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arXiv 2021年

作者： Gao, Guangwei Yu, Yi Yang, Jian Qi, Guo-Jun Yang, Meng Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210023 China Digital Content and Media Sciences Research Division National Institute of Informatics Tokyo101-8430 Japan School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China Department of Computer Science University of Central Florida OrlandoFL32816 United States School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University Guangzhou510006 China

Cross-resolution face recognition (CRFR), which is important in intelligent surveillance and biometric forensics, refers to the problem of matching a low-resolution (LR) probe face image against high-resolution (HR) gallery face images. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space where the resolution discrepancy is mitigated. However, little works consider how to extract and utilize the intermediate discriminative features from the noisy LR query faces to further mitigate the resolution discrepancy due to the resolution limitations. In this study, we desire to fully exploit the multilevel deep convolutional neural network (CNN) feature set for robust CRFR. In particular, our contributions are threefold. (i) To learn more robust and discriminative features, we desire to adaptively fuse the contextual features from different layers. (ii) To fully exploit these contextual features, we design a feature set-based representation learning (FSRL) scheme to collaboratively represent the hierarchical features for more accurate recognition. Moreover, FSRL utilizes the primitive form of feature maps to keep the latent structural information, especially in noisy cases. (iii) To further promote the recognition performance, we desire to fuse the hierarchical recognition outputs from different stages. Meanwhile, the discriminability from different scales can also be fully integrated. By exploiting these advantages, the efficiency of the proposed method can be delivered. Experimental results on several face datasets have verified the superiority of the presented algorithm to the other competitive CRFR approaches. Copyright © 2021, The Authors. All rights reserved.

关键词： Face recognition

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DLEDNet: A deep learning-based image encryption and decryption network for internet of medical things

arXiv

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arXiv 2020年

作者： Ding, Yi Wu, Guozheng Chen, Dajiang Zhang, Ning Gong, Linpeng Cao, Mingsheng Qin, Zhiguang Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongwan Guangdong China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan610054 China National Natural Science Foundation of China Beijing China Department of Computing Science Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States

With the rapid development of Internet of Medical Things (IoMT) technology, many medical imaging equipments are connected to the medical information network to facilitate the process of diagnosing and treating for doctors. As medical image contains sensitive information, it is of importance yet very challenging to encrypt the medical image to safeguard the privacy or security of the patient. In this work, a deep learning based encryption and decryption network (DLEDNet) is proposed to fulfill the process of encrypting and decrypting the medical image. Specifically, in DLEDNet, the Cycle-GAN network is employed as the main learning network to transfer the medical image from its original domain to the target domain. Target domain is regarded as a "Hidden Factors" to guide the learning model to realize the encryption process. The encrypted image is restored to the original (plaintext) image through a reconstruction network to achieve an image decryption process. In order to realize the data mining process direct from the privacy-protected environment, a ROI (Region of interest)-mining-network is proposed to extract the interested object directly from the encrypted image. The proposed DLEDNet is evaluated on the chest X-ray dataset. Extensive experimental results show that the proposed method can achieve a high level of security with a good performance in efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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ReCoSa: Detecting the relevant contexts with self-attention for multi-turn dialogue generation

arXiv

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arXiv 2019年

作者： Zhang, Hainan Y., Lan L., Pang J., Guo X., Cheng CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

In multi-turn dialogue generation, response is usually related with only a few contexts. Therefore, an ideal model should be able to detect these relevant contexts and produce a suitable response accordingly. However, the widely used hierarchical recurrent encoder-decoder models just treat all the contexts indiscriminately, which may hurt the following response generation process. Some researchers try to use the cosine similarity or the traditional attention mechanism to find the relevant contexts, but they suffer from either insufficient relevance assumption or position bias problem. In this paper, we propose a new model, named ReCoSa, to tackle this problem. Firstly, a word level LSTM encoder is conducted to obtain the initial representation of each context. Then, the self-attention mechanism is utilized to update both the context and masked response representation. Finally, the attention weights between each context and response representations are computed and used in the further decoding process. Experimental results on both Chinese customer services dataset and English Ubuntu dialogue dataset show that ReCoSa significantly outperforms baseline models, in terms of both metric-based and human evaluations. Further analysis on attention shows that the detected relevant contexts by ReCoSa are highly coherent with human's understanding, validating the correctness and interpretability of ReCoSa. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal encoding

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Graph wavelet neural network

arXiv

引用

arXiv 2019年

作者： Xu, Bingbing Shen, Huawei Cao, Qi Qiu, Yunqi Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China

We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained via a fast algorithm without requiring matrix eigendecomposition with high computational cost. Moreover, graph wavelets are sparse and localized in vertex domain, offering high efficiency and good interpretability for graph convolution. The proposed GWNN significantly outperforms previous spectral graph CNNs in the task of graph-based semi-supervised classification on three benchmark datasets: Cora, Citeseer and Pubmed. Copyright © 2019, The Authors. All rights reserved.

