Search Results (779)

In recent years, intelligent driving has attracted more and more attention due to its potential to revolutionize transportation safety and efficiency, emerging as a disruptive technology that reshapes the future landscape of transportation. Environmental perception serves as the primary and fundamental cornerstone of intelligent driving systems. To address the intrinsic blind spots in environmental perception, this paper presents a vehicle collaborative perception approach based on Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) semantic communication. Specifically, a Transformer-based semantic segmentation technique is proposed for application to images acquired from surrounding vehicles and ground-based cameras. Subsequently, the generated semantic segmentation maps are transmitted via V2V/V2I communication. In the receiver, a semantic-guided image reconstruction technique based on Generative Adversarial Networks (GANs) is developed to generate images with high realism. The generated Image images can be further fused with locally perceived data, facilitating intelligent collaborative perception. This method achieves effective elimination of blind spots. Furthermore, as only semantic segmentation maps—with a data size significantly smaller than that of raw images—are transmitted instead of the latter, it exhibits excellent adaptability to the dynamically time-varying characteristics of V2V/V2I channels. Even in poor channel condition, the proposed method maintains high reliability and real-time performance. Full article

To address the limited performance of transfer fault diagnosis for planetary gearboxes under cross-operating conditions, which is caused by the heterogeneous feature distribution of vibration data and insufficient feature extraction. An improved domain-adversarial neural network (IDANN) model based on a joint-adaptive-domain alignment component and a dual-branch feature extractor is proposed. Firstly, a joint domain adaptation alignment approach, integrating maximum mean discrepancy (MMD) and CORrelation ALignment (CORAL), is proposed to realize the correlation structure matching of features between the source and target domains of IDANN. Secondly, a dual-branch feature extractor composed of ResNet18 and Swin Transformer is proposed with an attention-weighted fusion mechanism to enhance feature extraction. Finally, validation experiments conducted on public planetary gearbox fault datasets show that the proposed method attains high accuracy and stable performance in cross-operating-condition transfer fault diagnosis. Full article

Predicting the degradation and lifespan of embedded computers relies critically on the accurate evaluation of key parameter degradation within computing systems. Accelerated high-temperature tests are frequently employed as an alternative to ambient-temperature degradation tests, primarily due to the excessive duration and cost of ambient-temperature testing. However, the scarcity of effective methodologies for correlating degradation trends across distinct temperature conditions persists as a prominent challenge. This study addresses this gap by leveraging adversarial learning to generate low-temperature degradation sequences from high-temperature datasets. The adversarial learning framework enables feature transfer across diverse operating conditions and facilitates domain adaptation learning. This empowers the model to extract features invariant to degradation trends across multiple temperature conditions. Furthermore, soft dynamic time warping (SDTW) is utilized to precisely align the generated low-temperature sequences with their real-world counterparts. This alignment methodology enables elastic matching of time series data exhibiting nonlinear temporal variations, thereby ensuring accurate comparison and synchronization of degradation sequences. Compared with prior methodologies, our proposed approach delivers superior performance on computer degradation data. It offers a more accurate and reliable solution for the degradation analysis and lifespan prediction of embedded computers, thereby advancing the reliability of computational systems. Full article

Cloud-native microservice architectures offer scalability and resilience but introduce complex interdependencies and new attack surfaces, making them vulnerable to resource-exhaustion Distributed Denial-of-Service (DDoS) attacks. These attacks propagate along service call chains, closely mimic legitimate traffic, and evade traditional detection and mitigation techniques, resulting in cascading bottlenecks and degraded Quality of Service (QoS). Existing Moving Target Defense (MTD) approaches lack adaptive, cost-aware policy guidance and are often ineffective against spatiotemporally adaptive adversaries. To address these challenges, this paper proposes ScaleShield, an adaptive MTD framework powered by Deep Reinforcement Learning (DRL) that learns coordinated, attack-aware defense policies for microservices. ScaleShield formulates defense as a Markov Decision Process (MDP) over multi-dimensional discrete actions, leveraging a Multi-Dimensional Double Deep Q-Network (MD3QN) to optimize service availability and minimize operational overhead. Experimental results demonstrate that ScaleShield achieves near 100% defense success rates and reduces compromised nodes to zero within approximately 5 steps, significantly outperforming state-of-the-art baselines. It lowers service latency by up to 72% under dynamic attacks while maintaining over 94% resource efficiency, providing robust and cost-effective protection against resource-exhaustion DDoS attacks in cloud-native environments. Full article

