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
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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 https://doi.org/10.4108/eetsis.7015 The article has been retracted due to authorship dispute.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 The retracted article will remain, and it has been watermarked as “RETRACTED”.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
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
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
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
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
KADP-SQL: Knowledge-Augmented Generation and Dual-Path Validation in Text-to-SQL
The widespread application of Large Language Models (LLMs) has significantly advanced the development of the Text-to-SQL task, which aims to translate natural language questions posed by users into executable structured query statements. This allows non-expert users to efficiently access databases. However, existing models still struggle to generate correct SQL statements in the absence of sufficient knowledge, particularly under complex query scenarios, where they are prone to syntactic errors and semantic deviations. Moreover, most current approaches rely solely on execution feedback for SQL correction via a single path, making it difficult to detect semantic errors. To address these challenges, we propose KADP-SQL, which divides the Text-to-SQL task into two main modules: Knowledge-Augmented Generation and Dual-Path Validation. This framework structurally represents manually annotated evidence and incorporates web-based external knowledge to enhance SQL generation. Additionally, we introduce a dual-path SQL validation method to detect and correct errors in generated SQL queries. Through extensive experiments conducted on multiple closed-source and open-source LLMs, our proposed KADP-SQL achieves an execution accuracy of 70.53% on the BIRD development set and 88.22% on the Spider test set. These results demonstrate the effectiveness and adaptability of the proposed method.
Copyright (c) 2026 Jun Peng, Jinguo You, Xiang Li, Jiaman Ding, Lianyin Jia https://creativecommons.org/licenses/by-nc-sa/4.0
An Artifact-Centric Process Mining Approach for Learning Style Analytics
As an integrated discipline encompassing data mining, machine learning, process modeling and analytics, process mining is increasingly being applied in the field of education and has emerged as a prominent research topic. Traditional business process modeling approaches, which are primarily based on control flow rather than data flow, exhibit a limited capacity to capture a holistic view of critical business data within complex business procedures. This study focuses on the impact of data-driven process modeling techniques on the performance of analytical models and proposes an artifact-centric process mining approach for learning style analysis. Based on the artifact life-cycle model, we extracted sequences of data attribute operations that encapsulate learning style features. The similarity among different data attribute operation sequences was quantified. The proposed method was evaluated using the OULAD, a benchmark dataset in the learning analytics domain. Experimental results demonstrate that the method effectively enhances the performance of learning style prediction models, with SVM and GBoost algorithms outperforming other modeling approaches.
Copyright (c) 2025 Jiehua Lu, Jun Li https://creativecommons.org/licenses/by-nc-sa/4.0
Building an Intelligent Home Perception System Based on Multi-Modal Information Interaction
In response to the problems of single interaction modality and weak perception ability in traditional smart homes, this paper proposes a multi-modal information perception Artificial Intelligence(AI) model invocation framework. It schedules visual, voice, and sensor data through natural language prompts, and combines the zero-shot visual recognition method of the cloud-based visual-language hybrid large model workflow to achieve cross-scene generalization ability without labeled training. This framework can innovatively solve the problems of heterogeneous data fusion and insufficient computing power of edge devices. Experimental results show that the multi-modal smart home perception system designed in this paper achieves an accuracy rate of over 90% in environmental perception and a precision rate as high as 92% in user intention recognition, which can provide new ideas and practical foundations for the multi-modal perception of future smart home technology.
