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arXiv 2025年

作者： Zhang, Yedi Huang, Lei Gao, Pengfei Song, Fu Sun, Jun Dong, Jin Song National University of Singapore Singapore ShanghaiTech University China ByteDance Inc China Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China Singapore Management University Singapore University of Chinese Academy of Sciences China Nanjing Institute of Software Technology China National University of Singapore Singapore Singapore ShanghaiTech University Shanghai China ByteDance Inc Beijing China Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Singapore Management University Singapore Singapore

In the rapidly evolving landscape of neural network security, the resilience of neural networks against bit-flip attacks (i.e., an attacker maliciously flips an extremely small amount of bits within its parameter storage memory system to induce harmful behavior), has emerged as a relevant area of research. Existing studies suggest that quantization may serve as a viable defense against such attacks. Recognizing the documented susceptibility of real-valued neural networks to such attacks and the comparative robustness of quantized neural networks (QNNs), in this work, we introduce BFAVerifier, the first verification framework designed to formally verify the absence of bit-flip attacks or to identify all vulnerable parameters in a sound and rigorous manner. BFAVerifier comprises two integral components: an abstraction-based method and an MILP-based method. Specifically, we first conduct a reachability analysis with respect to symbolic parameters that represent the potential bit-flip attacks, based on a novel abstract domain with a sound guarantee. If the reachability analysis fails to prove the resilience of such attacks, then we encode this verification problem into an equivalent MILP problem which can be solved by off-the-shelf solvers. Therefore, BFAVerifier is sound, complete, and reasonably efficient. We conduct extensive experiments, which demonstrate its effectiveness and efficiency across various network architectures, quantization bit-widths, and adversary capabilities. © 2025, CC BY.

关键词： Neural networks

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Write-Optimized Persistent Hash Index for Non-Volatile Memory

Write-Optimized Persistent Hash Index for Non-Volatile Memor...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Renzhi Xiao Dan Feng Yuchong Hu Yucheng Zhang Lanlan Cui Lin Wang School of Software Engineering Jiangxi University of Science and Technology Nanchang China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Engineering XI'AN University of Technology Xi'an China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

A hashing index provides rapid search performance by swiftly locating key-value items. Non-volatile memory (NVM) technologies have driven research into hashing indexes for NVM, combining hard disk persistence with DRAM-level performance. Nevertheless, current NVM-based hashing indexes must tackle data inconsistency challenges caused by NVM write reordering or partial writes, and mitigate rapid local wear due to frequent updates, considering NVM's limited endurance. The temporary allocation of buckets in NVM-based chained hashing to resolve hash collisions prolongs the critical path for writing, thus hampering write performance. This paper presents WOPHI, a write-optimized persistent hash index scheme for NVM. By utilizing log-free failure-atomic writes, WOPHI minimizes data consistency overhead and addresses hash conflicts with bucket pre-allocation. Experimental results underscore WOPHI's significant performance enhancements, with insertion latency slashed by up to 88.2% and deletion latency boosted by up to 82.6% compared to existing state-of-the-art schemes. Moreover, WOPHI substantially mitigates data consistency overhead, reducing cache line flushes by 59.3%, while maintaining robust write throughput for insert and delete operations.

关键词： Nonvolatile memory Europe Writing Throughput Hard disks Indexes Resource management

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Dynamic Bi-Elman Attention Networks (DBEAN): Dual-Directional Context-Aware Representation Learning for Enhanced Text Classification

arXiv

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arXiv 2025年

作者： Liao, Mengyao Lai, Zhenglin Xu, Dong College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Text classification, a fundamental task in natural language processing (NLP), aims to categorize textual data into predefined labels. Traditional methods struggled with complex linguistic structures and semantic dependencies. The advent of deep learning, particularly recurrent neural networks (RNNs) and Transformer-based models, has significantly advanced the field by enabling nuanced feature extraction and context-aware predictions. Despite improvements, existing models exhibit limitations in balancing interpretability, computational efficiency, and long-range contextual understanding. This paper proposes the Dynamic Bidirectional Elman with Attention Network (DBEAN), which integrates bidirectional temporal modelling with self-attention mechanisms. DBEAN dynamically assigns weights to critical segments of input, improving contextual representation while maintaining computational efficiency. Copyright © 2025, The Authors. All rights reserved.

关键词： Elman neural networks

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Global Feature Enhancing and Fusion Framework for Strain Gauge Status Recognition 25

Global Feature Enhancing and Fusion Framework for Strain Gau...

