EAI Endorsed Transactions on Scalable Information Systems
type: channel
description: 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
publisher: European Alliance for Innovation (EAI)
language: en
publication name: EAI Endorsed Transactions on Scalable Information Systems
issn: 2032-9407
copyright: This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
items: Generative AI-based privacy protection and security management mechanism for distributed financial systems
items: Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
items: Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
items: A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
items: Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
items: Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
items: Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
items: Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
items: Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
items: A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
items: An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
items: FreqEdgeViT: A Scalable and Efficient Reliability-Aware Transformer for Large-Scale Agricultural Information Systems
items: Chinese Online Violent Speech Detection Based on EBLA
items: Optimization of Urban Traffic Signal Control System on Hadoop Platform Based on Privacy Computing
items: NLP-Based Robust Object Detection and Recognition Through Multimodal Relation Graph Construction Using MPST AND DSWDCNN
items: Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
items: A Cloud Environment Security Access Control Scheme Based on Federated Learning and Fuzzy Logic Integration
items: Team structure optimization and talent flow prediction based on graph neural network
items: EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
items: Design and Implementation of a Collaborative Filtering Algorithm Based on Deep Learning and Matrix Factorization
items: A Point Cloud Instance Segmentation Framework with Attention Mechanisms and Semantic Refinement
items: The Optimization of Load Balancing Strategies in Heterogeneous Distributed Environments for Regional Industrial Upgrading
items: Scalable and Distributed Alignment Mechanisms for Autonomous and Controllable English Text Generation
items: Real Time Digital Twin Framework for Big Data-Driven Online Education Ecosystem
items: Computational Mathematical Model for Resource Scheduling in Cloud Computing Environments: Integrating Integer Programming and Reinforcement Learning
items: SEP-LLM: Professional QA in the SEP Domain Using Retrieval-Augmented LLMs
items: NAS-FD: Neural Architecture Search-Based Fraud Detection for Power Audit Data
items: Research on Intelligent Detection Method for Operation and Maintenance Violations of Power Distribution Equipment Based on YOLOv12
items: Cross-Modal Contrastive Representation Learning for Multimedia Retrieval with Noisy Supervision
items: MFSF-CEA: A Multi-Feature Similarity Fusion Model for Chinese Entity Alignment

Generative AI-based privacy protection and security management mechanism for distributed financial systems
type: item
description: In distributed financial systems, cross-institutional data sharing faces significant security challenges, including sensitive data leakage risks, unauthorized access, and inadequate privacy protection mechanisms. To address these issues, this paper proposes a generative AI-based privacy protection and security management mechanism for distributed financial systems. The proposed method employs a Gradient Penalty Generative Adversarial Network (GP-GAN) to generate privacy-preserving synthetic data that retains the statistical distribution characteristics of original financial data without exposing sensitive information such as account details and transaction records. A blockchain-based fine-grained access control model is constructed to allocate data access permissions according to user roles (ordinary users, financial institution administrators, regulators) and standardize access processes through smart contracts. The Probabilistic Neural Network (PNN) is restructured from risk prediction to abnormal attack detection, enabling real-time identification of side-channel attacks and data injection attacks by analyzing node data transmission characteristics. Furthermore, a game-theoretic weighting method balances multi-node security collaboration and data sharing efficiency. Experimental results demonstrate that the proposed mechanism achieves a privacy protection strength of 92.6%, an access control accuracy of 96.3%, an attack detection recall rate of 89.7%, and an edge node processing latency below 50 ms, effectively ensuring data security in distributed financial systems.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: distributed financial system
  topic: keywords
subject: privacy computing
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subject: generative artificial intelligence
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subject: access control
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subject: anomaly detection
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subject: data security
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subject: federated learning
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creator: Xiaochuan Tian
rights: Copyright (c) 2026 Xiaochuan Tian https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.4108/eetsis.12262

