Frontiers in Artificial Intelligence Research
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.
Theoretical Mechanisms of How AI-Enabled Driver Assistance Systems Influence Consumers’Automobile Choice Behavior
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.
Copyright (c) 2026 Yanqing Zhou, Bowen Shi (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Security-Constrained Graph Neural Clustering for Industrial Manufacturing Systems under OT and Hardware Constraints
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.
Copyright (c) 2026 Fu Wang, Liang Zhang (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Quantifying the Interplay Between Panic Propagation and Misinformation on Social Media Using Large Language Models
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.
Copyright (c) 2026 Chen Xie (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
SecHOT-GNC: Security-Oriented Hardware- and OT-Aware Graph Neural Clustering for Attack-Chain Community Detection in Industrial Fiber Manufacturing Systems
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
Learning Risk and Communities in Complex Systems: A Review of Temporal Models, Graph Neural Networks, and Fourier Approaches
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.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
A Hybrid Neural–Symbolic Framework for Interpretable Fault Diagnosis in Rotating Machinery
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.
Adversarially Robust Sequence Models with Frequency-Domain Consistency Regularization
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.
A Uncertainty-Calibrated Transformer for Long-Horizon Forecasting with Missing and Irregular Observations
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.
Retrieval-Augmented Graph Reasoning with Large Language Models for Explainable Incident Diagnosis
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.
Adaptive Query Decomposition via Learned Symbolic Primitives in Retrieval-Augmented Systems
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.
Learning Driven Decision Intelligence for Autonomous Driving Through Multimodal Understanding World Modeling and Policy Optimization
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.
Machine Learning Methods for Financial Forecasting in Enterprise Planning: Transitioning from Rule-Based Models to Predictive Analytics
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.
Infrastructure as Code and Observability Automation for Payment Systems in Cloud-Native Environments
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.
Building Billion-User Products from Zero to Scale: Full-Stack Engineering Patterns for Creator Monetization and Content Protection Systems
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.
Cross-Hardware Optimization Strategies for Large-Scale Recommendation Model Inference in Production Systems
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.
A Comprehensive Review of Deep Temporal, Graph-Based, and Bayesian Learning Frameworks for Risk Assessment and Community Detection
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 challenges and future research directions, such as scalable dynamic graph learning, unified temporal–graph–Bayesian modeling, and explainable uncertainty-aware systems. This survey aims to serve as a reference for researchers and practitioners seeking principled and reliable intelligent modeling approaches for complex, uncertain, and interconnected real-world systems.
Copyright (c) 2025 Edith Wales (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
RAG-Based AI Agents for Enterprise Software Development: Implementation Patterns and Production Deployment
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.
AI Agents; Enterprise Software Development
Dynamic Workflow Partitioning via Reinforcement Learning for Edge-Cloud Heterogeneous Systems
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.
Mixed Signal Approach Using C++ and DSP Hardware for Low Latency and Secure Speech Systems
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.
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
Improving Intrusion Detection Robustness Through Adversarial Training Methods
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.
Cross-Modal Attention Mechanisms for Inventory Optimization: Fusing IoT Sensor Data, News Sentiment, and Geospatial Information
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.
Reinforcement Learning Paradigms for Proactive Cybersecurity and Dynamic Risk Management
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.
AI Driven Systems for Improving Accounting Accuracy Fraud Detection and Financial Transparency
The use of artificial intelligence (AI) in accounting and finance is reshaping how organizations ensure accuracy, detect fraud, and maintain transparency in their financial operations. This paper reviews how AI-driven technologies-particularly machine learning (ML), deep learning (DL), and natural language processing (NLP)-are being applied to modern accounting systems. We discuss how these tools enhance financial accuracy by automating data processing, identifying anomalies in real time, and correcting errors intelligently. Advanced fraud detection systems based on supervised and unsupervised learning, neural networks, and ensemble methods are shown to recognize suspicious transactions and accounting irregularities with remarkable precision. The paper also explores how AI supports transparency through automated compliance checks, smart auditing systems, and blockchain-based solutions that build trust and accountability. In addition, we highlight recent developments in predictive analytics for financial forecasting, robotic process automation (RPA) in accounting workflows, and explainable AI (XAI) for regulatory compliance. Key implementation challenges are addressed, including data quality, algorithmic bias, model interpretability, and evolving regulatory frameworks. The review further considers how AI integrates with enterprise resource planning (ERP) systems, safeguards sensitive financial data, and raises new ethical questions around automation and human oversight. Finally, we identify emerging directions such as federated learning for cross-organization fraud detection, graph neural networks for analyzing complex transaction patterns, and hybrid human-AI collaboration models. These advancements point toward a future where continuous auditing, multimodal financial analysis, and AI-driven regulatory technologies transform the landscape of accounting and financial management.
