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IEEE Conference on Global Communications (GLOBECOM)

作者： Jingwei Liu Zihan Zhou Rong Sun Xiaojiang Du Mohsen Guizani Shaanxi Key Laboratory of Blockchain and Secure Computing Xidian University Xi'an China State Key Laboratory of ISN Xidian University Xi'an China Department of Electrical and Computer Engineering Stevens Institute of Technology Hoboken USA Department of Computer Science and Engineering Qatar University Doha Qatar

With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.

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Towards Depth-continuous Scene Representation with Displacement Field for Robust Light Field Depth Estimation

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IEEE Transactions on Multimedia 2025年

作者： Chen, Rongshan Sheng, Hao Yang, Da Cong, Ruixuan Cui, Zhenglong Wang, Sizhe Wang, Tun Zhao, Mingyuan Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing100191 China Beihang University Key Laboratory of Data Science and Intelligent Computing International Innovation Institute 166 Shuanghongqiao Street Pingyao Town Yuhang District Hangzhou31 1115 China Macao Polytechnic University Faculty of Applied Sciences Macao999078 China

The light field (LF) captures both the spatial and angular information of scenes, enabling accurate depth estimation. However, previous deep learning methods typically model surface depth only while ignoring the continuous nature of depth in 3D scenes. In this paper, we use the displacement field (DF) to describe this continuous property, and propose a novel depth-continuous scene representation for robust LF depth estimation. Experiments demonstrate that our representation enables the network to generate highly detailed depth maps with fewer parameters and faster speeds. Specifically, inspired by the signed distance field in 3D object descriptions, we aim to exploit the intrinsic depth-continuous property of 3D scenes via DF and define a novel depth-continuous scene representation. Then, we introduce a simple, yet general, learning framework for depth-continuous scene embedding, and the proposed network, DepthDF, achieves state-of-the-art performance on both synthetic and real-world LF datasets, ranking 1st on the HCI 4D light field benchmark. Furthermore, previous LF depth estimation methods can also be seamlessly integrated into this framework. Finally, we extend this framework beyond LF depth estimation to various tasks, including multiview stereo depth inference, LF superresolution, and LF salient object detection. Our experiments demonstrate an improved performance when continuous scene representation is applied, suggesting that our framework can potentially provide insights for more fields. © 1999-2012 IEEE.

关键词： Stereo image processing

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A Case for Adaptive Resource Management in Alibaba Datacenter Using Neural Networks

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Journal of computer Science & technology 2020年第1期35卷 209-220页

作者： Sa Wang Yan-Hai Zhu Shan-Pei Chen Tian-Ze Wu Wen-Jie Li Xu-Sheng Zhan Hai-Yang Ding Wei-Song Shi Yun-Gang Bao State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Peng Cheng Laboratory Shenzhen 518055China Alibaba Inc. Hangzhou 311121China Department of Computer Science Wayne State UniversityMichiganMI 48202U.S.A

Both resource efficiency and application QoS have been big concerns of datacenter operators for a long time,but remain to be *** resource utilization increases the risk of resource contention between co-located workload,which makes latency-critical(LC)applications suffer unpredictable,and even unacceptable *** of prior work devotes the effort on exploiting effective mechanisms to protect the QoS of LC applications while improving resource *** this paper,we propose MAGI,a resource management runtime that leverages neural networks to monitor and further pinpoint the root cause of performance interference,and adjusts resource shares of corresponding applications to ensure the QoS of LC *** is a practice in Alibaba datacenter to provide on-demand resource adjustment for applications using neural *** experimental results show that MAGI could reduce up to 87.3%performance degradation of LC application when co-located with other antagonist applications.

关键词： resource management neural network resource efficiency tail latency

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Poster: Revisiting linpack algorithm on large-scale CPU-GPU heterogeneous systems 25

Poster: Revisiting linpack algorithm on large-scale CPU-GPU ...

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25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2020

作者： Shui, Chaoyang Yu, Xianzhi Yan, Yujin Wang, Yinshan Meng, Ke Tan, Guangming State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

ISBN: (纸本)9781450368186

As the widening gap between GPU computing capability and other components (CPU, PCIe bus and communication network), it’s increasingly challenging to design high performance parallel algorithms for large CPU-GPU heterogeneous systems. There are mainly two reasons. Firstly, simply offloading the kernel library to GPU incurs large volume data transfer through low-speed PCIe bus. Secondly, communication overheads through network severely affects scalability. To solve the above issues, we advocate a paradigm shift to GPU-centric and fine-grained pipelining algorithm design. By taking Linpack benchmark as a case study, the new algorithm design paradigm shows its effectiveness. Our optimized Linpack program achieves 63.79PFlops on 16384 GPUs. Its floating-point efficiency outperforms the NVIDIA proprietary counterparts by 5% on average. © 2020 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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Quantum memory assisted entangled state verification with local measurements

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Physical Review Research 2025年第1期7卷 013003-013003页

作者： Siyuan Chen Wei Xie Ping Xu Kun Wang Hefei National Research Center for Physical Sciences at the Microscale and School of Physical Sciences University of Science and Technology of China Hefei 230026 China College of Physics Jilin University Changchun 130012 China School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha 410073 China

We consider the quantum memory assisted quantum state verification task, where an adversary prepares independent multipartite entangled states and sends them to the local verifiers, who then store several copies in the quantum memory and measure them collectively to make a decision. We establish an exact analytic formula for optimizing two-copy state verification, where the verifiers store two copies, and give a globally optimal two-copy strategy for multiqubit graph states involving only Bell measurements. When the verifiers can store arbitrarily many copies, we present a dimension expansion technique that designs efficient verification strategies for this case, showcasing its application to efficiently verifying Greenberger-Horne-Zeilinger (GHZ)-like states. These strategies become increasingly advantageous with growing memory resources, ultimately approaching the theoretical limit of efficiency. Our findings demonstrate that quantum memories enhance state verification efficiency, shedding light on error-resistant strategies and practical applications of large-scale quantum memory assisted verification.

