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CDCP: A Framework for Accelerating Dynamic Channel Pruning methods

CDCP: A Framework for Accelerating Dynamic Channel Pruning M...

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International conference on parallel and distributed Systems (ICPADS)

作者： Zhaohong Xiang Yigui Luo Yin Xie Haihong She Weiming Liang Laigang Zhao School of Software Engineering Tongji University Shanghai China Aichi Automobile (Shanghai) Company Limited

Dynamic channel pruning is a technique aimed at reducing the theoretical computational complexity and inference latency of convolutional neural networks. Dynamic channel pruning methods introduce complex additional modules for dynamically selecting channels for images. Due to the additional modules, dynamic channel pruning methods never achieve optimal acceleration effect in real world. To address this problem, we propose Consecutive Dynamic Channel Pruning (CDCP), a novel dynamic channel pruning framework unified for almost all dynamic pruning methods designed for continuous image processing. The core idea of CDCP stems from our observation that adjusting the network for all frames in semantically continuous scenes is unnecessary since adjacent frames often share similar network structures in dynamic channel pruning. CDCP introduces a simple binary classifier to determine whether the network structure needs to be adjusted for a new frame. Our method can also be used for semantically non-continuous image processing tasks with a slightly lower probability of model reuse. We validate the effectiveness of CDCP on three dynamic channel pruning methods and better acceleration effects are achieved when applied them with CDCP to the semantically continuous Waymo dataset, the nuScenes dataset, and the semantically discontinuous COCO dataset.

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Application Research of Vertical Federated Learning Technology in Banking Risk Control Model Strategy 21

Application Research of Vertical Federated Learning Technolo...

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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： Luo, Yong Lu, Zhi Yin, Xiaofei Lu, Songfeng Weng, Yiting Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan China China Minsheng Banking Corp. Ltd. Beijing China Shenzhen Huazhong University of Science and Technology Research Institute Wuhan China Shu Lun Pan Beijing China

ISBN: (纸本)9798350329223

This study centers on the application of vertical federated learning technology in the context of Internet banking loans, with a particular focus on innovations in data privacy protection, risk control model algorithms, and secure multi-party computation. Currently, banking risk control strategies mainly rely on traditional data processing technologies, which often fall short in protecting user privacy and ensuring data usage efficiency. We adopt vertical federated learning technology, offering an innovative solution for the Internet banking loan scenario. Firstly, regarding data privacy protection, we propose a differential privacy mechanism to safeguard user-sensitive data. Secondly, we innovatively apply risk control model algorithms, facilitating collaborative modeling across multiple Internet loan platforms through federated learning. Furthermore, we introduce secure multi-party computation technology to ensure the secure transmission of data and confidentiality of computation processes during federated learning. Through empirical experiments on real Internet loan datasets, we validate the effectiveness and feasibility of our proposed methods. After implementing our risk control model, the credit approval rate increased from 3.44% to 18.2%, with a single-day high reaching 25.53%. The average loan amount increased by 7,700 yuan, and the average interest rate slightly declined by 0.48%, marking a significant improvement and breakthrough compared to traditional risk control models. This study offers innovative solutions for data privacy protection and risk control in the Internet loan scenario, providing safer and more reliable services for financial institutions and users. Moreover, our methods possess high practicality and promotional value. The potential widespread impact on the industry is profound. © 2023 IEEE.

关键词： Risk management

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Research on Computer image Recognition Technology Based on Artificial Intelligence

Research on Computer Image Recognition Technology Based on A...

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International conference on Systems and Informatics (ICSAI)

作者： Jing Li Yuxi Liu Yahong Ma Feng Liu School of Electronic Information Xijing University Xian China

ISBN: (数字)9798331510138

ISBN: (纸本)9798331510145

In order to solve the problem of fast processing for ultra-high-resolution images in embedded systems, this paper proposes a multi-mode tracking and recognition method based on embedded processing architecture, through the construction of a multi-GPU hardware processing system, the large-scale image parallel processing algorithm is studied, and a variety of image processing modes such as full-frame, full-window tracking and tracking scanning are designed, and experimental verification is carried out on ultra-high-resolution images. The multi-GPU architecture and large-scale image parallel processing algorithm described in this paper have certain advantages over traditional processing methods.

关键词： image recognition Software design image resolution Software algorithms Computer architecture Switches parallel processing Hardware Artificial intelligence Informatics

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An intensity-density associative clustering method for big millimeter-wave image data

An intensity-density associative clustering method for big m...

