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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Zhibo Wang Jingxin Li Jiahui Hu Ju Ren Zhetao Li Yanjun Li State Key Laboratory for Novel Software Technology Nanjing University P. R. China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China School of Information Science and Engineering Central South University P. R. China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education College of Information Engineering Xiangtan University P. R. China School of Computer Science and Technology Zhejiang University of Technology P. R. China

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.

关键词： Task analysis Privacy Differential privacy Sensors Mobile handsets Cryptography Integer linear programming

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Dynamic layered-graph model for routing in cognitive radio network

Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of ...

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Jiefangjun Ligong Daxue Xuebao/Journal of PLA University of Science and technology (Natural Science Edition) 2008年第6期9卷 586-590页

作者： Li, Yang Dong, Yu-Ning Zhao, Hai-Tao College of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing 210003 China Jiangsu Provincial Key Lab. of Computer Information Processing Technology Soochow University Suzhou 215006 China

To analyze routing protocol in cognitive radio(CR) networks effectively, a dynamic routing model is necessary. Dynamic layered graphic(DG) was proposed aiming at the characteristic of dynamic spectrum access in CR networks. In DG model, nodes were split into many sub-nodes, and each sub-node state could denote whether the related channel was available or *** DG model, sub-node state was taken as a stochastic process and the model of cognitive radio network's dynamic topology was set up by analyzing the dynamic characteristic of sub-node state. Simulation results show that in the aspect of the adaptability to CR network's variable SOP and depiction of the topology, the DG model outperforms the layered graph(LG) model, and can adapt well to CR networks environment.

关键词： Cognitive radio

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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Multi-Task Self-Supervised Learning for Medical Image Segmentation

Multi-Task Self-Supervised Learning for Medical Image Segmen...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Bo Wang Hang Zhao Xiongfei Li Mingjie Tian Bo Huang Feiyang Yang Intelligent Information Processing Laboratory Yanbian University Yanji China College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China School of Artificial Intelligence Jilin University Changchun China

Although medical image segmentation has achieved remarkable results with supervised learning, obtaining labeled data remains challenging and costly. To counteract this, we present the MTSPSeg, a multi-task self-supervised learning framework. We establish the dynamic gradient learning rate (DGLR) strategy and the dynamic multi-task loss weight adjustment method (DWL) to relieve potential early convergence and diverse learning difficulties of self-supervised learning. Our method deeply integrates the prior knowledge inherent in medical images, facilitates collaborative learning among tasks, and delves further into the latent information within the images. Experimental results demonstrate that regardless of the proportion of labeled data, our method outperforms baseline models on the KiTS19 and LiTS datasets, achieving state-of-the-art performance on the KiTS19 dataset.

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Video-Based 3D Human Pose Estimation Research

Video-Based 3D Human Pose Estimation Research

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Siting Tao Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (纸本)9781665409858

At present, most of the video-based 3D human pose estimation methods process the video frame by frame, ignoring the timing information of the upper and lower frames of the video. When there is a single-frame estimation error caused by self-occlusion, the occlusion part will be Some strange movements appear due to information loss, and the estimated results make it difficult to produce accurate motion sequences. Therefore, considering the continuity of the upper and lower frames of the video, this paper adopts the HMR algorithm and introduces a bidirectional long-term memory network (BI-LSTM) in the encoding part to process the video sequence, and then outputs the parameters of the human body model through the SMPL parameters and inputs them into the discriminator for confrontation, train. In order for the discriminator to also take into account the semantic information of the temporal context, the discriminator in this paper also introduces a BI-LSTM network to improve the authenticity of the generated poses. Experimental results show that the accuracy of the model is improved when self-occlusion occurs.

关键词： Training Industrial electronics Solid modeling Estimation error Three-dimensional displays Biological system modeling Pose estimation

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

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IEEE International Conference on Unmanned Systems (ICUS)

作者： Hu Zhang Shuai Wang Tonglin Liu Aimin Zhou Yi Zhang Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China School of Mechanical Engineering Changzhou University Changzhou China Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP.

关键词： Missiles Optimization Path planning Turning Planning Target tracking Sociology

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Awareness and Settlement of IT Field key Challenges for Next Generation Development in World 2019

Awareness and Settlement of IT Field Key Challenges for Next...

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Proceedings of the 2019 7th International Conference on information technology: IoT and Smart City

作者： Muhammad Bilal Zhi Yu Abid Bashir Muhammad Ali Hussain Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Information Technology Services Manager MIS / Deputy Director of IT Department of Computer Science National Textile University Faisalabad Pakistan Management Science and Engineering School of Management and Economics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781450376631

information technology (IT) is playing a vital role in all aspects of our lives nowadays. IT is usually used for fast communication links and transferring useful information from one place to another. Nevertheless, around the world, unfortunately, IT is not properly handled. Without IT and information and Communication technology (ICT) world will become a dark place. Most of the research in the IT field is inadequate. A subjective IT field development framework designed with the assistance of better Internet features utilized properly in all developing countries around the world. The proposed IT development framework can be used innovatively by adopting a wireless-based network in developing country growth around the world. We analyzed wired and wireless systems based on six common key Internet feature parameters for IT field improvement in developing countries around the world. In this empirical research, a total of 72 IT professional specialists included based on the statistical technique judgment sampling. The questionnaire survey results signify that speed, security, and reliability Internet features in the wired network are better than the wireless network. Moreover, mobility, coverage area, and cost relevant Internet features in the wireless network are much better than a wired network. According to expert decision-makers' strategy, the wireless-based network transmission medium is better to develop improvement in the world. Therefore, it finally concluded that developing countries could also play a significant role in the IT field as well as other fields around the world if Internet features are provided in the form of a wireless-based network properly. In the future, security and speed relevant problems will be resolved in the wireless-based system through improving routing algorithms.

