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.
MCA-TAN: Cross-Modal Graph Neural Recommendation with Temporal Behavior and Social Network Fusion
This paper addresses the problem of how to integrate user behavior sequences and social network information to improve personalized recommendation. A recommendation model based on cross-modal graph neural networks is proposed. The method introduces a Modality-Coordinated Aggregation (MCA) mechanism to jointly model the user's behavior graph and social graph. This enables the model to capture the structural complementarity and semantic interaction between the two modalities. A Temporal-Aware Node (TAN) representation module is also introduced to fully extract temporal evolution patterns from behavior sequences, enhancing the model's ability to capture changes in user interests over time. The model first extracts high-order representations separately from the behavior graph and the social graph. Then, a modality gating mechanism is used to perform dynamic weighted fusion, generating a unified user representation vector. This fused representation is combined with the target item embedding and input into a matching function to produce the final recommendation results. Experiments on public datasets show that the proposed method outperforms several representative recommendation models across multiple mainstream evaluation metrics. It demonstrates notable advantages in precision, coverage, and ranking quality. Furthermore, ablation studies confirm the contribution of each module to performance gains. Cold-start analysis and modality weight sensitivity experiments further reveal the model's stability and adaptability. The experimental results strongly support the value of joint modeling of behavior and social graphs in addressing complex recommendation tasks. They also highlight the modeling capacity of graph neural architectures in multimodal recommendation scenarios.
Graph-Based Knowledge Representation and Semantic Alignment for Large Language Model-Driven Entity Recognition
This study proposes a large language model-based entity recognition method enhanced with external knowledge graphs to address the limitations of traditional approaches in complex semantic environments, ambiguous entities, and knowledge-sparse scenarios. The method builds a multilayer framework consisting of text encoding, knowledge retrieval, graph-based representation, and semantic fusion. The large language model extracts deep semantic features, while the knowledge graph supplements external knowledge through structured nodes and relations, which strengthen reasoning in boundary detection and semantic disambiguation. A dynamic knowledge selection mechanism and a graph encoding module are designed to achieve deep alignment between textual information and knowledge representations, allowing the model to use graph substructures that are most relevant to the context during inference. Experiments on an open-domain entity recognition dataset, along with sensitivity analyses, demonstrate that the method achieves significant improvements in Accuracy, Precision, Recall, and F1 score. It is especially stable in rare entity cases, ambiguous expressions, and long-distance dependency settings. Further analyses of training size, class imbalance, distribution perturbation, and knowledge injection strength confirm the robustness and scalability of the framework under different data conditions and training environments. Overall, the method achieves deep integration of large language models and knowledge graphs in entity recognition and provides an effective path toward building more accurate, interpretable, and structure-aware recognition systems.
Design and Performance Analysis of a Scalable Backend Architecture for High-Concurrency Web Applications
The rapid growth of online services has placed increasing demands on backend systems, particularly in terms of scalability, reliability, and response efficiency. As user traffic continues to rise, traditional monolithic architectures often face performance bottlenecks caused by limited scalability and inefficient resource utilization. This paper proposes a scalable backend architecture that integrates microservices, load balancing, Redis caching, and asynchronous task processing to improve system performance under high-concurrency workloads. The architecture was implemented using Spring Boot, MySQL, Redis, and Nginx, with Docker used for service deployment and management. Performance evaluation was conducted using Apache JMeter under workloads ranging from 500 to 5,000 concurrent users. Experimental results indicate that the proposed architecture maintained an average response time below 240 ms for up to 3,000 concurrent users. At the maximum workload of 5,000 concurrent users, the system achieved a throughput of 2,150 requests per second, representing a 38.6% improvement over the baseline monolithic implementation. The introduction of Redis caching reduced database read operations by approximately 67%, while CPU utilization remained below 72% during peak traffic. In addition, asynchronous processing reduced the execution time of background tasks by 43%, improving the responsiveness of user-facing services. These results demonstrate that the proposed backend architecture can effectively improve scalability and service stability while reducing system resource consumption. The study provides practical guidance for developing reliable backend systems for modern web applications.
Variational Autoencoder-Based Query Plan Anomaly Detection and Cost Deviation Warning for Database Systems
This paper proposes a variational autoencoder-based modeling approach for query plan anomaly detection and cost deviation warning in databases. The study first analyzes factors such as biased statistics, dynamic data distribution, and resource contention, which cause gaps between generated query plans and the true optimal execution, leading to performance fluctuation and resource waste. In the method design, an encoder maps query plans and execution features into a latent space, where the distribution is parameterized by mean and variance, and reconstruction is achieved through reparameterized sampling and a decoder to learn the latent structure of normal query plans. The objective function integrates reconstruction error, KL divergence, and a cost deviation term to jointly optimize query feature modeling and cost deviation representation. Through this mechanism, normal and abnormal plans can be effectively distinguished in the latent space, and deviations between estimated and actual costs can be sensitively captured. Furthermore, sensitivity experiments under feature missing ratios, latent dimension variations, and environmental disturbances show that the method achieves superior stability and robustness in AUC, ACC, Recall, and Precision compared with existing methods. Overall, this study provides a new technical path for query optimization and execution monitoring, enabling effective anomaly detection and early cost deviation warning in complexscenarios.
