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CorcPUM: Efficient Processing Using Cross-Point Memory via Cooperative Row-Column Access Pipelining and Adaptive Timing Optimization in Subarrays 23

CorcPUM: Efficient Processing Using Cross-Point Memory via C...

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

作者： Chengning Wang Dan Feng Wei Tong Jingning Liu Wuhan National Laboratory for Optoelectronics Key Laboratory of Information Storage System Engineering Research Center of Data Storage Systems and Technology Ministry of Education of China (School of Computer Science and Technology Huazhong University of Science and Technology) Wuhan China

ISBN: (纸本)9798350323481

Emerging cross-point memory can in-situ perform vector-matrix multiplication (VMM) for energy-efficient scientific computation. However, parasitic-capacitance-induced row charging and discharging latency is a major performance bottleneck of subarray VMM. We propose a memory-timing-compliant bulk VMM processing-using-memory design with row access and column access co-optimization from rethinking of read access commands and μ-op timing. We propose row-level-parallelism-adaptive timing termination mechanism to reduce tail latency of tRCD and tRP by exploiting row nonlinear charging and bulk-interleaved row-column-cooperative VMM access mechanism to reduce tRAS and overlap CL without increasing column ADC precision. Evaluations show that our design can achieve 5.03× performance speedup compared with an aggressive baseline.

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

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Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International Conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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OblivTime: Oblivious and Efficient Interval Skyline Query Processing Over Encrypted Time-Series Data

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IEEE Transactions on Services Computing 2025年

作者： Ouyang, Huajie Zheng, Yifeng Wang, Songlei Hua, Zhongyun Harbin Institute of Technology School of Computer Science and Technology Guangdong Shenzhen518055 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518055 China

Time-series data is prevalent in many applications like smart homes, smart grids, and healthcare. And it is now increasingly common to store and query time-series data in the cloud. Despite the benefits, data privacy concerns in such outsourced services are pressing, making it imperative to embed privacy assurance mechanisms from the outset. Most existing related works have been focused on querying for different types of aggregate statistics. In this paper, we instead focus on the secure support for advanced interval skyline queries, which allow to identify time series that are not dominated by any other time series within a query time interval. This is valuable for time-series data analytics in applications like remote health monitoring (e.g., identifying patients with high heart rates in a certain week). We present OblivTime, a new system framework for oblivious and efficient interval skyline query processing over encrypted time-series data. OblivTime is built from a synergy of time-series data analytics, lightweight cryptography, and GPU parallel computing, achieving stronger security guarantees and lower online query latency over the state-of-the-art prior work. Extensive experiments demonstrate that OblivTime can achieve up to 666\times speedup in online query latency over the state-of-the-art prior work. © 2008-2012 IEEE.

关键词： Structured Query Language

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Provably Secure Efficient Key-Exchange Protocol for Intelligent Supply Line Surveillance in Smart Grids

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IEEE Internet of Things Journal 2025年

作者： Ayub, Muhammad Faizan Li, Xiong Mahmood, Khalid Alenazi, Mohammed J.F. Das, Ashok Kumar Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu12599 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan Department of Computer Engineering Riyadh11451 Saudi Arabia International Institute of Information Technology Center for Security Theory and Algorithmic Research Hyderabad500 032 India Korea University College of Informatics Department of Computer Science and Engineering Anam-ro Seoul145 Korea Republic of Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan 250353 China

Intelligent supply line surveillance is critical for modern smart grids. Smart sensors and gateway nodes are strategically deployed along supply lines to achieve intelligent surveillance. They collect data continuously and transmit it to the control centre in real-time. It enables real-time monitoring, fault detection, and efficient energy management across distribution networks. This advanced surveillance system ensures continuous monitoring of supply lines, detecting anomalies, and optimizing operations to maintain the stability and reliability of the smart grid. However, the reliance of all participating nodes on public communication channels to transmit supply line surveillance data exposes these systems to critical cyber attacks. These cyber attacks include impersonation, physical tampering, ephemeral secret leakage, and desynchronization attacks. To address these issues, existing key-exchange protocols often fail to ensure robust security while imposing high computation and communication overheads, limiting their practicality for resource-constrained environments. Therefore, we propose a secure and efficient key exchange and tamper-resistant authentication protocol using Elliptic Curve Cryptography (ECC) and Physical Unclonable Functions (PUFs). The PUF mechanism provides robust resistance against physical tampering and cloning attacks, ensuring enhanced physical security for supply line devices. We validate the security robustness of the devised protocol using the Random or Real (ROR) model. Furthermore, informal security analysis demonstrates the protocol's robustness in rigorously resisting various attacks, including physical tampering, impersonation, ephemeral secret leakage and desynchronization. Moreover, we determine the performance evaluation that reveals the protocol's superior efficiency compared to competing protocols, achieving significant reductions of 28.64% in computation overhead and 9.96% in communication overhead. © 2014 IEEE.

