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2nd EAI International Conference on Robotic Sensor Networks, ROSENET 2018

作者： Jin, Xin Zhu, Shuyun Wu, Le Zhao, Geng Li, Xiaodong Zhou, Quan Lu, Huimin Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China CETC Big Data Research Institute Co. Ltd. Guizhou Guiyang China Department of Cyber Security Beijing Electronic Science and Technology Institute Beijing China National Engineering Research Center of Communications and Networking Nanjing University of Posts and Telecommunications Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Mechanical and Control Engineering Kyushu Institute of Technology Kitakyushu Japan

ISBN: (纸本)9783030177621

With the rapid progress of virtual reality and augmented reality technologies, 3D contents are the next widespread media in many applications. Thus, the protection of 3D models is primarily important. Encryption of 3D models is essential to maintain confidentiality. Previous work on encryption of 3D surface model often considers the point clouds, the meshes, and the textures individually. In this work, a multi-level chaotic maps model for 3D textured encryption was presented by observing the different contributions for recognizing cipher 3D models between vertices (point cloud), polygons, and textures. For vertices which make main contribution for recognizing, we use high-level 3D Lu chaotic map to encrypt them. For polygons and textures which make relatively smaller contributions for recognizing, we use 2D Arnold’s cat map and 1D logistic map to encrypt them, respectively. The experimental results show that our method can get similar performance with the other method and use the same high-level chaotic map for point cloud, polygons, and textures, while we use less time. Besides, our method can resist more method of attacks such as statistic attack, brute-force attack, and correlation attack. © 2020, Springer science and Business Media Deutschland GmbH. All rights reserved.

关键词： Textures

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Achieving the High Capacity and High Stability of Li-Rich Oxide Cathode in Garnet-Based Solid-State Battery

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Angewandte Chemie 2023年第1期136卷

作者： Butian Chen Prof. Jicheng Zhang Dr. Deniz Wong Tenghui Wang Taiguang Li Chong Liu Prof. Limei Sun Prof. Xiangfeng Liu Center of Materials Science and Optoelectronics Engineering College of Materials Science and Optoelectronic Technology University of Chinese Academy of Sciences Beijing 100049 China Contribution: Conceptualization (lead) Data curation (lead) Software (lead) Visualization (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Conceptualization (supporting) Data curation (supporting) Resources (supporting) Software (supporting) Helmholtz-Zentrum Berlin für Materialien und Energie Hahn-Meitner-Platz 1 14109 Berlin Germany Contribution: Data curation (supporting) Software (supporting) Validation (supporting) Contribution: Data curation (supporting) Methodology (supporting) Software (supporting) Contribution: Data curation (supporting) Software (supporting) Department of Nuclear Physics China Institute of Atomic Energy Beijing 102413 China Contribution: Data curation (supporting) Contribution: Funding acquisition (lead) Supervision (lead) Writing - original draft (supporting) Writing - review & editing (supporting)

Solid-state batteries (SSBs) based on Li-rich Mn-based oxide (LRMO) cathodes attract much attention because of their high energy density as well as high safety. But their development was seriously hindered by the interfacial instability and inferior electrochemical performance. Herein, we design a three-dimensional foam-structured GaN−Li composite anode and successfully construct a high-performance SSB based on Co-free Li 1.2 Ni 0.2 Mn 0.6 O 2 cathode and Li 6.5 La 3 Zr 1.5 Ta 0.5 O 12 (LLZTO) solid electrolyte. The interfacial resistance is considerably reduced to only 1.53 Ω cm 2 and the assembled Li symmetric cell is stably cycled more than 10,000 h at 0.1–0.2 mA cm −2 . The full battery shows a high initial capacity of 245 mAh g −1 at 0.1 C and does not show any capacity degradation after 200 cycles at 0.2 C (≈100 %). The voltage decay is well suppressed and it is significantly decreased from 2.96 mV/cycle to only 0.66 mV/cycle. The SSB also shows a very high rate capability (≈170 mAh g −1 at 1 C) comparable to a liquid electrolyte-based battery. Moreover, the oxygen anion redox (OAR) reversibility of LRMO in SSB is much higher than that in liquid electrolyte-based cells. This study offers a distinct strategy for constructing high-performance LRMO-based SSBs and sheds light on the development and application of high-energy density SSBs.

