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International Conference on Computer Supported Cooperative Work in Design

作者： Guohua Lv Xiyan Wang Zhonghe Wei Jinyong Cheng Guangxiao Ma Hanju Bao Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Faculty of Comuter Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Lianxin Intelligent Technology (Shandong) Co. Ltd Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The purpose of infrared and visible image fusion is to combine the background information of the visible images and the thermal target information of the infrared images. Current fusion methods often neglect the challenges of low-light conditions. In the night scene, the existing methods fail to capture texture information from the visible images that is hidden in the darkness, producing suboptimal fusion results that can hinder subsequent visual applications. Therefore, we propose a method for infrared and visible image fusion under night scenes, termed as IDFusion. Specifically, our method is divided into three parts, first, a dense auto-encoder is designed to obtain more useful features from the source images. Then we design a brightness enhancement network that removes visible degraded illumination maps to obtain brightness-enhanced features. Finally, a texture fusion network is designed so that the infrared features and enhanced visible features avoid texture loss during the fusion process. Experimental results show that our network can obtain better fusion results, outperforming state-of-the-art methods in terms of subjective visual effects and quantitative metrics.

关键词： Measurement Federated learning Brightness Lighting Visual effects Image fusion

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Dynamics and resonance fluorescence from a superconducting artificial atom doubly driven by quantized and classical fields

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Physical Review Research 2024年第3期6卷 033064-033064页

作者： Xinhui Ruan Jia-Heng Wang Dong He Pengtao Song Shengyong Li Qianchuan Zhao L. M. Kuang Jaw-Shen Tsai Chang-Ling Zou Jing Zhang Dongning Zheng O. V. Astafiev Yu-xi Liu Zhihui Peng Department of Automation Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center of Quantum Effects and Applications School of Integrated Circuits School of Physical Sciences Center for Quantum Computing Graduate School of Science CAS Key Laboratory of Quantum Information Hefei National Laboratory Hefei 230088 China School of Automation Science and Engineering MOE Key Lab for Intelligent Networks and Network Security Nobel Street 3 143026 Moscow Russia Institutskiy Pereulok 9 141701 Dolgoprudny Russia Egham Surrey TW20 0EX United Kingdom

We report an experimental demonstration of resonance fluorescence in a two-level superconducting artificial atom under two driving fields coupled to a detuned cavity. One of the fields is classical and the other is varied from quantum (vacuum fluctuations) to classical by controlling the photon number inside the cavity. The device consists of a transmon qubit strongly coupled to a one-dimensional transmission line and a coplanar waveguide resonator. We observe a sideband anticrossing and asymmetry in the emission spectra of the system through a one-dimensional transmission line, which is fundamentally different from the weak-coupling case. By changing the photon number inside the cavity, the emission spectrum of our doubly driven system approaches the case when the atom is driven by two classical bichromatic fields. We also measure the dynamical evolution of the system through the transmission line and study the properties of the first-order correlation function, Rabi oscillations, and energy relaxation in the system. The study of resonance fluorescence from an atom driven by two fields promotes understanding decoherence in superconducting quantum circuits and may find applications in superconducting quantum computing and quantum networks.

关键词： Light-matter interaction Optical pumping Quantum circuits Quantum coherence & coherence measures Quantum-to-classical transition Strong electromagnetic field effects

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Wireless powered Backscatter Communications with Spatially Random Tags

Wireless Powered Backscatter Communications with Spatially R...

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IEEE International Conference on Communications in China (ICCC)

作者： Yingting Liu Yinghui Ye Xingwang Li Zhongyu Ma Zhiyang Zhou School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China Shaanxi Key Laboratory of Information Communication Network and Security Xi’an University of Posts & Telecommunications Xi’an China School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China College of Computer Science and Engineering Northwest Normal University Lanzhou China

In this paper, a wireless powered backscatter communication (BackCom) system is considered, which consists of one dedicated radio-frequency source node (S), one destination node and multiple tags randomly located in the coverage region of S. The consumed power by tags is considered and an optimal dynamic reflection coefficient (RC) is used to maximize the power of the backscattered signals while just satisfying the tag’s power requirement. The random tag selection (RTS) and nearest tag selection (NTS) schemes are proposed and the corresponding outage probabilities and diversity orders are derived. Numerical results verify the theoretical analysis and show that the NTS scheme has the best performance compared to the RTS and farthest tag selection schemes. Moreover, numerical results also show that the performance of the adopted optimal dynamic RC is superior to that of the fixed RC.

