EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems is open access, a peer-reviewed scholarly journal focused on scalable distributed information systems, scalable, data mining, grid information systems, and more. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications. From 2024, the journal started to publish on a monthly frequency (12 issues per year). INDEXING: ESCI-WoS (IF: 1.3), Compendex, DOAJ, ProQuest, EBSCO, Crossref, Dimensions eISSN: 2032-9407
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Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
We, the Publisher, have retracted the following article: Fan et al. (2025). The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data. EAI Endorsed Scal Inf Syst. https://doi.org/10.4108/eetsis.7015 The article has been retracted due to authorship dispute. We informed the authors about this decision. The retracted article will remain, and it has been watermarked as “RETRACTED”.
Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
The rapid proliferation of rooftop photovoltaics and behind-the-meter battery storage is creating systemic risks in distribution networks. This includes local network constraint violations, insufficient outage resilience, and enlarged cybersecurity attack surfaces, leaving millions of demand-side distributed energy resources (DERs) to operate without coordinated oversight. Existing solutions (dynamic operating envelopes, virtual power plants, peer-to-peer trading, and single-household energy management systems) each address only partial aspects of this challenge. Based on multi-agent reinforcement learning, this paper proposes a Decentralised Coordination Framework (DCF) that organises the energy dispatch into a three-tier hierarchical architecture. Specifically, it has a user layer executing Proximal Policy Optimisation (PPO) for local dispatch, a feeder layer in which a dynamically elected L1 leader applies MADDPG to coordinate community flexibility via a Virtual Aggregation Unit (VAU); and a cross-feeder layer where an L2 leader manages inter-community balancing and market interfaces. It integrates a directed acyclic graph (DAG)-based verifiable execution ledger, Paillier homomorphic encryption, and LLM-based anomaly detection to enhance security. Potential market participation pathway and revenue distribution mechanism are proposed to align with the Australian National Electricity Market. The DCF provides a scalable, market-ready foundation for commercial demandside DER deployment under high renewable penetration.
Copyright (c) 2026 Bo Li, Yong-Feng Ge, Yuan Miao, Ruoxuan Cui, Xin Li, Kate Wang, Hua Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
This paper presents a novel Numerical Index Modulation-assisted Differential Chaos Shift Keying (NIMDCSK) system, architected on a multi-carrier framework where numerous information-bearing subcarriers are supported by a single common reference subcarrier. The proposed system introduces an additional dimension for data transmission by embedding information bits into the dynamic allocation of signal energy across subcarriers. This mechanism not only boosts the transmission rate but also significantly enhances physical layer security. Specifically, the NIM-DCSK scheme obfuscates its transmission characteristics through pseudo-random energy allocation patterns, where deliberately varying power levels create a complex and non-stationary signal signature. This fluctuating energy distribution effectively masks the underlying data structure, making it extremely difficult for unauthorized eavesdroppers to perform feature analysis or achieve successful decoding. Compared to conventional DCSK systems, comprehensive analyses demonstrate that the NIM-DCSK system achieves substantial gains in both energy efficiency (EE) and spectral efficiency (SE), alongside improved bit error rate (BER) performance under various channel conditions. These advantages are realized without a commensurate increase in structural complexity. The inherent security features, including inherent resistance to passive interception and a reduced vulnerability to statistical analysis, are key benefits. Consequently, this work facilitates the integration of index modulation with chaos-based communications, ensuring high-rate transmissions coupled with robust security. The proposed system is, therefore, highly suitable for applications demanding reliable and secure wireless links, such as secure sensor networks, tactical military communications, and confidential IoT deployments.
Multi-carrier differential chaos shift keying
Copyright (c) 2026 Wang Chen, Chao Duan https://creativecommons.org/licenses/by-nc-sa/4.0
Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
INTRODUCTION: As digital transformation progresses, power communication networks become exposed to increasingly complex security threats. Moreover, traditional routing algorithms cannot perceive or respond to dynamic security threats, making reliable data transmission difficult to achieve under network attack conditions. OBJECTIVES: Therefore, this study proposes a risk-aware secure routing (RASR) mechanism for power communication networks based on graph neural networks (GNNs). METHODS: The mechanism first introduces an autoencoder-graph neural network (AGNN) architecture for determining critical nodes, thus establishing a scoring prediction model customized to the structure of power communication backbone networks. It then integrates the essential scores of the nodes, historical failure rates, and traffic load factors to devise a path node risk quantification model. Finally, it incorporates risk quantification results into routing policies to enable proactive routing avoidance and thus bypass high-risk nodes. RESULTS: Experimental results show that the RASR algorithm effectively meets the differentiated service requirements of heterogeneous traffic—including delay-sensitive, bandwidth-sensitive, and reliability-sensitive traffic. Compared with traditional routing algorithms, it demonstrates greater stability and fault tolerance under high-risk attack scenarios while eliminating the need for redundant backup strategies, thereby markedly reducing resource overhead. CONCLUSION: Therefore, the proposed mechanism offers important theoretical support and a novel technical approach for establishing secure, reliable power communication networks.