Structure-Aware Multi-Scale Behavioral Learning for Distributed System Observability and Anomaly Identification
To address the challenges of complex anomaly behaviors, diverse propagation paths, and the intertwining of local disturbances and global imbalances in distributed system environments, this paper proposes an anomaly detection method based on multi-scale behavioral pattern learning. This method leverages the characteristics of frequent node interactions, tight service dependencies, and dynamic evolution of operational states in distributed systems. It organizes system monitoring data into a unified dynamic behavioral representation and constructs a multi-layered modeling framework for anomaly identification based on this representation. In terms of method design, firstly, the node states, edge relationships, and multi-source operational information in the system are structurally represented to form a dynamic graph representation that reflects service call relationships and behavioral coupling characteristics. Then, focusing on the behavioral change patterns over different time periods, a multi-scale temporal pattern extraction mechanism is introduced to jointly characterize short-term fluctuations and long-term drifts, thereby enhancing the model's ability to perceive complex anomaly evolution processes. Furthermore, by incorporating spatial dependencies between services, the propagation characteristics of anomaly signals along call chains and interaction paths are modeled, enabling local anomalies and associated disturbances to be co-represented in a unified representation space. Finally, by fusing temporal behavioral features and spatial dependency features, an anomaly scoring mechanism for deviations from normal state prototypes is constructed to achieve effective identification of anomaly samples. The proposed method effectively adapts to the diversity, hierarchy, and dynamism of anomaly patterns in distributed systems. It balances representational and discriminative capabilities during detection, enhancing the modeling depth for complex operational states. Related research indicates that this method significantly improves the accuracy and stability of anomaly detection in distributed systems, providing valuable technical support for the monitoring, anomaly identification, and intelligent operation and maintenanceof complexsoftwaresystems.
Spatiotemporal Transformer-Based Latency Prediction for Large-Scale Cloud Computing Clusters
This study addresses the key problem of latency prediction in cloud clusters and proposes a spatiotemporal Transformer-based modeling method to overcome the limitations of traditional approaches in capturing complex dependencies and achieving high accuracy in large-scale dynamic environments. The research first analyzes the spatiotemporal characteristics of latency, noting that latency shows both periodic fluctuations and sudden spikes in the time dimension, while in the spatial dimension, it is influenced by the coupling of topology structures and resource competition among nodes. To address this, a unified spatiotemporal Transformer framework is constructed, where temporal self-attention captures long-range dependencies, and spatial self-attention with adjacency matrices models node interaction features, enabling a comprehensive representation of dynamic latency patterns. The method design integrates an embedding module for high-dimensional representation of multi-source inputs, residual connections, and normalization to enhance stability and generalization, and a linear mapping output layer for precise latency predictions. Experiments are conducted on the public Google Cluster Trace dataset with comparisons against mainstream models and multidimensional sensitivity analyses. Results show that the proposed method achieves superior performance in MSE, RMSE, MAE, and R², significantly outperforming Transformer, LSTM, TimeMixer, and ITransformer. Furthermore, the method maintains stable performance under hyperparameter sensitivity tests, environmental disturbances, and data missing scenarios, demonstrating strong robustness and adaptability in complex distributed environments. In conclusion, the proposed framework achieves high accuracy and reliability in latency prediction tasks and provides effective technical support for performanceoptimization andresource management incloud computingsystems.
Self-Supervised Unified Representation Learning with Structural Dependency Modeling for Early Risk Detection in Distributed Systems
This study addresses the challenge of accurately modeling system states in distributed systems under highly dynamic workloads, multi-indicator coupling, and continuously evolving structures. A self-supervised unified representation framework is proposed for capturing multi-indicator association features. The method first constructs a high-dimensional temporal input matrixfrom multi-sourcemonitoring data and uses a nonlinear encoder to extract multi-scale features, including local trends, short-term disturbances, and long-term dependencies. A dependency graph based on structural consistency is then used to describe functional relations and potential propagation paths between nodes. A structural propagation mechanism integrates multi-indicator coupling relationships into a unified latent representation space. Predictive, contrastive, and reconstruction-based self-supervised tasks are further designed to enable the model to learn stable structural patterns and statistical regularities of normal operation without labels. Early risk signals are identified by detecting shifts in the latent space, providing efficient and reliable support for system safety. Experimental results show that the framework exhibits strong robustness and adaptability under conditions such as structural disturbances, node fault injection, and data noise. It can capture hidden early anomaly features and significantly enhance early risk detection. The method integrates temporal features, structural dependencies, and self-supervised signals in a unified modeling manner and offers a new technical pathway for understanding complex association behaviors in large-scale distributed systems.
