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作者： Zhang, Yuanming Wang, Pinghui Qi, Yiyan Cheng, Kuankuan Zhao, Junzhou Tian, Guangjian Guan, Xiaohong Xi'An Jiaotong University MOE Key Laboratory for Intelligent Networks and Network Security Xi'an710049 China Huawei Noah's Ark Lab Hong Kong Tsinghua University Center for Intelligent and Networked Systems Tsinghua National Lab for Information Science and Technology Beijing100084 China

The well-known Gumbel-Max Trick for sampling elements from a categorical distribution (or more generally a non-negative vector) and its variants have been widely used in areas such as machine learning and information retrieval. To sample a random element ii in proportion to its positive weight v_ivi, the Gumbel-Max Trick first computes a Gumbel random variable g_igi for each positive weight element ii, and then samples the element ii with the largest value of g_i+ln v_igi+lnvi. Recently, applications including similarity estimation and weighted cardinality estimation require to generate kk independent Gumbel-Max variables from high dimensional vectors. However, it is computationally expensive for a large kk (e.g., hundreds or even thousands) when using the traditional Gumbel-Max Trick. To solve this problem, we propose a novel algorithm, FastGM, which reduces the time complexity from O(kn^+)O(kn+) to O(k ln k + n^+)O(klnk+n+), where n^+n+ is the number of positive elements in the vector of interest. FastGM stops the procedure of Gumbel random variables computing for many elements, especially for those with small weights. We perform experiments on a variety of real-world datasets and the experimental results demonstrate that FastGM is orders of magnitude faster than state-of-the-art methods without sacrificing accuracy or incurring additional expenses. © 1989-2012 IEEE.

关键词： Learning systems

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Object Detection Algorithm Based on Second-Order Pooling network and Gaussian Mixture Attention 5

Object Detection Algorithm Based on Second-Order Pooling Net...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Li, Ningbo Yang, Xiaobao School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To improve the feature representation ability of the YOLOX algorithm and obtain better detection performance, an object detection algorithm based on second-order pooling network and gaussian mixture attention is proposed. Firstly, the second-order pooling network is added after the PAFPN, and the higher-order statistical information is obtained by calculating the covariance matrix between different channels, to enhance the non-linear modeling capability. Secondly, the mixture attention based on the gaussian function is added after the SPP to model global contexts in the spatial and channel dimensions respectively, which improves the network performance with almost no extra parameters. The experimental results show that the detection accuracy of the proposed algorithm on the PASCAL VOC dataset reaches 82.6 %, which is 1.6 % higher than the YOLOX algorithm. © 2022 ACM.

关键词： Object detection

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Secure fusion of encrypted remote sensing images based on Brovey

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science China(Information sciences) 2021年第2期64卷 243-245页

作者： Junzhi YANG Guohua CHENG Meng SHEN School of Information and Electronics Beijing Institute of Technology School of Computer Science Beijing Institute of Technology Key Lab of Information Network Security of Ministry of Public Security (The Third Research Institute of Ministry of Public Security)

Dear editor,In the field of image fusion, panchromatic images captured by the Satellite Pour l'Observation de la Terre(SPOT) [1]have high spatial resolutions, whereas their spectral resolutions are relatively low. On the other hand, multispectral images have high spectral resolutions; however, their spatial resolutions are low. Because the two types of images captured by satellites cannot meet the demand [2], image fusion is generally necessary in practice.

关键词： Information Systems and Communication Service

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Scalable Client-side Encrypted Deduplication beyond Secret Sharing of the Master key

Scalable Client-side Encrypted Deduplication beyond Secret S...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Yuchen Chen Guanxiong Ha Xuan Shan Chunfu Jia Qiaowen Jia The College of Cyber Science Nankai University Tianjin China Tianjin Key Laboratory of Network and Data Security Technology and DISSec Tianjin China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

Individuals and companies increasingly adopt encrypted deduplication systems for their enhanced security and efficiency benefits. Server-aided encrypted deduplication systems are the state-of-the-art scheme to resist brute-force attacks. However, it is overly reliant on a single centralized key server and vulnerable to a single point of failure. To this end, existing schemes have implemented distributed key servers based on secret sharing of the master key to resist a single point of failure. Nevertheless, this design has some inherent limitations in balancing security and scalability. Secret sharing of the master key effectively mitigates single points of failure, while negatively impacting system scalability. To address the above limitations, we propose a scalable client-side encrypted deduplication with distributed key servers based on secret sharing of the data key. To resist brute-force attacks, we also design a double-layer matching mechanism to achieve secure and effective duplicate check and key delivery. Additionally, drawing inspiration from random oracle models, we put forward a pseudo-random response strategy for key servers to safeguard key privacy effectively. Rigorous theoretical analysis and extensive experiments demonstrate that our scheme achieves both security and scalability, which is well-suited for deployment in large-scale systems and offers robust protection against a single point of failure.

