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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Zhang, Meng Chen, Tianshi Mu, Biqiang The Chinese University of Hong Kong School of Data Science Shenzhen Research Institute of Big Data Shenzhen518172 China Institute of Systems Science Academy of Mathematics and System Science Chinese Academy of Sciences Key Laboratory of Systems and Control Beijing100190 China

ISBN: (纸本)9798350316339

Regularized system identification is one of the major advances in the field of system identification in the last decade. One key issue is the hyper-parameter estimation, for which the generalized maximum likelihood (GML) estimator is a popular one closely related to the empirical Bayes (EB) method. Considering the rich theoretical results on the EB estimator, the asymptotic properties of the GML estimator has not been studied before and is critical for understanding its efficacy when the sample size is large. In this paper, we investigate the asymptotic properties of the GML estimator and show that the GML estimator is asymptotically equivalent to the EB estimator. Furthermore, Monte-Carlo simulations verify their asymptotic equivalence and also indicates the GML estimator outperform the EB estimator for small sample sizes. © 2024 IEEE.

关键词： Decentralized systems

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Fast Fractional Programming for Multi-Cell Integrated Sensing and Communications

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Yannan Feng, Yi Li, Xiaoyang Zhao, Licheng Shen, Kaiming School of Science and Engineering Shenzhen518172 China Shenzhen Research Institute of Big Data Shenzhen518172 China

This paper concerns the coordinate multi-cell beamforming design for integrated sensing and communications (ISAC). In particular, we assume that each base station (BS) has massive antennas. The optimization objective is to maximize a weighted sum of the data rates (for communications) and the Fisher information (for sensing). We first show that the conventional beamforming method for the multiple-input multiple-output (MIMO) transmission, i.e., the weighted minimum mean square error (WMMSE) algorithm, works for the ISAC problem case from a fractional programming (FP) perspective. However, the WMMSE algorithm frequently requires computing the N×N matrix inverse, where N is the number of transmit or receive antennas, so the algorithm becomes quite costly when antennas are massively deployed. To address this issue, we develop a nonhomogeneous bound and use it in conjunction with the FP technique to solve the ISAC beamforming problem without the need to invert any large matrices. It is further shown that the resulting new FP algorithm has an intimate connection with gradient projection, based on which we can accelerate the convergence via Nesterov’s gradient extrapolation. © 2002-2012 IEEE.

关键词： Mean square error

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Spatial and temporal variation of water clarity in typical reservoirs in the Beijing-Tianjin-Hebei region observed by GF 1-WFV satellite data

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Journal of Oceanology and Limnology 2024年第4期42卷 1048-1060页

作者： Chang CAO Junsheng LI Xiaodong JIA Shenglei WANG Bo WAN Liaoning Earthquake Agency Shenyang 11000China Key Laboratory of Digital Earth Science Aerospace Information Research InstituteChinese Academy of SciencesBeijing 100094China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China School of Electronic Electrical and Communication EngineeringUniversity of Chinese Academy of SciencesBeijing 100049China

Rapidly monitoring regional water quality and the changing trend is of great practical and scientific significance,especially for the Beijing-Tianjin-Hebei(BTH)region of China where water resources are relatively scarce and inland water bodies are generally *** remote sensing data of the GF 1 satellite launched in 2013 have characteristics of high spatial and temporal resolution,which can be used for the dynamic monitoring of the water environment in small lakes and ***,the water quality remote sensing monitoring model based on the GF 1 satellite data for lakes and reservoirs in BTH is still lacking because of the considerable differences in the optical characteristics of the lakes and *** this paper,the typical reservoirs in BTH-Guanting Reservoir,Yuqiao Reservoir,Panjiakou Reservoir,and Daheiting Reservoir are taken as the study *** the atmospheric correction of GF 1-WFV,the relative radiation normalized atmospheric correction was adopted after comparing it with other methods,such as 6 S and *** the water clarity retrieval,a water color hue angle based model was proposed and outperformed other available published models,with the R 2 of 0.74 and MRE of 31.7%.The clarity products of the four typical reservoirs in the BTH region in 2013-2019 were produced using the GF 1-WFV *** on the products,temporal and spatial changes in clarity were analyzed,and the main influencing factors for each water body were *** was found that the clarity of Guanting,Daheiting,and Panjiakou reservoirs showed an upward trend during this period,while that of Yuqiao Reservoir showed a downward *** the influencing factors,the water level of the water bodies can be an important factor related to the water clarity changes in this region.

