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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 393-404页

作者： Lin, Wenmin Zhou, Xinyi Sun, Lu Qi, Lianyong Tsai, Sang-Bing Yang, Yihong Liu, Hanwen Kou, Huaizhen Kong, Lingzhen Hangzhou Normal University Alibaba Business School Hangzhou311121 China College of Computer Science and Technology Qingdao266580 China International Engineering and Technology Institute Hong Kong999077 Hong Kong China University of Geosciences School of Information Engineering Beijing100083 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

As one of the most representative recommendation solutions, traditional collaborative filtering (CF) models typically have limitations in dealing with large-scale, sparse data to capture complex relationships between users and items. The rise of artificial intelligence (AI) provides powerful tools such as deep neural networks to overcome the data sparsity issue with typical CF models. Existing works on AI-driven recommender systems are focusing on improving recommendation accuracy by extracting rich user/item features from multisource data with deep learning tools to build more accurate user preference model. How to ensure the responsibility of AI-driven recommender systems is still a big challenge. On one hand, AI-driven recommender systems need to access raw data such as user profiles to train recommendation model, which face the risk of leaking user privacy information. On the other hand, the ever-increasing volume of user/item interaction records raises the efficiency challenge to provide recommendation results in a real-time manner. In view of those observations, we propose a collaborative recommendation method by adopting the locality-sensitive-hashing (LSH) technique (i.e., DisRecLSH), which aims to achieve the goal of privacy protection and calculation efficiency for AI-driven recommender systems. More specifically, we split DisRecLSH into three phases: 1) offline feature extraction: deep neural networks are applied to extract user/item features from multisource user/item data on each local dataset;2) offline user index building: LSH technique is adopted to map user features to hash codes to quickly recall correlated similar users for a given target user;and 3) online top-N items calculation: with similar users selected by phase 2, top-N items are calculated based on the ranking of predicted user-item rating score. Finally, extensive experiments are conducted on three public datasets to evaluate the efficiency of our proposal. © 2020 IEEE.

关键词： Recommender systems

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Noise-Mitigated Variational Quantum Eigensolver with Pre-training and Zero-Noise Extrapolation

Noise-Mitigated Variational Quantum Eigensolver with Pre-tra...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Sun, Wanqi Xu, Jungang Duan, Chenghua School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

As a hybrid quantum-classical algorithm, the variational quantum eigensolver is widely applied in quantum chemistry simulations, especially in computing the electronic structure of complex molecular systems. However, on existing noisy intermediate-scale quantum devices, some factors such as quantum decoherence, measurement errors, and gate operation imprecisions are unavoidable. To overcome these challenges, this study proposes an efficient noise-mitigating variational quantum eigensolver for accurate computation of molecular ground state energies in noisy environments. We design the quantum circuit with reference to the structure of matrix product states and utilize it to pre-train the circuit parameters, which ensures circuit stability and mitigates fluctuations caused by initialization. We also employ zero-noise extrapolation to mitigate quantum noise and combine it with neural networks to improve the accuracy of the noise-fitting function, which significantly eliminates noise interference. Furthermore, we implement an intelligent grouping strategy for measuring Hamiltonian Pauli strings, which not only reduces measurement errors but also improves sampling efficiency. We perform numerical simulations to solve the ground state energy of the H4 molecule by using MindSpore Quantum framework, and the results demonstrate that our algorithm can constrain noise errors within the range of O(10-2) ∼ O(10-1), outperforming mainstream variational quantum eigensolvers. This work provides a new strategy for high-precision quantum chemistry calculations on near-term noisy quantum hardware. © 2025 IEEE.

关键词： Error mitigation Matrix product states Variational quantum eigensolver Zero-noise extrapolation

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Multi-IRS-Aided Secure communication in UAV-MEC Networks

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IEEE Transactions on Vehicular technology 2025年第5期74卷 7327-7338页

作者： Gao, Yuan Wang, Zhenyu Zhang, Yu Lu, Weidang Tang, Jie Zhao, Nan Gao, Feifei Academy of Military Science of the PLA Beijing China Zhejiang University of Technology College of Information Engineering Hangzhou310023 China Dalian University of Technology School of Information and Communication Engineering Dalian116024 China Tsinghua National Laboratory for Information Science and Technology Beijing100084 China

