Frontiers in Artificial Intelligence Research
type: channel
description: Frontiers in Artificial Intelligence Research is a scholarly journal dedicated to advancing the field of artificial intelligence (AI) through high-quality research and interdisciplinary collaboration. The journal serves as a platform for AI experts, computer scientists, data analysts, and professionals from various domains to share the latest innovations, trends, and applications in AI technology.
publisher: INTERNATIONAL STUDY COUNSELOR
language: en
publication name: Frontiers in Artificial Intelligence Research
issn: 3079-6342
items: Research on Interface Design Optimization of Online Learning Platforms for Universities Based on Cognitive Load Theory
items: A Workflow Restructuring Method for Aircraft Maintenance with AR Guidance and Version-Consistent Configuration Management
items: Learning to Place Macros and Synthesize Power Grids Through Multi Agent Coordination
items: Graph Neural Networks for Timing Optimization in Advanced Node Placement
items: Toward Evasion-Resistant LLM Attribution with Multi-Scale Watermarking and Cryptographic Verification
items: Enhancing Domain-Specific Language Models with Knowledge Graph Injection and Graph Attention Networks
items: Integrating Symbolic Reasoning into Neural Networks: A Neuro-Symbolic Logic Programming Approach for Enhanced Explainability
items: Optimization of Adaptive Prompt Engineering for Large Language Models via Bayesian Inference in Low-Resource Settings
items: Theoretical Mechanisms of How AI-Enabled Driver Assistance Systems Influence Consumers’Automobile Choice Behavior
items: Security-Constrained Graph Neural Clustering for Industrial Manufacturing Systems under OT and Hardware Constraints
items: Quantifying the Interplay Between Panic Propagation and Misinformation on Social Media Using Large Language Models
items: SecHOT-GNC: Security-Oriented Hardware- and OT-Aware Graph Neural Clustering for Attack-Chain Community Detection in Industrial Fiber Manufacturing Systems
items: Learning Risk and Communities in Complex Systems: A Review of Temporal Models, Graph Neural Networks, and Fourier Approaches
items: A Hybrid Neural–Symbolic Framework for Interpretable Fault Diagnosis in Rotating Machinery
items: Adversarially Robust Sequence Models with Frequency-Domain Consistency Regularization
items: A Uncertainty-Calibrated Transformer for Long-Horizon Forecasting with Missing and Irregular Observations
items: Retrieval-Augmented Graph Reasoning with Large Language Models for Explainable Incident Diagnosis
items: Adaptive Query Decomposition via Learned Symbolic Primitives in Retrieval-Augmented Systems
items: Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization
items: Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics
items: Infrastructure as Code and Observability Automation for Payment Systems in Cloud-Native Environments
items: Building Billion-User Products from Zero to Scale: Full-Stack Engineering Patterns for Creator Monetization and Content Protection Systems
items: Cross-Hardware Optimization Strategies for Large-Scale Recommendation Model Inference in Production Systems
items: A Comprehensive Review of Deep Temporal, Graph-Based, and Bayesian Learning Frameworks for Risk Assessment and Community Detection
items: RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment
items: Dynamic Workflow Partitioning via Reinforcement Learning for Edge-Cloud Heterogeneous Systems
items: Mixed Signal Approach Using C++ and DSP Hardware for Low Latency and Secure Speech Systems
items: Improving Intrusion Detection Robustness Through Adversarial Training Methods
items: Cross-Modal Attention Mechanisms for Inventory Optimization: Fusing IoT Sensor Data, News Sentiment, and Geospatial Information
items: Reinforcement Learning Paradigms for Proactive Cybersecurity and Dynamic Risk Management

Research on Interface Design Optimization of Online Learning Platforms for Universities Based on Cognitive Load Theory
type: item
description: Against the backdrop of deepening digital transformation in education, online teaching platforms have gradually become an important carrier connecting online and offline teaching. The interface design of university learning platforms significantly influences the distribution and regulation of cognitive load during the learning process, thus having a significant impact on learning efficiency and effectiveness. This paper, based on cognitive load theory, studies the interface design of university learning platforms. It analyzes the key design aspects affecting students' cognitive load, as well as the shortcomings of current interface design, explores the intrinsic relationship between interface characteristics and cognitive load, and proposes corresponding optimization strategies, providing theoretical and practical references for the optimization and upgrading of university learning platforms.
subject: Articles
  topic: section
subject: Cognitive load
  topic: keywords
subject: university learning platform
  topic: keywords
subject: interface design
  topic: keywords
subject: learning efficiency
  topic: keywords
creator: Zhiqun Ren
creator: Ahmadrashidi Bin Hasan
rights: Copyright (c) 2026 Zhiqun Ren, Ahmadrashidi Bin Hasan (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-02-09
publication date: 2026-02-09
starting page: 113
ending page: 116
doi: 10.71465/fair638

A Workflow Restructuring Method for Aircraft Maintenance with AR Guidance and Version-Consistent Configuration Management
type: item
description: Aircraft maintenance often suffers from mismatched document versions and delays in updating task steps after software changes. This study introduces a method that delivers maintenance instructions through AR glasses and links each step to OTA version data. The method was tested in two airline hangars across 26 maintenance tasks involving junior and senior technicians. Results show that junior staff made 61.3% fewer errors, and average task time decreased by 27.5% when using AR guidance. After a software update, revised steps reached AR devices within 15 minutes, removing the usual delay found in paper and PDF procedures. These findings indicate that connecting AR guidance with OTA updates can reduce the risk of outdated instructions and improve task execution in routine maintenance. The study is limited to hangar work and a single device type, and broader testing is needed to assess performance in line maintenance and mixed fleet environments.
subject: Articles
  topic: section
subject: AR maintenance
  topic: keywords
subject: digital task cards
  topic: keywords
subject: OTA-linked instructions
  topic: keywords
subject: aviation maintenance
  topic: keywords
subject: error reduction
  topic: keywords
subject: task time
  topic: keywords
creator: Daniel Morrison
creator: Sophie Reynolds
creator: Liam Fraser
rights: Copyright (c) 2026 Daniel Morrison, Sophie Reynolds, Liam Fraser (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 106
ending page: 112
doi: 10.71465/fair634

Learning to Place Macros and Synthesize Power Grids Through Multi Agent Coordination
type: item
description: Modern chip design faces unprecedented challenges in optimizing macro placement and power grid synthesis simultaneously. Traditional Electronic Design Automation (EDA) approaches rely on sequential optimization strategies that fail to capture the complex interdependencies between macro positioning and power distribution networks. This paper presents a novel framework leveraging multi-agent reinforcement learning for coordinated macro placement and power grid synthesis. Our approach employs multiple specialized agents that collaboratively optimize placement objectives including wirelength minimization, congestion reduction, and power integrity. Through a hierarchical coordination mechanism, agents negotiate placement decisions while maintaining awareness of power delivery constraints. Experimental results on industrial benchmarks demonstrate that our multi-agent coordination framework achieves 12.3% improvement in wirelength, 15.7% reduction in congestion hotspots, and 18.2% enhancement in IR drop metrics compared to conventional single-agent and sequential optimization methods. The framework exhibits strong scalability properties, handling designs with over 1000 macros while maintaining solution quality. This work demonstrates that multi-agent coordination provides a promising paradigm for addressing the increasing complexity of modern chip design challenges.
subject: Articles
  topic: section
subject: Multi-agent reinforcement learning
  topic: keywords
subject: macro placement
  topic: keywords
subject: power grid synthesis
  topic: keywords
subject: chip design
  topic: keywords
subject: electronic design automation
  topic: keywords
subject: coordination mechanisms
  topic: keywords
creator: Ziyang Chen
rights: Copyright (c) 2026 Ziyang Chen (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 95
ending page: 105
doi: 10.71465/fair633

