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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ye, Xinggui Li, Jianping Wang, Peng School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China School of Computer Science and Technology Southwest Minzu University Chengdu610225 China

ISBN: (纸本)9781665410205

Quantum perturbation plays an important role in quantum movement. This paper proposes a three-stage perturbation (TSP) framework to enhance the performance of multi-scale quantum harmonic oscillator algorithm (MQHOA). Three perturbations are adopted in the population initialization process with opposition-based learning (OBL), in quantum harmonic oscillator (QHO) process with ensemble of three differential evolution (DE) strategies and in multi-scale (M) process with a rollback mechanism to enhance the diversity of the particles and prevent from falling into local optima. The proposed approach has been evaluated on several high-dimensional expensive problems from 50-D to 500-D. The empirical data are compared with recent MQHOA variants and some state-of-the-art similar metaheuristic algorithms (MAs). The experimental results reveal the superiority or competitiveness of the the proposed approach. © 2024 IEEE.

关键词： Harmonic analysis

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RIS-assisted multi-user visible light communication in IIoT scenarios: prototype design and feasibility study 6

RIS-assisted multi-user visible light communication in IIoT ...

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6th international conference on Optoelectronic science and Materials, ICOSM 2024

作者： Ye, Ziwei Li, Dun Zhang, Erfeng Zhu, Yijun Information Engineering University Henan Province Zhengzhou450000 China

ISBN: (数字)9781510686823

ISBN: (纸本)9781510686816

As the multi-user wireless communications in Industrial Internet of Things (IIoT) scenarios are increasingly demanded, conventional visible light communication (VLC) solutions suffer from the problems such as the inter-cell crosstalk and the latency when switching the cells. The introduction of reconfigurable intelligent surface (RIS) supplies new approach of reconfigurable optical cells based on digital holography. This paper proposes the prototype model of the holography-based multi-user VLC system. The computer-generated holograms (CGH) generation quality is analyzed, and the lens system to adjust the positions and the sizes of the generated cells is designed. The feasibility of the system is verified by simulation, which shows that the method can support 5 users data transmission. © 2024 SPIE.

关键词： computer generated holography

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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international Journal of Advanced computer science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings international conference on Software engineering and ACM international conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (science and Information Organization). All Rights Res

关键词： Software design

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Collaborative Learning for multi-Source Domain Adaptative Object Detection 4

Collaborative Learning for Multi-Source Domain Adaptative Ob...

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4th international conference on Neural Networks, Information and Communication engineering, NNICE 2024

作者： Cheng, Yansong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9798350394375

multi-Source domain adaptation is a more general scenario than single-source domain adaptation, however, it encounters challenges arising from the problem of model effect degradation caused by distribution differences between multiple source domains. To address this issue, we propose a novel approach, namely collaborative learning(CL). CL can retain public intra-domain knowledge and improve the generalization ability of the model. Extensive experiments on different adaptation scenarios demonstrate the effectiveness of the proposed model. © 2024 IEEE.

关键词： Object detection

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Comparative Study of Machine Learning and Deep Learning Models for Food Waste Detection Using Image Processing

Comparative Study of Machine Learning and Deep Learning Mode...

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2025 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2025

作者： Rajagopal, R. Chaithanya, K. Sonth, Ankita Kallappagoudar, Prasanna Basanagouda Mohammadishaq Nadaf, Mohammadzaid Ananda Theertha, H. Dept of Computer Science Alliance College of Engineering and Design Alliance University Bangalore India Bangalore India Alliance College of Engineering and Design Dept of Computer Science Bangalore India Alliance College of Engineering and Design Dept of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331529833

For the purpose of reducing financial losses and environmental impacts, food waste detection is essential. Inorder to detect food waste using image processing, this study investigates the efficiency of several machine learning algorithms, including Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), Random Forest, and Inception Model (IV3). A set of images of food are then processed. Before each algorithm is used for classification, pre-processing methods like segmentation and feature extraction are used. © 2025 IEEE.

关键词： k-nearest neighbors

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Enhancing Aspect-Based Sentiment Analysis Through multi-Granularity Information Sharing 2

Enhancing Aspect-Based Sentiment Analysis Through Multi-Gran...

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2nd IEEE international conference on Data science and Network Security, ICDSNS 2024

作者： Ilayaraja, N. Yuvaraj, S. Chowdhury, Rini Kumar, Prashant Yalagi, Pratibha C. Kaladeep Glory, E. Hindusthan College of Engineering and Technology Department of Information Technology Coimbatore India Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India Saltlake Telephone Exchange Department of It Project Circle Bsnl Block DE West Bengal Lalkuthi Kolkata India Walchand Institute of Technology Department of Computer Science and Engineering Maharashtra Solapur India Saveetha School of Engineering Simats Department of Biosciences Chennai India

ISBN: (纸本)9798350373110

multi-granular information contains sentences, words, phrases, etc. defining different user sentiments. Analyzing the sentiments using the available information requires disintegrated individual context-matching processes. This article proposes a Least Granular Context Matching Method (LGCMM) to analyze user sentiments through classification. The classification is performed using a decisive neural network with higher possible granular differentiation. Based on the learning output, the matching context for any granular entry is tested using training sentiment inputs. This matching with high similarity is used to decide the training for maximum differentiation. Therefore, irrespective of the granular information contexts, the sentiment assessment is improvised through the high degree of differentiation. The proposed method is analyzed using analysis rate, similarity index, and sentiment precision. © 2024 IEEE.