Copyright (c) 2026 Guo Zhanmiao, Qian Zhongli https://creativecommons.org/licenses/by-nc-sa/4.0
System Design and Development for Industry-Education Integration in Art Universities Based on Generative Adversarial Networks and Attention Mechanism
This study focuses on the algorithm design and system development of Generative Adversarial Networks and attention mechanisms in the context of industry-education integration at art universities. To address issues such as training instability, missing details, and insufficient personalization in the intelligent generation of art resources, a hybrid algorithm architecture integrating multi-head self-attention mechanisms and Wasserstein distance optimization is proposed. The generator incorporates multi-scale feature extraction and local-global joint attention mechanisms, significantly enhancing the style consistency and detail expression in image generation. The discriminator combines gradient penalty strategies to enhance the model's training stability. The study trains and evaluates using the COCO and ArtBench datasets, achieving excellent results in terms of generation quality, computational efficiency, and diversity. The highest image quality score is 94.8, and the diversity score is 92.1. The experimental results demonstrate the effectiveness of the designed algorithm in meeting the customized and high-quality resource generation needs of art universities, providing reliable technical support and application foundation for intelligent content generation in industry-education integration.
Copyright (c) 2025 Xingye Leng, Zhao Yifeng https://creativecommons.org/licenses/by-nc-sa/4.0
Mitigating Latency in Chord-Based Routing under IPv6
As the transition to IPv6 enables a massive expansion of the peer-to-peer (P2P) network landscape, traditional Chord-based routing protocols face significant performance bottlenecks due to the lack of awareness of the underlying physical network topology. In this paper, we propose eChord, a topology-aware routing system designed to mitigate latency in large-scale IPv6 environments by exploiting network locality. The key of eChord is a bifurcated routing architecture that maintains dual routing states: a localFinger table for intradomain routing within autonomous systems (AS) and a globalFinger table for inter-domain connectivity. By optimizing the identifier space and introducing a locality factor ρ to prioritize local lookups, eChord can effectively reduce the reliance on high-latency backbone links.We then perform amulti-dimensional efficiency analysis of the eChord system, in terms of expected hop number, end-to-end latency, and state maintenance overhead. Numerical simulations are finally provided to demonstrate that the proposed eChord system can significantly outperform standard Chord in various network scales. In particular, in a network of 106 nodes distributed across 500 ASs with a locality factor of ρ = 0.8, the proposed eChord system reduces the average routing latency by approximately 75% compared to the traditional, locality-agnostic Chord protocol.
AIGC - Empowered Covert Communications for Scalable Information Systems
Copyright (c) 2026 Xubin Lin, Feifei Hu, Liu Wu https://creativecommons.org/licenses/by-nc-sa/4.0
A Review of Reference Architectures for the Next-Generation Internet of Things Systems
The rapid evolution of the Internet of Things (IoT) demands robust architectural models capable of integrating emerging technologies and managing growing system complexity. This paper presents a structured review of IoT Reference Architectures (RAs), analysing their conceptual foundations, structural organization, and technological readiness in the context of Next-Generation IoT (NGIoT) technologies. The study examines RAs based on their support for disruptive technologies, including Edge/Fog/Cloud Computing, 5G, Artificial Intelligence, Digital Twins, Augmented Reality, Blockchain, and the Tactile Internet. Special emphasis is placed on the ASSIST-IoT RA, evaluated as a modular, cloud-native blueprint aligned with modern software design principles. Reflecting decentralization, scalability, interoperability, and resilience, ASSIST-IoT emerges as a production-ready framework for building intelligent and adaptive IoT systems. The findings synthesize current RAs trends and limitations, offering a forward-looking perspective that informs the development of NGIoT systems driven by data-driven and human-centric innovation.
