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39th IEEE International Conference on data engineering, ICDE 2023

作者： Zheng, Long Ye, Xiangyu Liu, Haifeng Wang, Qinggang Huang, Yu Gui, Chuangyi Yao, Pengcheng Liao, Xiaofei Jin, Hai Xue, Jingling Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Laboratory Wuhan430074 China Zhejiang Lab Hangzhou311121 China Unsw School of Computer Science and Engineering Sydney Australia

ISBN: (纸本)9798350322279

Multi-version graph processing has been widely used to solve many real-world problems. The process of the multi-version graph processing typically includes: (1) a history graph version switching at a specific time and (2) graph processing on this history graph. Existing multi-version graph systems assume ideally that every request for a particular graph version at a particular time will have a corresponding snapshot available. However, in most cases, this is not true. Then existing solutions usually have to settle with an "approximating"version as a substitute, leading to unexpected results for the underlying graph algorithm and thus reducing the practicality of a multi-version graph system for many application scenarios *** this paper, we observe that only a few graph updates have a great impact on the final results. We therefore present AFaVS, a novel multi-version graph system that can improve accuracy effectively in both time- and memory-efficient manners. The cornerstone of AFaVS lies in a novel concept "value"that characterizes the importance of graph updates. AFaVS proposes differential management of updates based on their values and achieves higher accuracy while preserving processing and memory efficiency. AFaVS is also equipped with value-guided version switching and locality-aware optimizations to boost its overall efficiency. Our results on a variety of real-world datasets show that AFaVS outperforms four state-of-the-art multi-version graph systems by 74.35%~95.72% in terms of accuracy improvement and 57.03%~90.44% in terms of memory reduction while introducing less than 2.96% extra computing time. We have deployed AFaVS in a disaster recovery system on the production cluster of Alibaba, achieving 78.8%~90.1% fewer error rates than advanced systems at a comparable efficiency. © 2023 IEEE.

关键词： Efficiency

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Layered Structure Aware Dependent Microservice Placement Toward Cost Efficient Edge Clouds 42

Layered Structure Aware Dependent Microservice Placement Tow...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Zeng, Deze Geng, Hongmin Gu, Lin Li, Zhexiong China University of Geosciences School of Computer Science Wuhan China Huazhong University of Science and Technology 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 Technology Wuhan China

ISBN: (纸本)9798350334142

Although the containers are featured by light-weightness, it is still resource-consuming to pull and startup a large container image, especially in relatively resource-constrained edge cloud. Fortunately, Docker, as the most widely used container, provides a unique layered architecture that allows the same layer to be shared between microservices so as to lower the deployment cost. Meanwhile, it is highly desirable to deploy dependent microservices of an application together to lower the operation cost. Therefore, the balancing of microservice deployment cost and the operation cost should be considered comprehensively to achieve minimal overall cost of an on-demand application. In this paper, we first formulate this problem into a Quadratic Integer Programming form (QIP) and prove it as a NP-hard problem. We further propose a Randomized Rounding-based Microservice Deployment and Layer Pulling (RR-MDLP) algorithm with low computation complexity and guaranteed approximation ratio. Through extensive experiments, we verify the high efficiency of our algorithm by the fact that it significantly outperforms existing state-of-the-art microservice deployment strategies. © 2023 IEEE.

关键词： Containers

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MeHyper: Accelerating Hypergraph Neural Networks by Exploring Implicit dataflows 31

MeHyper: Accelerating Hypergraph Neural Networks by Explorin...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Zhao, Wenju Yao, Pengcheng Chen, Dan Zheng, Long Liao, Xiaofei Wang, Qinggang Ma, Shaobo Li, Yu Liu, Haifeng Xiao, Wenjing Sun, Yufei Zhu, Bing Jin, Hai Xue, Jingling Huazhong University of Science and Technology 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 Technology Wuhan430074 China National University of Singapore School of Computing 119077 Singapore Guangxi University School of Computer Electronics and Information NanNing530004 China University of New South Wales School of Computer Science and Engineering SydneyNSW2052 Australia

ISBN: (纸本)9798331506476

Hypergraph Neural Networks (HGNNs) are increasingly utilized to analyze complex inter-entity relationships. Traditional HGNN systems, based on a hyperedge-centric dataflow model, independently process aggregation tasks for hyperedges and vertices, leading to significant computational redundancy. This redundancy arises from recalculating shared information across different tasks. For the first time, we identify and harness implicit dataflows (i.e., dependencies) within HGNNs, introducing the microedge concept to effectively capture and reuse intricate shared information among aggregation tasks, thereby minimizing redundant computations. We have developed a new microedge-centric dataflow model that processes shared information as fine-grained microedge aggregation tasks. This dataflow model is supported by the Read-Process-Activate-Generate execution model, which aims to optimize parallelism among these tasks. Furthermore, our newly developed MeHyper, a microedge-centric HGNN accelerator, incorporates a decoupled pipeline for improved computational parallelism and a hierarchical feature management strategy to reduce off-chip memory accesses for large volumes of intermediate feature vectors generated. Our evaluation demonstrates that MeHyper substantially outperforms the leading CPUbased system PyG-CPU and the GPU-based system HyperGef, delivering performance improvements of 1,032.23 × and 10.51 ×, and energy efficiencies of 1,169.03 × and 9.96 ×, respectively. © 2025 IEEE.

