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FlexIM: Efficient and Verifiable Index Management in Blockchain

IEEE Transactions on Knowledge and Data Engineering 2025年第6期37卷 3399-3412页

作者： Li, Binhong Lin, Licheng Zhang, Shijie Xu, Jianliang Xiao, Jiang Li, Bo Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China Hong Kong Baptist University School of Computer Science China Department of Computer Science and Engineering Hong Kong999077 Hong Kong

Blockchain-based query with its traceability and data provenance has become increasingly popular and widely adopted in numerous applications. Yet existing index-based query approaches are only efficient under static blockchain query workloads where the query attribute or type must be fixed. It turns out to be particularly challenging to construct an efficient index for dynamic workloads due to prohibitively long construction time and excessive storage consumption. In this paper, we present FlexIM, the first efficient and verifiable index management system for blockchain dynamic queries. The key innovation in FlexIM is to uncover the inherent characteristics of blockchain, i.e., data distribution and block access frequency, and then to optimally choose the index by utilizing reinforcement learning technique under varying workloads. In addition, we enhance and facilitate verifiability with low storage overhead by leveraging Root Merkle Tree (RMT) and Bloom Filter Merkle Tree (BMT). Our comprehensive evaluations demonstrate that FlexIM outperforms the state-of-the-art blockchain query mechanism, vChain+, by achieving a 26.5% speedup while consuming 94.2% less storage, on average, over real-world Bitcoin datasets. 1989-2012 IEEE.

关键词： Structured Query Language

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Impulsive Control of Nonlinear Multi-Agent Systems: A Hybrid Fuzzy Adaptive and Event-Triggered Strategy

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IEEE Transactions on Fuzzy Systems 2025年

作者： Han, Fang Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Wuhan430074 China

This paper presents a hybrid control approach that integrates an adaptive fuzzy mechanism with an event-triggered impulse strategy to address consensus control challenges in nonlinear Multi-Agent Systems (MASs) with uncertain information, both in leader and leaderless scenarios. Firstly, a fuzzy logic-based adaptive control mechanism is proposed to handle system nonlinearity and uncertainty. This mechanism dynamically adjusts fuzzy control rules in real time, enabling the system to adapt to changing conditions and enhancing its robustness against nonlinear disturbances. Secondly, to alleviate communication overhead and reduce control frequency, an event-triggered impulse strategy is implemented. This strategy activates impulse control only under specific conditions, effectively minimizing control actions while maintaining system consensus. Using Lyapunov stability theory, sufficient conditions for achieving system consensus are established, demonstrating the global consensus and robustness of the MASs under certain criteria. Finally, numerical simulations and experimental evaluations validate the proposed method's effectiveness and its practical advantages. © 2025 IEEE.

关键词： Multi agent systems

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MeG2: In-Memory Acceleration for Genome Graphs Analysis 23

MeG2: In-Memory Acceleration for Genome Graphs Analysis

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Proceedings of the 60th Annual ACM/IEEE Design Automation Conference

作者： Yu Huang Long Zheng Haifeng Liu Zhuoran Zhou Dan Chen Pengcheng Yao Qinggang Wang Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China and Zhejiang Lab Hangzhou China National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350323481

Genome graphs analysis has emerged as an effective means to enable mapping DNA fragments (known as reads) to the reference genome. It replaces the traditional linear reference with a graph-based representation to augment the genetic variations and diversity information, significantly improving the quality of genotyping. The in-depth characterization of genome graphs analysis uncovers that it is bottlenecked by the irregular seed index access and the intensive alignment operation, stressing both the memory system and computing *** on these observations, we propose MeG2, a lightweight, commodity DRAM-compliant, processing-in-memory architecture to accelerate genome graphs analysis. MeG2 is specifically integrated with the capabilities of both near-memory processing and bitwise in-situ computation. Specifically, MeG2 leverages the low access latency of near-memory processing with the index-centric offload mechanism to alleviate the irregular memory access in the seeding procedure, and harnesses the row-parallel capacity of in-situ computation with the distance-aware technique to exploit the intensive computational parallelism in the alignment process. Results show that MeG2 outperforms the CPU-, GPU-, and ASIC-based genome graphs analysis solutions by 502× (30.2×), 272× (15.1×), and 5.5× (8.3×) for short (long) reads, while reducing energy consumption by 1628× (85.6×), 1443× (77.1×), and 7.8× (11.7×), respectively. We also demonstrate that MeG2 offers significant improvements over existing PIM-based genome sequence analysis accelerators.