Flow Intelligence: Cross-Domain Deep Learning from Environmental Risk to Networked Community Systems
This study explores a unifying perspective on artificial intelligence as flow intelligence—a learning paradigm that adapts to the continuity of time, structure, and uncertainty. Building upon five empirical foundations—ranging from LSTM-based hazard prediction in the Yellow River Basin to hybrid graph-based community detection and sociological analysis of mental health—this research identifies the shared structural principles underlying intelligent systems. Rather than introducing new experiments, this work synthesizes and generalizes findings from these studies to construct a theoretical model of intelligence that integrates memory, modularity, and adaptation. The analysis reveals that when intelligence is designed to flow with systems rather than resist them, it achieves higher coherence, interpretability, and transferability across domains.
Real-Time Cold Storage Temperature Control via CFD–ML Hybrid Modeling
Cold storage facilities play a critical role in maintaining food quality and safety throughout the supply chain, with temperature uniformity and stability being essential for preserving product integrity and minimizing spoilage. Traditional temperature control systems often rely on simplified thermal models that fail to capture the complex three-dimensional airflow patterns and heat transfer phenomena occurring within large-scale cold storage environments. This research presents a novel hybrid modeling approach that combines Computational Fluid Dynamics (CFD) simulations with Machine Learning (ML) algorithms to achieve real-time temperature control optimization in cold storage facilities. The proposed CFD-ML hybrid model integrates high-fidelity CFD simulations for spatial temperature prediction with ML-based predictive control algorithms that can adapt to varying operational conditions and product loads. Our methodology employs a two-stage approach: offline CFD simulations generate comprehensive training datasets capturing diverse operational scenarios, while online ML models provide real-time control decisions based on current sensor measurements and predicted thermal behavior. Experimental validation was conducted in a 2,400 cubic meter commercial cold storage facility over a six-month period, comparing the hybrid approach against conventional PID control systems. Results demonstrate significant improvements in temperature uniformity, with spatial temperature variations reduced by 47% (from ±2.1°C to ±1.1°C) and energy consumption decreased by 23% while maintaining target temperature ranges within ±0.5°C. The ML component achieved prediction accuracies of 95.3% for temperature forecasting up to 2 hours ahead, enabling proactive control adjustments that prevent temperature excursions. The hybrid system demonstrated robust performance across varying ambient conditions, product loading scenarios, and equipment configurations, with average response times of 3.2 minutes for temperature corrections compared to 8.7 minutes for traditional control systems. This research contributes to the advancement of intelligent cold storage management by providing a scalable framework for integrating physics-based modeling with data-driven control strategies.
Copyright (c) 2025 Nathan Baker , Hannah Evans (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cross-Modal Feature Synthesis for Detecting Packaging and Visual Contaminants in Food Safety
Food packaging systems play a crucial role in maintaining food safety, yet they can simultaneously serve as sources of contamination through migration of packaging components and degradation products. Traditional single-modal detection approaches struggle to comprehensively assess the complex interactions between packaging materials, food matrices, and environmental factors that influence contamination dynamics. This research presents an innovative cross-modal feature synthesis framework that integrates active packaging monitoring, advanced biosensor technologies, and multi-modal sensing systems to achieve comprehensive detection of packaging-related and visual contaminants in food safety applications. Our approach combines intelligent packaging systems with nanomaterial-based biosensors and biomimetic sensing technologies to create a unified contamination detection platform. The integrated system utilizes Ti3C2 MXene/MoS2@AuNPs nanocomposite biosensors for electrochemical detection, chitosan-based active packaging systems for controlled-release monitoring, and electronic nose-tongue technologies for volatile and taste compound analysis. Through systematic evaluation across diverse food-packaging combinations, our cross-modal synthesis method demonstrates superior performance with detection accuracies exceeding 96.8% for packaging migrants and 94.2% for visual contaminants. The integration of Principal Component Analysis (PCA) and machine learning algorithms enables real-time processing of multi-dimensional sensor data, facilitating rapid identification of contamination events before they compromise food safety. Key innovations include the development of biomimetic sensor arrays that replicate human sensory perception, nanomaterial-enhanced electrochemical detection systems with improved sensitivity and selectivity, and adaptive data fusion algorithms that optimize detection parameters based on packaging type and food matrix characteristics. The research contributes to advancing intelligent food packaging concepts by providing automated monitoring capabilities that can be seamlessly integrated into existing packaging systems without compromising their primary protective functions.
Copyright (c) 2025 Matthew Young , Isabella Brooks (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Intelligent Task Planning for Reconfigurable Mechanisms Using Reinforcement Learning Based Structural Switching Strategy
Reconfigurable mechanisms can achieve multi-task execution through structural switching, but determining the optimal reconfiguration strategy in complex environments remains a major challenge. This study proposes an intelligent task planning method based on reinforcement learning. The structural switching process is modeled as a Markov decision process, where the action space corresponds to topology changes and the reward function jointly considers task completion rate and energy consumption. A deep Q-network is employed to train the optimal switching strategy. Experiments conducted in 15 task environments demonstrate that the proposed method achieves a 35% improvement in task completion rate and an 18% reduction in average energy consumption compared with baseline search algorithms. Moreover, after training, the decision-making speed on the simulation platform is approximately 10 times faster than that of traditional search methods. These results confirm that reinforcement learning can significantly enhance both efficiency and adaptability in reconfigurable mechanisms, providing an effective pathway for intelligent control of reconfigurable robots and adaptive mechanical systems.
