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3rd International Conference on Cloud Computing, Big Data Application and software engineering, CBASE 2024

作者： Liu, Xiangfeng Sun, Jianting Lei, Aidi Zhu, Junzhi Co. Ltd Chengdu610073 China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798331506582

The rapid growth of data has led to a significant increase in unstructured data, such as text, audio, and images, which dominate modern information processing. However, the complexity of unstructured data presents challenges for automated analysis and processing. Converting unstructured data into structured formats is crucial for tasks like data mining, information extraction, and knowledge graph construction. Traditional methods that rely on manual rules or statistical models struggle with complex, context-dependent data. Recently, large language models (LLMs), such as GPT-4 and BERT, have demonstrated great potential in unstructured data processing due to their powerful natural language understanding and generation capabilities. This study explores the application of large language models in transforming unstructured data into structured formats. It begins by reviewing the limitations of traditional approaches and then presents a framework for unstructured data processing using LLMs, covering data preprocessing, information extraction, and structured representation. The study's experiments demonstrate the superior performance of LLMs in handling various types of unstructured data, particularly in tasks like named entity recognition, relation extraction, and contextual understanding. The results show that LLMs achieve higher accuracy and generalization while reducing reliance on manual rules. The paper discusses the strengths and limitations of this method, proposing future improvements, such as combining domain-specific knowledge for model optimization and expanding applications to multimodal data processing. The research highlights the promising role of large language models in converting unstructured data into structured formats. © 2024 IEEE.

关键词： Structured Query Language

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YOLOv5s-FSDA: Fire and Smoke Detection Algorithm Based on Improved YOLOv5s

YOLOv5s-FSDA: Fire and Smoke Detection Algorithm Based on Im...

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2024 International Symposium on Digital Home, ISDH 2024

作者： Li, Yuhua Xu, Yanru Zhang, Mengyue Sheng, Jianqiang Software Engineering College Zhengzhou University of Light Industry Zhengzhou450001 China Shenzhen Institute of Information Technology Shenzhen518172 China

ISBN: (纸本)9798331509873

Fire and smoke detection are critical to reducing fire losses. Edge devices with limited resources require a model that is more lightweight while still maintaining the detection accuracy necessary for fire and smoke detection tasks. To solve these problems, this paper proposes a fire and smoke detection algorithm based on YOLOvSs, named YOLOvSsFSDA. Firstly, we incorporate the Cross-scale Feature Fusion (CFF) structure, enhancing the recognition and representation capabilities of features across different scales. Secondly, to further reduce the model burden, we adopt Ghost Convolution (GhostConv) technology to reduce model parameter count and computational load. Thirdly, to enhance the model's overall grasp of fire and smoke areas, we integrate the Content-Aware ReAssembly of Features (CARAFE) upsampling method. In addition, we introduce the Similarity Intersection over Union (SIoU) loss function to reduce the positioning error between the prediction and actual boxes, ensuring that fire and smoke targets of different scales can be located more accurately. Experimental results show that the YOLOvSs-FSDA algorithm, while maintaining the high detection accuracy of YOLOvSs, reduces model complexity by 30% and computational costs by 18%. © 2024 IEEE.

关键词： Smoke detectors

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Semi-Supervised Semantic Segmentation in Remote Sensing Images Using Active Learning and Deep Global Second Order Pooling Network 20

Semi-Supervised Semantic Segmentation in Remote Sensing Imag...

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20th International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2023

作者： Sarpong, Kwabena Qin, Zhinguang Addo, Daniel Patamia, Rutherford Agbesi Ekong, Favour Khamis, Asha Mzee School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China

ISBN: (纸本)9798350318982

Training convolutional neural networks (CNNs) for semi-semantic segmentation in remote sensing images is difficult due to the substantial amount of labelled data required. The scarcity of dense pixel-level annotations hampers the model's ability to extract meaningful information from the images. We propose a novel semi-supervised semantic segmentation network called 3S-Net, designed explicitly for high-resolution remote sensing imagery to overcome this challenge. This network leverages a limited number of labelled examples and many unlabeled examples to generate pseudo-labels, expanding the labelled training set. The Deep Global Second-Order Pooling Network (DG2oPN) is employed to learn discriminative and representative features from the entire image information throughout the network. Our approach involves two main steps: First, an active learning-based sampling strategy is utilized to select a representative set of labelled data. Secondly, we introduce the 3S-Net, which learns from limited annotated data and effectively leverages unlabeled images by utilizing the second-order statistics of the entire image. Experimental results on two public remote sensing datasets demonstrate that our semi-supervised semantic model efficiently and effectively addresses the issue of limited labelled samples, outperforming other state-of-the-art methods. © 2023 IEEE.

关键词： Semantic Segmentation

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Accessibility Evaluation Of Five Moroccan E-government Portals 33

Accessibility Evaluation Of Five Moroccan E-government Porta...