关键词： Redundancy

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RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion Models 32

RePaint-NeRF: NeRF Editting via Semantic Masks and Diffusion...

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32nd International Joint Conference on Artificial Intelligence, IJCAI 2023

作者： Zhou, Xingchen He, Ying Richard Yu, F. Li, Jianqiang Li, You College of Computer Science and Software Engineering Shenzhen University China China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China

ISBN: (纸本)9781956792034

The emergence of Neural Radiance Fields (NeRF) has promoted the development of synthesized high-fidelity views of the intricate real world. However, it is still a very demanding task to repaint the content in NeRF. In this paper, we propose a novel framework that can take RGB images as input and alter the 3D content in neural scenes. Our work leverages existing diffusion models to guide changes in the designated 3D content. Specifically, we semantically select the target object and a pre-trained diffusion model will guide the NeRF model to generate new 3D objects, which can improve the editability, diversity, and application range of NeRF. Experiment results show that our algorithm is effective for editing 3D objects in NeRF under different text prompts, including editing appearance, shape, and more. We validate our method on both real-world datasets and synthetic-world datasets for these editing tasks. Please visit https://*** for a better view of our results. © 2023 International Joint Conferences on Artificial Intelligence. All rights reserved.

关键词： Semantics

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Maverick: Personalized Edge-Assisted Federated Learning with Contrastive Training 25

Maverick: Personalized Edge-Assisted Federated Learning with...

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34th ACM Web Conference, WWW 2025

作者： Wang, Kaibin He, Qiang Dong, Zeqian Chen, Rui He, Chuan Chua, Caslon Chen, Feifei Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9798400712746

In an edge-assisted federated learning (FL) system, edge servers aggregate the local models from the clients within their coverage areas to produce intermediate models for the production of the global model. This significantly reduces the communication overhead incurred during the FL process. To accelerate model convergence, FedEdge, the state-of-the-art edge-assisted FL system, trains clients’ models in local federations when they wait for the global model in each training round. However, our investigation reveals that it drives the global model towards clients with excessive local training, causing model drifts that undermine model performance for other clients. To tackle this problem, this paper presents Maverick, a new edge-assisted FL system that mitigates model drifts by training personalized local models for clients through contrastive local training. It introduces a model-contrastive loss to facilitate personalized local federated training by driving clients’ local models away from the global model and close to their corresponding intermediate models. In addition, Maverick includes anomalous models in contrastive local training as negative samples to accelerate the convergence of clients’ local models. Extensive experiments are conducted on three widely-used models trained on three datasets to comprehensively evaluate the performance of Maverick. Compared to state-of-the-art edge-assisted FL systems, Maverick accelerates model convergence by up to 16.2x and improves model accuracy by up to 12.7%. © 2025 Copyright held by the owner/author(s).

关键词： Contrastive Learning

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Dispatcher: Resource-aware Nakamoto Blockchain via Hierarchical Topology and Adaptive Incentives

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Distributed Ledger Technologies: Research and Practice 2024年第2期3卷 1-20页

作者： Hai Jin Shuohua Dong Xiaohai Dai Yuandi Cai Jiang Xiao 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 Technology Huazhong University of Science and Technology Wuhan China

Mainstream blockchain systems such as Bitcoin and Ethereum are revolutionizing the financial industry by adopting the Nakamoto consensus protocol, i.e., Proof-of-Work (PoW). Only nodes with sufficient computing resources can work out the PoW difficulties, thereby increasing the mining cost of malicious attackers and ensuring the security of blockchain systems. Such an assumption of having abundant resources leads to drawbacks of low throughput and risk of centralization. In this article, we present Dispatcher, a novel distributed consensus protocol that takes resource heterogeneity into account to ensure resource-aware PoW with high efficiency. Dispatcher introduces a hierarchical topology to offer flexible PoW difficulties tailored for different nodes’ resources. In particular, it utilizes the limited resource of each node to jointly maximize the performance by concurrent mining. Moreover, we design an adaptive incentive mechanism to fit the available resource of blockchain nodes to rewards. Our experiments show that Dispatcher enjoys a substantial performance margin over the state-of-the-art. We can achieve a 50% throughput improvement compared with OHIE.

关键词： Blockchain resource heterogeneity incentive hierarchical architecture

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Autonomous Emergency Landing on 3D Terrains: Approaches for Monocular Vision-based UAVs 8

Autonomous Emergency Landing on 3D Terrains: Approaches for ...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Du, Weiming Lin, Junmou Du, Binqing Borhan, Uddin Md. Li, Jianqiang Chen, Jie College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

With the increasing use of unmanned aerial vehicles (UAVs) in a variety of applications, their safety has become a critical concern. UAVs face numerous emergencies during missions, in these situations, the UAVs need to autonomously find a suitable landing site, plan flight routes, and avoid obstacles in unstructured environments. However, due to the limitations of computing power and sensors, it is challenging to achieve this goal. This research aims to explore the monocular emergency autonomous landing algorithm. Specifically, this work focuses on extracting depth and vision information. A topology information extractor is designed to convert image to graph and assess the connectivity of terrain. Additionally, a depth information extractor is designed to calculate the slope and roughness of the ground. A 3D topology optimizer is designed to optimize the graph by depth information and evaluate the landing suitability by a heuristic strategy. To make the action decision, a hybrid decision network (DGN) based on 3D topology information is proposed. Finally, this work build a simulated scenario based on real scene to verify the efficiency of DGN. The results of the experiment show that DGN outperforms its counterparts in terms of action prediction accuracy and landing success rate. © 2023 IEEE.

