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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Dongcai Du Wei Zhao Linna Wei Siyang Lu Xuangou Wu School of Computer Science and Technology Anhui University of Technology Ma’anshan China Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet Ma’anshan China School of Software Beijing Jiaotong University Beijing China

Artificial intelligence combined with the Internet of Vehicles (IoV) can improve the performance of automatic driving and service quality of vehicles. However, data privacy protection of IoV has become a challenging problem. In order to effectively protect user data security and reduce resource consumption, we propose a lightweight homomorphic encryption federated learning framework based on blockchain. Firstly, we combine federated learning with blockchain to train a global model collaboratively without sharing their raw data. Meanwhile, the aggregation of shared models is conducted in the on-chain nodes of the blockchain instead of a single server with traditional federated learning. Secondly, considering the further security of the sharing model on the chain, we design a lightweight homomorphic encryption approach because of the high computational cost with the existing homomorphic encryption federated learning. Finally, we conducted comparison experiments with existing homomorphic encryption federated learning schemes, and the experimental results show that our scheme can effectively protect data privacy and reduce computational costs and storage space.

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A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19

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computers, Materials & Continua 2020年第9期64卷 1435-1452页

作者： Xiaorui Zhang Wenfang Zhang Wei Sun Tong Xu Sunil Kumar Jha Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow100031Poland

In the current dire situation of the corona virus COVID-19,remote consultations were proposed to avoid cross-infection and regional differences in medical ***,the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical *** ensure the integrity and security of medical images,this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest(ROI)and integer wavelet transform(IWT).First,the medical image is divided into two different parts,regions of interest and non-interest *** the integrity of ROI is verified using the hash algorithm,and the recovery data of the ROI region is calculated at the same ***,binary images with the basic information of patients are processed by logistic chaotic map encryption,and then the synthetic watermark is embedded in the medical carrier image using IWT *** performance of the proposed algorithm is tested by the simulation experiments based on the MATLAB program in CT images of the *** results show that the algorithm can precisely locate the distorted areas of an image and recover the original ROI on the basis of verifying image *** maximum peak signal to noise ratio(PSNR)value of 51.24 has been achieved,which proves that the watermark is invisible and has strong robustness against noise,compression,and filtering attacks.

关键词： Digital watermarking logistic hash algorithm ROI IWT

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Idea23D: Collaborative LMM Agents Enable 3D Model Generation from Interleaved Multimodal Inputs

arXiv

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arXiv 2024年

作者： Chen, Junhao Li, Xiang Ye, Xiaojun Li, Chao Fan, Zhaoxin Zhao, Hao Tsinghua University China Tsinghua Shenzhen International Graduate School Tsinghua University China School of Software and Microelectronics Peking University China College of Computer Science Zhejiang University China College of Computer Science and Technology Harbin Engineering University China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing School of Artificial Intelligence Beihang University China

With the success of 2D diffusion models, 2D AIGC content has already transformed our lives. Recently, this success has been extended to 3D AIGC, with state-of-the-art methods generating textured 3D models from single images or text. However, we argue that current 3D AIGC methods still don’t fully unleash human creativity. We often imagine 3D content made from multimodal inputs, such as what it would look like if my pet bunny were eating a doughnut on the table. In this paper, we explore a novel 3D AIGC approach: generating 3D content from IDEAs. An IDEA is a multimodal input composed of text, image, and 3D models. To our knowledge, this challenging and exciting 3D AIGC setting has not been studied before. We propose the new framework Idea23D, which combines three agents based on large multimodal models (LMMs) and existing algorithmic tools. These three LMM-based agents are tasked with prompt generation, model selection, and feedback reflection. They collaborate and critique each other in a fully automated loop, without human intervention. The framework then generates a text prompt to create 3D models that align closely with the input IDEAs. We demonstrate impressive 3D AIGC results that surpass previous methods. To comprehensively assess the 3D AIGC capabilities of Idea23D, we introduce the Eval3DAIGC-198 dataset, containing 198 multimodal inputs for 3D generation tasks. This dataset evaluates the alignment between generated 3D content and input IDEAs. Our user study and quantitative results show that Idea23D significantly improves the success rate and accuracy of 3D generation, with excellent compatibility across various LMM, Text-to-Image, and Image-to-3D models. Code and dataset are available at https://***/. © 2024, CC BY-NC-SA.