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Joint of the 33rd International Workshop on software Measurement and the 18th International Conference on software Process and Product Measurement, IWSM-MENSURA 2024

作者： Ouaziz, Mohammed Rida Cheikhi, Laila Idri, Ali Abran, Alain Software Project Management Team ENSIAS Mohammed V University in Rabat Morocco Department of Software Engineering Information Technology École de Technologie Supérieure Montréal Canada

In the present era, e-government portals are viewed as an effective means of providing online services to citizens. It is imperative for everyone to guarantee equitable access to these services, irrespective of their abilities or limitations. Hence, accessibility is vital for those with disabilities and advantageous for everyone. Web Content Accessibility Guidelines (WCAG) provide a comprehensive set of guidelines, in terms of success criteria, that are widely utilized in various tools to assess the accessibility of websites or portals. By following these guidelines, developers can ensure that their websites are accessible to everyone. This paper reports on a study of the accessibility evaluation of five Moroccan e-government portals using the AChecker tool with respect to WCAG. The aim was to prepare recommendations to improve the overall user experience across Moroccan e-government portals. The evaluation results analysis showed that the portals have some common as well as different failed success criteria with different numbers of errors per WCAG principle and conformance level. In particular, all the portals exhibited varying degrees of adherence to the four principles, and most of the portals struggled to meet levels A and AA, and none of them achieve perfect conformance across all levels. To address this issue, a set of recommendations is provided for each portal to enhance accessibility. The lessons learned from this study can contribute to more inclusive e-government portals and provide insights for governments and designers to prioritize accessibility enhancements. © 2024 Copyright for this paper by its authors.

关键词： Portals

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CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Downstream Vision Tasks Under Unknown Degradations

CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer fo...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Zhang, Yuwei Wu, Yan Liu, Yanming Peng, Xinyue College of Electronic and Information Engineering Tongji University Shanghai China School of Software Technology Zhejiang University Hangzhou China College of Electronic and Information Engineering Southeast University Nanjing China

ISBN: (纸本)9798350368741

Extracting valuable visual cues for downstream vision tasks poses a particular challenge under unknown degradations. A straightforward solution is to preprocess images using image restoration methods, but their high computational complexity renders them unsuitable for real-time tasks. Recent efforts have aimed to enhance image representations optimized by downstream task losses. However, these approaches are confined to known single degradation scenarios, constraining their practical applicability in unpredictable environments. Therefore, we propose a Chain-of-thought Prompted Adaptive Enhancer, CPA-Enhancer, for enhancing important features crucial for downstream vision tasks under unknown degradations. Specifically, CPA-Enhancer progressively adapts its enhancement strategies under the step-by-step guidance of CoT prompts that encode degradation-related information. Overall, CPA-Enhancer is a plug-and-play lightweight enhancement model that can be integrated into any vision pipeline and trained with task-related losses, without any prior knowledge of the degradation type. Extensive experiments demonstrate that CPA-Enhancer significantly improves performance across various vision tasks under unknown degradations. The codes is available at https://***/zyw-stu/CPA-Enhancer. © 2025 IEEE.

关键词： adaptive enhancer chain-of-thought prompting unknown degradations

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LKAW: A Robust Watermarking Method Based on Large Kernel Convolution and Adaptive Weight Assignment

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Computers, Materials & Continua 2023年第4期75卷 1-17页

作者： Xiaorui Zhang Rui Jiang Wei Sun Aiguo Song Xindong Wei Ruohan Meng Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China School of Instrument Science and Engineering Southeast UniversityNanjing211189China School of Teacher Education Nanjing University of Information Science&TechnologyNanjing210044China School of Computer Science Engineering Nanyang Technological UniversitySingapore

Robust watermarking requires finding invariant features under multiple attacks to ensure correct *** learning has extremely powerful in extracting features,and watermarking algorithms based on deep learning have attracted widespread *** existing methods use 3×3 small kernel convolution to extract image features and embed the ***,the effective perception fields for small kernel convolution are extremely confined,so the pixels that each watermarking can affect are restricted,thus limiting the performance of the *** address these problems,we propose a watermarking network based on large kernel convolution and adaptive weight assignment for loss *** uses large-kernel depth-wise convolution to extract features for learning large-scale image information and subsequently projects the watermarking into a highdimensional space by 1×1 convolution to achieve adaptability in the channel ***,the modification of the embedded watermarking on the cover image is extended to more *** the magnitude and convergence rates of each loss function are different,an adaptive loss weight assignment strategy is proposed to make theweights participate in the network training together and adjust theweight ***,a high-frequency wavelet loss is proposed,by which the watermarking is restricted to only the low-frequency wavelet sub-bands,thereby enhancing the robustness of watermarking against image *** experimental results show that the peak signal-to-noise ratio(PSNR)of the encoded image reaches 40.12,the structural similarity(SSIM)reaches 0.9721,and the watermarking has good robustness against various types of noise.

