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Evolving Safety Protocols: Deep Learning-Enabled Detection of Personal Protective Equipment 1

4th International Conference on computer Vision, High-Performance Computing, Smart Devices, and Networks, CHSN 2023

作者： Alahmid, Mustafa Bhimani, Kishankumar Saradva, Khushbu Ghildiyal, Sushil Saeedi, Sayed Aryan Ali, Saraa Yan, Han Filippov, Evgeny Faculty of Computer Science and Technology Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Laboratory on AI for Computational Biology Faculty of Computer Science HSE University Moscow Russia Department of Computer Science and Engineering Indian Institute of Technology Ropar Punjab India Laboratory of Methods for Big Data Analysis Faculty of Computer Science HSE University Moscow Russia Deggendorf Institute of Technology Bavaria Germany

ISBN: (数字)9789819777945

ISBN: (纸本)9789819777938

To give shift in safety protocols, we have employed advanced deep learning algorithms and frameworks (Shrestha and Mahmood in IEEE Access 7:53,040–53,065, 2019 [25]) to construct an innovative AI model. The designed model detects the usage of personal protective equipment (PPE) (Personal protective equipment. Geneva: World Health Organization, 2020 [18]) by workers in high-risk industries such as construction and manufacturing. We have used Google’s TensorFlow object detection API (Sai and Sasikala in Object detection and count of objects in image using tensor flow object detection API, pp 542–546, 2019 [22]) to modify and train a model for dual purposes: PPE detection and face recognition. The state-of-the-art of this research is to substantially enhance safety compliance by addressing the prevalent issue of PPE non-compliance. To emphasis this, we have developed a pioneering software prototype that synergizes PPE detection with a face recognition-based clock-in system. This prototype demonstrates impressive object detection metrics with a mean average precision (mAP) of 0.9 for vests and 0.85 for helmets. Moreover, it exhibited efficient face recognition with a successful threshold range of 17–20%. The implementation of AI in our system promises significant enhancements to worker safety, while concurrently reducing the financial burden associated with big hazards and accidents. Beyond the development and performance of the system, this paper provides a thorough exploration of the encountered challenges, potential real-world applications (particularly in employee monitoring and clock-in systems), and the future implications of this study on research and practical applications in the field of AI-integrated safety compliance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Software prototyping

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Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

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computer Modeling in Engineering & sciences 2023年第11期137卷 2025-2047页

作者： Huabin Wang Ruichao Mo Yuping Chen Weiwei Lin Minxian Xu Bo Liu School of Computer Science and Engineering Huizhou UniversityHuizhou516007China School of Computer Science and Engineering South China University of TechnologyGuangzhou510006China School of Computer Science and Technology South China Normal UniversityGuangzhou510631China Department of New Network Technologies Peng Cheng LaboratoryShenzhen518066China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile ***,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy *** addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge ***,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model *** results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher ***,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

关键词： Pedestrian and vehicle detection YOLOv4 channel pruning cloud-edge collaboration

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A New Deep Learning Architecture for Pneumonia Detection in Pediatrics 19

A New Deep Learning Architecture for Pneumonia Detection in ...

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19th IEEE International Multi-Conference on Systems, Signals and Devices, SSD 2022

作者： Remaigui, Ibrahim Kahloul, Laid Benharzallah, Saber Biskra University Computer Science Department Algeria Biskra University Linfi Laboratory Computer Science Department Algeria Batna 2 Univeristy Lamie Laboratory Computer Science Department Algeria

ISBN: (数字)9781665471084

ISBN: (纸本)9781665471084

This paper proposes a new convolutional neuronal network architecture for pneumonia detection in pediatric healthcare. The model was constructed from the scratch and was trained, validated, and tested using a public data set composed of 5863 images classified into two distinguished classes: normal and pneumonia. Indeed, the obtained results show an improvement compared to the existing works which have used the same data set. In this work, our achieved experimentations have reached an accuracy equal to 99.3% with a validation accuracy equal to 95.89% and a test accuracy equal to 97.10%. Thus, obtained results are very encouraging compared to existing models in the literature. © 2022 IEEE.