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Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

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Prophet: SSD Failure Analysis and Prediction Guided by Flash Reliability Characteristics in Data Centers

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IEEE Transactions on computers 2025年

作者： Song, Yunpeng Liang, Yujiong Liu, Jialin Shi, Liang East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application Shanghai China East China Normal University School of Computer Science and Technology Shanghai China Huazhong University of Science and Technology Wuhan National Laboratory for Optoelectronics Wuhan China

Solid-state drives (SSDs) are massively deployed in various fields, especially in data centers, for their excellent cost-effectiveness. However, SSDs may fail due to their imperfect manufacturing processes, resulting in system-level failures and even downtime in data centers. This makes SSD failure prediction critical. Current studies focus on dealing with data missing, numerical normalization, and other statistical issues in using machine learning methods, but the consideration of the reliability characteristics of the underlying flash media of SSDs and the timeliness (time duration between predicted failure and real failure) of SSD failure prediction result is missing. In this work, we study the failure characteristics of over 200,000 drives from industry data centers over a 4-year period, as well as daily data. The relationship between SSD attribute values and failures is first investigated. Then, we analyzed the SSD failure characteristics from several aspects (causes, differences between failures, and timeliness of prediction results) relying on flash reliability characteristics. Based on these, a novel SSD failure prediction method (Prophet) is proposed. Specifically, Prophet contains the following two components. First, to cope with the differences between failures, a diff-state method is proposed for differential machine learning modeling of SSDs in different "States". We define the "State" of an SSD, which represents the range of values in which the SSD currently lies in terms of some key attributes. Through flash reliability characteristics, we distinguish between different failures before training the model to obtain accurate predictions of different failure behaviors. Second, a recovery period method is proposed to enhance the timeliness of SSD failure prediction result by designing the sample selection method. The enhanced timeliness can be utilized by operations personnel to handle failed SSDs, such as replacement and repair. The evaluation results of

关键词： Failure rate

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Static code analyzer recommendation via preference mining

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Expert Systems with Applications 2025年 285卷

作者： Ge, Xiuting Fang, Chunrong Li, Xuanye Shang, Ye Zhang, Mengyao Pan, Ya China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education China The State Key Laboratory for Novel Software Technology Nanjing University China Department of Computer Science and Technology Southwest University of Science and Technology China

Static Code Analyzers (SCAs) have played a critical role in software quality assurance. However, SCAs with various static analysis techniques suffer from different levels of false positives and false negatives, thereby yielding varying effectiveness in SCAs. To detect more defects in a given project, it is a possible way to use more available SCAs for scanning this project. Due to producing unacceptable costs and overpowering warnings, invoking all available SCAs for the defect detection of a given project is impractical in real scenarios. To address the above problem, we are the first to propose a practical SCA recommendation approach via preference mining, which aims to select the most effective SCA for a given project. Specifically, our approach performs the SCA effectiveness evaluation to obtain the correspondingly optimal SCAs on projects under test. Subsequently, our approach performs the SCA preference mining via the project characteristics, thereby analyzing the intrinsic relation between projects under test and the correspondingly optimal SCAs. Finally, our approach constructs the SCA recommendation model based on projects under test and the associated characteristics. We conduct the experimental evaluation on three popular SCAs as well as 213 open-source and large-scale projects. The results present that our constructed SCA recommendation model outperforms four typical baselines by 2 ∼ 11 times. © 2025

关键词： Redundancy

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FairCoRe: Fairness-aware Recommendation through Counterfactual Representation Learning

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IEEE Transactions on Knowledge and Data Engineering 2025年

作者： Bin, Chenzhong Liu, Wenqiang Zhang, Feng Chang, Liang Gu, Tianlong Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Guilin University of Electronic Technology School of Business Guilin541004 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Guangzhou510632 China

Eliminating bias from data representations is crucial to ensure fairness in recommendation. Existing studies primarily focus on weakening the correlation between data representations and sensitive attributes, yet may inadvertently steer the user representations toward another potential bias direction of the target attribute. Furthermore, they often overlook the impact of user preferences on capturing sensitive information, incurring inadequate bias elimination. In this paper, we propose a Fair Counterfactual Representations (FairCoRe) learning framework, which aims to ensure the neutrality of representations among all bias directions. Firstly, we intervene on sensitive attributes to construct a counterfactual scenario. Then, two opposing attribute prediction tasks are respectively performed in ground-truth and counterfactual scenarios to encode sensitive information along different bias directions. Secondly, we design a bias-aware enhancement learning method that quantifies the respective correlation of user preferences and sensitive attributes to enhance sensitive information encoding. Finally, we introduce two mutual information optimization methods that optimize the representations to capture users' interests and disentangle sensitive factors. Moreover, we propose an attribute neutralization strategy that refines the learned representations, ensuring sensitive attribute neutrality. Extensive experiments demonstrate that our method achieves the optimal fairness and competitive accuracy compared to state-of-the-art methods. The source code is available at: https://***/FairCoRe2024/FairCoRe. © 1989-2012 IEEE.

