Artificial Intelligence and Computing Innovations
Artificial Intelligence and Computing Innovations (AICI) is an international, peer-reviewed, open-access quarterly journal, published twice a year in January and July, dedicated to advancing the frontiers of artificial intelligence and computing technologies. The journal provides a multidisciplinary platform for researchers, practitioners, and industry professionals to publish innovative findings that integrate AI theory, computational systems, and real-world applications. AICI focuses on promoting both theoretical and applied research in artificial intelligence, data science, and intelligent computing. The journal welcomes submissions that explore novel algorithms, architectures, and methodologies in areas such as deep learning, computer vision, natural language processing, robotics, cloud and edge computing, and intelligent decision-making. Contributions addressing emerging topics such as ethical AI, explainability, federated learning, and privacy-preserving computation are especially encouraged. The journal’s scope covers, but is not limited to, the following areas: Artificial Intelligence and Machine Learning: Deep neural networks, generative AI, reinforcement learning, and transfer learning. Data Science and Analytics: Big data frameworks, data mining, pattern recognition, and predictive modeling. Computing Systems and Architectures: Cloud-edge collaboration, distributed systems, quantum computing, and high-performance computation. Computer Vision and Multimedia Processing: Image analysis, object detection, multimodal learning, and video understanding. Natural Language Processing and Speech Intelligence: Large language models, dialogue systems, sentiment analysis, and multilingual communication. Robotics and Intelligent Control: Human–robot interaction, motion planning, and autonomous decision-making. AI Ethics, Security, and Sustainability: Fairness, interpretability, responsible innovation, and the societal impact of AI. Artificial Intelligence and Computing Innovations maintains a rigorous double-blind peer review process to ensure originality, technical quality, and relevance. The journal follows open-access principles, making all articles freely available to readers worldwide to promote rapid dissemination of scientific knowledge. With a distinguished international editorial board and a commitment to academic excellence, AICI strives to become a leading venue for cutting-edge research and cross-disciplinary dialogue in intelligent computing and AI-driven innovation.
Dependency-Aware Hierarchical Reinforcement Learning for Resource Allocation in Hybrid Cloud Microservices
Hybrid cloud platforms allow enterprises to combine private infrastructure with elastic public-cloud capacity, but they make microservice resource allocation a coupled control problem across heterogeneous machines, service dependencies, workload bursts, and cost constraints. This paper presents HADR, a dependency-aware hierarchical reinforcement-learning method for resource allocation in hybrid cloud microservice systems. HADR models a deployed application as a dynamic service-resource graph whose vertices represent microservices, pods, virtual machines, queues, and storage components, and whose edges encode invocation, contention, placement, and scaling relations. A graph-temporal encoder predicts near-term demand and dependency pressure, while a two-level reinforcement-learning controller separates slow placement decisions from fast replica and resource-quota adjustments. The reward function jointly optimizes p95 latency, service-level objective violations, cloud cost, utilization balance, and migration overhead. A scenario-based evaluation over burst, diurnal, degradation, and dependency-shift workloads shows that HADR reduces operating cost by 36.2%, lowers p95 latency by 24.7%, and improves mean resource utilization to 0.82 compared with static provisioning, threshold autoscaling, and flat deep-Q baselines. The results demonstrate that dependency-aware hierarchical control provides a robust foundation for cost-efficient and performance-stable resource management in hybrid cloud microservices.
Reinforcement Learning–Based Dynamic Decision Framework for Server Backend Resource Management
With the continuous development of cloud computing platforms, microservice architectures, and online service systems, server backend resource management faces challenges such as frequent load fluctuations, significant resource contention, complex service relationships, and diverse control objectives. Traditional management methods relying on static rules or local optimization are no longer adequate for the real-time control needs of highly dynamic environments. To address this issue, this paper studies reinforcement learning modeling and dynamic decision-making methods for server backend resource management, constructing a unified framework covering state representation, context awareness, policy learning, value assessment, and multi-objective reward constraints. At the state modeling level, operational information such as resource occupancy, task queuing, service latency, and dependencies is jointly represented to enhance the ability to perceive complex backend environments. At the decision modeling level, a context-aware mechanism and policy network are introduced to adaptively control resource scheduling behavior during continuous operation. At the optimization level, the stability of policy updates is improved by combining value functions and advantage estimation, and a comprehensive reward design is used to coordinate the relationship between throughput, latency, resource utilization efficiency, and operating costs. An experimental environment was built based on open-source cluster trajectory data, and compared with other methods in the same direction. The results show that the proposed method can achieve good overall performance in complex backend resource management scenarios, demonstrating strong decision-making effectiveness and system adaptability. The research results indicate that reinforcement learning can provide a more intelligent, systematic, and long-term optimization-capable modeling path for server backend resource allocation and dynamic scheduling.