With the merits of high mobility and easy deployment, mounting mobile edge computing (MEC) servers on unmanned aerial vehicles(UAVs) can efficiently fulfill the task offloading of ground users (GUs) over a large area. Nevertheless, data security is a challenging issue for the computation offloading in UAV-MEC networks, especially when there exist flying eavesdroppers. An intelligent reflecting surface (IRS) assisted secure communication scheme for a UAV-MEC network is proposed in this paper, wherein multiple IRSs are utilized to help the secure computation offloading from GUs against a UAV eavesdropper. Our aim is to maximize the secure computation capacity through the joint optimization of the IRS phase-shift, allocation of communication and computing resources and trajectory of UAV. We firstly solve the problem under a fixed UAV trajectory by alternating optimization to obtain the resource allocation and IRS phase-shift, wherein Dinkebach and Taylor expansion methods are used to transform the subproblems into tractable forms. Then, by adopting the proximal policy optimization, a joint optimization approach which further incorporates the UAV trajectory optimization is proposed. Numerical results verify that compared with benchmarks, the proposed scheme efficiently improves the system secure computation capacity. © 1967-2012 IEEE.

关键词： Computation offloading

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An Efficient Hybrid Quantum Variational Classifier With Matrix Product State

An Efficient Hybrid Quantum Variational Classifier With Matr...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

ISBN: (纸本)9798350368741

Matrix Product States have been extensively explored as a powerful tool for simulating quantum states in image classification task. However, most research has focused on classical simulations or computations involving high-dimensional unitaries, and significant challenges still exist in the practical preparation of Matrix Product States on quantum computers. This paper proposes a novel and practically feasible quantum variational algorithm based on Matrix Product States for image classification task. We design a hardware-efficient quantum circuit with several adjustable entangling operators to prepare the local tensors in Matrix Product States and integrate minimal residuals to ensure computational stability. We demonstrate that our algorithm can reduce the parameter complexity from growing exponentially with the system size to a linear scale. To validate the effectiveness of this quantum variational algorithm, we conducted experiments on the MNIST dataset, achieving accuracies of 99.95% and 95.96% for binary and ten-class classification tasks, which outperforms other related quantum algorithms. This work advances the practical application of quantum machine learning in resource-constrained environments of quantum computing. © 2025 IEEE.

关键词： Image classification Matrix product states Quantum circuit Variational quantum algorithm

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Emotion-Aware Synthetic Speech Detection with Multi-level Fusion Features 2nd

Emotion-Aware Synthetic Speech Detection with Multi-level Fu...

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2nd International Conference on the Frontiers of Robotics and Software engineering, FRSE 2024

作者： Xiang, Lingyun Zhou, Zhiguo Liu, Yangfan School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410114 China

ISBN: (纸本)9789819631605

Current synthetic speech detection methods often overlook the emotional distinctions between synthetic and real speech. To comprehensively leverage emotional information and deep features to further enhance the accuracy of distinguishing synthetic speeches, this paper proposes an emotion-aware synthetic speech detection method by fusing multi-level different features. Initially, a multi-scale emotional feature extraction model is devised to extract emotional features from 3-D log-Mels. Considering that the emotional features learned by a single model may not be comprehensive enough, the emotion2vec model is utilized to automatically learn additional emotional features from the temporal domain of the original speech. Subsequently, by employing a co-attention network, the extracted two kinds of emotional features are both incorporated into the deep features learned by the end-to-end model Rawnet2, thereby obtaining highly sensitive and weighted deep features. Finally, the extracted emotional features and the weighted deep features are fused to classify synthetic speech from real speech. Experimental results on several datasets demonstrate that the proposed method outperforms baseline methods, with a detection accuracy exceeding 0.97. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Speech recognition

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A Dam Deformation Prediction Model Based on SVM-RFE 3rd

A Dam Deformation Prediction Model Based on SVM-RFE

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3rd International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2024

作者： Cai, Shuo Zhang, Jie Gao, Huixin School of Computer and Communication Engineering Changsha University of Science and Technology Changsha410076 China

