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arXiv 2024年

作者： Zhao, Bowen Li, Jiuhui Xu, Peiming Li, Xiaoguo Pei, Qingqi Shen, Yulong Guangzhou Institute of Technology Xidian University Guangzhou510555 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Electric Power Research Institute CSG Guangdong Guangzhou510663 China Guangdong Provincial Key Laboratory of Power System Network Security Guangdong Guangzhou510663 China The College of Computer Science Chongqing University Chongqing400044 China The State Key Laboratory of Integrated Service Networks Xidian University Xi’an710126 China

Secure outsourced computation (SOC) provides secure computing services by taking advantage of the computation power of cloud computing and the technology of privacy computing (e.g., homomorphic encryption). Expanding computational operations on encrypted data (e.g., enabling complex calculations directly over ciphertexts) and broadening the applicability of SOC across diverse use cases remain critical yet challenging research topics in the field. Nevertheless, previous SOC solutions frequently lack the computational efficiency and adaptability required to fully meet evolving demands. To this end, in this paper, we propose a toolkit for TEE-assisted (Trusted Execution Environment) SOC over integers, named TRUST. In terms of system architecture, TRUST falls in a single TEE-equipped cloud server only through seamlessly integrating the computation of REE (Rich Execution Environment) and TEE. In consideration of TEE being difficult to permanently store data and being vulnerable to attacks, we introduce a (2, 2)-threshold homomorphic cryptosystem to fit the hybrid computation between REE and TEE. Additionally, we carefully design a suite of SOC protocols supporting unary, binary and ternary operations. To achieve applications, we present SEAT, secure data trading based on TRUST. Security analysis demonstrates that TRUST enables SOC, avoids collusion attacks among multiple cloud servers, and mitigates potential secret leakage risks within TEE (e.g., from side-channel attacks). Experimental evaluations indicate that TRUST outperforms the state-of-the-art and requires no alignment of data as well as any network communications. Furthermore, SEAT is as effective as the Baseline without any data protection. Copyright © 2024, The Authors. All rights reserved.

关键词： Side channel attack

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TEEM: Two-Factor Energy Evaluation Metric Toward Green big data system

TEEM: Two-Factor Energy Evaluation Metric Toward Green Big D...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Weidong Fang Chunsheng Zhu Mohsen Guizani Zhiqi Li Wuxiong Zhang Joel J.P.C. Rodrigues Science and Technology on Micro-system Laboratory Shanghai Institute of Micro-system and Information Technology Chinese Academy of Sciences Shanghai China University of Chinese Academy of Sciences Beijing China Shanghai Research and Development Center for Micro-Nano Electronics Shanghai China College of Big Data and Internet Shenzhen Technology University Shenzhen China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) UAE COPELABS Lusófona University Lisbon Portugal

Toward green big data system (BDS), one of the key requirements is to save energy consumption so that the system lifetime can be prolonged. Hence, the energy evaluation metric for the measurement of energy efficiency in green BDS, is very critical. Unfortunately, most current energy evaluation metrics are based on a single factor, which might be difficult to meet the diverse application requirements. In this paper, we propose a novel two-factor energy evaluation metric (TEEM) for green BDS. Specifically, the transmission distance and the modulation level are taken into account simultaneously, generating a metric named the bit-per energy consumption (BEC). Extensive simulation results demonstrate that the system performance in energy consumption can be more effectively evaluated by BEC.

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Hiding Scrambling Text Message in Speech Signal Based on Lightwight Hyperchaotic Map and Conditional Lsb Mechanisms

SSRN

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SSRN 2023年

作者： Al Sibahee, Mustafa A. Abduljaleel, Iman Qays Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Ma, Junchao National Engineering Laboratory for Big Data System Computing Technology Shenzhen Technology University 518060 China Department of Computer Science College of Computer Science and Information Technology University of Basrah 61004 Iraq College of Computer Science and Software Engineering Shenzhen University 518060 China Department of Computer Science College of Education for Pure Sciences University of Basrah Basrah61004 Iraq Technical Computer Engineering Department Al-kunooze University College Basrah61001 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518118 China College of Big Data and Internet Shenzhen Technology University Shenzhen518118 China

This study proposed a lightweight and secure audio steganography system for hiding text messages during transmission over the Internet to address the computational cost exaggeration, and Insufficient levels of security in earlier studies. The paper proposes a two-phase functioning mechanism. Text characters are transformed into ASCII code and then stored in a vector, which is then divided into three sub-vectors. The sub-vectors are scrambled using a two low complexity operation, namely a forwardbackward reading technique and an odd-even index. Two scrambling circuits can be performed: the first on tiny sub-vectors and another on the vector as a whole. In the hiding phase, the speech signal samples were divided into 256 blocks using only 200 values per block, and low complexity Quadratic and the Hénon maps to hide the speech signal in a random manner. This method utilizes the conditional LSB as low complexity algorithm to identify hidden bits and a special hyperchaotic map algorithm for randomly choosing locations. The proposed approach provides good security for a scrambled text message with high SNR and PSNR, small MSE error and PESQ, SSIM near one, BER close to zero, and MOS near five as well as the lowest computational hiding cost. © 2023, The Authors. All rights reserved.

