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arXiv 2023年

作者： Luo, Cheng Song, Siyang Xie, Weicheng Spitale, Micol Ge, Zongyuan Shen, Linlin Gunes, Hatice Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China Department of Computer Science University of Nottingham Ningbo China Ningbo315100 China Computer Sciences University of Exeter ExeterEX4 4PY United Kingdom Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FT United Kingdom Airdoc-Monash Research Centre Monash University Faculty of IT Monash University Melbourne Australia

In dyadic interaction, predicting the listener’s facial reactions is challenging as different reactions could be appropriate in response to the same speaker’s behaviour. Previous approaches predominantly treated this task as an interpolation or fitting problem, emphasizing deterministic outcomes but ignoring the diversity and uncertainty of human facial reactions. Furthermore, these methods often failed to model short-range and long-range dependencies within the interaction context, leading to issues in the synchrony and appropriateness of the generated facial reactions. To address these limitations, this paper reformulates the task as an extrapolation or prediction problem, and proposes an novel framework (called ReactFace) to generate multiple different but appropriate facial reactions from a speaker behaviour rather than merely replicating the corresponding listener facial behaviours. Our ReactFace generates multiple different but appropriate photo-realistic human facial reactions by: (i) learning an appropriate facial reaction distribution representing multiple different but appropriate facial reactions;and (ii) synchronizing the generated facial reactions with the speaker verbal and non-verbal behaviours at each time stamp, resulting in realistic 2D facial reaction sequences. Experimental results demonstrate the effectiveness of our approach in generating multiple diverse, synchronized, and appropriate facial reactions from each speaker’s behaviour. The quality of the generated facial reactions is intimately tied to the speaker’s speech and facial expressions, achieved through our novel speaker-listener interaction modules. Our code is made publicly available at https://***/lingjivoo/ReactFace. Copyright © 2023, The Authors. All rights reserved.

关键词： Extrapolation

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An Automatic Extraction Method of Coronary Arteries Centerline Based on Cellular Automata

An Automatic Extraction Method of Coronary Arteries Centerli...

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作者： Guangkun Ma Jinzhu Yang Hong Zhao School of Computer Science and Engineering Northeastern University School of Software Shenyang University of Technology Key Laboratory of Intelligent Computing in Medical Image Ministry of Education School of Computer Science and Engineering Northeastern University

Effectively obtaining the centerline of the coronary arteries is very important to the clinical medical diagnosis. Cellular automata are extensively used to find fine solutions in many complex hard. However, it has the problem of large amount of computation. In this paper, we propose an automatic extraction method of coronary centerline based on cellular automata. Different from the existing methods, the distance from boundary field is used to extract the centerline of the coronary arteries roughly, which can place the candidate voxel at the center of the coronary arteries and reduce the size of the data. Furthermore, the centerlines of the coronary arteries are extracted accurately by using cellular automata to find the shortest path. The proposed method can extract the centerlines of each branch of the coronary arteries simultaneously from a single source to multiple destinations. Furthermore, compared to the method in Ref [10], our proposed method works well, especially in the case of large vessel curvature, the voxels at the extracted centerline are relatively centered. In the last, quantitative evaluation is conducted on the coronary arteries and the results show that the proposed method achieves a smaller deviation distance.

关键词： Cellular automata Distance from Boundary Coronary arteries Computerized Tomography Angiography

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Physical Layer Security for Wireless-Powered Ambient Backscatter Cooperative Communication Networks

TechRxiv

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TechRxiv 2023年

作者： Li, Xingwang Jiang, Junjie Wang, Hao Chen, Gaojie Du, Jianhe Hu, Chunqiang Mumtaz, Shahid The School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo454000 China University of Surrey GuildfordGU2 7XH United Kingdom The State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China The School of Big Data & Software Engineering Chongqing University Chongqing400030 China The Institute of Telecommunications Aveiro3810078 Portugal The ARIES Research Center Universidad Antonio de Nebrija MadridE-28040 Spain

Low power consumption and high spectrum efficiency as the great challenges for multi-device access to Internet-of-Things (IoT) have put forward stringent requirements on the future intelligent network. Ambient backscatter communication (ABcom) is regarded as a promising technology to cope with the two challenges, where backscatter device (BD) can reflect ambient radio frequency (RF) signals without additional bandwidth. However, minimalist structural design of BD makes ABcom security vulnerable in wireless propagation environments. By virtue of this fact, this paper considers the physical layer security (PLS) of a wireless-powered ambient backscatter cooperative communication network threatened by an eavesdropper, where a BD with nonlinear energy harvesting model cooperates with decode-and-forward (DF) relay for secure communication. The PLS performance is investigated by deriving the secrecy outage probability (SOP) and secure energy efficiency (SEE). Specifically, the closed-form and asymptotic expressions of SOP are derived as well as the secrecy diversity order for the first time. As an energy-constrained device, balancing power consumption and security is major concern for BD, thus the SEE of the proposed network is studied. The results from numerical analysis show that the performance improvement of SOP and SEE is impacted by system parameters, including transmission power, secrecy rate threshold, reflection efficiency and distance between the source and BD, which provide guidance on balancing security and energy efficiency in ambient backscatter cooperative relay networks. © 2023, CC BY.