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LoCoCa: Location-Context-Capacity Aware Cost Economizing in Edge-Cloud Systems

LoCoCa: Location-Context-Capacity Aware Cost Economizing in ...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yuanze Li Chao Qiu Xiaofei Wang Cheng Zhang Wenyu Wang Shizhan Lan Jing Jiang College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Institute of Technology Tianjin University of Finance and Economics Tianjin China Shanghai Zhuichu Networking Technologies Co. Ltd. China China Mobile Guangxi institute Co Ltd Guangxi China Shaanxi Key Laboratory of Information Communication Network and Security Xi'an University of Posts and Telecommunications Xi'an China

Nowadays, real-time interactive content services have been the most dazzling sector of next-generation Internet. The high-quality perceptions of virtual scenes have given rise to the strict requirements of high bandwidth and low latency, where the edge-cloud system promises several benefits. However, there still remain prominent challenges, when taking the economical efficiency into consideration, including location-heterogeneity, context-directability, and capacity-exploitation. In this paper, we propose a location-context-capacity aware bandwidth cost economizing strategy in the edge-cloud system, i.e., LoCoCa. LoCoCa adopts server pools partition mechanism, then achieving the optimal burstable billing in each pool. Here, a location-aware graph construction and partition algorithm is designed to solve the server pools partition problem. Then an improved burstable billing optimization mechanism, with a context index and an adaptive bandwidth capacity, is also proposed to economize bandwidth costs. Finally, the realistic edge-cloud company's trace-based experimental results verify LoCoCa reduces bandwidth costs by 81.83 %, compared with the baselines.

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Disentangled Noisy Correspondence Learning

arXiv

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arXiv 2024年

作者： Dang, Zhuohang Luo, Minnan Wang, Jihong Jia, Chengyou Han, Haochen Wan, Herun Dai, Guang Chang, Xiaojun Wang, Jingdong The School of Computer Science and Technology The Ministry of Education Key Laboratory of Intelligent Networks and Network Security The Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Shaanxi Xi'An710049 China SGIT AI Lab State Grid Shaanxi Electric Power Company Limited State Grid Corporation of China Shaanxi China The School of Information Science and Technology University of Science and Technology China United Arab Emirates The Baidu Inc China

Cross-modal retrieval is crucial in understanding latent correspondences across modalities. However, existing methods implicitly assume well-matched training data, which is impractical as real-world data inevitably involves imperfect alignments, i.e., noisy correspondences. Although some works explore similarity-based strategies to address such noise, they suffer from sub-optimal similarity predictions influenced by modality-exclusive information (MEI), e.g., background noise in images and abstract definitions in texts. This issue arises as MEI is not shared across modalities, thus aligning it in training can markedly mislead similarity predictions. Moreover, although intuitive, directly applying previous cross-modal disentanglement methods suffers from limited noise tolerance and disentanglement efficacy. Inspired by the robustness of information bottlenecks against noise, we introduce DisNCL, a novel information-theoretic framework for feature Disentanglement in Noisy Correspondence Learning, to adaptively balance the extraction of MII and MEI with certifiable optimal cross-modal disentanglement efficacy. DisNCL then enhances similarity predictions in modality-invariant subspace, thereby greatly boosting similarity-based alleviation strategy for noisy correspondences. Furthermore, DisNCL introduces soft matching targets to model noisy many-to-many relationships inherent in multi-modal input for noise-robust and accurate cross-modal alignment. Extensive experiments confirm DisNCL's efficacy by 2% average recall improvement. Mutual information estimation and visualization results show that DisNCL learns meaningful MII/MEI subspaces, validating our theoretical analyses. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Joint object contour points and semantics for instance segmentation

arXiv

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arXiv 2020年

作者： Zhang, Wenchao Fu, Chong Zhu, Mai Cao, Lin Tie, Ming Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China Science and Technology on Space Physics Laboratory Beijing100076 China School of Computer Science University of Auckland New Zealand

