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Design and Implementation of Deep Learning Real-Time Streaming Video Data Processing System 1

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7th International conference on Smart Computing and Communication, SmartCom 2022

作者： Zhao, Qiming Wu, Jing Wu, Xiang Fan, Jie Wang, Linhao Wu, Fengling College of Computer Science Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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A high-performance Data Verification Mechanism in Blockchain-Based IoT Systems 21

A High-Performance Data Verification Mechanism in Blockchain...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Zhang, Jingyu Zhou, Pian Sun, Yongtao Luo, Lailong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha China National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering Changsha China

ISBN: (纸本)9798350329223

With the rapid development of the distributed network and communication, the Internet of Things (IoT) systems have been applied to all walks of life. Blockchain technology is one of the most popular research fields recently, which can provide a reliable storage solution and solve information security issues for IoT systems. Transaction information is the most critical fundamental data in the blockchain systems, which needs to be verified to avoid being tempered with by malicious nodes in the process of transmission. With the exponential growth of the number of IoT devices, the limitations of the current storage structure put too much pressure on the system nodes. How to improve verification efficiency has become a key challenge. To alleviate this problem, this paper proposes a novel high-performance verification mechanism for data security protection in blockchain-based IoT systems. We design a new storage structure based on Huffman trunk tree (HTT), and conduct the quantitative analysis of transaction weights. Transactions are stored in full-featured devices in the form of Huffman Merkle tree (HMT), and only the content of HTT is saved in lightweight devices. Finally, the performance superiority of our mechanism is proved through theoretical analysis and experimental evaluation. In blockchain-based IoT systems, our mechanism significantly reduces the data transmission cost and computation overhead, effectively improving the efficiency of data verification. © 2023 IEEE.

关键词： Efficiency

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Optimizing Matrix Multiplication on NERSC’s high performance computer Cori 8th

Optimizing Matrix Multiplication on NERSC’s High Performan...

引用

8th Future Technologies conference, FTC 2023

作者： Jiang, Xuan Lin, William Nagda, Ansh University of California Berkeley Berkeley United States

ISBN: (纸本)9783031474569

We use a variety of techniques to optimize the single-threaded operation of: C = C + A * B. A number of optimization techniques yielded significant speedups, including multi-level blocking, copy optimizations, and loop adjustments. However, we also tried a number of other optimizations that did not improve our program, including prefetching. In this report, we describe each of our optimizations in our final submission, present results with evidence that they work, and describe attempted optimizations that did not noticeably improve our overall performance. Unless otherwise noted, all of our benchmarks operate on matrices of size 1024 × 1024. In our attempts at optimizing matrix multiplication, we find that taking full advantage of spatial locality (through techniques like loop rearrangement or copy optimizations) and instruction-level parallelism dramatically improves the performance of our operation. Furthermore, techniques like prefetching can be a double-edged sword;it can help performance when used correctly but can also slow down performance. Finally, we find that the compiler adds optimizations out-of-the-box, like loop-unrolling, that help with performance underneath. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Matrix algebra

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Evidential Federated Learning for Skin Lesion Image Classification 27th

Evidential Federated Learning for Skin Lesion Image Classif...

引用

27th International conference on Pattern Recognition, ICPR 2024

作者： Hendrix, Rutger Proietto Salanitri, Federica Spampinato, Concetto Palazzo, Simone Bagci, Ulas Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Machine and Hybrid Intelligence Lab Department of Radiology Northwestern University Chicago United States

ISBN: (纸本)9783031781094

We introduce FedEvPrompt, a federated learning approach that integrates principles of evidential deep learning, prompt tuning, and knowledge distillation for distributed skin lesion classification. FedEvPrompt leverages two sets of prompts: b-prompts (for low-level basic visual knowledge) and t-prompts (for task-specific knowledge) prepended to frozen pre-trained Vision Transformer (ViT) models trained in an evidential learning framework to maximize class evidences. Crucially, knowledge sharing across federation clients is achieved only through knowledge distillation on attention maps generated by the local ViT models, ensuring enhanced privacy preservation compared to traditional parameter or synthetic image sharing methodologies. FedEvPrompt is optimized within a round-based learning paradigm, where each round involves training local models followed by attention maps sharing with all federation clients. Experimental validation conducted in a real distributed setting, on the ISIC2019 dataset, demonstrates the superior performance of FedEvPrompt against baseline federated learning algorithms and knowledge distillation methods, without sharing model parameters. In conclusion, FedEvPrompt offers a promising approach for federated learning, effectively addressing challenges such as data heterogeneity, imbalance, privacy preservation, and knowledge sharing. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Pseudo-label based and transformation-consistent self-ensembling model for underwater life images

Pseudo-label based and transformation-consistent self-ensemb...

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2023 International conference on Image, Signal Processing, and Pattern Recognition, ISPP 2023

作者： Huang, Shan Luo, Xiaonan Lu, Jiahao Huang, Chao Xu, Chengpei Yuan, Tianran School of Computer Science and Information Security Guilin University of Electronics Technology No. 1 Jinji Road Guilin541004 China

ISBN: (纸本)9781510666351

With the continuous development of underwater vision technology, high-precision object segmentation has received more attention from scholars. Due to the complexity of the underwater environment, the segmentation of underwater objects has become a new challenge. In order to alleviate such problems, this paper proposes a semi-supervised segmentation model based on pseudo-labels and transformation-consistent(PSTM). Due to the complexity of underwater images, PSTM preprocesses underwater biological images through an image enhancement module. A semi-supervised segmentation network with pseudo-labels and transformation-consistent is then used to obtain a more robust segmentation result. The method in this paper achieves better performance on the public underwater biological dataset DUT-USEG. © 2023 SPIE.

