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VKFQ:A Verifiable Keyword Frequency Query Framework with Local Differential Privacy in Blockchain

computers, Materials & Continua 2024年第3期78卷 4205-4223页

作者： Youlin Ji Bo Yin Ke Gu School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China

With its untameable and traceable properties,blockchain technology has been widely used in the field of data *** to preserve individual privacy while enabling efficient data queries is one of the primary issues with secure data *** this paper,we study verifiable keyword frequency(KF)queries with local differential privacy in *** the numerical and the keyword attributes are present in data objects;the latter are sensitive and require privacy ***,prior studies in blockchain have the problem of trilemma in privacy protection and are unable to handle KF *** propose an efficient framework that protects data owners’privacy on keyword attributes while enabling quick and verifiable query processing for KF *** framework computes an estimate of a keyword’s frequency and is efficient in query time and verification object(VO)size.A utility-optimized local differential privacy technique is used for privacy *** data owner adds noise locally into data based on local differential privacy so that the attacker cannot infer the owner of the keywords while keeping the difference in the probability distribution of the KF within the privacy *** propose the VB-cm tree as the authenticated data structure(ADS).The VB-cm tree combines the Verkle tree and the Count-Min sketch(CM-sketch)to lower the VO size and query *** VB-cm tree uses the vector commitment to verify the query *** fixed-size CM-sketch,which summarizes the frequency of multiple keywords,is used to estimate the KF via hashing *** conduct an extensive evaluation of the proposed *** experimental results show that compared to theMerkle B+tree,the query time is reduced by 52.38%,and the VO size is reduced by more than one order of magnitude.

关键词： Security data sharing blockchain data query privacy protection

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Predicting CircRNA-Disease Associations via Non-Negative Matrix Factorization Fused with Multiple Similarity Networks

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Journal of Shanghai Jiaotong University (Science) 2024年 1-11页

作者： Lu, Pengli Li, Shiying School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

CircRNAs, widely found throughout the human bodies, play a crucial role in regulating various biological processes and are closely linked to complex human diseases. Investigating potential associations between circRNAs and diseases can enhance our understanding of diseases and provide new strategies and tools for early diagnosis, treatment, and disease prevention. However, existing models have limitations in accurately capturing similarities, handling the sparse and noise attributes of association networks, and fully leveraging bioinformatical aspects from multiple viewpoints. To address these issues, this study introduces a new non-negative matrix factorization-based framework called NMFMSN. First, we incorporate circRNA sequence data and disease semantic information to compute circRNA and disease similarity, respectively. Given the sparse known associations between circRNAs and diseases, we reconstruct the network to complete more associations by imputing missing links based on neighboring circRNA and disease interactions. Finally, we integrate these two similarity networks into a non-negative matrix factorization framework to identify potential circRNA-disease associations. Upon conducting 5-fold cross-validation and leave-one-out cross-validation, the AUC values for NMFMSN reach 0.971 2 and 0.976 8, respectively, outperforming the currently most advanced models. Case studies on lung cancer and hepatocellular carcinoma show that NMFMSN is a good way to predict new associations between circRNAs and diseases. © Shanghai Jiao Tong University 2024.

关键词： Diseases

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Global Dense Two-Branch Cascade Network for Underwater Image Enhancement

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Journal of Shanghai Jiaotong University (Science) 2024年 1-17页

作者： Wang, Yan Wang, Likang Zhang, Jinfeng Fan, Xianghui School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China

In recent years, underwater image enhancement techniques has received a wide range of attention from related researchers with the rise of marine resource exploitation. As the existing network feature extraction is not sufficient and the enhancement results have the problems of incomplete defogging and inaccurate color bias correction, in this paper, an underwater image enhancement method based on global dense two-branch cascade network and spatial domain grayscale transformation is proposed. The global dense two-branch cascade network can amplify the global dimensional interaction features while reducing information reduction on the one hand, and extract spatial features by obtaining spatial information at different scales to achieve richer feature extraction on the other hand;the spatial domain grayscale transformation operation can improve the contrast while color correcting the image, which makes the image visual effect better. After the training is completed, an end-to-end inference can be performed on the underwater images. The experimental results show that this paper’s model works best on the EUVP dataset, and compared with the second best, this paper’s model obtains 3.371, 0.06, 0.716, 0.024, and 1.727 improvements in PSNR, SSIM, UIQM, UCIQE, and CCF, respectively. Compared with other representative methods, the proposed network achieves significant visual enhancement in dealing with severe color bias, low light, and detail loss in underwater images. © Shanghai Jiao Tong University 2024.

