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GCW-YOLOv8n: Lightweight Safety Helmet Wearing Detection Algorithm

computer engineering and Applications 2025年第3期61卷 144-154页

作者： Xu, Zhuang Qian, Yurong Yan, Feng Key Laboratory of Software Engineering Xinjiang University Urumqi830091 China Key Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830046 China School of Computer Science and Technology Xinjiang University Urumqi830046 China

China is a major industrial country in the world. In various construction environments, the falling of construction materials and collisions on construction sites are the main causes of casualties. Accidents caused by head injuries often occur, and wearing safety helmets can ensure the safety of construction personnel to the greatest extent possible. In order to solve the problems of poor timeliness and low management efficiency of manual management, the existing models have strict requirements for computing power, large memory requirements, and handling of load and data transmission delay of industrial equipment, and to achieve edge computing and real-time control, a modified helmet wearing detection algorithm based on YOLOv8n is proposed. Firstly, a new GS-C2f module is proposed, which introduces GhostConv and SE (squeeze and excitation) attention mechanism, effectively reducing the computational complexity of the model and helping the network extract features effectively. Secondly, the CBAM attention mechanism is introduced in the Neck section to enhance the model focus on effective features. Finally, Wise-IoUv3 is introduced to further improve the accuracy of the model. Through experiments, compared with the original YOLOv8n model, this model achieves a 21.24% reduction in computational parameters and a 0.01 improvement in recognition accuracy, achieving satisfactory results between model accuracy and complexity. © 2025 Journal of computer engineering and Applications Beijing Co., Ltd.;Science Press. All rights reserved.

关键词： Office equipment

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Delving into the reversal curse: how far can large language models generalize? 24

Delving into the reversal curse: how far can large language ...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhengkai Lin Zhihang Fu Kai Liu Liang Xie Binbin Lin Wenxiao Wang Deng Cai Yue Wu Jieping Ye State Key Lab of CAD & CG Zhejiang University and Alibaba Cloud Alibaba Cloud Alibaba Cloud and College of Biomedical Engineering & Instrument Science Zhejiang University College of Computer Science and Technology Zhejiang University of Technology School of Software Technology Zhejiang University and Fullong Inc. School of Software Technology Zhejiang University State Key Lab of CAD & CG Zhejiang University

ISBN: (纸本)9798331314385

While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A". In this paper, we examine the manifestation of the reversal curse across various tasks and delve into both the generalization abilities and the problem-solving mechanisms of LLMs. This investigation leads to a series of significant insights: (1) LLMs are able to generalize to "B is A" when both A and B are presented in the context as in the case of a multiple-choice question. (2) This generalization ability is highly correlated to the structure of the fact "A is B" in the training documents. For example, this generalization only applies to biographies structured in "[Name] is [Description]" but not to "[Description] is [Name]". (3) We propose and verify the hypothesis that LLMs possess an inherent bias in fact recalling during knowledge application, which explains and underscores the importance of the document structure to successful learning. (4) The negative impact of this bias on the downstream performance of LLMs can hardly be mitigated through training alone. These findings offer a novel perspective on interpreting LLMs' generalization through their intrinsic mechanisms and provide insights for developing more effective learning methods. https://***/alibaba/thinking_***

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A Novel Hierarchical Discourse Model for Scientific Article and It's Efficient Top-K Resampling-based Text Classification Approach

A Novel Hierarchical Discourse Model for Scientific Article ...

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2022 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2022

作者： Gao, Min Chen, Chun-Hua Gao, Zhi-Han Chen, Wei-Long Ren, Yuan Kwong, Sam Zhan, Zhi-Hui South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou510006 China South China University of Technology School of Software Engineering Guangzhou510006 China South China University of Technology School of Environment and Energy Guangzhou510006 China City University of Hong Kong Department of Computer Science Hong Kong Hong Kong

ISBN: (数字)9781665452588

ISBN: (纸本)9781665452588

Scientific articles contain rich knowledge that can significantly assists scientific research, but it is difficult to precisely extract knowledge information due to the complexity of the discourse structure of scientific articles. To provide more accurate scientific research knowledge for researchers in a specific academic domain, it is necessary to study the discourse structure of domain scientific articles and to propose an automatic annotation approach to automatically annotate discourse information from articles. Unfortunately, few works have studied the discourse structure of domain scientific articles and the corresponding automatic discourse annotation. To fill this gap, we take scientific articles of the wastewater-based epidemiology domain as a case to study how to automatically and efficiently annotate discourse information. This paper has three contributions. Firstly, we propose a hierarchical discourse model with two layers to cover all potential discourses in various domain scientific articles. Specifically, the first layer defines four core discourse concepts to describe the main process of a scientific research which can be applied in all scientific articles in various domains. The second layer defines fine-granular domain-specific structure, which can accurately describe the entire research contents of a specific domain. Secondly, based on the proposed model, we build a corpus dataset of 100 annotated scientific articles in the wastewater-based epidemiology domain. Thirdly, based on the model and dataset, we propose a simple yet efficient Top-K resampling-based approach to train a more effective classifier for automatic annotation. Extensive experiments verify the effectiveness and efficiency of our proposed hierarchical discourse model and the Top-K resampling-based classification approach. © 2022 IEEE.