关键词： Quantum entanglement

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GPU Parallelization and Optimization of a Combustion Simulation Application

GPU Parallelization and Optimization of a Combustion Simulat...

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IEEE International Conference on High Performance computing and Communications (HPCC)

作者： Zhixiang Liao Yongzhou Liu Yonggang Che State Key Laboratory of High Performance Computing Institute for Quantum Information College of Computer National University of Defense Technology Changsha China

Graphics processing units (GPUs) are widely used in the area of scientific computing. While GPUs provide much higher peak performance, efficient implementation of real applications on the GPU architectures is still a non-trivial task. It is crucial to realize efficient solution algorithms that can better utilize GPU architectures. This paper presents our efforts in parallelizing and optimizing LESAP, a CFD application for scramjet combustion simulation, on NVIDIA GPUs. The GPU parallelization is realized based on the CUDA programming model, with a data-parallel implicit time-marching method that is efficient on the GPU architecture. Furthermore, shared memory and redundant calculation are proposed to reduce memory access overhead during GPU computation, and data transfer between CPU and GPU is optimized by packing the data to be transferred. The experimental results show that the GPU version, when runs on four V100 GPUs, achieves a speedup of 11.26 times compared to the CPU version that runs on two 24-core Intel Skylake Gold 6240R CPUs. Excellent parallel scalability across multiple GPUs is also observed.

关键词： Gold Smart cities Computational modeling Graphics processing units computer architecture Combustion Central Processing Unit

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ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

arXiv

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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Xu, Daozhu Bai, Long Jin, Cheng Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Research Institute of Surveying and Mapping Xi’an710054 China

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

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computers in Biology and Medicine 2024年 176卷 108565-108565页

作者： Ma, Hongcheng Wu, Yajing Tang, Yongqiang Chen, Rui Xu, Tao Zhang, Wensheng School of Information and Communication Engineering Hainan University Haikou China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China Shanxi Key Laboratory of Big Data Analysis and Parallel Computing Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal. © 2024 Elsevier Ltd

关键词： Electroencephalography

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A Geometrical Characterization on Feature Density of Image Datasets

A Geometrical Characterization on Feature Density of Image D...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhen Liang Changyuan Zhao Wanwei Liu Bai Xue Wenjing Yang Institute for Quantum Information & State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Institute of Software Chinese Academy of Sciences Beijing China College of Computer Science and Technology National University of Defense Technology Changsha China

Recently, the interpretability and verification of deep learning have attracted enormous attention from both academic and industrial communities, aiming to gain users’ trust and ease their concerns. To guide learning procedures or data operations carried out in a more interpretable way, in this paper, we put a similar perspective on image datasets, the inputs of deep learning. Based on manifold learning, we work out an interpretable geometrical characterization on the curvity of manifolds to depict the feature density of datasets, which is represented with the ratio of the Euclidean distance and the geodesic distance. It is a noteworthy characteristic of image datasets and we take the dataset compression and enhancement problems as application instances via sample credit assignment with the geometrical information. Experiments on typical image datasets have justified the effectiveness and enormous prospect of the presented geometrical characteristic.

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Grain Size Distribution Analysis of Landslide Dams Caused by Dynamic Accumulation of Weathered Rock Landslides Using Discrete Element Modelling 29th

Grain Size Distribution Analysis of Landslide Dams Caused by...

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29th International Conference on Computational and Experimental Engineering and Sciences, ICCES 2023

作者： Peng, Ming Zhao, Qingxi Shen, Danyi Zhu, Yan Zhao, Qingxin Zhou, Jiawen Cai, Shuo Key Laboratory of Geotechnical & Underground Engineering of Ministry of Education Tongji University Shanghai China Department of Geotechnical Engineering College of Civil Engineering Tongji University Shanghai China Institute of Geotechnical Engineering College of Civil Engineering and Architecture Zhejiang University Hangzhou China Shanghai Research Center of Ocean & Shipbuilding Engineering China Shipbuilding NDRI Engineering Co. Ltd. Shanghai China State Key Laboratory Hydraulic & Mountain River Engineering Sichuan University Chengdu China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (纸本)9783031425141

The differences in the structure and composition materials of landslide dams often lead to great dissimilarity in dam stability, which often depend on the dynamic accumulation process of landslide dam. In order to investigate the response of dynamic characteristics of landslide to the composition material distribution of landslide dams, the migration and accumulation process of rock slopes with two weathering degrees was simulated by bonded particles and micro-cracks based on discrete element modelling. A new reconstructed distribution identification method was proposed in this paper to quantify the grain size distribution of dam. The results showed that the composition materials of dam, formed by weakly weathered slope, was poorly sorted, with medium particles located in surface layer, coarse particles located in middle and fine particles located in bottom of the valley. The material composition of dam, formed by strongly weathered slope, was well sorted, which spatial distribution showed well reverse segregation structure. It was found that the differences in particle size distribution between two kinds of landslide dams were mainly manifested in fine particles and coarse particles. The kinetic disintegration in weakly weathered slope was weakened, due to the accumulation of force coupling below hard rock at substrate, which occurred because of the fragmentation of particles aside it. As a result, there are more coarse particles and fewer fine particles in grain size distribution of its accumulation dam. This paper contributes to a better understanding of the structure and materials distribution of landslide dams, and assists the prediction of the stability of landslide dams. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle size

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