Copyright (c) 2026 Yanjun Zhao, Yue Zhang, Xiaowei Zhao, Liyu Liu, Xiang Wang, Kaiyue An https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
INTRODUCTION: Foundation models deployed in power grid infrastructure face escalating security threats from advancing quantum computing capabilities, which can compromise classical encryption protecting multi-gigabyte model parameters during distributed transmission and storage across geographically dispersed substations. OBJECTIVES: This research develops and validates a quantum-secured communication framework integrating quantum key distribution with hierarchical encryption mechanisms specifically designed for protecting foundation model parameters in operational power grid environments. METHODS: A dual-channel quantum communication architecture was deployed across five substations in Shanxi Province spanning 580 kilometers, implementing BB84 protocol with decoy state techniques for quantum key generation, dynamic key-data mapping algorithms for parameter encryption, and Ceph-based distributed storage with blockchain audit trails. The system underwent 30-day continuous operational validation protecting a 500-million-parameter Transformer model under real-world conditions including temperature variations (-5°C to 35°C), grid maintenance events, and concurrent SCADA traffic. RESULTS: The framework achieved 99.2% system availability with distance-dependent quantum key distribution rates ranging from 4.5 kbps (50 km) to 0.5 kbps (180 km), quantum bit error rates maintained between 3.2-11.4% within operational thresholds, hierarchical AES encryption throughput of 85 MB/s for model parameters, and storage system performance delivering 8,500-10,800 read IOPS with 1.05 ms average latency. CONCLUSION: This work validates the practical viability of quantum communication networks for securing distributed foundation models in critical power infrastructure, demonstrating information-theoretic security under operational network conditions while establishing integration protocols between quantum key distribution channels and encrypted data transmission pathways for large-scale AI model protection.
Copyright (c) 2026 Jiwu Liu, Kai Xue, Yachen Wang, Chunguang Ren, Xiaojian Zhang, Kai Han https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
INTRODUCTION: The synchronization of three-dimensional digital twin models across geographically distributed power grid control centers represents a critical challenge in modern power system operations. Traditional synchronization methods including Network Time Protocol (NTP) and Precision Time Protocol (PTP) achieve only millisecond-level accuracy, which proves fundamentally insufficient for monitoring and responding to critical power grid transients that occur on sub-millisecond timescales. The proliferation of renewable energy sources and distributed generation resources further intensifies synchronization requirements. OBJECTIVES: This research develops a comprehensive quantum communication-assisted synchronization framework designed to achieve unprecedented temporal accuracy for distributed 3D power grid models spanning multi-regional control centers while maintaining compatibility with existing infrastructure. METHODS: A hierarchical quantum-classical hybrid architecture is proposed that strategically utilizes quantum entanglement channels for time-critical synchronization signals and classical communication channels for bulk data transmission. An adaptive fault-tolerance mechanism dynamically adjusts quantum error correction strategies based on real-time quantum channel quality assessments, with graceful degradation to classical protocols when necessary. RESULTS: Extensive simulations utilizing 128 IEEE 118-bus system models demonstrate sub-10 nanosecond synchronization precision—representing 10,000-fold improvement over NTP—with end-to-end latencies maintained below 10 microseconds. The framework exhibits robust performance with synchronization success rates exceeding 90% under 15% node failure conditions and demonstrates logarithmic O(log M) time complexity compared to O(M²) scaling for classical consensus protocols. CONCLUSION: The quantum communication-assisted framework delivers superior accuracy, scalability, and fault tolerance compared to classical synchronization protocols, establishing viability for next-generation smart grids with extensive distributed energy resources.
Copyright (c) 2026 Youhui Chen, Zhonghua Lv, Ruixue Hu, Xinying Zhao, Dongxue Li https://creativecommons.org/licenses/by-nc-sa/4.0
Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
INTRODUCTION: The highly sensitive 3D cost estimation data in substation projects faces significant security challenges during distributed transmission, as conventional encryption approaches prove inadequate against quantum computing threats for large-scale engineering data protection. OBJECTIVES: This study aims to develop a quantum communication-based secure transmission framework for substation 3D cost estimation data, employing a three-tier quantum architecture to achieve information-theoretic security levels with robust defense against network attacks. METHODS: The proposed solution employs a three-tier quantum architecture encompassing physical, network, and application layers, with optimized quantum channels for engineering data flow. The framework utilizes the BB84 protocol with the decoy state method, implementing quantum key distribution and classical encryption integration to maintain data integrity through a distributed optimization strategy. RESULTS: Simulation validation across 10-50 node networks demonstrates quantum key generation rates of 8-40 kbps and 23% improvement in network throughput compared to star topology over traditional methods. The system achieves 99.99% reduction in key usage compared to pure quantum approaches while maintaining information-theoretic security levels. CONCLUSION: This research establishes fundamental principles and implementation pathways for quantum communication applications in engineering domains, offering significant advancement in secure data transmission for sensitive industrial applications and providing a reliable solution for protecting critical infrastructure engineering data.
Copyright (c) 2026 Junchao Li, Fei You, Dan Chen https://creativecommons.org/licenses/by-nc-sa/4.0
A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
INTRODUCTION: In the next-generation smart grid communication architecture, how to achieve secure, dynamic, and fine-grained network resource allocation to ensure differentiated QoS for various services has become a key challenge. OBJECTIVES: Therefore, this study proposes a lightweight resource allocation method based on constrained policy distillation to address the challenge of balancing lightweight deployment with strong security assurance in power communication networks. METHODS: By integrating Graph Neural Networks (GNNs) and Bidirectional LSTM (Bi-LSTM), the model extracts three-dimensional features of topology, service, and resources to construct a 128-dimensional joint state representation. Moreover, a multi-objective reward function is designed that employs a double Q-network to mitigate value overestimation and generate a high-fidelity decision trajectory library. Through service-constrained policy distillation, the model innovatively combines KL divergence loss, a squared hard-constraint loss, and a soft-constraint L2 loss to compress the teacher model into a student model, subsequently compiled and deployed at the edge. Finally, a rule engine layer dynamically adjusts priorities for intercepting critical violations and ensures the security of the power system. RESULTS: Experimental results based on real-world power grid datasets demonstrate that our model achieves superior performance in resource efficiency, security, and edge effectiveness, effectively balancing lightweight deployment with strong security assurance in resource allocation for power communication networks. CONCLUSION: It can be seen that this method enables distributed and secure resource allocation in power communication network environments, thus providing reliable QoS guarantees for new-type power systems.
