Journal of Computer, Signal, and System Research
The Journal of Computer, Signal, and System Research(JCSSR) is a peer-reviewed journal focusing on cutting-edge research in computer science, signal processing, and systems engineering. It covers a wide range of topics, including algorithms, signal analysis, system design, and computational methods, aiming to promote innovation and interdisciplinary collaboration in these fields.
From Data Pipelines to Platform Tooling: A Holistic Framework for Cloud-Native Backend Development
Cloud-native backend development has evolved rapidly, transitioning from monolithic architectures to microservices, serverless functions, and containerized applications managed by sophisticated orchestration platforms. This review provides a holistic framework for cloud-native backend development, tracing its historical evolution, examining key architectural patterns, and analyzing the tooling ecosystems that enable efficient development and deployment. We explore the transformation from data pipelines, which initially focused on data movement and transformation, towards comprehensive platform toolings that provide end-to-end development and deployment capabilities. Core themes covered include microservices architecture and serverless computing, highlighting their benefits and challenges in the cloud-native context. We also delve into containerization technologies like Docker and orchestration tools like Kubernetes, discussing their role in automating deployment and scaling applications. A critical comparison of different backend development approaches, focusing on trade-offs between cost, performance, scalability, and maintainability, is presented. This review also identifies key challenges faced by developers in adopting cloud-native practices, such as complexity in distributed systems, vendor lock-in, and security concerns. Finally, we discuss future perspectives, including the rise of low-code/no-code platforms and the increasing importance of observability and automated testing in ensuring the reliability and performance of cloud-native backends. This review serves as a comprehensive guide for developers and architects looking to navigate the complexities of cloud-native backend development and build scalable, resilient, and cost-effective solutions.
Copyright (c) 2026 Yingqiang Yuan (Author) https://creativecommons.org/licenses/by/4.0
Research on Feature Fusion and Multimodal Patent Text Based on Graph Attention Network
Aiming at the challenges of cross-modal feature fusion, low computational efficiency in long patent text modeling, and insufficient hierarchical semantic coherence in patent text semantic mining, this study proposes a novel deep learning framework termed HGM-Net. The framework integrates Hierarchical Comparative Learning (HCL), a Multi-modal Graph Attention Network (M-GAT), and Multi-Granularity Sparse Attention (MSA) to achieve robust, efficient, and semantically consistent patent representation learning. Specifically, HCL introduces dynamic masking, contrastive learning, and cross-structural similarity constraints across word-, sentence-, and paragraph-level hierarchies, enabling the model to jointly capture fine-grained local semantics and high-level thematic consistency. Contrastive and cross-structural similarity constraints are particularly enforced at the word and paragraph levels, effectively enhancing semantic discrimination and global coherence within complex patent documents. Furthermore, M-GAT models patent classification codes, citation relationships, and textual semantics as heterogeneous graph structures, and employs cross-modal gated attention mechanisms to dynamically fuse multi-source and multi-modal features, thereby improving representation completeness and robustness. To address the high computational cost of long-text processing, MSA adopts a hierarchical sparse attention strategy that selectively allocates attention across multiple granularities, including words, phrases, sentences, and paragraphs, significantly reducing computational overhead while preserving critical semantic information. Extensive experimental evaluations on patent classification and similarity matching tasks demonstrate that HGM-Net consistently outperforms existing state-of-the-art deep learning approaches. The results validate the effectiveness and generalization capability of the proposed framework, highlighting its theoretical innovation and practical value in improving patent examination efficiency and enabling large-scale technology relevance mining.
Copyright (c) 2026 Zhenzhen Song, Ziwei Liu, Hongji Li (Author) https://creativecommons.org/licenses/by/4.0
ACE-Sync: An Adaptive Cloud-Edge Synchronization Framework for Communication-Efficient Large-Scale Distributed Model Training
Large-scale deep learning models impose substantial communication overhead in distributed training, particularly in bandwidth-constrained or heterogeneous cloud-edge environments. Conventional synchronous or fixed-compression techniques often struggle to balance communication cost, convergence stability, and model accuracy. To address these challenges, we propose ACE-Sync, an Adaptive Cloud-Edge Synchronization Framework that integrates (1) an attention-based gradient importance predictor, (2) a differentiated parameter compression strategy, and (3) a hierarchical cloud-edge coordination mechanism. ACE-Sync dynamically selects which parameter groups to synchronize and determines appropriate compression levels under per-device bandwidth budgets. A knapsack-based optimization strategy is adopted to maximize important gradient preservation while reducing redundant communication. Furthermore, residual-based error compensation and device clustering ensure long-term convergence and cross-device personalization. Experiments show that ACE-Sync substantially reduces communication overhead while maintaining competitive accuracy. Compared with FullSync, ACE-Sync lowers communication cost from 112.5 GB to 44.7 GB (a 60% reduction) and shortens convergence from 41 to 39 epochs. Despite aggressive communication reduction, ACE-Sync preserves high model quality, achieving 82.1% Top-1 accuracy-only 0.3% below the full-synchronization baseline-demonstrating its efficiency and scalability for large-scale distributed training. These results indicate that ACE-Sync provides a scalable, communication-efficient, and accuracy-preserving solution for large-scale cloud-edge distributed model training.