关键词： Privacy data privacy Scalability Distributed databases Resists Brute force attacks Large-scale systems Cryptography Servers Protection

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SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

SaTrack: LoS/NLoS State-Aware WiFi Indoor Tracking System

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and networks (SECON)

作者： Yinnan Zhou Hao Zhou Luowei Li Siming Chen Zhi Liu Xiang-Yang Li LINKE Lab School of Computer Science and Technology University of Science and Technology of China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331519186

ISBN: (纸本)9798331519193

WiFi-based technology is appealing for indoor localization due to the widely deployed infrastructures. Recently, path separation solutions have been proposed to address the multipath effects and achieve decimeter-level localization accuracy in line-of-sight (LoS) scenarios. However, these solutions experience serious performance degradation in non-line-of-sight (NLoS) scenarios, and couldn't be used for mobile device tracking where continuous LoS/NLoS switching happens. In this paper, we propose SaTrack, a LoS/NLoS state-aware mobile device tracking system. SaTrack identifies LoS/NLoS states based on the diversity of the strongest estimated paths when using different reference antennas. With the observation of spatial aggregation and temporal continuity for the Tx-Rx direct path, SaTrack chooses the direct path through two-step clustering, i.e., clustering in the spatial domain to form candidates and clustering again in the temporal domain to select the winner. Extensive experiments are conducted to evaluate the effectiveness of SaTrack. In a typical indoor environment with abundant multipath, SaTrack achieves 0.64m and 1.27m for the median and 90th percentile tracking errors, outperforming the state-of-the-art (SOTA) solutions.

关键词： Location awareness Performance evaluation Accuracy Line-of-sight propagation Switches Mobile handsets Sensors Indoor environment Wireless fidelity Antennas

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UAV Visual Tracking Algorithm Based on Feature Fusion of the Attention Mechanism 5

UAV Visual Tracking Algorithm Based on Feature Fusion of the...

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5th International Conference on Artificial Intelligence and Pattern Recognition, AIPR 2022

作者： Ma, Sugang Zhang, Zixian Zhao, Zhixian Yang, Xiaobao Hou, Zhiqiang School of Computer Science and Technology Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China Xi'an Key Laboratory of Big Data and Intelligent Computing Xi'an University of Posts and Telecommunications Shaanxi Xi'an China

ISBN: (纸本)9781450396899

To enhance the expression ability of deep features and improve the tracking performance of the fully convolutional siamese network (SiamFC) in the UAV scene, we propose a UAV visual tracking algorithm based on feature fusion of the attention mechanism. By designing the local perception attention module and the global perception attention module to enhance the features extracted from the backbone network, a set of complementary local enhanced features and global enhanced features are obtained. And then, the tracking response map fused with the two features is then located, which effectively improves the tracking robustness of SiamFC in the UAV scene. The algorithm and nine other related algorithms such as SiamFC are tested on the DTB70 dataset. The experiments show that the algorithm has a good tracking performance and can adapt to the visual object tracking task in the UAV scene. © 2022 ACM.

关键词： Unmanned aerial vehicles (UAV)

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Disentangled Hyperbolic Representation Learning for Heterogeneous Graphs

arXiv

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arXiv 2024年

作者： Bai, Qijie Nie, Changli Zhang, Haiwei Dou, Zhicheng Yuan, Xiaojie The College of Computer Science Tianjin Key Laboratory of Network and Data Security Technology Nankai University Tianjin China Gaoling School of Artificial Intelligence Renmin University of China Beijing China