Predictive GPU Memory Modeling in Containerized Cloud Environments Using Self-Supervised Multi-Scale Learning
This paper addresses the complexity and challenges of GPU memory usage prediction in containerized cloud environments and proposes a framework based on self-supervised learning and multi-scale time series modeling. The study first analyzes the dynamic characteristics of GPU memory utilization under multi-tenant and multi-task concurrency and points out that its usage patterns show significant non- stationarity and multi-scale dependencies. To address this, a multi-scale embedding mechanism is designed to unify sequence information at different time granularities, and a self-attention mechanism is applied to capture global dependencies, enhancing the representation of both short-term fluctuations and long-term trends. At the same time, a self-supervised pretraining module is introduced, which extracts latent temporal patterns from large-scale unlabeled data through a masking reconstruction task, reducing the reliance on labeled data and improving model generalization. In the prediction stage, the model uses fused feature representations to perform regression learning of GPU memory usage and adopts a joint optimization objective to improve accuracy while maintaining stability. Comparative and sensitivity experiments are conducted, covering mainstream methods such as LSTM, MLP, 1DCNN, and TimeMixer. The results show that the proposed method outperforms existing models in R², MAPE, MAE, and RMSE, and demonstrates robustness across multiple conditions, including window size, missing rate, and workload variations. These findings confirm the practical value and wide applicability of the framework in containerized cloud environments.
Heterogeneity-Aware Federated Optimization with Secure Aggregation for Distributed Data Mining
This paper addresses the challenges of collaborative modeling in distributed data mining scenarios, stemming from difficulties in data centralization, strict privacy constraints, and significant client heterogeneity. It proposes an intelligent modeling framework integrating federated learning. This method uses communication rounds as its core organizational structure, enabling parameter-only collaborative training between a central server and multiple edge nodes. Clients define empirical risks based on local data and perform gradient updates, using regularization terms and proximal constraints to suppress local drift and improve optimization stability. The server performs weighted aggregation based on client sample proportions and the participating set, combining update increment metrics and pruning mechanisms to limit the impact of anomalous updates on the global model. Simultaneously, secure aggregation and update perturbation strategies are introduced to enhance the privacy protection and controllable leakage risk of uploaded information without exposing the original data. The method has a clear, modular, and composable overall structure, adaptable to varying client participation sizes and communication constraints, while balancing prediction performance and probabilistic reliability. Experiments are conducted under a unified evaluation protocol, comparing the proposed framework with several representative methods to verify its effectiveness and applicability in distributed datamining tasks.
Game-Theoretic Reinforcement Learning for Stable Competitive Resource Allocation in Dynamic Cloud Environments
With the increasing prevalence of multi-tenant concurrent access, heterogeneous task hybrid operation, and dynamic fluctuations in resource demand in cloud computing platforms, the stability problem of competitive allocation under limited resource conditions is becoming increasingly prominent. Addressing the shortcomings of traditional methods in simultaneously considering resource utilization, fairness constraints, and system stability in complex competitive environments, this paper proposes a cloud resource competitive allocation method based on the fusion of game theory and reinforcement learning. This method first starts from the multi-agent resource competition relationship in the cloud environment, modeling resource requesters and the scheduling process as a dynamic interaction process. State vectors are used to uniformly characterize key information such as remaining resources, queue pressure, task priority, and competition intensity, thereby enhancing the model's ability to describe real-world cloud resource competition scenarios. Based on this, a game- theoretic interaction mechanism and a hybrid strategy selection mechanism are introduced to model the strategy coupling relationship between different agents under limited resource constraints, enabling resource allocation decisions to better reflect the mutual influence and equilibrium characteristicsin the multi-agent competition process. Furthermore, by combining reinforcement learning strategy optimization mechanisms, a reward-shaping guidance model simultaneously focuses on resource utilization efficiency, fair allocation effectiveness, and conflict suppression capabilities during long-term interactions. Stability regularization constraints are used to reduce fluctuations and imbalances in resource allocation, thereby improving the continuity, coordination, and robustness of the schedulingstrategy in dynamic environments. After constructing a resource competition allocation scenario using public cloud cluster data, this paper systematically validates the proposed method. Results show that this method effectively improves the overall performance of resource competition allocation, demonstrating good application potential in resource utilization, fairness assurance, system stability, and overall coordination. The research indicates that the synergistic integration of game theory mechanisms and reinforcement learning strategy Artificial Intelligenceand ComputingInnovations optimization provides a feasible and interpretable technical path for studying the stabilityof resourcecompetition allocationin complexcloud environments.