关键词： Classification (of information)

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TaskComplexity: A Dataset for Task Complexity Classification with In-Context Learning, FLAN-T5 and GPT-4o Benchmarks

arXiv

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arXiv 2024年

作者： Rasheed, Areeg Fahad Zarkoosh, M. Abbas, Safa F. Al-Azzawi, Sana Sabah College of Information Engineering Al-Nahrain University Baghdad Iraq Software Engineering Baghdad Iraq Computer Techniques Engineering Department Al-Rasheed University College Baghdad Iraq EISLAB Machine Learning Lule University of Technology Luleå Sweden

This paper addresses the challenge of classifying and assigning programming tasks to experts, a process that typically requires significant effort, time, and cost. To tackle this issue, a novel dataset1 containing a total of 4,112 programming tasks was created by extracting tasks from various websites. Web scraping techniques were employed to collect this dataset of programming problems systematically. Specific HTML tags were tracked to extract key elements of each issue, including the title, problem description, input/output, examples, problem class, and complexity score. Examples from the dataset are provided in the appendix to illustrate the variety and complexity of tasks included. The dataset's effectiveness has been evaluated and benchmarked using two approaches;the first approach involved fine-tuning the FLAN-T5 small model on the dataset, while the second approach used in-context learning (ICL) with the GPT-4o mini. The performance was assessed using standard metrics: accuracy, recall, precision, and F1-score. The results indicated that in-context learning with GPT-4o-mini outperformed the FLAN-T5 model. © 2024, CC BY.

关键词： Natural language processing systems

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WiHunter: Enabling Real-time Small Object Detection via Wireless Sensing

WiHunter: Enabling Real-time Small Object Detection via Wire...

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International Workshop on Quality of Service

作者： Xiang Zou Jianwei Liu Jinsong Han Wei Xi Zhi Wang School of Computer Science and Technology Xi'an Jiaotong University China School of Cyber Science and Technology Zhejiang University China School of Software Engineering Xi'an Jiaotong University China

Rodent infestation is a great danger to human society, continuously threatening food safety and inducing disease spread. Existing methods to deal with rodent infestation are mainly based on passive bait traps and poisoning. These methods lack timeliness and effectiveness due to the missing of real-time detection. In this paper, we develop WiHunter, a new wireless sensing system to discover small objects (e.g., rat). Our idea is to exploit reflection signal effect of wireless channels induced by the movement of small objects around the receiver antenna. However, existing wireless sensing works usually employ customized or costly device-dependency Network Interface Cards(NIC), which are impractical to be densely deployed in reality. We implement WiHunter with several CSI-enabled standalone IoT nodes. We show how such devices enable moving small object detection via WiFi signal. The rationale behind this is 1) thanks to the widespread deployments of WiFi infrastructures and IoT devices, the WiFi signal covers almost every location of the corner, 2) the signal amplitude of each device is related to the small object near the receiver antenna. This ability gives us the opportunity to sense object as small as a rat. We implement WiHunter with ESP32 microcontroller on Espressif IoT Development Framework (both of them are cheap commodity off-the-shelf (COTS) devices) and design a practical small object intrusion detection system. Comprehensive and real-world experiments demonstrate that our system is effective in detecting the presence of small objects with an average accuracy of 92.1%.

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WTDUN: Wavelet Tree-Structured Sampling and Deep Unfolding Network for Image Compressed Sensing

arXiv

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arXiv 2024年

作者： Han, Kai Wang, Jin Shi, Yunhui Cai, Hanqin Ling, Nam Yin, Baocai Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence School of Information Science and Technology Beijing University of Technology China School of Computer Science Beijing University of Technology China The Department of Statistics and Data Science The Department of Computer Science University of Central Florida United States The Department of Computer Science and Engineering Santa Clara University United States

Deep unfolding networks have gained increasing attention in the field of compressed sensing (CS) owing to their theoretical interpretability and superior reconstruction performance. However, most existing deep unfolding methods often face the following issues: 1) they learn directly from single-channel images, leading to a simple feature representation that does not fully capture complex features;and 2) they treat various image components uniformly, ignoring the characteristics of different components. To address these issues, we propose a novel wavelet-domain deep unfolding framework named WTDUN, which operates directly on the multi-scale wavelet subbands. Our method utilizes the intrinsic sparsity and multi-scale structure of wavelet coefficients to achieve a tree-structured sampling and reconstruction, effectively capturing and highlighting the most important features within images. Specifically, the design of tree-structured reconstruction aims to capture the inter-dependencies among the multi-scale subbands, enabling the identification of both fine and coarse features, which can lead to a marked improvement in reconstruction quality. Furthermore, a wavelet domain adaptive sampling method is proposed to greatly improve the sampling capability, which is realized by assigning measurements to each wavelet subband based on its importance. Unlike pure deep learning methods that treat all components uniformly, our method introduces a targeted focus on important subbands, considering their energy and sparsity. This targeted strategy lets us capture key information more efficiently while discarding less important information, resulting in a more effective and detailed reconstruction. Extensive experimental results on various datasets validate the superior performance of our proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Compressed sensing

