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RDC-YOLOv5: Improved Safety Helmet Detection in Adverse Weather 30th

RDC-YOLOv5: Improved Safety Helmet Detection in Adverse Wea...

引用

30th International Conference on MultiMedia Modeling, MMM 2024

作者： Yao, Dexu Li, Aimin Liu, Deqi Cheng, Mengfan Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9783031533105

Outdoor construction sites are frequently affected by fog and various adverse weather conditions, resulting in a decline in the quality of the captured images. This deterioration ultimately leads to a significant drop in the performance of helmet-wear detection systems at construction sites. To address this challenge, we proposed an improved YOLOv5 model. Firstly, we set up a restoration network to effectively restore hazy image quality and enhance intricate details. Secondly, we introduced a micro-scale detection layer, enabling more efficient capture of smaller objects and effectively mitigating the adverse weather-induced variations in object scale. Finally, we added a cross-layer connection to meticulously amplify the fine-grained features of objects within the network’s shallower layers. Our comprehensive evaluation of the enhanced YOLOv5 model, conducted on two distinct datasets, yielded a final mean average precision (mAP) value of 95.1%. This substantial improvement effectively reduces both false detections and missed detections, enhances overall robustness, and significantly improves detection performance in challenging adverse weather conditions. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deterioration

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Fruit Deformity Classification Through Single-Input and Multi-input Architectures Based on CNN Models Using Real and Synthetic Images 27th

Fruit Deformity Classification Through Single-Input and Mul...

引用

27th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, computer Vision, and Applications, CIARP 2024

作者： Beltran, Tommy D. Villao, Raul J. Chuquimarca, Luis E. Vintimilla, Boris X. Velastin, Sergio A. ESPOL CIDIS ESPOL Polytechnic University Guayaquil Ecuador UPSE FACSISTEL UPSE Santa Elena Peninsula State University La Libertad Ecuador School of EECS Queen Mary University of London London United Kingdom Department of Computer Science University Carlos III Madrid Spain

ISBN: (纸本)9783031766060

The present study focuses on detecting the degree of deformity in fruits such as apples, mangoes, and strawberries during the process of inspecting their external quality, employing Single-Input and Multi-Input architectures based on convolutional neural network (CNN) models using sets of real and synthetic images. The datasets are segmented using the Segment Anything Model (SAM), which provides the silhouette of the fruits. Regarding the single-input architecture, the evaluation of the CNN models is performed only with real images, but a methodology is proposed to improve these results using a pre-trained model with synthetic images. In the Multi-Input architecture, branches with RGB images and fruit silhouettes are implemented as inputs for evaluating CNN models such as VGG16, MobileNetV2, and CIDIS. However, the results revealed that the Multi-Input architecture with the MobileNetV2 model was the most effective in identifying deformities in the fruits, achieving accuracies of 90%, 94%, and 92% for apples, mangoes, and strawberries, respectively. In conclusion, the Multi-Input architecture with the MobileNetV2 model is the most accurate for classifying levels of deformity in fruits. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Teaching computer science in a School Camp Using Gaming Technologies

Teaching Computer Science in a School Camp Using Gaming Tech...

引用

International Conference on Innovative Approaches to the Application of Digital Technologies in Education and Research, SLET 2022

作者： Ararat-Isaeva, Maria Grigoryev, Sergey Moscow City University Moscow Russia

ISBN: (纸本)9783031787751

This article examines the issues of methodology and practice in teaching computer science. Special attention is given to the method of applying game technologies, game-based learning, and gamification in the education of students in grades 3–4, which help activate thinking and enhance students independence, and also contribute to developing an interest in computer science. The article presents a methodical system consisting of three parts: a computer science curriculum for students in grades 3–4, gamification, and game-based learning, organized within the framework of a school camp. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Educational technology

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Attribute Relevance and Discretisation in Knowledge Discovery: A Study in Stylometric Domain 23rd

Attribute Relevance and Discretisation in Knowledge Discov...

