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Efficient distributed algorithms for holistic aggregation functions on random regular graphs

science China(Information sciences) 2022年第5期65卷 32-50页

作者： Lin JIA Qiang-Sheng HUA Haoqiang FAN Qiuping WANG Hai JIN National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology Institute for Interdisciplinary Information Science Tsinghua University

In this paper, we propose efficient distributed algorithms for three holistic aggregation functions on random regular graphs that are good candidates for network topology in next-generation data *** three holistic aggregation functions include SELECTION（select the k-th largest or smallest element）,DISTINCT（query the count of distinct elements）, MODE（query the most frequent element）. We design three basic techniques — Pre-order Network Partition, Pairwise-independent Random Walk, and Random Permutation Delivery, and devise the algorithms based on the techniques. The round complexity of the distributed SELECTION is Θ（log N） which meets the lower bound where N is the number of nodes and each node holds a numeric element. The round complexity of the distributed DISTINCT and MODE algorithms are O（log3N/log log N） and O（log2N log log N） respectively. All of our results break the lower bounds obtained on general graphs and our distributed algorithms are all based on the CON GE S T model, which restricts each node to send only O（log N） bits on each edge in one round under synchronous communications.

关键词： distributed algorithms holistic aggregation function random regular graph $\mathcal{CONGEST}$ model communication complexity round complexity

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A Transformer-Based Video Colorization Method Fusing Local Self-attention and Bidirectional Optical Flow 10th

A Transformer-Based Video Colorization Method Fusing Local S...

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10th International Conference of Pioneering computer Scientists, Engineers and Educators, ICPCSEE 2024

作者： Yuan, Lin Duan, Xun Xiang, Lingjie Kong, Guangqian Wu, Yun State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China

ISBN: (纸本)9789819787456

Video colorization encounters two principal challenges: colorization quality and temporal flicker. Balancing colorization quality and temporal consistency is a significant challenge. To address the aforementioned issues, we employ the transformer model in the field of video colorization and introduce a pioneering video colorization method named VCTR. Initially, to guarantee the quality of video coloring, we employ a pretrained image coloring network to add color to the grayscale video frame. Next, the feature extraction module for VCTR is utilized to perform feature extraction and propagation on the color frames during the colorization process. Finally, the transformer module is designed to fully leverage local information via the local feature self-attention layer. Additionally, motion information from bidirectional optical flow is utilized to identify correlations across video frames for feature fusion, guaranteeing both the coloring effect and temporal consistency. The experimental results demonstrate that VCTR outperforms existing methods on two publicly available datasets, namely, DAVIS and Videvo. VCTR attains the top ranking in the long-series dataset, Videvo, based on the CTBI (Colorization and Temporal-Consistency Balance Index). This achievement underscores VCTR’s ability to strike a commendable balance between colorization quality and temporal consistency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Optical flows

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Joint DRL and ASL-Based 'Cloud-Edge-End' Collaborative Caching Optimization for Metaverse Scenarios 30

Joint DRL and ASL-Based 'Cloud-Edge-End' Collaborative Cachi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Wan, Zheng Zhao, Shenglu Dong, Xiaogang Huang, Zhekai Tan, Yifeng Jiangxi University of Finance and Economics School of Computer and Artificial Intelligence Nanchang China Jiujiang University School of Computer and Big Data Science Jiujiang China

ISBN: (纸本)9798331515966

With the emergence of the Metaverse concept, the rendering and transmission of 3D virtual scenes demand high-bandwidth, high-quality real-time rendering technology, as well as ultra-reliable low-latency communication (URLLC). However, the current 5G network technology faces unprecedented challenges. The 3D Gaussian Splatting technique renders realistic scenes from sparse input views, providing a novel solution to these challenges. This paper proposes a cloud-edge-end collaborative caching optimization scheme based on deep reinforcement learning and asynchronous federated learning (termed Gaussian Splatting Actor-Critic, GSAC) for 3D Gaussian Splatting rendering near terminals. This scheme leverages asynchronous federated learning technology to predict user behavior trajectories in real time while ensuring user data privacy. It integrates the ActorCritic strategy of deep reinforcement learning and graph convolutional network (GCN) technology to dynamically adjust caching strategies, thereby improving cache hit rates and reducing cache switching frequency. Simulation experiments demonstrate that compared to existing technologies, the GSAC scheme significantly outperforms in terms of target sub-scenario cache hit rates and cache switching frequency. © 2024 IEEE.