关键词： Energy harvesting

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Research on Application of Heterogeneous Resonance Integral for Double Heterogeneous System

SSRN

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SSRN 2024年

作者： Qin, Shuai Zhang, Qian Wang, Kai Huang, Dong Li, Song Liang, Yuechao Laboratory for Advanced Nuclear Energy Theory and Applications Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China Zhejiang Institute of Modern Physics Department of Physics Zhejiang University Zhejiang Hangzhou310027 China School of Nuclear Science and Technology Xi’an Jiaotong University 28 West Xianning Road Shaanxi Xi’an710049 China College of Nuclear Science and Technology Naval University of Engineering Wuhan China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Hunan Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Hunan Changsha410073 China

The Double Heterogeneous (DH) system, where fuel particles are randomly dispersed in the non-fissile matrix, is challenging for the reactor physics calculation. The Sanchez-Pomraning method accurately handles the DH system, but integrating it into existing reactor physics code requires code development. This study adopts the Sanchez-Pomraning coupled Ultra-Fine-Group (SP-UFG) slowing-down calculation to generate the heterogeneous Resonance Integral (RI) for DH system treatment with simple volume homogenization. Fully Ceramic Micro-encapsulated (FCM) fuel pin-cells and plates with varying configurations are calculated for verification. Effective cross-sections (XSs) and keff calculated by the heterogeneous RI are compared with SP-UFG results. Results show that the maximum bias of XSs and keff caused by the XS biases are less than 5% and 200 pcm, respectively. The maximum bias of keff when compared with Monte Carlo calculated results is -213 pcm, demonstrating that only considering the DH effect in the resonance energy region is acceptable. © 2024, The Authors. All rights reserved.

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Adaptive fusion affinity graph with noise-free online low-rank representation for natural image segmentation

arXiv

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arXiv 2021年

作者： Zhang, Yang Liu, Moyun Zhang, Huiming Sun, Guodong He, Jingwu School of Mechanical Engineering Hubei University of Technology Wuhan430068 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

Affinity graph-based segmentation methods have become a major trend in computer vision. The performance of these methods rely on the constructed affinity graph, with particular emphasis on the neighborhood topology and pairwise affinities among superpixels. Due to the advantages of assimilating different graphs, a multi-scale fusion graph has a better performance than a single graph with single-scale. However, these methods ignore the noise from images which influence the accuracy of pairwise similarities. Multi-scale combinatorial grouping and graph fusion also generate a higher computational complexity. In this paper, we propose an adaptive fusion affinity graph (AFA-graph) with noise-free low-rank representation in an online manner for natural image segmentation. An input image is first over-segmented into superpixels at different scales and then filtered by the proposed improved kernel density estimation method. Moreover, we select global nodes of these superpixels on the basis of their subspace-preserving presentation, which reveals the feature distribution of superpixels exactly. To reduce time complexity while improving performance, a sparse representation of global nodes based on noise-free online low-rank representation is used to obtain a global graph at each scale. The global graph is finally used to update a local graph which is built upon all superpixels at each scale. Experimental results on the BSD300, BSD500, MSRC, SBD, and PASCAL VOC show the effectiveness of AFA-graph in comparison with state-of-the-art approaches. The code is available at https://***/Yangzhangcst/AFA-graph. © 2021, CC BY.

关键词： Graphic methods

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Group Signatures with Decentralized Tracing 15th

Group Signatures with Decentralized Tracing

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15th International Conference on Information Security and Cryptology, Inscrypt 2019

作者： Lu, Tingting Li, Jiangtao Zhang, Lei Lam, Kwok-Yan Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China State Key Laboratory of Cryptology P.O. Box 5159 Beijing China Shanghai Institute for Advanced Communication and Data Science Shanghai China School of Computer Engineering and Science Shanghai University Shanghai China Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030429201

Group signature is a useful cryptographic primitive that allows a message to be signed by a user on behalf of a group which is managed by some trusted authority, namely the group manager. However, group signature schemes typically place a disturbingly high level of trust on the group manager, which has become a major deployment issue in cyber applications where there is no centralized trust management. In this paper, we investigate mechanisms that aim to achieve a balance between anonymity and accountability in group signatures by decentralizing the operation of tracing the signer. We propose a practical group signature scheme with decentralized tracing. When comparing with a similar result by Benjumea et al. (FC 2008), our proposal has the advantage of a shorter signature size. © 2020, Springer Nature Switzerland AG.

