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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Pyramids Higher Inst. for Engineering & Technology Dept. of Electronics and Communications Engineering Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798350387131

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8's versa-tility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22,424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making P-YOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8's architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024 IEEE.

关键词： Convolutional neural networks

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A Fine-Grained Attribute Pre-Labeling Method Based on Label Dependency and Feature Similarity Dynamics

A Fine-Grained Attribute Pre-Labeling Method Based on Label ...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hao-Chiang Shao Yu-Hsien Lin Chia-Wen Lin Inst. Data Science and Information Computing National Chung Hsing University Taiwan Dept. Electrical Engineering National Tsing Hua University Taiwan

In this paper, we proposed a fine-grained attribute pre-labeling method based on the multi-label recovery techniques. Given a fine-grained image dataset with overlooked attributes in its annotation vectors, our method can predict those missing attribute labels by learning the between-label dependency based on the estimated similarity between known attributes and the similarity of extracted deep image features. Furthermore, to prevent the learnable label dependency matrix from converging to a trivial solution, we designed a trace-loss to penalize the self-dependency of attributes. Comprehensive experiments on the CUB-200-2011 dataset show that, given a training set with 40% of attribute labels randomly dropped: i) our approach achieves a pre-labeling performance with an mAP value of 30.7 on a blind testing set, and ii) the missing attributes in the training set can be corrected with an accuracy of 89%. Our method can effectively and robustly perform the fine-grained pre-labeling task.

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Logging Multi-Component Supply Chain Production in Blockchain 2021

Logging Multi-Component Supply Chain Production in Blockchai...

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4th International Conference on Computers in Management and Business, ICCMB 2021

作者： Madhwal, Yash Chistiakov, Ivan Yanovich, Yury Faculty of Computer Science National Research University Higher School of Economics Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Institute of Science and Technology Russia Center for Computational and Data-Intensive Science and Engineering Skolkovo Inst. of Sci. and Technol. and Lab. of Data Mining and Predictive Modelling Inst. for Info. Transmiss. Prob. Russia

ISBN: (纸本)9781450388610

The supply chain is a thriving industry where numerous parties have different interests. Subsequently, the immense volume of data produced is difficult to audit. Some information can be lost or intentionally distorted in the process. Blockchain as an open, public, borderless, neutral, and censorship-resistant architecture can significantly complement supply chains. A new supply chain architecture is proposed in this work, where the tokenized directed acyclic hypergraph (DAG) represents real-world production processes. An anti-aerosol respirator manufacturing is used as an illustration example. By tokenizing all parts of multi-component products, supply chain data is automatically timestamped and secured. Moreover, the DAG design allows one to trace-back all the elements of the final product to their origin. Blockchain can formally audit the entire supply chain without the need to go from place to place. A single incorruptible operations log creates an enabling environment for an unbiased reputation system to emerge. © 2021 ACM.

关键词： Smart contract

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Chinese Emergency Event Extraction Based on Contrastive Learning with Event Semantic Features 19

Chinese Emergency Event Extraction Based on Contrastive Lear...

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19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2023

作者： Sheng, Xinyi Gu, Jinguang Yan, Youcheng Xu, Fangfang Wuhan University of Science and Technology College of Computer Science and Technology Wuhan430065 China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Wuhan430065 China The Key Lab. of Rich-Media Knowledge Org. and Serv. of Digit. Publ. Content Inst. of Sci. and Tech. Info. of China Beijing100038 China

ISBN: (纸本)9798350304398

Extracting emergency events from a large amount of unstructured information is essential for improving early warning and emergency response. Existing event extraction methods for specialist fields often rely on well-designed syntactic dependencies or external knowledge graphs, which can provide high accuracy in their specialist fields. However, these approaches may have poor generalization capabilities and are difficult to apply directly to the emergency field. In addition, most existing methods ignore the interdependencies between event triggers and event arguments, which may be crucial semantic features for event extraction. To solve these problems, we propose a Chinese emergent event extraction method based on contrastive learning with event semantic features(CNEmergEE). Firstly, the proposed approach automatically expands the pre-trained model's vocabulary on a domain corpus. Then, continuing learning on the same corpus allows the model to complete domain adaptation. Finally, we use contrast learning to enable models to learn semantic representations of events. Experimental results on the CEC (Chinese Emergency Corpus) dataset show that our approach achieves better improvement. © 2023 IEEE.

关键词： Semantics

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SPENT+: A Category- And Region-aware Successive POI Recommendation Model

SPENT+: A Category- And Region-aware Successive POI Recommen...