Graph Neural Networks for Timing Optimization in Advanced Node Placement
type: item
description: The escalating complexity of modern integrated circuit design demands innovative approaches to address timing optimization challenges in advanced technology nodes. Graph Neural Networks (GNNs) have emerged as a transformative paradigm for modeling circuit representations and optimizing placement decisions. This paper presents a comprehensive investigation of GNN applications in timing-driven placement optimization for sub-10nm process technologies. We propose a novel framework that leverages GNN architectures to encode circuit connectivity patterns, predict timing metrics, and guide placement algorithms toward solutions that minimize critical path delays while maintaining acceptable wirelength overhead. Our methodology employs a two-stage GNN model integrating global placement refinement with local timing optimization subroutines. The framework captures spatial dependencies between circuit components through message passing mechanisms while incorporating timing constraints directly into the optimization objective. Experimental evaluations on industry benchmark circuits demonstrate that GNN-based timing optimization achieves 18-24% reduction in worst negative slack compared to conventional analytical placement methods, with runtime improvements of 3-5x over traditional static timing analysis iterations. The proposed approach maintains placement quality metrics including wirelength increase below 7% and demonstrates robust convergence across diverse circuit topologies ranging from processor cores to memory controllers. This research establishes GNNs as viable alternatives to conventional timing-driven placement algorithms and opens new directions for machine learning integration in electronic design automation workflows.
subject: Articles
  topic: section
subject: Graph Neural Networks
  topic: keywords
subject: Timing Optimization
  topic: keywords
subject: Placement Algorithm
  topic: keywords
subject: Advanced Process Nodes
  topic: keywords
subject: Electronic Design Automation
  topic: keywords
subject: Critical Path Analysis
  topic: keywords
subject: Wirelength Minimization
  topic: keywords
creator: Jiahao Liu
creator: Pengfei Wu
creator: Robert Klein
rights: Copyright (c) 2026 Jiahao Liu, Pengfei Wu, Robert Klein (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 83
ending page: 94
doi: 10.71465/fair632

Toward Evasion-Resistant LLM Attribution with Multi-Scale Watermarking and Cryptographic Verification
type: item
description: Large language models (LLMs) have transformed natural language generation capabilities across numerous applications, yet their proliferation raises critical concerns regarding content attribution, intellectual property protection, and potential misuse. Watermarking techniques have emerged as promising solutions for embedding verifiable signals into LLM outputs, but existing approaches remain vulnerable to sophisticated evasion attacks that exploit detection mechanisms through adversarial modifications. This paper introduces a novel watermarking framework that integrates multi-scale semantic embedding with cryptographic verification to achieve robust attribution of LLM-generated text. Our approach operates across multiple granularity levels, from token-level perturbations to discourse-level structural patterns, while incorporating error-correcting codes and cryptographic signatures to ensure detection integrity even under aggressive tampering attempts. Through comprehensive evaluation on diverse text generation tasks, we demonstrate that our framework achieves superior robustness against paraphrasing attacks, token substitution, and deletion operations while maintaining high text quality with perplexity comparable to unwatermarked outputs. The integration of cryptographic primitives enables public verifiability without exposing watermarking keys, addressing critical security requirements for real-world deployment. Our results show detection accuracy exceeding 94 percent under various attack scenarios while preserving semantic coherence and stylistic naturalness of generated text.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Watermarking
  topic: keywords
subject: Attribution, Evasion Attacks
  topic: keywords
subject: Cryptographic Verification
  topic: keywords
subject: Multi-Scale Embedding
  topic: keywords
subject: Error-Correcting Codes
  topic: keywords
subject: Content Provenance
  topic: keywords
creator: Pieter Janssen
creator: Elisa Conti
rights: Copyright (c) 2026 Pieter Janssen, Elisa Conti (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 72
ending page: 82
doi: 10.71465/fair631

Enhancing Domain-Specific Language Models with Knowledge Graph Injection and Graph Attention Networks
type: item
description: The rapid evolution of Large Language Models has revolutionized natural language processing, yet these models frequently exhibit limitations when deployed in specialized high-stakes domains such as medicine, law, and engineering. A primary deficiency is the propensity for hallucination and the inability to access up-to-date, structured factual knowledge that was not present or emphasized during the pre-training phase. This paper proposes a novel architecture that integrates Domain-Specific Knowledge Graphs with pre-trained language models utilizing Graph Attention Networks. By employing a dual-stream mechanism that processes textual input alongside structured graph data, we facilitate a deep injection of semantic relationships into the latent space of the language model. The Graph Attention Network component dynamically weighs the importance of neighboring entities within the knowledge graph, allowing the model to attend to the most relevant factual context corresponding to the input query. We evaluate this approach on two distinct domain-specific datasets involving biomedical and legal texts. Our experimental results demonstrate that this injection mechanism significantly outperforms standard fine-tuning approaches in terms of factual accuracy and reasoning capabilities. The proposed method offers a scalable pathway toward creating more reliable and logically sound domain-specific artificial intelligence systems.
subject: Articles
  topic: section
subject: Knowledge Graphs
  topic: keywords
subject: Graph Attention Networks
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Domain Adaptation
  topic: keywords
creator: Jean Dupont
creator: Marie Laurent
creator: Michael Reynolds
rights: Copyright (c) 2026 Jean Dupont, Marie Laurent, Michael Reynolds (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-30
publication date: 2026-01-30
starting page: 63
ending page: 71
doi: 10.71465/fair603

Integrating Symbolic Reasoning into Neural Networks: A Neuro-Symbolic Logic Programming Approach for Enhanced Explainability
type: item
description: The dichotomy between sub-symbolic connectionist approaches and symbolic logic-based systems constitutes a fundamental divide in the history of artificial intelligence. While deep neural networks have achieved unprecedented success in perceptual tasks such as image recognition and natural language processing, they continue to suffer from a lack of interpretability and a tendency to fail in scenarios requiring rigorous logical consistent reasoning. Conversely, symbolic systems offer high explainability and verifiable reasoning chains but struggle with the noise and ambiguity inherent in real-world sensory data. This paper proposes a unified Neuro-Symbolic Logic Programming framework that integrates differentiable logic layers within deep neural architectures. By mapping logical predicates to continuous real-valued tensors and relaxing Boolean operators into differentiable functions, we enable end-to-end training of systems that possess both the learning capability of neural networks and the reasoning structure of logic programming. Our experimental results demonstrate that this hybrid approach not only matches state-of-the-art performance in complex reasoning tasks but also significantly outperforms baseline models in terms of explainability and data efficiency. The framework allows for the extraction of explicit logical rules from trained networks, providing a window into the decision-making process of the model and bridging the gap between data-driven learning and knowledge-based reasoning.
subject: Articles
  topic: section
subject: Neuro-Symbolic AI
  topic: keywords
subject: Differentiable Logic
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Deep Learning Integration
  topic: keywords
creator: Arthur Hamilton
creator: Claire Vance
creator: Eleanor P. Wright
rights: Copyright (c) 2026 Arthur Hamilton, Claire Vance, Eleanor P. Wright (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-30
publication date: 2026-01-30
starting page: 54
ending page: 62
doi: 10.71465/fair602