Distributed and Secure Resource Allocation
Copyright (c) 2026 Yue Zhang, Zongtao Li, Si Chen, Guoqiang Hu, Pengcheng Li, Ruimei Wu https://creativecommons.org/licenses/by-nc-sa/4.0
An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
Federated learning, as a distributed machine learning paradigm, allows multiple participants to collaboratively train a shared model without sharing their local data. However, the increasing demand for privacy protection during data aggregation within distributed systems underscores the persistent challenge of ensuring both security and efficiency. Many existing Privacy-Preserving Machine Learning (PPML) schemes relying on homomorphic encryption introduce substantial computational overhead during aggregation, rendering them impractical for large-scale PPML applications involving resource-constrained participant devices. Moreover, device dropout events and data poisoning attacks perpetrated by malicious clients adversely affect the integrity of the aggregated results. To address these challenges, this paper proposes an efficient privacy-preserving secure aggregation scheme capable of tolerating participant dropout at arbitrary stages and securing data against both semi-honest and malicious participants. By integrating input verification protocols and applying gradient masking techniques, the scheme enhances its resilience against malicious attacks while ensuring user data privacy. Leveraging the additive homomorphic property of Shamir's secret sharing enables efficient global mask recovery, significantly optimizing the scheme's efficiency. Experimental results demonstrate that the proposed scheme significantly outperforms baseline methods in computational efficiency, communication overhead, and security robustness. By effectively balancing high privacy protection with practical feasibility, this scheme presents a promising solution for secure multi-party aggregation in large-scale distributed systems.
Copyright (c) 2026 Zijun Guo, Yuteng Sun, Xinyue Zhang, Lingling Wu https://creativecommons.org/licenses/by-nc-sa/4.0
FreqEdgeViT: A Scalable and Efficient Reliability-Aware Transformer for Large-Scale Agricultural Information Systems
With the exponential growth of data in modern agriculture, satellite image time series (SITS) has become an important data source for scalable information systems that analyze global crop distribution. However, processing these massive, high-dimensional data streams poses significant challenges; existing semantic segmentation models suffer from prohibitive computational overhead and lack scalability. Furthermore, they are vulnerable to high-frequency non-phenological perturbations and mixed-pixel boundary ambiguity, which degrades reliability in agricultural Internet of Things (IoT) applications. In this work, we propose FreqEdgeViT, an efficient, reliability-aware, and boundary-guided Vision Transformer. Designed for scalable SITS processing, FreqEdgeViT integrates a factorized spatiotemporal architecture with two novel mechanisms. First, in the temporal domain, we introduce a Phenology-Aware Frequency Filter (PAFF) combined with Reliability-Aware Token Merging (Ra-ToMe). This combination utilizes spectral analysis to filter environmental noise and dynamically prunes temporal redundancy based on signal reliability, significantly reducing data throughput requirements. Second, in the spatial domain, we propose a Boundary-Guided Spatial Encoder (BGSE) that enforces explicit geometric constraints to resolve edge blurring in mixed pixels. Experimental results on two public SITS datasets demonstrate that FreqEdgeViT achieves state-of-the-art accuracy with significantly reduced computational costs. The proposed architecture offers a scalable solution for processing large-scale agricultural data, providing precise support for crop policy formulation and enhancing the value of agricultural information systems.
Copyright (c) 2026 Li Qu, Le Sun, Yimin Yu, Hemant Ghayvat https://creativecommons.org/licenses/by-nc-sa/4.0
Chinese Online Violent Speech Detection Based on EBLA
INTRODUCTION: The Internet's features of transcending time and space and anonymity have fostered more rampant and covert online violent speech. Thus, accurate and effective management of online public opinion is of great significance. In recent years, scholars both domestically and internationally have conducted extensive research on online violent speech detection, but current challenges include extracting semantics from diverse and implicit expressions in Chinese online violent short texts. OBJECTIVES: This paper aims to propose the EBLA model for online violent speech detection, based on the ERNIE knowledge-enhanced semantic understanding pre-training model and the BiLSTM-Attention network, to precisely identify relevant textual semantic information and provide an effective method for online content moderators. METHODS: The model is trained using publicly available Chinese datasets related to online violence. It enhances deep, sentence-level feature extraction by integrating an attention mechanism into the BiLSTM layer on top of the ERNIE pre-training model. The model consists of vector transformation, deep text feature extraction, and text classification prediction phases. RESULTS: Results show that the precision of this model in identifying Chinese online violence tasks surpasses the BERT pre-training model by 3.7% and outperforms the BiLSTM combined with the attention mechanism by 13.84%. Empirical studies on additional datasets confirm the model's robustness and transferability. CONCLUSION: The EBLA model provides a strong basis for online violent speech detection, though it has limitations such as not accounting for identity bias or dynamic speech nature. Future improvements will focus on multimodal analysis and dynamic monitoring capabilities.