关键词： GF 1 satellite atmospheric correction clarity Beijing-Tianjin-Hebei spatial and temporal change analysis

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Pedestrian wind flow prediction using spatial-frequency generative adversarial network

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Building Simulation 2024年第2期17卷 319-334页

作者： Pengyue Wang Maozu Guo Yingeng Cao Shimeng Hao Xiaoping Zhou Lingling Zhao School of Architecture and Urban Planning Beijing University of Civil Engineering and ArchitectureBeijing100044China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing100044China School of Electrical and Information Engineering Beijing University of Civil Engineering and ArchitectureBeijing100044China School of Computer Science and Technology Harbin Institute of TechnologyHarbin150001China

Pedestrian wind flow is a critical factor in designing livable residential environments under growing complex urban *** pedestrian wind flow during the early design stages is essential but currently suffers from inefficiencies in numerical *** learning,particularly generative adversarial networks(GAN),has been increasingly adopted as an alternative method to provide efficient prediction of pedestrian wind ***,existing GAN-based wind flow prediction schemes have limitations due to the lack of considering the spatial and frequency characteristics of wind flow *** study proposes a novel approach termed SFGAN,which embeds spatial and frequency characteristics to enhance pedestrian wind flow *** the spatial domain,Gaussian blur is employed to decompose wind flow into components containing wind speed and distinguished flow edges,which are used as the embedded spatial *** information of wind flow is obtained through discrete wavelet transformation and used as the embedded frequency *** spatial and frequency characteristics of wind flow are jointly utilized to enforce consistency between the predicted wind flow and ground truth during the training phase,thereby leading to enhanced *** results demonstrate that SFGAN clearly improves wind flow prediction,reducing Wind_MAE,Wind_RMSE and the Fréchet Inception Distance(FID)score by 5.35%,6.52%and 12.30%,compared to the previous best method,*** also analyze the effectiveness of incorporating the spatial and frequency characteristics of wind flow in predicting pedestrian wind *** reduces errors in predicting wind flow at large error intervals and performs well in wake regions and regions surrounding *** enhanced predictions provide a better understanding of performance variability,bringing insights at the early design stage to improve pedestrian wind *** proposed spatial-frequen

关键词： pedestrian wind flow prediction generative adversarial network Gaussian kernel wavelet transform objective function

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Towards Stricter Black-box Integrity Verification of Deep Neural Network Models 24

Towards Stricter Black-box Integrity Verification of Deep Ne...

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32nd ACM International Conference on Multimedia, MM 2024

作者： He, Chaoxiang Bai, Xiaofan Ma, Xiaojing Zhu, Bin B. Hu, Pingyi Fu, Jiayun Jin, Hai Zhang, Dongmei Huazhong University of Science and Technology Hubei Wuhan China Shanghai Jiao Tong University Shanghai China Microsoft Corporation Beijing China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

ISBN: (纸本)9798400706868

Cloud-based machine learning services offer significant advantages but also introduce the risk of tampering with cloud-deployed deep neural network (DNN) models. Black-box integrity verification (BIV) allows model owners and end-users to determine if a cloud-deployed DNN model has been tampered with by examining only the top-1 label responses. Fingerprinting generates fingerprint samples to query the model, achieving BIV with no impact on the model's accuracy. In this paper, we present BIVBench, the first comprehensive benchmark for BIV of DNN models. BIVBench covers 16 types of model modifications, providing extensive coverage of practical modification scenarios. Our analysis reveals that existing fingerprinting methods, which are typically focused on significant tampering, lack the sensitivity needed to effectively detect subtle yet common and potentially severe modifications. To address this limitation, we propose MiSentry (Model Integrity Sentry), a novel fingerprinting method that leverages meta-learning. MiSentry strategically incorporates a few subtly modified models into the meta-learning model zoo and maximizes the divergence of output predictions between the target model and the modified models in the model zoo to generate highly sensitive, generalizable, and effective fingerprint samples. Extensive evaluations using BIVBench demonstrate that MiSentry outperforms existing state-of-the-art methods overall and significantly surpasses them in detecting subtle modifications. The BIVBench and supplementary materials are available at: https://***/CGCL-codes/BIVBench. © 2024 ACM.