Edge Computing Communication Privacy Protection Method Based on Federated Learning Algorithm
type: item
description: Data heterogeneity, the complexity of privacy budget allocation, and the imbalance between privacy and performance lead to a limited scope of privacy protection constraints and fail to ensure data integrity. Therefore, an edge computing communication privacy protection method based on federated learning algorithm is proposed. Participants use federated learning to locally train the sensing data to obtain a local model, avoiding the interaction of raw data with edge computing nodes and the perception platform. The parameter values of the trained model are perturbed by noise using the adaptive differential privacy technology and uploaded to the edge computing node. The edge computing node performs edge aggregation on the noisy model parameters and uploads them to the perception platform to complete the global aggregation operation, realizing edge computing communication privacy protection. A performance loss constraint mechanism suitable for federated learning is proposed and designed, and the performance loss of the adaptive differential privacy federated model is reduced by optimizing the constraint scope of the loss function, improving the privacy protection effect. Experiments show that this method can effectively add noise to the local model parameters and achieve privacy protection for edge computing communication; the performance loss of this method’s privacy protection is small, about 0.4; when transmitting different types of data in edge computing communication, the data integrity after privacy protection processing by this method is above 0.98, with excellent privacy protection effect.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: federated learning algorithm
  topic: keywords
subject: edge computing
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subject: communication privacy protection
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subject: local model
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subject: edge aggregation
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subject: adaptive differential
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creator: Jiamei Xue
creator: Wengang Yan
rights: Copyright (c) 2026 Jiamei Xue, Wengang Yan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/eetsis.12254

Retraction Notice: The Quality Evaluation of Innovation and Entrepreneurship Education in Colleges and Universities in the Context of Big Data
type: item
description: 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”.
subject: Retractions
  topic: section
creator: Xiaoxue Fan
creator: Bingxin Zhang
creator: Ping Zhang
rights: Copyright (c) 2026 Xiaoxue Fan, Ping Zhang, Bingxin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-04
publication date: 2026-03-04
doi: 10.4108/eetsis.12091

A Decentralised Coordination Framework for Demand-Side Solar-Storage and Its Electricity Market Participation
type: item
description: 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.
subject: Research articles
  topic: section
subject: Distributed energy resources
  topic: keywords
subject: Multi-agent reinforcement learning
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subject: Decentralised coordination
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subject: Electricity market participation
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creator: Bo Li
creator: Yong-Feng Ge
creator: Yuan Miao
creator: Ruoxuan Cui
creator: Xin Li
creator: Kate Wang
creator: Hua Wang
rights: 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
date: 2026-03-19
publication date: 2026-03-19
doi: 10.4108/eetsis.12071

Numerical Index Modulation Aided Multi-Carrier DCSK System: A Secure Transmission Scheme for Physical Layer Security
type: item
description: 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.
subject: Research articles
  topic: section
subject: Multi-carrier differential chaos shift keying
  topic: keywords
subject: numerical index modulation
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subject: physical layer security
  topic: keywords
creator: Wang Chen
creator: Chao Duan
rights: Copyright (c) 2026 Wang Chen, Chao Duan https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12043

Risk-Aware Secure Routing Mechanism for Power Communication Networks Based on GNNs
type: item
description: 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.
subject: Research articles
  topic: section
subject: Cyberattack
  topic: keywords
subject: Power Communication Network
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subject: Key Node Identification
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subject: Risk-Aware
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subject: Secure Routing
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creator: Yanjun Zhao
creator: Yue Zhang
creator: Xiaowei Zhao
creator: Liyu Liu
creator: Xiang Wang
creator: Kaiyue An
rights: Copyright (c) 2026 Yanjun Zhao, Yue Zhang, Xiaowei Zhao, Liyu Liu, Xiang Wang, Kaiyue An https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12042

Quantum Communication Networks for Secure Foundation Model Distribution Across Shanxi Power Grid Substations
type: item
description: 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 A…
subject: Research articles
  topic: section
subject: Quantum Communication Networks
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subject: Foundation Model Security
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subject: Power Grid Cybersecurity
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subject: Encrypted Transmission Protocols
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subject: Quantum Key Distribution
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creator: Jiwu Liu
creator: Kai Xue
creator: Yachen Wang
creator: Chunguang Ren
creator: Xiaojian Zhang
creator: Kai Han
rights: Copyright (c) 2026 Jiwu Liu, Kai Xue, Yachen Wang, Chunguang Ren, Xiaojian Zhang, Kai Han https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12041

Quantum Communication-Assisted Synchronization Framework for Distributed 3D Power Grid Models Across Multi-Regional Control Centers
type: item
description: 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 sy…
subject: Research articles
  topic: section
subject: Quantum communication
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subject: Power grid
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subject: Digital twin
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subject: Quantum entanglement
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subject: Distributed energy resources
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creator: Youhui Chen
creator: Zhonghua Lv
creator: Ruixue Hu
creator: Xinying Zhao
creator: Dongxue Li
rights: Copyright (c) 2026 Youhui Chen, Zhonghua Lv, Ruixue Hu, Xinying Zhao, Dongxue Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12031