关键词： Recommendation fairness Recommender system Representation learning

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DNMDR: Dynamic Networks and Multi-view Drug Representations for Safe Medication Recommendation

arXiv

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arXiv 2025年

作者： Liu, Guanlin Yu, Xiaomei Liu, Zihao Li, Xue Fan, Xingxu Zheng, Xiangwei School of Information Science and Engineering Shandong Normal University Jinan250358 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan250358 China State Key Laboratory of High-end Server and Storage Technology Jinan250300 China

Medication Recommendation (MR) is a promising research topic which booms diverse applications in the healthcare and clinical domains. However, existing methods mainly rely on sequential modeling and static graphs for representation learning, which ignore the dynamic correlations in diverse medical events of a patient's temporal visits, leading to insufficient global structural exploration on nodes. Additionally, mitigating drug-drug interactions (DDIs) is another issue determining the utility of the MR systems. To address the challenges mentioned above, this paper proposes a novel MR method with the integration of dynamic networks and multi-view drug representations (DNMDR). Specifically, weighted snapshot sequences for dynamic heterogeneous networks are constructed based on discrete visits in temporal EHRs, and all the dynamic networks are jointly trained to gain both structural correlations in diverse medical events and temporal dependency in historical health conditions, for achieving comprehensive patient representations with both semantic features and structural relationships. Moreover, combining the drug co-occurrences and adverse drug-drug interactions (DDIs) in internal view of drug molecule structure and interactive view of drug pairs, the safe drug representations are available to obtain high-quality medication combination recommendation. Finally, extensive experiments on real world datasets are conducted for performance evaluation, and the experimental results demonstrate that the proposed DNMDR method outperforms the state-of-the-art baseline models with a large margin on various metrics such as PRAUC, Jaccard, DDI rates and so on. Copyright © 2025, The Authors. All rights reserved.

关键词： Electronic health record

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Approximation Methods for Simulation and Equivalence Checking of Noisy Quantum Circuits

arXiv

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arXiv 2025年

作者： Huang, Mingyu Guan, Ji Fang, Wang Ying, Mingsheng Key Laboratory of System Software Chinese Academy of Sciences State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing101408 China School of Informatics University of Edinburgh EdinburghEH8 9UR United Kingdom Centre for Quantum Software and Information University of Technology Sydney UltimoNSW2007 Australia

In the current NISQ (Noisy Intermediate-Scale Quantum) era, simulating and verifying noisy quantum circuits is crucial but faces challenges such as quantum state explosion and complex noise representations, constraining simulation, and equivalence checking to circuits with a limited number of qubits. This paper introduces an approximation algorithm for simulating and assessing the equivalence of noisy quantum circuits, specifically designed to improve scalability under low-noise conditions. The approach utilizes a novel tensor network diagram combined with singular value decomposition to approximate the tensors of quantum noises. The implementation is based on Google’s TensorNetwork Python package for contraction. Experimental results on realistic quantum circuits with realistic hardware noise models indicate that our algorithm can simulate and check the equivalence of QAOA (Quantum Approximate Optimization Algorithm) circuits with around 200 qubits, surpassing the state-of-the-art methods by noise number and execution time. © 2025, CC BY.

关键词： Qubits

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Alternating refinement relations for fuzzy concurrent game structures

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Theoretical computer Science 2025年 1044卷

作者： Zhang, Shasha Zhou, Jie Ma, Yanfang Pan, Haiyu Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China Mathematics and Science College Shanghai Normal University Shanghai200234 China School of Computer Science and Information Engineering Changzhou Institute of Technology Jiangsu Changzhou213032 China

Fuzzy concurrent game structures (FCGSs), an extension of concurrent game structures in the fuzzy setting, combine both fuzzy and nondeterministic choices. They are therefore a powerful semantic foundation for modeling fuzzy systems that interact with their environments. In recent years, refinement relations in the field of formal verification, including trace containment and simulation, have been successfully introduced to special classes or related ones of FCGSs, such as fuzzy transition systems and fuzzy automata, and have been developed. However, these refinement relations are not directly applicable to FCGSs as they constrain the possible behaviors of the environment. To further satisfy the need for analysis and verification of fuzzy systems, this paper introduces alternating refinement relations for FCGSs. First, we present two general notions of alternating trace containment for FCGSs. Although these notions coincide with the corresponding classical ones, they do not coincide for FCGSs. Then, we provide algorithms to solve the problems of deciding two types of alternating trace containments in finite FCGSs under memoryless strategies. On the other hand, we propose two types of alternating simulations for FCGSs via weight functions and closed sets, and discover a novel result that they have different discriminating power, with one alternating simulation implying the other. Finally, we investigate the relationships among these notions mentioned above. © 2025 Elsevier B.V.

关键词： Alternating simulation Bisimulation Fuzzy automata Fuzzy language

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