关键词： Deep neural networks

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Personalized Intelligent Job Recommendation Based on Deep Learning in Elasticsearch Environment

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 339-352页

作者： Cui, Xiu-Yan Hwang, David Hebei Software Institute Hebei Province University Intelligent Interconnection Equipment and Multimodal Big Data Application Technology Research and Development Center Baoding071000 China College of Engineering St. Paul University Philippines Tuguegarao3500 Philippines

Aiming at the issue that the jobs predicted by the existing job recommendation methods cannot be precisely matched with the job seekers, this paper designs a personalized intelligent job recommendation method based on deep learning in the Elas-ticsearch environment. Firstly, to deal with the data sparsity and cold start issue of traditional collaborative filtering algorithms, the similarity calculation formula of Pearson’s correlation in the algorithm is modified to enhance the accuracy of similarity calculation. Secondly, on the ground of the history of different job seekers, the keywords of job seekers are constructed in terms of various weights, and input into ElasticSearch to recall the set of jobs that are strongly related to the job seekers. Then adopting Convolutional Neural Network (CNN) to extract the text data features of job seekers and positions, and meanwhile, integrating the attention mechanism to get the auxiliary features of job seekers and positions, and finally, integrating the implicit feedback data with other at-tribute features of job seekers and positions as the input of the improved collaborative filtration algorithm, so that we can realize the intelligent recommendation of jobs for job seekers. Simulation outcome indicates that the Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE) and Root Mean Square Percentage Error (RMSPE) of the suggested method are 0.0681, 0.0519, 0.0834 and 0.0628 respectively, which are lower than those of the comparison method, and the accuracy of the recommendation is effectively enhanced. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Convolutional neural networks

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Backdoor Attack Based on Privacy Inference against Federated Learning 7

Backdoor Attack Based on Privacy Inference against Federated...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Wu, Duo Hao, Lingguang Wei, Bing Hao, Kuangrong Han, Tao He, Langping Donghua University College of Information Science and Technology Shanghai China China Telecom Research Institute College of Big Data and Artificial Intelligence Shanghai China

ISBN: (纸本)9798350363173

As a pioneering distributed learning framework, federated learning (FL) has gained widespread adoption. It operates collaboratively among participants, with communication limited to sharing model parameters between the server and participants. However, FL is also more susceptible to active attacks from malicious insiders. Poisoned updates submitted by attackers can degrade the performance of the global model. Previous research only considered using the naive data of malicious clients for backdoor poisoning, and therefore achieved limited backdoor attack success rates. In this paper, we propose a novel Federated Backdoor Attack based on Privacy Inference (FBA-PI). Combining privacy inference based on generative adversarial networks (GAN), the attacker first infers sensitive information from victim participants and then injects backdoor triggers into the naive and generated data. Finally, malicious clients can embed backdoor semantics into the global model by participating in regular federated aggregation. Extensive experiments on the MNIST dataset verify the effectiveness of our proposed method. © 2024 IEEE.