引用

23rd International Conference on Computational science, ICCS 2023

作者： Stańczyk, Urszula Zielosko, Beata Baron, Grzegorz Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Institute of Computer Science University of Silesia in Katowice Bȩdzińska 39 Sosnowiec41-200 Poland

ISBN: (纸本)9783031360206

The paper demonstrates the research methodology focused on observations of relations between attribute relevance, displayed by rankings, and discretisation. Instead of transforming all continuous attributes before data exploration, the variables were gradually processed, and the impact of such a change on the performance of a classifier was studied. Considerable experiments carried out on stylometric data illustrate that selective discretisation could be more advantageous to predictive accuracy than some uniform transformation of all features. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Metadata

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Traditional Chinese Medicine Health Status Identification with Graph Attention Network 1

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Fu, Amin Ma, Jishun Wang, Chuansheng Zhou, Changen Li, Zuoyong Teng, Shenghua College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao266590 China Department of Automatic Control Technical Polytechnic University of Catalonia Barcelona08034 Spain Research Base of Traditional Chinese Medicine Syndrome Fujian University of Traditional Chinese Medicine Fuzhou350122 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering Minjiang University Fuzhou350121 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The Traditional Chinese Medicine Health Status Identification plays an important role in TCM diagnosis and prescription recommendation. In this paper, we propose a method of Status Identification via Graph Attention Network, named SIGAT, which captures the complex medical correlation in the symptom-syndrome graph. More specifically, we construct a symptom-syndrome graph in that symptoms are taken as nodes and the edges are connected by syndromes. And we realize automatic induction of symptom to state element classification by using the attention mechanism and perceptron classifier. Finally, we conduct experiments by using hamming loss, coverage, 0/1 error, ranking loss, average precision, macro-F1 score, and micro-F1 score as evaluation metrics. The results demonstrate that the SIGAT model outperforms comparison algorithms on Traditional Chinese Medicine Prescription Dictionary dataset. The case study results suggest that the proposed method is a valuable way to identify the state element. The application of the graph attention network classification algorithm in TCM health status identification is of high precision and methodological feasibility. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Classification (of information)

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SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream 17th

SpikeGS: Learning 3D Gaussian Fields from Continuous Spike ...

引用

17th Asian Conference on computer Vision, ACCV 2024

作者： Yu, Jinze Peng, Xin Lu, Zhengda Kneip, Laurent Wang, Yiqun College of Computer Science Chongqing University Chongqing China Motovis Co. Ltd. Shanghai China University of Chinese Academy of Sciences Beijing China Mobile Perception Lab ShanghaiTech University Shanghai China

ISBN: (纸本)9789819609710

A spike camera is a specialized high-speed visual sensor that offers advantages such as high temporal resolution and high dynamic range compared to conventional frame cameras. These features provide the camera with significant advantages in many computer vision tasks. However, the tasks of novel view synthesis based on spike cameras remain underdeveloped. Although there are existing methods for learning neural radiance fields from spike stream, they either lack robustness in extremely noisy, low-quality lighting conditions or suffer from high computational complexity due to the deep fully connected neural networks and ray marching rendering strategies used in neural radiance fields, making it difficult to recover fine texture details. In contrast, the latest advancements in 3DGS have achieved high-quality real-time rendering by optimizing the point cloud representation into Gaussian ellipsoids. Building on this, we introduce SpikeGS, the method to learn 3D Gaussian fields solely from spike stream. We designed a differentiable spike stream rendering framework based on 3DGS, incorporating noise embedding and spiking neurons. By leveraging the multi-view consistency of 3DGS and the tile-based multi-threaded parallel rendering mechanism, we achieved high-quality real-time rendering results. Additionally, we introduced a spike rendering loss function that generalizes under varying illumination conditions. Our method can reconstruct view synthesis results with fine texture details from a continuous spike stream captured by a moving spike camera, while demonstrating high robustness in extremely noisy low-light scenarios. Experimental results on both real and synthetic datasets demonstrate that our method surpasses existing approaches in terms of rendering quality and speed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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AIMSM - A Mechanism to Optimize Systems with Multiple AI Models: A Case Study in computer Vision for Autonomous Mobile Robots 23rd

AIMSM - A Mechanism to Optimize Systems with Multiple AI M...

引用

23rd EPIA Conference on Artificial Intelligence, EPIA 2024

作者： Ferreira, Bruno Georgevich Sousa, Armando Jorge Reis, Luis Paulo Augusto de Sousa, António Rodrigues, Rui Rossetti, Rosaldo Edge Innovation Center Federal University of Alagoas Maceió Brazil FEUP - Faculty of Engineering of the University of Porto Porto Portugal LIACC - Member of LASI LA - Artificial Intelligence and Computer Science Laboratory Porto Portugal INESC TEC - INESC Technology and Science Porto Portugal