关键词： Garnet-Based Solid Electrolytes Interfacial Modification Lithium-Rich Oxide Cathode Solid-State Battery Suppression of Li Dendrites

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A robust and generalized framework for adversarial graph embedding

arXiv

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arXiv 2021年

作者： Li, Jianxin Fu, Xingcheng Peng, Hao Wang, Senzhang Zhu, Shijie Sun, Qingyun Yu, Philip S. He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China School of Computer Science and Engineering Central South University Changsha410083 China Department of Computer Science University of Illinois at Chicago ChicagoIL60607 United States Department of Computer Science and Engineering Lehigh University BethlehemPA18015 United States

Graph embedding is essential for graph mining tasks. With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high quality graph embedding vectors various types of graphs. However, most existing methods usually randomly select the negative samples from the original graph to enhance the training data without considering the noise. In addition, most of these methods only focus on the explicit graph structures and cannot fully capture complex semantics of edges such as various relationships or asymmetry. In order to address these issues, we propose a robust and generalized framework for adversarial graph embedding based on generative adversarial networks. Inspired by generative adversarial network, we propose a robust and generalized framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution, and enables the discriminator and generator to jointly learn each node's robust and generalized representation. Based on this framework, we propose three models to handle three types of graph data and derive the corresponding optimization algorithms, i.e., UG-AGE and DG-AGE for undirected and directed homogeneous graphs, respectively, and HIN-AGE for heterogeneous information networks. Extensive experiments show that our methods consistently and significantly outperform existing state-of-the-art methods across multiple graph mining tasks, including link prediction, node classification, and graph reconstruction. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

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Ar-Manet: A Low-Quality Image Restoration Method Based on Multi-Feature Fusion

SSRN

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SSRN 2024年

作者： Zhang, Yugui Zhou, Qi Jia, Tingting Ke, Zunwang Jia, YiZhen Wang, Gang Yang, Guang Du, Minghua Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Hunan RAMON Science & Technology Co. Ltd. Changsha410138 China Department of Stomatology The First Medical Centre Chinese PLA General Hospital Beijing100853 China School of Software Xinjiang University Urumqi830091 China School of Computing and Data Engineering NingboTech University Ningbo315100 China National Heart and Lung Institute Imperial CollegeLondon LondonSW7 2BX United Kingdom The Royal Brompton Hospital LondonSW3 6NP United Kingdom Medical School of Chinese PLA China

Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, taking care of both overexposure and underexposure problems. On this basis, we improve the U-Net model by introducing the Strengthen-Operate-Subtract (SOS) feature enhancement module and a multi-scale aggregate back-projection module to achieve finer image restoration and improve the propagation of feature information. In addition, we introduce an atrous residual dense block at the highest scale to expand the receptive field of the network and efficiently capture local information to improve the quality of the exposure-corrected images. These innovations significantly optimize the effect of image exposure correction and improve the problems of loss of image details, oversaturation of regional contrast, and imbalance of color naturalness in existing techniques. Through extensive and in-depth analytical evaluations, we have validated the effectiveness of our model, which also demonstrates excellent performance compared with the most recent methods on the test database. © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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Attention-augmented and depthwise separable convolutional message passing for robust fraud detection in large-scale graphs

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Journal of Advanced Research 2025年

作者： Ijeoma A. Chikwendu Xiaoling Zhang Chiagoziem C. Ukwuoma Chibueze D. Ukwuoma Isaac Adjei-Mensah Chinedu I. Otuka Chukwuebuka Joseph Ejiyi Yeong Hyeon Gu Mugahed A. Al-antari School of Information and Communication Engineering University of Electronic Science and Technology of China 611731 Chengdu China College of Nuclear Technology and Automation Engineering Chengdu University of Technology Sichuan 610059 China Sichuan Engineering Technology Research Center for Industrial Internet Intelligent Monitoring and Application Chengdu University of Technology Sichuan 610059 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan 610059 China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Chengdu Sichuan China Department of Artificial Intelligence and Data Science College of AI Convergence Daeyang AI Center Sejong University Seoul 05006 Republic of Korea

