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4th International Conference on Intelligent technology and Embedded Systems, ICITES 2024

作者： Lin, Hanqing Hao, Zongbo Yu, Can Mo, Jikang Tao, Zixv Lin, Di University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331506292

Synthetic Aperture Radar (SAR) image speckle is a type of image noise caused by the coherence of terrain, which can degrade the resolution and detection capability of SAR images. To address this challenge, this paper proposes a speckle suppression network based on Transformer and frequency features. The network first performs initial denoising on the images, followed by wavelet transformation to retain high-frequency information, and then inputs the results into the network to guide speckle removal. The main body of the network employs Transformer as the encoder for feature extraction, facilitating better learning of the relationships between different regions. Experiments conducted on both synthetic and real datasets validate the effectiveness of the proposed method, showing superior performance compared to existing classical methods. © 2024 IEEE.

关键词： Radar imaging

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FedDPC: A Density Peak Clustering-based Federated Learning Algorithm 4

FedDPC: A Density Peak Clustering-based Federated Learning A...

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4th International Conference on Artificial Intelligence, Robotics, and Communication, ICAIRC 2024

作者： Du, Jiayi Zhao, Taiyin Li, Xucheng University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331531225

Federated learning (FL) is widely applied in edge computing scenarios to protect data privacy, but the assumption of independent and identically distributed (IID) data across clients often does not hold in practice, leading to performance degradation. Among the many methods proposed to address data heterogeneity, clustered federated learning (CFL) has emerged as a popular approach. Current CFL methods commonly adopt the expectation-maximization (EM) algorithm, which mitigates the problem of data heterogeneity. However, they still face challenges such as requiring more clients than traditional federated learning and introducing randomness in the clustering process. To overcome these limitations, we propose FedDPC, which integrates the density peak clustering (DPC) algorithm. This approach provides the advantages of not requiring additional clients and having a non-random partitioning strategy. Finally, we conducted experiments to demonstrate the feasibility and effectiveness of our proposed method. © 2024 IEEE.

关键词： Federated learning

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Application of optimized deformable convolution in underwater biological detection algorithm 3

Application of optimized deformable convolution in underwate...

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3rd International Symposium on Computer Applications and Information Systems, ISCAIS 2024

作者： Zhuang, Hongwei Liu, Weisheng School of Computer and Software Engineering Liaoning University of Science and Technology Liaoning Anshan China

ISBN: (纸本)9781510681316

Underwater target detection is an important method for marine life detection. However, the accuracy of target detection and recognition is affected by the problems of image occlusion, blurred water quality and complex background of underwater targets. An improved YOLOV7 underwater biological target detection method is proposed to address the above problems. Firstly, the DCN structure is improved by incorporating the residual structure into the DCN, with new jump connections and 1×1 convolutional branches, so that the network can learn the residual information between the input and the output. It can improve the expressive ability of the model, reduce the number of parameters, and enhance the adaptability to the shape of the object. Finally, COT3 is integrated, which utilises a self-attention mechanism to weight the input feature maps so that the model can focus on important contextual information around the target and improve the model's ability to handle complex scenes. The experimental results show that the improved YOLOv7 model achieves an average detection accuracy mAP value of 80.4%, which is 2.8% higher than the original algorithm, and the algorithm is better than the original YOLOv7 algorithm in terms of detection accuracy and speed, which is more suitable for the detection of underwater targets. © 2024 SPIE.

关键词： Water quality

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Beyond Code: Evaluate Thought Steps for Complex Code Generation 30

Beyond Code: Evaluate Thought Steps for Complex Code Generat...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Cao, Liuwen Cai, Yi He, Hongkui Huang, Hailin Wang, Jiexin School of Software Engineering South China University of Technology China Ministry of Education China

ISBN: (纸本)9782493814104

Code generation aims to generate code in a general-purpose programming language, such as C++, based on natural language intents. Existing efforts primarily focus on relatively simple programming problems and fail to evaluate the thought process involved in complex programming scenarios. In this paper, we introduce "steps-guided code generation", a task that assesses the quality of both thought steps and code implementation to evaluate the overall management of handling a complex programming problem. To support this task, we construct CodeStepsEval, a real-world scenario dataset of complex programming problems in the C++ programming language with varying levels of difficulty. Comprehensive experiments on this dataset demonstrate the importance of high-quality steps in enhancing code generation performance and the challenges faced by the code LLMs in this task. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Quality control

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Generative AI — The End of Systematic Reviews in PhD Projects?