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OUTLIER SYNTHESIS VIA HAMILTONIAN MONTE CARLO FOR OUT-OF-DISTRIBUTION DETECTION

arXiv

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arXiv 2025年

作者： Li, Hengzhuang Zhang, Teng National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Out-of-distribution (OOD) detection is crucial for developing trustworthy and reliable machine learning systems. Recent advances in training with auxiliary OOD data demonstrate efficacy in enhancing detection capabilities. Nonetheless, these methods heavily rely on acquiring a large pool of high-quality natural outliers. Some prior methods try to alleviate this problem by synthesizing virtual outliers but suffer from either poor quality or high cost due to the monotonous sampling strategy and the heavy-parameterized generative models. In this paper, we overcome all these problems by proposing the Hamiltonian Monte Carlo Outlier Synthesis (HamOS) framework, which views the synthesis process as sampling from Markov chains. Based solely on the in-distribution data, the Markov chains can extensively traverse the feature space and generate diverse and representative outliers, hence exposing the model to miscellaneous potential OOD scenarios. The Hamiltonian Monte Carlo with sampling acceptance rate almost close to 1 also makes our framework enjoy great efficiency. By empirically competing with SOTA baselines on both standard and large-scale benchmarks, we verify the efficacy and efficiency of our proposed HamOS. Our code is available at: https://***/Fir-lat/HamOS_OOD. © 2025, CC BY.

关键词： Markov chains

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FIRE: combining multi-stage filtering with taint analysis for scalable recurring vulnerability detection 24

FIRE: combining multi-stage filtering with taint analysis fo...

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Proceedings of the 33rd USENIX Conference on Security Symposium

作者： Siyue Feng Yueming Wu Wenjie Xue Sikui Pan Deqing Zou Yang Liu Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China Nanyang Technological University Singapore National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Cyber Science and Engineering Huazhong University of Science and Technology China and Jinyinhu Laboratory China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9781939133441

With the continuous development of software open-sourcing, the reuse of open-source software has led to a significant increase in the occurrence of recurring vulnerabilities. These vulnerabilities often arise through the practice of copying and pasting existing vulnerabilities. Many methods have been proposed for detecting recurring vulnerabilities, but they often struggle to ensure both high efficiency and consideration of semantic information about vulnerabilities and patches. In this paper, we introduce FIRE, a scalable method for large-scale recurring vulnerability detection. It utilizes multi-stage filtering and differential taint paths to achieve precise clone vulnerability scanning at an extensive scale. In our evaluation across ten open-source software projects, FIRE demonstrates a precision of 90.0% in detecting 298 recurring vulnerabilities out of 385 ground truth instance. This surpasses the performance of existing advanced recurring vulnerability detection tools, detecting 31.4% more vulnerabilities than VUDDY and 47.0% more than MOVERY. When detecting vulnerabilities in large-scale software, FIRE outperforms MOVERY by saving about twice the time, enabling the scanning of recurring vulnerabilities on an ultra-large scale.

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It Takes Two to Tango: Serverless Workflow Serving via Bilaterally Engaged Resource Adaptation

arXiv

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arXiv 2025年

作者： Wu, Jing Wang, Lin Deng, Quanfeng Yu, Chen Zhang, Dong Yan, Bingheng Liu, Fangming National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology China Paderborn University Germany Inspur Data Co. Ltd. China Peng Cheng Laboratory China

Serverless platforms typically adopt an early-binding approach for function sizing, requiring developers to specify an immutable size for each function within a workflow beforehand. Accounting for potential runtime variability, developers must size functions for worst-case scenarios to ensure service-level objectives (SLOs), resulting in significant resource inefficiency. To address this issue, we propose Janus, a novel resource adaptation framework for serverless platforms. Janus employs a late-binding approach, allowing function sizes to be dynamically adapted based on runtime conditions. The main challenge lies in the information barrier between the developer and the provider: developers lack access to runtime information, while providers lack domain knowledge about the workflow. To bridge this gap, Janus allows developers to provide hints containing rules and options for resource adaptation. Providers then follow these hints to dynamically adjust resource allocation at runtime based on real-time function execution information, ensuring compliance with SLOs. We implement Janus and conduct extensive experiments with real-world serverless workflows. Our results demonstrate that Janus enhances resource efficiency by up to 34.7% compared to the state-of-the-art. © 2025, CC BY-NC-ND.

关键词： Resource allocation

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Comprehensive Architecture Search for Deep Graph Neural Networks

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IEEE Transactions on Big Data 2025年

作者： Dong, Yukang Pan, Fanxing Gui, Yi Jiang, Wenbin Wan, Yao Zheng, Ran Jin, Hai National Engineering Research Center for Big Data Technology Huazhong University of Science and Technology Wuhan430074 China Huazhong University of Science and Technology Service Computing Technology and System Laboratory Wuhan430074 China Huazhong University of Science and Technology Cluster and Grid Computing Laboratory Wuhan430074 China Huazhong University of Science and Technology School of Computer Science and Technology Wuhan430074 China Zhejiang Lab Hangzhou311121 China