Learning Collaborative and Robust Scheduling Policies for Microservice Backends under Uncertainty
With the continuous development of cloud computing, containerized deployment, and service-oriented architecture, microservice server backends face higher demands in handling high-concurrency requests, coordinating complex service dependencies, and managing dynamic resources. Traditional scheduling methods relying on static rules or local optimization are no longer suitable for operating environments with multiple coupled services, rapid state changes, and abnormal disturbances. To address this issue, this paper proposes a reinforcement learning-based collaborative scheduling and robust optimization method for microservice server backends. Starting from the overall system operation process, it unifies information such as queue state, resource consumption, dependency strength, and latency fluctuations into a scheduling state representation. Based on this, a joint decision-making mechanism is constructed, incorporating request allocation, instance scaling, and resource reallocation into a unified action space to improve overall coordination capabilities under complex service chain conditions. Furthermore, to address uncertainties in the backend environment, such as traffic surges, node degradation, and resource jitter, this paper introduces robust optimization concepts into the policy learning process. Through disturbance modeling and constraint control, it enhances the stability and adaptability of the scheduling strategy in complex operating scenarios. To ensure the engineering usability of the method, this paper simultaneously considers service quality constraints, resource capacity limitations, and system operating costs, enabling the proposed model to balance deployment rationality and control feasibility while pursuing improved throughput and response efficiency. Related research results show that the proposed method can achieve good overall scheduling performance in microservice server backend scenarios, demonstrating strong advantages in service processing capacity, resource utilization, and operational stability. This research provides a modeling approach for intelligent scheduling in microservice environments that considers global collaboration, dynamic decision-making, and robust control, and also offers methodological references for research on cloud platform backend resource management and service governance.
Causal Inference-Guided Bias Correction and Hallucination Suppression for Trustworthy Text Summarization
We propose a bias correction and illusion suppression method that integrates causal inference. This method treats the summary generation process as a conditional generation decision influenced by potential confounding factors. By constructing a target distribution for causal correction, it weakens the systematic influence of corpus distribution and narrative style on information selection. Furthermore, it introduces a reweighting strategy based on propensity scoring to calibrate bias-related conditions during the learning phase, improving the consistency and robustness of the generation mechanism across different contexts. To reduce the hallucination risk caused by unfounded completion and over-certainty, the method explicitly establishes the alignment relationship between the summary statement and the input evidence fragments during generation and incorporates consistency constraints to make the output more inclined towards facts and entity information supported by the original text. Simultaneously, the method constructs semantically equivalent input variants through counterfactual perturbations and uses distribution consistency constraints to suppress the model's sensitivity to non-causal surface factors, thereby reducing generation bias caused by style differences and rewriting. This paper compares and analyzes the proposed method with related methods under a unified evaluation index system. The results show that the proposed method performs more balancedly in terms of semantic similarity, factual consistency, and entity alignment, and can effectively control the risks associated with hallucination. This verifies the role of causal correction and evidence consistency constraints in improving the credibility and interpretability of the abstract.
Copyright (c) 2025 Artificial Intelligence and Computing Innovations
Collaborative Decision Optimization for Timely Order Fulfillment and Service Quality Enhancement in E-Commerce Supply Chains
To address the decision-making complexity arising from the large order volume, long fulfillment chain, and high service requirements in e-commerce, this paper proposes a supply chain collaborative decision optimization method for e-commerce fulfillment scenarios. This method constructs a unified collaborative modeling framework based on multi-dimensional business information such as order processing, seller response, product attributes, customer region, and fulfillment execution. It integrates heterogeneous features through a state fusion mechanism, uses collaborative correlation analysis to characterize the intrinsic relationship between orders and fulfillment conditions, and combines seller embedding representations to enhance the supporting role of supply-side features in decision generation, ultimately forming a decision output that balances timeliness and service performance. Compared with traditional methods that only focus on local links or single rule allocation, the proposed method can more systematically describe the organizational relationships under the combined influence of multiple subjects, factors, and constraints in the order fulfillment process, thereby improving the integrity and coordination of the supply chain decision-making process. Comparative analysis of related methods shows that the proposed method has better overall performance in terms of on-time order delivery, fulfillment stability, service quality, and collaborative execution capabilities, indicating that it can effectively support order fulfillment organization and service management in e-commerce scenarios. The research results show that building a collaborative decision-making optimization framework around the fulfillment process can help improve the operational quality of the e-commerce supply chain and provide a methodological basis for intelligent decision-making modeling in relevant business scenarios.