Joint Optimization of Dual Retrieval and Adaptive Re-Ranking for Retrieval-Augmented Generation
This paper proposes a Retrieval-Augmented Generation framework based on sparse–dense dual retrieval fusion combined with an adaptive re-ranking algorithm to improve ranking precision and generation consistency. Sparse retrieval ensures high coverage, dense retrieval provides semantic matching ability, and dynamic weight allocation is introduced during re-ranking to balance the importance of evidence from different sources. An end-to-end optimization objective is constructed that jointly constrains ranking relevance and generation consistency, enabling retrieval and generation to be improved in coordination. The framework is systematically evaluated across hyperparameters, environment, and data sensitivities, covering candidate size, caching strategy, domain transfer, label sparsity, and feedback noise. Results show that the proposed method outperforms existing approaches across multiple metrics, maintaining stable advantages in candidate relevance, recall rate, and semantic consistency of generation. This study demonstrates that dual-path fusion and adaptive re-ranking effectively mitigate the conflict between coverage and precision in complex retrieval environments and provide new ideas and methods for building robust RAG systems.
Reinforcement Learning-Based Task Scheduling Under Energy Budget Constraints in Edge-Cloud Systems
This paper addresses the energy constraint challenge in task scheduling within edge-cloud collaborative architectures by proposing a scheduling method named EA-BCRL (Energy-Aware Budget-Constrained Reinforcement Learning). The method integrates reinforcement learning with an energy-aware mechanism. It constructs an energy budget modeling module to dynamically estimate the current energy state and budget utilization of the system, enabling real-time modeling and feedback of budget constraints. A budget-guided reward shaping mechanism is introduced to embed energy constraint signals into the policy optimization process, guiding the reinforcement learning agent to avoid budget violations while ensuring task performance. The overall framework achieves coordinated optimization of energy modeling, policy updating, and budget control during task offloading and resource scheduling. Experiments are conducted on a real-world dataset to validate the method at scale, with evaluation metrics including task success rate, average energy consumption, budget violation rate, and average latency. Comparative results show that the proposed method outperforms existing mainstream scheduling approaches across multiple key performance indicators. Ablation studies are further carried out to analyze the independent contributions of each module. Multi-dimensional sensitivity tests are also performed on learning rate, task scale, data disturbance, and task heterogeneity. These results confirm the stability and adaptability of the method. Overall, the findings demonstrate that the proposed approach enables efficient and stable energy-constrained scheduling under complex resource environments and diverse workload conditions.
Intelligent Domain Text Classification through Fine-Grained Semantic Representation Learning
This study focuses on fine-grained discriminative feature extraction for domain text classification and proposes a BERT-based feature enhancement method to address the limitations of traditional models in domain adaptation and fine-grained modeling. The approach first uses a pretrained language model to generate contextual representations of input texts, and then introduces a multi-head attention mechanism to capture both global dependencies and local semantic details, leading to more discriminative feature representations. A contrastive learning constraint is further designed to optimize the separability of positive and negative samples in the semantic space, enabling the model to maintain strong discriminative ability even under blurred category boundaries and subtle semantic differences. In addition, regularization and joint optimization strategies are employed to ensure feature separability while preserving stability and generalization. To evaluate the effectiveness of the method, sensitivity experiments were conducted on key hyperparameters, including learning rate, weight decay, maximum sequence length, contrastive learning temperature coefficient, number of attention heads, and hidden dimension, followed by comparative analyses of model performance under different conditions. Experimental results show that the proposed method achieves superior performance on accuracy, precision, recall, and F1-score, demonstrating higher-level fine-grained discrimination in complex domain text classification tasks. The study not only presents innovation in method design but also shows robustness and effectiveness through multi-dimensional experimental validation, providing new technical insights and practical directions for domain text classification.
Enhancing Semantic Matching and Text Robustness through Masked Language Modeling
This paper investigates the application of masked language models in semantic mining. It proposes a deep semantic modeling method based on selfsupervised learning. The method randomly masks parts of the input text and uses contextual information to predict the masked words. This enables effective modeling of deep semantic structures in text. A unified training framework is designed, integrating a cross-entropy loss function with a regularization mechanism. This improves both the semantic representation ability and training stability of the model. The model structure retains the core features of masked language model pretraining. It also incorporates task-specific classification layers to support various downstream semantic mining tasks. The experiments evaluate the model from multiple perspectives, including classification performance, semantic match, differences in text length, and robustness to text perturbations. Results show that the proposed method outperforms mainstream pretrained language models across several metrics. It is especially strong in semantic matching and contextual understanding. The study also constructs multiple semantic perturbation scenarios to analyze model robustness. It confirms the model's ability to preserve semantic integrity under complex variations. By integrating corpus structure, prediction behavior, and task adaptability, the proposed semantic mining method offers an effective solution to enhance semantic understanding in natural language processing systems.