Copyright (c) 2026 Yi Yang, Ziyu Lin, Liesheng Wei (Author) https://creativecommons.org/licenses/by/4.0
Reverse Incentive Generation Mechanism for Agent Calibration and Stability
This paper focuses on the reverse incentive generation mechanism for agent calibration and stability. Firstly, it clarifies the background and core significance of researching this mechanism, then conducts an in-depth analysis of various factors affecting agent calibration and stability. Finally, aiming at these issues, it discusses in detail the operational principles and specific methods of the reverse incentive generation mechanism in agent calibration and stability improvement. By establishing a reasonable reverse incentive model combined with practical cases, the paper analyzes its application effects, aiming to provide theoretical support and practical guidance for enhancing agent performance, strengthening its reliability and stability in practical applications, and promoting the development and application of agent technology in various fields.
Copyright (c) 2026 Juyi Yang (Author) https://creativecommons.org/licenses/by/4.0
Customer Retention Optimization for SMEs Using Predictive Machine Learning Models
Customer retention is a critical determinant of sustainable performance for small and medium-sized enterprises (SMEs), yet many SMEs continue to rely on reactive and experience-based approaches to manage customer churn. The increasing availability of customer data creates new opportunities for more proactive and data-driven retention strategies. This study develops a conceptual framework for optimizing customer retention in SMEs through the application of predictive machine learning models. The framework integrates customer data, predictive analytics, and risk-based decision-making to support early identification of churn risk and targeted retention actions. By emphasizing model interpretability, practical implementation, and resource efficiency, the study highlights how predictive insights can be effectively embedded into SME operational processes. The analysis demonstrates the potential of predictive machine learning to transform customer retention management from a reactive function into a proactive and strategic capability under SME-specific constraints.
Copyright (c) 2026 Zhijun Liu (Author) https://creativecommons.org/licenses/by/4.0
Cross-Region Few-Shot Remote Sensing Image Captioning via Adaptive Vision-Language Feature Fusion
Remote Sensing Image Captioning (RSIC) enables automated interpretation of aerial imagery by converting complex visual scenes into coherent natural language descriptions. A key challenge in RSIC is the scarcity of annotated data and the significant domain shifts across geographic regions. Models trained on specific regional features often degrade in performance when applied to visually distinct landscapes such as agricultural or coastal areas. To address this, we propose the Adaptive Vision-Language Feature Fusion (AVLF) network, a few-shot learning framework designed to achieve robust cross-region transfer with minimal data. The AVLF framework bridges the semantic gap between visual and linguistic representations through an adaptive gating mechanism that dynamically balances visual and language features during caption generation. Extensive experiments on cross-region splits of multiple remote sensing datasets demonstrate that AVLF achieves state-of-the-art performance, maintains high captioning quality with limited support sets, generalizes effectively to unseen semantic categories, and incurs minimal computational overhead. Feature space visualizations show well-separated class distributions, while attention maps confirm that the model focuses on semantically relevant geographic objects. Ablation studies further highlight the importance of the adaptive fusion strategy in overcoming domain discrepancies and enhancing few-shot learning capability.
Copyright (c) 2026 Qikun Zuo (Author) https://creativecommons.org/licenses/by/4.0
UniGuard-Cascade: A Unified LLM-Driven Safety Scoring and Multi-Stage Audit Framework for Cross-Platform User Comments
User-generated comments across social media, e-commerce platforms, and online forums pose increasing challenges for automated content safety management. Existing moderation systems typically target a single platform, rely on inconsistent labeling standards, and struggle to balance accuracy with operational cost. To address these limitations, we propose UniGuard-Cascade, a unified safety scoring and cascading moderation framework that integrates large language models (LLMs), lightweight classifiers, and retrieval-augmented verification. UniGuard-Cascade introduces a platform-agnostic unified safety taxonomy covering toxicity, hate speech, spam, sexual/NSFW content, and misinformation, generated through LLM-based label alignment and normalization. The system operates in a three-stage cascade: (1) Fast Path Screening using a DistilBERT-based multi-label classifier for low-cost filtering; (2) Slow Path LLM Examination that provides refined labels and natural-language explanations; and (3) RAG-Enhanced Misinformation Verification, retrieving external evidence to validate factuality. Experiments conducted on four real-world datasets-Twitter, Amazon Reviews, Reddit, and YouTube Comments-show that UniGuard-Cascade consistently outperforms existing moderation baselines, achieving a Micro-F1 of 0.91 and a ROC-AUC of 0.97. The framework further reduces LLM usage by 87 percent compared with single-stage GPT-4 moderation, yielding a 5.7×reduction in overall inference cost while maintaining state-of-the-art multi-platform safety performance.