Dynamic Low-Rank Routing for Efficient Multi-Task Instruction Fine-Tuning of Large Language Models
This paper proposes a multi-task instruction fine-tuning method that integrates low-rank adaptation with dynamic routing to address the challenges of insufficient adaptation efficiency and severe task conflicts in large-scale language models. The method first applies low-rank decomposition to restrict parameter updates to a low-dimensional subspace, which reduces redundant computation and storage costs while preserving semantic representation capacity. On this basis, a dynamic routing mechanism is introduced to adaptively select optimal paths and task-specific modules according to input instruction features, enabling differentiated modeling for different tasks within a shared representation space. This mechanism effectively alleviates performance interference and gradient conflicts commonly encountered in multi-task training, ensuring stable coordination across tasks. To achieve unified optimization, a joint objective function is constructed by combining task loss, low-rank update regularization, and routing distribution constraints, thereby improving stability and controllability during training while maintaining performance. The experimental design covers multi-dimensional analyses of hyperparameter sensitivity, environmental sensitivity, and data sensitivity, verifying the superiority of the proposed method in key metrics such as instruction accuracy, average multi-task performance, parameter efficiency, and task conflict. The results demonstrate that the method achieves efficient fine-tuning and robust performance in complex task environments, providing a solution that balances efficiency and stability for multi-task instruction modeling.
Enterprise Audit Risk Identification Through Temporal Context and Entity Relationship Anomaly Modeling
This paper addresses the challenges of concealed risk clues, scarce labels, and coupling of heterogeneous information from multiple sources in enterprise auditing scenarios. It proposes an audit risk identification framework based on anomaly detection, simultaneously characterizing anomalies at the attribute, temporal context, and relational structure levels from a unified modeling perspective. First, robust preprocessing of audit records weakens scaling differences and extreme value interference. Representation learning is then used to map records to a latent space, reconstructing the deviation measure to assess the strength of deviations from normal patterns. Subsequently, a time window mechanism is introduced to construct a local contextual baseline, calculating the contextual deviation of the latent representation relative to the baseline to identify unconventional behavioral fragments within the business context. Simultaneously, a subject relationship graph is constructed, and structural information is aggregated to form a subject structural representation, which uses structural deviations to characterize abnormal associations and suspicious links. Finally, multi-source deviation signals are fused into a unified risk score, and probability mapping is used to obtain a risk intensity expression that can be used for verification ranking and threshold decision-making, thus outputting a set of high-risk records and their associated clues. Comparative experimental results show that this method outperforms other methods in terms of accuracy, precision, recall, and AUROC, validating the effectiveness and stability of the multi-source anomaly characterization and fusion scoring mechanism in audit risk identification tasks.
Policy-Aware Enterprise Risk Assessment through LLM-Based Cross-Source Representation and Reasoning
Against the backdrop of frequent policy adjustments and continuous changes in the business environment, traditional enterprise risk identification methods generally suffer from insufficient perception of external institutional constraints, limited ability to integrate multi-source heterogeneous information, and weak semantic interpretability in the risk judgment process. These shortcomings make it difficult to meet the practical needs of dynamic identification and comprehensive assessment of enterprise risks in complex governance scenarios. To address these issues, this paper proposes an LLM Agent-based enterprise risk judgment method that integrates policy text analysis and enterprise operational status modeling. This method incorporates regulatory requirements, constraint logic, and objects of action from policy texts with the enterprise's financial performance, operational status, and abnormal signals into a unified analytical framework, enabling cross-source semantic modeling and intelligent judgment of enterprise risks. Methodologically, the policy text is first structurally analyzed to extract institutional constraint information and regulatory semantic clues related to enterprise risks. Then, the enterprise's operational status is dynamically represented, constructing a multi-dimensional state representation that reflects solvency, profitability, cash flow pressure, and operational stability. Based on this, a cross-source alignment mechanism is used to map policy semantics and enterprise state information to a unified representation space, and the LLM Agent's memory organization and reasoning decision-making capabilities are combined to complete the risk judgment. This method not only enhances the modeling ability of the correlation between policy information and enterprise operational information but also improves the integrity, consistency, and semantic expressiveness of the risk assessment process. Comparative experimental results show that the proposed method exhibits good overall performance in enterprise risk identification tasks, indicating that the constructed policy text parsing and enterprise operational status joint modeling framework can provide effective support for enterprise risk assessment in complex scenarios.
Cross-Domain Optimization of Large Language Models via Data-Centric Representation Alignment with Self-Supervised Learning
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including natural language understanding, generation, and reasoning. However, their performance is often constrained by challenges such as data heterogeneity, distribution shifts, and inefficient utilization of large-scale corpora, particularly in real-world scenarios where data sources are diverse and continuously evolving. These limitations can lead to unstable training dynamics and reduced generalization when models are transferred to unseen domains. To address these challenges, this paper proposes a data-centric optimization framework that integrates adaptive representation alignment and self-supervised auxiliary tasks to enhance generalization and stability in LLM training. The framework focuses on improving the quality and consistency of learned representations by aligning feature distributions across domains while simultaneously leveraging unlabeled data through self-supervised learning objectives. This dual strategy enables the model to capture both domain-invariant features and task-relevant semantics, thereby improving robustness under distributional variations. The proposed method jointly optimizes task-specific objectives and distribution alignment constraints within a unified multi-objective learning paradigm, allowing for effective coordination between performance optimization and cross-domain consistency. In addition, an adaptive weighting mechanism is introduced to dynamically balance multiple learning objectives during training, improving convergence efficiency and mitigating optimization conflicts. Extensive experiments conducted on multiple benchmark datasets demonstrate that the proposed approach achieves improved accuracy, reduced divergence across data distributions, and enhanced training efficiency compared to conventional fine-tuning strategies. These results highlight the effectiveness of the framework in enabling scalable, robust, and generalizable LLM training in heterogeneous data environments.