Optimization of Adaptive Prompt Engineering for Large Language Models via Bayesian Inference in Low-Resource Settings
type: item
description: The rapid proliferation of Large Language Models (LLMs) has necessitated the development of effective prompt engineering strategies to harness their full potential. However, the stochastic nature of LLM outputs and the vast, discrete combinatorial space of natural language make manual prompt design a laborious and often suboptimal process. While automated prompt optimization techniques exist, they typically require significant computational resources, massive datasets, or access to model gradients, rendering them inaccessible for low-resource environments and edge computing applications. This paper proposes a novel framework for optimizing adaptive prompt engineering using Bayesian Inference. We introduce a sample-efficient methodology that treats prompt selection as a black-box optimization problem, utilizing Gaussian Processes to model the latent manifold of prompt effectiveness. By strategically balancing exploration and exploitation through acquisition functions, our approach converges on high-performing prompts with significantly fewer model queries than traditional grid search or reinforcement learning paradigms. We demonstrate that this method achieves state-of-the-art performance on reasoning benchmarks while reducing token consumption by an order of magnitude, making advanced prompt engineering feasible for academic laboratories and limited-budget applications.
subject: Articles
  topic: section
subject: Bayesian Optimization
  topic: keywords
subject: Prompt Engineering
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Low-Resource NLP.
  topic: keywords
creator: Arthur J. Williams
creator: Katherine L. McDough
creator: Thomas S. Halloway
rights: Copyright (c) 2026 Arthur J. Williams, Katherine L. McDough, Thomas S. Halloway (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-04-30
publication date: 2026-04-30
starting page: 46
ending page: 53
doi: 10.71465/fair601

Theoretical Mechanisms of How AI-Enabled Driver Assistance Systems Influence Consumers’Automobile Choice Behavior
type: item
description: This study develops a theoretical mechanism-based framework to explain how artificial intelligence (AI)-enabled driver assistance systems influence consumers’ automobile choice behavior. While research on AI-enabled driver assistance has largely focused on engineering feasibility, system reliability, and safety performance, consumer research has not sufficiently theorized how AI systems’ autonomy-related properties reshape consumers’ cognitive evaluations and decision processes. Distinct from conventional vehicle attributes, AI-enabled driver assistance systems constitute intelligent socio-technical configurations that participate in driving decision-making, thereby introducing unique psychological concerns such as perceived autonomy, controllability, transparency, and responsibility ambiguity. Building on prior work in consumer choice, technology acceptance, and trust–risk theories, this study conceptualizes four key perceived attributes—perceived intelligence, perceived autonomy, perceived controllability, and perceived transparency—and argues that AI-enabled driver assistance affects automobile choice behavior primarily through mediated pathways of trust formation and risk/uncertainty appraisal. The framework further clarifies that the choice effects of AI-enabled driver assistance are not uniformly positive; rather, they depend on consumers’ perceived trade-offs between anticipated technological benefits and psychological uncertainty. By theorizing AI-enabled driver assistance as an autonomy-bearing technology element in consumption contexts, the study extends consumer choice theorizing into AI settings, refines conceptual distinctions between objective technical capability and subjective perceived attributes, and offers an integrative mechanism model that can guide future empirical work and managerial practice in intelligent vehicle design and communication.
subject: Articles
  topic: section
subject: AI-enabled driver assistance
  topic: keywords
subject: consumer choice
  topic: keywords
subject: perceived intelligence
  topic: keywords
subject: perceived autonomy
  topic: keywords
subject: perceived controllability
  topic: keywords
subject: perceived transparency
  topic: keywords
subject: trust in AI
  topic: keywords
subject: risk perception
  topic: keywords
subject: uncertainty
  topic: keywords
creator: Yanqing Zhou
creator: Bowen Shi
rights: Copyright (c) 2026 Yanqing Zhou, Bowen Shi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-14
publication date: 2026-01-14
starting page: 36
ending page: 45
doi: 10.71465/fair579

Security-Constrained Graph Neural Clustering for Industrial Manufacturing Systems under OT and Hardware Constraints
type: item
description: Industrial manufacturing systems increasingly rely on tightly coupled operational technology (OT) networks and heterogeneous hardware platforms, which exposes them to sophisticated multi-stage cyber attacks that propagate across devices, control logic, and production processes. Existing graph-based security analytics often overlook OT process semantics and hardware constraints, resulting in unstable clustering results and limited practical deployability. This paper proposes a Security-Constrained Graph Neural Clustering framework for industrial manufacturing systems that explicitly incorporates OT semantics and hardware constraints into attack-chain analysis. The manufacturing environment is modeled as a heterogeneous graph integrating OT assets, communication and control relationships, process-stage dependencies, and device-level resource limitations. A security-oriented clustering objective is designed to aggregate related assets into attack-chain communities while enforcing OT-consistent propagation and hardware-feasible inference. The proposed framework further enhances robustness under incomplete or noisy telemetry and provides interpretable community-level risk indicators to support security operations. Experimental results on industrial manufacturing datasets demonstrate that the proposed approach achieves more coherent and stable attack-chain communities than representative baselines, while maintaining computational efficiency suitable for deployment in resource-constrained OT environments.
subject: Articles
  topic: section
subject: Industrial Cybersecurity
  topic: keywords
subject: OT Security
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Security-Constrained Clustering
  topic: keywords
subject: Attack-Chain Detection
  topic: keywords
subject: Community Detection
  topic: keywords
subject: Hardware-Aware Learning
  topic: keywords
creator: Fu Wang
creator: Liang Zhang
rights: Copyright (c) 2026 Fu Wang, Liang Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-14
publication date: 2026-01-14
starting page: 25
ending page: 35
doi: 10.71465/fair577