Quantum Communication Technology for Optimizing 3D Cost Estimation and Secure Data Transmission in Substation Engineering
type: item
description: 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.
subject: Research articles
  topic: section
subject: Quantum communication
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subject: Secure data transmission
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subject: Substation engineering
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subject: 3D cost estimation
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subject: Distributed systems
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creator: Junchao Li
creator: Fei You
creator: Dan Chen
rights: Copyright (c) 2026 Junchao Li, Fei You, Dan Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.12021

A Distributed and Secure Resource Allocation Method for Power Communication Networks Based on Policy Distillation
type: item
description: 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.
subject: Research articles
  topic: section
subject: Power Communication Networks
  topic: keywords
subject: Reinforcement Learning
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subject: Policy Distillation
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subject: Distributed and Secure Resource Allocation
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creator: Yue Zhang
creator: Zongtao Li
creator: Si Chen
creator: Guoqiang Hu
creator: Pengcheng Li
creator: Ruimei Wu
rights: Copyright (c) 2026 Yue Zhang, Zongtao Li, Si Chen, Guoqiang Hu, Pengcheng Li, Ruimei Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.11995

An Efficient Privacy-Preserving Secure Aggregation Scheme for Federated Learning with Input Verification and Dropout Resistance
type: item
description: 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.
subject: Research articles
  topic: section
subject: Secure Aggregation
  topic: keywords
subject: Privacy Preservation
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subject: Secret Sharing
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subject: Zero-Knowledge Proofs
  topic: keywords
creator: Zijun Guo
creator: Yuteng Sun
creator: Xinyue Zhang
creator: Lingling Wu
rights: Copyright (c) 2026 Zijun Guo, Yuteng Sun, Xinyue Zhang, Lingling Wu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-16
publication date: 2026-03-16
doi: 10.4108/eetsis.11991

FreqEdgeViT: A Scalable and Efficient Reliability-Aware Transformer for Large-Scale Agricultural Information Systems
type: item
description: 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.
subject: Research articles
  topic: section
subject: Vision transformer
  topic: keywords
subject: Satellite image time series
  topic: keywords
subject: Crop type semantic segmentation
  topic: keywords
subject: Scalable Data Processing
  topic: keywords
creator: Li Qu
creator: Le Sun
creator: Yimin Yu
creator: Hemant Ghayvat
rights: Copyright (c) 2026 Li Qu, Le Sun, Yimin Yu, Hemant Ghayvat https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-31
publication date: 2026-03-31
doi: 10.4108/eetsis.11866

Chinese Online Violent Speech Detection Based on EBLA
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Internet public opinion management
  topic: keywords
subject: online violent speech detection
  topic: keywords
subject: Text classification
  topic: keywords
subject: ERNIE
  topic: keywords
subject: Attention mechanism
  topic: keywords
creator: Hongliang Wang
creator: Shoumin Zhang
creator: Na Li
creator: Jing Liu
creator: Peng Zhang
rights: Copyright (c) 2026 Hongliang Wang, Shoumin Zhang, Na Li, Jing Liu, Peng Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.4108/eetsis.10318

Optimization of Urban Traffic Signal Control System on Hadoop Platform Based on Privacy Computing
type: item
description: With the acceleration of urbanization, the number of motor vehicles has surged, and problems such as urban traffic congestion, low traffic efficiency, and resource waste have become increasingly prominent. Traditional traffic signal control systems rely on fixed allocation or simple sensing control, which cannot adapt to the dynamic changes in traffic flow and cannot meet the needs of refined control. This article aims to leverage the Hadoop platform empowered by privacy computing, balance the efficiency of processing massive traffic data with data privacy and security, optimize urban traffic signal control strategies, and enhance traffic operation efficiency and intelligent control level. In terms of methods, firstly, multi-dimensional traffic data such as traffic flow, speed, and queue length are collected through traffic detectors, monitoring devices, etc., and sensitive traffic data is desensitized and encrypted using privacy computing technology. Combining HDFS distributed storage technology on the Hadoop platform to achieve secure data storage and compliant calling, utilizing components such as MapReduce and Spark to perform data cleaning, mining, and analysis under privacy protection, and constructing a traffic flow prediction model. Based on the predicted results, design a dynamic signal timing optimization algorithm to replace the traditional fixed timing mode. The results show that the optimized traffic signal control system can respond to changes in traffic flow in real time, effectively shorten the average queuing time of vehicles by 15% -25%, improve intersection traffic efficiency by about 20%, reduce vehicle idle fuel consumption and exhaust emissions, and achieve the coordinated promotion of traffic data privacy and security and data utilization. The Hadoop platform empowered by privacy computing can efficiently and securely process massive heterogeneous sensitive data in urban transportation, providing reliable data support, privacy protection, and …
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
subject: Privacy computing
  topic: keywords
subject: Hadoop platform
  topic: keywords
subject: Urban transportation
  topic: keywords
subject: Signal control system
  topic: keywords
subject: Dynamic timing
  topic: keywords
subject: traffic flow prediction
  topic: keywords
creator: Sun Yaping
creator: Cao Sen
rights: Copyright (c) 2026 Sun Yaping, Cao Sen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-11
publication date: 2026-05-11
doi: 10.4108/eetsis.11814