关键词： Pediatrics

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Autonomous Aspect-Image Instruction A2II: Q-Former Guided Multimodal Sentiment Classification 30

Autonomous Aspect-Image Instruction A2II: Q-Former Guided Mu...

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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： Feng, Junjia Lin, Mingqian Shang, Lin Gao, Xiaoying State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Nanjing University China School of Engineering and Computer Science Victoria University of Wellington New Zealand

ISBN: (纸本)9782493814104

Multimodal aspect-oriented sentiment classification (MABSC) task has garnered significant attention, which aims to identify the sentiment polarities of aspects by combining both language and vision information. However, the limited multimodal data in this task has become a big gap for the vision-language multimodal fusion. While large-scale vision-language pretrained models have been adapted to multiple tasks, their use for MABSC task is still in a nascent stage. In this work, we present an attempt to use the instruction tuning paradigm to MABSC task and leverage the ability of large vision-language models to alleviate the limitation in the fusion of textual and image modalities. To tackle the problem of potential irrelevance between aspects and images, we propose a plug-and-play selector to autonomously choose the most appropriate instruction from the instruction pool, thereby reducing the impact of irrelevant image noise on the final sentiment classification results. We conduct extensive experiments in various scenarios and our model achieves state-of-the-art performance on benchmark datasets, as well as in few-shot settings. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Sentiment analysis

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A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

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IEEE Internet of Things Journal 2025年第11期12卷 18115-18124页

作者： Gao, Suo Zhang, Zheyi Iu, Herbert Ho-Ching Ding, Siqi Mou, Jun Erkan, Uğur Toktas, Abdurrahim Li, Qi Wang, Chunpeng Cao, Yinghong Dalian Polytechnic University School of Information Science and Engineering Dalian116034 China The University of Western Australia School of Electrical Electronic and Computer Engineering PerthWA6009 Australia Dalian University of Technology School of Mathematical Sciences Dalian116024 China Karamanoglu Mehmetbey University Faculty of Engineering Department of Computer Engineering Karaman70200 Turkey Ankara University Engineering Faculty Department of Artificial Intelligence and Data Engineering Ankara06830 Turkey Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan250000 China

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%. © 2014 IEEE.

关键词： Encryption algorithms

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Kernel Density Visualization for Big Geospatial Data: Algorithms and Applications 24

Kernel Density Visualization for Big Geospatial Data: Algori...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Chan, Tsz Nam Leong Hou, U. Choi, Byron Xu, Jianliang Cheng, Reynold Hong Kong Baptist University Department of Computer Science Hong Kong University of Macau Department of Computer and Information Science China The University of Hong Kong Department of Computer Science Hong Kong Guangdong-Hong Kong-Macau Joint Laboratory Hong Kong

ISBN: (纸本)9798350341010

The use of Kernel Density Visualization (KDV) has become widespread in a number of disciplines, including geography, crime science, transportation science, and ecology, for analyzing geospatial data. However, the growing scale of massive geospatial data has rendered many commonly used software tools unable of generating high-resolution KDVs, leading to concerns about the inefficiency of KDV. This 90-minute tutorial aims to raise awareness among database researchers about this important, emerging, database-related, and interdisciplinary topic. It is structured into four parts: a thorough discussion of the background of KDV, a review of state-of-the-art methods for generating KDVs, a discussion of key variants of KDV, including network kernel density visualization (NKDV) and spatiotemporal kernel density visualization (STKDV), and an outline of future directions for this topic. © 2023 IEEE.

关键词： Visualization

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Coverage-Based Fault Localization in Haskell

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计算机科学技术学报（英文版） 2025年第1期40卷 158-177页

作者：李丰王国庆王萌郝丹 1Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing 100871China 2Department of Computer Science University of Bristol Bristol BS81UB U.K.