Introduction Graph Neural Networks (GNNs) have shown great promise in fraud detection tasks on graph-structured data. However, they struggle with challenges such as label imbalance and the presence of heterophilic neighbours, which can obscure fraudulent behaviour by embedding fraudsters among benign users. Objectives Existing GNN-based models often address these issues by modifying the graph structure to favour homophily, frequently ignoring heterophilic neighbours during message passing. This approach weakens their capacity to detect fraud in real-world graphs. Addressing this gap is crucial to enhance the effectiveness of fraud detection in large-scale graph. Methods This study proposes Attention-Augmented and Depthwise Separable Convolutional Message Passing (ADSCMP), a novel GNN framework. ADSCMP actively partitions neighbours into homophilic, heterophilic, and unknown groups during message passing. It integrates lightweight attention mechanisms to highlight critical nodes and employ depthwise separable convolutions to reduce computational overhead. Furthermore, this study dynamically generates root-specific weight matrices and incorporate both spectral and spatial features to better handle complex graph topologies. Results This study evaluated ADSCMP on four benchmark datasets Amazon, YelpChi, T-Finance, and T-Social. In supervised experiments (40 % labelled data), ADSCMP achieved AUC scores of 97.91 %, 94.17 %, 97.51 %, and 99.75 %, respectively. Even with only 1 % labelled data in semi-supervised settings, the model maintained strong performance with AUCs of 93.15 %, 84.53 %, 94.92 %, and 98.59 %. Our ablation study confirmed that each model component contributed meaningfully to its overall performance. Additionally, ADSCMP reduced inference time compared to competitive baselines, making it suitable for real-time fraud detection. Conclusion By actively learning from both homophilic and heterophilic neighbours and optimizing message passing with efficient con

关键词： Attention mechanism Depthwise separable convolution Graph fraud detection Graph neural networks Message passing

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Generative Adversarial Networks for Image Super-Resolution: A Survey

arXiv

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arXiv 2022年

作者： Tian, Chunwei Zhang, Xuanyu Zhu, Qi Zhang, Bob Lin, Jerry Chun-Wei School of Software Northwestern Polytechnical University the National Engineering Laboratory for Integrated Aero-space-Ground-Ocean Big Data Application Technology 1 Dongxiang Rd Shaanxi Xi’an710129 China School of Software Northwestern Polytechnical University 1 Dongxiang Rd Shaanxi Xi’an710129 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics 101 Section 2 Kuang-Fu Road Jiangsu Nanjing China PAMI Research Group University of Macau 101 Section 2 Kuang-Fu Road Macao999078 China Department of Computer Science Western Norway University of Applied Sciences Bergen Norway

Single image super-resolution (SISR) has played an important role in the field of image processing. Recent generative adversarial networks (GANs) can achieve excellent results on low-resolution images with small samples. However, there are little literatures summarizing different GANs in SISR. In this paper, we conduct a comparative study of GANs from different perspectives. We first take a look at developments of GANs. Second, we present popular architectures for GANs in big and small samples for image applications. Then, we analyze motivations, implementations and differences of GANs based optimization methods and discriminative learning for image super-resolution in terms of supervised, semi-supervised and unsupervised manners, where these GANs are analyzed via integrating different network architectures, prior knowledge, loss functions and multiple tasks. Next, we compare performance of these popular GANs on public datasets via quantitative and qualitative analysis in SISR. Finally, we highlight challenges of GANs and potential research points for SISR. © 2022, CC BY-NC-ND.

