition InceptionNeXt dilated convolution channel shuffling

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An Exploratory Study on Code Quality, Testing, Data Accuracy, and Practical Use Cases of IoT Wearables 7

An Exploratory Study on Code Quality, Testing, Data Accuracy...

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7th Conference on Cloud and Internet of Things, CIoT 2024

作者： Minani, Jean Baptiste El Fellah, Yahia Ahmed, Sanam Sabir, Fatima Moha, Naouel Gueheneuc, Yann-Gael Concordia University Computer Science and Software Engineering Department MontrealQC Canada École de Technologie Supérieure Département de Génie Logiciel et des Ti MontrealQC Canada University of the Punjab Department of Computer Sciences and Software Engineering Lahore Pakistan

ISBN: (纸本)9798350356717

The growth of the Internet of Things (IoT), particularly in wearable devices like Fitbits, has raised challenges related to source code quality, testing, data accuracy, and practical applications. This paper investigates issues in Fitbit apps by (1) analyzing GitHub repositories of Fitbit projects to identify code quality issues, (2) using Large Language Models (LLMs) to automate testing, (3) comparing data variations across different Fitbit models, and (4) experimenting with real-world use cases for Fitbit devices. Our analysis of 16 GitHub repositories revealed code quality issues in Fitbit apps, highlighting the need for better practices. Using LLMs like ChatGPT-4, we generated unit tests with 100% coverage. Data comparisons across Fitbit Versa models showed consistent accuracy. Finally, we showed the potential of wearable devices in the real-world with two practical use cases: health monitoring with robotic assistance and location-based tracking. These findings open new avenues for research in wearables. © 2024 IEEE.

关键词： Data accuracy

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Predicting Travel Time in Complex Road Structures using Deep Learning 14

Predicting Travel Time in Complex Road Structures using Deep...

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2024 IEEE 14th Annual Computing and Communication Workshop and Conference, CCWC 2024

作者： Nampalli, Vignaan Vardhan Gudla, Charan Rana, Md Shohel Mississippi State University Computer Science and Engineering StarkvilleMS United States Florida Gulf Coast University Computing and Software Engineering Fort MyersFL United States

ISBN: (纸本)9798350360134

Vehicular traffic and congestion is a major challenge worldwide because of rapid growth in urban population. The congestion can be mitigated to enhance traffic management by predicting accurate travel time of the vehicles in the traffic. This research developed a novel methodology utilizing machine learning on real-time traffic data collected through Bluetooth sensors deployed at traffic intersections to estimate travel time. The research evaluates performance and accuracy of five different prediction systems for travel time estimation highlighting the effectiveness of the machine learning models in accurately predicting travel time. The research also explores the development of the machine learning model predicting the travel time during peak hours, considering traffic lights impact on travel time between intersections. This research findings contribute to the efficient and reliable travel time prediction systems development, helping commuters making informed decisions and improve traffic management strategies. © 2024 IEEE.

关键词： Travel time

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Actor-Oriented Self-Organizing Maps: A novel distributed design for Self-Organizing Systems 22

Actor-Oriented Self-Organizing Maps: A novel distributed des...

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10th International Conference on computer and Communications Management, ICCCM 2022

作者： Thomas, Penelope Clara Coulter, Duncan Anthony Academy of Computer Science and Software Engineering University of Johannesburg South Africa

ISBN: (纸本)9781450396349

This paper explores the implementation of a distributed model of the Self-Organizing Map (SOM) and its subsequent validation through the implementation of a proof-of-concept prototype using the Typed-Akka tool kit. This work is a part of an on-going study into the intersection of the actor-model and SOMs following on from previous work on lattice initialization. A comprehensive software architecture model is given using various UML diagrams, specifically the behavioral and finite state-oriented aspects of the design are given using activity and state diagrams respectively. A parameter-study was conducted that focused specifically on the neighborhood definition function in the context of an actor-oriented SOM. © 2022 ACM.

关键词： Self organizing maps

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