NLP-Based Robust Object Detection and Recognition Through Multimodal Relation Graph Construction Using MPST AND DSWDCNN
type: item
description: 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.
subject: Research articles
  topic: section
subject: Object Detection and Recognition
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Human Computer Interface (HCI) Applications
  topic: keywords
subject: Multimodal Relation Graph Construction
  topic: keywords
subject: Entity Relation Identification
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
creator: Bo Feng
creator: Jiayi Yang
creator: Jingyue Xue
rights: Copyright (c) 2026 Bo Feng, Jiayi Yang, Jingyue Xue https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-27
publication date: 2026-04-27
doi: 10.4108/eetsis.11813

Deep Reinforcement Learning-Driven Adaptive Control for Intelligent Manufacturing Systems: A Multi-Sensor Fusion Framework for Real-Time Anomaly Detection
type: item
description: 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.
subject: Research articles
  topic: section
subject: Deep Reinforcement Learning
  topic: keywords
subject: Intelligent Manufacturing Systems
  topic: keywords
subject: Multi-Sensor Fusion
  topic: keywords
subject: Residual-Based Anomaly Detection
  topic: keywords
subject: Adaptive Control
  topic: keywords
subject: Proximal Policy Optimization
  topic: keywords
creator: Shanshan Kong
creator: Peng Zhou
rights: Copyright (c) 2026 Shanshan Kong, Peng Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-29
publication date: 2026-04-29
doi: 10.4108/eetsis.11781

A Cloud Environment Security Access Control Scheme Based on Federated Learning and Fuzzy Logic Integration
type: item
description: 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.
subject: Data Security and Privacy Protection in New Distributed Networks and System
  topic: section
creator: Hongbo Li
rights: Copyright (c) 2026 Hongbo Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-29
publication date: 2026-04-29
doi: 10.4108/eetsis.11731

Team structure optimization and talent flow prediction based on graph neural network
type: item
description: This paper proposes a graph neural network-based framework for team structure optimization and talent flow prediction to address the limitations of traditional methods in modeling complex organizational dynamics. By constructing an organizational graph structure containing 12,547 employees and 56,892 flow records, we develop a hybrid model combining graph attention networks and multi-layer perceptrons to accurately capture team collaboration relationships and talent mobility patterns. The proposed method achieves an F1 score of 78.6% in teamwork prediction, and 81.2% accuracy with 0.864 AUC in talent flow prediction, outperforming traditional methods by over 10%. Experimental results demonstrate that graph neural networks can effectively model complex dependencies in organizational structures, providing data-driven decision support for team optimization and talent management.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: graph neural network
  topic: keywords
subject: co-simulation
  topic: keywords
subject: optimization of team structure
  topic: keywords
subject: talent flow forecasting
  topic: keywords
subject: organizational management
  topic: keywords
subject: Attention mechanism
  topic: keywords
creator: Shuangshuang Chen
creator: Huaqiang Lai
rights: Copyright (c) 2026 Shuangshuang Chen, Huaqiang Lai Lai https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-04
publication date: 2026-05-04
doi: 10.4108/eetsis.11146

EBTM: Web Vulnerability Attack Behavior Recognition Method Based on Feature Fusion
type: item
description: 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.
subject: Research articles
  topic: section
subject: Vulnerability Attack Behavior
  topic: keywords
subject: Large-Scale Traffic
  topic: keywords
subject: False Positive Rate
  topic: keywords
subject: Feature Fusion
  topic: keywords
subject: Deep Learning
  topic: keywords
creator: Xiangnan Lin
creator: Bin Lu
rights: Copyright (c) 2026 Xiangnan Lin, Bin Lu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-21
publication date: 2026-01-21
doi: 10.4108/eetsis.11636