Scheduling optimization and load balancing in scalable distributed systems
Copyright (c) 2026 Hongliang Wang, Shoumin Zhang, Na Li, Jing Liu, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
NLP-Based Robust Object Detection and Recognition Through Multimodal Relation Graph Construction Using MPST AND DSWDCNN
INTRODUCTION: Nowadays, object detection and recognition play an important role in various applications such as surveillance, autonomous driving, robotics, and medical imaging. However, none of the traditional works focuses on analyzing the explicit relationships, logical dependencies, and semantic conflicts in text-rich or complex scenes, affecting object recognition accuracy. OBJECTIVES: A Natural Language Processing (NLP)-based robust object detection and recognition framework is developed through multimodal relation graph construction using Minimum Persistence Spanning Tree (MPST) and Deep Swim Wishart Distribution Convolutional Neural Network (DSWDCNN). METHODS: Initially, image with their corresponding captions is collected. Then, the image preprocessing and text preprocessing are done independently. From the preprocessed image, the object is detected using You Aspect-ratio Adaptive Anchors Only Look Once version-8 (YAAAOLOv8), followed by visualization. Meanwhile, from the preprocessed text, entity relations are identified. The multimodal relation graph is constructed using MPST. Further, the features from preprocessed text, relation graphs, detected objects, and visualized-images are extracted. Next, the multimodal analysis is carried out. RESULTS: In the meantime, the word embedding is performed on the preprocessed texts. Finally, the object recognition is carried out using DSWDCNN. CONCLUSION: The proposed framework achieves an object recognition accuracy of 98.8569%, demonstrating its effectiveness under weakly supervised conditions.
Human Computer Interface (HCI) Applications
Copyright (c) 2026 Bo Feng, Jiayi Yang, Jingyue Xue https://creativecommons.org/licenses/by-nc-sa/4.0
Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
One of the principal requirements for Intelligent Manufacturing Systems that are working in complicated, nonlinear, and uncertain environments is the robust real-time monitoring and an adaptive control system. In this paper, a deep reinforcement learning-based adaptive control framework is proposed that combines multi-sensor fusion, residual-based anomaly detection, and robust state estimation. The framework is tested on a multi-sensor manufacturing dataset that consists of a total of 10,000 time steps of four different heterogeneous sensor streams collected under both normal and anomalous operating conditions. The sensor data is initially preprocessed by noise filtering, normalization, synchronization, and feature extraction, after which an Unscented Kalman Filter (UKF) is used for sensor fusion and state estimation. Residual analysis along with Support Vector Machine (SVM) allows for real-time anomaly detection and classification, while Multi-Model Adaptive Estimation (MMAE) technique improves the robustness of the state estimation. A Proximal Policy Optimization (PPO) agent uses the updated system state and the anomaly information for adaptive control. The results of the experiments reveal perfect anomaly classification with an ROC-AUC of 1.000, highly stable state estimation in all state dimensions, and continuous control performance increase with the average reward of 1.79, thereby validating the proposed framework's suitability for anomaly-aware adaptive control in intelligent manufacturing systems.
Copyright (c) 2026 Shanshan Kong, Peng Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
A Cloud Environment Security Access Control Scheme Based on Federated Learning and Fuzzy Logic Integration
In cloud environments, the coexistence of multi-source heterogeneous nodes, cross-domain data sharing, and dynamic access control requirements are mutually intertwined. The lack of capability to address dynamic node risks and differentiated access demands necessitates a solution to these challenges. To this end, a cloud environment security access control scheme integrating federated learning and fuzzy logic is proposed. Firstly, using fuzzy logic to quantitatively evaluate the multidimensional dynamic attributes of nodes in the cloud environment, the results serve as a prerequisite for selecting participating nodes in federated learning; Secondly, a blockchain based federated learning architecture is constructed, and a ciphertext policy attribute based encryption algorithm is introduced to deeply couple access control policies with the federated learning process, achieving fine-grained control where only authorized nodes can participate in model aggregation and decryption. Experimental results demonstrate that this control scheme effectively evaluates the security state of the cloud environment, identifies and defends against multiple attack behaviours, achieves precise permission control for users of varying identities, and ensures the security, reliability, and dynamic adaptability of access control within the cloud environment.
Data Security and Privacy Protection in New Distributed Networks and System
Copyright (c) 2026 Hongbo Li https://creativecommons.org/licenses/by-nc-sa/4.0
EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
INTRODUCTION: With the continuous expansion of network scale, effectively detecting web vulnerability attacks has become a critical challenge for ensuring network security. Existing research, primarily focused on balanced sample recognition, proves inadequate for real-world scenarios due to high false alarm rates in large-scale traffic and poor recognition performance for minority attack samples caused by imbalanced data distribution. OBJECTIVES: This paper aims to address the limitations of current web vulnerability attack detection methods in imbalanced real-world environments. The objective is to propose a novel recognition method that reduces the false positive rate and improves the identification performance for minority attack samples under highly skewed data distributions. METHODS: We propose a feature fusion-based recognition method named EBTM for imbalanced samples. The method integrates expert knowledge to optimize feature selection, focusing on key information and ensuring a more uniform mapping of URL requests. It employs three output features from different advantageous models for feature fusion, thereby generating a richer and more discriminative feature representation for the final recognition task. RESULTS: Experimental results demonstrate that the proposed EBTM method significantly enhances the recognition of web vulnerability attack behaviors. Under a realistic imbalanced condition where attack samples constitute only about 3% of the data, the model achieves a macro-average F1 score of 99.1% and reduces the false positive rate to 0.054%. CONCLUSION: The EBTM method effectively improves the efficiency and accuracy of web vulnerability attack behavior recognition in practical, imbalanced scenarios. By combining expert-guided feature optimization and multi-model feature fusion, it successfully addresses key challenges of high false alarms and poor minority class recognition, offering a robust solution for securing large-scale network environments.
