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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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
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
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.
Scheduling optimization and load balancing in scalable distributed systems
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
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
Deep Learning Based Power Load Prediction in Smart Grid Networks
This paper presents a novel deep learning scheme for power load prediction in smart grid networks, combining temporal modeling with adaptive feature integration to tackle the complex dynamics of electricity consumption. The proposed scheme features a hybrid architecture that merges recurrent neural networks with attention mechanisms, enabling simultaneous capture of long-term load patterns and dynamic weighting of external influences like weather conditions and temporal features. Moreover, the model incorporates specialized preprocessing to decompose load data into periodic and volatile components while employing robust normalization techniques to handle non-stationary behavior. Then, a dual-objective loss function is used to enhance both prediction accuracy and resilience to outliers, supported by adaptive optimization with regularization. Simulation results are provided to demonstrate the proposed scheme’s superior performance, achieving 96.1% prediction accuracy with 5 hidden layers. The attention mechanism proves particularly effective, reducing weather-related prediction errors by 22% while maintaining faster convergence rates than conventional methods. This comprehensive solution offers grid operators a reliable tool for demand-side management, renewable integration, and operational planning in modern power systems.
Copyright (c) 2025 Yifan Tian, Xiaoyi Chen, Yuanyuan Dai, Jiajia Shao, Jiankai Ma, Xiaoyu Yan, Zan Huang, Qi Wang, Lijun Wu, Zhijing Zhang, Shiyun Huang https://creativecommons.org/licenses/by-nc-sa/4.0
A Cholesky decomposition and fusion clustering based spectrum sensing method
Spectrum sensing is a key technology to detect unused frequency bands, and is widely applied in spectrum sharing and dynamic channel allocation. However, it is a challenge to provide high sensing accuracy under low signal-to-noise-ratio (SNR) environments. To address this issue, this paper proposes a novel method based on feature extraction and fusion clustering. First, the sampling matrix of the received signal is decomposed into two orthogonal components I and Q, and Cholesky decomposition is performed on the covariance matrices of I and Q components to extract their two-dimensional feature vectors. Then, the fusion clustering algorithm is proposed, where the GMM clustering algorithm is performed to classify the feature vectors, and the initial parameters of GMM, such as centroids, weights and covariance matrices, are generated by K-means clustering. Simulation results show that the proposed method accelerates the convergence speed of GMM and improves the classification accuracy. It effectively enhances the performance of spectrum sensing compared to other mainstream methods.
Copyright (c) 2025 Jian Li, Yue Li, Xinpei Zhao, Xiaoxu Chen https://creativecommons.org/licenses/by-nc-sa/4.0
On the Performance of RIS-Enhanced RSMA Networks with On-Off Control
As a pivotal enabling technology for the 6G wireless communication system, Reconfigurable intelligent surface (RIS) is capable of dynamically adjusting its electromagnetic elements, thereby significantly enhancing wireless network coverage, spectral efficiency, and energy sustainability. In this context, this paper investigates the integration of RIS into the rate-splitting multiple access (RSMA) framework, where the base station employs the RIS to transmit superimposed signals to multiple users simultaneously. To reduce computational complexity, a On-Off control scheme is adopted, considering both imperfect successive interference cancellation (ipSIC) and perfect successive interference cancellation (pSIC) scenarios. closed-form expressions for the exact outage probability of the $k$-th user under Rayleigh fading channels are derived. Furthermore, to more comprehensively evaluate system performance, asymptotic outage probability expressions for ipSIC/pSIC in the high signal-to-noise ratio (SNR) regime are also derived. The results demonstrate that, under identical system configurations, the RSMA-based communication scheme outperforms the non-orthogonal multiple access (NOMA) scheme, exhibiting superior spectral resource utilization and interference management capabilities. In the pSIC scenario, the user diversity order is contingent upon the number of RIS reflecting elements and the channel ordering approach. Increasing the number of RIS reflecting elements can effectively compensate for the performance degradation caused by residual interference in the ipSIC scenario, thereby further enhancing system performance
Copyright (c) 2025 Haiyan Huang, Dongjie Jiang, Nina Zhang, Linlin Liang, Hongyan Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Direction-of-Arrival Estimation for Deterministic Networks
Direction-of-arrival (DOA) estimation is a key physical-layer technique for guaranteeing the stringent latency and reliability requirements of emerging deterministic networks. This paper investigates a generalized dual-subarray linear array model in which the inter-subarray displacement vectors can be arbitrarily specified, and devises two subspace-based DOA estimation schemes tailored to this setting. The first scheme is a spectral-search (SS) estimator that exploits the structured relationship between the signal subspaces of the two subarrays via a parametrized phase-rotation matrix, where the DOAs are obtained by searching over angles that induce a rank deficiency in a residual matrix constructed from the estimated signal subspace, yielding a high-resolution spectral function analogous to, but more flexible than the conventional schemes. The second scheme is a search-free (SF) estimator that, under mild geometric assumptions on the linear array, reformulates the same criterion as a polynomial in a complex exponential variable and recovers the DOAs from the roots closest to the unit circle, thereby eliminating grid search and significantly reducing computational complexity. Simulation results are provided for deterministic-network scenarios to show that, for a 2 × 5-element dual-subarray and moderate SNRs, the proposed SF-based estimator achieves an RMSE below 1 while conventional MUSIC exhibits an RMSE around 10, demonstrating roughly an order-of-magnitude accuracy gain and confirming the superior performance and scalability of the proposed SS and SF schemes.