Historical inference attacks pose a critical privacy threat in mobile edge computing (MEC), where adversaries exploit long-term task and location patterns to infer users’ sensitive information. To address this challenge, we propose a privacy-preserving task scheduling framework that adaptively balances privacy protection and system performance under dynamic vehicular environments. First, we introduce a dynamic privacy-aware adaptation mechanism that adjusts privacy levels in real time according to vehicle mobility and network dynamics. Second, we design a dual-algorithm framework composed of two complementary solutions: a Markov Approximation-Based Online Algorithm (MAOA) that achieves near-optimal scheduling with provable convergence, and a Privacy-Aware Deep Q-Network (PAT-DQN) algorithm that leverages deep reinforcement learning to enhance adaptability and long-term decision-making. Extensive simulations demonstrate that our proposed methods effectively mitigate privacy leakage while maintaining high task completion rates and low energy consumption. In particular, PAT-DQN achieves up to 14.2% lower privacy loss and 19% fewer handovers than MAOA in high-mobility scenarios, showing superior adaptability and convergence performance. Full article

Predictive process monitoring estimates the future behaviour of running process instances based on historical event logs, with typical tasks including next-activity prediction, remaining-time estimation, and risk assessment. Existing recurrent and Transformer-based models achieve strong accuracy on individual logs but transfer poorly across processes and underuse the rich graph structure of event data. This paper introduces ProcessGFM, a domain-specific graph pretraining prototype for predictive process monitoring on event graphs. ProcessGFM employs a hierarchical graph neural architecture that jointly encodes event-level, case-level, and resource-level structure and is pretrained in a self-supervised manner on multiple benchmark logs using masked activity reconstruction, temporal order consistency, and pseudo-labelled outcome prediction. A multi-task prediction head and an adversarial domain alignment module adapt the pretrained backbone to downstream tasks and stabilise cross-log generalisation. On the BPI 2012, 2017, and 2019 logs, ProcessGFM improves next-activity accuracy by 2.7 to 4.5 percentage points over the best graph baseline, reaching up to 89.6% accuracy and 87.1% macro-F1. For remaining-time prediction, it attains mean absolute errors between 0.84 and 2.11 days, reducing error by 11.7% to 18.2% relative to the strongest graph baseline. For case-level risk prediction, it achieves area-under-the-curve scores between 0.907 and 0.934 and raises precision at 10% recall by 6.7 to 8.1 percentage points. Cross-log transfer experiments show that ProcessGFM retains between about 90% and 96% of its in-domain next-activity accuracy when applied zero-shot to a different log. Attention-based analysis highlights critical subgraphs that can be projected back to Petri net fragments, providing interpretable links between structural patterns, resource handovers, and late cases. Full article

With the rapid growth and increasing sophistication of malicious software (malware), traditional detection methods face significant challenges in addressing emerging threats. Machine learning-based detection approaches rely on manual feature engineering, making it difficult for them to adapt to diverse attack patterns. In contrast, while deep learning methods can automatically extract features, they remain vulnerable to data imbalance and sample scarcity, which lead to poor detection performance for minority-class samples. To address these issues, this study proposes a semantic data augmentation approach based on a Conditional Wasserstein Generative Adversarial Network with Gradient Penalty (CWGAN-GP), and designs a malware detection model that combines a Text Convolutional Neural Network (TextCNN) with a Transformer Encoder, termed Mal-CGP-TTN. The proposed model establishes a symmetry between local feature extraction and global semantic representation, where the convolutional and attention-based components complement each other to achieve balanced learning. First, the proposed method enriches the semantic diversity of the training data by generating high-quality synthetic samples. Then, it leverages multi-scale convolution and self-attention mechanisms to extract both local and global features of malicious behaviors, thereby enabling hierarchical semantic modeling and accurate classification of malicious activities. Experimental results on two public datasets demonstrate that the proposed method outperforms traditional machine learning and mainstream deep learning models in terms of accuracy, precision, and F1-score. Notably, it achieves substantial improvements in detecting minority-class samples. Full article