关键词： Steganography

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Multi-Object Tracking Based on Anchor-Free Detection and Message Passing Association

Multi-Object Tracking Based on Anchor-Free Detection and Mes...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhou Fang 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 Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (纸本)9781665409858

Due to the current multi-target tracking work, in the scene of dense and staggered targets, the tracked targets are prone to ID switch, which greatly affects the tracking effect. Aiming at this problem, this paper proposed a new multi-target tracking algorithm: using an improved anchor-free detection method and extracting features from multiple aspects, combined with the message passing graph neural network to participate in the data association process to further improve the exchange of feature information, so as to improve the algorithm's ability to associate target information data between frames and improve the tracking accuracy. After conducting quantitative analysis and comparison experiments on datasets such as MOT17 and MOT20, the experimental results show that the algorithm in this paper can effectively improve the problem of target ID switching and provide better tracking accuracy and performance.

关键词： Industrial electronics Target tracking Statistical analysis Message passing Switches Feature extraction Real-time systems

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TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded Network

TomoSAR-ALISTA: Efficient TomoSAR Imaging via Deep Unfolded ...

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2022 International Conference on Radar systems, RADAR 2022

作者： Wang, Muhan Zhang, Zhe Wang, Yue Gao, Silin Qiu, Xiaolan Key Laboratory of Technology in Geo-spatial Information Processing and Application System Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Aerospace Big Data Application Technology Suzhou215123 China Suzhou Aerospace Information Research Institute Suzhou215123 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China Electrical and Computer Engineering Department George Mason University FairfaxVA22030 United States

ISBN: (纸本)9781839537776

Synthetic aperture radar (SAR) tomography (TomoSAR) has attracted remarkable interest for its ability in achieving three-dimensional reconstruction along the elevation direction from multiple observations. In recent years, compressed sensing (CS) technique has been introduced into TomoSAR considering for its super-resolution ability with limited samples. Whereas, the CS-based methods suffer from several drawbacks, including weak noise resistance, high computational complexity and complex parameter fine-tuning. Among the different CS algorithms, iterative soft-thresholding algorithm (ISTA) is widely used as a robust reconstruction approach, however, the parameters in the ISTA are manually chosen, which usually requires a time-consuming fine-tuning process to achieve the best performance. Aiming at efficient TomoSAR imaging, a novel sparse unfolding network named analytic learned ISTA (ALISTA) is proposed towards the TomoSAR imaging problem in this paper, and the key parameters of ISTA are learned from training data via deep learning to avoid complex parameter fine-tuning and significantly relieves the training burden. In addition, experiments verify that it is feasible to use traditional CS algorithms as training labels, which provides a tangible supervised training method to achieve better 3D reconstruction performance even in the absence of labeled data in real applications. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Compressed sensing

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Real-time detection of intelligent transportation system based on 3D point cloud

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Information Fusion 2025年 123卷

作者： Wang, Shitao Xiao, Jiongen Liu, Yi Zhang, Si Yan, Gengxiang School of Mathematics Sun Yat-sen University Guangzhou510275 China International Business School Guangdong University of Finance and Economics Guangzhou510320 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing101399 China

In the field of large-scale 3D point cloud processing, multi-object tracking (MOT) is a key technology in the field of intelligent transportation and autonomous driving, which aims to detect and track multiple targets accurately in real time. Existing multi-object tracking methods usually rely on a single detection or tracking module, which is difficult to provide efficient and stable tracking performance in complex dynamic environments. To address this problem, we propose a real-time detection framework for intelligent transportation systems based on 3D vision, which combines a dynamic object detection module (DODTM), a target tracking module (TTMDM), a temporal modeling module (Transformer), and a decision support module. Experimental results show that our method achieves 81.8% HOTA and 91.0% MOTA on the KITTI dataset, which is better than existing methods, especially in reducing identity switching (IDSW), which is only 7. Ablation experiments also verify the effectiveness of each module. Removing any module leads to a significant drop in performance, which proves the importance of each module in the system. © 2025

关键词： Object detection

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IMPERFECT KNOWLEDGE BASE SELF-ORGANIZATION in ROBOTIC INTELLIGENT COGNITIVE CONTROL: QUANTUM SUPREMACY on big data ANALYSIS 9

IMPERFECT KNOWLEDGE BASE SELF-ORGANIZATION in ROBOTIC INTELL...