AI-Driven Multi-Omics Integration for Biomarker Discovery and Disease Risk Prediction
The rapid growth of high-throughput biological data has created new opportunities for understanding disease mechanisms, identifying biomarkers, and improving precision medicine. However, biological datasets such as genomics, transcriptomics, proteomics, and metabolomics are often high-dimensional, heterogeneous, noisy, and difficult to integrate using traditional statistical methods. This paper proposes an AI-driven multi-omics integration framework for biomarker discovery and disease risk prediction. The proposed method combines deep representation learning, attention-based feature selection, and graph neural networks to capture complex relationships among genes, proteins, metabolites, and clinical phenotypes. By constructing a biological interaction graph and learning cross-omics representations, the model can identify disease-associated molecular patterns and improve predictive performance. Experimental analysis on public multi-omics datasets demonstrates that the proposed framework achieves higher accuracy, F1-score, and area under the receiver operating characteristic curve compared with conventional machine learning baselines. The results suggest that AI-based multi-omics integration can provide a powerful computational tool for discovering reliable biomarkers, supporting early diagnosis, and advancing personalized biomedical research.
Multi-Scale Attention-Enhanced Vision Transformer for Automated Tumor Segmentation in Medical Imaging
Accurate tumor segmentation is a critical step in computer-aided diagnosis, treatment planning, and disease progression monitoring. However, medical images often contain tumors with irregular shapes, heterogeneous textures, and blurred boundaries, making precise segmentation challenging for conventional convolutional neural networks. This paper proposes a multi-scale attention-enhanced Vision Transformer framework for automated tumor segmentation in medical imaging. The proposed model integrates local convolutional feature extraction with global self-attention modeling to capture both fine-grained anatomical details and long-range contextual dependencies. A multi-scale feature fusion module is designed to improve the representation of tumors with varying sizes, while an attention-guided boundary refinement mechanism is introduced to enhance segmentation accuracy around ambiguous lesion margins. Experimental evaluation on public medical imaging datasets demonstrates that the proposed method improves Dice coefficient, Intersection over Union, and boundary accuracy compared with baseline CNN-based and transformer-based models. The results suggest that combining multi-scale representation learning with attention-based global modeling can provide a more reliable and clinically useful approach for tumor segmentation in complex medical imaging scenarios.
Task Graph Modeling and Constraint-Aware Scheduling for Reliable Large Language Model Agent Execution
This paper addresses the instability of large language model agents under real-world constraints, proposing an integrated approach of robust task decomposition and constraint-aware scheduling to improve the controllability, compliance, and stability of long-link interactive tasks. The method first structures high-level instructions into a task graph, explicitly modeling subtask dependencies, resource sharing, and executable conditions. Verifiable pre-checks, alternative paths, and fallback actions are configured for key steps to reduce error accumulation caused by incomplete information and environmental changes. Subsequently, during the scheduling phase, latency and risk are incorporated into a unified priority decision, dynamically selecting the execution order and allocating resources based on critical path identification and failure cost assessment. When tools become unavailable, constraints are triggered, or execution deviations occur, rapid recovery is achieved through local reordering and path switching, avoiding the high overhead of global rework. Comparative experimental results show that this method exhibits superior overall performance in key agent metrics such as task completion, tool invocation reliability, constraint violation control, interaction efficiency, and recovery capability. It can maintain a more stable end-to-end execution process under a limited budget and uncertain environments, providing a feasible agent execution paradigm for real-world business process automation and multi-tool collaboration scenarios.
Research on Hierarchical Multi-Agent Reinforcement Learning Resource Orchestration for Large-Scale Heterogeneous Distributed Clusters with Dynamic Load Awareness
To address the challenges of complex resource states, random task arrivals, significant differences in node capabilities, and multiple couplings of scheduling objectives in large-scale heterogeneous distributed clusters, a hierarchical multi-agent reinforcement learning resource orchestration method integrating dynamic load awareness is constructed. This method first extracts time-varying load information from machine resource occupancy, task queuing status, service deployment density, and node capacity constraints, and constructs a state representation with global context awareness capabilities by combining cluster topology relationships to enhance the method's ability to characterize complex operating environments. Based on this, a hierarchical collaborative decision-making mechanism is adopted, dividing the resource orchestration process into global coordination by the upper-level manager and local control by the lower-level executors, achieving effective connection between global planning and node-level execution through sub-objective propagation. Furthermore, to address the matching problem between heterogeneous nodes and task requirements, a compatibility-aware scoring mechanism is introduced to improve the rationality of resource allocation, execution stability, and orchestration accuracy. This method can balance resource utilization, task waiting control, load balancing, and scheduling success capability within a unified framework, thereby improving the overall resource organization quality in complex cluster environments. Comparative experimental results show that the proposed method exhibits good overall performance across multiple key evaluation metrics, indicating that the method can effectively adapt to large-scale heterogeneous distributed cluster resource orchestration scenarios driven by dynamic loads, and has strong method effectiveness and application value.
