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Provable Data Possession with Outsourced Tag Generation for AI-Driven E-Commerce

computers, Materials & Continua 2025年第5期83卷 2719-2734页

作者： Yi Li Wenying Zheng Yu-Sheng Su Meiqin Tang School of Computer Science Nanjing University of Information Science and TechnologyNanjing210044China School of Computer Science and Technology(School of Artificial Intelligence) Zhejiang Sci-Tech UniversityHangzhou310018China Department of Computer Science and Information Engineering National Chung Cheng UniversityChiayi621301TaiwanChina Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng UniversityChiayi621301TaiwanChina Department of Computer Science and Engineering National Taiwan Ocean UniversityKeelung202301TaiwanChina School of Integrated Circuits Wuxi Vocational College of Science and TechnologyWuxi214028China

AI applications have become ubiquitous,bringing significant convenience to various *** e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy ***,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI ***,it is essential to verify the integrity of e-commerce *** existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in *** address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded ***,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its ***,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.

关键词： Provable data possession data auditing cloud computing e-commerce bloom filter

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FedRFD: Heterogeneous Federated Learning via Refining Feature-level Distillation 24

FedRFD: Heterogeneous Federated Learning via Refining Featur...

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13th International Conference on Computing and Pattern Recognition, ICCPR 2024

作者： Yu, Peng Yuan, Yuwen Wang, Youquan Shi, Wei Shi, Zhihui Hua, Yue College of Information Engineering Nanjing University of Finance and Economics Jiangsu Nanjing China School of Computer Science and Engineering Taizhou Institute of Sci.&Tech. NJUST Jiangsu Taizhou China College of Liberal Arts and Sciences University of Illinois at Chicago ChicagoIL United States Department of Financial Technology Jiangsu Sushang Bank Co. Ltd. Jiangsu Nanjing China School of Marxism Jiangsu University Jinagsu Zhenjiang China

ISBN: (纸本)9798400717482

Federated learning faces a significant challenge in effectively aggregating knowledge from diverse local models with varying learning capabilities while preserving privacy through collaborative, local updates on private data. Previous methods for addressing such challenges involve combining distilled logits-based knowledge from various local models using weights derived from their respective learning capabilities as weighting coefficients. However, logits-based knowledge distillation suffers from information loss, expression diversity limitations, and model dependency, leading to a less affluent knowledge transfer. Therefore, we propose heterogeneous federated learning via refining feature-level distillation (FedRFD), which leverages richer knowledge of intermediate layer features for distillation. Specifically, (1) To facilitate communication between heterogeneous models, we align their feature-level with public data, eliminating the need for an additional global model. (2) To address feature alignment challenges in models with varying feature-level numbers, we propose the knowledge refining mechanism (KRM). (3) To increase the efficiency of collaborative learning, we propose a novel method to dynamically assign weights to each client. Extensive experiments confirm that our approach significantly outperforms state-of-the-art algorithms in heterogeneous model scenarios. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Efficient Batch Verification and Privacy-Preserving Data Aggregation Scheme in V2G Networks

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IEEE Transactions on Vehicular technology 2025年

作者： Wang, Congcong Wang, Chen Shen, Jian Vasilakos, Athanasios V. Wang, Bin Wang, Weizheng Department of Software Engineering Nanjing University of Information Science and Technology Nanjing210044 China the Zhejiang Key Laboratory of Digital Fashion and Data Governance the Zhejiang Provincial International Cooperation Base for Science and Technology on Cloud Computing Security and Data Aggregation Zhejiang Sci-Tech University Hangzhou310018 China Zhejiang Provincial Innovation Center of Advanced Textile Technology Shaoxing312000 China College of Computer Science and Information Technology IAU Saudi Arabia Grimstad4879 Norway Network and Data Security Hangzhou310053 China Computer Science Department City University of Hong Kong Hong Kong the Division of Computer Science University of Aizu Fukushima965–8580 Japan