Copyright (c) 2026 Hao Tan, Ziming Chen (Author) https://creativecommons.org/licenses/by/4.0
AI Driven Payment System Security Improvement and User Privacy Protection Mechanism
With the continuous expansion of electronic payment systems and the rapid evolution of sophisticated cyber-attack methodologies, traditional security measures are increasingly finding it difficult to address the multifaceted risk challenges of the modern era. Leveraging the high-dimensional feature extraction and adaptive learning characteristics of artificial intelligence, this paper establishes a comprehensive AI-driven payment security and privacy protection framework. In terms of system security, the proposed architecture utilizes a residual attention mechanism for precise anomaly detection, while incorporating graph neural networks to analyze and cluster complex account relationship topologies. Furthermore, reinforcement learning is integrated to dynamically adjust risk control strategies in real-time, facilitating the construction of a collaborative defense system through the fusion of multi-source information. Regarding data privacy and integrity, the system adopts homomorphic encryption to enable complex model operations within an encrypted state, which is further combined with blockchain technology to ensure the rigorous traceability and immutability of the entire data flow. The implementation of this integrated technological architecture significantly enhances the intelligent defense capabilities of payment systems, providing a robust and scalable solution for safeguarding digital transactions and sensitive information in high-risk environments. This research not only offers a theoretical advancement in payment security but also provides a practical implementation roadmap for developing next-generation resilient financial information systems.
Copyright (c) 2026 Yue Qi (Author) https://creativecommons.org/licenses/by/4.0
Research on the Design and Optimization of Automated Data Collection and Visual Dashboard in the Medical Industry
With the ongoing advancement of medical informatization, automated data collection and visual dashboards have become critical for patient management, operational efficiency, and intelligent decision-making. Standardized data collection systems and display platforms improve the timeliness and accuracy of information gathering. To support diverse devices and complex use cases, system architecture, data transmission, display logic, and multi-terminal adaptation must be optimized to enhance data interoperability, boost efficiency, and maximize the value of intelligent healthcare systems.
Copyright (c) 2026 Xiangtian Hui (Author) https://creativecommons.org/licenses/by/4.0
A Dynamic Intrusion Detection System Integrating Concept Drift Detection and Incremental Learning
The dynamic, evolving, and inherently non-stationary characteristics of modern network environments present a fundamental challenge to traditional intrusion detection systems that rely on static learning paradigms. As network traffic patterns, system usage behaviors, and threat manifestations continuously change over time, the statistical properties underlying detection data are prone to both explicit and implicit variations, commonly described as concept drift. Such drift leads to a gradual mismatch between previously learned models and current data distributions, resulting in performance degradation, delayed responses, and reduced practical effectiveness of fixed detection mechanisms. To address these limitations and support the construction of an intelligent defense system with long-term adaptability, this study conducts a systematic theoretical investigation into dynamic intrusion detection from the perspective of learning evolution. On this basis, a unified framework is proposed that tightly integrates concept drift detection mechanisms with incremental learning strategies, enabling models to identify distributional changes in a timely manner and update their knowledge without retraining from scratch. The framework emphasizes continuity, stability, and adaptability, aiming to balance detection accuracy with computational efficiency under continuously changing conditions. By clarifying the internal relationship between drift detection and incremental model updating, this work provides a structured theoretical foundation for the development of adaptive intrusion detection systems capable of maintaining robust performance in complex and evolving network scenarios.
Copyright (c) 2026 Zhuoqi Liao (Author) https://creativecommons.org/licenses/by/4.0
DeepSeqCaus: A Deep Sequential Causal Inference Framework for User Churn Prediction and Optimal Retention Intervention Generation
In digital platforms, understanding and mitigating user churn is crucial for sustaining long-term engagement and revenue. Traditional machine learning approaches often rely on correlation-based predictive models without explicitly accounting for causal relationships underlying user behavior. This study proposes DeepSeqCaus, a unified deep sequential causal inference framework that integrates sequence modeling and treatment effect estimation to enable accurate churn prediction and optimal retention intervention policy generation. DeepSeqCaus consists of a dual-branch architecture: (1) a Temporal Feature Encoder using gated convolution and bidirectional gated recurrent networks to extract multi-granular temporal representations from behavioral sequences; and (2) a Causal Effect Estimator based on counterfactual representation learning to estimate heterogeneous treatment effects (HTEs) for candidate interventions such as personalized notifications, discount offers, or content recommendations. Using large-scale user interaction logs from an online service, we conduct extensive experiments comparing DeepSeqCaus with conventional predictive models and causal inference baselines. The results showed that DeepSeqCaus outperformed the baseline model in all cases. The proposed framework provides actionable insights for targeted retention and demonstrates strong potential for deployment in intelligent customer management systems.