Quantifying the Interplay Between Panic Propagation and Misinformation on Social Media Using Large Language Models
type: item
description: The digital age has catalyzed a phenomenon where information diffusion occurs at unprecedented velocities, often outpacing the capacity for verification. This paper investigates the symbiotic relationship between panic propagation and the spread of misinformation on social media platforms during crisis events. While traditional sentiment analysis and fact-checking systems have operated in isolation, we propose a novel framework that utilizes Large Language Models (LLMs) to jointly model these phenomena. By leveraging the semantic reasoning capabilities of state-of-the-art LLMs, we quantify the causality and temporal lag between exposure to falsified narratives and the subsequent escalation of collective anxiety. Our methodology introduces the Panic-Misinformation Interaction Index (PMII), a metric derived from high-dimensional embedding spaces, to measure the volatility of public discourse. We evaluate our approach on a massive dataset curated from social media feeds during recent global health emergencies. The results demonstrate that misinformation does not merely accompany panic but acts as a primary accelerant, with a quantifiable amplification factor. Furthermore, our LLM-driven approach outperforms baseline deep learning models in predictive accuracy regarding the trajectory of social hysteria.
subject: Articles
  topic: section
subject: Large Language Models
  topic: keywords
subject: Social Media Analysis
  topic: keywords
subject: Misinformation
  topic: keywords
subject: Panic Propagation
  topic: keywords
subject: Crisis Informatics
  topic: keywords
creator: Chen Xie
rights: Copyright (c) 2026 Chen Xie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-11
publication date: 2026-01-11
starting page: 1
ending page: 8
doi: 10.71465/fair575

SecHOT-GNC: Security-Oriented Hardware- and OT-Aware Graph Neural Clustering for Attack-Chain Community Detection in Industrial Fiber Manufacturing Systems
type: item
description: Industrial fiber manufacturing systems increasingly rely on tightly coupled operational technology (OT) networks and heterogeneous hardware controllers, making them vulnerable to multi-stage attack chains that traverse cyber–physical dependencies. Existing graph-based security analytics often ignore hardware constraints and OT process semantics, leading to unstable communities, weak attack-chain consistency, and limited deployability on shop-floor compute. This paper proposes SecHOT-GNC, a security-oriented, hardware- and OT-aware graph neural clustering framework for attack-chain community detection in industrial fiber manufacturing. We model the plant as a multi-layer heterogeneous graph that integrates OT assets (PLCs, HMIs, drives, sensors), communication flows, process topology, and hardware attributes (resource budgets, firmware/OS class, interface types, timing constraints). SecHOT-GNC couples OT-aware message passing with a security-driven clustering objective that aligns communities with plausible attacker paths by jointly optimizing (i) attack-chain consistency, (ii) OT-process coherence, (iii) hardware feasibility under on-device constraints, and (iv) robustness to noisy/partial telemetry. The framework further produces interpretable community rationales via edge/feature attributions and yields community-level risk scores to support prioritization of defense actions. Experiments on industrial fiber manufacturing datasets and attack simulations demonstrate that SecHOT-GNC improves attack-chain community quality and stability over representative baselines, while maintaining practical inference latency and memory footprints suitable for edge/plant deployment.
subject: Articles
  topic: section
subject: Security-Oriented Graph Neural Networks
  topic: keywords
subject: OT Cybersecurity
  topic: keywords
subject: ardware-Aware Learning
  topic: keywords
subject: Attack-Chain Detection
  topic: keywords
subject: Community Detection
  topic: keywords
subject: Graph Neural Clustering
  topic: keywords
subject: Industrial Control Systems (ICS)
  topic: keywords
subject: Industrial Fiber Manufacturing Systems
  topic: keywords
creator: Liang Zhang
creator: Fu Wang
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 9
ending page: 24
doi: 10.71465/fair571

Learning Risk and Communities in Complex Systems: A Review of Temporal Models, Graph Neural Networks, and Fourier Approaches
type: item
description: Risk assessment and community detection are two central problems in data-driven decision systems, spanning finance, infrastructure, cybersecurity, transportation, and social networks. In modern settings, both tasks are increasingly defined by (i) temporal complexity (non-stationarity, regime shifts, delays), (ii) relational structure (interacting agents and cascading effects), and (iii) multi-scale frequency behavior (smooth trends vs. abrupt anomalies), motivating learning frameworks that unify deep temporal models, graph representation learning, and Fourier/spectral operators. This review synthesizes progress across three complementary axes: deep temporal learning (e.g., sequence models and transformers for forecasting, anomaly detection, and early-warning), graph-based learning (GNNs, graph transformers, spatio-temporal GNNs, and temporal graph neural networks), and Fourier/spectral learning (graph Fourier transform, spectral filters, wavelets, and frequency-aware graph architectures). We provide a taxonomy that maps model families to learning settings and objectives, compare methods under shared evaluation protocols, and highlight practical design trade-offs such as scalability, stability, interpretability, and robustness. Finally, we outline open challenges—dataset realism, dynamic community ground truth, distribution shift, and frequency-domain generalization—and propose a benchmarking checklist to support reproducible research across risk prediction and community discovery.
subject: Articles
  topic: section
subject: Risk Assessment
  topic: keywords
subject: Community Detection
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
creator: Ethan Parker
creator: Dylan Collins
creator: Luke Richardson
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 686
ending page: 694
doi: 10.71465/fair568

A Hybrid Neural–Symbolic Framework for Interpretable Fault Diagnosis in Rotating Machinery
type: item
description: The advent of Industry 4.0 has precipitated a paradigm shift in the maintenance strategies of complex industrial systems, particularly rotating machinery, moving from reactive to predictive maintenance regimes. While deep learning models have achieved state-of-the-art performance in fault diagnosis due to their powerful feature extraction capabilities, they suffer from a critical lack of transparency, often described as the black-box problem. This opacity hinders their adoption in safety-critical environments where understanding the etiology of a fault is as significant as its detection. This paper proposes a novel Hybrid Neural–Symbolic Framework (HNSF) that integrates the perceptual capabilities of deep neural networks with the inferential transparency of symbolic logic. We utilize a One-Dimensional Convolutional Neural Network (1D-CNN) to extract high-level latent features from raw vibration signals, which are subsequently mapped to semantic concepts within a predefined knowledge graph. A differentiable reasoning layer then applies First-Order Logic rules to these concepts to deduce fault classes, ensuring that the model's predictions are consistent with domain knowledge. Experimental validation on the Case Western Reserve University (CWRU) bearing dataset demonstrates that our framework not only achieves classification accuracy comparable to pure deep learning models but also provides human-readable explanations for its decisions. The results suggest that bridging the subsymbolic and symbolic gap offers a robust pathway toward trustworthy industrial artificial intelligence.
subject: Articles
  topic: section
subject: Fault Diagnosis
  topic: keywords
subject: Neural-Symbolic Computing
  topic: keywords
subject: Rotating Machinery
  topic: keywords
subject: Explainable AI
  topic: keywords
creator: Haoran Yuan
creator: Fei Tian
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 674
ending page: 685
doi: 10.71465/fair540

Adversarially Robust Sequence Models with Frequency-Domain Consistency Regularization
type: item
description: The deployment of sequence models in safety-critical applications, ranging from automated financial trading to clinical narrative analysis, is currently hindered by their susceptibility to adversarial perturbations. While adversarial training has emerged as a rigorous defense mechanism, it predominantly operates in the time or token domain, often failing to account for the spectral characteristics of the input data where adversarial noise tends to concentrate. In this paper, we introduce Frequency-Domain Consistency Regularization (FDCR), a novel architectural constraint that enforces semantic invariance across the spectral decomposition of latent representations. By leveraging the Discrete Fourier Transform (DFT) within the training loop, FDCR penalizes discrepancies between the high-frequency components of clean and perturbed sequences, effectively filtering out non-robust features that contribute to model fragility. We provide a theoretical analysis demonstrating that spectral regularization tightens the generalization bound for recurrent architectures under min-max perturbation constraints. Extensive experiments on standard benchmarks verify that FDCR significantly outperforms state-of-the-art adversarial defense methods in maintaining robust accuracy while mitigating the trade-off with clean performance.
subject: Articles
  topic: section
subject: Adversarial Robustness
  topic: keywords
subject: Sequence Modeling
  topic: keywords
subject: Frequency Domain
  topic: keywords
subject: Consistency Regularization
  topic: keywords
creator: Tao Mao
creator: Emily Wilson
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 665
ending page: 673
doi: 10.71465/fair539