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2022 International conference on Optical and Photonic Engineering, icOPEN 2022

作者： Wan, Shuai Ding, Li He, Huagang Qian, Zujian Li, Ping Shanghai Key Lab of Modern Optical System University of Shanghai for Science and Technology Shanghai China Terahertz Spectrum and Imaging Technology Cooperative Innovation Center Shanghai China

ISBN: (纸本)9781510661905

The three-dimensional (3D) millimeter-wave (MMW) image is a big data with redundant information that not only blurs the image but also increases the computational load on the denoising procedure. To address this problem, we propose an intensity-density associative clustering method which mainly consists of two phases. Specifically, an intensity-based clustering algorithm, e.g., K-Means, is firstly applied on the amplitude of the image data and initially achieves denoising and data compression. Then, a density-based spatial clustering algorithm, e.g., DBSCAN, is used to further extract object information. Due to the label transferring from the retained amplitude, only valid image data can forward spatial information to DBSCAN, and as a result, the computational load on DBSCAN decreases. Also, a multi-threaded parallel computing framework is developed to exploit the distributed multicore processing for its implementation. Therefore, the proposed method can be well-adapted for 3D MMW image data in a computational efficient way. Simulations and experimental results confirm the effectiveness of our method that has good efficiency on big MMW image data with respect to the noise suppression and object information extraction. © 2022 SPIE.

关键词： Millimeter waves

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A distributed multi MIMO radar imaging system 3

A distributed multi MIMO radar imaging system

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3rd International conference on Computer Vision, image and Deep Learning and International conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Liu, Renjun Liu, Yunqing Changchun University of Technology Electronic Science and Technology Jilin Province Changchun City China

ISBN: (纸本)9781665459112

At present, MIMO radar is more and more widely used in the field of radar imaging. With its unique multi transmit and multi receive system, after appropriate processing, the radar can be equivalent to a virtual array that can transmit and receive independently at the same time, which is equivalent to increasing the radar aperture and improving the resolution of radar imaging. However, in some cases, when the radar needs to provide high azimuth resolution images under certain conditions, the imaging range and resolution provided by a single radar may not be enough. The distributed multi MIMO radar imaging system model described in this paper is composed of multiple radars with the same system. Under a certain signal processing mode, through timing control, the static distributed multi MIMO radar can realize high-resolution object images in the imaging area at the same time. Under the above conditions, using certain data processing methods, the imaging system proposed in this paper can provide a larger imaging range and higher resolution, especially angular resolution, compared with a single radar. © 2022 IEEE.

关键词： Radar imaging

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FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing image Super-Resolution

FCTNet: A CNN-Transformer Hybrid for Single Remote Sensing I...

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International Symposium on parallel and distributed processing with Applications, ISPA

作者： Ning Shi Hui Zhou Chunyang Ye Biyuan Yao Yilin Wu Bo Li Hainan University Haikou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Convolutional Neural Network (CNN) and Transformer architectures have been extensively applied in the domain of remote sensing image super-resolution. However, to achieve optimal performance, many existing methods are designed with a large number of parameters, thereby increasing the complexity of the model and hindering practical deployment. To address this issue, we propose an innovative model that synergizes CNN and Transformer architectures while employing a minimal number of their respective modules. This approach significantly reduces the parameter count while maintaining superior performance. Firstly, by constructing additional shallow feature representations as input, we enhance the feature extraction capabilities for individual images. Secondly, we utilize residual connections between various modules to integrate multi-scale, high-dimensional feature information, thus ensuring efficient transmission. Finally, the image reconstruction module is employed to restore the high-resolution image. Experimental results show that FCTNet significantly outperforms existing methods while maintaining a substantially lower parameter count, as demonstrated through evaluations on two public datasets.

关键词： Visualization distributed processing Superresolution Feature extraction Transformers Sensors image restoration Convolutional neural networks Remote sensing image reconstruction

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Ancient Chinese Character image Retrieval Based on Fusing Deep Features and Skeleton Features

Ancient Chinese Character Image Retrieval Based on Fusing De...

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International conference on parallel and distributed Systems (ICPADS)

作者： Xinhui Wang Xuedong Tian School of Cyber Security and Computer Hebei University Baoding China Institute of Intelligent Image and Document Information Processing Hebei University Baoding China

The complex glyph structures and diverse writing styles of ancient Chinese character images lead to suboptimal performance when existing image retrieval methods are directly applied to datasets of these images. Addressing the impact on retrieval caused by the complex glyph structure and rich detail information characteristic of ancient Chinese character images, a multi-layer feature adaptive fusion model for ancient Chinese character image retrieval is designed. Firstly, a local feature extraction module is constructed to obtain low-level feature maps with different spatial receptive fields. Secondly, to better fuse features of varying scales, an improved ASFF(Adaptively Spatial Feature Fusion) method is employed to build the PSASFF(Pixel Shuffle Adaptively Spatial Feature Fusion) module for adaptive fusion of multi-layer features extracted by the designed network. Finally, in order to reduce the influence of the writing style of ancient Chinese characters on retrieval, the cosine similarity scores of image retrieval before and after fine processing are weighted and used as the ultimate similarity scores. The retrieval method proposed achieves an average retrieval accuracy of mAP@50 and mAP@30 of 0.8537 and 0.9576 respectively on the ancient Chinese character image dataset. Experimental results demonstrate the effectiveness of this method for ancient Chinese character image retrieval.

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distributed Deep Reinforcement Learning with Graph Neural Networks for Personalized Drug Interaction Analysis 7

Distributed Deep Reinforcement Learning with Graph Neural Ne...