This study develops a tri-level adversarial robust optimization framework for cyber–physical scheduling in smart grids, addressing the intertwined challenges of coordinated cyberattacks, defensive resource allocation, and stochastic operational uncertainties. The upper level represents the attacker’s objective to maximize system disruption and conceal detection, the middle level models the defender’s optimization of detection and redundancy deployment under budgetary constraints, and the lower level performs economic dispatch given tampered data and uncertain renewable generation. The model integrates Distributionally Robust Optimization (DRO) based on a Wasserstein ambiguity set to safeguard against worst-case probability distributions, ensuring operational stability even under unobserved adversarial scenarios. A hierarchical reformulation using Karush–Kuhn–Tucker (KKT) conditions and Mixed-Integer Second-Order Cone Programming (MISOCP) transformation converts the nonconvex tri-level problem into a tractable bilevel surrogate solvable through alternating direction optimization. Numerical case studies on multi-node systems demonstrate that the proposed method reduces system loss by up to 36% compared to conventional stochastic scheduling, while maintaining 92% dispatch efficiency under high-severity attack scenarios. The results further reveal that adaptive defense allocation accelerates robustness convergence by over 50%, and that the risk–reward frontier stabilizes near a Pareto-optimal equilibrium between cost and resilience. This work provides a unified theoretical and computational foundation for adversarially resilient smart grid operation, bridging cyber-defense strategy, uncertainty quantification, and real-time economic scheduling into one coherent optimization paradigm. Full article

Accurate and real-time evaluation of energy expenditure is crucial for optimizing exoskeleton control laws. Conventional regression-based prediction approaches are strongly affected by inter-individual variability in surface electromyography (sEMG) signals, limiting their generalization across subjects. To address this limitation, we reformulate the evaluation task as a comparative classification problem, instead of predicting absolute metabolic values, the proposed method directly judges which of two control strategies induces lower energy expenditure. We design a Control Laws Evaluation Network (CLEN) based on a Siamese architecture, which captures pairwise sEMG representations to compare assistance strategies. To further mitigate subject-specific variability, we introduce a Dual Adversarial Adaptive Optimization Strategy (DAAOS) that aligns feature distributions across domains using maximum classifier discrepancy and domain confusion. Experimental results on both public and local datasets demonstrate that the proposed domain-adaptive framework significantly outperforms regression-based approaches, achieving accuracies of $77.6\pm 3.1\%$ on the public dataset and $73.3\pm 4.7\%$ on the local dataset across unseen subjects. The findings indicate that the proposed framework provides an effective and generalizable metric for optimizing exoskeleton control, with potential applications in mobility assistance. Full article

This paper presents a comprehensive review of motion capture (MoCap) data recovery techniques, with a particular focus on the suitability of artificial intelligence (AI) for addressing missing or corrupted motion data. Existing approaches are classified into three categories: non-data-driven, data-driven (AI-based), and hybrid methods. Within the AI domain, frameworks such as generative adversarial networks (GANs), transformers, and graph neural networks (GNNs) demonstrate strong capabilities in modeling complex spatial–temporal dependencies and achieving accurate motion reconstruction. Compared with traditional methods, AI techniques offer greater adaptability and precision, though they remain limited by high computational costs and dependence on large, high-quality datasets. Hybrid approaches that combine AI models with physics-based or statistical algorithms provide a balance between efficiency, interpretability, and robustness. The review also examines benchmark datasets, including CMU MoCap and Human3.6M, while highlighting the growing role of synthetic and augmented data in improving AI model generalization. Despite notable progress, the absence of standardized evaluation protocols and diverse real-world datasets continues to hinder generalization. Emerging trends point toward real-time AI-driven recovery, multimodal data fusion, and unified performance benchmarks. By integrating traditional, AI-based, and hybrid approaches into a coherent taxonomy, this review provides a unique contribution to the literature. Unlike prior surveys focused on prediction, denoising, pose estimation, or generative modeling, it treats MoCap recovery as a standalone problem. It further synthesizes comparative insights across datasets, evaluation metrics, movement representations, and common failure cases, offering a comprehensive foundation for advancing MoCap recovery research. Full article