关键词： Wavelet transforms

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Parameter estimation with the ordered 2 regularization via an alternating direction method of multipliers

arXiv

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arXiv 2019年

作者： Humayoo, Mahammad Cheng, Xueqi CAS Key Laboratory of Network Data Science & Technology Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China

Regularization is a popular technique in machine learning for model estimation and for avoiding overfitting. Prior studies have found that modern ordered regularization can be more effective in handling highly correlated, high-dimensional data than traditional regularization. The reason stems from the fact that the ordered regularization can reject irrelevant variables and yield an accurate estimation of the parameters. How to scale up the ordered regularization problems when facing large-scale training data remains an unanswered question. This paper explores the problem of parameter estimation with the ordered 2-regularization via Alternating Direction Method of Multipliers (ADMM), called ADMM-O2. The advantages of ADMM-O2 include (i) scaling up the ordered 2 to a large-scale dataset, (ii) predicting parameters correctly by excluding irrelevant variables automatically, and (iii) having a fast convergence rate. Experimental results on both synthetic data and real data indicate that ADMM-O2 can perform better than or comparable to several state-of-the-art baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Parameter estimation

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Fair and autonomous sharing of federate learning models in mobile Internet of Things

arXiv

引用

arXiv 2020年

作者： Hao, Xiaohan Ren, Wei Xiong, Ruoting Zheng, Xianghan Zhu, Tianqing Xiong, Neal N. School of Computer Science China University of Geoscience Wuhan China Guangxi Key Laboratory of Cryptography and Information Security Guilin541004 China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Mingbyte Technology QingDao China School of Software University of Technology Sydney UltimoNSW2007 Australia Dept. of Mathematics and Computer Science Northeastern State University United States

Federate learning can conduct machine learning as well as protect the privacy of self-owned training data on corresponding ends, instead of having to upload to a central trusted data aggregation server. In mobile scenarios, a centralized trusted server may not be existing, and even though it exists, the delay will not be manageable, e.g., smart driving cars. Thus, mobile federate learning at the edge with privacy-awareness is attracted more and more attentions. It then imposes a problem - after data are trained on a mobile terminal to obtain a learned model, how to share the model parameters among others to create more accurate and robust accumulative final model. This kind of model sharing confronts several challenges, e.g., the sharing must be conducted without a third trusted party (autonomously), and the sharing must be fair as model training (by training data)is valuable. To tackle the above challenges, we propose a smart contract and IPFS (Inter-Planetary File System) based model sharing protocol and algorithms to address the challenges. The proposed protocol does not rely on a trusted third party, where individual-learned models are shared/stored in corresponding ends. Conducted through extensive experiments, three main steps of the proposed protocol are evaluated. The average executive time of the three steps are 0.059s, 0.060s and 0.032s, demonstrating its efficiency. Copyright © 2020, The Authors. All rights reserved.

关键词： Smart contract

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Mixture-of-Experts for Distributed Edge computing with Channel-Aware Gating Function

arXiv

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arXiv 2025年

作者： Song, Qiuchen Jing, Shusen Zhang, Shuai Zhang, Songyang Huang, Chuan Shenzhen Future Network of Intelligence Institute School of Science and Engineering Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China Department of Radiation Oncology University of California San FranciscoCA94118 United States Department of Data Science New Jersey Institute of Technology NewarkNJ07102 United States Department of Electrical and Computer Engineering University of Louisiana at Lafayette LafayetteLA70504 United States School of Science and Engineering Shenzhen Future Network of Intelligence Institute Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Shenzhen518172 China

In a distributed mixture-of-experts (MoE) system, a server collaborates with multiple specialized expert clients to perform inference. The server extracts features from input data and dynamically selects experts based on their areas of specialization to produce the final output. Although MoE models are widely valued for their flexibility and performance benefits, adapting distributed MoEs to operate effectively in wireless networks has remained unexplored. In this work, we introduce a novel channel-aware gating function for wireless distributed MoE, which incorporates channel conditions into the MoE gating mechanism. To train the channel-aware gating, we simulate various signal-to-noise ratios (SNRs) for each expert’s communication channel and add noise to the features distributed to the experts based on these SNRs. The gating function then utilizes both features and SNRs to optimize expert selection. Unlike conventional MoE models which solely consider the alignment of features with the specializations of experts, our approach additionally considers the impact of channel conditions on expert performance. Experimental results demonstrate that the proposed channel-aware gating scheme outperforms traditional MoE models. Copyright © 2025, The Authors. All rights reserved.