Copyright (c) 2025 Daniel J. Carter, Xiaoling Wu, Emily R. Davies, Thomas H. Bennett, Olivia M. Clarke (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Adaptive Hierarchical RL for Dynamic Workload Management in Programmatic Advertising
Dynamic workload management in programmatic advertising environments presents complex multi-objective optimization challenges that conventional Reinforcement Learning (RL) approaches struggle to address effectively. The inherent difficulties include sparse reward signals, long-term credit assignment problems, and the need for hierarchical decision-making across multiple temporal scales. This study proposes an Adaptive Hierarchical Reinforcement Learning (AHRL) framework specifically designed for programmatic advertising workload management, drawing inspiration from the Manager-Worker paradigm established in hierarchical RL literature. The framework employs a dual-layer architecture where a high-level Manager module operates at reduced temporal resolution to establish strategic goals, while a low-level Worker module executes tactical actions at native system frequency. Through innovative goal embedding mechanisms and transition policy gradient algorithms, the AHRL system effectively decomposes complex advertising placement decisions into manageable hierarchical components. Experimental evaluation demonstrates significant improvements over baseline approaches, with performance gains of 18-25% in revenue optimization and 15-20% in cost efficiency, validating the effectiveness of hierarchical decomposition in programmatic advertising contexts.
Copyright (c) 2025 Vincent Rossi, Min-Jae Park (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Attention-Enhanced Cross-Modal Learning for Detecting Anomalies in System Software
System software anomaly detection has become increasingly critical as modern computing systems grow in complexity and scale. Traditional approaches typically analyze single data modalities in isolation, failing to capture the rich interplay between different system components that often signals anomalous behavior. This paper presents a novel attention-enhanced cross-modal learning framework specifically designed for detecting anomalies in system software environments. Our approach integrates multiple data modalities including system logs, performance metrics, network traffic patterns, and process execution traces through sophisticated convolutional neural network architectures and probabilistic graphical models. The framework employs deep learning-based feature extraction with specialized attention mechanisms that learn to focus on the most discriminative cross-modal relationships, combined with mixture-of-experts approaches that dynamically select the most reliable detection mechanisms based on system context. We introduce adaptive fusion techniques that leverage hierarchical Dirichlet process hidden Markov models for capturing temporal dependencies and switching dynamics in system behavior. Comprehensive evaluation on real-world datasets from enterprise environments demonstrates that our cross-modal approach achieves superior detection performance compared to single-modal baselines, with improvements of 23% in precision and 18% in recall for detecting zero-day attacks and system vulnerabilities. The probabilistic framework provides interpretable explanations for detected anomalies through confidence-based fusion mechanisms that enable security analysts to quickly understand the root causes and take appropriate remediation actions.
Copyright (c) 2025 Rachel Stein (Author) https://creativecommons.org/licenses/by-nc-nd/4.0
Cross-Domain Applications of Temporal Heterogeneous Graph Contrastive Learning: From Credit Cards to Insurance Claims
Temporal Heterogeneous Graph Contrastive Learning (THGCL) has emerged as a revolutionary approach for modeling complex dynamic relationships in multi-entity financial systems. This research presents a comprehensive cross-domain framework that demonstrates the transferability of THGCL methodologies between credit card fraud detection and insurance claim analysis. Through systematic investigation of heterogeneous graph structures containing multiple entity types including credit card numbers, merchant identifiers, and transaction records, we develop a unified architecture capable of capturing both structural relationships and temporal dynamics across diverse financial domains. Our methodology employs a sophisticated multi-module framework incorporating multi-dimensional gated update mechanisms, noise-aware adversarial modeling, and multi-layer embedding contrastive learning to address challenges including data sparsity, temporal heterogeneity, and class imbalance prevalent in financial fraud detection scenarios. Experimental results demonstrate that our THGCL-based approach achieves superior performance metrics with detection rates reaching 91.2% in credit card fraud detection and 87.8% in insurance claim analysis. The cross-domain adaptation framework exhibits remarkable knowledge transfer capabilities, reducing training time by 43% while maintaining detection accuracy above 89% across both domains. These findings establish THGCL as a robust foundation for developing scalable fraud detection systems capable of adapting to evolving fraudulent patterns across diverse financial sectors.
Copyright (c) 2025 Nathan Hall, Amelia Ward (Author) https://creativecommons.org/licenses/by-nc-nd/4.0