Copyright (c) 2026 Sorin-Daniel Gheorghe https://creativecommons.org/licenses/by-nc-sa/4.0
An Integrated Cybersecurity Defense Framework for Attack Intelligence Analysis, Counteraction, and Traceability in Complex Network Architectures
INTRODUCTION: In response to the increasingly severe and complex cybersecurity threats posed by advanced persistent threats (APTs) and other sophisticated attacks, this paper proposes an integrated security defense framework for attack intelligence analysis, counteraction, and traceability in complex network architectures. Consolidating threat intelligence from multiple sources that provide different types of intelligence is a major obstacle for the cyber security defense community, particularly when serious challenges arise from complex advanced persistent threats (APTs) involving multiple entities. Inconsistent data formats and methods of structuring data further complicate analyzing the intelligence and creating improved strategies for defense from an all-source intelligence model. OBJECTIVES: The proposed approach leverages both internal and external threat intelligence sources, standardizes and integrates them into a heterogeneous threat intelligence knowledge graph, and transforms it into a homogeneous representation to facilitate analysis. Here introduces a framework that allows integration of a heterogeneous threat intelligence knowledge graph, Bayesian game theoretic modeling, and a Graph Attention Network (GAT). It is a method that converts multi-source intelligence into a single, homogeneous graph that allows for more informative analysis and a greater adaptive decision-making capacity. METHODS: To model the strategic interaction between attackers and defenders under incomplete information and resource constraints, we construct a Network Attack-Defense Game Model (NADGM) based on Bayesian game theory and derive the equilibrium strategies using linear programming and Harsanyi transformation. RESULTS: Furthermore, a graph attention network (GAT) is applied to perform node classification on the threat intelligence reports, exploiting the semantic relations between entities to enhance the accuracy of organization-level attribution. The framework is validated through experiments using real-world APT reports and a case study on ransomware attack-defense scenarios. Experimental results demonstrate that the proposed method achieves superior classification performance, effective strategy optimization, and reasonable attack-defense situation evolution compared to baseline models such as GCN and GraphSAGE. Integrating heterogeneous threat intelligence from diverse sources is a significant challenge in cybersecurity, especially when dealing with complex, advanced persistent threats (APTs). The variation in data formats and structures complicates analysis and defense strategy optimization. Key Contributions This paper introduces a framework that integrates a heterogeneous threat intelligence knowledge graph with Bayesian game-theoretic modeling and a Graph Attention Network (GAT). It standardizes multi-source intelligence into a homogeneous graph for improved analysis and adaptive decision-making. Results Experimental results show that the proposed framework outperforms traditional models, achieving a classification accuracy of 0.81 for threat intelligence reports. This leads to enhanced detection performance and optimized strategic defense decisions. CONCLUSION: The findings suggest that integrating threat intelligence, game-theoretic modeling, and graph-based learning can significantly improve the efficiency of threat detection, response, and decision-making in large-scale, complex network environments. The novelty is to integrate a mixed threat intelligence knowledge graph with the application of Bayesian game-theoretic modeling and classifier based on Graph Attention Network (GAT). The classifier leads to a single structured representation of different threat data, recommends defenses that are best and at the same time, enhances the overall detection accuracy across multiple datasets.
Copyright (c) 2025 Xiangyu Le, Hushuang Zeng https://creativecommons.org/licenses/by-nc-sa/4.0
Ensuring Security in Multi-Tag Backscatter Communication Systems
This paper studies the security performance of a multi-tag backscatter communication system, where a reader exchanges its information with multiple distributed tags in the presence of an eavesdropper. The system security performance is assessed using the secrecy outage probability (SOP), which quantifies the likelihood that the system secrecy capacity drops below a specified threshold. The paper provides a mathematical analysis on the communication links, considering Rayleigh fading, and investigates the effect of system monostatic and bistatic RFID configurations on the overall security. An optimal tag selection scheme is proposed to maximize the secrecy capacity by choosing the tag with the best link between the reader and the eavesdropper. Moreover, an analytical expression is derived for the system SOP, which incorporates the joint probability distributions of channel gains and the asymptotic SOP is provided as the signal-to-noise ratio increases. Simulation results are presented to verify the theoretical analysis, where the simulation results closely match the analytical predictions, confirming the accuracy of the proposed SOP analysis. Moreover, the asymptotic analysis offers an upper bound on the outage probability, consistent with theoretical analysis.
Copyright (c) 2026 Zilin You, Zhihong Liang, Siliang Suo, Lifeng Mai, Qiaoling Lin https://creativecommons.org/licenses/by-nc-sa/4.0