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Reference Implementation of Scalable I/O Low-Level API on Intel Paragon

Journal of Computer Science & technology 1999年第3期14卷 206-223页

作者：孙凝晖 National Research Center for Intelligent Computing systems Institute of Computing Technology Chinese Academy of Sciences Beijing P.R. China

The Scalable I/O (SIO) Initiative's Low-Level Application Pro- gramming Interface (SIO LLAPI)provides file system implementers with a simplelow-Level interface to support high-level parallel I/O interfaces efficiently and ef fectively. This paper describes a reference implementation and the evaluation of the SIO LLAPI on the Intel Paragon multicomputer. The implementation provides the file system structure and striping algorithm compatible with the Parallel File System (PFS) of Intel Paragon, and runs either inside the kernel or as a user level library. The scatter-gather addressing read/write, asynchronous I/O, client caching and prefetching mechanism, file access hint mechanism, collective I/O and highly efficient file copy have been implemented. The preliminary experience shows that the SIO LLAPI provides opportunities of significant performance improvement and is easy to implement. Some high level file system interfaces and applications, such as PFS, ADIO and Hartree-Fock application, are also implemented on top of SIO. The performance of PFS is at least the same as that of Intel's native PFS, and in many cases, such as small sequential file access, huge I/O requests and collective I/O, it is stable and much better. The SIO features help to support high level interfaces easily, quickly and more efficiently, and the cache, prefetching, hints are useful to get better performance based on different access models. The scalability and per formance of SIO are limited by the network latency, network scalable bandwidth,memory copy bandwidth, memory size and pattern of I/O requests. The tradeoff between generality and efficiency should be considered in implementation.

关键词： scalable I/O low-level API parallel file system scalability client cache collective I/O Intel Paragon

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Learning to Learn Transferable Generative Attack for Person Re-Identification

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IEEE Transactions on Image Processing 2025年 PP卷 PP页

作者： Bian, Yuan Liu, Min Wang, Xueping Ma, Yunfeng Wang, Yaonan Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha China Hunan Normal University Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing College of Information Science and Engineering Hunan Changsha China

Deep learning-based person re-identification (reid) models are widely employed in surveillance systems and inevitably inherit the vulnerability of deep networks to adversarial attacks. Existing attacks merely consider cross-dataset and cross-model transferability, ignoring the cross-test capability to perturb models trained in different domains. To powerfully examine the robustness of real-world re-id models, the Meta Transferable Generative Attack (MTGA) method is proposed, which adopts meta-learning optimization to promote the generative attacker producing highly transferable adversarial examples by learning comprehensively simulated transfer-based crossmodel&dataset&test black-box meta attack tasks. Specifically, cross-model&dataset black-box attack tasks are first mimicked by selecting different re-id models and datasets for meta-train and meta-test attack processes. As different models may focus on different feature regions, the Perturbation Random Erasing module is further devised to prevent the attacker from learning to only corrupt model-specific features. To boost the attacker learning to possess cross-test transferability, the Normalization Mix strategy is introduced to imitate diverse feature embedding spaces by mixing multi-domain statistics of target models. Extensive experiments show the superiority of MTGA, especially in cross-model&dataset and cross-model&dataset&test attacks, our MTGA outperforms the SOTA methods by 20.0% and 11.3% on mean mAP drop rate, respectively. © 1992-2012 IEEE.

关键词： Generative adversarial networks

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China 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 Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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A Stealthy False Data Injection Attack Scheme Against Sensor Measurements Using Partial System Knowledge

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IEEE Transactions on Automatic Control 2025年

作者： Guo, Haibin Pang, Zhong-Hua Sun, Jian Han, Qing-Long Liu, Guo-Ping North China University of Technology Key Laboratory of Fieldbus Technology and Automation of Beijing Beijing100144 China Beijing University of Technology School of Information Science and Technology Beijing100124 China Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing100081 China Swinburne University of Technology School of Science Computing and Engineering Technologies MelbourneVIC3122 Australia Southern University of Science and Technology School of Automation and Intelligent Manufacturing Shenzhen518055 China

Attack design is indispensable for analyzing potential risks of networked control systems (NCSs). However, the remote control center is usually well protected and its knowledge is difficult to be disclosed, which becomes a major obstacle in developing stealthy false data injection (FDI) attack scheme because only partial system knowledge (i.e., the system matrices of the physical plant) could be used. To meet this challenge, a novel stealthy FDI attack scheme against the sensor measurement is proposed by employing the normal and compromised self-governed filters held by malicious attackers, where the normal one is adopted to estimate the system state and the compromised one is used as the virtual attacked target. The corresponding attack strategy is obtained by maximizing the estimation error of the compromised self-governed filter. Then, the residual of the compromised system is derived to prove attack stealthiness. Next, it is derived and found that the attack impact on system estimation performance is the same as that based on full system knowledge. Furthermore, the divergence condition of NCSs under the attack is presented. Finally, all the theoretical analyses are verified by simulation results. © 1963-2012 IEEE.