Enhancing Cloud Security with Graph Neural Network-Driven Anomaly Detection in Multi-Tenant Environments
This paper addresses the key problem of anomaly detection in multitenant cloud environments and proposes an intelligent detection method based on graph neural networks. The study first analyzes the complexity and security risks faced during the process of resource sharing among multiple tenants. It points out that traditional methods based on rules or single features fail to accurately capture tenant interactions and potential abnormal patterns. On this basis, the multi-tenant environment is abstracted into a graph structure, where nodes represent tenants or resources and edges represent interactions or dependencies. Graph neural networks are then used to take advantage of information propagation and feature aggregation, enabling global modeling and representation of tenant behaviors. The method combines graph convolution and attention mechanisms, allowing the model to dynamically focus on key neighbor nodes within multi-level dependencies. This enhances sensitivity and discriminative ability toward abnormal behaviors. At the same time, a joint loss function is constructed to balance feature reconstruction and regularization constraints. This design effectively strengthens the generalization ability and robustness of the model. To verify the effectiveness of the proposed method, systematic experiments were conducted on public cloud computing datasets. Sensitivity analyses were also performed on hyperparameters and environmental conditions. The results show that the method outperforms baseline models in key metrics such as AUC, ACC, F1-Score, and Precision. It maintains stable performance under different conditions. These findings confirm that the proposed anomaly detection framework with graph neural networks has strong practical value and application potential in complex multi-tenant cloud scenarios.
Efficient Anomaly Detection in Distributed Edge-Cloud Systems Using Lightweight Modeling
This paper addresses the problem of anomaly detection in edge-cloud collaborative environments and proposes a lightweight model design method. The study first analyzes the challenges of anomaly detection under multi-tenant sharing and heterogeneous resource conditions, including high latency, bandwidth consumption, and privacy risks. To solve these issues, a detection framework combining edge computing with cloud collaboration is developed. At the edge side, input projection and feature encoding are performed, and temporal dependencies are modeled through a lightweight attention mechanism. The cloud side then aggregates and optimizes features from different nodes to achieve global anomaly recognition in distributed scenarios. The method incorporates edge memory caching and reconstruction mechanisms, enabling the model to reduce computation and storage costs while maintaining accuracy. To further enhance performance, the framework applies regularization and privacy constraints to improve robustness against noise and data heterogeneity in distributed environments. Experiments using real cloud workload trace datasets demonstrate that the proposed method outperforms comparison approaches in F1-Score, Precision, Recall, and ACC, confirming its effectiveness and stability in complex conditions. The findings enrich the theoretical system of lightweight detection methods in edge-cloud collaboration and provide feasible technical support for the secure operation of distributed intelligent systems.
Automatic Audit Judgment Using Causal Inference and Counterfactual Risk Estimation
This paper addresses the instability and insufficient interpretation of automatic judgment in audit scenarios caused by large data scale, complex triggering mechanisms, and the coexistence of bias and drift. It proposes an automatic audit judgment algorithm framework that incorporates causal inference. The method models audit trigger signals as processing variables, uses transaction behavior, account status, and time context as observation features, and takes risk outcomes as the judgment target, constructing a unified process from processing mechanism modeling to bias-free discriminant learning. First, propensity probability modeling characterizes the probability of trigger signals occurring under given feature conditions to mitigate the interference of selection bias and confounding factors on the judgment boundary. Then, inverse probability weighting is introduced to achieve sample reweighting, and a dual robust counterfactual estimation is combined to improve the reliability of effect characterization. Based on this, a risk discriminator is trained to output a risk score, and causal consistency constraints are added to promote the alignment of the judgment output with counterfactual differences, thereby enhancing the auditability and verifiability of the results. The framework was compared with several related methods under the same data and process settings. The results show that the proposed method achieves more balanced performance on commonly used classification evaluation indicators and can provide an audit clue organization method that takes into account both risk ranking and mechanism explanation, providing practical technical support for digital auditing and intelligent risk control applications.
Reinforcement Learning-Based Elastic Scaling Policy Optimization for Microservices
This paper focuses on the intelligent optimization of elastic scaling strategies in microservice architectures. It proposes an adaptive resource control method based on reinforcement learning. The method constructs a multi-dimensional state space that incorporates key performance indicators such as CPU utilization, response time, and queue length. A policy network is used to make dynamic decisions on scaling actions. To improve learning efficiency and system responsiveness, the method introduces an advantage function for policy optimization. It also integrates a temporal dependency modeling mechanism to enhance the model's awareness of historical workload patterns. A reward function is designed based on multiple system metrics. It jointly considers resource cost, performance assurance, and system stability, enabling fine-grained guidance of the policy behavior. In the experimental section, a sensitivity analysis is conducted with different discount factors, dynamic workload patterns, and environmental disturbances. The evaluation systematically examines the stability and robustness of the proposed method. Comparative results with mainstream baselines show significant improvements in accuracy, precision, and recall. The results demonstrate strong policy convergence and efficient scheduling, confirming the effectiveness of the proposed approach in microservice elastic control tasks.