Copyright (c) 2026 Tangtang Wang, Wen Ding (Author) https://creativecommons.org/licenses/by/4.0
A Secure Federated Learning Algorithm for Emotion Recognition Towards Multimodal Speaker Signals on the Client Side
With the widespread application of multimodal data in dialog emotion recognition, effectively integrating text, audio, and visual information while addressing data heterogeneity across multiple clients and ensuring user privacy has become a key research challenge. This paper integrates a Transformer self-distillation model with attention scores and federated learning algorithms to propose a multimodal emotion recognition framework. The framework employs intra-modal and inter-modal Transformers to capture multimodal interactions, enhances modality representations through attention weights, and incorporates a federated learning structure to safeguard data privacy. A global model distance-weighted aggregation strategy is introduced to mitigate model bias caused by heterogeneous data. Experimental results on the IEMOCAP dataset demonstrate that the proposed framework achieves superior emotion recognition accuracy and exhibits more stable model convergence compared to existing baseline models.
Copyright (c) 2026 Xin Wang, Longlong Qiao, Guangxin Dai, Quanping Chen, Yan Zhang, Wensong Li (Author) https://creativecommons.org/licenses/by/4.0
AI-Empowered Technical Knowledge Modelling and Transformation Mechanism in Cross-Cloud Environments
For technical knowledge in multi-cloud and cross-cloud environments, there exist characteristics such as structural diversity, meaning differences, and dynamic continuity. The existing knowledge management methods cannot adapt to this form of cooperation. This study focuses on proposing and answering how to establish and transform technical knowledge in a cross-cloud environment. It has established an AI modeling system of "extraction - matching - integration - update", proposed quantitative expressions suitable for cross-cloud knowledge, and developed strategies for automatic model extraction, semantic matching, and dynamic knowledge network construction. In terms of knowledge transformation, a cross-cloud technology knowledge transformation model centered on AI has been established, and a reasoning and decision-making mechanism has been introduced to accelerate the transformation efficiency. The effectiveness of the proposed method has been proved through typical cases, providing technical path support for cross-cloud knowledge organization and intelligent management.
Copyright (c) 2025 Zhengrui Lu (Author) https://creativecommons.org/licenses/by/4.0
Research on AI Security Strategies and Practical Approaches for Risk Management
The rapid integration of Artificial Intelligence (AI) into critical infrastructures has exposed significant security vulnerabilities that traditional cybersecurity paradigms fail to address. Unlike deterministic IT systems, AI models are stochastic and data-dependent, making them susceptible to unique threats such as data poisoning, adversarial evasion, and model inversion. This dissertation investigates the comprehensive landscape of AI security, aiming to bridge the gap between isolated technical defenses and holistic organizational risk management. Through a systematic review and taxonomic analysis, this research categorizes AI risks across three distinct layers: data integrity, model robustness, and system deployment. The study evaluates current defense strategies, demonstrating that technical measures like adversarial training and differential privacy are necessary but insufficient when applied in isolation. Consequently, a practical AI Risk Management Framework is proposed, structured around a continuous lifecycle of mapping, measuring, managing, and monitoring risks. The findings suggest that effective AI security requires a "Defense-in-Depth" strategy that integrates robust MLOps infrastructure with rigorous governance policies. The dissertation concludes that shifting from static security controls to dynamic, lifecycle-based assurance is essential for deploying trustworthy AI systems in high-stakes environments.
Copyright (c) 2025 Chong Lam Cheong (Author) https://creativecommons.org/licenses/by/4.0
Research on Privacy-Preserving Sharing and Collaborative Computing of Transaction Data for Intelligent Risk Control
This paper conducts a systematic study of the challenges in privacy-preserving sharing of transaction data faced by the financial intelligent risk control sector. It first analyzes how data silos lead to specific operational bottlenecks, including fraud detection models that "cannot see and cannot track", credit assessment that "cannot clearly perceive and cannot accurately evaluate", and group-wide risk management that "cannot fully observe and cannot effectively prevent". It also reveals the dual dilemma of traditional plaintext data-sharing models in terms of legal compliance and commercial trust. On this basis, the paper proposes an "One Core, Two Wings, Three Drivers" application framework for privacy-preserving collaborative computing designed to resolve these conflicts. This framework takes business value and joint risk prevention as its core objective, supported by two wings-technical integration and governance collaboration-and driven by three key elements: scenarios, compliance, and performance. The paper provides a systematic and actionable theoretical reference and practical solution for financial institutions to achieve secure data value circulation and collaborative intelligent analytics across institutions and scenarios, while strictly adhering to data security and privacy compliance requirements.
