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34th ACM Web Conference, WWW 2025

作者： Yang, Jiyu He, Qiang Zhou, Zheyu Dai, Xiaohai Chen, Feifei Tian, Cong Yang, Yun Swinburne University of Technology Melbourne Australia Huazhong University of Science and Technology Wuhan China Deakin University Melbourne Australia Xidian University Xi’an China 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

Machine learning (ML) models are widely deployed on edge nodes, such as mobile phones and edge servers, to power a wide range of AI applications over the web. Ensuring the integrity of these edge models is paramount, as they are subject to corruption caused by software/hardware exceptions and malicious tampering, which may undermine model performance, incur economic losses, and pose health risks. Existing data integrity mechanisms designed for files stored on disks cannot properly verify the integrity of models running in GPUs or mitigate the new integrity threats against edge models. This paper proposes EdgeThemis, a novel mechanism for verifying the integrity of edge models through sentinel verification. To enable verifiability for a model M, EdgeThemis embeds a sentinel backdoor and a verification module into M. Then, a challenger can send verification requests to the edge node hosting M to verify its integrity. Next, the sentinel activates the verification module to generate a unique integrity proof tied to the identity of the edge node for verification. Finally, the challenger can verify the integrity proof to detect model corruption. Theoretical analysis proves that EdgeThemis can properly mitigate potential integrity threats against edge models. Experiments demonstrate that EdgeThemis achieves a verification accuracy of 100.00% across various models and different types of model corruption with robustness against replay attacks, theft attacks, and replacement attacks. © 2025 Copyright held by the owner/author(s).

关键词： Backpropagation

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Seastar: A Cache-Efficient and Load-Balanced Key-Value Store on Disaggregated Memory

Seastar: A Cache-Efficient and Load-Balanced Key-Value Store...

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2024 IEEE International Conference on Cluster Computing, CLUSTER 2024

作者： Du, Jingwen Wang, Fang Feng, Dan Zeng, Dexin Yi, Sheng Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System China School of Computer Science and Technology Huazhong University of Science and Technology China Shenzhen Huazhong University of Science and Technology Research Institute China

ISBN: (纸本)9798350358711

In modern datacenters, memory disaggregation un-packs monolithic servers to build independent network-connected compute and memory pools, greatly improving resource uti-lization. Existing memory-disaggregated key-value stores adopt ownership sharing or ownership partitioning for request scheduling and they all show poor performance and scalability. The former generates multiple cache misses and multiple network round trips due to poor cache locality, while the latter fails to fully use resources on all Compute Nodes (CNs) due to load imbalance. In this paper, we propose Seastar, a fast and scalable key-value store that achieves high cache locality as well as load balancing. To enable high concurrency and ensure linearizability, Seastar exploits a self-verification version chain to provide out-of-place atomic updates and ensure consistent reads. To improve cache locality, Seastar leverages a pair-based ownership assigning scheme to assign the processing permission of requests corre-sponding to each key to a CN pair through two independent hash functions. To ensure load balancing among CNs, Seastar proposes a decentralized power-of-two-choices routing scheme to route the requests to the less-loaded CNs in CN pairs for handling. Experimental results demonstrate that Seastar improves the throughput by up to 6.7x and significantly reduces the latency compared with state-of-the-art memory-disaggregated key-value stores. © 2024 IEEE.

关键词： Scalability

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Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

arXiv

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arXiv 2024年

作者： Wang, Xianlong Li, Minghui Liu, Wei Zhang, Hangtao Hu, Shengshan Zhang, Yechao Zhou, Ziqi Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China

Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples;(ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, i.e., even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at https://***/CGCL-codes/UnlearnablePC Copyright © 2024, The Authors. All rights reserved.

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iCREPCP:A deep learning-based web server for identifying base-resolution cis-regulatory elements within plant core promoters

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Plant Communications 2023年第1期4卷 213-216页

作者： Kaixuan Deng Qizhe Zhang Yuxin Hong Jianbing Yan Xuehai Hu College of Informatics Hubei Engineering Technology Research Center of Agricultural Big DataHuazhong Agricultural UniversityWuhanHubeiP.R.China National Key Laboratory of Crop Genetic Improvement Huazhong Agricultural UniversityWuhan 430070P.R.China

Dear Editor,A central question of plant biology is how to specify the temporal and spatial patterns as well as the quantitative levels of gene expression,which are significantly associated with important agronomic *** has been a growing consensus in the past decade that the two key factors determining gene expression level are cis-regulatory modules(CRMs)and trans-acting factors(TAFs)(Schmitz et al.,2022).Common CRMs include gene-proximal promoters and distal enhancers,which are both considered to be complex assemblies of cis-regulatory elements(CREs).It is the binding or interaction between CREs and TAFs(often transcription factors[TFs])in a ubiquitous or cell-specific manner that determines in which cell,at what time,and at what level a gene is ***,the identification of plant CRMs or critical CREs will not only help us understand transcriptional regulatory mechanisms in plants but also serve as an essential prerequisite for plant breeding 4.0—breeding by genome editing(Gao,2021).