Intelligent Backend Failure Detection with Uncertainty Quantification and Asymmetric Risk Optimization
Backend systems in cloud computing and microservice environments exhibit highly dynamic behavior, strong coupling, and substantial noise. These characteristics lead to challenges such as prediction instability and asymmetric risk in failure detection. To address the limitations of traditional approaches that rely on deterministic decisions, fail to represent predictive confidence, and ignore differences in error costs, this paper proposes a backend failure detection method that integrates uncertainty estimation with cost-sensitive learning. The method represents system states through probabilistic modeling. It outputs failure decisions while explicitly characterizing predictive uncertainty. Uncertainty information is further incorporated into a risk-weighted mechanism to suppress the influence of noisy samples and low-confidence predictions during model updates. In addition, a cost-sensitive learning objective is constructed so that the model explicitly accounts for the impact of different detection errors on system stability and service continuity during training. This design enables risk-aware decisions that better align with practical operational requirements. The proposed framework is evaluated under unified data and evaluation settings and compared with several representative methods. The results show consistent advantages in overall discriminative capability, decision stability, and risk control. The study demonstrates that joint modeling of predictive uncertainty and error cost improves the reliability and engineering applicability of backend failure detection in complex environments. It also provides an effective modeling approach for intelligent failure detection systems in real operational scenarios.
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.
Memory-Enhanced Transformer for Backend Microservice Anomaly Detection
With the widespread adoption of cloud-native architecture and service-oriented deployment, backend microservice systems, while enhancing business flexibility, also exhibit characteristics such as cross-service propagation, enhanced contextual coupling, and complex temporal evolution of abnormal behavior, posing higher demands on anomaly detection. Addressing the shortcomings of existing methods, such as insufficient utilization of historical states, inadequate characterization of long-distance dependencies, and limited ability to fuse multi-source runtime information, this paper proposes a backend microservice anomaly detection method based on memory-enhanced Transformers. This method, based on logs, monitoring metrics, and link observation data, first constructs continuous temporal input through a unified representation. Then, an external memory unit is introduced to store and retrieve key historical patterns, enabling the model to continuously incorporate anomaly-related contextual clues during current state discrimination. Subsequently, a gating fusion mechanism and Transformer encoding structure are combined to collaboratively model current observation features and historical memory information, thereby enhancing the ability to express complex anomaly semantics and long-term correlations. To achieve anomaly detection, this paper further utilizes reconstruction bias to construct an anomaly scoring method, enabling the model to characterize normal behavioral structures and anomaly deviations from the latent representation space. A comparative study was conducted on anomaly data of open-source microservices. The results show that the proposed method has good recognition ability and stable performance in backend microservice anomaly detection tasks. It can more effectively characterize the anomaly features in complex operating environments and provide a feasible methodological support for anomaly monitoring in intelligent operation and maintenance scenarios.
Intelligent Decision-Making for Advertising Recommendation via Data Mining and Graph Neural Networks
This paper addresses the challenges of data sparsity, complex interactions, difficulty in fully characterizing latent preferences, and lack of structural awareness in the decision-making process of advertising recommendation tasks. It proposes an intelligent decision-making method for advertising recommendation that combines hard data mining and graph neural networks. First, this method extracts multi-source information, including user, ad, and contextual information, from the original ad interaction logs. Then, it uses a hard data mining mechanism to identify high-value behavioral patterns and key relationship clues to reduce noise interference and enhance the ability to express effective information. Based on this, a heterogeneous interaction graph for advertising recommendation scenarios is constructed, unifying user interests, ad attributes, and environmental information into a graph structure modeling framework. Graph propagation learning is used to mine high-order dependencies between nodes, thereby more accurately representing potential associations in complex recommendation scenarios. Simultaneously, a graph reweighting and adaptive fusion mechanism is introduced to strengthen key structural information and improve the synergy between initial features and deep graph representations, making the resulting decision representation more consistent with the actual needs of advertising recommendation tasks. Furthermore, based on a unified decision modeling strategy, the matching relationship between users and ads is effectively estimated, thereby completing ad ranking and recommendation output. Comparative experimental results show that the proposed method achieves superior performance on multiple recommendation evaluation indicators, verifying its effectiveness and practicality in intelligent decision-making tasks for advertising recommendation.