In recent years, Neural Architecture Search (NAS) has emerged as a promising approach for automatically discovering superior model architectures for deep Graph Neural Networks (GNNs). Different methods have paid attention to different types of search spaces. However, due to the time-consuming nature of training deep GNNs, existing NAS methods often fail to explore diverse search spaces sufficiently, which constrains their effectiveness. To crack this hard nut, we propose CAS-DGNN, a novel comprehensive architecture search method for deep GNNs. It encompasses four kinds of search spaces that are the composition of aggregate and update operators, different types of aggregate operators, residual connections, and hyper-parameters. To meet the needs of such a complex situation, a phased and hybrid search strategy is proposed to accommodate the diverse characteristics of different search spaces. Specifically, we divide the search process into four phases, utilizing evolutionary algorithms and Bayesian optimization. Meanwhile, we design two distinct search methods for residual connections (All-connected search and Initial Residual search) to streamline the search space, which enhances the scalability of CAS-DGNN. The experimental results show that CAS-DGNN achieves higher accuracy with competitive search costs across ten public datasets compared to existing methods. © 2015 IEEE.

关键词： Neural network models

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Working Smarter Not Harder: Hybrid Cooling for Deep Learning in Edge Datacenters

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Pei, Qiangyu Yuan, Yongjie Hu, Haichuan Wang, Lin Zhang, Dong Yan, Bingheng Yu, Chen Liu, Fangming 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 1037 Luoyu Road Wuhan430074 China Paderborn University TU Darmstadt Germany Jinan Inspur Data Co. Ltd. China Huazhong University of Science and Technology Peng Cheng Laboratory China

The proliferation of deep-learning-based mobile and IoT applications has driven the increasing deployment of edge datacenters equipped with domain-specific accelerators. The unprecedented computing power offered by these accelerators puts a heavy burden on the cooling system, motivating more potent cooling techniques like cold water cooling. However, we observe that cold water cooling results in significant energy waste in edge datacenters due to the fluctuating resource utilization both spatially and temporally. To tackle this issue, we propose the concept of "working smarter"by slowing down accelerators deliberately whenever possible and enabling warm water cooling during these times to achieve cooling efficiency. Based on this concept, we develop Hyco - a hybrid water cooling system tailored for edge datacenters running deep learning workloads. First, Hyco features a zone-based cooling architecture enabling dynamic switching between cold water and warm water cooling. Then, based on a lightweight latency estimation method, Hyco incorporates a learning-based scheduling scheme to determine "which"accelerator workers and "when"to slow down through an adaptive and intelligent power-latency trade-off for deep learning models. The simulation with real-world traces shows that Hyco reduces the cooling energy consumption by up to 34.74× while satisfying latency constraints more than 99% of the time for deep-learning-based applications. © 2016 IEEE.

关键词： Deep reinforcement learning

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Efficient Modeling Attack on Multiplexer PUFs via Kronecker Matrix Multiplication

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Wang, Hongfei Wan, Caixue Jin, Hai Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 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 Wuhan430074 China

The Physical Unclonable Function (PUF) is valued for its lightweight nature and unique functionality, making it a common choice for securing hardware products requiring authentication and key generation mechanisms. In response to the susceptibility of individual PUFs to modeling attacks, advanced PUF variants have been developed to improve security measures. One notable type in this regard is the multiplexer-based composition of arbiter PUFs, known as MPUF, which aims to meet high reliability and security standards simultaneously. Current research on attacking MPUF encounters challenges such as substantial demands for training CRPs and low success rates. In this work, we propose a novel numerical modeling attack strategy for MPUFs. Using Kronecker products from mathematical perspective, this method precisely describe the MPUF model without using complex network architectures, boosting attack accuracy and overall efficiency. Experiment comparison with state-of-the-art works demonstrates that our method achieves better performance in terms of attack accuracy, robustness, and efficiency. Our method is able to successfully attack a (512, 8)-MPUF in 32.71 minutes with 97.14% accuracy, outperforming all existing attack methods on MPUFs. More, we validate our method through experiments with hardware implementations on FPGAs. The advantages of our method also include the adaptability to be employed to attack other MPUF variations like cMPUF and rMPUF, and the capability to be integrated with an existing attack method for launching efficient attack on MPUFs leveraging reliability information. © 2025 IEEE.

关键词： modeling attack multiplexer-based PUF Physical unclonable function

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Exploring Semantic Association Rules Between Keyword and Visual Feature clusters for Web Image Retrieval

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电子学报(英文版) 2008年第1期17卷 59-65页

作者： JIN Hai HE Ruhan TAO Wenbing Services Computing Technology and System Laboratory Cluster and Grid Computing Laboratory Huazhong University of Science and Technology Wuhan 430074 China

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback (RF) and Latent semantic indexing (LSI) to alleviate the gap. This paper proposes an approach base on Frequent itemset mining (FIM) and Association rule (AR) techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained offline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web images. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The experiments are carried out in our web image retrieval system named VAST (VisuAl & SemanTic image search), and the results show the effectiveness of the proposed approach.

关键词：图像处理自适应算法图像恢复语义联合规则视觉特征

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