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Design, Automation and Test in Europe Conference and Exhibition

作者： Xin Hong Dingchao Gao Sanjiang Li Shenggang Ying Mingsheng Ying Key Laboratory of System Software (Chinese Academy of Sciences) and State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Centre for Quantum Software and Information University of Technology Sydney Sydney Australia

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checking quantum systems by providing efficient image computation algorithms for quantum transition systems, which play a fundamental role in model checking. In our approach, we represent quantum circuits as tensor networks and design algorithms by leveraging the properties of tensor networks and tensor decision diagrams. Our experiments demonstrate that our contraction partition-based algorithm can greatly improve the efficiency of image computation for quantum transition systems.

关键词： Quantum system Tensors Europe Model checking Software Hardware Partitioning algorithms Computational efficiency Quantum circuit

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Studying of Machine Learning Models for Forecasting Macroeconomic Indicators

Studying of Machine Learning Models for Forecasting Macroeco...

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International Conference on Advanced Communication technology (ICACT)

作者： Lan Dong Thi Ngoc Ngo-Thi-Thu-Trang Kyoung-Mok Kim Vung Pham Ha-Nam Nguyen Department of Economic Information System Academy of Finance Hanoi Viet Nam Faculty of Information Technology University of Engineering and Technology Vietnam National University Hanoi Viet Nam Posts and Telecommunications Institute of Technology Hanoi CO Vietnam Department of Medical Information Sahmyook Health University Seoul Republic of Korea Computer Science Department Sam Houston State University Huntsville Texas TX USA Faculty of Information Technology Electric Power University Hanoi Viet Nam

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This study explores the application of machine learning models in forecasting macro-economic indicators, including GDP, inflation rate, unemployment rate, and exchange rate across 11 Southeast Asian countries. The models used include Linear Regression, ARIMA, Random Forest, XGBoost, LSTM, and SVM. We conducted a performance comparison of each model based on MAE, RMSE, and R 2 metrics to evaluate the accuracy of the *** experimental results indicate that Random Forest and XGBoost models excel in predicting nonlinear and complex indicators such as GDP and unemployment rate, while ARIMA and Linear Regression models perform better in time series with clear regular patterns, like inflation rate. The LSTM model shows inconsistent effective-ness, requiring large data volumes and complex optimization processes. SVM demonstrates potential in handling nonlinear data but requires careful *** study concludes that using machine learning models presents significant potential for improving the accuracy of macroeconomic forecasting. However, model tuning and optimization are essential to match the characteristics of each type of economic indicator. Future research directions include developing hybrid models and integrating additional factors such as market sentiment, social and environmental indicators (ESG) to enhance forecasting outcomes.

关键词： Support vector machines Economic indicators Biological system modeling Predictive models Macroeconomics Forecasting Unemployment Random forests Optimization Long short term memory

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Big-data-driven vessel destination prediction for smart port management

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engineering Applications of Artificial Intelligence 2025年 154卷

作者： Chen, Jin Zhang, Qiang Liang, Maohan Peng, Chang Chen, Chen Sanya Institute Hunan University of Science and Technology China National Key Laboratory of Space Integrated Information System China Hubei Key Laboratory of Inland Shipping Technology School of Navigation Wuhan University of Technology China Department of Civil and Environmental Engineering National University of Singapore Singapore College of Computer Science and Electronic Engineering Hunan University China

The accurate prediction of vessel destinations is crucial for enhancing maritime traffic efficiency, optimizing port management, and improving regional economic analysis. However, destination information in Automatic Identification system (AIS) data is often missing or inaccurate, which undermines the reliability of maritime analytic. Traditional vessel destination prediction methods primarily focus on measuring trajectory similarities, which results in high computational complexity. This study develops a deep learning approach to vessel destination prediction by transforming the problem into an image classification task. Rasterized images of historical destination ports and vessel trajectories are generated, incorporating AIS data within a fixed spatial context. A multi-scale residual convolutional network is constructed to extract relevant trajectory and port distribution features. To enhance the representation of trajectory endpoints, which are critical for predicting the destination port, a multi-attention mechanism is introduced. This mechanism increases the learning weight assigned to endpoint features, improving prediction accuracy. Finally, a classification network predicts the destination port based on the extracted features. The performance of the proposed method is evaluated using AIS data from the Denmark Strait. Experimental results demonstrate that the model outperforms existing methods, highlighting its potential for applications in smart port management and maritime traffic optimization. © 2025 Elsevier Ltd

关键词： Waterway transportation

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Multi-label feature selection with feature reconstruction and label correlations

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Expert systems with Applications 2025年 285卷

作者： Zhang, Ning Wang, Ao Lu, Pengwei Feng, Tao Xu, Ying Du, Guodong School of Information Science and Engineering Yanshan University Qinhuangdao066004 China Hebei Key Laboratory of Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China Department of Computer Science and Technology Tsinghua University Beijing100084 China Shenzhen Research Institute of Yanshan University Shenzhen518063 China