关键词： 5G mobile communication systems

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LF-DETR: Lightweight Detection Network for Floating Surface Garbage Based on Adaptive Channel Shuffling 22

LF-DETR: Lightweight Detection Network for Floating Surface ...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Xu, ShouKun Gong, Yan Yang, Gaochao Li, Ning School of Software Changzhou University School of Computer Science and Artificial Intelligence Aliyun School of Big Data Changzhou China

ISBN: (纸本)9798331509712

To address the issues of low accuracy and efficiency in water surface target detection caused by uneven lighting and water ripples, this study proposes a channel modeling-based DETR-like model. Starting from efficient partial convolutions and inverted residual blocks, the concept of dynamic sparsity is introduced, and a Flexible Masked Residual Block (FMRB) for a lightweight backbone is designed. Based on the design principle of channel grouping, an adaptive channel shuffling strategy is derived. Our model achieved an mAP of 79.8% on the FGDD dataset and 61.7% on the Pascal VOC2007 dataset. Compared to the original model, the FLOPs of our LF-DETR model are reduced by 24.9%, down to 42.8G, and the number of parameters is reduced by 29.4%. Extensive experimental results demonstrate that our model exhibits an excellent balance between efficiency and accuracy across multiple benchmarks, highlighting its great potential in practical applications. © 2024 IEEE.

关键词： Object detection

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Evaluating Pretrained Deep Learning Models for COVID-19 Detection in Medical Imaging 1

Evaluating Pretrained Deep Learning Models for COVID-19 Dete...

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1st Renewable Energy Technologies and Modern Communications Systems: Future and Challenges Conference, RETMCS 2024

作者： AL-Shehri, Bushra A. Sabbeh, Sahar F. Dessouky, Mohamed M. Department of Data Science College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Faculty of Computers and Artificial Intelligence Information Systems Department Benha University Benha13518 Egypt Department of Computer Science and Artificial Intelligence College of Computer Science and Engineering University of Jeddah Jeddah23218 Saudi Arabia Department of Computer Science and Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9798331519445

COVID-19 is a respiratory disease for which reverse transcription-polymerase chain reaction (RT-PCR) is the standard detection method. This study introduces a hybrid deep learning approach to support the diagnosis of respiratory abnormalities, using COVID-19 as a case study. Multiple datasets were utilized, including the COVID-19 Radiography dataset (Kaggle), the SARS-CoV-2 CT-Scan dataset, and proprietary data from the King Faisal Medical Complex. Seven pre-trained deep learning models—VGG16, VGG19, MobileNet, DenseNet201, ResNet50, InceptionResNetV2, and InceptionV3—were evaluated for their performance. InceptionResNetV2 and InceptionV3 achieved perfect accuracy (100%) on the COVID-19 Radiography dataset and 99% accuracy on the SARS-CoV-2 CT-Scan dataset. Additionally, augmentation techniques were explored to improve model robustness. This research contributes significantly to medical imaging, offering practical insights for advancing diagnostic and monitoring practices for COVID-19 and other respiratory conditions. ©2024 IEEE.

关键词： COVID-19

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MapsTP: HD Map Images Based Multimodal Trajectory Prediction for Automated Vehicles 26

MapsTP: HD Map Images Based Multimodal Trajectory Prediction...

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26th Irish Machine Vision and Image Processing Conference, IMVIP 2024

作者： Sharma, Sushil Das, Arindam Sistu, Ganesh Halton, Mark Eising, Ciarán Department of Electronic & Computer Engineering University of Limerick Ireland SFI CRT Foundations in Data Science University of Limerick Ireland

ISBN: (纸本)9781837242672

Predicting ego vehicle trajectories remains a critical challenge, especially in urban and dense areas due to the unpredictable behaviours of other vehicles and pedestrians. Multimodal trajectory prediction enhances decision-making by considering multiple possible future trajectories based on diverse sources of environmental data. In this approach, we leverage ResNet-50 to extract image features from high-definition map data and use IMU sensor data to calculate speed, acceleration, and yaw rate. A temporal probabilistic network is employed to compute potential trajectories, selecting the most accurate and highly probable trajectory paths. This method integrates HD map data to improve the robustness and reliability of trajectory predictions for autonomous vehicles. © This is an open access article published by the IET under the Creative Commons Attribution License (http://***/licenses/by/3.0/)

关键词： Autonomous Vehicles HDMap Images MultiModel Trajectory Prediction Probabilistic Network

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Expressive Access Control Encryption With Bilateral Policy Privacy for Consumer Electronics in the Metaverse

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 90-101页

作者： Peng, Zongfeng Peng, Changgen Xu, Dequan Ding, Hongfa Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Guizhou University of Finance and Economics College of Information Guiyang550025 China