Copyright (c) 2026 Xiangnan Lin, Bin Lu https://creativecommons.org/licenses/by-nc-sa/4.0
Design and Implementation of a Collaborative Filtering Algorithm Based on Deep Learning and Matrix Factorization
INTRODUCTION: Collaborative filtering (CF) algorithms based on deep learning and matrix factorization aim to learn deeper latent features of users and items from user ratings. However, such methods often face challenges due to the sparsity of rating data, which limits their recommendation performance. OBJECTIVES: This study aims to design a hybrid recommendation algorithm named auto-encoder deep learning and matrix factorization (AED-MF), which integrates deep learning and matrix factorization to address the sparsity issue in rating data and improve the extraction of deeper latent features of users and items. METHODS: The AED-MF algorithm combines an auto-encoder-based deep learning approach with matrix factorization to model both deeper hidden representations and nonlinear relationships between users and items. The methodology includes data downloading, mounting, cleaning, model training, and experimental evaluation, with matrix factorization applied to predict missing ratings in the rating matrix. RESULTS: The proposed AED-MF algorithm demonstrated strong performance in key recommendation metrics, effectively learning deeper user and item representations and applying them to capture complex nonlinear relationships. It also reduced the impact of rating data sparsity while maintaining high recommendation accuracy. CONCLUSION: The AED-MF recommendation algorithm successfully alleviates the problem of sparse rating data and enhances recommendation accuracy by leveraging the strengths of both deep learning and matrix factorization, offering an effective solution for improving collaborative filtering-based recommender systems.
Copyright (c) 2026 Meiyu Fang, Zhe Zhu, Leyi Cai, Zelin Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Scalable and Distributed Alignment Mechanisms for Autonomous and Controllable English Text Generation
INTRODUCTION: Large-scale English text generation models have shown remarkable capabilities across diverse applications, yet they still face significant challenges in controllability and alignment, especially when handling complex, multi-constraint instructions that require precise intent following and output consistency. OBJECTIVES: To address the lack of a systematic end-to-end alignment framework for large models, this work aims to develop an autonomous and controllable mechanism that ensures high-fidelity generation under intricate user directives. METHODS: We propose a unified alignment architecture composed of three synergistic modules: (1) an instruction parser that converts raw instructions and constraints into structured task representations; (2) a constraint-aware reinforcement learning controller that optimizes token selection via learnable rewards based on alignment and constraint metrics; and (3) a fine-grained aligner that enforces local semantic consistency through differentiable cross-attention between input and output. RESULTS: Evaluated on a custom Instruction-Gen dataset and public benchmarks, our method achieves 84.7% intent alignment accuracy and 88.3% constraint satisfaction, improving by 6.9 and 7.1 percentage points over the PPO-pt baseline, respectively (p < 0.01), while maintaining comparable generation quality (BLEU, ROUGE-L) and textual diversity. CONCLUSION: This work provides a systematic solution for controllable text generation under complex instructions, offering both methodological advances in alignment and practical utility in applications such as intelligent writing and dialogue systems.
Copyright (c) 2026 Xu Gong, Xiaoyu Wang https://creativecommons.org/licenses/by-nc-sa/4.0
Computational Mathematical Model for Resource Scheduling in Cloud Computing Environments: Integrating Integer Programming and Reinforcement Learning
Cloud computing, on the other hand, is very fast and very easy to scale while still having to rely on traditional scheduling methods that cause delays, SLA violations, and a lack of flexibility, mainly when the workloads change. These problems occur in large-scale environments that are very inefficient and hence consume a lot of resources and power. A hybrid solution combining Integer Programming (IP) and Reinforcement Learning (RL) is offered by the authors to make the resource allocation adaptive, energy-efficient, and at the same time provide SLA compliance. The system is supplied with a real Cloud Workload Dataset that offers comprehensive details at both task and system levels. The first step involves Integer Programming, which is used to produce a constraint-aware basic allocation and to ascertain its feasibility by considering not only resource availability but also the limitations imposed by deadlines. At this point, RL takes over from the allocation and thereby improves it further by utilizing Q-learning and ε-greedy policy for making real-time adjustments according to the states of the system and feedback. The interdependence between learning and optimization is thus continuous, leading to better scheduling outcomes over time. The system's performance is evaluated through the application of standard cloud performance metrics such as SLA compliance, resource utilization, task completion time, energy efficiency, and makespan. The hybrid IP–RL framework achieves 98.6% SLA compliance, 99.6% allocation efficiency, and a 12–18% reduction in task completion time when compared to the baseline. Furthermore, it requires 15% less energy and achieves a 92.4% better makespan efficiency. The given results prove that the hybrid method beats the traditional scheduling models in performance concerning all the metrics and opens a door for a cloud resource scheduling that is both scalable and adaptive.