A Uncertainty-Calibrated Transformer for Long-Horizon Forecasting with Missing and Irregular Observations
type: item
description: The proliferation of Internet of Things (IoT) devices and distributed sensor networks has resulted in an explosion of time-series data, yet the utility of this data is frequently compromised by irregularities, such as missing observations, non-uniform sampling rates, and sensor failures. While Transformer-based architectures have established a new state-of-the-art in sequence modeling, standard implementations rely implicitly on fixed-interval discrete time steps, rendering them suboptimal for irregular temporal environments. Furthermore, long-horizon forecasting inherently involves accumulating errors, necessitating robust uncertainty quantification to support downstream decision-making processes. This paper introduces the Uncertainty-Calibrated Continuous Transformer (UCCT), a novel architecture designed to address these dual challenges simultaneously. We propose a continuous-time positional encoding mechanism that leverages the temporal timestamps directly, decoupling the model from the rigid index-based sequence assumption. Additionally, we integrate a probabilistic decoding head that outputs distributional parameters rather than point estimates, calibrated via a composite loss function balancing accuracy and aleatoric uncertainty estimation. Extensive experiments on real-world datasets, including energy consumption, meteorology, and healthcare telemetry, demonstrate that the proposed UCCT outperforms current deterministic and stochastic baselines. Specifically, the model achieves a reduction in Mean Squared Error by approximately 14% in scenarios with 50% missing data, while providing reliable confidence intervals that accurately capture the increasing variance over long forecast horizons.
subject: Articles
  topic: section
subject: Time Series Forecasting
  topic: keywords
subject: Transformer Networks
  topic: keywords
subject: Irregular Sampling
  topic: keywords
subject: Uncertainty Quantification
  topic: keywords
creator: Joseph Hernandez
creator: Barbara Hall
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-30
publication date: 2025-12-30
starting page: 656
ending page: 664
doi: 10.71465/fair538

Retrieval-Augmented Graph Reasoning with Large Language Models for Explainable Incident Diagnosis
type: item
description: The increasing complexity of modern distributed systems has created significant challenges in incident diagnosis and root cause analysis. Traditional approaches often lack explainability and struggle with the dynamic nature of system failures, while pure machine learning methods suffer from limited interpretability and contextual understanding. This paper proposes a novel framework that integrates Retrieval-Augmented Generation (RAG) with graph-based reasoning and Large Language Models (LLMs) to enable explainable incident diagnosis in complex systems. The proposed approach leverages knowledge graphs to capture causal relationships among system components, employs retrieval mechanisms to access relevant historical incident data, and utilizes LLMs to generate human-interpretable explanations for diagnosed incidents. Through comprehensive evaluation on real-world incident datasets, our method demonstrates superior performance in fault localization accuracy, achieving 92.3% precision while providing transparent reasoning paths that enhance engineer trust and accelerate remediation workflows. The framework addresses critical limitations in existing approaches by combining the structured reasoning capabilities of graph neural networks with the semantic understanding and generation abilities of large language models, thereby advancing the state-of-the-art in intelligent operations and system reliability engineering.
subject: Articles
  topic: section
subject: Retrieval-Augmented Generation
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Graph Neural Networks
  topic: keywords
subject: Incident Diagnosis
  topic: keywords
subject: Root Cause Analysis
  topic: keywords
subject: Explainable AI
  topic: keywords
subject: Causal Reasoning
  topic: keywords
creator: Yuxiang He
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-26
publication date: 2025-12-26
starting page: 640
ending page: 655
doi: 10.71465/fair531

Adaptive Query Decomposition via Learned Symbolic Primitives in Retrieval-Augmented Systems
type: item
description: Retrieval-Augmented Generation systems have emerged as a promising paradigm for enhancing large language models with external knowledge retrieval capabilities. However, complex user queries often require sophisticated decomposition strategies to effectively retrieve relevant information from heterogeneous knowledge bases. This paper presents a novel framework for adaptive query decomposition that leverages learned symbolic primitives to intelligently break down complex queries into retrievable sub-components. Our approach combines neural representation learning with symbolic reasoning mechanisms to create a hybrid architecture that maintains interpretability while achieving robust performance across diverse query types. The proposed system employs a hierarchical decomposition strategy where queries are first analyzed to identify semantic components, which are then mapped to learned symbolic primitives representing fundamental information-seeking patterns through semantic pattern tree construction. These primitives guide the retrieval process through structured query reformulation and contextual expansion across distributed processing layers. Experimental evaluation demonstrates that our method achieves significant improvements in retrieval precision and answer quality compared to baseline neural ranking approaches, particularly for multi-hop reasoning tasks requiring integration of information from multiple sources.
subject: Articles
  topic: section
subject: retrieval-augmented generation
  topic: keywords
subject: query decomposition
  topic: keywords
subject: symbolic learning
  topic: keywords
subject: neural-symbolic integration
  topic: keywords
subject: information retrieval
  topic: keywords
subject: semantic pattern trees
  topic: keywords
creator: Tomáš Novák
creator: Isabelle Fournier
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-25
publication date: 2025-12-25
starting page: 635
ending page: 639
doi: 10.71465/fair530

Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization
type: item
description: Autonomous driving represents one of the most challenging applications of artificial intelligence (AI), requiring sophisticated decision-making capabilities that integrate perception, prediction, and planning under dynamic and uncertain conditions. Recent advances in learning-driven approaches have demonstrated remarkable potential in addressing these challenges through multimodal understanding, world modeling, and policy optimization. Deep learning (DL) techniques enable vehicles to process heterogeneous sensory inputs including camera images, LiDAR point clouds, and radar signals to construct comprehensive environmental representations. World models provide predictive frameworks that simulate future scenarios and potential outcomes, allowing autonomous systems to anticipate complex traffic dynamics and make informed decisions. Reinforcement learning (RL) and imitation learning methods optimize driving policies through interaction with real and simulated environments, progressively improving decision quality and safety. This review examines the current state of learning-driven decision intelligence in autonomous driving, analyzing how multimodal perception architectures extract meaningful features from diverse sensor modalities, how world modeling techniques enable forward-looking planning capabilities, and how policy optimization frameworks translate environmental understanding into safe and efficient driving behaviors. We synthesize recent developments in transformer-based architectures, neural rendering approaches, and end-to-end learning systems that directly map sensory inputs to control actions. The integration of these components presents both significant opportunities and substantial challenges, including handling distribution shifts between training and deployment scenarios, ensuring robustness to adversarial conditions, and achieving the safety guarantees required for real-world deployment.
subject: Articles
  topic: section
subject: Autonomous driving
  topic: keywords
subject: Decision intelligence
  topic: keywords
subject: Multimodal learning
  topic: keywords
subject: World models
  topic: keywords
subject: Policy optimization
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Reinforcement learning
  topic: keywords
creator: Fengyuan Zhang
creator: Jie-Si Yang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-24
publication date: 2025-12-24
starting page: 616
ending page: 634
doi: 10.71465/fair529

Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics
type: item
description: The transition from traditional rule-based forecasting systems to machine learning (ML) approaches represents a fundamental shift in enterprise financial planning methodologies. This review examines the evolution of financial forecasting techniques, analyzing how ML algorithms have transformed predictive analytics in corporate environments. Traditional rule-based models, while offering interpretability and deterministic outputs, often struggle with complex non-linear patterns and dynamic market conditions. In contrast, ML methods including deep learning (DL), ensemble techniques, and hybrid models demonstrate superior performance in capturing intricate relationships within financial data. This paper synthesizes recent literature on ML applications in enterprise financial forecasting, evaluating methodologies such as recurrent neural networks (RNN), long short-term memory (LSTM) networks, gradient boosting machines, and transformer architectures. The review explores implementation challenges including data quality requirements, model interpretability concerns, regulatory compliance, and organizational change management. Empirical evidence suggests that ML-based forecasting systems can achieve accuracy improvements of 15-40% compared to traditional approaches, though success depends heavily on data infrastructure, talent capabilities, and strategic integration. The paper concludes by identifying emerging trends including explainable artificial intelligence (AI), automated machine learning (AutoML), and federated learning approaches that address current limitations while maintaining the predictive advantages of ML systems.
subject: Articles
  topic: section
subject: machine learning
  topic: keywords
subject: financial forecasting
  topic: keywords
subject: enterprise planning
  topic: keywords
subject: predictive analytics
  topic: keywords
subject: deep learning
  topic: keywords
subject: rule-based models
  topic: keywords
subject: time series analysis
  topic: keywords
subject: neural networks
  topic: keywords
subject: gradient boosting
  topic: keywords
subject: model interpretability
  topic: keywords
creator: Zifan Chen
creator: Jingyi Liu
creator: Jiaying Chen
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-14
publication date: 2025-12-14
starting page: 541
ending page: 564
doi: 10.71465/fair527

Infrastructure as Code and Observability Automation for Payment Systems in Cloud-Native Environments
type: item
description: The rapid adoption of cloud-native architectures has fundamentally transformed the deployment and operation of payment systems, necessitating automated approaches to infrastructure management and system monitoring. Infrastructure as Code (IaC) enables the declarative definition and version-controlled management of computational resources, while observability automation provides real-time insights into complex distributed payment workflows. This review examines the convergence of IaC and observability automation in modern payment infrastructures, analyzing their combined impact on system reliability, compliance, and operational efficiency. The integration of IaC frameworks with observability platforms addresses critical challenges in payment system management, including deployment consistency, security compliance, and performance optimization. Through systematic analysis of recent literature, this paper synthesizes current approaches to automated infrastructure provisioning, distributed tracing, metrics collection, and log aggregation within payment processing environments. The review identifies emerging patterns in declarative infrastructure management, automated monitoring configuration, and intelligent alerting mechanisms that collectively enhance the resilience of cloud-native payment systems. Findings indicate that organizations implementing comprehensive IaC and observability automation achieve significant improvements in deployment velocity, incident response times, and regulatory compliance adherence. This paper contributes to the understanding of how automation technologies reshape payment infrastructure management and provides insights into future research directions for autonomous system operations.
subject: Articles
  topic: section
subject: Infrastructure as Code
  topic: keywords
subject: Observability Automation
  topic: keywords
subject: Payment Systems
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Distributed Tracing
  topic: keywords
subject: Monitoring Automation
  topic: keywords
subject: DevOps
  topic: keywords
subject: latform Engineering
  topic: keywords
subject: Site Reliability Engineering
  topic: keywords
subject: Microservices
  topic: keywords
creator: Zhuoqi Zeng
creator: Han Lin
creator: Jingyi Liu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-18
publication date: 2025-12-18
starting page: 589
ending page: 615
doi: 10.71465/fair526

Building Billion-User Products from Zero to Scale: Full-Stack Engineering Patterns for Creator Monetization and Content Protection Systems
type: item
description: The rapid expansion of digital creator economies has necessitated sophisticated engineering approaches to build platforms capable of supporting billion-user scales while maintaining robust monetization and content protection mechanisms. This review examines the full-stack engineering patterns employed in developing large-scale creator platforms, focusing on the technical architectures that enable creator monetization and content protection systems. Modern platforms face unprecedented challenges in balancing user experience, creator compensation, and intellectual property protection while managing massive concurrent user loads. This paper synthesizes current research on microservices architecture (MSA), distributed systems, blockchain technology, machine learning (ML), and cloud-native infrastructure to present comprehensive engineering patterns for building scalable creator platforms. The review analyzes payment processing systems, content delivery networks (CDN), digital rights management (DRM), and fraud detection mechanisms that constitute the technical foundation of billion-user products. Through examination of contemporary platforms and emerging technologies, this paper identifies critical design patterns including event-driven architecture (EDA), eventual consistency models, horizontal scaling strategies, and zero-trust security frameworks. The findings reveal that successful billion-user creator platforms require careful orchestration of backend infrastructure, real-time data processing, intelligent caching strategies, and multi-layered security approaches. This review contributes to the understanding of engineering tradeoffs in building creator economy platforms and provides insights into future technological directions for supporting the next generation of digital content creation and distribution systems.
subject: Articles
  topic: section
subject: creator economy
  topic: keywords
subject: full-stack engineering
  topic: keywords
subject: scalability
  topic: keywords
subject: content protection
  topic: keywords
subject: monetization systems
  topic: keywords
subject: distributed architecture
  topic: keywords
subject: billion-user platforms
  topic: keywords
creator: Ziyang Zeng
creator: Guanyu Ding
creator: Shiyu Yang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 565
ending page: 588
doi: 10.71465/fair525