Copyright (c) 2025 Zhijian Liu (Author) https://creativecommons.org/licenses/by/4.0
RAGN-IIoT: A Retrieval-Augmented NL2SQL Framework with Dynamic Sensor-Selection Guardrails for Industrial IoT Time-Series Data Warehouses
Industrial Internet of Things (IIoT) systems generate massive time-series data streams characterized by high dimensionality, heterogeneous sensor modalities, and complex domain-specific schemas. Traditional SQL-based data access remains a barrier for operational engineers, limiting fine-grained and real-time insights in production environments. To address this challenge, we propose RAGN-IIoT, a Retrieval-Augmented Generation (RAG) guided Natural Language to SQL (NL2SQL) framework tailored for large-scale IIoT time-series data warehouses. RAGN-IIoT integrates (1) schema-aware context retrieval, (2) a domain-adaptive large language model for SQL synthesis, and (3) a novel Sensor-Selection Guardrail Module that constrains hallucinations by validating referenced sensors, KPIs, or device identifiers through a semantic index. Extensive experiments on two real-world industrial IIoT datasets and one synthetic benchmark demonstrate that the proposed RAGN-IIoT framework substantially improves NL2SQL performance across all evaluation metrics. Compared with the strongest baseline, RAG-SQL, RAGN-IIoT achieves an absolute +11.7 percent gain in exact-match accuracy and a +13.4 percent improvement in execution accuracy, while raising schema precision to 96.1 percent and reducing guardrail violations to only 1.8 percent. Ablation studies further confirm the contribution of each module-sensor-selection guardrails, retrieval augmentation, and temporal templates-to overall model stability and correctness. These results highlight RAGN-IIoT's robustness and its practical suitability for natural-language analytics in industrial time-series data warehouses.
Copyright (c) 2025 Haoyan Wei, Yuqing Wu, Mengtong Li (Author) https://creativecommons.org/licenses/by/4.0
A Deep Q-Learning Framework for Modeling and Nonlinear Adaptive Control of Autonomous Space Rocket Maneuvers
In this paper, we develop a mathematical model and a deep Q-learning-assisted adaptive controller for autonomous space-rocket maneuvers. Our approach focuses on executing a multi-phase trajectory alignment without relying on external markers or inter-rocket communication. The proposed feedback mechanism uses onboard sensors to track the rocket's current position and orientation, applying standard robotics nomenclature for translational and rotational states. We formulate a general kinematic description of the rocket system, treating the maneuver as a tracking problem with respect to polynomial-based reference paths, which are generated online for each phase. A deep Q-learning module refines the control policy in real time by exploring actions that optimize the rocket's flight profile under uncertainties, such as unknown velocities. Simulation results verify that the integrated controller is capable of accurately following the desired trajectories and can robustly adapt to dynamic variations, illustrating the effectiveness of our proposed method for autonomous rocket guidance.
Copyright (c) 2025 Xiaoan Zhan (Author) https://creativecommons.org/licenses/by/4.0
The Construction of a Quality and Schedule Coupling Evaluation System in Smart Manufacturing Projects
The integration of smart manufacturing technologies has revolutionized production processes, enhancing efficiency and quality control. However, managing both quality and schedule remains a significant challenge. Existing research often treats quality control and project scheduling independently, neglecting their interdependence in complex manufacturing environments. This study aims to develop a coupled evaluation model that integrates quality control strategies with project scheduling in smart manufacturing projects. The model assesses the impact of various quality control strategies, such as predictive maintenance and real-time monitoring, on project delivery time and product defect rates. Through case studies, simulation modeling, and comparative analysis, the study demonstrates that the integrated model outperforms traditional scheduling methods, reducing project delays by up to 25% while maintaining product quality. The results show that adopting a proactive quality-schedule coupling approach optimizes resource utilization, reduces costs, and improves overall project outcomes. This research contributes to the academic field by providing a comprehensive framework for managing both quality and schedule in smart manufacturing, offering valuable insights for project managers to optimize decision-making in real-world projects. The findings have significant implications for improving efficiency and competitiveness in manufacturing industries.
Copyright (c) 2025 Dan Wang (Author) https://creativecommons.org/licenses/by/4.0
Group Anomaly Detection and Risk Control of Commodity Sales Volume Data Based on LSTM-VAE Framework
In recent years, the increasing demand for sales-volume analytics has driven growing interest in deep-learning-based anomaly detection methods for intelligent supply-chain management and operational risk control. Using point-of-sale data from brick-and-mortar retail stores, this study proposes a group-anomaly detection approach based on an LSTM-VAE framework to identify contiguous anomalous segments that are likely associated with stock-out events or supply disruptions, thereby enabling timely early warning. The LSTM-VAE (Long Short-Term Memory-Variational Auto-Encoder) integrates LSTM networks to model temporal dependencies in time-series data with the generative learning capability of a variational auto-encoder. The model is trained exclusively on historical data representing normal operational weeks, allowing it to learn baseline sales dynamics and effectively distinguish normal patterns from anomalous behaviors. During the testing phase, time-series segments exhibiting persistently high reconstruction errors are identified as potential periods of stock-out risk or supply-chain disruption. The dataset consists of weekly sales records collected from 45 retail stores in the United States between 2010 and 2023, comprising more than 50 000 observations. Each record includes sales volume as the target variable, along with inventory levels, weekly timestamps, store identifiers, product identifiers, temperature information, macroeconomic indicators, and special-holiday dummy variables. Data preprocessing procedures include missing-value imputation, outlier handling, and feature standardization to ensure model robustness. Experimental results show that the proposed LSTM-VAE model performs effectively in group-anomaly detection by accurately capturing contiguous abnormal segments associated with inventory shortages, thus providing practical insights for optimizing replenishment strategies. Overall, the proposed approach offers a scalable and efficient solution for risk control in large-scale product-level sales monitoring scenarios.