Copyright (c) 2026 Zhiyan Li, Xiaohui Zhang, Baoxia Jin https://creativecommons.org/licenses/by-nc-sa/4.0
NAS-FD: Neural Architecture Search-Based Fraud Detection for Power Audit Data
Power data auditing is the cornerstone of a reliable and efficient modern power system. Various deep learning models have been successfully applied to fraud detection in power audit data. However, most of these methods rely on manual trial-and-error and expert knowledge to design the neural architectures and hyper-parameters. To address this limitation, this paper proposes an innovative automated deep learning approach for fraud detection model design using genetic algorithm (GA)-based neural architecture search (NAS), termed NAS-FD. In NAS-FD, convolutional neural network (CNN) is employed as the primary detection model, leveraging its strong data learning and feature extraction capabilities. First, an effective encoding scheme is developed to represent the nueral architectures and hyper-parameters of CNN, as these parameters significantly influence the detection performance. Then, considering detection performance as the objective function, well-designed GA-based evolutionary operations are implemented to optimize the neural architectures and hyper-parameters of CNN, obtaining the optimized CNN. The detection performance of the proposed NAS-FD method is validated using an electricity theft dataset from the power auditing domain. Experimental results demonstrate that NAS-FD achieves superior detection performance compared with manually designed deep learning models in terms of four performance indices including accuracy, precision, recall, and F1-score.
Copyright (c) 2026 Yuanzhong Zuo, Jingyi Hu, Rao Kuang, Ping Chen, Zhigao Zheng, Ze Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Intelligent Detection Method for Operation and Maintenance Violations of Power Distribution Equipment Based on YOLOv12
INTRODUCTION: With the emergence of new equipment and technologies, the difficulty of operation and maintenance (O&M) of power distribution equipment (PDE) has been continuously increasing. Traditional manual supervision and monitoring methods have been unable to meet the requirements of real-time performance and accuracy. OBJECTIVES: In order to effectively reduce operational safety risks, we propose an intelligent O&M violation detection method. METHODS: This paper optimizes the architecture of YOLOv12 and constructs three models: a security tool violation carrying recognition model, a general violation operation behavior recognition model, and a specific task violation operation behavior recognition model, this paper also uses the 3D electronic fence and real-time acquisition of each operator's 3D joint coordinates, and predicts the 3D joint coordinates of operation and maintenance personnel based on the Kalman filter. RESULTS: The method achievies accurate detection of O&M violations. In addition, this paper successfully establishes a 3D electronic fence for the O&M environment of PDE, and also achieves the recognition and early warning of violations related to spatial locations. CONCLUSION: The intelligent analysis and evaluation system for power distribution equipment operation and maintenance safety based on multimodal data fusion developed based on this method has been deployed and applied in the PDE O&M environment, achieving intelligent recognition of violations in power distribution equipment operation and maintenance and significantly improving the level of intelligence in on-site safety control.
Copyright (c) 2026 Yuexing Hu https://creativecommons.org/licenses/by-nc-sa/4.0
Cross-Modal Contrastive Representation Learning for Multimedia Retrieval with Noisy Supervision
Cross-modal contrastive representation learning has shown great potential for multimedia retrieval tasks by aligning heterogeneous modalities into a shared embedding space. However, its performance often degrades severely in real-world scenarios where supervision signals are noisy, such as mislabeled cross-modal pairs or ambiguous annotations. To address this challenge, we propose Adaptive Noise-Robust Contrastive Learning (ANRCL), a novel framework designed to enhance cross-modal representation robustness under noisy supervision. Specifically, ANRCL introduces an adaptive noise-robust contrastive loss that jointly exploits cross-modal consistency and intra-modal coherence to dynamically reweight training samples according to their estimated reliability. This mechanism effectively suppresses the influence of noisy pairs while reinforcing the contribution of high-confidence pairs. Experimental results on multiple benchmark datasets demonstrate that ANRCL consistently outperforms state-of-the-art methods in noisy supervision settings, achieving significant improvements in retrieval accuracy and robustness without sacrificing computational efficiency.
Copyright (c) 2026 Hui Zhi https://creativecommons.org/licenses/by-nc-sa/4.0
MFSF-CEA: A Multi-Feature Similarity Fusion Model for Chinese Entity Alignment
INTRODUCTION: Entity alignment across multi-source encyclopedic knowledge bases is crucial for constructing high-quality knowledge graphs. This task is particularly challenging in specialized vertical domains like Chinese cultural relics, where heterogeneous data sources and diverse descriptive patterns render single-feature alignment methods inadequate. OBJECTIVES: To address this challenge, we propose MFSF-CEA, a Multi-feature Similarity Fusion model featuring dual-layer optimization: multi-granularity semantic modeling and domain-adaptive dynamic weight fusion. METHODS: This approach employs a three-tiered semantic capture structure: character-level similarity using Longest Common Subsequence for variant character matching; word-level similarity via TF-IDF for core concept association; and sentence-level semantic similarity through Latent Dirichlet Allocation for deep topic alignment. Beyond feature extraction, we introduce an Entropy-AHP combined weighting mechanism that dynamically balances objective information contribution and domain expert knowledge, overcoming limitations of fixed-weight fusion strategies. Experimental evaluation on a Chinese cultural relics dataset demonstrates that MFSF-CEA significantly outperforms baseline methods in precision, recall, and F1-score. The sentence-level contextual features contribute most substantially to alignment accuracy, while the multi-feature fusion effectively compensates for the limitations of any single feature type, particularly the sparsity of word-level abstract features. RESULTS: The proposed framework successfully addresses the unique challenges of entity alignment in cultural relic texts by leveraging complementary features across multiple linguistic levels. CONCLUSION: This work provides an effective and extensible solution for knowledge fusion in vertical domains, advancing entity alignment from traditional string matching toward deeper semantic integration.