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22nd Asia-Pacific Network Operations and Management Symposium, APNOMS 2021

作者： Lai, Hsu-Chao Lu, Yi-Shu Wang, Mu-Fan Chen, Yi-Cheng Shih, Wen-Yueh Huang, Jiun-Long Inst. of Computer Science and Engineering National Yang Ming Chiao Tung University Taiwan National Central University Department of Information Management Taiwan Inst. of Data Science and Engineering National Yang Ming Chiao Tung University Taiwan

ISBN: (纸本)9784885523328

To facilitate successive Point-of-Interests (POI) recommendation, the categories of POIs and the regions where POIs are located are seldom considered in existing models. In view of this, we extend a state-of-the-art model SPENT, named SPENT+, by taking the category and the region into considerations. In SPENT+, we formulate category- and region-aware check-in sequences, design the similarity trees to aggregate similar features, and finally establish the category latent vectors and region latent vectors, respectively. The above two latent vectors are aggregated as the category-region-aware latent vectors. Therefore, the category-region-latent vectors are sent to an LSTM together with conventional check-in sequences to improve successive POI recommendation. We conduct two real datasets, Gowalla and Foursquare, and compare with state-of-the-art methods in experiments. Results show that SPENT+ outperforms the baselines in terms of precision and recall. © 2021 IEICE.

关键词： Vectors

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Comparison between Grad-CAM and EigenCAM on YOLOv5 detection model

Comparison between Grad-CAM and EigenCAM on YOLOv5 detection...

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2022 International Symposium on Electronics and Smart Devices, ISESD 2022

作者： Rahman, Arief Nur Andriana, Dian MacHbub, Carmadi Bandung Inst. of Technol. Res. Center for Telecommunications National Research and Innovation Agency School of Electrical Engineering and Informatics Bandung Indonesia Telkom University Research Center for Data and Information Science National Research and Innovation Agency School of Computing Bandung Indonesia

ISBN: (数字)9798350396607

ISBN: (纸本)9798350396607

This paper presents a comparison between Grad-CAM and EigenCAM methods on the YOLOv5 detection model. Explainable Artificial Intelligence (XAI) is a tool to understand and interpret machine learning models. XAI is used to understand which pixel from an image contributes significantly to the model's final output. EigenCAM is quite simple but a very powerful technique for producing visual explanations, but Grad-CAM is better for a low illumination environment. © 2022 IEEE.

关键词： Cams

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P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

arXiv

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arXiv 2024年

作者： Elshamy, Mohamed R. Emara, Heba M. Shoaib, Mohamed R. Badawy, Abdel-Hameed A. Klipsch School of ECE New Mexico State University Las CrucesNM88003 United States Dept. of Electronics and Communications Engineering Pyramids Higher Inst. for Engineering & Technology Egypt College of Computing and Data Science Nanyang Technological University Singapore

Distracted driving is a critical safety issue that leads to numerous fatalities and injuries worldwide. This study addresses the urgent need for efficient and real-time machine learning models to detect distracted driving behaviors. Leveraging the Pretrained-YOLOv8 (P-YOLOv8) model, a real-time object detection system is introduced, optimized for both speed and accuracy. This approach addresses the computational constraints and latency limitations commonly associated with conventional detection models. The study demonstrates P-YOLOv8’s versatility in both object detection and image classification tasks using the Distracted Driver Detection dataset from state farm, which includes 22, 424 images across ten behavior categories. Our research explores the application of P-YOLOv8 for image classification, evaluating its performance compared to deep learning models such as VGG16, VGG19, and ResNet. Some traditional models often struggle with low accuracy, while others achieve high accuracy but come with high computational costs and slow detection speeds, making them unsuitable for real-time applications. P-YOLOv8 addresses these issues by achieving competitive accuracy with significant computational cost and efficiency advantages. In particular, P-YOLOv8 generates a lightweight model with a size of only 2.84 MB and a lower number of parameters, totaling 1, 451, 098, due to its innovative architecture. It achieves a high accuracy of 99.46% with this small model size, opening new directions for deployment on inexpensive and small embedded devices using Tiny Machine Learning (TinyML). The experimental results show robust performance, making PYOLOv8 a cost-effective solution for real-time deployment. This study provides a detailed analysis of P-YOLOv8’s architecture, training, and performance benchmarks, highlighting its potential for real-time use in detecting distracted driving. © 2024, CC BY.

关键词： Convolutional neural networks

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p-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving

p-YOLOv8: Efficient and Accurate Real-Time Detection of Dist...