关键词： Authentication Protocol

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Bandwidth-Efficient Two-Server ORAMs with O(1) Client Storage

arXiv

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arXiv 2025年

作者： Wang, Wei Zhang, Xianglong Xu, Peng Chen, Rongmao Yang, Laurence Tianruo School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Jinyinhu Laboratory Wuhan430040 China School of Computer National University of Defense Technology Changsha410073 China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou450001 China Department of Computer Science St. Francis Xavier University AntigonishNSB2G 2W5 Canada

Oblivious RAM (ORAM) allows a client to securely retrieve elements from outsourced servers without leakage about the accessed elements or their virtual addresses. Two-server ORAM, designed for secure two-party RAM computation, stores data across two non-colluding servers. However, many two-server ORAM schemes suffer from excessive local storage or high bandwidth costs. To serve lightweight clients, it is crucial for ORAM to achieve concretely efficient bandwidth while maintaining O(1) local storage. Hence, this paper presents two new client-friendly two-server ORAM schemes that achieve practical logarithmic bandwidth under O(1) local storage, while incurring linear symmetric key computations. The core design features a hierarchical structure and a pairwise-area setting for the elements and their tags. Accordingly, we specify efficient read-only and write-only private information retrieval (PIR) algorithms in our schemes to ensure obliviousness in accessing two areas respectively, so as to avoid the necessity of costly shuffle techniques in previous works. We empirically evaluate our schemes against LO13 (TCC’13), AFN17 (PKC’17), and KM19 (PKC’19) in terms of both bandwidth and time cost. The results demonstrate that our schemes reduce bandwidth by approximately 2 ∼ 4× compared to LO13, and by 16 ∼ 64× compared to AFN17 and KM19. For a database of size 214 blocks, our schemes are over 64× faster than KM19, while achieving similar performance to LO13 and AFN17 in the WAN setting, with a latency of around 1 second. Copyright © 2025, The Authors. All rights reserved.

关键词： Virtual addresses

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FedMHO: Heterogeneous One-Shot Federated Learning Towards Resource-Constrained Edge Devices

arXiv

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arXiv 2025年

作者： Yao, Dezhong Shi, Yuexin Liu, Tongtong Xu, Zhiqiang 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 Wuhan430074 China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Federated Learning (FL) is increasingly adopted in edge computing scenarios, where a large number of heterogeneous clients operate under constrained or sufficient resources. The iterative training process in conventional FL introduces significant computation and communication overhead, which is unfriendly for resource-constrained edge devices. One-shot FL has emerged as a promising approach to mitigate communication overhead, and model-heterogeneous FL solves the problem of diverse computing resources across clients. However, existing methods face challenges in effectively managing model-heterogeneous one-shot FL, often leading to unsatisfactory global model performance or reliance on auxiliary datasets. To address these challenges, we propose a novel FL framework named FedMHO, which leverages deep classification models on resource-sufficient clients and lightweight generative models on resource-constrained devices. On the server side, FedMHO involves a two-stage process that includes data generation and knowledge fusion. Furthermore, we introduce FedMHO-MD and FedMHO-SD to mitigate the knowledge-forgetting problem during the knowledge fusion stage, and an unsupervised data optimization solution to improve the quality of synthetic samples. Comprehensive experiments demonstrate the effectiveness of our methods, as they outperform state-of-the-art baselines in various experimental setups. Our code is available at https://***/YXShi2000/FedMHO. © 2025, CC BY.