关键词： 3D modeling

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CM-MLP: Cascade Multi-scale MLP with Axial Context Relation Encoder for Edge Segmentation of Medical Image

CM-MLP: Cascade Multi-scale MLP with Axial Context Relation ...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Lv, Jinkai Hu, Yuyong Fu, Quanshui Zhang, Zhiwang Hu, Yuqiang Lv, Lin Yang, Guoqing Li, Jinpeng Zhao, Yi Henan Institute of Advanced Technology Zhengzhou University Zhengzhou China The Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences Beijing China Suining Central Hospital Suining China Hwa Mei Hospital University of Chinese Academy of Sciences Ningbo China School of Electrical and Information Engineering The University of Sydney Sydney Australia School of Mathematics and Computer Sciences NanChang University Nanchang China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665468190

The convolutional-based methods provide good segmentation performance in the medical image segmentation task. However, those methods have the following challenges when dealing with the edges of the medical images: (1) Previous convolutional-based methods do not focus on the boundary relationship between foreground and background around the segmentation edge, which leads to the degradation of segmentation performance when the edge changes complexly. (2) The inductive bias of the convolutional layer cannot be adapted to complex edge changes and the aggregation of multiple-segmented areas, resulting in its performance improvement mostly limited to segmenting the body of segmented areas instead of the edge. To address these challenges, we propose the CM-MLP framework on MFI (Multiscale Feature Interaction) block and ACRE (axial context relation encoder) block for accurate segmentation of the edge of medical image. In the MFI block, we propose the cascade multi-scale MLP (Cascade MLP) to process all local information from the deeper layers of the network simultaneously and utilize a cascade multiscale mechanism to fuse discrete local information gradually. Then, the ACRE block is used to make the deep supervision focus on exploring the boundary relationship between foreground and background to modify the edge of the medical image. The segmentation accuracy (Dice) of our proposed CM-MLP framework reaches 96.96%, 96.76%, and 82.54% on three benchmark datasets: CVC-ClinicDB dataset, sub-Kvasir dataset, and our inhouse dataset, respectively, which significantly outperform the state-of-the-art method. The source code and trained models will be available at https://***/ProgrammerHyy/CM-MLP. © 2022 IEEE.

关键词： Semantic Segmentation

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Photonic Frequency Space Simulation of Tight-Binding Lattices Using Integrated LNOI Ring Resonators

Photonic Frequency Space Simulation of Tight-Binding Lattice...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Armandas Balčytis Hiep Dinh Tomoki Ozawa Yasutomo Ota Toshihiko Baba Satoshi Iwamoto Arnan Mitchell Thach G. Nguyen Integrated Photonics and Applications Centre RMIT University Melbourne VIC Australia Advanced Institute for Materials Research Tohoku University Sendai Japan Department of Applied Physics and Physico-Informatics Keio University Yokohama Japan Department of Electrical and Computer Engineering Yokohama National University Hodogaya-ku Yokohama Japan Research Center for Advanced Science and Technology The University of Tokyo Meguro-ku Tokyo Japan

We use electro-optically modulated ring cavities, integrated on thin-film lithium niobate, to model frequency dimension tight-binding lattices with versatile connectivity. Inter-mode coupling range, strength, and hopp... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Frequency modulation Lithium niobate Resonant frequency Lattices Electro-optic modulators Ring lasers Laser modes

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Quantum reversible circuits for multiplicative inverse

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EPJ Quantum technology 2022年第1期9卷 1-17页

作者： Luo, Qing-bin Yang, Guo-wu Li, Xiao-yu Li, Qiang Department of Computer Science and Technology School of Information Engineering Hubei Minzu University Enshi China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China Big data research Center & School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China