Fault localization is to identify faulty program *** a large number of fault localization ap-proaches in the literature,coverage-based fault localization,especially spectrum-based fault localization(SBFL),has been intensively studied due to its effectiveness and *** the rich literature,almost all existing fault local-ization approaches and studies have been conducted on imperative programming languages such as Java and C,leaving a gap in other programming *** this paper,we aim to study fault localization approaches for the functional pro-gramming paradigm,using the Haskell language as a *** the best of our knowledge,we build up the first dataset on real Haskell projects,including both real and seeded *** dataset enables the research of fault localiza-tion for functional *** it,we explore fault localization techniques for *** particular,as is typical for SBFL approaches,we study methods for coverage collection and formulae for suspiciousness score computation,and care-fully adapt these two components to Haskell considering the language features and characteristics,resulting in a series of adaption ***,we design a learning-based approach and a transfer learning based approach to take ad-vantage of data from imperative *** approaches are evaluated on our dataset to demonstrate the promises of the direction.

关键词： debugging fault localization Haskell programming paradigms

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Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images 24

Dual Cross-Stage Partial Learning for Detecting Objects in D...

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24th IEEE International Conference on Data Mining, ICDM 2024

作者： Zhao, Jinbiao Zhang, Zhao Ren, Jiahuan Zhang, Haijun Zhao, Zhongqiu Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Harbin Institute of Technology Department of Computer Science Shenzhen China

ISBN: (纸本)9798331506681

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP. © 2024 IEEE.

关键词： Demulsification

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Solving the Tourist Planning Problem Using a Hybrid Genetic Tabu Algorithm 4

Solving the Tourist Planning Problem Using a Hybrid Genetic ...

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4th International Conference on Embedded and Distributed Systems, EDiS 2024

作者： Bouatouche, Mourad Belkadi, Khaled University of Relizane Department of Computer Science Cité Bourmadia Relizane BP Relizane48000 Algeria USTO-MB Laboratory Simpa Department of Computer Science oran BP 1505 El M'naouer Oran31000 Algeria

ISBN: (数字)9798331519872

ISBN: (纸本)9798331519872

The goal of this work is to apply metaheuristics to the problem of planning tourist trips. The tourist trip planning problem is the preparation of an optimal personalized itinerary based on a maximization of the tourists' preference factors while respecting the time constraint, provided that a tourist site is visited only once. This problem can be modeled as an Orientation Problem and its variants. To achieve our goal, we propose a Hybrid Genetic Tabu algorithm. In our algorithm, we use genetic operators such as the crossover operator, which is adopted for the variable-size chromosome. In mutation, the exchange operator and the insertion operator are used, and then we improve the solution found by adding a tabu search. This approach takes advantage of the complementarity of hybrid methods. The results obtained are discussed and compared with other known results in the literature. The computational results demonstrate the efficacy and efficiency of our proposed algorithm. © 2024 IEEE.

关键词： Genetic algorithms

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Movements Classification Based on Surface Electromyography Using Time-frequency Domain Features

Movements Classification Based on Surface Electromyography U...

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International Conference on Soft Computing and Measurements (SCM)

作者： Ivan V. Kuznetsov Department of Computer Science and Computer Graphics Far Eastern State Transport University Khabarovsk Russia

ISBN: (数字)9798350363708

ISBN: (纸本)9798350363715

Feature selection makes significant role in movement classification based on electromyography data. It is assumed that the efficiency of movement classification is improved when time-domain (TD) and frequency-time-domain (TFD) features are used together. To validate the hypothesis, classifiers based on Support Vector Machines (SVM), K Nearest Neighbors (KNN), Random Forest (RF) were trained using NinaPro DB5 dataset. Classification efficiencies of 91.3% and 92.8% were achieved for Recall and Precision metrics, respectively, using KNN. The proposed methods can be used to create human-machine interfaces using muscle activity data.

关键词： Support vector machines Time-frequency analysis Accuracy Human-machine systems Muscles Feature extraction Electromyography

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