关键词： Managers

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A unified light framework for real-time fault detection of freight train images

arXiv

引用

arXiv 2021年

作者： Zhang, Yang Liu, Moyun Yang, Yang Guo, Yanwen Zhang, Huiming School of Mechanical Engineering Hubei University of Technology Wuhan430068 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

Real-time fault detection for freight trains plays a vital role in guaranteeing the security and optimal operation of railway transportation under stringent resource requirements. Despite the promising results for deep learning based approaches, the performance of these fault detectors on freight train images, are far from satisfactory in both accuracy and efficiency. This paper proposes a unified light framework to improve detection accuracy while supporting a real-time operation with a low resource requirement. We firstly design a novel lightweight backbone (RFDNet) to improve the accuracy and reduce computational cost. Then, we propose a multi region proposal network using multi-scale feature maps generated from RFDNet to improve the detection performance. Finally, we present multi level position-sensitive score maps and region of interest pooling to further improve accuracy with few redundant computations. Extensive experimental results on public benchmark datasets suggest that our RFDNet can significantly improve the performance of baseline network with higher accuracy and efficiency. Experiments on six fault datasets show that our method is capable of real-time detection at over 38 frames per second and achieves competitive accuracy and lower computation than the state-of-the-art detectors. © 2021, CC BY-NC-SA.

关键词： Fault detection

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Robust Self-Ensembling Network for Hyperspectral Image Classification

arXiv

引用

arXiv 2021年

作者： Xu, Yonghao Du, Bo Zhang, Liangpei The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Institute of Artificial Intelligence Wuhan University Wuhan430079 China Vienna1030 Austria School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430079 China The State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430079 China

Recent research has shown the great potential of deep learning algorithms in the hyperspectral image (HSI) classification task. Nevertheless, training these models usually requires a large amount of labeled data. Since the collection of pixel-level annotations for HSI is laborious and time-consuming, developing algorithms that can yield good performance in the small sample size situation is of great significance. In this study, we propose a robust self-ensembling network (RSEN) to address this problem. The proposed RSEN consists of two subnetworks including a base network and an ensemble network. With the constraint of both the supervised loss from the labeled data and the unsupervised loss from the unlabeled data, the base network and the ensemble network can learn from each other, achieving the self-ensembling mechanism. To the best of our knowledge, the proposed method is the first attempt to introduce the self-ensembling technique into the HSI classification task, which provides a different view on how to utilize the unlabeled data in HSI to assist the network training. We further propose a novel consistency filter to increase the robustness of self-ensembling learning. Extensive experiments on three benchmark HSI datasets demonstrate that the proposed algorithm can yield competitive performance compared with the state-of-the-art methods. Code is available online (https://***/YonghaoXu/RSEN). Copyright © 2021, The Authors. All rights reserved.

关键词： Learning algorithms

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Inconsistency-aware uncertainty estimation for semi-supervised medical image segmentation

arXiv

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arXiv 2021年

作者： Shi, Yinghuan Zhang, Jian Ling, Tong Lu, Jiwen Zheng, Yefeng Yu, Qian Qi, Lei Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China National Institute of Healthcare Data Science Nanjing University China Department of Automation Tsinghua University China Tencent Jarvis Lab China School of Data and Computer Science Shandong Women’s University China School of Computer Science and Engineering Southeast University Nanjing211189 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210046 China

In semi-supervised medical image segmentation, most previous works draw on the common assumption that higher entropy means higher uncertainty. In this paper, we investigate a novel method of estimating uncertainty. We observe that, when assigned different misclassification costs in a certain degree, if the segmentation result of a pixel becomes inconsistent, this pixel shows a relative uncertainty in its segmentation. Therefore, we present a new semi-supervised segmentation model, namely, conservative-radical network (CoraNet in short) based on our uncertainty estimation and separate self-training strategy. In particular, our CoraNet model consists of three major components: a conservative-radical module (CRM), a certain region segmentation network (C-SN), and an uncertain region segmentation network (UC-SN) that could be alternatively trained in an end-to-end manner. We have extensively evaluated our method on various segmentation tasks with publicly available benchmark datasets, including CT pancreas, MR endocardium, and MR multi-structures segmentation on the ACDC dataset. Compared with the current state of the art, our CoraNet has demonstrated superior performance. In addition, we have also analyzed its connection with and difference from conventional methods of uncertainty estimation in semi-supervised medical image segmentation. Copyright © 2021, The Authors. All rights reserved.

关键词： Pixels

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The Concert between Geology and Artificial Intelligence:Automatic Identification of Heavy Minerals from River Sand

引用

Acta Geologica Sinica(English Edition) 2019年第S01期93卷 21-23页

作者： HAO Huizhen HU Xiumian GUO Ronghua GU Qing Software Institute Nanjing UniversityNanjingChina School of Communication Engineering Nanjing Institute of TechnologyNanjingChina School of Earth Sciences and Engineering Nanjing UniversityNanjingChina State Key Laboratory for Novel Software Technology Nanjing UniversityNanjingChina

Heavy minerals with densities higher than 2.8 ***-3 are generally considered minor components of sand or sandstone,typically forming 1%of the weight in the samples(Mange and Wright,2007).Heavy-mineral analysis is an effective tool for studying the sedimentary provenance of siliciclastic rocks,reconstructing sedimentary sources to sink routes,subdividing and correlating non-fossiliferous siliciclastic strata.

关键词： river sand heavy mineral artificial intelligence image analysis

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