ISBN: (纸本)9789819624676

Dams play a pivotal part in national economic development, and to protect the operation of dams, monitoring and prediction of dam deformation are crucial. In response to the fact that the commonly used HST (hydrostatic, season, time) model for predicting dam deformation in the past cannot explain the influence of environmental variables well, the measured temperature was used to replace the harmonic function in the HST model as the temperature component, and the lagged water levels were added as an influencing factor. Then, the feature set was filtered using the Recursive Feature Elimination (RFE) algorithm, which reduces the dimensionality of the original feature set to the same level as the HST model to alleviate the computational burden of the model and the Support Vector Machine (SVM) was used to build the model. Aiming at the sensitivity of SVM model prediction accuracy to parameters, the Particle Swarm Optimization algorithm was introduced to find the best combination of parameters for SVM, and the SVM-RFE dam deformation prediction model was established. The measured data of Shuibuya concrete-faced rockfill dam verified the validity of the model. The final analysis indicate that compared with the traditional model, the improved SVM-RFE dam deformation prediction model obviously improves the prediction performance, reflecting the superiority of the model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Harmonic functions

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Cross-Coupling Matrix Reconfiguration Using the Levenberg-Marquardt Algorithm on Orthogonal Groups

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IEEE Microwave and Wireless technology Letters 2025年第4期35卷 380-383页

作者： Wang, Xiyuan Ding, Hai Di, Yingjie Beijing Information Science and Technology University School of Information and Communication Engineering Beijing100101 China Comba Telecom Ltd. Guangdong Guangzhou510663 China

This letter introduces a Levenberg-Marquardt (LM) algorithm on the orthogonal group to reconfigure the coupling matrix (CM) for cross-coupled resonator filters of general topology. By leveraging the framework of Lie groups and the exponential map, we perform LM steps locally in the linear space of skew-symmetric matrices, the Lie algebra associated with the orthogonal group. In each LM step, we derive and explicitly formulate a regularized least-squares (LSs) problem in matrix-vector form, and then map the solution back to the orthogonal matrix to transform the CM toward the one with the desired structure. In addition, we integrate the proposed LM algorithm with homotopy optimization to enhance global convergence, particularly for lossy CM reconfiguration. The proposed algorithm demonstrates fast convergence and high accuracy, as evidenced by the results of filter synthesis and fabrication. © 2023 IEEE.

关键词： Least squares approximations

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TCMS: A Multi-Sequence Log Parsing Method Based on Token Conversion

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IEEE Transactions on Dependable and Secure Computing 2025年第3期22卷 3028-3045页

作者： Wei, Mingkuan Wen, Jigang He, Shiming Xie, Kun Liang, Wei Xie, Gaogang Li, Kenli Zhu, Ziyu Hunan University Ministry of Education Key Laboratory of "Fusion Computing of Supercomputing and Artificial Intelligence" College of Computer Science and Electronics Engineering Changsha410012 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China Changsha University of Science and Technology School of Computer and Communication Engineering Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha410114 China Beijing100045 China Columbia University Electrical Engineering New YorkNY10027 United States

Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from low parsing accuracy and slow efficiency due to the ignorance of variable characteristics and the use of costly pairwise comparison methods. In this paper, we propose a TCMS framework to parse logs, consisting of two main technologies. First, by studying 16 public log datasets, we find that most log variable tokens are structured variable tokens. Based on this discovery, we propose a token conversion algorithm to improve parsing accuracy. This algorithm converts the changed parts in structured variable tokens into wildcards (‘’), preventing these tokens from being directly identified as constant tokens. Second, to improve efficiency, we propose the LogMLCS algorithm, which intelligently constructs a graph to facilitate the extraction of common parts from multiple log messages at once, instead of using pairwise comparisons. Comprehensive experiments conducted on 16 log datasets reveal that our TCMS outperforms seven other parsing methods, achieving the highest parsing accuracy at the fastest speed. Furthermore, experimental results from running a log anomaly detection algorithm in conjunction with different log parsing methods demonstrate that TCMS significantly boosts detection accuracy. For instance, on the OpenStack dataset, our TCMS-facilitated log anomaly detection algorithm achieves a perfect F1-score, precision, and recall of 100% each, surpassing the best peer method by 32.2, 0.8, and 19.5 percentage points, respectively. © 2004-2012 IEEE.