关键词： Edge computing

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Faking photon number on a transition-edge sensor

arXiv

引用

arXiv 2021年

作者： Chaiwongkhot, Poompong Zhong, Jiaqiang Huang, Anqi Qin, Hao Shi, Sheng-Cai Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics Faculty of Science Mahidol University Bangkok10400 Thailand Bangkok10110 Thailand Purple Mountain Observatory and Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua road Nanjing210033 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China CAS Quantum Network Co. Ltd. 99 Xiupu road Shanghai201315 China Russian Quantum Center Skolkovo Moscow121205 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

We study potential security vulnerabilities of a single-photon detector based on superconducting transition-edge sensor. In a simple experiment, we show that an adversary could fake a photon number result at a certain wavelength by sending a larger number of photons at a longer wavelength. In another experiment, we show that the detector can be blinded by bright continuous-wave light and then, a controlled response simulating single-photon detection can be produced by applying a bright light pulse. We model an intercept- and-resend attack on a quantum key distribution system that exploits the latter vulnerability and, under certain assumptions, succeeds to steal the key. © 2021, CC BY.

关键词： Photons

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Dynamic MCMC sampling

arXiv

引用

arXiv 2019年

作者： Feng, Weiming He, Kun Sun, Xiaoming Yin, Yitong State Key Laboratory for Novel Software Technology Nanjing University CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

The Markov chain Monte Carlo (MCMC) methods are the primary tools for sampling from Gibbs distributions arising by various graphical models, e.g. Markov random fields (MRF). Traditional MCMC sampling algorithms are focused on a classic static setting, where the input is fixed. In this paper we study the problem of sampling from a MRF when the graphical model itself is changing dynamically with time. The problem is well motivated by the growing volume and velocity of data in today's applications of the MCMC methods. For the two major MCMC approaches, respectively for the approximate and perfect sampling, namely, the Gibbs sampling and the coupling from the past (CFTP), we give dynamic versions for the respective MCMC sampling algorithms. On MRF with n variables and bounded maximum degrees, these dynamic sampling algorithms can maintain approximate samples within 1/Poly(n) total variation errors, or perfect samples, while the MRF is dynamically changing. Furthermore, the dynamic sampling algorithms are efficient with O(n) space cost, and O(log2 n) incremental time cost upon each local update to the input MRF, as long as certain decay conditions are satisfied in each step by natural couplings of the corresponding single-site chains. These decay conditions were well known in the literature of couplings for rapid mixing of Markov chains, and now for the first time, are used to imply efficient dynamic sampling algorithms. Consequently, we have efficient dynamic (approximate or perfect) sampling algorithms with O(n) space cost and O (log2 n) incremental time cost, for the following models when the maximum degree is bounded: generalMRF satisfying the Dobrushin-Shlosman condition (for approximate sampling);Ising model with temperature β where e-2|β| > 1 - 2 β+1 (for both approximate and perfect samplings);hardcore model with fugacity λ 2λ (for approximate sampling);or q > 2Δ2 + 3Δ (for perfect sampling). These results show that the coupling of single-site Markov chains that

关键词： Learning algorithms

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Facial Expression Recognition for In-the-wild Videos

Facial Expression Recognition for In-the-wild Videos

引用

International Conference on Automatic Face and Gesture Recognition

作者： Hanyu Liu Jiabei Zeng Shiguang Shan Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Computer Science Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728130798

ISBN: (纸本)9781728130804

In this paper, we propose a method for facial expression recognition for in-the-wild videos. Our method combines Deep Residual network (ResNet) and Bidirectional Recurrent Neutral network with Long-Short-Term Memory Unit (BLSTM). This method won the 2 nd place in the seven basic expression classification track of Affective Behavior Analysis in-the-wild Competition held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020, achieving 66.9% accuracy and 40.8% final metric on the test set. We also visualize the learned attention maps and analyze the importance of different regions in facial expression recognition.

关键词： Face recognition Videos Feature extraction Training Conferences Computer vision Mouth

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