关键词： server web breeding

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Metamorphic Testing for Traffic Sign Detection and Recognition 23

Metamorphic Testing for Traffic Sign Detection and Recogniti...

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23rd IEEE International Conference on Software Quality, Reliability, and security Companion, QRS-C 2023

作者： Long, Yuanyuan Fan, Yong Pan, Ya School of Computer Science and Technology of Southwest University of Science and Technology Mianyang China Sichuan Big Data and Intelligent System Engineering Technology Research Center Mianyang China

ISBN: (纸本)9798350359398

Traffic sign detection and recognition (TSDR) is a crucial technology to realize vehicle autonomous driving and maintain road safety. Misdetection or misclassification of traffic signs can lead to serious traffic accidents. Therefore, it is essential to ensure the correctness of traffic sign detection and recognition systems. However, most existing methods use labeled data, but it isn't easy to obtain labeled data. The metamorphic testing can reduce the dependence on the labeled data, but the current metamorphic relations do not consider the domain characteristics of traffic signs, so it is difficult to effectively detect traffic sign detection and recognition systems faults. To solve the above problems, this paper introduces metamorphic testing to verify traffic sign detection and recognition systems and designs and implements a tool, MTSDR, which provides a series of metamorphic relations in the traffic domain. It can generate many follow-up test images based on these metamorphic relations and automatically performs tests for evaluation, focusing on practical failure situations. The experimental evaluation was carried out on four advanced traffic sign detection and recognition models on open datasets. The results show that MTSDR can generate natural images and find thousands of traffic sign detection and recognition faults. Compared with the previous methods, our method has a higher fault detection capability, and the highest fault detection rate reaches more than 40%. In addition, it finds that retraining the traffic sign detection and recognition models using the images that found the fault can improve the model's performance, reduce its fault, and reduce the fault rate by 11.77% on average. © 2023 IEEE.

关键词： Traffic signs

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FRMT: RMT with Frequency Feature learning Framework for land cover classification 3

FRMT: RMT with Frequency Feature learning Framework for land...

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3rd International Conference on Environmental Remote Sensing and Geographic Information technology, ERSGIT 2024

作者： Ou, Zhihui Chen, Fang Wang, Lei Yu, Bo Wang, Ning School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China International Research Center of Big Data for Sustainable Development Goals Beijing100094 China

ISBN: (纸本)9781510690219

The classification of land cover is of great significance in the management and sustainable development of land resources. In recent years, deep learning methods have made remarkable progress in the field of remote sensing image processing. However, due to the complex difference in structure and scale of surface objects in remote sensing images, most of the current methods are prone to the problem of unclear boundary extraction, and the classification results of different scale surface objects are poor. In this letter, we proposed FRMT by introducing frequency domain information into RMT framework. A frequency feature learning module (FFLM) is proposed. The frequency domain features are decomposed into high frequency and low frequency features, and the frequency features are learned by spiking convolution to strengthen the model's learning of boundary and different scale features. We performed experimental validation on the very challenging multi-class remote sensing dataset GID-15 and Huawei Ascend Cup competition dataset, and the experimental results show that our model achieves the best results compared to five other recently proposed models, which mIoU achieves 77.91% and 71.85%, respectively. © 2025 SPIE.

关键词： Frequency domain analysis

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Controlling update distance and enhancing fair trainable prototypes in federated learning under data and model heterogeneity

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Defence technology(防务技术) 2025年第5期47卷 15-31页

作者： Kangning Yin Zhen Ding Xinhui Ji Zhiguo Wang School of Information and Software Engineering University of Electronic Science and Technology of ChinaChengdu 610054China Institute of Public Security Kash Institute of Electronics and Information IndustryKash 844000China Shanghai New Energy Vehicle Public Data Collection and Monitoring Research Center Shanghai 200241China

Heterogeneous federated learning(HtFL)has gained significant attention due to its ability to accommodate diverse models and data from distributed combat *** prototype-based HtFL methods were proposed to reduce the high communication cost of transmitting model *** methods allow for the sharing of only class representatives between heterogeneous clients while maintaining ***,existing prototype learning approaches fail to take the data distribution of clients into consideration,which results in suboptimal global prototype learning and insufficient client model personalization *** address these issues,we propose a fair trainable prototype federated learning(FedFTP)algorithm,which employs a fair sampling training prototype(FSTP)mechanism and a hyperbolic space constraints(HSC)mechanism to enhance the fairness and effectiveness of prototype learning on the server in heterogeneous ***,a local prototype stable update(LPSU)mechanism is proposed as a means of maintaining personalization while promoting global consistency,based on contrastive *** experimental results demonstrate that FedFTP achieves state-of-the-art performance in HtFL scenarios.