Copyright (c) 2026 Min Zhang, Luya Yang, Yaxian Gao, Lina Han, Bai Caimei https://creativecommons.org/licenses/by-nc-sa/4.0
Object Detection and Segmentation of Power Equipment in Infrared Images via Improved YOLOv8 and Prompt-Optimized SAM
To achieve automated infrared monitoring of power equipment in substations, this paper proposes an object detection and segmentation method based on improved YOLOv8 and Prompt-Optimized SAM (Segment Anything Model). Firstly, to address the issues of poor resolution and strong background interference in infrared images, the small object feature extraction capability and bounding box regression accuracy of YOLOv8 are improved by introducing a multi-scale feature extraction module, a robust feature downsampling module, and an improved loss function. The Spatial Pyramid Pooling Fast module is improved using large-kernel depthwise separable convolution, enhancing the extraction capability for both global and local features. Secondly, to improve segmentation accuracy, this paper proposes a method that converts detection boxes into prompt points. GrabCut, combined with colour saliency and a superpixel algorithm, is used to segment high-confidence target regions. Zero-shot prompt point segmentation for SAM is achieved by performing clustering on the regions. Experimental validation on an infrared dataset covering seven types of power equipment shows that the improved object detection model achieves an mAP@0.5 of 95.7%, which is 2.3% higher than the original model, with a detection speed of 107.5 FPS. The proposed segmentation method achieves higher accuracy in complex backgrounds than both bounding box-prompted SAM and GrabCut. This study lays a foundation for the precise processing of infrared images of substation power equipment.
Copyright (c) 2026 Bing Xue, Zehui Liu, Zhanhong Wang, Wenyuan Zhou, Baoning Wang, Xukun Yang https://creativecommons.org/licenses/by-nc-sa/4.0
Risk Early Warning for Police-Related Online Public Opinion Based on Deep Learning
INTRODUCTION: Police-related online public opinion is highly sensitive and can easily have a negative impact on social stability. OBJECTIVES: This paper aims to address the crucial need for early warning systems for the risks associated with police-related online public opinion to ensure social harmony and effectively prevent and resolve major social risks. METHODS: Based on a literature review and deep learning methods, this research constructs an indicator system from four dimensions, analyzing data from representative police-related online public opinion incidents over the past five years. A CNN-BiLSTM sentiment classification model is built for sentiment analysis, and an optimized SSA-CNN-LSTM-Attention model is used for public opinion risk early warning. RESULTS: The experimental results demonstrate that the SSA-CNN-LSTM-Attention model has the minimum error. CONCLUSION: This research provides a theoretical reference for public security organs in responding to and preventing police-related online public opinion.
Copyright (c) 2025 Juan Wang, Yuxiang Guan, Nan Wang, Jie Pan, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Food Derived Biostimulants Technology Revealed and Retrieved by Natural Language Processing
Food-derived biostimulants support sustainable agriculture; however, the scale and heterogeneity of the field hinder their synthesis. We profiled 2005–2025 innovation by mining 2,586 PATENTSCOPE filings and Web of Science articles; texts were analyzed with KH Coder and topic models, with large language models assisting in interpretation. Patent activity surged after 2018, emphasizing plant growth promotion, yield stability, and abiotic stress tolerance (amino acids, seaweed extracts, polyphenols, humic substances, and microbial consortia). In parallel, academic papers have shifted from descriptive trials to mechanism-level work on drought/salinity responses, gene expression, and metabolomics. Together, these signals outline a translation path in which deployable biological inputs converge with mechanistic evidence. Our NLP pipeline distilled heterogeneous texts into actionable indicators, yielding a reproducible map from patent/literature trends to testable hypotheses for formulation, dose, and seed stage delivery.
Copyright (c) 2025 Yoshiyuki Kobayashi, Kakeru Ota, Maya Iwano, Itsuki Kageyama, Kota Kodama, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
Heterogeneous Distributed Computing-Based AI Video Generation: Real-Time Load Balancing and Intelligent Scheduling in New Media Art
INTRODUCTION: The rapid proliferation of Generative AI (AIGC) in new media art has intensified the need for real-time, distributed video generation with stable performance and low latency. Conventional centralized rendering and static scheduling frameworks often encounter load imbalance and communication bottlenecks in heterogeneous environments, resulting in degraded visual coherence and responsiveness. To address these challenges, this study develops a unified and adaptive distributed framework, termed H-RLSCO (Heterogeneity-aware Reinforcement Learning and Scheduling Co-Optimization), designed to enhance both computational efficiency and artistic consistency in large-scale AI video generation. The framework integrates three complementary modules: a Heterogeneity Perception Module (HPM) for node profiling and adaptive task partitioning, a Reinforcement Learning Scheduling Controller (RLSC) for dynamic task migration, and a Generation-Scheduling Co-Optimization (GSCO) mechanism that incorporates content-complexity feedback into scheduling decisions to maintain multimodal synchronization. Experiments on the ArtScene-4K and StageSyn-Real datasets demonstrate that H-RLSCO reduces average latency by 14.4% and decreases Fréchet Video Distance by approximately 12.5% compared with the RL-Scheduler baseline, while limiting performance fluctuation to within 3% under multi-noise conditions (p < 0.01). These gains remain consistent across varying bandwidths and node capabilities on a five-node heterogeneous cluster, confirming robust real-time behavior and balanced utilization. Nevertheless, the scalability of H-RLSCO remains constrained when applied to large-scale node clusters, suggesting future work should explore multi-agent reinforcement learning and lightweight diffusion-Transformer architectures to enhance efficiency and expand applicability.