Design and Implementation of a Collaborative Filtering Algorithm Based on Deep Learning and Matrix Factorization
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Collaborative filtering
  topic: keywords
subject: Deep learning
  topic: keywords
subject: Matrix factorization
  topic: keywords
subject: Recommendation algorithm
  topic: keywords
subject: Data sparsity
  topic: keywords
subject: Recall
  topic: keywords
creator: Meiyu Fang
creator: Zhe Zhu
creator: Leyi Cai
creator: Zelin Chen
rights: Copyright (c) 2026 Meiyu Fang, Zhe Zhu, Leyi Cai, Zelin Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
doi: 10.4108/eetsis.10935

A Point Cloud Instance Segmentation Framework with Attention Mechanisms and Semantic Refinement
type: item
description: INTRODUCTION: Point cloud instance segmentation, a critical 3D computer vision task, faces significant challenges in complex indoor environments. Methods often suffer from insufficient feature extraction and a strong coupling between semantic prediction and instance features, where semantic errors cascade and limit overall accuracy. OBJECTIVES: This paper proposes an innovative approach using attention mechanisms and semantic refinement to address these limitations. The primary goal is to enhance feature representation and alleviate the strong dependency between semantic prediction and instance segmentation. METHODS: We introduce three key innovations: 1) A reverse attention mechanism to improve multi-level feature fusion; 2) An instance soft clustering strategy incorporating semantic scores to weaken the semantic-instance coupling; and 3) A self-attention-based instance refinement network. Finally, a dual-branch scoring mechanism, combining classification and mask scores, jointly determines confidence levels to further mitigate semantic errors. RESULTS: Evaluated on the S3DIS dataset, our model achieved 74.1% mean Precision(mPrec) on the Area 5 test. In the more rigorous six-fold cross-validation, it achieved 76.8% mPrec and 72.3% mean recall rate(mRec), outperforming the state-of-the-art (SOTA) model SoftGroup by 1.5% and 2.5%, respectively. CONCLUSION: The proposed method significantly improves instance segmentation accuracy and demonstrates stronger robustness in complex scenes. It effectively resolves the strong coupling issue, providing a novel technical pathway for point cloud instance segmentation. While primarily optimized for dense indoor scans, adapting this framework for extremely sparse outdoor point clouds remains a compelling direction for future exploration.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: point cloud instance segmentation
  topic: keywords
subject: reverse attention mechanism
  topic: keywords
subject: self-attention mechanism
  topic: keywords
subject: feature interleaving
  topic: keywords
creator: Xi Chen
creator: Meiji Chen
creator: Hao Lin
rights: Copyright (c) 2026 Xi Chen, Meiji Chen, Hao Lin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-19
publication date: 2026-05-19
doi: 10.4108/eetsis.11524

The Optimization of Load Balancing Strategies in Heterogeneous Distributed Environments for Regional Industrial Upgrading
type: item
description: INTRODUCTION: With the ongoing advancement of regional industrial upgrading, efficient load balancing in heterogeneous distributed environments has emerged as a pivotal technical challenge. Conventional load balancing methods, typically designed for homogeneous systems or based on static scheduling rules, are ill-equipped to handle the dynamic task demands and diverse resource capabilities characteristic of modern industrial computing infrastructures. OBJECTIVES: This study seeks to bridge this gap by developing an intelligent, adaptive load balancing strategy specifically tailored for heterogeneous environments, with the goal of maximizing resource utilization, minimizing response latency, and supporting sustainable industrial transformation. METHODS: The proposed framework synergistically combines real-time dynamic resource monitoring, multi-dimensional task characterization and scheduling, and reinforcement learning-driven decision-making to construct a responsive and self-optimizing load distribution mechanism that continuously adapts to system state changes. RESULTS: Comprehensive experiments show that the approach significantly outperforms traditional strategies (e.g., round-robin, shortest job first) and state-of-the-art deep reinforcement learning methods across key performance metrics (p < 0.05), achieving up to a 44% reduction in average response time and a remarkable 91.4% system-wide resource utilization. CONCLUSION: By enhancing both adaptability and efficiency in complex heterogeneous settings, the proposed strategy offers a practical and scalable solution with strong potential for deployment in smart manufacturing, cloud computing, edge computing, and other industrial digitalization scenarios.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Heterogeneous distributed environments
  topic: keywords
subject: load balancing optimization
  topic: keywords
subject: reinforcement learning
  topic: keywords
subject: adaptive scheduling
  topic: keywords
subject: regional industrial upgrading
  topic: keywords
creator: Zhigang Guo
creator: Zhen Li
rights: Copyright (c) 2026 Zhigang Guo, Zhen Li https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-14
publication date: 2026-05-14
doi: 10.4108/eetsis.11448