The synthesis of quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are discussed in this paper. We first convert the multiplicative inverse operation in $\operatorname{GF}(2^{8})$ to arithmetic operations in the composite field $\operatorname{GF}((2^{4})^{2})$ , and then discuss the expressions of the square calculation, the inversion calculation and the multiplication calculation separately in the finite field $\operatorname{GF}(2^{4})$ , where the expressions of multiplication calculation in $\operatorname{GF}(2^{4})$ are given directly in $\operatorname{GF}(2^{4})$ and given through being transformed into the composite field $\operatorname{GF}((2^{2})^{2})$ . Then the quantum circuits of these calculations are realized one by one. Finally, two quantum circuits for multiplicative inverse over $\operatorname{GF}(2^{8})$ are synthesized. They both use 21 qubits, the first quantum circuit uses 55 Toffoli gates and 107 CNOT gates and the second one uses 37 Toffoli gates and 209 CNOT gates. As an example of the application of multiplication inverse, we apply these quantum circuits to the implementations of the S-box quantum circuit of the AES cryptographic algorithm. Two quantum circuits for implementing the S-box of the AES cryptographic algorithm are presented. The first quantum circuit uses 21 qubits, 55 Toffoli gates, 131 CNOT gates and 4 NOT gates and the second one uses 21 qubits, 37 Toffoli gates, 233 CNOT gates and 4 NOT gates. Through the evaluation of quantum cost, the two quantum circuits of the S-box of AES cryptographic algorithm use less quantum resources than the existing schemes.

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Manipulating Piezoelectric and Electro-Strain Properties of BiFeO3-BaTiO3-Based Ceramics Through Chemical Doping-Controlled Domain-Size engineering

SSRN

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SSRN 2025年

作者： Lin, Jiamin Liu, Bing Liu, Mengxiang Yang, Shan Zhou, Linming Hong, Zijian Zhu, Xiaoli Wu, Yongjun Li, Juan Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Institute of Fundamental and Transdisciplinary Research Zhejiang University Hangzhou310027 China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou311100 China College of Electronic Information and Engineering Hangzhou Dianzi University Zhejiang Hangzhou310018 China Zhejiang Key Laboratory of Advanced Solid State Energy Storage Technology and Applications Taizhou Institute of Zhejiang University Zhejiang Taizhou318000 China School of Engineering Hangzhou City University Zhejiang Hangzhou310015 China College of Materials and Science Engineering Zhejiang University of Technology Zhejiang Hangzhou310006 China

Ferroelectric domains are crucial for the performance of piezoelectric ceramics, as the size and switching dynamics affect polarization response directly, manipulating both ferroelectric and piezoelectric properties. In this study, we achieved BiFeO3-BaTiO3 ceramics with domain sizes from macroscopic to nanoscale by incorporating a small amount of antiferroelectric phase. Contrary to the long-standing belief that smaller ferroelectric domains own lower polarization switching barriers and yield higher piezoelectricity, we found that in chemical doping-controlled domain size engineering, the smaller the domain size, the more difficult it is for polarization switching. Ceramics with the largest domain exhibited superior positive piezoelectric performance due to the sufficient polarization switching and reduced domain wall pinning, achieving a piezoelectric coefficient (d33) of 460 pC/N at 350°C. Middle-sized domains achieved optimal electro-strain performance determined by the trade-off between the domain walls pinning and the increased local tetragonal or orthorhombic symmetries, with a strain (0.29%) and an excellent piezoelectric strain coefficient (d33*) of 726.4 pm/V at 160°C. This work provides novel insights into how domain-size engineering affects domain switching and local distortion, offering guidance for performance optimization of piezoelectric ceramics. © 2025, The Authors. All rights reserved.