关键词： Heterogeneous federated learning Model heterogeneity data heterogeneity Contrastive learning

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Breaking Barriers in Physical-World Adversarial Examples: Improving Robustness and Transferability via Robust Feature 39

Breaking Barriers in Physical-World Adversarial Examples: Im...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yichen Chou, Yuxuan Zhou, Ziqi Zhang, Hangtao Wan, Wei Hu, Shengshan Li, Minghui National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China

ISBN: (纸本)157735897X

As deep neural networks (DNNs) are widely applied in the physical world, many researches are focusing on physical-world adversarial examples (PAEs), which introduce perturbations to inputs and cause the model's incorrect outputs. However, existing PAEs face two challenges: unsatisfactory attack performance (i.e., poor transferability and insufficient robustness to environment conditions), and difficulty in balancing attack effectiveness with stealthiness, where better attack effectiveness often makes PAEs more perceptible. In this paper, we explore a novel perturbation-based method to overcome the challenges. For the first challenge, we introduce a strategy Deceptive RF injection based on robust features (RFs) that are predictive, robust to perturbations, and consistent across different models. Specifically, it improves the transferability and robustness of PAEs by covering RFs of other classes onto the predictive features in clean images. For the second challenge, we introduce another strategy Adversarial Semantic Pattern Minimization, which removes most perturbations and retains only essential adversarial patterns in AEs. Based on the two strategies, we design our method Robust Feature Coverage Attack (RFCoA), comprising Robust Feature Disentanglement and Adversarial Feature Fusion. In the first stage, we extract target class RFs in feature space. In the second stage, we use attention-based feature fusion to overlay these RFs onto predictive features of clean images and remove unnecessary perturbations. Experiments show our method's superior transferability, robustness, and stealthiness compared to existing state-of-the-art methods. Additionally, our method's effectiveness can extend to Large Vision-Language Models (LVLMs), indicating its potential applicability to more complex tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep neural networks

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CLNX: Bridging Code and Natural Language for C/C++ Vulnerability-Contributing Commits Identification

arXiv

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arXiv 2024年

作者： Qin, Zeqing Wu, Yiwei Han, Lansheng School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security China

Large Language Models (LLMs) have shown great promise in vulnerability identification. As C/C++ comprises half of the Open-Source Software (OSS) vulnerabilities over the past decade and updates in OSS mainly occur through commits, enhancing LLMs' ability to identify C/C++ Vulnerability-Contributing Commits (VCCs) is essential. However, current studies primarily focus on further pre-training LLMs on massive code datasets, which is resource-intensive and poses efficiency challenges. In this paper, we enhance the ability of BERT-based LLMs to identify C/C++ VCCs in a lightweight manner. We propose CodeLinguaNexus (CLNX) as a bridge facilitating communication between C/C++ programs and LLMs. Based on commits, CLNX efficiently converts the source code into a more natural representation while preserving key details. Specifically, CLNX first applies structure-level naturalization to decompose complex programs, followed by token-level naturalization to interpret complex symbols. We evaluate CLNX on public datasets of 25,872 C/C++ functions with their commits. The results show that CLNX significantly enhances the performance of LLMs on identifying C/C++ VCCs. Moreover, CLNX-equipped CodeBERT achieves new state-of-the-art and identifies 38 OSS vulnerabilities in the real *** Codes 68M25 Copyright © 2024, The Authors. All rights reserved.

关键词： Open source software

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Refining Idioms Semantics Comprehension via Contrastive Learning and Cross-Attention 30

Refining Idioms Semantics Comprehension via Contrastive Lear...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Wu, Mingmin Su, Guixin Zhang, Yongcheng Huang, Zhongqiang Sha, Ying College of Informatics Huazhong Agricultural University Wuhan China Key Laboratory of Smart Farming for Agricultural Animals Wuhan China Hubei Engineering Technology Research Center of Agricultural Big Data Wuhan China Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education China

ISBN: (纸本)9782493814104

Chinese idioms on social media demand a nuanced understanding for correct usage. The Chinese idiom cloze test poses a unique challenge for machine reading comprehension due to the figurative meanings of idioms deviating from their literal interpretations, resulting in a semantic bias in models' comprehension of idioms. Furthermore, given that the figurative meanings of many idioms are similar, their use as suboptimal options can interfere with optimal selection. Despite achieving some success in the Chinese idiom cloze test, existing methods based on deep learning still struggle to comprehensively grasp idiom semantics due to the aforementioned issues. To tackle these challenges, we introduce a Refining Idioms Semantics Comprehension Framework (RISCF) to capture the comprehensive idioms semantics. Specifically, we propose a semantic sense contrastive learning module to enhance the representation of idiom semantics, diminishing the semantic bias between figurative and literal meanings of idioms. Meanwhile, we propose an interference-resistant cross-attention module to attenuate the interference of suboptimal options, which considers the interaction between the candidate idioms and the blank space in the context. Experimental results on the benchmark datasets demonstrate the effectiveness of our RISCF model, significantly outperforming state-of-the-art methods. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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