关键词： Color

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Wind Speed Short-Term Prediction Based on Empirical Wavelet Transform, Recurrent Neural Network and Error Correction

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Journal of Shanghai Jiaotong university(Science) 2024年第2期29卷 297-308页

作者：朱昶胜朱丽娜 School of Computer and Communication Lanzhou University of TechnologyLanzhou 730050China

Predicting wind speed accurately is essential to ensure the stability of the wind power system and improve the utilization rate of wind ***,owing to the stochastic and intermittent of wind speed,predicting wind speed accurately is difficult.A new hybrid deep learning model based on empirical wavelet transform,recurrent neural network and error correction for short-term wind speed prediction is proposed in this *** empirical wavelet transformation is applied to decompose the original wind speed *** long short term memory network and the Elman neural network are adopted to predict low-frequency and high-frequency wind speed sub-layers respectively to balance the calculation efficiency and prediction *** error correction strategy based on deep long short term memory network is developed to modify the prediction *** actual wind speed series are utilized to verify the effectiveness of the proposed *** empirical results indicate that the method proposed in this paper has satisfactory performance in wind speed prediction.

关键词： wind speed prediction empirical wavelet transform deep long short term memory network Elman neural network error correction strategy

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Topological phase transition in network spreading

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Chinese Physics B 2023年第3期32卷 629-641页

作者：年福忠张霞 School of Computer&Communication Lanzhou University of TechnologyLanzhou 730050China

This paper investigates information spreading from the perspective of topological phase ***,a new hybrid network is constructed based on the small-world networks and scale-free ***,the attention mechanism of online users in information spreading is studied from four aspects:social distance,individual influence,content richness,and individual activity,and a dynamic evolution model of connecting with spreading is ***,numerical simulations are conducted in three types of networks to verify the validity of the proposed dynamic evolution *** simulation results show that topological structure and node influence in different networks have undergone phase transition,which is consistent with the phenomenon that followers and individual influence in real social networks experience phase transition within a short *** infection density of networks with the dynamic evolution rule changes faster and reaches higher values than that of networks without the dynamic evolution ***,the simulation results are compared with the real data,which shows that the infection density curve of the hybrid networks is closer to that of the real data than that of the small-world networks and scale-free networks,verifying the validity of the model proposed in this paper.

关键词： social network information spreading network structure phase transition

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Optical Space Time Pulse Position Modulation over Exponential Weibull Turbulence Channel

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Chinese Journal of Electronics 2024年第2期33卷 527-535页

作者： Yue ZHANG Huiqin WANG Xuemei MA Minghua CAO Qingbin PENG School of Computer and Communication Lanzhou University of Technology School of Foreign Languages Lanzhou University of Technology

An optical space time pulse position modulation(OSTPPM) scheme is proposed to satisfy the communication requirement of high transmission rate and better reliability. For the OSTPPM scheme, an improved threshold judgment-based orthogonal matching pursuit(IT-OMP) algorithm with low-complexity and near optimal performance is proposed. The average bit error rate of OSTPPM-IT-OMP scheme is investigated over the exponential Weibull channel, and its analytical expression is verified via Monte Carlo simulation. With the same simulation parameters, the signal to noise ratio of(4,4,2)-OSTPPM-IT-OMP is respectively 3.75 d B and 8.5 d B better than that of spatial pulse position modulation(SPPM) scheme and generalized spatial pulse position modulation(GSPPM)scheme at a bit error rate of 1 × 10-3. With the same transmission bits per symbol, the computational complexity of(3,4,2)-OSTPPM-IT-OMP scheme is reduced by 90.47% and 75.4% compared with(16,4,2)-SPPM and(5,4,2)-GSPPM schemes, respectively.

关键词： Optical wireless communication Optical spatial modulation Pulse position modulation Compressed sensing

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HA-FGOVD: Highlighting Fine-Grained Attributes via Explicit Linear Composition for Open-Vocabulary Object Detection

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IEEE Transactions on Multimedia 2025年 27卷 3171-3183页

作者： Ma, Yuqi Liu, Mengyin Zhu, Chao Yin, Xu-Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Open-vocabulary object detection (OVD) models are considered to be Large Multi-modal Models (LMM), due to their extensive training data and a large number of parameters. Mainstream OVD models prioritize object coarse-grained category rather than focus on their fine-grained attributes, e.g., colors or materials, thus failed to identify objects specified with certain attributes. Despite being pretrained on large-scale image-text pairs with rich attribute information, their latent feature space does not highlight these fine-grained attributes. In this paper, we introduce HA-FGOVD, a universal and explicit method that enhances the attribute-level detection capabilities of frozen OVD models by highlighting fine-grained attributes in explicit linear space. Our approach uses a LLM to extract attribute words in input text as a zero-shot task. Then, token attention masks are adjusted to guide text encoders in extracting both global and attribute-specific features, which are explicitly composited as two vectors in linear space to form a new attribute-highlighted feature for detection tasks. The composition weight scalars can be learned or transferred across different OVD models, showcasing the universality of our method. Experimental results show that HA-FGOVD achieves state-of-the-art performance on the FG-OVD benchmark and demonstrates promising generalization on the OVDEval benchmark, suggesting that our method addresses significant limitations in fine-grained attribute detection and has potential for broader fine-grained detection applications. © 1999-2012 IEEE.

