With the widespread adoption of microservice architectures, software systems have become increasingly complex and dynamic, leading to more diverse types of anomalies. Traditional single-modal anomaly detection methods are no longer sufficient for accurately identifying system anomalies and locating their root causes. To address these challenges, this paper proposes GRAFlow, an anomaly detection and root cause localization method based on cross-modal feature fusion and multi-scale graph attention networks. GRAFlow integrates three key modalities-logs, performance metrics, and traces-and employs a cross-modal attention mechanism to dynamically model the semantic interactions among these heterogeneous data sources, thereby enhancing detection accuracy and robustness. Additionally, a multi-scale graph attention network is introduced to capture both local dependencies and long-range propagation paths among microservices, enabling more comprehensive system state modeling. The model jointly optimizes both anomaly detection and root cause localization tasks. Experimental results on two real-world microservice datasets, TrainTicket and SocialNetwork, demonstrate that GRAFlow significantly outperforms existing state-of-the-art methods across multiple evaluation metrics, including accuracy, F1 score, HR@K, and NDCG@K, confirming its effectiveness and robustness in complex system environments.
Skin cancer is a prevalent and potentially life-threatening condition, where early and accurate detection is crucial for effective treatment. Traditional diagnosis, based on visual assessment, faces challenges due to inter-class variability and the similarity between benign and malignant lesions. Although automated diagnostic systems are gaining attention, few offer comprehensive evaluations. This study addresses this gap by systematically assessing such systems and reporting extensive performance metrics.This study contributes to both artificial intelligence and dermatological image analysis by introducing an automated system for multi-class skin lesion classification. The system was evaluated under various configurations, including original and artifact-inpainted images. Three approaches were employed: deep learning, radiomics-based and hybrid. Additionally, to enhance the interpretability, the system generates segmentation masks and attribution maps.Models were evaluated using five-fold cross-validation on the International Skin Imaging Collaboration (ISIC) 2018 Challenge Task 3 training dataset, with final performance assessed on the official test set. The best-performing model achieved a micro accuracy of 79.76 +/- 0.73, macro F1-score of 73.62 +/- 1.38, precision of 74.80 +/- 0.84, and recall of 73.93 +/- 2.19, surpassing comparable methods reported in the literature.The contribution to the research community is the release of a novel set of 57630 binary artifact labels for the ISIC-2018 Task 3 dataset, enabling research on the impact of visual artifacts such as hair, rulers, and interface fluids. This contribution is supported by statistical analysis and attribution map assessment, revealing significant differences in artifact occurrence across lesion classes and highlighting the utility of these labels in identifying and mitigating bias in skin lesion classification.
The integration of intelligent transportation systems and autonomous driving is reshaping modern mobility by mitigating the longstanding trade-off between traffic efficiency and road safety. Enabled by vehicle-to-everything (V2X) communications, connected and autonomous vehicles (CAVs) are increasingly integrated into vehicle-road-cloud collaborative networks, resulting in measurable improvements in traffic capacity, energy efficiency, and collision avoidance. However, the coexistence of CAVs, human-driven vehicles, and vulnerable road users (VRUs) introduces complex challenges in collaborative control, real-time risk perception, and data security. To address these issues, this review synthesizes recent advances through a tripartite lens: collaborative driving, risk-aware perception, and energy-efficient operation. Our analysis identifies three recurring scientific questions: (i) how to effectively couple dynamic risk perception with cooperative control under uncertainty; (ii) how to enable secure, privacy-preserving collaboration across heterogeneous agents; and (iii) how to guarantee VRU safety in mixed-autonomy traffic. Representative approaches fall into three categories: (1) multimodal collaborative decision-making frameworks combining hierarchical deep reinforcement learning and model predictive control; (2) federated learning architectures that preserve data privacy while enabling cross-vehicle knowledge sharing; and (3) human-centric safety mechanisms leveraging ultra-wideband sensing and heterogeneous graph neural networks for VRU detection and intent prediction. Collectively, these findings demonstrate that risk-aware, energy-efficient cooperative driving is technically feasible, yet its large-scale deployment hinges on interdisciplinary innovation, standardized communication protocols, and regulatory alignment.
Anomaly prediction is vital for ensuring the safe operation of pump station facilities. However, traditional models are often hampered by spurious correlation stemming from unobservable confounding factors, thereby degrading detection accuracy. To address this, this paper proposes the Causal Intervention with Front-door Adjustment Model (CIFAM) to eliminate spurious correlation in complex features. Initially, CIFAM integrates video and signal data, utilizing graph attention networks and clustering to construct a multi-level structural attribute graph that represents dynamic operational dependencies. The model incorporates causal dilated convolution to capture long-term memory while preventing information leakage, alongside a random edge dropping strategy to simulate causal intervention. By applying the front-door adjustment criterion, CIFAM extracts robust intermediate representations to block interference paths. Furthermore, contrastive learning and Adaptive Instance Normalization (AdaIN) are employed to decouple features, bolstering model robustness in complex scenarios. Experimental results demonstrate that CIFAM achieves state-of-the-art performance across multiple industrial benchmarks, specifically reaching 91.1% accuracy and a 90.6% Micro-F1 score on the OURS dataset. The model exhibits exceptional robustness and sample utilization efficiency. Furthermore, with minor modifications, this algorithm can be generalized to various monitoring scenarios, such as power generator set monitoring, semiconductor production lines, and UAV-assisted monitoring for oil pipelines or high-voltage power line maintenance.
This paper investigates future purchase prediction in e-commerce using sequences of user visits and behavioural micro-interactions. The study is based on anonymised production data from three e-commerce domains, comprising approximately 690,000 users and 2.61 million visits. User histories are modelled as temporally ordered sequences of visits, with each visit represented by 21 features. Six models are evaluated: three tabular approaches (SVM, XGBoost, CatBoost) and three sequential architectures (RNN, LSTM, GRU), each tested in two variants addressing class imbalance. The experimental setup employs a temporal train-test split, five random seeds, and bootstrap-based significance testing. The top-performing sequential models achieve an AUC-PR of approximately 0.379, significantly outperforming the strongest tabular baseline, XGBoost (AUC-PR approximate to 0.364). This advantage remains consistent across domain-specific evaluations as well as in a leave-one-shop-out setting. Further analysis shows that mouse movement and scrolling-related features provide the strongest predictive signal, whereas extending user histories beyond 40 visits yields only marginal performance gains.