关键词： information technology Internet Decision-makers Developing countries Transmission medium

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An experimental-based review of image enhancement and image restoration methods for underwater imaging

arXiv

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arXiv 2019年

作者： Wang, Yan Song, Wei Fortino, Giancarlo Qi, Lizhe Zhang, Wenqiang Liotta, Antonio Academy for Engineering & Technology Fudan University China College of Information Technology Shanghai Ocean University Shanghai201306 China University of Calabria Italy Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China Napier Edinburgh University School of Computing Edinburgh United Kingdom

Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium. Although major breakthroughs have been made recently in the general area of image enhancement and restoration, the applicability of new methods for improving the quality of underwater images has not specifically been captured. In this paper, we review the image enhancement and restoration methods that tackle typical underwater image impairments, including some extreme degradations and distortions. Firstly, we introduce the key causes of quality reduction in underwater images, in terms of the underwater image formation model (IFM). Then, we review underwater restoration methods, considering both the IFM-free and the IFM-based approaches. Next, we present an experimental-based comparative evaluation of state-of-the-art IFM-free and IFM-based methods, considering also the prior-based parameter estimation algorithms of the IFM-based methods, using both subjective and objective analysis (the used code is freely available at https://***/wangyanckxx/Single-UnderwaterImage-Enhancement-and-Color-Restoration). Starting from this study, we pinpoint the key shortcomings of existing methods, drawing recommendations for future research in this area. Our review of underwater image enhancement and restoration provides researchers with the necessary background to appreciate challenges and opportunities in this important field. Copyright © 2019, The Authors. All rights reserved.

关键词： Image reconstruction

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Find the Bad Apples: An efficient method for perfect key recovery under imperfect SCA oracles – A case study of Kyber

IACR Transactions on Cryptographic Hardware and Embedded Sys...

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IACR Transactions on Cryptographic Hardware and Embedded Systems 2022年第1期2023卷 89-112页

作者： Shen, Muyan Cheng, Chi Zhang, Xiaohan Guo, Qian Jiang, Tao Hubei Key Laboratory of Intelligent Geo-Information Processing School of Computer Science China University of Geosciences Wuhan China State Key Laboratory of Integrated Services Networks Xidian University Xian China Lund University Lund Sweden Research Center of 6G Mobile Communications School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China

Side-channel resilience is a crucial feature when assessing whether a post-quantum cryptographic proposal is sufficiently mature to be deployed. In this paper, we propose a generic and efficient adaptive approach to improve the sample complexity (i.e., the required number of traces) of plaintext-checking (PC) oracle-based side-channel attacks (SCAs), a major class of key recovery chosen-ciphertext SCAs on lattice-based key encapsulation mechanisms (KEMs). This new approach is preferable when the constructed PC oracle is imperfect, which is common in practice, and its basic idea is to design new detection codes that can determine erroneous positions in the initially recovered secret key. These secret entries are further corrected with a small number of additional traces. This work benefits from the generality of PC oracle and thus is applicable to various schemes and implementations. Our main target is Kyber since it has been selected by NIST as the KEM algorithm for standardization. We instantiated the proposed generic attack on Kyber512 and then conducted extensive computer simulations against Kyber512 and FireSaber. We further mounted an electromagnetic (EM) attack against an optimized implementation of Kyber512 in the pqm4 library running on an STM32F407G board with an ARM Cortex-M4 microcontroller. These simulations and real-world experiments demonstrate that the newly proposed attack could greatly improve the state-of-the-art in terms of the required number of traces. For instance, the new attack requires only 41% of the EM traces needed in a majority-voting attack in our experiments, where the raw oracle accuracy is fixed. © 2022, Ruhr-University of Bochum. All rights reserved.

关键词： Standardization

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FELSDiff: Image Super-Resolution Based on Diffusion Model Fusing Feature Extraction with Latent Space

FELSDiff: Image Super-Resolution Based on Diffusion Model Fu...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Cancan Wu Zhi Zhang College of Computer Science and Technology Wuhan University of Science and Technology Wuhan Hubei China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan Hubei China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

In addressing issues like an excessive number of network parameters and the loss of fine texture details in image super-resolution reconstruction approaches based on diffusion models, this passage introduces a novel image super-resolution model (FELSDiff). This model relies on the fusion of diffusion models, feature extraction, and latent space. FELSDiff utilizes a Pre-trained CNN for initial super-resolution to extract the primary low-band features and part high-band features of the image. The preliminary super-resolved image is then concatenated as a condition to this model. We introduce a cross-attention mechanism guided by the high-band features of the preliminary super-resolved image to enhance the network's feature extraction capability and obtain more effective features. In the U-Net structure, we incorporate a latent space strategy, enabling the model's training and inference processes to be conducted in the latent space, thereby reducing the number of network parameters. Experimental results on multiple standard datasets indicate that this method outperforms other advanced methods overall.

关键词： Training Runtime Computational modeling Superresolution Memory management Feature extraction Diffusion models Convolutional neural networks Image reconstruction Standards

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