关键词： Generative adversarial networks

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Spatial-Temporal Dynamic Graph Attention Networks for Ride-hailing Demand Prediction

arXiv

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arXiv 2020年

作者： Pian, Weiguo Wu, Yingbo Qu, Xiangmou Cai, Junpeng Kou, Ziyi Automotive Collaborative Innovation Center Chongqing University China School of Big Data & Software Engineering Chongqing University China Department of Computer Science and Engineering University of Notre Dame United States

Ride-hailing demand prediction is an essential task in spatial-temporal data mining. Accurate Ride-hailing demand prediction can help to pre-allocate resources, improve vehicle utilization and user experiences. Graph Convolutional Networks (GCN) is commonly used to model the complicated irregular non-Euclidean spatial correlations. However, existing GCN-based ride-hailing demand prediction methods only assign the same importance to different neighbor regions, and maintain a fixed graph structure with static spatial relationships throughout the timeline when extracting the irregular non-Euclidean spatial correlations. In this paper, we propose the Spatial-Temporal Dynamic Graph Attention Network (STDGAT), a novel ride-hailing demand prediction method. Based on the attention mechanism of GAT, STDGAT extracts different pair-wise correlations to achieve the adaptive importance allocation for different neighbor regions. Moreover, in STDGAT, we design a novel time-specific commuting-based graph attention mode to construct a dynamic graph structure for capturing the dynamic time-specific spatial relationships throughout the timeline. Extensive experiments are conducted on a real-world ride-hailing demand dataset, and the experimental results demonstrate the significant improvement of our method on three evaluation metrics RMSE, MAPE and MAE over state-of-the-art baselines. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Solvers for Large-Scale Electronic Structure Theory: ELPA and ELSI

arXiv

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arXiv 2025年

作者： Karpov, Petr Marek, Andreas Melson, Tobias Pöppl, Alexander Yu, Victor Wen-Zhe Hourahine, Ben Garcia, Alberto Dawson, William Yao, Yi Huhn, William Moussa, Jonathan Hall, Sam Maurer, Reinhard Herath, Uthpala Lion, Konstantin Kokott, Sebastian Blum, Volker Max Planck Computing and Data Facility Garching Germany Intel Corporation Santa ClaraCA United States Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Department of Physics SUPA University of Strathclyde John Anderson Building107 Rottenrow GlasgowG4 0NG United Kingdom Institut de Ciència de Materials de Barcelona ICMAB-CSIC Campus UAB Bellaterra08193 Spain RIKEN Center for Computational Science Kobe650-0047 Japan Molecular Simulations from First Principles e.V. BerlinD-14195 Germany Intel Corporation 500 Beaver Brook Road BoxboroughMA01719 United States Molecular Sciences Software Institute BlacksburgVA24060 United States University of Warwick United Kingdom Department of Chemistry Duke University DurhamNC27708 United States

In this contribution, we give an overview of the ELPA library and ELSI interface, which are crucial elements for large-scale electronic structure calculations in FHI-aims. ELPA is a key solver library that provides efficient solutions for both standard and generalized eigenproblems, which are central to the Kohn-Sham formalism in density functional theory (DFT). It supports CPU and GPU architectures, with full support for NVIDIA and AMD GPUs, and ongoing development for Intel GPUs. Here we also report the results of recent optimizations, leading to significant improvements in GPU performance for the generalized eigenproblem. ELSI is an open-source software interface layer that creates a well-defined connection between "user" electronic structure codes and "solver" libraries for the Kohn-Sham problem, abstracting the step between Hamilton and overlap matrices (as input to ELSI and the respective solvers) and eigenvalues and eigenvectors or density matrix solutions (as output to be passed back to the "user" electronic structure code). In addition to ELPA, ELSI supports solvers including LAPACK and MAGMA, the PEXSI and NTPoly libraries (which bypass an explicit eigenvalue solution), and several others. ELSI, ELPA, and other solver libraries supported in ELSI are mature, well-tested software, ensuring efficient support for large-scale simulations on current HPC systems. Future plans for ELPA include further optimization of GPU routines, particularly integrating the GPU collective communication libraries (NVIDIA’s NCCL and AMD’s RCCL) into the solution tridiagonal of the matrix problem and backtransformation of the eigenvectors in standard eigenproblem solvers. Similarly expanding support for Intel GPUs through deeper integration with Intel’s oneAPI libraries, specifically oneCCL, is planned. These advancements aim to ensure that ELPA, accessible either with its own Fortran/C/C++ APIs or via ELSI, remains at the forefront of large-scale computations, offering researchers