Modeling Financial Market Dynamics with Temporal and Relational Learning: An LSTM-GNN Approach
This study proposes a stock market prediction method based on long short-term memory network (LSTM) and graph neural network (GNN) to simultaneously capture the time series characteristics of individual stocks and the market network relationship. Traditional stock prediction methods mainly rely on time series models such as LSTM and GRU, but these methods often ignore the complex interactive relationships between stocks. To solve this problem, this study constructs a graph structure of the stock market and uses GNN for relationship modeling, so that the prediction model can comprehensively consider the historical trading data of individual stocks and the overall market dynamics. The experiment is verified based on the trading data of the S&P 500 index in 2018, and MLP, 1D-CNN, GRU and Transformer are used as comparison models. The experimental results show that LSTM+GNN achieves the best performance in terms of mean square error (MSE), root mean square error (RMSE) and coefficient of determination (R²), verifying the effectiveness of integrating time series and market structure information. In addition, ablation experiments and optimizer sensitivity experiments further illustrate the contribution of GNN structure and AdamW optimizer in improving prediction accuracy. Future research can combine multimodal data and reinforcement learning to further optimize the model and explore more efficient financial market prediction methods to enhance the decision-making ability of intelligent trading systems.
Towards Intelligent Backend Operations via Unified Temporal Representation and Prediction Modeling
This study proposes a unified modeling approach for long- and short-term temporal dependencies to address the challenges of complex metric structures, dynamic distribution changes, and intertwined multidimensional dependencies in backend systems, aiming to achieve high-accuracy and high-robustness system state prediction. The method first performs structured preprocessing and normalization on multi-source monitoring data, extracts multidimensional temporal features through a feature encoding module, and constructs a dual-channel architecture for short-term dynamic modeling and long-term context capture to fully characterize temporal properties of local fluctuations and global trends. On this basis, a multi-scale fusion mechanism is introduced to enable adaptive interaction and integration of features across different temporal granularities, enhancing the model's ability to represent non-stationary signals, sudden load changes, and cross-dimensional dependencies. In addition, residual calibration and dynamic aggregation strategies are designed to mitigate feature shifts in high-noise environments, ensuring stable prediction performance and strong generalization under complex operating conditions. Validation across various dynamic scenarios shows that the proposed method outperforms existing baseline models on key metrics such as MSE, MAE, MAPE, and RMSE, while exhibiting strong adaptability to data disturbances, environmental changes, and sampling granularity variations. The results demonstrate that the proposed approach effectively captures multi-level dependencies in complex time series, providing strong technical support for state modeling, performance prediction, and anomaly detection in backend systems, and laying a methodological foundation for building high-precision intelligent operations and maintenance systems.
Modeling Evolving Service Dependencies: Dynamic Graph Learning for Microservice Anomaly Detection
Addressing the challenges of anomaly detection in cloud-native microservice systems due to frequent changes in backend dependencies and strong time-varying coupling of runtime states, this paper investigates the problem of dynamic graph temporal anomaly detection and proposes a joint structural and temporal modeling framework. The method organizes the service dependency graph using time-slice sequences, unifying the representation of service nodes, call relationships, and their multi-source attributes. It encodes dependency interaction strength and local structural consistency through relation-aware neighborhood aggregation, and uses a temporal aggregation module to characterize the cross-time state evolution patterns and long-term dependencies. Based on conditional prediction of normal evolution trajectories, an anomaly metric centered on prediction residuals is constructed, transforming the impact of structural perturbations and behavioral drift on the representation space into quantifiable anomaly scores, thereby supporting node-level and subgraph-level anomaly detection and event characterization. Experiments are conducted on open-source microservice monitoring data, and multiple evaluation metrics verify the effectiveness of the proposed method in terms of anomaly discrimination capability and overall recognition quality.