The attributes of object contours has great significance for instance segmentation task. However, most of the current popular deep neural networks do not pay much attention to the object edge information. Inspired by the human annotation process when making instance segmentation datasets, in this paper, we propose Mask Point R-CNN aiming at promoting the neural network’s attention to the object boundary. Specifically, we innovatively extend the original human keypoint detection task to the contour point detection of any object. Based on this analogy, we present an contour point detection auxiliary task to Mask R-CNN, which can boost the gradient flow between different tasks by effectively using feature fusion strategies and multi-task joint training. As a consequence, the model will be more sensitive to the edges of the object and can capture more geometric features. Quantitatively, the experimental results show that our approach outperforms vanilla Mask R-CNN by 3.8% on Cityscapes dataset and 0.8% on COCO dataset. Copyright © 2020, The Authors. All rights reserved.

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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Trust and Risk Based on Access Control Model in Social Internet of Things

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International Journal of network security 2023年第5期25卷 736-744页

作者： Zhang, Hongbin Liu, Jian Zhao, Dongmei Liu, Bin Wang, Yanmei Fan, Fan School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Scienc and Technology Shijiazhuang050000 China Hebei Geological Worker’s University Shijiazhuang050000 China

Integrating social networks and the Internet of Things has formed an emerging field of Social Internet of Things (IoT). Aiming to address the shortcomings of traditional TBAC (Trust based on Access Control) and ABAC (Attribute based on Access Control) in dynamic authorization and security, this paper proposes a hybrid access control model based on trust and risk for SIoT. The model supports social relationship-based trust assessment and risk prediction between SIoT nodes and integrates trust and risk values into SIoT security values through reasonable weight allocation. Furthermore, by simulating the safety requirements of different scenarios, it was verified that the model was efficient and more secure than the current models. © (2023). All Rights Reserved.

关键词： Authorization

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MedX-Net: Hierarchical Transformer with Large Kernel Convolutions for 3D Medical Image Segmentation

MedX-Net: Hierarchical Transformer with Large Kernel Convolu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lin Lu Qingzhi Zou Key Laboratory of Computing Power Network and Information Se-curity Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Due to the exceptional performance of Transform-ers in 2 $D$ medical image segmentation, recent work has also introduced them into 3D medical segmentation tasks. For instance, Swin UNETR and other hierarchical Transformers have reintroduced prior knowledge from several convolutional networks, further enhancing the volume segmentation capabilities of models. The efficacy of these hybrid methodologies is primarily attributed to the substantial quantity of parameters and the nonlocal self-attention mechanism with a large receptive field. We argue that the behavior of these methods' large receptive fields can be simulated by employing fewer parameters through the utilization of depth-wise convolutions with large kernel. Within this manuscript, we introduce a lightweight volume segmentation model called MedX-Net, which uses convolutional network modules to simulate hierarchical Transformers for robust volume segmentation. Firstly, inspired by the hierarchical Transformer module of Swin UNETR, we investigate large-kernel depth-wise convolutions with different sizes to achieve a reduced model parameter count while maintaining a large global receptive field. Secondly, we replace the multilayer perceptron (MLP) in the hierarchical Transformer module with Inverted Bottleneck with Depthwise Convolution Enhancement(DWCE) to improve model performance with fewer activation and normalization layers, further reducing the parameter count. We validate the effectiveness and efficiency of our model for volume segmentation on three public datasets: Synapse, BTCV and ACDC. On the Synapse dataset, compared to Swin UNETR, our model achieves an improvement from 83.48% to 87.21% in Dice score. Compared to the result of 86.57% achieved by nnFormer, our model achieves superior performance while reducing the model parameter count by 64%.

关键词： Image segmentation Solid modeling Visualization Three-dimensional displays Current transformers Multilayer perceptrons Convolutional neural networks Kernel Synapses Biomedical imaging

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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