With the arrival of the big data era, multi-label data have frequently appeared in various fields. However, multi-label data often contain rich feature information, leading to a high-dimensional feature space. Thus, multi-label feature selection plays a crucial role in addressing this challenge as a preprocessing step. Multi-label data assign a set of labels to each instance simultaneously;therefore, capturing the correlations among labels is an effective means of enhancing the learning performance. However, current multi-label feature selection methods seldom consider label correlations. Moreover, redundant features can cause overfitting, increase the complexity, and reduce the interpretability. To address these issues, we propose a multi-label feature selection method that incorporates feature reconstruction and label correlations. Specifically, we construct a low-dimensional embedding from the original feature space using induction, which preserves the manifold structure of the feature space. Next, we explore the global and local label correlations using a manifold regularization term designed by the generated low-dimensional space. We then achieve feature reconstruction by minimising the reconstruction error and sparse regularization terms to learn the relationships between the features. Finally, we construct an optimal objective function with 2,1-norm regularization to facilitate multi-label feature selection and ensure convergence. Comparative experiments on 11 datasets show that the proposed method exhibits notable performance improvements over existing methods(e.g., achieves 5.6 % higher Average precision than RFS on Scene and 3.6 % lower Coverage than CRASG on Science), validating the effectiveness and superiority of the proposed method. © 2025 Elsevier Ltd

关键词： Feature extraction

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BeamVQ: Beam Search with Vector Quantization to Mitigate Data Scarcity in Physical Spatiotemporal Forecasting

arXiv

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arXiv 2025年

作者： Wang, Weiyan Shi, Xingjian Shu, Ruiqi Gao, Yuan Chen, Rui Ray Wang, Kun Xu, Fan Xue, Jinbao Li, Shuaipeng Tao, Yangyu Wang, Di Wu, Hao Huang, Xiaomeng TEG Tencent China Boson AI United States Department of Earth System Science Ministry of Education Key Laboratory for Earth System Modeling Institute for Global Change Studies Tsinghua University China Institute for Interdisciplinary Information Sciences Tsinghua University China School of Computer Science and Engineering Nanyang Technological University Singapore Department and Computer and Science University of Science and Technology of China China

In practice, physical spatiotemporal forecasting can suffer from data scarcity, because collecting large-scale data is non-trivial, especially for extreme events. Hence, we propose BeamVQ, a novel probabilistic framework to realize iterative self-training with new self-ensemble strategies, achieving better physical consistency and generalization on extreme events. Following any base forecasting model, we can encode its deterministic outputs into a latent space and retrieve multiple codebook entries to generate probabilistic outputs. Then BeamVQ extends the beam search from discrete spaces to the continuous state spaces in this field. We can further employ domain-specific metrics (e.g., Critical Success Index for extreme events) to filter out the top-k candidates and develop the new self-ensemble strategy by combining the high-quality candidates. The self-ensemble can not only improve the inference quality and robustness but also iteratively augment the training datasets during continuous self-training. Consequently, BeamVQ realizes the exploration of rare but critical phenomena beyond the original dataset. Comprehensive experiments on different benchmarks and backbones show that BeamVQ consistently reduces forecasting MSE (up to 39%), enhancing extreme events detection and proving its effectiveness in handling data scarcity. Our codes are available at https://***/easylearningscores/BeamVQ. © 2025, CC BY.

关键词： Spatio-temporal data

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Provably Secure Authenticated Key-Management Mechanism for e-Healthcare Environment

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IEEE Internet of Things Journal 2025年

作者： Saleem, Muhammad Asad Li, Xiong Mahmood, Khalid Ghaffar, Zahid Xie, Yong Wang, Guijuan University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin Douliu64002 Taiwan National Yunlin University of Science and Technology Graduate School of Engineering Science and Technology Douliu64002 Taiwan Qinghai Institute of Technology School of Computer and Information Science China Qinghai Provincial Laboratory for Intelligent Computing and Application China Key Laboratory of Computing Power Network and Information Security Jinan250353 China Shandong Provincial Key Laboratory of Industrial Network and Information System Security China Shandong Fundamental Research Center for Computer Science Jinan250353 China

The Internet of Things (IoT) is rapidly permeating all aspects of human life, involving a network of devices that share sensitive data. A notable application is the e-healthcare systems, which employ connected sensors, medical servers, and wearable devices. However, the public nature of communication in e-healthcare systems poses challenges such as security, privacy, and authentication of participating entities. Recently, many authentication protocols have been introduced to address these challenges. However, most of these protocols remain vulnerable to various security attacks, including device or medical server impersonation, denial of service, physical or cloning, and de-synchronization attacks. Therefore, we introduce an authenticated key-management protocol utilizing hash functions and Cipher-Block Chaining-Advanced Encryption Standard encryption (CBC-AES) encryption. The proposed protocol also employs the Physical Unclonable Function (PUF), which makes it more robust and efficient in resisting physical or cloning attacks. Additionally, the proposed scheme resists various security threats, including impersonation, session key leakage, ephemeral secret leakage, and de-synchronization attacks. We analyze the scheme’s security and reliability through formal and informal analysis. The informal analysis demonstrates that the scheme encompasses crucial security features, while the formal analysis substantiates. Moreover, performance analysis of the proposed protocol with various competing results indicates that our protocol achieves an average reduction in communication and computation overheads by 36.03.% and 41.79%, respectively. © 2014 IEEE.