关键词： Open source software

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CAN-D: A modular four-step pipeline for comprehensively decoding controller area network data

arXiv

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arXiv 2020年

作者： Verma, Miki E. Bridges, Robert A. Sosnowski, Jordan J. Hollifield, Samuel C. Iannacone, Michael D. Cyber & Applied Data Analytics Division Oak Ridge National Laboratory Oak RidgeTN United States Department of Computer Science & Software Engineering Auburn University

Controller area networks (CANs) are a broadcast protocol for real-time communication of critical vehicle subsystems. Original equipment manufacturers of passenger vehicles hold secret their mappings of CAN data to vehicle signals, and these definitions vary according to make, model, and year. Without these mappings, the wealth of real-time vehicle information hidden in the CAN packets is uninterpretable, severely impeding vehicle-related research, including CAN cybersecurity and privacy studies, aftermarket tuning, efficiency and performance monitoring, and fault diagnosis to name a few. Guided by the four-part CAN signal definition, we present CAN-D (CAN-Decoder), a modular, four-step pipeline for identifying each signal's boundaries (start bit and length), endianness (byte ordering), signedness (bit-to-integer encoding), and by leveraging diagnostic standards, augmenting a subset of the extracted signals with meaningful, physical interpretation. En route to CAN-D, we provide a comprehensive review of the CAN signal reverse engineering research. All previous methods ignore endianness and signedness, rendering them incapable of decoding many standard CAN signal definitions. Incorporating endianness grows the search space from 128 to 4.72E21 signal tokenizations and introduces a web of changing dependencies. In response, we formulate, formally analyze, and provide an efficient solution to an optimization problem, allowing identification of the optimal set of signal boundaries and byte orderings. In addition, we provide two novel, state-of-the-art signal boundary classifiers-both of which are superior to previous approaches in precision and recall in three different test scenarios-and the first signedness classification algorithm, which exhibits a > 97% F-score. Overall, CAN-D is the only solution with the potential to extract any CAN signal that is also the state of the art. In evaluation on 10 vehicles of different makes, CAN-D's average l1 error is five times bette

关键词： Control system synthesis

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Real-Time Agriculture Yield Monitor System (AYMS) Using Deep Feedforward (DFF) Neural Network

SSRN

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SSRN 2020年

作者： C S, Manoj Athreya H R, Mohith Gowda K A, Amogh Babu Ravishankar, R. Software Engineer Bangalore India Bachelors of Engineering in Computer Science PES College of Engineering Mandya India Implementation Engineer Data Science Enthusiast Bangalore India Department of IS&E Adichunchanagiri Institute of Technology Chikmagalur India

Agriculture stands as the backbone of our Nation, by Contributing 7% of the total Indian Economy. The drivers of agriculture are facing a huge problem in predicting the yield in different varieties of soil. Currently, the use of sophisticated technologies in the field of agriculture is underdeveloped when compared to other sectors over the past few decades. Self-mortality rates of farmers considerably increasing from the past four-five years. This is mainly due to the debt overhead usually caused by low yield. Crops yield decline considerably due to unpredictable weather, environmental changes, and diseases. This can say that agricultural landowners fear to use new technologies and tend to follow the age-old tradition of farming. The sphere of computing with its rigorous learning capabilities is inevitable to find a novel solution for agriculture-related issues. In this paper, this issue is addressed and have come up with an improvised idea to help farmers get a better yield for their crops. Deep learning has been used to predict the yield. This technology is made handy for every farmer to learn and use it effectively via a simple IoT device installed in their fields and a smartphone application. This improvised system has been named as Agriculture Yield Monitor System (AYMS). This has proved to be more efficient and beneficial to farmers. © 2020, The Authors. All rights reserved.

关键词： Internet of things

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