Copyright (c) 2025 Feifei Hu, Yu Huang, Xubin Lin, Liu Wu https://creativecommons.org/licenses/by-nc-sa/4.0
Enhancing Stakeholder Analysis with AI: A Comparative Study of Productivity and Quality in the Educational Context
This paper examines the application of generative artificial intelligence in stakeholder management while studying the business aspects of software development and project management in two different universities. It explores a novel intersection of AI with software development and project management practices, offering valuable insights for both academia and industry. By investigating how students use AI alongside traditional methods under supervision, this study evaluates the effectiveness, quality of results, and creativity of students’ project assignments in identifying stakeholders and defining communication strategies. The findings suggest that AI can enhance work completion speed and contribute to greater project success due to a more complete identification of stakeholders and formulation of innovative stakeholder engagement strategies. There is a consensus, within this context, that while AI can be invaluable for project stakeholder management, human judgment remains essential.
System Analysis and Design Project Management
Copyright (c) 2025 Vijay Kanabar, Kalinka Kaloyanova https://creativecommons.org/licenses/by-nc-sa/4.0
Knowledge Graph Fusion for Cross-Modal Semantic Communication
This paper proposes a knowledge graph-enhanced multi-source fusion (KG-MSF) scheme, a novel cross-modal semantic communication system to robustly fuse visual and textual data for tasks such as visual question answering (VQA) over wireless channels. The proposed KG-MSF scheme integrates knowledge graph reasoning into a multi-stage fusion and encoding pipeline, utilizing bidirectional cross attention between modalities and structured semantic triplets to enhance semantic preservation and resilience to channel impairments. Specifically, image objects and question tokens are first aligned via cross-modal attention, then enriched with shallow and deep semantic triplets extracted through knowledge graphs, which are subsequently fused and transmitted using joint source-channel coding. Extensive simulation results are provided to demonstrate that the proposed KG-MSF scheme significantly outperforms the competing ones under both AWGN and Rayleigh fading channels, indicating KG-MSF’s superior semantic robustness and efficient cross-modal reasoning in wireless environments.
Copyright (c) 2025 Yanrong Yang, Tianxiang Zhong, Mengting Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Leveraging Relation Attention Mechanisms for Enhanced Knowledge Graph Completion with Embedding Translation
In this paper, we propose a novel knowledge graph completion framework to leverage a relation-specific attention mechanism integrated with an embedding translation strategy to improve the accuracy and contextual understanding of link prediction tasks. Unlike traditional models that rely on fixed transformation spaces, the proposed method dynamically captures fine-grained relational semantics by combining hierarchical candidate categorization, relation-guided entity projection, and asymmetric score functions. Specifically, the proposed model employs K-means clustering and principal component analysis (PCA) to identify semantically consistent entity sets, and integrates attention-weighted multi-attribute embeddings to construct robust relational representations. A margin-based ranking loss with normalized embedding constraints ensures effective optimization, further supported by Xavier initialization and stochastic gradient descent. Extensive experiments on two benchmark datasets, WN18 and FB15K, demonstrate the superiority of the proposed method. Specifically, on WN18, the proposed method achieves the lowest mean rank (MR) of 144, with competitive results in mean reciprocal rank (MRR) (0.902), Hits@1 (89.0%), Hits@3 (90.4%), and Hits@10 (96.3%), closely rivaling state- of-the-art models like QuatE and ComplEx. On FB15K, the proposed method again delivers the best Mean Rank of 21, along with strong scores in MRR (0.831), Hits@1 (72.2%), Hits@3 (88.4%), and the highest Hits@10 (92.5%) among all compared methods.