Copyright (c) 2025 Ruihan Luo, Jinlin Hu, Qingyu Sun (Author) https://creativecommons.org/licenses/by/4.0
Intelligent Quality Governance Framework and Adaptive Verification Model in Cloud Integration Scenarios
The background of cloud integration is a huge system composed of numerous distributed services. The relationship between data flow and service invocation is constantly changing dynamically, thus making quality risks highly concealed and contagious. However, the traditional approach relies on manual analysis and static scanning to assess quality, which is difficult to meet the complex environmental requirements of various types, large quantities, high data density and frequent changes. For this purpose, this paper proposes the Intelligent Quality Governance Framework (IQGF), which establishes a representation system of quality measurement by integrating monitoring data, logs and call chains. At the same time, a self-regulating inspection model is proposed, which can automatically adjust the inspection range and plan according to the real-time risk information to achieve dynamic detection based on the operation status. This method shifts quality management from passive detection to active management, providing reliable quality assurance for large-scale cloud integration systems.
Copyright (c) 2025 Mingde Guo (Author) https://creativecommons.org/licenses/by/4.0
Cross-Lingual Sponsored Search via Dual-Encoder and Graph Neural Networks for Context-Aware Query Translation in Advertising Platforms
Cross-lingual sponsored search is crucial for global advertising platforms, where users from different language backgrounds interact with multilingual ads. Traditional machine translation methods often fail to capture query-specific contextual cues, leading to semantic ambiguities that negatively impact click-through rates (CTR) and conversion rates (CVR). To address this challenge, we propose AdGraphTrans, a novel dual-encoder framework enhanced with graph neural networks (GNNs) for context-aware query translation in advertising. Specifically, user queries and ad contents are independently encoded using multilingual Transformer-based encoders (mBERT/XLM-R), and contextual relations-such as co-clicked ads, user search sessions, and query-ad co-occurrence-are modeled as a heterogeneous graph. A graph attention network (GAT) is then applied to refine embeddings by leveraging semantic and behavioral context. These embeddings are aligned via contrastive learning to reduce translation ambiguity. Experiments conducted on a cross-lingual sponsored search dataset collected from Google Ads and Amazon Ads (EN-ZH, EN-ES, EN-FR pairs) demonstrate that AdGraphTrans significantly improves query translation quality, achieving a BLEU score of 38.9 and semantic similarity (cosine score) of 0.83, outperforming strong baselines such as mBERT and M2M-100. Moreover, in downstream ad retrieval tasks, AdGraphTrans yields +4.67% CTR and +1.72% CVR improvements over baseline methods. These results confirm that incorporating graph-based contextual signals with dual-encoder translation provides a robust solution for enhancing cross-lingual sponsored search in advertising platforms.
Copyright (c) 2025 Ziyang Gao, Yuanliang Qu, Yi Han (Author) https://creativecommons.org/licenses/by/4.0
Research on Collaborative Development Mode of C# And Python in Medical Device Software Development
C# and Python are two widely used programming languages, each with unique advantages, and they play a crucial role in the development of medical device software. C# is particularly effective in designing and implementing graphical user interfaces (GUIs) and managing core business logic, providing a stable and high-performance environment for software front-end and system integration. Python, on the other hand, excels in data analysis, scientific computing, algorithm development, and machine learning, offering flexibility and rapid prototyping capabilities that are essential for advanced data-driven functionalities in medical devices. This paper presents a comprehensive study on the joint development model of C# and Python, focusing on the design of an effective cooperative framework that leverages the strengths of both languages in the field of medical device software development. The proposed framework provides a structured approach for integrating front-end GUI design, core process management, and advanced computational modules, and details the interaction and cooperation between C# and Python during the development lifecycle with specific task-oriented guidance. To validate the proposed approach, integration testing and performance evaluation were conducted to ensure the reliability, efficiency, and robustness of the system. The results demonstrate that the combined use of C# and Python not only improves software performance and maintainability but also enhances the flexibility and scalability of medical device applications. Finally, the practical implementation of this joint development model in real-world projects confirms its effectiveness, providing a valuable reference for future development of high-quality, data-driven, and user-friendly medical device software.
