2895 papers
This empirical study examines the experiences of creative practitioners in Iran with Generative AI, revealing how geopolitical constraints and technical limitations shape their adoption through dominant director mental models, compensatory labor, and the emergence of bottom-up "Folk Ethics" to navigate systemic inequities.
This paper demonstrates that integrating an automated security-audit skill into the "vibe coding" process drastically improves the security of AI-generated web applications, reducing unmitigated risk by 99% and eliminating all high-severity vulnerabilities in a controlled comparison of two builds of the same e-commerce demo.
This paper proposes GTAN, a Graph-Temporal Attention Network that combines Temporal Convolutional and Graph Attention encoders with a Focal Contrastive Loss to effectively detect rare intrusion attacks in highly imbalanced Cyber-Physical Systems traffic by modeling both temporal sequences and topological relationships.
This paper proposes SPAT-UAD, a robust fake information detection framework that integrates semantics-preserving adversarial training with uncertainty-aware adaptive decision-making to significantly outperform existing baselines in handling surface-level variations and improving model reliability across multiple datasets.
This paper introduces Symbolic Log Shield, a neurosymbolic framework that significantly mitigates the vulnerability of Large Language Models to adversarial jailbreak attacks in network log classification, effectively restoring and even enhancing their anomaly detection performance from severely degraded levels.
This paper presents a data-driven framework for LAPAN-IPB LEO satellites that combines a Hybrid SARIMAX-LSTM voltage predictor and a Random Forest classifier to generate auditable, evidence-based mission recommendations, thereby replacing reliance on undocumented operator experience with a reproducible, objective scheduling process.
The paper proposes MRT, a scene text recognition framework that combines Neighborhood Attention-enhanced Mamba for efficient local spatial aggregation with a weight-shared Reusable Transformer for bidirectional global context, achieving superior accuracy-efficiency trade-offs across multiple benchmarks.
This systematic review analyzes federated learning architectures, applications, and challenges for distributed cyber threat detection, highlighting the shift toward decentralized models to address privacy and bandwidth limitations while identifying non-IID data and adversarial attacks as critical barriers to effective implementation.
This paper proposes a fast one-step multi-view clustering method that unifies spectral clustering and nonnegative matrix factorization with tensor log-determinant regularization to effectively capture high-order cross-view correlations and achieve superior performance and scalability compared to state-of-the-art methods.
This paper proposes an REAE-LTransformer model optimized by an Improved Four-Vector Intelligent Metaheuristic (IFVIM) algorithm to accurately predict lithium-ion battery remaining useful life by effectively extracting degradation features, capturing long-term dependencies, and minimizing prediction errors, achieving superior performance with an MAE under 0.024 and R² exceeding 0.99 across multiple datasets.