Copyright (c) 2025 Qian Fu https://creativecommons.org/licenses/by-nc-sa/4.0
Reinforcement Learning-Based Robust Resource Scheduling for Dynamic LEO Satellite Networks
INTRODUCTION: Low Earth Orbit (LEO) satellite communication extends Narrowband Internet of Things (NB-IoT) coverage for global 6G IoT services. However, long propagation delays and high mobility cause outdated channel state information (CSI), degrading system performance. OBJECTIVES: This study aims to design a robust resource scheduling strategy that mitigates CSI outdatedness, improves resource utilization, and supports large-scale IoT connectivity in dynamic LEO satellite environments. METHODS: We propose a reinforcement learning–based robust scheduling framework. The Chebyshev inequality transforms probabilistic signal-to-noise ratio (SNR) constraints into deterministic bounds using statistical moments. A multi-objective optimization problem is formulated to maximize served terminals and minimize resource fragmentation. The Proximal Policy Optimization (PPO) algorithm enables intelligent allocation across network slices under dynamic conditions. RESULTS: Simulation results demonstrate that the proposed approach achieves higher scheduling success rates and better resource utilization compared with baseline methods. The reinforcement learning agent adapts effectively to environmental variations, maintaining stable performance even under severe CSI outdatedness. CONCLUSION: The robust reinforcement learning–based scheduling provides an effective solution for NB-IoT over LEO satellites, enhancing reliability and scalability of future 6G global IoT networks.
Copyright (c) 2026 N.N. Song, Y. Z. Li, Y. K. Zhao, J. Li, W. Li https://creativecommons.org/licenses/by-nc-sa/4.0
Method for Analysing Information on Psychosomatic Issues Based on Service Recipient Dissatisfaction
Recently, the proliferation of social networking services has facilitated free sharing of opinions and complaints by consumers. For companies, such reviews are a valuable source of information for product and service evaluation, and for identifying areas for improvement. In this investigation, the cosmetics industry was used as a case study for this phenomenon. Content related to ‘skin’ was extracted from consumer review information and related physical and mental problems were analysed. Specifically, a polarity dictionary specialized for cosmetics was built, and related reviews were extracted by identifying expressions of dissatisfaction related to the skin, such as ‘itchiness’, ‘redness’, and ‘dryness’. By classifying and analysing problem types, this analysis revealed the trends in consumer dissatisfaction and related issues, discovering insights useful for improving services and products. Therefore, the present study identified the types and causes of problems in providing practical knowledge that contributes to enhanced consumer satisfaction, reduced product risk, and effective marketing strategies.
Copyright (c) 2025 Maya Iwano, Yoshiyuki Kobayashi, Kakeru Ota, Kazuhiko Tsuda https://creativecommons.org/licenses/by-nc-sa/4.0
Research on the Application of Large Language Model in Data Integration
INTRODUCTION: Large Language Models (LLMs), a major breakthrough in artificial intelligence, have been widely applied across various domains in recent years. Their powerful capabilities in language comprehension and generation enable effective handling of diverse natural language processing tasks, such as text generation, question answering, machine translation, and information retrieval. This paper investigates the application of LLM technology in data integration, a core aspect of data governance. In contrast to end-to-end black-box approaches, we reframe data integration as a problem of discovering interpretable mapping rules through symbolic regression. OBJECTIVES: We begin by defining the fundamental problem of data integration. We then propose a general-purpose large model framework for data governance, built on a deep symbolic regression foundation. The framework comprises a symbolic expression generator and a metadata-enhanced executor, aiming to achieve both high accuracy and interpretability. METHODS: The model is trained using a combination of recurrent neural networks and reinforcement learning techniques, for expression generation and the execution of the discovered rules is structured based on a Transformer encoder architecture enhanced with a dedicated metadata embedding layer. To enhance performance, we incorporate metadata fine-tuning, where the generated symbolic expressions serve as key metadata to guide the integration process. RESULTS: Finally, the proposed model is evaluated on two representative data integration tasks, with experimental results demonstrating its effectiveness. CONCLUSION: The results validate its practical quality and highlight the advantage of the symbolic regression paradigm in enhancing interpretability.
Copyright (c) 2026 Zhanfang Chen, Yuan Ren, Xiaoming Jiang, Ruipeng Qi https://creativecommons.org/licenses/by-nc-sa/4.0
A Method for Extracting Dissatisfaction Entities in the Pharmaceutical Sector Using Large Language Model
In today’s VUCA environment, pharmaceutical companies face mounting pressure to enhance both innovation and patient satisfaction amid shrinking domestic markets and intensifying global competition. Despite the growing emphasis on Patient Centricity—the integration of patients' voices into the drug development process—Japan still lags in the systematic incorporation of patient feedback. This study proposes a novel, patient-centered approach that leverages large language models (LLMs) and prompt engineering to extract dissatisfaction entities from patient-generated content, automatically generate a Dissatisfaction Dictionary, and visualize the interrelationships between these entities. Using the “Medical & Welfare_Pharmaceuticals” category from the Dissatisfaction Survey Dataset provided by the National Institute of Informatics, we analysed 300 patient comments with a Conversation Chain built on the GPT-4o API and LangChain. As a result, dissatisfaction entities were extracted and vectorized using TF-IDF and cosine similarity to form thematic clusters. This analysis revealed interconnected concerns related to pricing, efficacy, usability, and medical service processes. For example, discrepancies in pricing and perceived ineffectiveness of generics frequently co-occurred with complaints about pharmacist communication. Our findings offer pharmaceutical firms a systematic, scalable framework to reflect patient dissatisfaction in drug development, thereby enhancing patient engagement, satisfaction, and strategic alignment with Patient Centricity principles.
Copyright (c) 2025 Kakeru Ota, Takumi Uchida, Maya Iwano, Tsuda Kazuhiko https://creativecommons.org/licenses/by-nc-sa/4.0