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9th International Conference "Distributed Computing and Grid Technologies in Science and Education", GRID 2021

作者： Ulyanov, Sergey Shevchenko, Andrey Shevchenko, Alla Reshetnikov, Andrey Joint Institute for Nuclear Research Laboratory of Information Technology 6 Joliot-Curie St Dubna 141980 Russia Dubna State University Institute of System Analysis and Control 19 Universitetskaya St Dubna 141982 Russia Plekhanov Russian University of Economics Laboratory of Cloud Technologies and Big Data analytics Stremyanny Lane 36 Moscow117997 Russia

The smart control design with secured achievement of information-thermodynamic trade-off interrelations is main goal for quantum self-organization algorithm of imperfect KB. Quantum genetic algorithm applied on line for the quantum correlation's type searching between unknown solutions in quantum superposition of imperfect knowledge bases of intelligent controllers designed on soft computing. Disturbance conditions of analytical information-thermodynamic trade-off interrelations between main control quality measures (as new design laws) discussed. Sophisticated synergetic quantum information effect in autonomous robot in unpredicted control situations) and swarm robots with imperfect KB exchanging between "master - slaves" introduced.A new robust smart controller on line designed from responses on unpredicted control situations of any imperfect KB applying quantum hidden information extracted from quantum correlation discussed. Within the toolkit of classical intelligent control, the achievement of the similar synergetic information effect is impossible. © 2021 CEUR-WS. All rights reserved.

关键词： Economic and social effects

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Xiaoke Zhu Danyang Li Xiaopan Chen Fumin Qi Fan Zhang Xiao-Yuan Jing School of Computer and Information Engineering Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China Department of System Operation National Supercomuting Center in Shenzhen Shenzhen China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Facial image based kinship verification aims to decide whether there exists kinship between the given facial images. In practice, the cross-generation differences will cause adverse effects on kinship verification, which limits the performance. Therefore, how to mine the implied similarity from facial images with large cross-generation divergence is an important problem in kinship verification, which has not yet been well studied. In view of this, we propose a Similarity Mining via Implicit matching pattern LEarning (SMILE) approach for kinship verification. Specifically, SMILE mainly consists of two modules, including a Semi-coupled Multi-pattern Similarity Learning (SMSL) module and a Cross-Generation Feature Normalization (CGFN) module. The SMSL module is designed to learn multiple semi-coupled matching patterns for mining the implicit facial similarity information from different perspectives. The CGFN module aims to reduce the divergence between facial images of parent and child. Extensive experiments demonstrate that the proposed approach outperforms the existing state-of-the-art methods.

关键词： Pattern matching Similarity learning

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CUE: Compound Uniform Encoding for Writer Retrieval

CUE: Compound Uniform Encoding for Writer Retrieval

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jiakai Luo Hongwei Lu Xin Nie Shenghao Liu Xianjun Deng Chenlu Zhu Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Hubei Chutian Expressway Digital Technology Co. Ltd. China

Writer retrieval is crucial in document forensics and historical document analysis. However, due to the difference in syntactic structure between Chinese and other languages, the existing methods may not be directly applied to Chinese writer retrieval. Previous work on Chinese writer retrieval does not overcome the performance degradation problem when the number of samples grows. In this paper, we propose a novel compound uniform encoding algorithm (CUE) for Chinese writer retrieval, which mainly consists of a combined feature extraction module (CFE) and a prototype substitution module (PS). The CFE module combines two complementary features from image filter response and character contour. It counts local symmetries and edge co-occurrence pairs. PS module substitutes the outliers with the class prototypes to alleviate the influence of the outliers. Finally, the weighted Chi-square distance is applied to measure the similarity between writer and text. To verify the superiority of our proposed method, experiments are conducted on four public datasets and our built dataset. The results validate that CUE outperforms the state-of-the-art algorithms on mAP metric.

关键词： Weight measurement Measurement Text analysis Image edge detection Prototypes Syntactics Feature extraction

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Light from space illuminating the polar silk road

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International Journal of Digital Earth 2022年第1期15卷 2028-2046页

作者： Xiao-Ming Li Yujia Qiu Yacheng Wang Bingqing Huang Haiming Lu Min Chu Han Fu Fengming Hui International Research Center of Big Data for Sustainable Development Goals BeijingPeople’s Republic of China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China University of Chinese Academy of Sciences BeijingPeople’s Republic of China Space Star Technology CO. LTDBeijingPeople’s Republic of China COSCO Shipping Specialized Carriers CO. LTDGuangzhouPeople’s Republic of China CMA Earth System Modeling and Prediction Centre (CEMC) BeijingPeople’s Republic of China State Key Laboratory of Severe Weather (LaSW) BeijingPeople’s Republic of China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information EngineeringBeijingPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen Universityand Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)ZhuhaiPeople’s Republic of China

The rapid decline of Arctic sea ice has been reminding us the significant impacts caused by global ***,the other side of the coin is that this opens a window to utilize the Arctic sea routes in the summer seasons,bringing remarkable economic benefits for ocean transportation between Asia and ***,commercial vessels with low ice classes must tackle substantial environmental challenges in the Arctic sea routes,particularly those caused by variable sea ice,even in the melting ***,the science-based support for shipping safety in the Arctic sea routes is being given more *** satellite remote sensing technology plays a critical role in environmental monitoring in the *** paper reviews state-of-the-art satellite observations on monitoring sea ice and potential applications on supporting shipping activities in the Arctic ***,we introduced a recently developed system based on satellite observations to support the safe transportation of Chinese cargo vessels in the Arctic northern sea route,demonstrating the efforts by both the science and business communities to promote the development of the polar silk road.

关键词： Arctic northern sea route earth observation digital earth sustainable development

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