关键词： Kalman filters

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BADA-LAT: Efficient Local Attention Transformer for Chinese Named Entity Recognition with Boundary and LLM-Based Data Augmentation 27th

BADA-LAT: Efficient Local Attention Transformer for Chinese ...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Qiu, Xiaoping Yang, Ke Du, Shiling School of Computing and Artificial Intelligence Southwest Jiaotong University No. 999 Xi’an Road Sichuan Chengdu611756 China Tangshan Institute of Southwest Jiaotong University No. 6 Jingqu Road Hebei Tangshan063000 China National United Engineering Laboratory of Integrated and Intelligent Transportation No. 111 North Section 1 Second Ring Road Sichuan Chengdu610031 China Sichuan Key Laboratory of Manufacturing Industry Chain Collaboration and Information Support Technology No. 999 Xi’an Road Sichuan Chengdu610016 China Engineering Research Center of Sustainable Urban Intelligent Transportation No. 999 Xi’an Road Sichuan Chengdu611756 China Sichuan Institute of Industrial Software Technology No. 61 Sha’wan Road Sichuan Chengdu610031 China

ISBN: (纸本)9783031784941

Progress in Chinese Named Entity Recognition (CNER) has highlighted lexicon-based methods that use word information to boost performance. However, these methods neglect two crucial aspects: the regularity of word boundary and the characteristics of the NER task. To address these shortcomings, we introduce BADA-LAT, a Transformer architecture that incorporates word boundary information and introduce local attention computation. It can enhance entity boundary recognition and concentrate the attention weight of the target token on adjacent characters and matched words. Furthermore, to mitigate the issue of class imbalance, we augment the original training data using large language model (LLM). Our method outperforms other lexicon-based ones, as shown in experiments on four Chinese datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： boundary augmentation Chinese NER data augmentation local attention

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A Joint Visual Compression and Perception Framework for Neuralmorphic Spiking Camera

arXiv

引用

arXiv 2025年

作者： Feng, Kexiang Jia, Chuanmin Ma, Siwei Gao, Wen Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China National Engineering Research Center of Visual Technology Peking University Beijing100871 China Wangxuan Institute of Computer Technology Peking University Beijing100080 China

The advent of neuralmorphic spike cameras has garnered significant attention for their ability to capture continuous motion with unparalleled temporal resolution. However, this imaging attribute necessitates considerable resources for binary spike data storage and transmission. In light of compression and spike-driven intelligent applications, we present the notion of Spike Coding for Intelligence (SCI), wherein spike sequences are compressed and optimized for both bit-rate and task performance. Drawing inspiration from the mammalian vision system, we propose a dual-pathway architecture for separate processing of spatial semantics and motion information, which is then merged to produce features for compression. A refinement scheme is also introduced to ensure consistency between decoded features and motion vectors. We further propose a temporal regression approach that integrates various motion dynamics, capitalizing on the advancements in warping and deformation simultaneously. Comprehensive experiments demonstrate our scheme achieves state-of-the-art (SOTA) performance for spike compression and analysis. We achieve an average 17.25% BD-rate reduction compared to SOTA codecs and a 4.3% accuracy improvement over SpiReco for spike-based classification, with 88.26% complexity reduction and 42.41% inference time saving on the encoding side. Copyright © 2025, The Authors. All rights reserved.

关键词： Image compression

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Real-world Cyber Security Demonstration for Networked Electric Drives

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Yang, He Yang, Bowen Coshatt, Stephen Li, Qi Hu, Kun Hammond, Bryan Cooper Ye, Jin Parasuraman, Ramviyas Song, Wenzhan The Intelligent Power Electronics and Electric Machine Laboratory University of Georgia AthensGA30602 United States The Power Systems Engineering Center National Renewable Energy Laboratory GoldenCO80401 United States The Center for Cyber-Physical Systems University of Georgia AthensGA30602 United States Eaton ClevelandOH44122 United States Sino-German College of Intelligent Manufacturing Shenzhen Technology University Guangdong Shenzhen518118 China Research Lab the School of Computing University of Georgia AthensGA30602 United States