关键词： Landing

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LibAMM: Empirical Insights into Approximate computing for Accelerating Matrix Multiplication 38

LibAMM: Empirical Insights into Approximate Computing for Ac...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Zeng, Xianzhi Jiang, Wenchao Zhang, Shuhao National Engineering Research Center for Big DataTechnology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Nanyang Technological University Singapore Singapore University of Technology and Design Singapore

Matrix multiplication (MM) is pivotal in fields from deep learning to scientific computing, driving the quest for improved computational efficiency. Accelerating MM encompasses strategies like complexity reduction, parallel and distributed computing, hardware acceleration, and approximate computing techniques, namely AMM algorithms. Amidst growing concerns over the resource demands of large language models (LLMs), AMM has garnered renewed focus. However, understanding the nuances that govern AMM's effectiveness remains incomplete. This study delves into AMM by examining algorithmic strategies, operational specifics, dataset characteristics, and their application in real-world tasks. Through comprehensive testing across diverse datasets and scenarios, we analyze how these factors affect AMM's performance, uncovering that the selection of AMM approaches significantly influences the balance between efficiency and accuracy, with factors like memory access playing a pivotal role. Additionally, dataset attributes are shown to be vital for the success of AMM in applications. Our results advocate for tailored algorithmic approaches and careful strategy selection to enhance AMM's effectiveness. To aid in the practical application and ongoing research of AMM, we introduce LibAMM -a toolkit offering a wide range of AMM algorithms, benchmarks, and tools for experiment management. LibAMM aims to facilitate research and application in AMM, guiding future developments towards more adaptive and context-aware computational solutions. © 2024 Neural information processing systems foundation. All rights reserved.

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Defect Screening on Nuclear Power Plant Concrete Structures: A Two-staged Method Based on Contrastive Representation Learning 8

Defect Screening on Nuclear Power Plant Concrete Structures:...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： Huang, Wenlian Zhu, Guanming Huang, Qiang Chen, Zhuangzhuang Chen, Jie Li, Jianqiang College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9798350300178

Intelligent defect detection methods are important for the surface of the containment of nuclear power plants and face many challenges in the field of computer vision. Due to the irregular shapes and large variation of defects, as well as the similarity of the features between some defects and the background. Most existing deep learning-based defect detection networks suffer from insufficient feature extraction capabilities, making it difficult to detect defects from the background. Inspired by discrete representation learning methods, we propose a two-stage defect detection model called ConVQVAE, which uses a semi-supervised method for representation learning to achieve binary classification for defect detection. In addition, contrastive learning is performed in two stages to enhance the model's diversity representation ability and inter-class disentanglement representation ability. Finally, we experimentally verify that the proposed method can obtain good classification results. © 2023 IEEE.

关键词： Defects

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IBATree: A Novel Method for Interpretable Cancer Cell Diagnosis Using Information Bottleneck Attribution

IBATree: A Novel Method for Interpretable Cancer Cell Diagno...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Raza, Muhammad Umair Chen, Jie Nawaz, Adil Saeed, Faisal Leung, Victor C.M. Li, Jianqiang Wang, Zhaoxia Shenzhen University College of Computer Science and Software Engineering China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology China Shenzhen Children's Hospital Department of Gastroenterology China

ISBN: (纸本)9798350386226

Deep Neural Networks (DNNs) have demonstrated remarkable performance in classification and regression tasks on RGB-based pathological inputs. The network's prediction mechanism must be interpretable to establish trust in the clinical routine. One principal approach to interpretation is feature attribution. Feature attribution methods identify the importance of input features for the output prediction. Building on the Information Bottleneck Attribution (IBA) method, we recognize the RGB's input regions with high mutual information with the network's output for each prediction. IBA identifies input regions that have sufficient predictive information. In this paper, we introduce "IBATree", a novel approach that combines IBA with decision trees to enhance both the interpretability and accuracy of DNNs in cancer cell classification. Our method leverages the information bottleneck framework to inject noise into feature maps and then isolates the most informative features for model predictions while maintaining high classification performance. We evaluated our proposed approach on three datasets - CNMC, ISBI2016, and BreaKHis - demonstrating competitive accuracy and producing clear interpretations. This makes IBATree particularly suitable for clinical applications, where understanding the rationale behind predictions is crucial. Our results show that IBATree provides reliable predictions and valuable insights into feature importance, paving the way for its application in various biomedical domains. © 2024 IEEE.

关键词： Diseases

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