Scalable and Distributed Alignment Mechanisms for Autonomous and Controllable English Text Generation
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: English text generation
  topic: keywords
subject: large models
  topic: keywords
subject: alignment mechanism
  topic: keywords
subject: controllability
  topic: keywords
subject: reinforcement learning
  topic: keywords
creator: Xu Gong
creator: Xiaoyu Wang
rights: Copyright (c) 2026 Xu Gong, Xiaoyu Wang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-23
publication date: 2026-04-23
doi: 10.4108/eetsis.11447

Real Time Digital Twin Framework for Big Data-Driven Online Education Ecosystem
type: item
description: INTRODUCTION: The rapid expansion of online education platforms has created unprecedented opportunities for personalized learning but also presents challenges in monitoring student engagement, learning outcomes, and instructional quality in real-time. OBJECTIVES: This research proposes a real-time digital twin framework for a big data-driven online education ecosystem, designed to continuously capture and analyze large-scale educational data that enhances learning and teaching effectiveness. METHODS: The digital twin online learning dataset with 345 students is obtained. The obtained data are preprocessed by the data cleaning for removing duplicate entries and z-score normalization to normalize the numerical features in the dataset. By integrating big data analytics and Deep Learning (DL) techniques based on an Adaptive Monarch Butterfly Optimized Graph convolutional with Long Short-Term Memory (AMB-GC-LSTM) that combines Long Short-Term Memory (LSTM) networks for sequential student behavior prediction and Graph Convolutional Networks (GCNs) for modeling collaborative learning relationships, the system enables accurate prediction of student engagement, performance trends, and early identification of at-risk learners. RESULTS: The AMB is employed to optimize the GC-LSTM parameters for higher accuracy and cost computation reduction. Experimental evaluation demonstrates that the digital twin-driven ecosystem improves engagement, learning outcomes, and teaching efficiency. CONCLUSION: Comparison results provide an enhanced accuracy (0.965), precision (0.945), recall (0.974), and F1-score (0.959) with the Python implementation. This research presents a novel approach for integrating digital twin technology, big data analytics, and deep learning to create an intelligent, responsive, and scalable online education ecosystem capable of supporting continuous improvement in teaching and learning.
subject: Research articles
  topic: section
subject: Digital Twin
  topic: keywords
subject: Online Education
  topic: keywords
subject: Big Data Analytics
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: Student Behavior Prediction
  topic: keywords
subject: Personalized Learning
  topic: keywords
creator: Yanyu Qian
creator: Na Zhang
rights: Copyright (c) 2026 Yanyu Qian, Na Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-07
publication date: 2026-05-07
doi: 10.4108/eetsis.11312

Computational Mathematical Model for Resource Scheduling in Cloud Computing Environments: Integrating Integer Programming and Reinforcement Learning
type: item
description: 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.
subject: Research articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Resource Scheduling
  topic: keywords
subject: Integer Programming
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Predictive Optimization
  topic: keywords
creator: Zhiyan Li
creator: Xiaohui Zhang
creator: Baoxia Jin
rights: Copyright (c) 2026 Zhiyan Li, Xiaohui Zhang, Baoxia Jin https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-03-27
publication date: 2026-03-27
doi: 10.4108/eetsis.11134