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Towards Efficient Auction Design with ROI Constraints 24

Towards Efficient Auction Design with ROI Constraints

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xinyu Tang Hongtao Lv Yingjie Gao Fan Wu Lei Liu Lizhen Cui School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University & Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798400704864

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3 over 2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5 over 4; when it falls below 2, the lower bound becomes 3+γ over 2+3γ-γ2.

关键词： incentive compatibility mechanism design return on investment

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Method of Formation of Basic Practical Skills of Data Analysis using Google Services 14

Method of Formation of Basic Practical Skills of Data Analys...

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14th International Conference on Advanced computer Information Technologies, ACIT 2024

作者： Lutsyk, Iryna Lutsyk, Illia Potapchuk, Olha Havryshchak, Halyna Zahorodnii, Roman Rak, Volodymyr Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Computer Technologies Ternopil Ukraine Lviv Polytechnic National University Software Department Lviv Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Service Technology and Occupational Safety Ternopil Ukraine Ternopil Volodymyr Hnatiuk National Pedagogical University Department of Mechanical Engineering and Transport Ternopil Ukraine

ISBN: (纸本)9798350350036

The article reveals the key aspects of the methodology of studying data analysis, which is aimed at forming students' basic practical analytical skills with the use of cloud-based digital tools. The interdisciplinary nature of data analysis and its importance in the process of digital transformation of the social and industrial spheres are revealed. It has been established that the study of data analysis by students should be based on practice-oriented learning, taking into account the development of technologies and professional direction. In the process of studying data analysis, it is also important to integrate it with other disciplines, focus on the formation of students' strategic thinking and the ability to professionally use digital technologies. To form basic practical skills of data analysis, it is suggested to use Google cloud services in combination with classic means of collecting and processing information. This combination allows you to gradually familiarize yourself with the analysis of tabular data: from the basics of creating tables and visualizing results in the Google Sheets environment to the technologies of using specialized tools using the Google Colab service. The results of the ascertainment stage of the research are given, which confirm the feasibility of implementing the proposed methodology in the practical preparation of students for future professional activities. © 2024 IEEE.

关键词： Data assimilation

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Glare sign recognition based on improved Yolox algorithm

Glare sign recognition based on improved Yolox algorithm

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Teng Xu Ling Ren Tian-Wei Shi Yuan Gao Jian-Bang Ding Rong-Chen Jin School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan Liaoning China School of Innovation and entrepreneurship University of Science and Technology Liaoning Anshan Liaoning China

Traffic sign recognition is one of the most critical aspects of autonomous driving. To improve detection accuracy without increasing the number of model parameters too much, this paper uses Conditionally Parameterized Convolutions (condconv) to replace the convolutional blocks in Yolox-s. To understand the transfer of feature maps within the deep learning network, a gradient mapping using Gradient-weighted Class Activation Mapping (Grad-CAM) is used to generate a heat map. As the existing traffic sign dataset does not include data for glare situations, data augmentation was used to simulate traffic signs in a glare context. Experiments show that the proposed improvements can improve detection accuracy (+2.3%) with only a small increase in model parameters $(+0.1 \mathrm{M})$. The effectiveness of the proposed data augmentation method is demonstrated by using the trained model for real-world detection.

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Unsupervised Transfer Learning for Generative Image Inpainting with Adversarial Edge Learning 22

Unsupervised Transfer Learning for Generative Image Inpainti...

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Proceedings of the 2022 5th International Conference on Sensors, Signal and Image Processing

作者： Yiming Zhao Yuxiang Zhang Zishuo Sun Qingdao School of Software School of Computer Science and Technology China University of Petroleum (East China) China School of information and electrical engineering Hebei University of EngineeringHandan China

ISBN: (纸本)9781450397124

Deep learning-based image restoration techniques have made great progress in recent years, and EdgeConnect network has achieved good results in image restoration. We find that EdgeConnect suffers from a complex training process and poor migratability, which reduces its usability in practical applications. We explore the reasons for the poor transferability learning and generalization of the EdgeConnect model, and propose a small-sample unsupervised joint transfer learning method for the case of small datasets and low data similarity. The method combines a large amount of Fine-tune with a small amount of direct migration training to enable the network to learn new knowledge of the target domain while avoiding overfitting and negative migration. We perform migration learning and evaluation on 600 images from Paris StreetView with a pre-trained model obtained on the CelebA dataset, and show that it outperforms other current methods in terms of quality.

关键词： Transfer learning Pre-trained model Image inpainting Fine-tune EdgeConnect

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