Cross-Hardware Optimization Strategies for Large-Scale Recommendation Model Inference in Production Systems
type: item
description: Large-scale recommendation systems have become indispensable components of modern digital platforms, processing billions of user interactions daily to deliver personalized content and services. The computational demands of recommendation model inference in production environments present significant challenges, particularly when deploying across heterogeneous hardware architectures. This review examines cross-hardware optimization strategies for large-scale recommendation model inference, focusing on techniques that enable efficient deployment across graphics processing units (GPUs), central processing units (CPUs), tensor processing units (TPUs), and field-programmable gate arrays (FPGAs). We systematically analyze recent advances in model compression, including quantization and pruning techniques specifically designed for recommendation models. The paper explores hardware-aware neural architecture search (NAS) methods that optimize model structures for target hardware platforms while maintaining prediction accuracy. We investigate dynamic resource allocation strategies and load balancing mechanisms that improve throughput in multi-device production systems. Additionally, we examine emerging heterogeneous computing frameworks that enable seamless model deployment across diverse hardware infrastructures. Our analysis reveals that successful cross-hardware optimization requires careful consideration of model architecture, hardware characteristics, and system-level constraints. The review identifies critical research gaps in real-time inference optimization, automated hardware selection, and energy-efficient deployment strategies. We conclude that integrated optimization approaches combining multiple techniques offer the most promising path toward efficient large-scale recommendation system deployment in heterogeneous production environments.
subject: Articles
  topic: section
subject: recommendation systems
  topic: keywords
subject: cross-hardware optimization
  topic: keywords
subject: model inference
  topic: keywords
subject: heterogeneous computing
  topic: keywords
subject: neural architecture search
  topic: keywords
subject: model compression
  topic: keywords
subject: production systems
  topic: keywords
subject: GPU acceleration
  topic: keywords
creator: Zijian Shen
creator: Zimeng Wang
creator: Yang Liu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-11
publication date: 2025-12-11
starting page: 521
ending page: 540
doi: 10.71465/fair524

A Comprehensive Review of Deep Temporal, Graph-Based, and Bayesian Learning Frameworks for Risk Assessment and Community Detection
type: item
description: Risk assessment and structural pattern discovery in complex systems—such as environmental monitoring networks, spatiotemporal infrastructures, and large-scale relational data—pose significant challenges due to nonlinear temporal dynamics, latent graph structures, and pervasive uncertainty. In recent years, the rapid development of deep learning has led to a diverse body of methods integrating temporal neural models, graph-based representation learning, and Bayesian inference to address these challenges. However, existing studies are often scattered across different application domains and methodological paradigms, lacking a unified and systematic perspective. This survey presents a comprehensive review of recent advances in deep temporal modeling, graph neural networks, and uncertainty-aware Bayesian learning frameworks for risk assessment and community detection. We first examine sequence-based models, including Long Short-Term Memory (LSTM) networks and Transformer architectures, highlighting their strengths and limitations in capturing long-range temporal dependencies for hazard source identification and risk prediction. We then review graph neural network–based approaches for community detection, with particular emphasis on hybrid frameworks that combine graph convolution or attention mechanisms with classical clustering and modularity optimization to enhance structural awareness and interpretability. Furthermore, we analyze Bayesian deep learning models and operator-learning frameworks that incorporate probabilistic reasoning, Markov priors, Fourier spectral modeling, and gauge-equivariant constraints to achieve calibrated prediction, robustness, and trustworthy decision support. To provide a structured understanding of the field, we introduce a methodological taxonomy and comparative analysis across key dimensions, including temporal modeling capability, graph structure integration, uncertainty quantification, and interpretability. Finally, we discuss open cha…
subject: Articles
  topic: section
subject: Risk assessment
  topic: keywords
subject: Community detection
  topic: keywords
subject: Deep learning
  topic: keywords
creator: Edith Wales
rights: Copyright (c) 2025 Edith Wales (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-23
publication date: 2025-12-23
starting page: 521
ending page: 525
doi: 10.71465/fair489

RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment
type: item
description: The integration of artificial intelligence (AI) agents into enterprise software development has emerged as a transformative approach to enhance productivity and code quality. Retrieval-Augmented Generation (RAG) represents a paradigm shift in how AI systems access and utilize knowledge, combining the generative capabilities of large language models (LLMs) with dynamic information retrieval mechanisms. This review paper examines the current state of RAG-based AI agents in enterprise software development contexts, focusing on implementation patterns and production deployment strategies. We analyze the architectural foundations of RAG systems, including vector databases, embedding models, and retrieval mechanisms that enable AI agents to access up-to-date code repositories, documentation, and organizational knowledge bases. The paper explores various implementation patterns such as code completion agents, automated testing assistants, documentation generators, and intelligent code review systems. We investigate production deployment challenges including scalability, latency optimization, security considerations, and integration with existing development workflows. Through systematic analysis of recent research and industrial applications, we identify key success factors for deploying RAG-based AI agents in enterprise environments, including context window management, retrieval accuracy, and hallucination mitigation strategies. The review also addresses emerging trends such as multi-agent collaboration, fine-tuning strategies for domain-specific tasks, and hybrid retrieval approaches. Our findings suggest that while RAG-based AI agents demonstrate significant potential for transforming enterprise software development, successful deployment requires careful consideration of organizational context, data privacy requirements, and continuous model evaluation frameworks.
subject: Articles
  topic: section
subject: Retrieval-Augmented Generation
  topic: keywords
subject: AI Agents; Enterprise Software Development
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Vector Databases
  topic: keywords
subject: Code Generation
  topic: keywords
subject: Production Deployment
  topic: keywords
subject: Implementation Patterns
  topic: keywords
subject: Knowledge Retrieval
  topic: keywords
subject: Software Engineering
  topic: keywords
creator: Xiuyuan Zhao
creator: Tiejiang Sun
creator: Shaochen Ren
creator: Jingyun Yang
creator: Yang Liu
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-02
publication date: 2025-12-02
starting page: 501
ending page: 520
doi: 10.71465/fair456

Dynamic Workflow Partitioning via Reinforcement Learning for Edge-Cloud Heterogeneous Systems
type: item
description: The proliferation of Internet of Things devices and edge computing infrastructure has created unprecedented opportunities for distributed workflow execution across heterogeneous edge-cloud environments. However, optimal workflow partitioning in such dynamic systems remains a significant challenge due to the complexity of resource heterogeneity, network variability, and diverse application requirements. This paper proposes a novel Dynamic Workflow Partitioning framework leveraging Deep Reinforcement Learning to intelligently distribute workflow tasks between edge nodes and cloud data centers. The framework employs a Deep Q-Network architecture enhanced with a Graph Neural Network encoder to capture workflow dependencies and system state representations. Through comprehensive evaluation using real-world workflow applications including CyberShake, Epigenomics, Inspiral, Montage, and Sipht, our approach demonstrates superior performance in minimizing execution time, reducing network overhead, and maintaining quality of service guarantees compared to traditional heuristic-based methods. The experimental results show that the proposed approach achieves up to 32% reduction in average workflow completion time and 41% improvement in resource utilization efficiency across various heterogeneous edge-cloud configurations.
subject: Articles
  topic: section
subject: Edge computing
  topic: keywords
subject: cloud computing
  topic: keywords
subject: workflow partitioning
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: deep Q-network
  topic: keywords
subject: heterogeneous systems
  topic: keywords
subject: task scheduling
  topic: keywords
subject: resource optimization
  topic: keywords
creator: Yuexin Zhang
creator: Caleb Morris
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-12-01
publication date: 2025-12-01
starting page: 483
ending page: 500
doi: 10.71465/fair455