The metaverse offers an unprecedented immersive experience and a fresh connection paradigm for consumers, which brings security threats related to unauthorized data access, potential misuse, and increasing attacks in the bilateral information flow. Therefore, it is necessary to provide an efficient and secure access control scheme. However, current efforts struggle to address these more complicated security issues. To do this, in this paper, we present an expressive access control encryption (EACE) scheme for bilateral control of information flow in metaverse-enabled consumer electronics. Our EACE supports fine-grained and bilateral control of senders and receivers by carefully taking advantage of access control encryption, dual-policy attribute-based encryption, and matchmaking encryption. Furthermore, we propose a novel sanitization algorithm with sender anonymity and bilateral policy privacy against the malicious sanitizer. We formalize the corresponding security definitions and give rigorous proofs. Performance analysis demonstrates that our EACE can simultaneously achieve bilateral control with policy privacy, sender anonymity against sanitizer, and lightweight decryption. The experimental results show that our proposal costs less computational and storage overhead in the symmetric bilinear group. The impressive performance indicates that our EACE is an excellent choice to balance security and efficiency when consumer electronics meet the metaverse. © 1975-2011 IEEE.

关键词： Differential privacy

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Hierarchical vectorization for facial images

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Computational Visual Media 2024年第1期10卷 97-118页

作者： Qian Fu Linlin Liu Fei Hou Ying He School of Computer Science and Engineering Nanyang Technological University639798SingaporeSingapore Data61 Commonwealth Scientific and Industrial Research OrganisationSydney2015Australia Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group639798SingaporeSingapore State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

The explosive growth of social media means portrait editing and retouching are in high *** portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly *** at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical *** base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression *** middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of *** top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and *** train a deep generative model that can produce high-frequency residuals *** to the inherent meaning in vector primitives,editing portraits becomes easy and *** particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic *** quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider *** new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait *** evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.

关键词： face editing vectorization Poisson editing color transfer illumination editing expression editing

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Successful Integration of IoT and Blockchain Technologies Using Several Machine Learning Algorithms 5

Successful Integration of IoT and Blockchain Technologies Us...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Calarany, C. Indumathy, M. Senthilraja, P. Suganya, D. Gopi Vardhan, V. Rahul, Yelisetty Shanmukh Tagore Government Arts and Science College Department of Computer Science Puducherry India Rajiv Gandhi College of Engineering & Technology Department of Information Technology Kirumampakkam India K.S.Rangasamy College of Technology Department of Computer Science Tamil Nadu India Rajiv Gandhi College of Engineering & Technology Department of Computer Science Kirumampakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Internet of Things (IoT) produces massive amounts of data that need to be processed and saved securely. The strong features of Blockchain makes it as a best candidate for storing the data received from IoT sensors. However, there is a need of concern to take care of the challenges associated with both IoT and blockchain paradigms. Firstly, the enormous amount of data should be effortlessly handled by Blockchain network, without adding much complexity. Secondly, the heterogeneous nature of data that are received from various IoT Sensors should be stored within the blockchain. In this paper, a conceptual framework based on Multimodal Machine Learning (MML) algorithm is proposed as an interface between the IoT sensors and Blockchain networks. The MML algorithm is used to understand the different modalities of the data and provides a way to manage the heterogeneity in that data. The MML algorithms provides an intelligent data processing that allows for real-time data storage in the blockchain networks. The conceptual frame assumes that audio, video, and text are the three types of data as received from IoT. These inputs are processed in simplistic Recurrent neural network RNN that acts as MML and then passed to Blockchain networks for secure storage. Simulation of the above said conceptual framework is performed assuming all other relevant requirements of IoT, MML and Blockchain are taken into account with default parameters and values. The results obtained provide a promising note for future scope in this direction. © 2024 IEEE.

关键词： IoT MML RNN

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Efficient and Reliable Federated Recommendation System in Temporal Scenarios 18th

Efficient and Reliable Federated Recommendation System in ...

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18th International Conference on Green, Pervasive, and Cloud Computing, GPC 2023

作者： Ye, Jingzhou Lin, Hui Wang, Xiaoding Dong, Chen Liu, Jianmin The College of Computer and Cyber Security Fujian Normal University Fuzhou350117 China College of Computer and Data Science Fuzhou University Fuzhou350116 China

ISBN: (纸本)9789819998951

Addressing privacy concerns and the evolving nature of user preferences, it is crucial to explore collaborative training methods for federated recommendation models that match the performance of centralized models while preserving user privacy. Existing federated recommendation models primarily rely on static relational data, overlooking the temporal patterns that dynamically evolve over time. In domains like travel recommendations, factors such as the availability of attractions, introduction of new activities, and media coverage constantly change, influencing user preferences. To tackle these challenges, we propose a novel approach called FedNTF. It leverages an LSTM encoder to capture multidimensional temporal interactions within relational data. By incorporating tensor factorization and multilayer perceptrons, we project users and items into a latent space with time encoding, enabling the learning of nonlinear relationships among diverse latent factors. This approach not only addresses the privacy concerns by preserving the confidentiality of user data but also enables the modeling of temporal dynamics to enhance the accuracy and relevance of recommendations over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Recommender systems

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