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7th International conference on image Information processing, ICIIP 2023

作者： Vyas, Narayan Jadhav, Nitin N. Raje, Vaishali V. Choudhary, Chahil Chandigarh University Department of Cse Mohali India Krishna Institute of Medical Sciences Krishna Vishwa Vidyapeeth Department of Medicine Karad India Krishna Institute of Medical Sciences Krishna Vishwa Vidyapeeth Department of Psm Karad India

ISBN: (纸本)9798350371406

This research pivots around the intricate domain of personalized drug interaction analysis, harnessing the power of Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). Emphasizing the need for personalized medicine, the study bridges the gap between static drug interaction data and dynamic patient-specific requirements. The principal purpose is to delve into the amalgamation of GNNs and DRL, targeting precise, adaptive, and safer drug recommendations tailored to individual patient profiles. The significance of this work lies in its potential to revolutionize the pharmaceutical landscape, shifting from a one-size-fits-all model to bespoke drug therapies. Preliminary outcomes have underscored the capability of this integrated approach to outperform traditional methods, illuminating a promising trajectory for future medical interventions and setting a benchmark in patient-centric drug interaction analysis. © 2023 IEEE.

关键词： Reinforcement learning

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MiDedup: A Restore-Friendly Deduplication Method on Docker image Storage Systems 20th

MiDedup: A Restore-Friendly Deduplication Method on Docker ...

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20th IFIP WG 10.3 International conference on Network and parallel Computing, NPC 2024

作者： Qin, Lisha Tan, Haoliang Zou, Xiangyu Ou, Wenhao Wei, Yan Huang, Rubing Xia, Wen Harbin Institute of Technology Shenzhen China Peng Cheng Laboratory Shenzhen China Zhongruan International Technology Service Co. Ltd. Shenzhen China Macau University of Science and Technology Macao China

ISBN: (纸本)9789819628292

Docker image storage systems, like Docker registry, often employ deduplication to reduce storage overhead. Existing deduplication methods for these systems detect redundancy at either layer or file level, each with its own advantages and limitations. Layer-level deduplication offers much faster restoration but lower deduplication ratio, as it eliminates duplicate layers while ignoring duplicate files across layers. Conversely, file-level deduplication offers a higher deduplication ratio but slower restoration, as removing duplicate files scatters the files of a layer, leading to multiple disk read I/O during restoration. Our analysis reveals that the slow restoration in file-level deduplication partly stems from ignoring file-layer relationships. In this paper, we propose MiDedup, a new Docker deduplication method. MiDedup detects redundancy at file-level and organizes files into a restore-friendly layout by considering file-layer relationships to enhance restore speed. Our studies also show that duplicated files of one layer are not uniformly distributed across different layers. MiDedup uses this insight to skip deduplicating scattered files to further optimize the file layout for faster restoration. Evaluations indicate that MiDedup achieves restore speeds that are 8–19× faster than file-level deduplication (slightly slower than layer-level one), while maintaining 9–47% higher deduplication ratios than layer-level deduplication. © IFIP International Federation for Information processing 2025.

关键词： Redundancy

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CUTTANA: Scalable Graph Partitioning for Faster distributed Graph Databases and Analytics 51st

CUTTANA: Scalable Graph Partitioning for Faster Distributed ...

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51st International conference on Very Large Data Bases, VLDB 2025

作者： Hajidehi, Milad Rezaei Sridhar, Sraavan Seltzer, Margo University of British Columbia Vancouver Canada

Graph partitioning plays a pivotal role in various distributed graph processing applications, including graph analytics, graph neural network training, and distributed graph databases. A "good" graph partitioner reduces workload execution time, worker imbalance, and network overhead. Graphs that require distributed settings are often too large to fit in the main memory of a single machine. This challenge renders traditional in-memory graph partitioners infeasible, leading to the emergence of streaming solutions. Streaming partitioners produce lower-quality partitions, because they work from partial information and must make premature decisions before they have a complete view of a vertex’s neighborhood. We introduce CUTTANA, a streaming graph partitioner that partitions massive graphs (Web/Twitter scale) with superior quality compared to existing streaming solutions. CUTTANA uses a novel buffering technique that prevents the premature assignment of vertices to partitions and a scalable coarsening and refinement technique that enables a complete graph view, improving the intermediate assignment made by a streaming partitioner. We implemented a parallel version for CUTTANA that offers nearly the same partitioning latency as existing streaming partitioners. Our experimental analysis shows that CUTTANA consistently yields better partitioning quality than state-of-the-art streaming vertex partitioners in terms of both edge-cut and communication volume metrics. We also evaluate the workload latencies that result from using CUTTANA and other partitioners in distributed graph analytics and databases. CUTTANA outperforms the other methods in most scenarios (algorithms, datasets). In analytics applications, CUTTANA improves runtime performance by up to 59% compared to various streaming partitioners (i.e., HDRF, Fennel, Ginger, HeiStream). In graph database tasks, CUTTANA results in higher query throughput by up to 23%, without hurting tail latency. © 2024, VLDB Endowment. All

关键词： Graph Databases

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