Enhancing Trustworthiness of Retrieval-Augmented Large Language Models via Confidence Calibration and Selective Rejection
This study proposes a robust monitoring method based on structure-aware feature learning to address the challenges of complex multidimensional feature coupling, dynamically changing dependencies, and diverse anomaly patterns in backend systems. The method builds upon multi-source monitoring data, integrating temporal dynamic features and topological dependency structures to achieve unified system state representation across different time scales and structural hierarchies. First, a feature encoder is employed to extract temporal features and construct a dynamic dependency graph that captures latent structural relationships among service nodes. Second, a structure- aware mechanism and graph propagation layer are introduced to perform cross-node feature fusion and apply dependency consistency constraints, enhancing the model's stability and generalization capability in complex topological environments. Finally, variational regularization and a robust optimization objective are applied to further improve anomaly detection reliability under high noise and non-stationary distributions. Experimental results show that the proposed method outperforms existing models across multiple key metrics, including F1-Score, AUROC, and Robust Recall, while maintaining stable monitoring performance under structural perturbations, workload fluctuations, and feature heterogeneity. These results validate the effectiveness of structure-aware feature learning in complex system monitoring tasks, demonstrating its ability to accurately model anomaly propagation paths and achieve system-level semantic recognition in the feature space, thereby providing strong support for building highly reliable and interpretable backend monitoring frameworks.
Adaptive Scheduling for Multi-Model Collaborative Distributed Inference under Resource Heterogeneity and Dynamic Workloads
This paper addresses the scheduling complexity of distributed inference systems in multi-model collaborative scenarios under resource heterogeneity, model diversity, and dynamic request conditions. An adaptive scheduling method for distributed inference systems with multi-model collaboration is proposed. From a system-level perspective, the method integrates model selection and resource allocation into a unified scheduling decision process. Coordinated management of multi-model inference workflows is achieved through comprehensive characterization of system states. The proposed scheduling mechanism dynamically adjusts its decisions in response to changes in the operating environment and maintains a reasonable execution order under shared resource constraints, thereby improving overall system performance. Comparative experiments conducted in multi-model collaborative inference settings demonstrate that the proposed method achieves clear advantages in resource utilization efficiency, load distribution rationality, system stability, and scheduling responsiveness. The method effectively mitigates performance degradation caused by resource contention and model heterogeneity during parallel multi-model execution. The results indicate that a unified adaptive scheduling design plays an important role in supporting complex intelligent inference services and provides valuable guidance for the engineering implementation and optimization of distributed inference systems.
Adaptive and Learning-Driven Resource Optimization Framework for Scalable Cloud Computing Systems
With the rapid development of cloud computing technologies in 2024, large-scale distributed systems face increasing challenges in resource allocation, latency control, and energy efficiency. This paper proposes a scalable and adaptive resource optimization framework that integrates deep learning-based prediction models with dynamic scheduling strategies. By leveraging workload forecasting and real-time feedback mechanisms, the proposed framework improves resource utilization and system stability. Experimental results demonstrate that the framework achieves significant improvements in latency reduction and energy efficiency compared with conventional approaches. The proposed method provides a robust solution for modern cloud environments characterized by high variability and heterogeneous workloads.
Spatiotemporal Deep Learning for Ocean Wave Forecasting and Vessel Motion Modeling
Reliable prediction of ocean wave dynamics and vessel motion is essential for maritime safety and operational efficiency. This paper presents a deep learning framework that integrates spatiotemporal oceanographic data with vessel kinematic signals to jointly model wave evolution and ship response. The proposed model employs a hybrid architecture combining convolutional neural networks for spatial feature extraction and transformer-based temporal modeling to capture long-range dependencies. The dataset includes 10 years of buoy-based wave measurements and onboard sensor data from cargo vessels operating in the North Pacific, totaling over 3.5 TB of multivariate time-series data. Experimental results show that the model achieves a wave height prediction RMSE of 0.21 m and improves vessel roll motion prediction accuracy by 18% compared to physics-based simulation methods. In extreme sea states (significant wave height > 5 m), the model maintains stable performance with less than 12% degradation.
Self-Supervised Temporal-Structural Learning for Industrial Fault Diagnosis under Data Imbalance
Industrial fault diagnosis in complex manufacturing systems is challenged by highly imbalanced data distributions and limited labeled samples. This study proposes a self-supervised temporal-structural learning framework that captures both temporal dynamics and inter-component dependencies in industrial sensor networks. The approach leverages contrastive learning to pretrain representations from unlabeled multivariate time-series data, followed by a graph-based encoder that models structural relationships among system components. The model is evaluated on a large-scale industrial IoT dataset comprising over 500 sensors and 8 million time-series samples collected from a real production line. Under extreme imbalance conditions (fault ratio < 2%), the proposed method achieves an F1-score of 0.81 and recall of 0.87, significantly outperforming supervised baselines such as CNN-LSTM (F1 0.69) and Random Forest (F1 0.62). The framework also demonstrates strong robustness under noise perturbations and missing data scenarios. These results indicate that self-supervised temporal-structural learning provides an effective and scalable solution for real-world industrial fault diagnosis, particularly in scenarios with sparse annotations and evolving system dynamics.