Hierarchical Communication and Computation Scheduling for Efficient Federated Learning in Cloud-Edge Collaborative Intelligent Systems
As federated learning and cloud-edge collaborative training are increasingly applied in distributed intelligent computing, issues such as high communication overhead, strong heterogeneity of computing resources, and insufficient efficiency of multi-layer node collaboration have gradually become key factors restricting system performance improvement. To address these issues, this paper studies a joint scheduling optimization method for communication and computation, addressing the distributed training needs in cloud computing scenarios, and constructs a unified modeling framework suitable for collaborative training across terminals, edge nodes, and the cloud. In terms of method design, firstly, starting from the multi-layer collaborative structure, a systematic description of client access relationships, edge node activation states, and cloud coordination processes is provided, incorporating communication latency, local computation overhead, and regional forwarding costs during training into a unified latency model. Secondly, to improve the effectiveness of node participation and the rationality of resource allocation, a utility evaluation mechanism integrating data quality, training status, and resource consumption is introduced to achieve dynamic selection of participating nodes under constraints of limited bandwidth and computational budget. Subsequently, combining the characteristics of cloud-edge layered training, a joint optimization objective for resource-constrained environments is further constructed, considering latency control, training quality, and energy consumption costs collaboratively, and enhancing the organizational capacity and execution feasibility among multiple nodes through hierarchical scheduling constraints. Finally, an aggregation mechanism combining latency and data contribution is designed during the model update phase to mitigate the adverse effects of heterogeneous node differences on the global model evolution. Experimental results show that the proposed method performs well in terms of accuracy, overall performance, and system coordination capabilities, effectively improving resource utilization and overall training efficiency in federated learning and cloud-edge collaborative training scenarios.
Retrieval-Augmented Long-Text Reasoning with Semantic Conflict Detection and Cross-Paragraph Evidence Integration
This study addresses common challenges in long-text reasoning, including retrieval noise, dispersed evidence, semantic drift, and inconsistent generation, and proposes a retrieval-augmented generation framework for long-text understanding. The framework performs hierarchical semantic retrieval, cross-paragraph evidence graph modeling, semantic conflict detection and calibration, and evidence-guided generation to extract key segments from large-scale long texts, correct semantic relations, and produce reasoning outputs that align closely with factual content. In the retrieval stage, the model maps queries into a multi-scale semantic space and applies structured candidate filtering to improve cross-document evidence recall. In the evidence construction stage, graph structures represent semantic associations and potential conflicts across paragraphs, forming a unified semantic center that constrains subsequent generation. In the generation stage, evidence-aware attention and conditional control ensure that outputs rely strictly on real paragraph content, reducing hallucinations and enhancing the logical consistency of the reasoning chain. The experiments evaluate the model across answer accuracy, reasoning consistency, factual faithfulness, and cross-paragraph structural integration, and conduct sensitivity analyses on learning rate, context window length, retrieval candidate size, and training data scale. The results show that the framework achieves stable advantages across multiple metrics and generates high-quality reasoning outputs in complex long-text environments. Overall, this study provides a complete technical pathway that covers retrieval, integration, and generation, offering an effective solution for multi-source and multi-paragraph long-text tasks and establishing a methodological foundation for reliable long-text reasoning in knowledge-intensive application scenarios.
Dynamic User Interest Evolution Modeling for Personalized Recommendation Systems Based on Multi-Scale Temporal Awareness and Attention Mechanisms
This paper addresses the problem of dynamic user interest changes in recommendation systems by proposing a deep recommendation method based on time awareness and interest evolution modeling. The method introduces multi-scale time windows, time decay factors, and attention mechanisms to hierarchically model user behavior sequences. It extracts both short-term and long-term interest features and integrates them into a dynamic interest representation. The model considers not only the sequential nature of behaviors but also incorporates time intervals, behavior frequency, and the weighting of key interaction segments to improve the accuracy of interest modeling. In the experimental section, multiple evaluations are conducted on the MovieLens 1M dataset. The results show that the proposed method significantly outperforms existing mainstream models in terms of Precision@10, Recall@10, and NDCG@10. In addition, sensitivity experiments are conducted on learning rate, optimizer, and interest evolution speed. These experiments confirm the model's stability and robustness to parameter changes. Overall, this study builds a recommendation framework that performs well in dynamic environments. It effectively improves the accuracy and practicality of personalized recommendations.
Log Semantic Graph Construction and System Event Anomaly Detection via Convolutional Neural Networks
This paper proposes a system event anomaly detection method that integrates convolutional semantic encoding with structure-aware graph fusion. The method first introduces a convolution-based log semantic modeling module, which uses a multi-scale convolutional perception mechanism to extract contextual features and build embedding representations from raw log sequences. This significantly enhances the ability to capture local semantic dependencies and behavioral cues. Then, a structure-aware graph fusion module is designed to construct dynamic graphs based on event associations, modeling and reinforcing the potential interactions among logs. This captures multiple dependencies of log events across both temporal and structural dimensions. To improve the discriminability of the embedding space and the consistency of structural representations, a joint optimization objective is constructed. It combines contrastive loss and structural alignment constraints for collaborative training, enabling accurate semantic differentiation and anomaly detection at the event level. The proposed method demonstrates strong semantic parsing and structural modeling capabilities, achieving precise anomaly detection and pattern extraction in high-dimensional and sparse system logs. Modeling experiments on real log data verify the effectiveness and applicability of the approach in complex system scenarios.