关键词： Electronic health record

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DRSC: Dual-Reweighted Siamese Contrastive Learning Network for Cross-Domain Rotating Machinery Fault Diagnosis With Multi-Source Domain Imbalanced Data

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IEEE Internet of Things Journal 2025年

作者： Bi, Yuanguo Fu, Rao Jiang, Cunyu Zhang, Xiaoling Li, Fengyun Zhao, Liang Han, Guangjie Northeastern University School of Computer Science and Engineering Shenyang110169 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110169 China Shenyang University of Technology Institute of Artificial Intelligence Shenyang110870 China Shenyang Aerospace University School of Computer Science Shenyang110135 China Hohai University Key Laboratory of Maritime Intelligent Network Information Technology Ministry of Education China

To enhance the reliability of rotating machinery, cross-domain fault diagnosis becomes vital for detecting faults under unknown operating conditions. However, multi-source domain imbalanced data present significant challenges, as divergent label distributions across domains cause complex domain-class shifts and degrade the performance of cross-domain fault diagnosis. Moreover, diagnostic models often struggle to learn features from minority classes due to label imbalance within each domain, which may degrade the performance in diagnosing these minority classes. To address these challenges, we propose a Dual-Reweighted Siamese Contrastive learning network (DRSC) for cross-domain fault diagnosis with multi-source domain imbalanced data. In DRSC, we design a Siamese feature extractor based on a wide-kernel convolutional neural network to capture short-term characteristics and leverage the convenience in extracting domain-invariant features. Subsequently, to alleviate domain-class shifts, we design a reweighted contrastive domain-class alignment mechanism that strategically pulls domain-class pairs together while pushing other health conditions away. Finally, to enable the diagnostic model to learn from minority health conditions, a reweighted health condition classifier is developed by assigning higher weights to the minority classes. Evaluation results on two public datasets illustrate DRSC outperforms comparison models in cross-domain fault diagnosis. © 2014 IEEE.

关键词： Convolutional neural networks

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Deep Learning Approaches for Battery Capacity and State of Charge Estimation with the NASA B0005 Dataset

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computers, Materials and Continua 2025年第3期83卷 4795-4813页

作者： Zeyang Zhou Zachary James Ryan Utkarsh Sharma Tran Tien Anh Shashi Mehrotra Angelo Greco Jason West Mukesh Prasad School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo NSW 2007 Australia School of Data Science and Engineering Indian Institute of Science Education and Research Bhopal 462066 India Research Institute of Marine Systems Engineering Seoul National University Seoul 08826 Republic of Korea Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad 201204 India Battery System Modelling Department CLARIOS VARTA Hannover GmbH Hanover 30419 Germany Science and Innovation Group Bureau of Meteorology Brisbane 4000 Australia

Accurate capacity and State of Charge (SOC) estimation are crucial for ensuring the safety and longevity of lithium-ion batteries in electric vehicles. This study examines ten machine learning architectures, Including Deep Belief Network (DBN), Bidirectional Recurrent Neural Network (BiDirRNN), Gated Recurrent Unit (GRU), and others using the NASA B0005 dataset of 591,458 instances. Results indicate that DBN excels in capacity estimation, achieving orders-of-magnitude lower error values and explaining over 99.97% of the predicted variable’s variance. When computational efficiency is paramount, the Deep Neural Network (DNN) offers a strong alternative, delivering near-competitive accuracy with significantly reduced prediction times. The GRU achieves the best overall performance for SOC estimation, attaining an R 2 of 0.9999, while the BiDirRNN provides a marginally lower error at a slightly higher computational speed. In contrast, Convolutional Neural Networks (CNN) and Radial Basis Function Networks (RBFN) exhibit relatively high error rates, making them less viable for real-world battery management. Analyses of error distributions reveal that the top-performing models cluster most predictions within tight bounds, limiting the risk of overcharging or deep discharging. These findings highlight the trade-off between accuracy and computational overhead, offering valuable guidance for battery management system (BMS) designers seeking optimal performance under constrained resources. Future work may further explore advanced data augmentation and domain adaptation techniques to enhance these models’ robustness in diverse operating conditions.

关键词： Battery capacity estimation state of charge deep learning prediction efficiency energy storage systems

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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