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Ethereum smart contract security research:survey and future research opportunities

Frontiers of Computer science 2021年第2期15卷 143-160页

作者： Zeli WANG Hai JIN Weiqi DAI Kim-Kwang Raymond CHOO Deqing ZOU National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabClusters and Grid Computing LabHubei Engineering Research Center on Big Data SecurityWuhan430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan430074China School of Cyber Science and Engineering Huazhong University of Science and TechnologyWuhan430074China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057China Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Blockchain has recently emerged as a research trend,with potential applications in a broad range of industries and *** particular successful Blockchain technology is smart contract,which is widely used in commercial settings(e.g.,high value financial transactions).This,however,has security implications due to the potential to financially benefit from a security incident(e.g.,identification and exploitation of a vulnerability in the smart contract or its implementation).Among,Ethereum is the most active and ***,in this paper,we systematically review existing research efforts on Ethereum smart contract security,published between 2015 and ***,we focus on how smart contracts can be maliciously exploited and targeted,such as security issues of contract program model,vulnerabilities in the program and safety consideration introduced by program execution *** also identify potential research opportunities and future research agenda.

关键词： smart contract security blockchain vulnerability unreliable data

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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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GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerators

GoPIM: GCN-Oriented Pipeline Optimization for PIM Accelerato...

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IEEE Symposium on High-Performance Computer Architecture

作者： Siling Yang Shuibing He Wenjiong Wang Yanlong Yin Tong Wu Weijian Chen Xuechen Zhang Xian-He Sun Dan Feng The State Key Laboratory of Blockchain and Data Security Zhejiang University Zhejiang Lab Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Zhejiang Key Laboratory of Big Data Intelligent Computing Washington State University Vancouver Illinois Institute of Technology Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics

ISBN: (数字)9798331506476

ISBN: (纸本)9798331506483

Graph convolutional networks (GCNs) are popular for a variety of graph learning tasks. ReRAM-based processing-in-memory (PIM) accelerators are promising to expedite GCN training owing to their in-situ computing capability. However, existing accelerators can be severely underutilized even with pipelines, due to the oversight of the skewed execution times of various GCN stages and the ignorance of skewed degrees of graph vertices. In this work, we propose GOPIM, a GCN-oriented pipeline optimization for PIM accelerators to expedite GCN training. First, GOPIM proposes an ML-based scheme that allocates crossbar resources to the most needed stages to streamline the overall pipeline. Second, GOPIM utilizes a selective vertex updating technique that evenly distributes vertices on crossbars by interleaved mapping. These techniques collectively reduce the overall execution time without losing much accuracy. We also provide a practical architecture design for GOPIM. Our experimental results show that, GoPIM achieves up to 191 × speedup and 16.1 × energy saving, compared to the state-of-the-art work.

关键词： Training Accuracy Graph convolutional networks Pipelines Energy conservation Computer architecture Machine learning Parallel processing Optimization

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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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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

arXiv

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arXiv 2024年

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an China National University of Singapore Singapore

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024, CC BY.

关键词： Extraction

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A Novel Interpretable Ozone Forecasting Approach Based on Deep Learning with Masked Residual Connections

SSRN

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SSRN 2025年

作者： Jiménez-Navarro, M.J. Asencio-Cortés, Gualberto Martínez-Álvarez, Francisco Martínez-Ballesteros, María del Mar Department of Computer Languages and Systems University of Seville Av. Reina Mercedes S/N Seville41012 Spain Data Science and Big Data Lab Pablo de Olavide University Ctra. de Utrera 1 Seville41013 Spain

Air pollution is a growing threat, especially in low- and middle-income countries, causing 4.2 million premature deaths annually. Ground-level ozone is a major concern, necessitating accurate and interpretable prediction systems for public health. This study presents a machine learning-based forecasting approach applied in Andalusia, Spain, to improve ozone pollution predictions. Traditional time-series forecasting struggles with handling both linear and non-linear relationships in multi-horizon scenarios. Deep learning models often fail to adapt to these complexities. To address this, the proposed methodology integrates residual connections and feature selection, allowing features with linear relationships to bypass non-linear transformations while creating a hierarchical structure for better adaptability. This approach enhances flexibility, interpretability, and robustness, improving pollution level estimates. Additionally, it provides insights into the impact of individual features, supporting better decision-making in public health. The results demonstrate significant improvements in prediction accuracy and feature relevance assessment. © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Joint Participation Incentive and Network Pricing Design for Federated Learning 42

Joint Participation Incentive and Network Pricing Design for...