关键词： Contrastive Learning

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Mutli-Step Chain-of-Thought in Geometry Problem Solving 4

Mutli-Step Chain-of-Thought in Geometry Problem Solving

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4th international conference on Electronic Information engineering and computer science, EIECS 2024

作者： Wang, Long Ma, Jianhui University of Science and Technology of China Hefei China

ISBN: (纸本)9798331531409

In the realm of natural language processing, large language models (LLMs) have demonstrated superb performance in human-level reasoning and text generation, which has inspired a large number of researchers to conduct extensive research on the automatic solution of mathematical word problems. The latest work has concentrated on the construction of extensive geometry datasets for fine-tuning, leveraging huge datasets to enhance the mathematical reasoning capabilities of models. However, the majority of these work focus on calculation problem solving, and little attention is given to the more challenge proving problems. To address this gap, we introduce a multi-step chain of thought approach. By employing two-step generation approach, our method enables the model to concentrate on distinct task, thereby augmenting the model's comprehension and mathematical reasoning ability. Furthermore, we explore the application of multi-modal large language models in geometry proving problems, proposing an evaluation framework that employ the existing powerful LLMs to evaluate model's performance. © 2024 IEEE.

关键词： Chains

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Research on the Application of multi-Source Radar Information Fusion Based on Digital Twin 7

Research on the Application of Multi-Source Radar Informatio...

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7th international conference on computer Information science and Application Technology, CISAT 2024

作者： Zhao, Sheng Xie, Yanan Lu, Tegang Bai, Hao Northwest Institute of Mechanical and Electrical Engineering Xianyang China Aeronautics Computing Technology Research Institute Xian China

ISBN: (纸本)9798350375107

This paper proposes a multi-source radar information fusion technology grounded in digital twin technology, considering the future development trajectories of radar information fusion and the current status and advancements in digital twin technology. It systematically examines the specific applications of digital twin technology in multi-source radar information fusion, encompassing system architecture design, data acquisition and processing, real-time simulation and prediction, fault diagnosis, and health management. Additionally, the potential applications of digital twin technology in fault diagnosis and health management within the context of multi-source radar information fusion are explored, proposing data-driven preventive maintenance strategies to further enhance system reliability and maintainability. The paper also identifies challenges such as high-precision modeling, data transmission, and processing efficiency in the application of digital twin technology within multi-source radar information fusion, providing reference directions for future development. © 2024 IEEE.

关键词： Data fusion

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A multi-fault diagnosis method based on the fusion of time-space information 7

A multi-fault diagnosis method based on the fusion of time-s...

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7th international conference on computer Information science and Application Technology, CISAT 2024

作者： Zhou, Bingjing Shanghai Maritime University Logistics Engineering College Electrical Engineering and Automation Pudong New Area Shanghai China

ISBN: (纸本)9798350375107

In industrial processes, equipment data are often characterized by dynamics, strong nonlinearity and strong coupling, which makes it difficult to achieve multi-fault diagnosis when multiple faults occur simultaneously. To solve this problem, this paper proposes a multi-fault diagnosis method based on time-space information fusion. The method is realized by designing a time-space information fusion model and combining it with the contribution graph method. Firstly, the Euclidean distance is used to mine the information of the samples in the time direction;then, the spatial features are extracted from the mined temporal information to achieve the time-space information fusion;and finally, the fault variables are identified by combining with the contribution graph method to achieve the multi-fault diagnosis. Verified by numerical cases and actual data from the equipment of an inlet lift pump room of a wastewater treatment plant in Shanghai, the time-space information fusion model can effectively extract and use time-space information to separate fault samples, and the subsequent diagnostic results can accurately locate the fault variables. © 2024 IEEE.

关键词： Information fusion

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An Entity Relation Extraction Framework Based on Large Language Model and multi-Tasks Iterative Prompt engineering

An Entity Relation Extraction Framework Based on Large Langu...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Geng, Haibin Shi, Chenglong Jiang, Xuesong Kong, Zan Liu, Song Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Jinan China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan China

ISBN: (纸本)9781665410205

Document-level entity relation extraction is an important task in the field of natural language processing, which plays an important role in semantic understanding and knowledge graph construction. However, existing deep neural networks and graph neural networks models are limited by their performance and parameters number, which can not capture global semantics and have poor generalization ability. Furthermore, existing methods employing large language model for entity relation extraction do not establish good relationships among multi-tasks of entity relation extraction task, resulting in more information can not be effectively shared and transmitted between tasks. In addition, previous approaches can not effectively eliminate false entities and relationships. To solve these problems, we propose an entity relation extraction framework based on large language model and multi-tasks iterative prompt engineering. In our model, we design an iterative prompt engineering, which can better establish the relationship among multi-tasks, and ensure every task to obtain the optimal results. Moreover, we design semantic merging, group disambiguation and self-verification modules to eliminate the false entity relations and noise nodes. Additionally, we design summary prompts to provide sufficient global semantics for better text segmentation. Finally, we evaluated our model on wikiann, wikineural, ACE2005, CoNLL2003, CoNLL2004, and SciERC datasets and compared it with other baseline models. © 2024 IEEE.

关键词： Graph neural networks

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