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IEEE Conference on High Performance Extreme Computing (HPEC)

作者： Mohamed R. Elshamy Heba M. Emara Mohamed R. Shoaib Abdel-Hameed A. Badawy Klipsch School of ECE New Mexico State University Las Cruces NM United States Dept. of Electronics and Communications Engineering Pyramids Higher Inst. for Engineering & Technology Egypt College of Computing and. Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350387131

ISBN: (纸本)9798350387148

关键词： Training Performance evaluation Adaptation models Accuracy Computational modeling Tiny machine learning Computer architecture Real-time systems Computational efficiency Image classification

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Private algorithms for stochastic saddle points and variational inequalities: beyond euclidean geometry 24

Private algorithms for stochastic saddle points and variatio...

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

作者： Raef Bassily Cristóbal Guzmán Michael Menart Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute

ISBN: (纸本)9798331314385

In this work, we conduct a systematic study of stochastic saddle point problems (SSP) and stochastic variational inequalities (SVI) under the constraint of (ε, δ)-differential privacy (DP) in both Euclidean and non-Euclidean setups. We first consider Lipschitz convex-concave SSPs in the ℓp/ℓq setup, p, q ∈ [1,2]. That is, we consider the case where the primal problem has an ℓp-setup (i.e., the primal parameter is constrained to an ℓp bounded domain and the loss is ℓp-Lipschitz with respect to the primal parameter) and the dual problem has an ℓq setup. Here, we obtain a bound of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$ on the strong SP-gap, where n is the number of samples and d is the dimension. This rate is nearly optimal for any p, q ∈ [1,2]. Without additional assumptions, such as smoothness or linearity requirements, prior work under DP has only obtained this rate when p = q = 2 (i.e., only in the Euclidean setup). Further, existing algorithms have each only been shown to work for specific settings of p and q and under certain assumptions on the loss and the feasible set, whereas we provide a general algorithm for DP SSPs whenever p, q ∈ [1, 2]. Our result is obtained via a novel analysis of the recursive regularization algorithm. In particular, we develop new tools for analyzing generalization, which may be of independent interest. Next, we turn our attention towards SVIs with a monotone, bounded and Lipschitz operator and consider ℓp-setups, p ∈ [1, 2]. Here, we provide the first analysis which obtains a bound on the strong VI-gap of $\tilde{O}\big(\frac{1}{\sqrt{n}} + \frac{\sqrt{d}}{n\epsilon}\big)$. For p - 1 = Ω(1), this rate is near optimal due to existing lower bounds. To obtain this result, we develop a modified version of recursive regularization. Our analysis builds on the techniques we develop for SSPs as well as employing additional novel components which handle difficulties arising from adapting the recursive regularizat

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Public-data assisted private stochastic optimization: power and limitations 24

Public-data assisted private stochastic optimization: power ...

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

作者： Enayat Ullah Michael Menart Raef Bassily Cristóbal Guzmán Raman Arora Meta Department of Computer Science & Engineering The Ohio State University and Department of Computer Science University of Toronto Vector Institute Department of Computer Science & Engineering Translational Data Analytics Institute (TDAI) The Ohio State University Inst. for Mathematical and Comput. Eng. Fac. de Matemáticas and Esc. de Ingeniería Pontificia Universidad Católica de Chile Department of Computer Science Johns Hopkins University

ISBN: (纸本)9798331314385

We study the limits and capability of public-data assisted differentially private (PA-DP) algorithms. Specifically, we focus on the problem of stochastic convex optimization (SCO) with either labeled or unlabeled public data. For complete/labeled public data, we show that any (ε, δ)-PA-DP has excess risk $\tilde{\Omega}\big(\min(\frac{1}{\sqrt{n_{\text{pub}}}},\frac{1}{\sqrt{n}}+\frac{\sqrt{d}}{n\epsilon} ) \big)$, where d is the dimension, npub is the number of public samples, npriv is the number of private samples, and n = npub + npriv. These lower bounds are established via our new lower bounds for PA-DP mean estimation, which are of a similar form. Up to constant factors, these lower bounds show that the simple strategy of either treating all data as private or discarding the private data, is optimal. We also study PA-DP supervised learning with unlabeled public samples. In contrast to our previous result, we here show novel methods for leveraging public data in private supervised learning. For generalized linear models (GLM) with unlabeled public data, we show an efficient algorithm which, given Õ(nprivε) unlabeled public samples, achieves the dimension independent rate $\tilde{O}\big(\frac{1}{\sqrt{{n_{\text{priv}}}}} + \frac{1}{\sqrt{{n_{\text{priv}}}\epsilon}}\big)$. We develop new lower bounds for this setting which shows that this rate cannot be improved with more public samples, and any fewer public samples leads to a worse rate. Finally, we provide extensions of this result to general hypothesis classes with finite fat-shattering dimension with applications to neural networks and non-Euclidean geometries.

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