关键词： Federated learning

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Preserving Location Privacy of IoT Devices in Heterogeneous Edge Computing Architecture Through Deniability-Based Authentication

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IEEE Transactions on Consumer Electronics 2025年

作者： Ali, Ikram Li, Jianqiang Chen, Jie Chen, Yong Ullah, Shamsher Wakeel, Abdul Shenzhen University National Engineering Laboratory of Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Automation Engineering Chengdu611731 China National University of Sciences and Technology Military College of Signals Department of Electrical Engineering Islamabad Pakistan

Edge computing moves cloud services closer to consumer Internet of Things (IoT) devices, reducing latency and bandwidth usage. This setup enables faster responses but also introduces new security challenges, particularly concerning location privacy when sender and receiver use different security techniques. The authentication process in such architectures can expose the location of IoT devices. To address this issue, researchers have proposed various privacy-preserving schemes. However, these are computationally heavy for IoT devices. To tackle this issue, we propose two novel heterogeneous deniable authentication schemes: HDA-IoT-I and HDA-IoT-II. These schemes use public-key infrastructure and identity-based cryptography to protect the location privacy of IoT devices in heterogeneous edge computing environments. Our schemes enable the designated edge device to verify the source of a message without being able to prove its origin to a third party, thus preserving privacy. Additionally, they incorporate a batch verification method, which speeds up the verification of multiple deniable authenticators. The proposed schemes are formally proven secure in the random oracle model based on the hardness assumption of elliptic curve inverse computational Diffie-Hellman problem. The performance evaluation demonstrate that our schemes significantly enhance computational and communication efficiency, making them suitable for IoT devices. © 2025 IEEE.

关键词： Differential privacy

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Applying system Thinking to Learn from Accident of Modern Automatic Control systems

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Chinese Journal of Electronics 2025年第2期23卷 409-414页

作者： Ru Niu Yuan Cao Xiaocheng Ge Tao Tang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Department of Computer Science University of York York UK

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events, rather than a simple cause-effect chain. We take railway Train control systems (TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP - one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Sociotechnical systems Analytical models systematics Railway accidents Transportation Medical services Rail transportation systems thinking Research and development Accidents

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OneForecast: A Universal Framework for Global and Regional Weather Forecasting

arXiv

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arXiv 2025年

作者： Gao, Yuan Wu, Hao Shu, Ruiqi Dong, Huanshuo Xu, Fan Chen, Rui Yan, Yibo Wen, Qingsong Hu, Xuming Wang, Kun Wu, Jiahao Li, Qing Xiong, Hui Huang, Xiaomeng Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China TEG Tencent China Department and Computer and Science University of Science and Technology of China China Institute for Interdisciplinary Information Sciences Tsinghua University China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong AI Thrust The Hong Kong University of Science and Technology Guangzhou China Squirrel Ai Learning China School of Computer Science and Engineering Nanyang Technological University Singapore Department of Computing The Hong Kong Polytechnic University Hong Kong

Accurate weather forecasts are important for disaster prevention, agricultural planning, and water resource management. Traditional numerical weather prediction (NWP) methods offer physically interpretable high-accuracy predictions but are computationally expensive and fail to fully leverage rapidly growing historical data. In recent years, deep learning methods have made significant progress in weather forecasting, but challenges remain, such as balancing global and regional high-resolution forecasts, excessive smoothing in extreme event predictions, and insufficient dynamic system modeling. To address these issues, this paper proposes a global-regional nested weather forecasting framework based on graph neural networks (GNNs). By combining a dynamic system perspective with multi-grid theory, we construct a multi-scale graph structure and densify the target region to capture local high-frequency features. We introduce an adaptive information propagation mechanism, using dynamic gating units to deeply integrate node and edge features for more accurate extreme event forecasting. For high-resolution regional forecasts, we propose a neural nested grid method to mitigate boundary information loss. Experimental results show that the proposed method performs excellently across global to regional scales and short-term to long-term forecasts, especially in extreme event predictions (e.g., typhoons), significantly improving forecast accuracy. Our codes are available at https://***/YuanGao-YG/OneForecast. Copyright © 2025, The Authors. All rights reserved.

关键词： Weather forecasting

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