The limitations of conventional rule-based security systems have been exposed by the quick evolution of cyber threats, necessitating more proactive, intelligent, and flexible solutions. In cybersecurity, Artificial Intelligence (AI) has emerged as a transformative factor, offering improved threat detection, prediction, and automated response capabilities. This paper explores the advantages of using AI in strengthening cybersecurity, focusing on its applications in machine learning, Deep Learning, Natural Language Processing, and reinforcement learning. We highlight the improvement brought by AI in terms of real-time incident response, detection accuracy, scalability, and false positive reduction while processing massive datasets. Furthermore, we examine the challenges that accompany the integration of AI into cybersecurity, including adversarial attacks, data quality constraints, interpretability, and ethical implications. The study concludes by identifying potential future directions, such as integration with blockchain and IoT, Explainable AI and the implementation of autonomous security systems. By presenting a comprehensive analysis, this paper underscores exceptional potential of AI to transform cybersecurity into a field that is more robust, adaptive, and predictive. Full article

As unconventional hydrocarbon resources gain increasing importance, the risk of lost circulation during drilling operations has also grown significantly. Accurate and reliable risk diagnosis methods are essential to ensure safety and operational efficiency in complex drilling environments. This study proposes a novel lost circulation risk monitoring framework based on time-series shapelet transformation, integrated with Generative Adversarial Network (GAN)-based data augmentation and real-time model updating strategies. GANs are employed to synthesize diverse, high-quality samples, enriching the training dataset and improving the model’s ability to capture rare or latent lost circulation signals. Shapelets are then extracted from the time series using a supervised shapelet transform that searches for discriminative subsequences maximizing the separation between normal and lost-circulation samples. Each time series is subsequently represented by its minimum distances to the learned shapelets, so that critical local temporal patterns indicative of early lost circulation can be explicitly captured. To further enhance adaptability during field applications, a real-time model updating mechanism is incorporated. The system incrementally refines the classifier using newly incoming data, where high-confidence predictions are selectively added for online updating. This strategy enables the model to adjust to evolving operating conditions, improves robustness, and provides earlier and more reliable risk warnings. We implemented and evaluated Support Vector Machine (SVM), k-Nearest Neighbors (kNNs), Logistic Regression, and Artificial Neural Networks (ANNs) on the transformed datasets. Experimental results demonstrate that the proposed method improves prediction accuracy by 6.5%, measured as the accuracy gain of the SVM classifier after applying the shapelet transformation (from 84.7% to 91.2%) compared with using raw, untransformed time-series features. Among all models, SVM achieves the best performance, with an accuracy of 91.2%, recall of 90.5%, and precision of 92.3%. Moreover, the integration of real-time updating further boosts accuracy and responsiveness, confirming the effectiveness of the proposed monitoring framework in dynamic drilling environments. The proposed method offers a practical and scalable solution for intelligent lost circulation monitoring in drilling operations, providing a solid theoretical foundation and technical reference for data-driven safety systems in dynamic environments. Full article

Electroencephalogram (EEG)-based emotion recognition has emerged as a key enabler for semantic communication systems in next-generation networks (5G-Advanced/6G), where the goal is to transmit task-relevant semantic information rather than raw signals. However, domain adaptation approaches for EEG emotion recognition typically assume closed-set label spaces and fail when unseen emotional classes arise, leading to negative transfer and degraded semantic fidelity. To address this challenge, we propose a Coarse-to-Fine Open-set Domain Adaptation (C2FDA) framework, which aligns with the semantic communication paradigm by extracting and transmitting only the emotion-related semantics necessary for task performance. C2FDA integrates a cognition-inspired spatio-temporal graph encoder with a coarse-to-fine sample separation pipeline and instance-weighted adversarial alignment. The framework distinguishes between known and unknown emotional states in the target domain, ensuring that only semantically relevant information is communicated, while novel states are flagged as unknown. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate that C2FDA achieves superior open-set adaptation performance, with average accuracies of 41.5% (SEED → SEED-IV), 42.6% (SEED → SEED-V), and 48.9% (SEED-IV → SEED-V), significantly outperforming state-of-the-art baselines. These results confirm that C2FDA provides a semantic communication-driven solution for robust EEG-based emotion recognition in 6G-oriented human–machine interaction scenarios. Full article