关键词： Coarse-grained modeling

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WSN Mobile Target Tracking Based on Improved Snake-Extended Kalman Filtering Algorithm

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Journal of Beijing Institute of technology 2024年第1期33卷 28-40页

作者： Duo Peng Kun Xie Mingshuo Liu School of Computer and Communication of Lanzhou University of Technology Lanzhou Gansu 730050China

A wireless sensor network mobile target tracking algorithm(ISO-EKF)based on improved snake optimization algorithm(ISO)is proposed to address the difficulty of estimating initial values when using extended Kalman filtering to solve the state of nonlinear mobile target ***,the steps of extended Kalman filtering(EKF)are ***,the ISO is used to adjust the parameters of the EKF in real time to adapt to the current motion state of the mobile ***,the effectiveness of the algorithm is demonstrated through filtering and tracking using the constant velocity circular motion model(CM).Under the specified conditions,the position and velocity mean square error curves are compared among the snake optimizer(SO)-EKF algorithm,EKF algorithm,and the proposed *** comparison shows that the proposed algorithm reduces the root mean square error of position by 52%and 41%compared to the SOEKF algorithm and EKF algorithm,respectively.

关键词： wireless sensor network(WSN)target tracking snake optimization algorithm extended Kalman filter maneuvering target

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Context-aware Proactive Edge Caching for Vehicular Edge Computing Based on Asynchronous Federated Learning

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IEEE Internet of Things Journal 2025年第13期12卷 23195-23206页

作者： Liao, Zhuofan Liu, Pang Zheng, Bin Tang, XiaoYong Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410114 China

Edge caching is a promising technique for effectively reducing backhaul pressure and content access latency in the Internet of Vehicles (IoV). The existing content caching solutions still face the following challenges: 1) Contents cached on edge servers are outdated quickly as time and user preferences change. 2) The large amount of vehicle data causes huge communication overheads. 3) Limited storage resources of edge servers. Simultaneously considering these issues to reduce transmission latency is a large-scale 0-1 constraint problem, which is NP-hard, and boosting cache hit rates is a key entry point. In this work, we propose a Context-aware Proactive Caching Strategy (CPCS) based on asynchronous federated learning, which works as follows. To improve the accuracy of content popularity prediction, thus improving the cache hit rate, we combine contextual information between different contents and use long and short-term memory networks to analyze the dynamic preferences of vehicle users. After that, vehicles complete the model training and upload via an asynchronous federation learning to complete the popularity prediction. To explore the problem of local models being outdated in asynchronous federated learning, CPCS integrates model compression algorithms, enhancing system efficiency and prediction accuracy. With the prediction results, CPCS gives a content placement algorithm based on the prediction results to approximate the optimal caching scheme. Simulation results show that the CPCS can improve the cache hit rate by 17% at most compared to existing state-of-the-art caching strategies. © 2014 IEEE.

关键词： Prediction models

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Individualized Driving Intention Prediction With Inverse Reinforcement Learning

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IEEE Transactions on Intelligent Transportation Systems 2025年第6期26卷 8125-8139页

作者： Liu, Siqi Li, Xinyang Chen, Jiansheng Guo, Chenghao Wu, Jiehui Luo, Qifeng Ma, Huimin University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Advanced Driver Assistance Systems (ADAS) are designed to prevent collisions, identify the condition of drivers while operating vehicles, and provide additional information to enhance drivers' awareness of potential hazards on the road. Today, ADAS are capable of predicting drivers' actions several seconds in advance, preparing for potential future hazards to prevent accidents or reduce injuries to occupants. Most previous works have achieved prediction results by analyzing and processing a vast amount of driving data from multiple drivers, based on the macro intention preferences of multiple drivers and external environmental features, collectively referred to as the generalized intention prediction network. This network utilizes extensive driving data to predict the common driving intentions of the overall driving population, without considering individualized driving styles. However, according to our research, different drivers exhibit distinct latent preferences in real-world driving scenarios. The generalized intention prediction network is influenced by these latent preferences, resulting in poor generalization capabilities and inaccurate predictions across different drivers. In this study, we propose a individualized driver intention prediction network. Based on Inverse Reinforcement Learning (IRL), it extracts individualized driving intention feature preferences that influence driving intentions from the driver's historical behavior to improve generalized prediction results and achieve individualized driving intention prediction. We demonstrate that preferences vary among different drivers in the driving domain, leading to biases in model predictions. Upon experimental validation, the method we have proposed demonstrates remarkable efficacy on both the Brain4Cars and IESDD datasets, thereby showcasing its enhanced applicability in real-world scenarios. © 2000-2011 IEEE.

关键词： Advanced driver assistance systems

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