SEP-LLM: Professional QA in the SEP Domain Using Retrieval-Augmented LLMs
type: item
description: INTRODUCTION: Question answering tasks in the Standard Essential Patent (SEP) domain impose high demands on models for professional terminology comprehension, regulatory interpretation, and factual accuracy. Existing general-purpose large language models show limitations in this field, mainly in knowledge retrieval accuracy, semantic matching, and legal compliance of generated content. Therefore, there is an urgent need to develop a specialized intelligent QA system tailored for the SEP domain. OBJECTIVES: This paper aims to develop an intelligent QA system for the SEP domain, SEP-LLM, to improve knowledge retrieval, semantic matching, and content compliance, providing high-quality automated answers to SEP-related questions. METHODS: We collected and curated a large set of SEP-related regulations, technical standards, and judicial cases to build a high-quality QA dataset. Leveraging the LightRAG framework, a large language model was used to extract entities and relationships from documents, constructing a structured SEP knowledge graph with incremental updates to ensure dynamic completeness. In retrieval, a two-layer strategy addresses both fine-grained entity queries and broader thematic searches, improving accuracy and coverage. In generation, DeepSeek-LLM-7B was fine-tuned with LoRA on SEP-specific instructions and terminology, enhancing the model’s understanding and generation capabilities while significantly reducing training and inference resource requirements. RESULTS: Experimental results demonstrate that SEP-LLM significantly outperforms leading general-purpose models, including GPT-4o and Qwen3-235B, across three key metrics: BLEU-4, ROUGE-L, and Accuracy. These findings underscore its superior performance and promising potential for professional Quality Assurance within SEP domain. CONCLUSION: The LightRAG-based SEP-LLM system effectively enhances knowledge retrieval, semantic understanding, and compliance in SEP QA tasks, demonstrating the potential of r…
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Standard Essential Patent(SEP)
  topic: keywords
subject: Retrieval-Augmented Generation (RAG)
  topic: keywords
subject: Knowledge Graph
  topic: keywords
subject: Low-Rank Adaptation (LoRA)
  topic: keywords
subject: Instruction Fine-Tuning
  topic: keywords
creator: Chenchen Guo
creator: Kehao Wang
creator: Dianhui Mao
creator: Yunlong Xiong
creator: Yiwen Lyu
creator: Junhua Chen
rights: Copyright (c) 2026 Chenchen Guo, Kehao Wang, Dianhui Mao, Yunlong Xiong, Yiwen Lyu, Junhua Chen https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-05-11
publication date: 2026-05-11
doi: 10.4108/eetsis.10653

NAS-FD: Neural Architecture Search-Based Fraud Detection for Power Audit Data
type: item
description: 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.
subject: Research articles
  topic: section
subject: Modern power systems
  topic: keywords
subject: power audit data
  topic: keywords
subject: fraud detection
  topic: keywords
subject: convolutional neural network
  topic: keywords
subject: neural architecture search
  topic: keywords
creator: Yuanzhong Zuo
creator: Jingyi Hu
creator: Rao Kuang
creator: Ping Chen
creator: Zhigao Zheng
creator: Ze Zhou
rights: Copyright (c) 2026 Yuanzhong Zuo, Jingyi Hu, Rao Kuang, Ping Chen, Zhigao Zheng, Ze Zhou https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-01-14
publication date: 2026-01-14
doi: 10.4108/eetsis.10860

Research on Intelligent Detection Method for Operation and Maintenance Violations of Power Distribution Equipment Based on YOLOv12
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Intelligent detection method
  topic: keywords
subject: O&M violation
  topic: keywords
subject: PDE
  topic: keywords
subject: YOLOv12
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Object detection
  topic: keywords
creator: Yuexing Hu
rights: Copyright (c) 2026 Yuexing Hu https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-22
publication date: 2026-04-22
doi: 10.4108/eetsis.10801

Cross-Modal Contrastive Representation Learning for Multimedia Retrieval with Noisy Supervision
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
creator: Hui Zhi
rights: Copyright (c) 2026 Hui Zhi https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-20
publication date: 2026-04-20
doi: 10.4108/eetsis.10757

MFSF-CEA: A Multi-Feature Similarity Fusion Model for Chinese Entity Alignment
type: item
description: 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.
subject: Scheduling optimization and load balancing in scalable distributed systems
  topic: section
subject: Entity alignment
  topic: keywords
subject: multi-feature
  topic: keywords
subject: entity abstract
  topic: keywords
subject: LDA
  topic: keywords
creator: Min Zhang
creator: Luya Yang
creator: Yaxian Gao
creator: Lina Han
creator: Bai Caimei
rights: Copyright (c) 2026 Min Zhang, Luya Yang, Yaxian Gao, Lina Han, Bai Caimei https://creativecommons.org/licenses/by-nc-sa/4.0
date: 2026-04-08
publication date: 2026-04-08
doi: 10.4108/eetsis.10735