Copyright (c) 2025 Jiahao Shi, Zhengping Lin, Yuzhong Zhou, Yuliang Yang, Jie Lin https://creativecommons.org/licenses/by-nc-sa/4.0
Smart Data Prefetching Using KNN to Improve Hadoop Performance
Hadoop is an open-source framework that enables the parallel processing of large data sets across a cluster of machines. It faces several challenges that can lead to poor performance, such as I/O operations, network data transmission, and high data access time. In recent years, researchers have explored prefetching techniques to reduce the data access time as a potential solution to these problems. Nevertheless, several issues must be considered to optimize the prefetching mechanism. These include launching the prefetch at an appropriate time to avoid conflicts with other operations and minimize waiting time, determining the amount of prefetched data to avoid overload and underload, and placing the prefetched data in locations that can be accessed efficiently when required. In this paper, we propose a smart prefetch mechanism that consists of three phases designed to address these issues. First, we enhance the task progress rate to calculate the optimal time for triggering prefetch operations. Next, we utilize K-Nearest Neighbor clustering to identify which data blocks should be prefetched in each round, employing the data locality feature to determine the placement of prefetched data. Our experimental results demonstrate that our proposed smart prefetch mechanism improves job execution time by an average of 28.33% by increasing the rate of local tasks.
Copyright (c) 2025 Rana Ghazali, Douglas G. Down https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Hybrid Path Planning Algorithms for UAVs in Complex Environments
INTRODUCTION: This paper investigates a UAV path planning algorithm in a UAV-assisted network scenario, integrating both global and local path planning. Firstly, the ASPSO (Adaptive Spherical Vector-Based Particle Swarm Optimization) algorithm is proposed for offline path planning to obtain key global path points, providing a general flight strategy for the UAV. During the flight, the UAV continuously detects surrounding obstacles in real-time. If newly detected obstacles are encountered, the ECAVF (Enhanced Collision Avoidance Vector Field) algorithm is employed for local path planning to dynamically avoid obstacles and ensure the safety of the UAV. OBJECTIVES: The objective of this paper is to enhance the path planning capability of existing algorithms in complex three-dimensional environments, enabling UAVs to operate efficiently and safely. METHODS: The proposed ASPSO algorithm determines parameter ranges for different scenarios during the initialization phase, effectively reducing initialization time. Additionally, a multi-strategy optimization approach is introduced during the search process. Expanding the search space in the early iterations helps escape local optima, while minor perturbations are introduced in the later iterations to continue exploring within the neighbourhood of high-quality solutions. Finally, a method utilizing virtual control points for path refinement is proposed to smooth the trajectory. The ECAVF algorithm incorporates a dynamic adjustment factor based on relative velocity to optimize the vector field in the presence of multiple moving obstacles. By integrating factors such as distance and velocity, a hybrid vector field is constructed, demonstrating superior robustness in complex multi-obstacle scenarios. RESULTS: The proposed method is compared with the PSO (Particle Swarm Optimization), the Spherical Vector-based PSO, and the original CAVF (Collision Avoidance Vector Field) method. The results demonstrate that the proposed method exhibits higher initialization efficiency, superior initial solution quality, and the ability to obtain a more optimal global path. Additionally, it shows stronger dynamic obstacle avoidance capabilities and a higher success rate in avoiding obstacles. CONCLUSION: These results demonstrate that the proposed method effectively enhances the quality of global path planning solutions and improves the success rate of dynamic obstacle avoidance.