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42nd IEEE International Conference on Computer Communications, INFOCOM 2023

作者： Ding, Ningning Gao, Lin Huang, Jianwei Northwestern University Department of Electrical and Computer Engineering EvanstonIL60208 United States Shenzhen Research Institute of Big Data Shenzhen518172 China Harbin Institute of Technology Sch. of Electronics and Info. Eng. and the Guangdong Prov. Key Lab. of Aerosp. Commun. and Networking Technol. Shenzhen China The Chinese University of Hong Kong Shenzhen School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen518172 China

ISBN: (纸本)9798350334142

Federated learning protects users' data privacy though sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under both vertical and horizontal interaction structures of the participants. We show that the vertical structure is better than the horizontal one, as it avoids the interests misalignment between the server and the network operator. Numerical results based on real-world datasets show that our proposed mechanisms decrease server's cost by up to 24.87% comparing with the state-of-the-art benchmarks. © 2023 IEEE.

关键词： Costs

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E1 TTS: Simple and Fast Non-Autoregressive TTS

arXiv

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arXiv 2024年

作者： Liu, Zhijun Wang, Shuai Zhu, Pengcheng Bi, Mengxiao Li, Haizhou School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen China Fuxi AI Lab NetEase Inc. Hangzhou China

This paper introduces Easy One-Step Text-to-Speech (E1 TTS), an efficient non-autoregressive zero-shot text-to-speech system based on denoising diffusion pretraining and distribution matching distillation. The training of E1 TTS is straightforward;it does not require explicit monotonic alignment between the text and audio pairs. The inference of E1 TTS is efficient, requiring only one neural network evaluation for each utterance. Despite its sampling efficiency, E1 TTS achieves naturalness and speaker similarity comparable to various strong baseline models. Audio samples are available at ***. © 2024, CC BY.

关键词： Neural networks

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DenoiseRep: denoising model for representation learning 24

DenoiseRep: denoising model for representation learning

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zhengrui Xu Guan'an Wang Xiaowen Huang Jitao Sang School of Computer Science and Technology Beijing Jiaotong University School of Computer Science and Technology Beijing Jiaotong University and Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University and Key Laboratory of Big Data & Artificial Intelligence in Transportation(Beijing Jiaotong University) Ministry of Education

ISBN: (纸本)9798331314385

The denoising model has been proven a powerful generative model but has little exploration of discriminative tasks. Representation learning is important in discriminative tasks, which is defined as "learning representations (or features) of the data that make it easier to extract useful information when building classifiers or other predictors" [4]. In this paper, we propose a novel Denoising Model for Representation Learning (DenoiseRep) to improve feature discrimination with joint feature extraction and denoising. DenoiseRep views each embedding layer in a backbone as a denoising layer, processing the cascaded embedding layers as if we are recursively denoise features step-by-step. This unifies the frameworks of feature extraction and denoising, where the former progressively embeds features from low-level to high-level, and the latter recursively denoises features step-by-step. After that, DenoiseRep fus es the parameters of feature extraction and denoising layers, and theoretically demonstrates its equivalence before and after the fusion, thus making feature denoising computation-free. DenoiseRep is a label-free algorithm that incrementally improves features but also complementary to the label if available. Experimental results on various discriminative vision tasks, including re-identification (Market-1501, DukeMTMC-reID, MSMT17, CUHK-03, vehicleID), image classification (ImageNet, UB200, Oxford-Pet, Flowers), object detection (COCO), image segmentation (ADE20K) show stability and impressive improvements. We also validate its effectiveness on the CNN (ResNet) and Transformer (ViT, Swin, Vmamda) architectures. Code is available at https://***/wangguanan/DenoiseRep.

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Scenario-aware Multimodal Chain-of-Thought Prompting for Rationales of Video Social Relations

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Qin, Penggang Xu, Tong Zhang, Chao Wang, Heda Hu, Yao Chen, Enhong University of Science and Technology of China Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science Hefei 230026 China Xiaohongshu China

Recently, audiences have increasingly watched diverse dramas and movies on streaming platforms, prompting platform administrators to enhance their understanding of video semantics. In particular, capturing and interpreting social relations among characters is critical for content-driven intelligent services and enhancing user experience. However, most existing research has solely approached social relations recognition as a classification problem, without justifiable interpretation of prediction results within the video context. To address this issue, we study cognitive science research and the Chain-of-Thought (CoT) strategy. Based on these foundations, we propose SaMo-CoT, an approach that leverages large language models (LLMs) in step-by-step reasoning. This approach simulates human cognitive processes by combining social scenarios in videos and incorporating empirical social interaction knowledge. In this way, we enable verbal rationales for determining social relations in video understanding. Furthermore, we present an innovative doubly-right social relation recognition framework that predicts both correct social relation labels and correct scenario rationales. Specifically, we translate verbal CoT into multimodal CoT by leveraging scenario-aware prompts and contrastive learning. Extensive experiments demonstrate significant improvements in classification accuracy and interpretability compared to traditional approaches. © 1991-2012 IEEE.

关键词： Chain of thoughts Large language models Social relationship recognition Video understanding

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