In this paper, we present the design and implementation of a cyber-physical security testbed for networked electric drive systems, aimed at conducting real-world security demonstrations. To our knowledge, this is one of the first security testbeds for networked electric drives, seamlessly integrating the domains of power electronics and computer science, and cybersecurity. By doing so, the testbed offers a comprehensive platform to explore and understand the intricate and often complex interactions between cyber and physical systems. The core of our testbed consists of four electric machine drives, meticulously configured to emulate small-scale but realistic information technology (IT) and operational technology (OT) networks. This setup both provides a controlled environment for simulating a wide array of cyber attacks, and mirrors potential real-world attack scenarios with a high degree of fidelity. The testbed serves as an invaluable resource for the study of cyberphysical security, offering a practical and dynamic platform for testing and validating cybersecurity measures in the context of networked electric drive systems. As a concrete example of the testbed’s capabilities, we have developed and implemented a Python-based script designed to execute step-stone attacks over a wireless local area network (WLAN). This script leverages a sequence of target IP addresses, simulating a real-world attack vector that could be exploited by adversaries. To counteract such threats, we demonstrate the efficacy of our developed cyberattack detection algorithms, which are integral to our testbed’s security framework. Furthermore, the testbed incorporates a real-time visualization system using InfluxDB and Grafana, providing a dynamic and interactive representation of networked electric drives and their associated security monitoring mechanisms. This visualization component not only enhances the testbed’s usability but also offers insightful, real-time data for researchers and

关键词： Testbeds

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

引用

15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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COph100: A comprehensive fundus image registration dataset from infants constituting the "RIDIRP" database

arXiv

引用

arXiv 2025年

作者： Hu, Yan Gong, Mingdao Qiu, Zhongxi Liu, Jiabao Shen, Hongli Yuan, Mingzhen Zhang, Xiaoqing Li, Heng Lu, Hai Liu, Jiang Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Beijing Tongren Eye Center Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Beijing China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

Retinal image registration is vital for diagnostic therapeutic applications within the field of ophthalmology. Existing public datasets, focusing on adult retinal pathologies with high-quality images, have limited number of image pairs and neglect clinical challenges. To address this gap, we introduce COph100, a novel and challenging dataset known as the Comprehensive Ophthalmology Retinal Image Registration dataset for infants with a wide range of image quality issues constituting the public "RIDIRP" database1. COph100 consists of 100 eyes, each with 2 to 9 examination sessions, amounting to a total of 491 image pairs carefully selected from the publicly available dataset. We manually labeled the corresponding ground truth image points and provided automatic vessel segmentation masks for each image. We have assessed COph100 in terms of image quality and registration outcomes using state-of-the-art algorithms. This resource enables a robust comparison of retinal registration methodologies and aids in the analysis of disease progression in infants, thereby deepening our understanding of pediatric ophthalmic conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image registration

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MS-Glance: Bio-Inspired Non-Semantic Context Vectors and Their Applications in Supervising Image Reconstruction

MS-Glance: Bio-Inspired Non-Semantic Context Vectors and The...

引用

IEEE Workshop on Applications of Computer Vision (WACV)

作者： Ziqi Gao Wendi Yang Yujia Li Lei Xing S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China Department of Radiation Oncology Stanford University Stanford CA USA Key Laboratory of Precision and Intelligent Chemistry USTC CAS Hefei Anhui

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Non-semantic context information is crucial for visual recognition, as the human visual perception system first uses global statistics to process scenes rapidly before identifying specific objects. However, while semantic information is increasingly incorporated into computer vision tasks such as image reconstruction, non-semantic information, such as global spatial structures, is often overlooked. To bridge the gap, we propose a biologically informed non-semantic context descriptor, MS-Glance, along with the Glance Index Measure for comparing two images. A Global Glance vector is formulated by randomly retrieving pixels based on a perception-driven rule from an image to form a vector representing non-semantic global context, while a local Glance vector is a flattened local image window, mimicking a zoom in observation. The Glance Index is defined as the inner product of two standardized sets of Glance vectors. We evaluate the effectiveness of incorporating Glance supervision in two reconstruction tasks: image fitting with implicit neural representation (INR) and undersampled MRI reconstruction. Extensive experimental results show that MS-Glance outperforms existing image restoration losses across both natural and medical images. The code is available at https://***/Z7Gao/MSGlance.

关键词： Computer vision Visualization Magnetic resonance imaging Fitting Vectors Image restoration Indexes Image reconstruction Biomedical imaging Visual perception

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