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ACM Inroads 2024年第2期15卷 48-50页

作者： Zamani, Sanaz Sinha, Roopak Department of Computer Science and Software Engineering Auckland University of Technology Auckland New Zealand School of Information Technology Deakin University Melbourne Australia

In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the ubiquitous availability of Generative AI tools shape PhD research and supervision, and how can we capitalize on such disruptions? We report our reflections about early-stage PhD research from 10+ PhD supervisions completed by Sinha, and a recently started PhD project with Zamani as the student. We conducted a systematic literature review following traditional methods for the first four months and then revisited the task using ChatGPT and other related tools. We opine that while SRs still have value for new students, Generative AI must be incorporated, with care, to significantly enhance student learning, reinvest time savings into other, often-ignored skills, and improve the quality (breadth and depth) of SRs. © 2024 Association for Computing Machinery. All rights reserved.

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Fast feature selection algorithm of EEG data based on convolutional neural network 6

Fast feature selection algorithm of EEG data based on convol...

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6th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2024

作者： Liu, Bin Cao, Liming Fang, Tianke Xiamen University of Technology School of Software Engineering Xiamen China Mapua University School of Information Technology Manila Philippines Xiamen University of Technology School of Computing and Information Engineering Xiamen China

ISBN: (纸本)9798331516550

Electroencephalography (EEG) data is invaluable for investigating EEG physiology, neuroscience and clinical medicine. However, EEG signals are characterized by temporal nature, high dimensionality, and noise, which make it difficult for traditional feature selection methods for EEG data to extract useful information effectively with high computational effort. To solve this problem, this paper proposed a fast feature selection algorithm for multichannel EEG data based on a convolutional neural network (FFSEEG-GNN). Specifically, the algorithm pre-processes the multichannel EEG data, then extracts the waveform features and builds a CUDA architecture using GPUs to convolutional neural network operations. Finally, the most representative subset of features is selected in order of feature importance and feature extraction is performed. In addition, the simulation comparison experiment proves that the algorithm achieves 92.60% in terms of precision rate. Therefore, the program has accurate feature selection capability, which is expected to play an essential role in clinical medicine. © 2024 IEEE.

关键词： Convolutional neural networks

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Ontology Evaluation Tools: Current and Future Research

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Recent Advances in Computer Science and Communications 2023年第6期16卷 2-15页

作者： Debnath, Narayan C. Patel, Archana Department of Software Engineering School of Computing and Information Technology Eastern International University Thu Dau Mot Viet Nam

In recent years, the increasing interest in ontologies resulted in the developing and publishing of many ontologies in the same or different domains. When users try to reuse the existing ontologies in their applications, they may encounter problems determining the most appropriate ontology based on their needs. Ontology evaluation is a process that helps users to examine the quality of the ontology based on different attributes. Many accessible and usable tools for ontology evaluation have been studied in the literature. However, finding an efficient ontology evaluation tool, following ontology specifications and their requirements (advantages/disadvantages), is still missing, limiting the researchers from determining possible future research. This paper aims to help new researchers and practitioners identify appropriate ontology evaluation tools based on their requirements and provide guidelines for future research directions on the same topic. This paper provides a detailed description of the different types of ontologies and classifies the available ontology evaluation tools into two categories, namely domain dependant ontology evaluation tools and domain-independent ontology evaluation tools. © 2023 Bentham Science Publishers.

关键词： Ontology

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SparseCoder: Identifier-Aware Sparse Transformer for File-Level Code Summarization 31

SparseCoder: Identifier-Aware Sparse Transformer for File-Le...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Wang, Yanlin Huang, Yanxian Guo, Daya Zhang, Hongyu Zheng, Zibin School of Software Engineering Sun Yat-sen University China School of Computer Science and Technology Sun Yat-sen University China School of Big Data and Software Engineering Chongqing University China