关键词： Robust watermarking large kernel convolution adaptive loss weights high-frequency wavelet loss deep learning

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Analysis and Prediction of E-Bank Suspicious Accounts Based on Ensemble Learning Under Imbalance Data

Analysis and Prediction of E-Bank Suspicious Accounts Based ...

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作者： Jiang, Song Fengli, Zhang School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Information Technology Center Sichuan Rural Credit Union Chengdu China

Due to its convenience, the bank transaction payment system has become the most common channel of money laundering transactions, and has become the forward and main battlefield of anti-money laundering transactions. The current anti-money laundering abnormal transaction identification rules of the domestic banking industry are mainly designed around the abnormal transaction standards issued by regulatory agencies. With the advancement and diversification of payment technology and electronic banking (E-Bank) business, various new abnormal transaction methods emerge in endlessly. The banking business is facing great challenges. In the massive transactions of commercial banks, the proportion of abnormal transactions is very small, and there is a serious sample imbalance problem. In order to improve the identification accuracy of abnormal transactions in massive E-Banks a mixed sampling method is proposed to solve the problem of centralized category of imbalance in the E-Bank transaction flow data set, while using the integrated learning method to build a commercial bank E-Bank account abnormal transaction identification model (SADA model). After data verification, the SADA model can effectively identify abnormal accounts in commercial bank E-Bank accounts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Learning systems

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MedNetV3: A Deep Learning Model for MRI Brain Tumor Detection with MobileNetV3 and Advanced Data Augmentation 21

MedNetV3: A Deep Learning Model for MRI Brain Tumor Detectio...

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21st International Computer Conference on Wavelet Active Media technology and information Processing, ICCWAMTIP 2024

作者： Tariq, Hussain Ahmed Fengli, Zhang Salim Shakib, Muhamud Md Javed, Iram School of Information and Software Engineering University of Electronic Science and Technology of China China University of Agriculture Department of Computer Science Faisalabad Pakistan

ISBN: (纸本)9798331519254

In this paper, we present a novel deep learning model medical network (MedNetV3) developed for brain tumor detection. It incorporates advanced data augmentation techniques based on the MobileNetV3 architecture. MedNetV3 is developed for higher classification accuracy at the cost of slightly lower computational efficiency and is therefore suitable for clinical use. The proposed model overcomes the class imbalance problem and can make the model robust to the variations of MRI scans included in the extensive data augmentation methods, such as random rotations, flips. The proposed model trained and evaluated using a publicly available MRI dataset. It achieved a high Area Under the Curve (AUC) value of 0.99 for both the Precision-Recall and Receiver Operating Characteristic (ROC) curves. The results show that MedNetV3 is effective in correctly distinguishing brain tumors and therefore has the potential to be used in medical image classification. © 2024 IEEE.

关键词： Deep learning

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EPM: Evolutionary Perception Method for Anomaly Detection in Noisy Dynamic Graphs

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IEEE Transactions on Knowledge and Data engineering 2025年第7期37卷 4035-4048页

作者： Wang, Huan Chen, Junyang Wu, Yirui Leung, Victor C. M. Wang, Di King Abdullah University of Science and Technology Division of Computer Electrical and Mathematical Sciences and Engineering Saudi Arabia Huazhong Agricultural University College of Informatics China Shenzhen University College of Computer Science and Software Engineering China Hohai University College of Computer and Information China

With the rapid expansion of interactions across various domains such as knowledge graphs and social networks, anomaly detection in dynamic graphs has become increasingly critical for mitigating potential risks. However, existing anomaly detection methods often assume noise-free dynamic graphs, overlooking the prevalence of noisy dynamic graphs in real-world applications. Specifically, noisy dynamic graphs affected by structural noises-such as spurious and missing nodes and edges-struggle to consistently provide reliable structural evidence for anomaly detection. To tackle this challenge, we propose an Evolutionary Perception Method (EPM) for identifying anomalous nodes in noisy dynamic graphs by resisting the interference of structural noises. EPM primarily consists of two components: a dynamic fitter and a filtering reviser. The dynamic fitter characterizes the interaction dynamics of nodes that removes and generates links at each period as a multiple superposition state, utilizing various link prediction algorithms to fit evolutionary mechanisms. Additionally, the filtering reviser designs evolutional entropies to quantify the evolutional uncertainty in multiple superposition states, further designing the Kalman filter to optimize these entropies. Extensive experiments show that the proposed EPM method surpasses state-of-the-art approaches in detecting anomalous nodes in noisy dynamic graphs. © 1989-2012 IEEE.

关键词： Kalman filters

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Project Assessment in Offshore software Maintenance Outsourcing Using Deep Extreme Learning Machines

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Computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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