In recent years, against the backdrop of smart grids, vehicle-to-grid (V2G) interaction has become a hot topic. With the increasing number of new energy vehicles, handling massive amounts of electricity transaction data has become an urgent problem in need of resolution. Ensuring the security of the vast quantities of electricity transaction data has emerged as a significant challenge. Therefore, two data aggregation schemes are proposed in this paper, both supporting batch verification and privacy protection in centralized and distributed scenarios, respectively. Firstly, an improved Paillier cryptosystem is utilized to achieve lightweight encryption in the proposed schemes. In addition, a novel distributed data aggregation structure that combines a large number of geographically distributed fog nodes with centralized cloud servers is introduced. This structure not only meets the requirements of lightweight encryption and resistance against single point failures but also reduces the communication complexity of distributed data aggregation to O(n). According to the security proofs and experimental analysis, it can be demonstrated that both proposed schemes are effective and secure, meeting the lightweight requirements of modern smart grid systems. © 2025 IEEE. All rights reserved,

关键词： Data aggregation

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Throughput Analysis of Full-Duplex Network Coding in Two-Way Relay Channel

Throughput Analysis of Full-Duplex Network Coding in Two-Way...

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2017 17th IEEE International Conference on Communication technology (ICCT 2017)

作者： Li weizheng Tu Xiumei Computer Sci.& Tech College Nanjing University of Technology ZTE Nanjing R&D Center

In two-way relay channel(TWRC), terminal nodes exchange information assisted by a relay node. To exchange 1 bit information over TWRC, the traditional save-andforward scheme takes 4 time slots. The traditional network coding scheme can reduce the number of time slots to 3 since its relay node can mix the information from tow terminal nodes and then broadcast them in 1 time slot. Further, the physical network coding(PNC) and analog network coding(ANC) schemes, which allow terminal nodes send the signal simultaneously, only need 2 time slots to complete the information exchange. In this paper, by combining physical network coding and full-duplex(FD) techniques, the proposed scheme full-duplex physical network coding(FD-PNC) and full-duplex analog network coding(FD-ANC) can nearly reduce the required time slots to 1. Therein, full-duplex technology can make the nodes send and receive the signal at the same time and in the same frequency band by eliminating the self interference signal. Considering the channel error, we analyze their achievable throughput under the symmetric channel. We believe th at t aking full-duple x technology into network coding research may open many possibilities for future work.

关键词： full-duplex physical network coding analog network coding symmetric channel

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Text emotion distribution learning via multi-task convolutional neural network 27

Text emotion distribution learning via multi-task convolutio...

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27th International Joint Conference on Artificial Intelligence, IJCAI 2018

作者： Zhang, Yuxiang Fu, Jiamei Zhang, Ying Yang, Jufeng College of Computer Science and Technology Civil Aviation University of China Tianjin China College of Computer and Control Engineering Nankai University Tianjin China College of Comp. Sci. and Tech. Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9780999241127

Emotion analysis of on-line user generated textual content is important for natural language processing and social media analytics tasks. Most of previous emotion analysis approaches focus on identifying users' emotional states from text by classifying emotions into one of the finite categories, e.g., joy, surprise, anger and fear. However, there exists ambiguity characteristic for the emotion analysis, since a single sentence can evoke multiple emotions with different intensities. To address this problem, we introduce emotion distribution learning and propose a multi-task convolutional neural network for text emotion analysis. The end-to-end framework optimizes the distribution prediction and classification tasks simultaneously, which is able to learn robust representations for the distribution dataset with annotations of different voters. While most work adopt the majority voting scheme for the ground truth labeling, we also propose a lexicon-based strategy to generate distributions from a single label, which provides prior information for the emotion classification. Experiments conducted on five public text datasets (i.e., SemEval, Fairy Tales, ISEAR, TEC, CBET) demonstrate that our proposed method performs favorably against the state-of-the-art approaches. © 2018 International Joint Conferences on Artificial *** right reserved.