ISBN: (纸本)9798350330663

Code summarization aims to generate natural language descriptions of source code, facilitating programmers to understand and maintain it rapidly. While previous code summarization efforts have predominantly focused on method-level, this paper studies file-level code summarization, which can assist programmers in understanding and maintaining large source code projects. Unlike method-level code summarization, file-level code summarization typically involves long source code within a single file, which makes it challenging for Transformer-based models to understand the code semantics for the maximum input length of these models is difficult to set to a large number that can handle long code input well, due to the quadratic scaling of computational complexity with the input sequence length. To address this challenge, we propose SparseCoder, an identifier-aware sparse transformer for effectively handling long code sequences. Specifically, the SparseCoder employs a sliding window mechanism for self-attention to model short-term dependencies and leverages the structure message of code to capture long-term dependencies among source code identifiers by introducing two types of sparse attention patterns named global and identifier attention. To evaluate the performance of SparseCoder, we construct a new dataset FILE-CS for file-level code summarization in Python. Experimental results show that our SparseCoder model achieves state-of-the-art performance compared with other pre-trained models, including full self-attention and sparse models. Additionally, our model has low memory overhead and achieves comparable performance with models using full self-attention mechanism. Furthermore, we verify the generality of SparseCoder on other code understanding tasks, i.e., code clone detection and code search, and results show that our model outperforms baseline models in both tasks, demonstrating that our model can generate better code representations for various downstream tasks. Our

关键词： Semantics

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Masked hybrid attention with Laplacian query fusion and tripartite sequence matching for medical image segmentation

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Neural Computing and Applications 2025年第8期37卷 5891-5911页

作者： Ekong, Favour Yu, Yongbin Patamia, Rutherford Agbeshi Sarpong, Kwabena Ukwuoma, Chiagoziem C. Wang, Xiangxiang Ukot, Akpanika Robert Cai, Jingye School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu611731 China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan Chengdu610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan Chengdu610059 China

Medical image segmentation is pivotal in computer-aided diagnosis systems, demanding high precision and contextual understanding. Vision Transformer-based approaches have gained much attention recently due to their excellent performance and ability to capture long-range dependencies in medical images. However, research shows they suffer from inadequate multi-scale feature integration, poor object localization, and inconsistent mask predictions, leading to sub-optimal segmentation performance. This paper addresses these challenges by redefining semantic medical image segmentation through learnable object queries within an enhanced transformer framework with a masked hybrid attention querying mechanism, optimizing multi-scale feature fusion, object localization, and instance-specific segmentation. First, this study presents a novel transformer-based masked hybrid attention mechanism using Laplacian query fusion on learnable query features and incorporating a novel tripartite sequence matching technique as part of the enhanced decoder block to improve the consistency of mask predictions and optimize decoder queries. The designed hybrid multi-head self- and cross-attention mechanisms aim to selectively integrate multi-scale features, ensuring optimal feature combinations for precise segmentation. Secondly, multiple class tokens are incorporated to improve object localization and capture class-specific characteristics within the transformer framework, leveraging the transformer decoders’ ability to learn distinct instance representations. Experimental results and extensive ablation studies demonstrate the effectiveness of the proposed approach on three publicly available datasets, obtaining better segmentation results compared to various state-of-the-art approaches using various evaluation metrics. Specifically, the proposed model achieves a Dice Score of 95.25%, 92.75%, and 85.25% on LUNA, ISIC, and DRIVE datasets, respectively. © The Author(s), under exclusive licence

关键词： Laplace transforms

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Exploration and Practice of Deep Learning Classroom Teaching of 'Computer Network' Course Based on the Integration of Specialization and Innovation 5

Exploration and Practice of Deep Learning Classroom Teaching...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Huang, Min Peng, ShaoWu Yuan, Hua School of Software Engineering South China University of Technology Guangzhou China School of Computer Science South China University of Technology Guangzhou China

ISBN: (纸本)9798331528409

In the era of rapid development of artificial intelligence technologies, traditional teaching models are unable to meet the employment needs of enterprises, and talent cultivation in universities faces more challenges, so teachers should consider how to better cultivate students' innovative abilities in classroom teaching and guiding them in practical applications. The paper focuses on teaching reform and practice of 'Computer Networks' course by deep learning classroom, which mainly includes three aspects: 1) Integrating ideological and political education and innovative education into the process of professional education of in the curriculum, by which to enhance students' professional innovation awareness;2) Inviting experts from enterprises to participate in classroom teaching and practical guidance to help students learn about the ability requirements and related positions in the IT enterprises for talents in the era of AI;3) Designing practice projects and guiding students to implement them by 'design thinking' methods, and also adopting an evaluation method that can reflect the innovative ability of students. Works of the paper can better reflect the support and deepening the teaching philosophy of studentcentered, and provide an attempt to improve teaching effectiveness and talent cultivation quality. © 2025 IEEE.

关键词： Cultivation

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