关键词： Accuracy Anomaly Detection Clustering Algorithms Peer To Peer Computing Electronic Mail Source Coding Particle Separators Codes Tokenization Itemsets Log Parsing Log Analysis Token Conversion Log MLCS Log Parsing Pairwise Comparisons Low Accuracy Public Datasets Anomaly Detection Common Part Multiple Messages Long Short Term Memory Point Source Target Domain Logarithm Of The Number Captive Animals Multi Objective Optimization Problem Regular Expressions Biological Sequences Rule Based Methods Point In The Graph Kinds Of Datasets Template Generation Number Of Templates Similar Template Parse Tree Syntax Errors Harmonic Average Of Precision Log Length Earliest Methods Common Subsequence Precision And Recall Leaf Node Part Of The Message

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Across-Platform Detection of Malicious Cryptocurrency Accounts via Interaction Feature Learning

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IEEE Transactions on Information Forensics and Security 2025年 20卷 4783-4798页

作者： Che, Zheng Shen, Meng Tan, Zhehui Du, Hanbiao Wang, Wei Chen, Ting Zhao, Qinglin Xie, Yong Zhu, Liehuang Beijing Institute of Technology School of Computer Science Beijing100081 China Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Xi'an Jiaotong University Key Laboratory for Intelligent Networks and Network Security Ministry of Education Xi'an710049 China University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Macau University of Science and Technology School of Computer Science and Engineering Taipa China Qinghai Institute of Technology School of Computer and Information Science Xining810016 China

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious accounts is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious account detection methods rely on large amounts of labeled data and suffer from low generalization. Label-efficient and generalizable malicious account detection remains a challenging task. In this paper, we propose ShadowEyes, a framework for detecting malicious accounts by leveraging interaction feature learning with only a small labeled dataset. Specifically, We first propose a generalized account representation named TxGraph, which captures the universal interaction features of Ethereum and Bitcoin. Then we carefully design an account representation augmentation method tailored to simulate the evolution of malicious accounts to generate positive pairs. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the scenario of across-platform malicious account detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. In the zero-shot learning scenario, it can achieve an F1 score of 79.56% for detecting gambling accounts, surpassing the SOTA method by 10.44%. © 2005-2012 IEEE.

关键词： Zero-shot learning

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MCFTNet: Multimodal Cross-layer Fusion Transformer Network for Hyperspectral and LiDAR Data Classification

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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2025年 18卷 12803-12818页

作者： Huang, Wei Wu, Tianren Zhang, Xueyu Li, Liangliang Lv, Ming Jia, Zhenhong Zhao, Xiaobin Ma, Hongbing Vivone, Gemine Xinjiang University School of Computer Science and Technology Urumqi830017 China Xinjiang University Key Laboratory of Signal Detection and Processing Urumqi830017 China Guangxi University School of Computer and Electronic Information Nanning530004 China Guangxi University Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Beijing Institute of Technology School of Information and Electronics Beijing100081 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Tsinghua University Department of Electronic Engineering Beijing100084 China National Research Council Tito85050 Italy NBFC National Biodiversity Future Center Palermo90133 Italy

Remote sensing image classification is a popular yet challenging field. Many researchers have combined convolutional neural networks (CNNs) and Transformers for hyperspectral image (HSI) classification tasks. However, in traditional Transformers, shallow-level information does not propagate well to deeper layers, which can lead to spatial variations and overfitting. Moreover, traditional Transformer models use an external classification (CLS) token that is randomly initialized and often struggles to generalize effectively. In this paper, we combine the strengths of HSI and light detection and ranging (LiDAR) data, using LiDAR as an external CLS token, which significantly enhances classification accuracy and reliability. We propose a new multimodal cross-layer fusion transformer network (MCFTNet), integrating CNNs with the latest Transformer networks. It includes a CNN for extracting spatial features and a hybrid cross-patch attention mechanism (HyCrossAttention) for land cover classification, leveraging LiDAR data to generate CLS and HSI patch tokens. More importantly, to reduce the loss of valuable information during the layer-by-layer propagation, we designed cross-layer skip connections. Through adaptive learning, these cross-layer fusions help address the gradient vanishing problem in deep networks while preserving early-layer features. This enables the model to better integrate information from different layers, enhancing both its stability and performance. We carried out in - depth experiments on commonly - used benchmark datasets, specifically, the university of Houston dataset, the Trento dataset, and the university of Southern Mississippi Gulf Park (MUUFL) dataset. We compared the results of the proposed MCFTNet model with those obtained from state-of-the-art Transformer models, classical CNNs, and traditional classifiers. As a result, the MCFTNet model outshone them all in terms of performance. © 2008-2012 IEEE.

关键词： Convolutional neural networks

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