关键词： Classification (of information)

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Design of Robust Projection Matrix Using Prior Information for Signal Compression 17

Design of Robust Projection Matrix Using Prior Information f...

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2017 17th IEEE International Conference on Communication technology (ICCT 2017)

作者： Gang Li Aihua Yu Zhihui Zhu Beiping Hou School of Automation & Electrical Engr. Zhejiang Univ.of Sci.& Tech. College of Information Engr. Zhejiang University of Technology Dept.of Electrical Engr.and Computer Science at the Colorado School of Mines

ISBN: (纸本)9781509039456;9781509039449

This paper deals with designing optimal projection matrix for signal compression. With prior information taken into account, a novel measure is proposed, which allows us to design optimal robust projection matrix such that the sparse representation error is minimized and the clean signal is sensed as much as possible. An analytical solution is derived for the optimal projection matrix. Simulation and experiments, carried out using synthetic data and real image signals, show that the CS system with the proposed projection matrix outperforms the prevailing ones in terms of reducing the effect of sparse representation errors.

关键词： optimal projection matrix signal compression mutual coherence sparse representation errors

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Estimating Key Phenological Dates of Multiple Rice Accessions Using Unmanned Aerial Vehicle-Based Plant Height Dynamics for Breeding

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Rice science 2024年第5期31卷 617-628,I0066-I0070页

作者： HONG Weiyuan LI Ziqiu FENG Xiangqian QIN Jinhua WANG Aidong JIN Shichao WANG Danying CHEN Song State Key Laboratory of Rice Biology and Breeding China National Rice Research InstituteHangzhou 311400China College of Computer Science and Technology Zhejiang Sci-Tech UniversityHangzhou 310018China College of Agriculture Yangtze UniversityJingzhou 434025China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjing 210095China

Efficient and high-quality estimation of key phenological dates in rice is of great significance in breeding work. Plant height(PH) dynamics are valuable for estimating phenological dates. However, research on estimating the key phenological dates of multiple rice accessions based on PH dynamics has been limited. In 2022, field traits were collected using unmanned aerial vehicle(UAV)-based images across 435 plots, including 364 rice varieties. PH, dates of initial heading(IH) and full heading(FH), and panicle initiation(PI), and growth period after transplanting(GPAT) were collected during the rice growth stage. PHs were extracted using a digital surface model(DSM) and fitted using Fourier and logistic models. Machine learning algorithms, including multiple linear regression, random forest(RF), support vector regression, least absolute shrinkage and selection operator, and elastic net regression, were employed to estimate phenological dates. Results indicated that the optimal percentile of the DSM for extracting rice PH was the 95th(R^(2) = 0.934, RMSE = 0.056 m). The Fourier model provided a better fit for PH dynamics compared with the logistic models. Additionally, curve features(CF) and GPAT were significantly associated with PI, IH, and FH. The combination of CF and GPAT outperformed the use of CF alone, with RF demonstrating the best performance among the algorithms. Specifically, the combination of CF extracted from the logistic models, GPAT, and RF yielded the best performance for estimating PI(R^(2) = 0.834, RMSE = 4.344 d), IH(R^(2) = 0.877, RMSE = 2.721 d), and FH(R^(2) = 0.883, RMSE = 2.694 d). Overall, UAV-based rice PH dynamics combined with machine learning effectively estimated the key phenological dates of multiple rice accessions, providing a novel approach for investigating key phenological dates in breeding work.

关键词： phenological date plant height unmanned aerial vehicle machine learning rice breeding

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SoVAR: Build Generalizable Scenarios from Accident Reports for Autonomous Driving Testing 24

SoVAR: Build Generalizable Scenarios from Accident Reports f...