Explainable Deep Learning for Credit Risk Prediction via Multi-Modal Financial Representation Learning
Accurate credit risk prediction is critical for financial institutions in managing loan portfolios and mitigating systemic risk. This paper proposes an explainable deep learning framework that integrates structured financial records with unstructured textual data, including transaction descriptions and credit reports, through a multi-modal representation learning architecture. The model combines a Wide & Deep network with a transformer-based text encoder, enabling joint feature extraction and nonlinear interaction modeling. Experiments are conducted on a real-world dataset containing over 1.2 million loan records from U.S.-based lending platforms, with an observed default rate of 13.7%. The proposed model achieves an AUC of 0.921 and a KS statistic of 0.68, outperforming traditional logistic regression (AUC 0.842) and gradient boosting methods (AUC 0.895). Furthermore, an integrated SHAP-based interpretability module reveals key risk drivers such as debt-to-income ratio, transaction volatility, and semantic indicators of financial distress in borrower narratives. The results demonstrate that incorporating multi-modal information significantly enhances predictive performance while maintaining model transparency, making the approach suitable for regulatory-compliant financial decision-making systems.
Dual-Channel Attention-Based Multimodal Sentiment Analysis Model Integrating Text and Image Features
With the rise of multimodal data in social media, sentiment analysis based solely on text has become insufficient to capture the richness of human emotion. To address this limitation, this paper proposes a dual-channel multimodal sentiment analysis model based on attention mechanisms, named ACMSA (Attention Channel Multimodal Sentiment Analysis). The model integrates textual and visual features to improve emotional understanding and classification accuracy. Text features are extracted using the BERT model and processed through a CNN-BiGRU-Attention dual-channel architecture to capture both local and global semantic dependencies. Image features are obtained via ResNet152, enhanced by a Channel-Spatial Attention Module (CSAM) that adaptively emphasizes salient regions. The fusion of multimodal features is achieved through a Co-Attention mechanism, enabling fine-grained interaction between textual and visual representations. Experimental evaluations on the MVSA-Single and MVSA-Multi Twitter datasets demonstrate that ACMSA outperforms state-of-the-art baselines, achieving an accuracy of 77.08% and 74.42%, respectively. The results verify that attention-guided dual-channel modeling effectively strengthens cross-modal correlation and interpretability. This framework provides a robust and extensible solution for sentiment analysis in multimedia-rich environments, offering valuable implications for emotion recognition, social media monitoring, and intelligent interaction systems.
Performance Analysis of Front-End and Back-End Separation in Java Web Development
This paper conducts an in-depth investigation into the performance impact of front-end and back-end separation architecture in Java Web development and discusses corresponding optimization strategies. Front-end and back-end separation has become a widely adopted architectural paradigm in modern Web application development. By decoupling the user interface layer (front-end) from the data processing layer (back-end), this approach improves development efficiency, enhances resource management, and strengthens system scalability. The paper first introduces the fundamental concept of front-end and back-end separation and elaborates on its implementation technologies and development models, including commonly used frameworks and tools. Subsequently, it analyzes potential performance challenges introduced by this architecture, such as increased network requests and more complex front-end logic. Finally, strategies including resource optimization, code modularization, API optimization, and load balancing are proposed to mitigate these challenges and improve overall system performance.
Adaptive Instruction Tuning for Multi-Domain Dialogue Generation in Large Language Models
Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, adapting LLMs to multi-domain dialogue systems remains challenging due to domain distribution shifts, instruction diversity, and varying conversational contexts. This paper proposes an adaptive instruction tuning framework that enables LLMs to dynamically adjust dialogue generation strategies across heterogeneous domains. The method integrates domain-aware instruction embeddings, reinforcement-based response optimization, and context-aware decoding mechanisms. The framework improves response relevance, coherence, and adaptability across domains such as healthcare, finance, and customer service. Experimental evaluations demonstrate that the proposed method outperforms baseline instruction-tuned models in both automatic metrics and human evaluation.
Intelligent Robot Navigation Using Deep Reinforcement Learning for Dynamic Environments
Autonomous mobile robots operating in dynamic environments must effectively perceive environmental changes and make adaptive navigation decisions in real time. Traditional path planning algorithms often rely on static environment assumptions, which limits their performance in real-world scenarios where obstacles and environmental conditions continuously change. This paper proposes a deep reinforcement learning-based navigation framework that integrates perception, decision-making, and motion control into a unified learning architecture. The system employs convolutional neural networks to extract spatial features from sensor observations and utilizes a reinforcement learning policy network to generate navigation actions. A simulation-based evaluation environment is constructed to analyze navigation efficiency, collision rate, and trajectory stability. Experimental results demonstrate that the proposed framework achieves improved adaptability and higher success rates compared with classical navigation methods in dynamic environments.