Copyright (c) 2025 Minkang Zhang (Author) https://creativecommons.org/licenses/by/4.0
Intervention Research and Optimization Strategies for Neuromuscular Function Degeneration in the Context of Aging
With the aging of the population in China, neuromuscular function degradation has become one of the most important problems affecting the health level and quality of life of the elderly. The degradation process of this neuromuscular function involves a decrease in nerve conduction velocity, muscle strength, coordination, and balance, which seriously affects the daily activity and self-care abilities of the elderly. This article is based on the perspectives of sports science and physical therapy, and elaborates on the physiological mechanisms of neuromuscular degeneration. It summarizes the mainstream intervention paths of aerobic exercise, resistance training, and proprioceptive training, and proposes suggestions and improvement plans for functional evaluation, personalized training, multidisciplinary integration, and information-based assistance to improve neuromuscular degeneration. The aim is to provide theoretical basis and practical reference for the development of a scientific and effective neuromuscular function intervention system for the elderly.
Copyright (c) 2025 Yihao Wang (Author) https://creativecommons.org/licenses/by/4.0
Construction And Optimization Of AI-Based Real-Time Clinical Decision Support System
With the rapid development of AI technology, its role in clinical decision support systems (CDSS) has become increasingly prominent. This paper focuses on the construction ideas of real-time clinical decision support systems, elaborates in detail the key elements of multi-source data fusion, high-precision prediction and real-time reasoning, and proposes improvement paths to accelerate system response, strengthen model iteration and optimize interaction structure, aiming to provide reliable, efficient and sustainable technical support for smart medical practice.
Copyright (c) 2025 Danyating Shen (Author) https://creativecommons.org/licenses/by/4.0
Application of Multi-Source Remote Sensing Data and Lidar Data Fusion Technology in Agricultural Monitoring
The fusion technology of multiple and LiDAR data has important value in agricultural monitoring process, which can contribute to the accuracy of crop identification, real-time monitoring of crop growth status, and evaluation of advanced land resource characteristics. Due to the large-scale coverage and complex spectral features of remote sensing images, they have the potential to identify vegetation and soil characteristics; And LiDAR images have precise spatial structural characteristics, such as tree crown height, ground morphology, etc., which can complement the latter in structural and informational spaces, filling the limitations of single source data acquisition in representation. Of course, having great technological potential does not necessarily mean that it can be fully realized. In fact, it also faces some challenges, such as mismatched temporal spatial resolution, cumbersome data acquisition and processing processes, high development costs for hardware facilities and algorithm software, etc., all of which hinder the effectiveness and depth of the development of this technology. This article mainly starts from the agricultural and forestry scenarios, explores suitable integration development paths and strategies based on their existing problems, in order to enhance the practical value and intelligence level of agricultural remote sensing data integration, promote the intelligent and refined development of agriculture, and provide technical support for its development.
Copyright (c) 2025 Chuying Lu (Author) https://creativecommons.org/licenses/by/4.0
Constructing a Dynamic Causal Inference Framework for Digital
In digital scenarios, traditional linear causal inference models struggle to accurately depict complex causal relationships due to the breadth of data sources and intricate environmental dynamics. This study establishes a dynamic causal inference model suited to digital contexts based on dynamic systems theory. The model comprises multiple layers: data input, generation and evaluation of causal relationships, feedback and learning of causal relationships, and visualization of outcomes. This model automatically adjusts and refines causal structures, exhibiting adaptability and stability. Through time-varying modelling and machine learning optimization strategies, it achieves greater flexibility and interpretability in high-dimensional, non-stationary data scenarios. The research serves as a tool to address practical challenges in digital governance, intelligent decision-making, and social science experimentation.
Copyright (c) 2025 Jing Xie (Author) https://creativecommons.org/licenses/by/4.0
Enhancing Small Business Customer Engagement through Sentiment Analysis and Predictive Modeling
In today's highly competitive business environment, small enterprises face multiple challenges, including limited resources, high customer acquisition costs, and insufficient customer engagement. Customer engagement not only impacts sales and brand loyalty but also directly affects the long-term growth of the business. This paper explores the practical value and strategies of applying sentiment analysis and predictive modeling to enhance customer engagement in small enterprises. First, the study analyzes the current state and challenges of customer engagement for small businesses, including fragmented data, difficulties in personalized marketing, delayed response to customer feedback, and intense market competition. It then discusses how sentiment analysis can identify customer emotions, satisfaction levels, and potential pain points by analyzing feedback, social media comments, and survey data, thereby helping businesses optimize their service and interaction strategies. Predictive modeling, by combining historical behavioral data with emotional information, forecasts future customer behaviors, purchase tendencies, and churn risks, providing data-driven support for personalized marketing and proactive customer care. Furthermore, the paper examines the integration of sentiment analysis and predictive modeling, creating a closed-loop system from emotion perception to behavior prediction. This approach enhances the accuracy and timeliness of customer interactions, enables early detection of potential issues, and improves customer satisfaction and loyalty. Implementation steps are also outlined, including data collection, cleaning, analysis, modeling, and strategy execution, while potential challenges and mitigation strategies are discussed, such as insufficient data, technical costs, employee capability limitations, and privacy compliance concerns. Finally, the paper highlights future development directions, including multi-channel data integration, advanced emotion recognition (e.g., voice and image analysis), AI-driven automation in customer interactions, and continuous optimization of data-driven strategies. The study demonstrates that the combination of sentiment analysis and predictive modeling can significantly enhance customer engagement in small enterprises while providing data-driven decision support, enabling small businesses to achieve growth and sustainable competitive advantage despite limited resources.