Mixed Signal Approach Using C++ and DSP Hardware for Low Latency and Secure Speech Systems
type: item
description: Real-time speech systems need to provide quick responses while remaining resistant to security risks. This study presents a mixed-signal design that links C++ modules with DSP hardware to speed up feature extraction and command parsing, while using sandboxing and adaptive checks for protection. Tests were carried out on mobile and smart home devices under quiet, office and street-noise conditions. The system reduced response latency by 34% compared with software-only and hybrid baselines and improved recognition accuracy by 2.5–4.6 percentage points. Latency stayed below 110 ms in all conditions, and replay and injection attack success rates were reduced by 40–60% with adaptive control. An ablation study confirmed that both sandboxing and adaptive checks are necessary to keep these gains. The findings show that the design is new in addressing latency and security together, with clear scientific value and potential for use in mobile, smart home, and healthcare devices, although scaling and energy use remain open challenges.
subject: Articles
  topic: section
subject: real-time speech systems
  topic: keywords
subject: mixed-signal design
  topic: keywords
subject: DSP acceleration
  topic: keywords
subject: latency reduction
  topic: keywords
subject: sandboxing
  topic: keywords
subject: replay attack
  topic: keywords
subject: smart devices
  topic: keywords
creator: Jonathan R. Clark
creator: Matthew D. Harris
creator: Charlotte L. Chan
creator: Emily K. Wong
creator: Olivia J. Taylor
rights: Copyright (c) 2025 Jonathan R. Clark, Matthew D. Harris, Charlotte L. Chan, Emily K. Wong, Olivia J. Taylor (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-11-30
publication date: 2025-11-30
starting page: 483
ending page: 489
doi: 10.71465/fair449

Improving Intrusion Detection Robustness Through Adversarial Training Methods
type: item
description: Network Intrusion Detection Systems (NIDS) leveraging deep learning architectures have demonstrated exceptional performance in identifying cyber threats through automated feature learning and pattern recognition. However, recent investigations reveal critical vulnerabilities when these systems encounter adversarial attacks, where malicious actors introduce carefully crafted perturbations to evade detection mechanisms. This paper presents a comprehensive study of adversarial training methodologies specifically designed to enhance the robustness of deep neural network-based NIDS against sophisticated evasion techniques. We systematically investigate multiple adversarial training approaches, integrating both Fast Gradient Sign Method (FGSM) and Projected Gradient Descent (PGD) attack generation with deep learning architectures including fully-connected Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN). Through extensive experimentation on benchmark intrusion detection datasets, our adversarially-trained models achieve detection accuracy exceeding 94 percent even under strong adversarial perturbations, while maintaining competitive performance on clean network traffic. The research demonstrates that incorporating adversarial examples during training fundamentally reshapes decision boundaries, enabling intrusion detection systems to maintain operational effectiveness in adversarial environments.
subject: Articles
  topic: section
subject: Intrusion Detection Systems
  topic: keywords
subject: Adversarial Training
  topic: keywords
subject: Deep Neural Networks
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Network Security
  topic: keywords
subject: Robust Machine Learning
  topic: keywords
subject: Recurrent Neural Networks
  topic: keywords
creator: Kenji Sato
creator: Priya Nair
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-30
publication date: 2025-10-30
starting page: 457
ending page: 482
doi: 10.71465/fair423

Cross-Modal Attention Mechanisms for Inventory Optimization: Fusing IoT Sensor Data, News Sentiment, and Geospatial Information
type: item
description: Modern supply chain management faces unprecedented challenges in maintaining optimal inventory levels amid volatile market conditions, disruption risks, and rapidly changing consumer demands. This paper proposes a novel cross-modal attention framework that integrates Internet of Things (IoT) sensor data, news sentiment analysis, and geospatial information to enhance inventory optimization decisions. The proposed architecture leverages a transformer-based encoder-decoder structure with multi-head attention mechanisms to dynamically weight and fuse heterogeneous data sources, enabling real-time adaptive inventory management. Through the implementation of global attention layers that compute context vectors across all input modalities, the framework captures complex inter-modal dependencies that traditional single-modality approaches fail to represent. Experimental validation demonstrates that the cross-modal attention approach achieves significant improvements in demand forecasting accuracy, reducing mean absolute percentage error by 18.7% compared to conventional methods, while simultaneously improving service levels from 92.3% to 96.8%. Visualization of learned attention alignment matrices reveals interpretable patterns where the model dynamically focuses on relevant information sources based on market conditions. The integration of news sentiment provides early warning signals for demand fluctuations, IoT sensors enable granular monitoring of inventory conditions, and geospatial analysis optimizes distribution network configurations. This work contributes to the emerging field of multimodal fusion in supply chain analytics by demonstrating how attention mechanisms can effectively integrate diverse data modalities for superior decision-making.
subject: Articles
  topic: section
subject: Cross-modal attention
  topic: keywords
subject: Inventory optimization
  topic: keywords
subject: IoT sensors
  topic: keywords
subject: Sentiment analysis
  topic: keywords
subject: Geospatial information
  topic: keywords
subject: Supply chain management
  topic: keywords
subject: Multimodal fusion
  topic: keywords
creator: Emma Johnson
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 422
ending page: 435
doi: 10.71465/fair418

Reinforcement Learning Paradigms for Proactive Cybersecurity and Dynamic Risk Management
type: item
description: The escalating sophistication of cyber threats and the dynamic nature of modern network environments necessitate intelligent, adaptive security mechanisms capable of autonomous decision-making and continuous learning. Reinforcement learning (RL) has emerged as a promising paradigm for addressing these challenges by enabling security systems to learn optimal defense strategies through interaction with complex cyber environments. This review examines recent advances in RL applications for proactive cybersecurity and dynamic risk management, focusing on threat detection, intrusion prevention, malware analysis, and adaptive defense strategies. The paper synthesizes current research on various RL paradigms including deep Q-networks (DQN), policy gradient methods, actor-critic algorithms, and multi-agent reinforcement learning (MARL) in security contexts. We analyze how RL-based systems can autonomously discover vulnerabilities, respond to zero-day attacks, optimize security resource allocation, and adapt defense mechanisms in real-time. The review addresses critical challenges including reward function design, exploration-exploitation trade-offs in adversarial environments, sample efficiency, and the interpretability of learned security policies. Emerging trends such as adversarial RL, transfer learning for security applications, and the integration of RL with other artificial intelligence (AI) techniques are discussed. The findings indicate that while RL offers substantial potential for enhancing cybersecurity through autonomous learning and adaptation, practical deployment requires careful consideration of training stability, adversarial robustness, computational constraints, and the need for explainable decision-making in security-critical contexts. This comprehensive review provides researchers and practitioners with insights into the current state, open challenges, and future directions of RL-based cybersecurity systems.
subject: Articles
  topic: section
subject: Reinforcement Learning
  topic: keywords
subject: Cybersecurity
  topic: keywords
subject: Intrusion Detection
  topic: keywords
subject: Dynamic Risk Management
  topic: keywords
subject: Deep Q-Networks
  topic: keywords
subject: Policy Gradient
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: Adversarial Machine Learning
  topic: keywords
subject: Autonomous Defense
  topic: keywords
subject: Threat Intelligence
  topic: keywords
creator: Shaochen Ren
creator: Shiyang Chen
creator: Qun Zhang
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
date: 2025-10-25
publication date: 2025-10-25
starting page: 436
ending page: 456
doi: 10.71465/fair417