Heterogeneous graphs have attracted a lot of research interests recently due to the success for representing complex real-world systems. However, existing methods have two pain points in embedding them into low-dimensional spaces: the mixing of structural and semantic information, and the distributional mismatch between data and embedding spaces. These two challenges require representation methods to consider the global and partial data distributions while unmixing the information. Therefore, in this paper, we propose Dis-H2GCN, a Disentangled Hyperbolic Heterogeneous Graph Convolutional network. On the one hand, we leverage the mutual information minimization and discrimination maximization constraints to disentangle the semantic features from comprehensively learned representations by independent message propagation for each edge type, away from the pure structural features. On the other hand, the entire model is constructed upon the hyperbolic geometry to narrow the gap between data distributions and representing spaces. We evaluate our proposed Dis-H2GCN on five real-world heterogeneous graph datasets across two downstream tasks: node classification and link prediction. The results demonstrate its superiority over state-of-the-art methods, showcasing the effectiveness of our method in disentangling and representing heterogeneous graph data in hyperbolic spaces. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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DCSim: Computing and networking Integration based Container Scheduling Simulator for data Centers

arXiv

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arXiv 2024年

作者： Hu, Jinlong Rao, Zhizhe Liu, Xingchen Deng, Lihao Dong, Shoubin Guangdong Key Lab of Multimodal Big Data Intelligent Analysis School of Computer Science and Engineering South China University of Technology Guangzhou China

The increasing prevalence of cloud-native technologies, particularly containers, has led to the widespread adoption of containerized deployments in data centers. The advancement of deep neural network models has increased the demand for container-based distributed model training and inference, where frequent data transmission among nodes has emerged as a significant performance bottleneck. However, traditional container scheduling simulators often overlook the influence of network modeling on the efficiency of container scheduling, primarily concentrating on modeling computational resources. In this paper, we focus on a container scheduling simulator based on collaboration between computing and networking within data centers. We propose a new container scheduling simulator for data centers, named DCSim. The simulator consists of several modules: a data center module, a network simulation module, a container scheduling module, a discrete event-driven module, and a data collection and analysis module. Together, these modules provide heterogeneous computing power modeling and dynamic network simulation capabilities. We design a discrete event model using SimPy to represent various aspects of container processing, including container requests, scheduling, execution, pauses, communication, migration, and termination within data centers. Among these, lightweight virtualization technology based on Mininet is employed to construct a software-defined network. An experimental environment for container scheduling simulation was established, and functional and performance tests were conducted on the simulator to validate its scheduling simulation capabilities. Copyright © 2024, The Authors. All rights reserved.

关键词： Software testing

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A Novel Under-sampling Method with Gaussian Mixture and Jensen-Shannon Divergence 2022

A Novel Under-sampling Method with Gaussian Mixture and Jens...

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4th International Conference on Image, Video and Signal Processing, IVSP 2022

作者： Pan, Xueling Li, Guohe Liu, Shunxin Yu, Qiuyue Li, Ying Beijing Key Lab of Data Mining for Petroleum Data China University of Petroleum Beijing102249 China College of Information Science and Engineering China University of Petroleum Beijing102249 China Department of Computer Science and Technology China University of Petroleum-Beijing at Keramay China

ISBN: (纸本)9781450387415

Due to bias towards major class samples, the traditional classifier cannot obtain excellent performance in imbalanced data. The under-sampling approach is one of the effective methods in imbalance classification. Nowadays, most of the under-sampling methods do not consider the global and local distribution of samples and the intra-class imbalance problem simultaneously. In addition, the sampling quantity cannot be adjusted adaptively in the practical application. To handle the above problems, in this paper, we propose a novel under-sampling approach with the Gaussian mixture model and Jensen-Shannon divergence, called GD-US. Firstly, it utilizes a Gaussian mixture model to cluster the majority class samples. And then, it calculates the number of sampling in each cluster according to its sparsity. In addition, GD-US adopts the global and local distribution of samples to decide the probability of deleting samples. Eventually, the Jensen-Shannon divergence is used to control the number of sampling. The experimental results show that the proposal can help solve the imbalance problem and enhance the classification accuracy. © 2022 ACM.

关键词： data mining

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Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information

arXiv

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arXiv 2024年

作者： Zhang, Mingkun Li, Jianing Chen, Wei Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of AI Safety Institute of Computing Technology Chinese Academy of Sciences Beijing China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

Adversarial purification is one of the promising approaches to defend neural networks against adversarial attacks. Recently, methods utilizing diffusion probabilistic models have achieved great success for adversarial purification in image classification tasks. However, such methods fall into the dilemma of balancing the needs for noise removal and information preservation. This paper points out that existing adversarial purification methods based on diffusion models gradually lose sample information during the core denoising process, causing occasional label shift in subsequent classification tasks. As a remedy, we suggest to suppress such information loss by introducing guidance from the classifier confidence. Specifically, we propose Classifier-cOnfidence gUided Purification (COUP) algorithm, which purifies adversarial examples while keeping away from the classifier decision boundary. Experimental results show that COUP can achieve better adversarial robustness under strong attack methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

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