Copyright (c) 2025 Zongze Li (Author) https://creativecommons.org/licenses/by/4.0
Optimization of Large Models for Efficient Inference: Algorithm, Compiler, and System Co-Design
Large language models have achieved remarkable capabilities in natural language processing, yet their inference cost remains a significant challenge due to high computation, memory usage, and latency. This study presents a cross-layer co-optimization framework that integrates algorithmic, compiler, and system-level strategies to enhance inference efficiency. Algorithmic techniques, including structured pruning, sparsity, quantization, and dynamic inference, reduce computational workload and memory footprint. Compiler optimizations, such as operator fusion, graph rewriting, and layout specialization, translate algorithmic improvements into hardware-efficient execution. System-level strategies, encompassing parallel execution, memory management, and KV cache optimization, further improve resource utilization and reduce latency. The framework synergistically coordinates these layers, providing a theoretically grounded approach for reducing FLOPs, memory consumption, and inference latency. Its adaptability extends to cloud, edge, and interactive deployment scenarios, offering a unified methodology for efficient and scalable large-model inference. This work contributes a systematic and extensible pathway for accelerating model inference without relying on empirical performance measurements.
Copyright (c) 2025 Shengyi Gao (Author) https://creativecommons.org/licenses/by/4.0
A Context-Aware Personalized Recommendation Framework Integrating User Clustering and BERT-Based Sentiment Analysis
With the rapid growth of e-commerce platforms, there is an increasing demand for highly accurate and personalized recommendation systems. Traditional recommendation algorithms often struggle to capture the complex and dynamic nature of user preferences, especially when dealing with heterogeneous data sources. This study introduces a novel recommendation framework that integrates user clustering, BERT-based sentiment analysis, contextual encoding, and deep learning techniques. Utilizing a real-world dataset from Kaggle, the proposed model incorporates user behavior records, review texts, and contextual information to construct detailed user and item representations. Dimensionality reduction and clustering methods are employed to identify latent user groups, while BERT is used to extract deep semantic features from user-generated reviews. The resulting feature vectors are then fed into a multi-layer perceptron to generate personalized recommendations. Extensive experiments demonstrate that the K-means + BERT + MLP model consistently outperforms a variety of traditional and hybrid baselines across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC. The results validate the effectiveness and robustness of the proposed approach, showcasing the potential of multi-source feature fusion and advanced modeling techniques in next-generation recommender systems.
Copyright (c) 2025 Siyu Li, Kuangcong Liu, Xuanjing Chen (Author) https://creativecommons.org/licenses/by/4.0
Edge-Intelligence-Based Dynamic Spectrum Allocation for 6G Wireless Networks
The rapid proliferation of ultra-dense 6G networks has intensified the challenges of real-time spectrum management, as traditional centralized or static allocation methods struggle to achieve a balance among responsiveness, energy efficiency, and scalability. Existing approaches that rely on global coordination incur considerable signaling overhead and fail to adapt effectively to non-stationary wireless environments. To address these limitations, this study introduces an Edge-Intelligence-Based Dynamic Spectrum Allocation (EI-DSA) framework that integrates deep reinforcement learning (DRL) with federated learning (FL) for localized spectrum prediction and distributed decision-making. Utilizing empirical parameters derived from the NTT Docomo Tokyo 6G Testbed and Huawei Futian CBD Field Trials, the proposed framework achieves notable improvements-8.8% higher spectrum utilization, 26% lower latency, and 43% better energy efficiency-compared with centralized RL and proportional fairness baselines. The findings validate that embedding edge intelligence within radio access networks enables real-time, energy-aware, and privacy-preserving control. Theoretically, this research bridges communication engineering and intelligent optimization, presenting a scalable paradigm for AI-native 6G systems. Practically, it provides design guidance for developing green, autonomous, and adaptive wireless infrastructures that align with next-generation communication and electronic engineering advancements.
Copyright (c) 2025 Ruoxi Yu (Author) https://creativecommons.org/licenses/by/4.0