关键词： Generative adversarial networks

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Multivariable financial time series forecasting based on phase space reconstruction compensation

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Neural Computing and Applications 2025年第3期37卷 1389-1402页

作者： Li, Jincheng Zhou, Linli Li, Xuefei Wu, Di Xiong, Jianqiao Song, Liangtu School of Big Data and Statistics Anhui University Hefei230039 China Intelligent Agriculture Engineering Laboratory of Anhui Province Institute of Intelligent Machines Hefei Institutes of Physical Science Chinese Academy of Sciences Hefei230031 China University of Science and Technology of China Hefei230026 China

Accurate financial time series forecasting is an important challenge in the financial field due to varying levels of interaction among multiple financial time series, complicating the extraction of valid information from these variables. This study introduces an effective and efficient multivariable financial time series forecasting model based on phase space reconstruction compensation: Phase Space Reconstruction Compensation Long Short-Term Memory (PSR-LSTM). The PSR-LSTM model leverages the long short-term memory (LSTM) network to analyze short-term data behaviors, reconstructs multiple long-term variables by phase space reconstruction method, and utilizes multivariable trend attention to capture trend information in correlated variables. This trend information is finally used to correct the LSTM network’s predictive result. Experimental results demonstrate that the PSR-LSTM outperforms existing multivariable forecasting models by effectively mitigating noise interference while achieving optimal forecasting performance. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phase space methods

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Evaluating RISC-V Vector Instruction Set Architecture Extension with Computer Vision Workloads

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Journal of Computer science & Technology 2023年第4期38卷 807-820页

作者：李若时彭平邵志远金海郑然 National Engineering Research Center for Big Data Technology and System Huazhong University of Science and Technology Wuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and TechnologyWuhan 430074 China Cluster and Grid Computing Lab Huazhong University of Science and TechnologyWuhan 430074China

Computer vision(CV)algorithms have been extensively used for a myriad of applications *** the multimedia data are generally well-formatted and regular,it is beneficial to leverage the massive parallel processing power of the underlying platform to improve the performances of CV *** Instruction Multiple data(SIMD)instructions,capable of conducting the same operation on multiple data items in a single instruction,are extensively employed to improve the efficiency of CV *** this paper,we evaluate the power and effectiveness of RISC-V vector extension(RV-V)on typical CV algorithms,such as Gray Scale,Mean Filter,and Edge *** our examinations,we show that compared with the baseline OpenCV implementation using scalar instructions,the equivalent implementations using the RV-V(version 0.8)can reduce the instruction count of the same CV algorithm up to 24x,when processing the same input ***,the actual performances improvement measured by the cycle counts is highly related with the specific implementation of the underlying RV-V *** our evaluation,by using the vector co-processor(with eight execution lanes)of Xuantie C906,vector-version CV algorithms averagely exhibit up to 2.98x performances speedups compared with their scalar counterparts.

关键词： RISC-V vector extension single instruction multiple data(SIMD) computer vision OpenCV

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An Elite Archive-Assisted Multi-Objective Evolutionary Algorithm for mRNA Design 13

An Elite Archive-Assisted Multi-Objective Evolutionary Algor...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Hong, Wenjing Chen, Cheng Zhu, Zexuan Tang, Ke Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen China

ISBN: (纸本)9798350308365

Messenger RNA (mRNA) vaccines have emerged as highly effective strategies in the prophylaxis and treatment of diseases. mRNA design, a key to the success of mRNA vaccines, in-volves finding optimal codons and increasing secondary structure stability to lengthen mRNA half-life, ultimately enhancing protein expression. Despite receiving widespread attention, most methods primarily rely on manual design, which is time-consuming and labor-intensive. While optimization approaches can alleviate this issue, existing methods still exhibit critical limitations caused by conflicts between codon usage and mRNA structural stability, compounded by the vast design space of mRNA resulting from the presence of synonymous codons. In this paper, a novel multi-objective evolutionary optimization-based mRNA design method is proposed. We first formulate the mRNA design problem as a multi-objective optimization problem and then develop an Elite Archive-Assisted Multi-Objective Evolutionary algorithm for mRNA Design, namely EAA-MOED, by incorporating a novel elite archive-assisted method into a weighted optimization framework to improve search efficiency. Experimental studies, involving two state-of-the-art mRNA design methods and five well-known MOEAs, show the competitiveness of the proposed EAA-MOED in mRNA design. © 2024 IEEE.

关键词： Vaccines

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