Modeling Financial Risk Propagation and Systemic Contagion via Stability-Aware Graph Neural Networks
The increasing complexity and interconnectivity of global financial systems have intensified the challenge of identifying and mitigating systemic risks. Traditional econometric and network-based approaches often fail to capture nonlinear dependencies and dynamic contagion effects that characterize modern financial networks. To address this issue, this paper proposes a Graph Neural Network (GNN)-based framework for financial risk propagation and systemic stability analysis. The model leverages both structural and temporal dependencies among financial entities-such as banks, corporations, and markets-represented as nodes and edges in a dynamic graph. Through message-passing mechanisms, the proposed model aggregates information across neighboring nodes to learn latent risk representations, enabling accurate modeling of cascading failures and contagion paths. A novel stability-aware loss function is introduced to penalize high-risk clustering and enhance robustness under volatile market conditions. Experiments conducted on real-world interbank transaction and equity exposure datasets demonstrate that the GNN-based approach achieves superior performance compared to baseline methods including LSTM, VAR, and classical contagion models. The results highlight that GNNs can effectively uncover hidden risk linkages, forecast systemic vulnerability, and provide valuable insights for regulatory supervision and stress testing. This study contributes to a growing body of research bridging graph deep learning and financial system analysis, offering a scalable and interpretable paradigm for risk-aware financial modeling.
IoT-Based Intelligent Trash Can Monitoring System for Scenic Areas Using Multi-Sensor Detection and LoRa Communication
Traditional manual inspection of trash cans in scenic areas often faces low efficiency, delayed anomaly detection, and high maintenance costs due to the wide distribution of bins and complex terrain. To address these issues, this paper designs an intelligent trash can monitoring system based on the Internet of Things (IoT) that integrates multi-sensor fusion and low-power wide-area communication technology. The proposed system employs ultrasonic and pressure sensors to monitor the overflow state in real time, while temperature and orientation sensors detect fire and tilting anomalies. The STM32F103 microcontroller serves as the main control unit, responsible for data acquisition and system coordination. The collected data are transmitted via a LoRa communication module, enabling long-distance and low-power data exchange across mountainous scenic environments. The software design includes real-time sensing, data fusion, and event-triggered communication to enhance energy efficiency and response speed. Experimental validation demonstrates that the system can accurately detect overflow, fire, and tilt states, providing reliable real-time monitoring and alerting. This solution significantly improves the management efficiency of waste collection in scenic areas and supports the sustainable development of smart tourism infrastructure.
Contrastive Representation Learning for Anomaly Detection in Cloud-Based Backend Services
This study proposes an anomaly detection method based on contrastive representation learning to address the challenges of complex multi-metric coupling, dynamic dependency variation, and label scarcity in backend service systems. The method first employs a multi-scale temporal encoding module to extract dynamic features at different time granularities, capturing both short-term fluctuations and long-term trends in system operation. An adaptive dependency modeling mechanism is then constructed, which generates dynamic graph structures through feature similarity projection to characterize semantic relationships and temporal dependencies among services. On this basis, a contrastive learning objective is introduced, where structured constraints between positive and negative sample pairs enable stable feature aggregation and boundary separation under unsupervised or weakly supervised conditions. A temporal consistency regularization term is also incorporated to maintain state smoothness across time steps, enhancing robustness under non-stationary distributions. The proposed method is validated on multidimensional cloud workload datasets, and results show that it achieves high detection accuracy and stability even with incomplete labels and noisy pseudo-labels. Overall, the method not only realizes self-supervised and structure-aware anomaly detection but also demonstrates strong performance in feature representation, anomaly discrimination, and distribution shift adaptation, providing an efficient and general solution for intelligent backend operations and multi-source time-series analysis.
A Study on Robust Monitoring Methods for Backend Systems Using Structure-Aware Feature Learning
This study proposes a robust monitoring method based on structure-aware feature learning to address the challenges of complex multidimensional feature coupling, dynamically changing dependencies, and diverse anomaly patterns in backend systems. The method builds upon multi-source monitoring data, integrating temporal dynamic features and topological dependency structures to achieve unified system state representation across different time scales and structural hierarchies. First, a feature encoder is employed to extract temporal features and construct a dynamic dependency graph that captures latent structural relationships among service nodes. Second, a structure-aware mechanism and graph propagation layer are introduced to perform cross-node feature fusion and apply dependency consistency constraints, enhancing the model's stability and generalization capability in complex topological environments. Finally, variational regularization and a robust optimization objective are applied to further improve anomaly detection reliability under high noise and non-stationary distributions. Experimental results show that the proposed method outperforms existing models across multiple key metrics, including F1-Score, AUROC, and Robust Recall, while maintaining stable monitoring performance under structural perturbations, workload fluctuations, and feature heterogeneity. These results validate the effectiveness of structure-aware feature learning in complex system monitoring tasks, demonstrating its ability to accurately model anomaly propagation paths and achieve system-level semantic recognition in the feature space, thereby providing strong support for building highly reliable and interpretable backend monitoring frameworks.