Automatic Orchestration of Enterprise ETL Processes via Graph Neural Network and Large Language Model Collaborative Reasoning
As data source types, field semantics, quality rules, and task dependencies in enterprise data platforms become increasingly complex, traditional ETL process orchestration methods relying on manual configuration and static rules struggle to meet the demands for automation, consistency, and reliability in large-scale enterprise data processing. Addressing the limitations of traditional ETL process orchestration methods, which suffer from insufficient cross-source relationship identification, inadequate business semantic understanding, and the ever-increasing complexity of contract types, field semantics, quality rules, and task dependencies, this paper proposes an automatic orchestration method based on collaborative reasoning using graph neural networks and a large language model. This method first models data sources, fields, rules, and tasks as a unified ETL state graph and uses relation-aware graph representation learning to capture high-order relationships between field mappings, task dependencies, and rule constraints. Subsequently, it constructs a structural semantic joint context by combining business requirement text, data pattern information, and rule descriptions, enabling the large language model to complete task decomposition, process reasoning, and candidate ETL process generation under graph-structured evidence constraints. Furthermore, the method introduces constraint consistency verification and a multi-criteria scoring mechanism to screen candidate processes based on dependency rationality, rule coverage, and execution feasibility, thereby improving the stability and reliability of the automatic orchestration results. Comparative experiments based on publicly available enterprise data integration scenarios show that the proposed method has good overall performance in terms of process generation correctness, dependency consistency, rule coverage, and execution success rate, and can provide effective support for enterprise data warehouse construction, automated data governance, and intelligent data engineering.
Deep Learning-Based Multi-View Behavioral Modeling and Trend Regression for Cloud Resource Prediction
This paper proposes a prediction framework for cloud computing environments that integrates a Behavior-Aware Modeling Module (BAMM) and a Multi-Scale Trend Regression Mechanism (MSTRM) to address the challenge of modeling complex access behaviors and their impact on bandwidth usage. BAMM employs multi-view embedding to jointly represent multi-dimensional features such as request type, access path, tenant information, and geographic location. It combines dilated temporal convolution and self-attention mechanisms for multi-dimensional temporal modeling and uses a service call graph for cross-service structural modeling. These three pathways are fused through residual connections and gated mechanisms to generate behavioral representations that capture both fine-grained local patterns and global structural dependencies. On this basis, MSTRM performs multi-scale regression modeling to simultaneously capture short-term fluctuations and long-term trends in bandwidth usage, enabling accurate prediction across multiple periods. The framework incorporates a task-specific loss function to balance prediction accuracy across different time scales while maintaining stable performance in dynamic multi-tenant scenarios. Experiments on a real-world cloud workload dataset demonstrate that the proposed method offers significant advantages in accurately characterizing bandwidth usage trends, reducing prediction errors, and improving robustness in complex environments, providing strong support for high-precision and fine-grained resource usage prediction.
Reinforcement Learning-Based Intelligent Decision Modeling for Large-Scale Distributed Systems
To address challenges in large-scale distributed systems, including high-dimensional state spaces, dynamic operating environments, and strongly coupled multi-node decisions, this study proposes a reinforcement learning based approach for intelligent decision modeling that provides a unified sequential formulation of the scheduling process. System states are abstracted into structured representations, while policy networks and value evaluation mechanisms jointly model the long-term impact of scheduling actions on system objectives. This enables integrated optimization of task completion efficiency, resource utilization, and load distribution. The method explicitly accounts for parallel operation across multiple nodes and the requirement of global objective consistency, allowing stable and effective scheduling behavior under complex dependencies. Comparative evaluations conducted in a public distributed system data environment demonstrate clear advantages across multiple system-level metrics, confirming the effectiveness and practical value of reinforcement learning driven intelligent decision modeling for improving the overall operational quality of large-scale distributed systems.
Learning-Driven Collaborative Scheduling for Multi-Tenant Distributed Inference Services
To address the scheduling complexity of multi-tenant distributed inference services under shared resources, model heterogeneity, and dynamic request conditions, this paper proposes a learning driven collaborative scheduling mechanism that models inference scheduling from a holistic system perspective. The approach abstracts system states and formulates scheduling decisions as a learnable mapping process. This enables the scheduling policy to dynamically adapt to workload variations and resource conditions during continuous operation. To capture intrinsic relationships in multi-tenant concurrent scenarios, collaborative utility modeling is incorporated into the scheduling objective. This guides scheduling behavior toward coordinated constraints among resource utilization efficiency, service stability, and tenant fairness. The proposed framework avoids strong dependence on specific execution details and provides a unified system-level treatment of request assignment and resource scheduling. It mitigates performance degradation caused by resource contention and workload fluctuation in multi-tenant parallel inference. System-level evaluation in multi-tenant distributed inference scenarios demonstrates that the method achieves strong overall performance in service response, system throughput, operational stability, and tenant fairness. These results confirm the effectiveness and practicality of learning driven collaborative scheduling in complex inference service environments.