ISBN: (纸本)9783031735028

This article proposes the Artificial Intelligence Models Switching Mechanism (AIMSM), a novel approach to optimize system resource utilization by allowing systems to switch AI models during runtime in dynamic environments. Many real-world applications utilize multiple data sources and various AI models for different purposes. In many of those applications, every AI model doesn’t have to operate all the time. The AIMSM strategically allows the system to activate and deactivate these models, focusing on system resource optimization. The switching of each AI model can be based on any information, such as context or previous results. In the case study of an autonomous mobile robot performing computer vision tasks, the AIMSM helps the system to achieve a significant increment in performance, with a 50% average increase in frames per second (FPS) rate, for this specific case study, assuming that no erroneous switching occurred. Experimental results have demonstrated that the AIMSM can improve system resource utilization efficiency when properly implemented, optimize overall resource consumption, and enhance system performance. The AIMSM presented itself as a better alternative to permanently loading all the models simultaneously, improving the adaptability and functionality of the systems. It is expected that using the AIMSM will yield a performance improvement that is particularly relevant to systems with multiple AI models of a complex nature, where such models do not need to be all continuously executed or systems that will benefit from lower resource usage. Code is available at https://***/BrunoGeorgevich/AIMSM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Overview of DDoS Attack Research Under SDN 1

引用

4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Guo, Lei Jing, Shan Zhao, Chuan School of Information Science and Engineering University of Jinan Jinan China Quan Cheng Laboratory Jinan China

ISBN: (数字)9783031200991

ISBN: (纸本)9783031200984

Software Defined Network (SDN) has been developed and applied gradually in recent years. SDN decouples the data plane from the control plane to implement centralized control, improving network operation efficiency and simplifying network management. However, SDN is vulnerable to Distributed Denial of Service (DDoS) attacks, especially on the control plane, which affects the whole network. In this paper, DDoS attack detection and defense under SDN are comprehensively reviewed, mainly classified according to different detection methods. On this basis, different methods are analyzed and compared in detail, which involves the advantages and disadvantages of different methods, application scenarios and specific DDoS attack types targeted by the methods. After analyzing and comparing different kinds of methods, this paper also points out the current difficulties in the field of DDoS detection and defense under the SDN architecture, and provides ideas for future research. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Denial-of-service attack

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How Language of Interaction Affects the User Perception of a Robot 15th

How Language of Interaction Affects the User Perception of...

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15th International Conference on Social Robotics, ICSR 2023

作者： Sienkiewicz, Barbara Sejnova, Gabriela Gajewski, Paul Vavrecka, Michal Indurkhya, Bipin Cognitive Science Department Jagiellonian University Krakow Poland Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Prague Czech Republic Institute of Computer Science AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9789819987146

Spoken language is the most natural way for a human to communicate with a robot. It may seem intuitive that a robot should communicate with users in their native language. However, it is not clear if a user’s perception of a robot is affected by the language of interaction. We investigated this question by conducting a study with twenty-three native Czech participants who were also fluent in English. The participants were tasked with instructing the Pepper robot on where to place objects on a shelf. The robot was controlled remotely using the Wizard-of-Oz technique. We collected data through questionnaires, video recordings, and a post-experiment feedback session. The results of our experiment show that people perceive an English-speaking robot as more intelligent than a Czech-speaking robot (z = 18.00, p-value = 0.02). This finding highlights the influence of language on human-robot interaction. Furthermore, we discuss the feedback obtained from the participants via the post-experiment sessions and its implications for HRI design. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feedback

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ComPAT: A Compiler Principles Course Assistant 1

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17th International Conference on Knowledge science, Engineering and Management (KSEM)

作者： Cai, Shubin Chen, Honglong Huang, Youyi Ming, Zhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China Lab Artificial Intelligence & Digital Econ Shenzh Shenzhen Peoples R China Shenzhen Technol Univ Shenzhen Peoples R China

ISBN: (数字)9789819754892

ISBN: (纸本)9789819754885;9789819754892

Students in compiler principle course often encounter difficulties in understanding abstract programming concepts and need first-aid assistants in the context of independent learning. An innovative Large Language Model-based assistant ComPAT (Compiler Principles course AssisTant) is proposed in this paper. ComPAT uses vector database for text segmentation and similarity queries, meta prompting for answer refinement and challenging questions for heuristic learning. A compiler principles domain-specific dataset with 1520 questions is constructed. Compared to ChatGPT3.5, ERNIE3.5, and ChatGLM4, ComPAT has shown superior performance in handling fill-in-the-blank and question-and-answer. ComPAT demonstrates an innovative and promising use of LLM-based teaching/learning assistant system in computer education.

关键词： computer Education Large Language Model Teaching/Learning Assistant

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