Existing Generative Adversarial Networks (GANs) frequently yield remote sensing images with blurred fine details, distorted textures, and compromised spatial structures when applied to super-resolution (SR) tasks, so this study proposes a Multi-Attention Fusion Generative Adversarial Network (MAF-GAN) to address these limitations: the generator of MAF-GAN is built on a U-Net backbone, which incorporates Oriented Convolutions (OrientedConv) to enhance the extraction of directional features and textures, while a novel co-calibration mechanism—incorporating channel, spatial, gating, and spectral attention—is embedded in the encoding path and skip connections, supplemented by an adaptive weighting strategy to enable effective multi-scale feature fusion, and a composite loss function is further designed to integrate adversarial loss, perceptual loss, hybrid pixel loss, total variation loss, and feature consistency loss for optimizing model performance; extensive experiments on the GF7-SR4×-MSD dataset demonstrate that MAF-GAN achieves state-of-the-art performance, delivering a Peak Signal-to-Noise Ratio (PSNR) of 27.14 dB, Structural Similarity Index (SSIM) of 0.7206, Learned Perceptual Image Patch Similarity (LPIPS) of 0.1017, and Spectral Angle Mapper (SAM) of 1.0871, which significantly outperforms mainstream models including SRGAN, ESRGAN, SwinIR, HAT, and ESatSR as well as exceeds traditional interpolation methods (e.g., Bicubic) by a substantial margin, and notably, MAF-GAN maintains an excellent balance between reconstruction quality and inference efficiency to further reinforce its advantages over competing methods; additionally, ablation studies validate the individual contribution of each proposed component to the model’s overall performance, and this method generates super-resolution remote sensing images with more natural visual perception, clearer spatial structures, and superior spectral fidelity, thus offering a reliable technical solution for high-precision remote sensing applications. Full article

Optical Wireless Communication (OWC) technologies are emerging as promising complements to radio-frequency systems, offering high bandwidth, spatial confinement, and license-free operation. Within this domain, Visible Light Communication (VLC) and Optical Camera Communication (OCC) represent two distinct paradigms with divergent performance and security profiles. While VLC leverages LED-photodiode links for high-speed data transfer, OCC exploits ubiquitous image sensors to decode modulated light patterns, enabling flexible but lower-rate communication. Despite their potential, both remain vulnerable to various attacks, including eavesdropping, jamming, spoofing, and privacy breaches. This work applies—and extends—the Risk Matrix (RM) methodology to systematically evaluate the security of VLC and OCC across reconnaissance, denial, and exploitation phases. Unlike prior literature, which treats VLC and OCC separately and under incompatible threat definitions, we introduce a unified, domain-specific risk framework that maps empirical channel behavior and attack feasibility into a common set of impact and likelihood indices. A normalized risk rank (NRR) is proposed to enable a direct, quantitative comparison of heterogeneous attacks and technologies under a shared reference scale. By quantifying risks for representative threats—including war driving, Denial of Service (DoS) attacks, preshared key cracking, and Evil Twin attacks—our analysis shows that neither VLC nor OCC is intrinsically more secure; rather, their vulnerabilities are context-dependent, shaped by physical constraints, receiver architectures, and deployment environments. VLC tends to concentrate confidentiality-driven exposure due to optical leakage paths, whereas OCC is more sensitive to availability-related degradation under adversarial load. Overall, the main contribution of this work is the first unified, standards-aligned, and empirically grounded risk-assessment framework capable of comparing VLC and OCC on a common security scale. The findings highlight the need for technology-aware security strategies in future OWC deployments and demonstrate how an adapted RM methodology can identify priority areas for mitigation, design, and resource allocation. Full article