关键词： Piezoelectricity

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Positional Encoding-Guided Transformer-Based Multiple Instance Learning for Histopathology Whole Slide Images Classification

SSRN

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SSRN 2024年

作者： Shi, Jun Sun, Dongdong Wu, Kun Jiang, Zhiguo Kong, Xue Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Anhui Province Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Province Hefei230601 China Beijing102206 China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering China

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slidelevel classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches which is likely to strengthen the discriminative ability of WSI-level features. In this paper, we propose a novel position encoding-guided transformerbased multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatialaware features. After incorporating the semantic features and spatial embeddings, multi-head selfattention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial-semantic consistency and generalization ability of features. The proposed method is evaluated on one public TCGA dataset and two in-house datasets. Experimental results validate it is effective in the tasks of lung cancer subtyping and gene mutation status prediction for lung cancer and gastrointestinal stromal tumor (GIST), and superior to the state-of-the-art MIL-based methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Modeling Inter-Intra Heterogeneity for Graph Federated Learning

arXiv

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arXiv 2024年

作者： Yu, Wentao Chen, Shuo Tong, Yongxin Gu, Tianlong Gong, Chen School of Computer Science and Engineering Nanjing University of Science and Technology China Center for Advanced Intelligence Project RIKEN Japan State Key Laboratory of Complex & Critical Software Environment Beihang University China Jinan University China Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

Heterogeneity is a fundamental and challenging issue in federated learning, especially for the graph data due to the complex relationships among the graph nodes. To deal with the heterogeneity, lots of existing methods perform the weighted federation based on their calculated similarities between pairwise clients (i.e., subgraphs). However, their inter-subgraph similarities estimated with the outputs of local models are less reliable, because the final outputs of local models may not comprehensively represent the real distribution of subgraph data. In addition, they ignore the critical intra-heterogeneity which usually exists within each subgraph itself. To address these issues, we propose a novel Federated learning method by integrally modeling the Inter-Intra Heterogeneity (FedIIH). For the inter-subgraph relationship, we propose a novel hierarchical variational model to infer the whole distribution of subgraph data in a multi-level form, so that we can accurately characterize the inter-subgraph similarities with the global perspective. For the intra-heterogeneity, we disentangle the subgraph into multiple latent factors and partition the model parameters into multiple parts, where each part corresponds to a single latent factor. Our FedIIH not only properly computes the distribution similarities between subgraphs, but also learns disentangled representations that are robust to irrelevant factors within subgraphs, so that it successfully considers the inter- and intra- heterogeneity simultaneously. Extensive experiments on six homophilic and five heterophilic graph datasets in both non-overlapping and overlapping settings demonstrate the effectiveness of our method when compared with nine state-of-the-art methods. Specifically, FedIIH averagely outperforms the second-best method by a large margin of 5.79% on all heterophilic datasets. Code is available at https://***/blgpb/FedIIH. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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Innovative Poverty Estimation through Machine Learning Approaches

Innovative Poverty Estimation through Machine Learning Appro...

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International Conference on Emerging Technologies, ICET

作者： Ahmad Mashhood Zain ul Abideen Usama Arshad Raja Hashim Ali Ammad Aslam Khan Bahram Khan Faculty of Computer Science & Engineering GIK Institute of Engg. Sciences & Technology Topi Pakistan BCMaterials Basque Center for Materials Applications & Nanostructure Leioa Basque Country Spain Dept. of Technology & Software Engineering University of Europe for Applied Sciences Berlin Germany Dept. of Biological Sciences M.A. Road Satellite Town Virtual University of Pakistan Rawalpindi Pakistan

This paper delves into the application and capabilities of machine learning methodologies in forecasting poverty scenarios, underlining the importance of varied data sources, along with the interpretability and explainability of models to refine the precision and transparency of poverty prediction mechanisms. It primarily utilizes an iteration of the LightGBM algorithm to infer poverty stages premised on household variables. This investigation showcases the potential of machine learning methodologies in poverty estimation, elucidating the relevance of additional, diverse data resources, and the critical role of model interpretability in enhancing model accuracy and clarity. The LightGBM version employed in our study demonstrated high efficiency on the validation set, affirming its effectiveness in projecting poverty conditions based on household features. In analyzing SHAP values, we unearthed crucial insights into the contributing elements to poverty. These findings can aid in shaping policies aimed at poverty alleviation and elevating social welfare. Our work provides the basis for future research into poverty prediction and affirms the potential of machine learning methodologies in tackling intricate social challenges.

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