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39th ACM/IEEE International Conference on Automated Software Engineering, ASE 2024

作者： Guo, An Zhou, Yuan Tian, Haoxiang Fang, Chunrong Sun, Yunjian Sun, Weisong Gao, Xinyu Luu, Anh Tuan Liu, Yang Chen, Zhenyu State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798400712487

Autonomous driving systems (ADSs) have undergone remarkable development and are increasingly employed in safety-critical applications. However, recently reported data on fatal accidents involving ADSs suggests that the desired level of safety has not yet been fully achieved. Consequently, there is a growing need for more comprehensive and targeted testing approaches to ensure safe driving. Scenarios from real-world accident reports provide valuable resources for ADS testing, including critical scenarios and high-quality seeds. However, existing scenario reconstruction methods from accident reports often exhibit limited accuracy in information extraction. Moreover, due to the diversity and complexity of road environments, matching current accident information with the simulation map data for reconstruction poses significant *** this paper, we design and implement SoVAR, a tool for automatically generating road-generalizable scenarios from accident reports. SoVAR utilizes well-designed prompts with linguistic patterns to guide the large language model (LLM) in extracting accident information from textual data. Subsequently, it formulates and solves accident-related constraints in conjunction with the extracted accident information to generate accident trajectories. Finally, SoVAR reconstructs accident scenarios on various map structures and converts them into test scenarios to evaluate its capability to detect defects in industrial ADSs. We experiment with SoVAR, using the accident reports from the National Highway Traffic Safety Administration's (NHTSA) database to generate test scenarios for the industrial-grade ADS Apollo. The experimental findings demonstrate that SoVAR can effectively generate generalized accident scenarios across different road structures. Furthermore, the results confirm that SoVAR identified 5 distinct safety violation types that contributed to the crash of Baidu Apollo. © 2024 Copyright is held by the owner/author(s). Publication ri

关键词： Highway administration

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Network structure cascade for reversible logic

Network structure cascade for reversible logic

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3rd International Conference on Natural Computation, ICNC 2007

作者： Guan, Zhijm Qin, Xiao Lin Zhang, Yiqing Shi, Quan College of Infomation Sci. and Tech. Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Nantong University Nantong 226007 College of Science Nantong University Nantong Jiangsu 226004 China College of Computer Science and Technology Nantong University Nantong Jiangsu 226019 China

ISBN: (纸本)0769528759

A reversible logic function maps each of the 2m input patterns to a unique output pattern. The network cascade structure problem is to realize a reversible function by a cascade of primitive reversible gates. We present a method that synthesizes a network with family of Toffoli gates. The cascade algorithm finds cascade of Toffoli gates with no backtracking and minimal look-ahead, and apply transformations that reduce the number of gates in the network. We have synthesized all three input and output reversible functions and compare our results to the other optimal results. We also present the results of applying our synthesis method to obtain networks for number of benchmark function. © 2007 IEEE.

关键词： Logic circuits

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Secret and reliable coding mechanism for noncoherent multisource network coding

Secret and reliable coding mechanism for noncoherent multiso...

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IEEE International Conference on Communication Software and Networks

作者： Chen, Siguang Wu, Meng Lu, Weifeng Jin, Yi College of Computer Science Nanjing University of Posts and Telecommunications Nanjing China College of Telecommunications and Information Engineering Nanjing Univ. of Posts and Telecommunications Nanjing China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of Education China College of Informatics and Electronics Zhejiang Sci-Tech University Hangzhou China

ISBN: (纸本)9781612844855

This work considers the communication scenario where multiple sources transmit information to one or more receivers and these receivers request the information from all the sources. We construct a secret and reliable coding mechanism for noncoherent multisource network coding by extending the work [9, 10] to multisource case. Although the network scenario suffers from an adversary that can re-select the tapping links in different time slot and inject z erroneous packets into network, and the transmission links suffer from a packet erasures, our scheme can retrieve the original information secretly and accurately from the corrupt and deficient information. Meanwhile, the secret and reliable multisource network coding scheme can obtain an asymptotic achievable rate of k2z3. © 2011 IEEE.

关键词： Network coding

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