关键词： Deep neural networks

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Analysis and Deployment of Applications Acceleration Environment for Xilinx Hardware-Accelerated Platforms 37

Analysis and Deployment of Applications Acceleration Environ...

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37th conference on Design of Circuits and Integrated Circuits (DCIS)

作者： Santiago, Adrian Muguira, Leire Jimenez, Jaime Sun, Le Lazaro, Jesus Univ Basque Country Dept Tecnol Elect Leioa Spain

ISBN: (数字)9781665459501

ISBN: (纸本)9781665459501

high performance Computing is a practice that consists of adding computing power above what any typical computer or workstation can provide. Greater performance is achieved by solving complex problems. The aim is to obtain a more efficient, reliable, and faster processing than traditional methods. The foundation of this technology is based on the parallelization of the processing units so that a higher performance per unit of time is achieved. This can be get with highperformance hardware or by combining simpler hardware, giving rise to clusters or supercomputers, depending on the form of grouping. Once the general acceleration process of an application and the methodology on which it is based have been understood, a complete analysis of the performance of a specific algorithm or program has been carried out and implemented using different programming languages and technologies. Besides, a series of conclusions and results related to the acceleration of applications through acceleration hardware have been obtained, determining its potential and the possible inconveniences it entails.

关键词： Hardware Acceleration Accelerated Applications high-performance Computing Reconfigurable Hardware

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Bidirectional Weighted Co-Association-Based Clustering Ensemble Algorithm for high-Speed Train Delay Prediction 29th

Bidirectional Weighted Co-Association-Based Clustering Ensem...

引用

29th International conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Zhao, Yi Bai, Muchen Ni, Panpan Jiang, Yunan Feng, Xuan Zhang, Xiaobo School of Design SouthWest Jiaotong University Chengdu611756 China School of Computing and Artificial Intelligence SouthWest Jiaotong University Chengdu611756 China Sichuan Institute of Computer Sciences Chengdu610042 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China

ISBN: (纸本)9783031425141

In recent years, high-speed trains are one of the popular research topics in the field of transportation and have attracted much attention from scholars at home and abroad in view of their high efficiency and safety features. high-speed trains are susceptible to unexpected situations of delayed arrival due to the disturbance of external factors including wind and temperature during the trip. The delay of high-speed trains has a vital impact on people's work efficiency and living standards. To solve the problem of intelligent forecast of high-speed train delay, we design and implement a clustering ensemble algorithm based on text data of bullet train operation environment monitoring. We firstly combine the concepts of cosine similarity, Jaccard similarity and information entropy, then fuse a bidirectional weighting formula, and further propose a novel Clustering Ensemble algorithm based on Bidirectional Weighted Co-association (CE-BWC). This approach consists of an inter-cluster weighting formula used to obtain the implicit generalized information by weighting each cluster between clusters, and a reliability weighting formula transferring the cluster information to the data item itself to measure the correlation degree of the data group. Experiments prove that our CE-BWC has better prediction performance for high-speed train delay and outperforms the state-of-the-art benchmark algorithms. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Efficiency

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A Byzantine Client Detection Method for Federated Learning Based on Encrypted Repeated Median Linear Regression 24

A Byzantine Client Detection Method for Federated Learning B...

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5th International conference on computer Science and Management Technology, ICCSMT 2024

作者： Guo, Jinlong Yang, Ming Li, Huijuan Wu, Xiaoming He, Yunpeng Wang, Xin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Shandong Jinan China Shandong Evayinfo Technology Co. Ltd. Shandong Jinan China

ISBN: (纸本)9798400709999

Federated learning is an emerging distributed machine learning technology that allows participants to jointly train machine learning models locally without the need for large-scale data transmission and sharing, thereby greatly improving data security. However, some Byzantine clients may intentionally tamper with local model parameter updates with the intention of manipulating the global model's update direction, which can lead to a decline in the overall performance of federated learning. Besides, existing Byzantine client detection research is conducted in plain text, which will lead to the leakage of private information. To solve the above issues, we propose a Byzantine client detection method for federated learning based on encrypted repeated median linear regression. Specifically, it combines the repeated median linear regression algorithm with homomorphic encryption, which can detect and eliminate clients with Byzantine behavior in the ciphertext state, ensuring the security and reliability of the parameters in the entire federated learning process. Additionally, we also designed an adaptive gradient clipping method based on historical iteration to clip the gradient to a reasonable range, which can not only reduce communication and computing overhead, but also improve the convergence speed and stability of the model. Experiments show that the proposed method can maintain high accuracy under the influence of malicious behavior of Byzantine clients. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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ESNet: Evolution and Succession Network for high-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the high-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with high-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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Impact of Gamma-Ray Radiation on high Speed Silicon Optical Modulators

Impact of Gamma-Ray Radiation on High Speed Silicon Optical ...

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2023 Asia Communications and Photonics conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023

作者： Zhao, Nengyang Wu, Longsheng Qiu, Chao Bi, Dawei Ding, Yanyue Zhang, Enxia Wu, Aimin Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences University of Chinese Academy of Sciences National Key Laboratory of Materials for Integrated Circuits Shanghai200050 China University of Central Florida Department of Electrical and Computer Engineering OrlandoFL32801 United States

ISBN: (纸本)9798350312614

The performance of the Silicon Photonic Mach-Zehnder modulator (MZM) was studied under gamma-ray radiation. The experimental results showed that when the total dose of gamma-ray radiation reached up to 10 Mrad(Si), the influence on modulation efficiency of the silicon photonic MZM was negligible. However, the electro-optical(EO) bandwidth decreased by 13 % compared to that of pre-irradiation. © 2023 IEEE.

关键词： gamma-ray radiation optical communication silicon photonic MZM silicon photonics

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