Copyright (c) 2025 Xinyue Chang, Liang Ye, Lin Ma, Shuyi Chen https://creativecommons.org/licenses/by-nc-sa/4.0
Wireless Federated Learning Based Building Temperature Estimation With Latency Constraint
The paper proposes a novel approach for temperature estimation in buildings using wireless federated learning (FL) while considering latency constraints. The proposed model utilizes a hierarchical federated learning architecture within a wireless network, incorporating base stations (BS), access points (APs), and user equipment (UEs). Each UE performs local learning and shares model updates with APs, which aggregate them and forward them to the BS for final aggregation. The system aims to minimize both latency and energy consumption while ensuring accurate temperature predictions. Simulation results show the effectiveness of the proposed scheme in comparison to deep reinforcement learning (DRL) and genetic algorithm (GA) approaches. Specifically, at a latency threshold of 10 seconds, the proposed scheme achieves a prediction accuracy of approximately 0.60, while DRL reaches 0.50 and GA stays around 0.48. These results highlight the superior performance of the proposed federated learning-based method, especially in high-latency scenarios, and demonstrate its potential for real-time applications in smart building environments under wireless communication constraints.
Copyright (c) 2025 Kemin Zhang https://creativecommons.org/licenses/by-nc-sa/4.0
Research on Optimization of Unmanned Aerial Vehicle Communication Based on Wireless Communication Artificial Intelligence
In the last few years, there has been an increasing use of Unmanned Aerial Vehicles (UAVs), leading to a demand for robust and efficient communication technologies. But by their nature, UAVs operate dynamically and relatively sparsely in the sky, making it inconvenient to perform traditional communication. To overcome communication hurdles, the authors of this research propose an AI approach to optimize wireless communications to enhance UAV (Unmanned Aerial Vehicles) performance. The study will investigate the development of an AI-based algorithm to adjust communication parameters concerning various aspects, including UAV movement, environmental conditions, and network constraints. It will increase the data transmission quality, reduce latency, and service larger areas the network covers for UAVs. Simulations and actual world experiments will be conducted to evaluate the proposed approach, showing that AI can enhance UAV communication. This work could significantly improve the performance of UAVs and optimize their application in many fields ranging from surveillance to delivery services.
Copyright (c) 2025 Yu Geng, Yuqing Tang, Qiang Wang https://creativecommons.org/licenses/by-nc-sa/4.0
The Cutting-Edge Hadoop Distributed File System: Un-leashing Optimal Performance
Despite the widespread adoption of 1000-node Hadoop clusters by the end of 2022, Hadoop implementation still encounters various challenges. As a vital software paradigm for managing big data, Hadoop relies on the Hadoop Distributed File System (HDFS), a distributed file system designed to handle data replication for fault tolerance. This technique involves duplicating data across multiple DataNodes (DN) to ensure data reliability and availability. While data replication is effective, it suffers from inefficiencies due to its reliance on a single-pipelined paradigm, leading to time wastage. To tackle this limitation and optimize HDFS performance, a novel approach is proposed, utilizing multiple pipelines for data block transfers in-stead of a single pipeline. Additionally, the proposed approach incorporates dynamic reliability evaluation, wherein each DN updates its reliability value after each round and sends this information to the NameNode (NN). The NN then sorts the DN based on their reliability values. When a client requests to upload a data block, the NN responds with a list of high-reliability DN, ensuring high-performance data transfer. This proposed approach has been fully implemented and tested through rigorous experiments. The results reveal significant improvements in HDFS write operations, providing a promising solution to overcome the challenges associated with traditional HDFS implementations. By leveraging multiple pipelines and dynamic reliability assessment, this approach enhances the overall performance and responsiveness of Hadoop's distributed file system.
Copyright (c) 2025 Anish Gupta, P. Santhiya, C. Thiyagarajan, Anurag Gupta, Manish Gupta, Rajendra Kr. Dwivedi https://creativecommons.org/licenses/by-nc-sa/4.0
Improved Authentication in Information Systems through a Mobile Identity Management Scheme (MoIdM-MSS) Utilizing Mobile Signature Service
In today's digital economy, work processes are increasingly digitized using computer information systems. An essential aspect of employees' reliance on these systems is trust in their reliability. Mobile devices and apps play a vital role in this digital landscape, with Mobile Identity at the forefront. Mobile Identity extends the concept of digital identity through mobile networks, acting as a tool for login and transactions and as a crucial element in communication and interaction. This paper introduces a Mobile Identity Management Scheme based on the Mobile Signature Service for information systems. The scheme enables digital signatures on mobile devices for various purposes, enhancing security by leveraging the user's private key and the system's authentication challenge. Through this approach, authentication is ensured by permitting only users with the correct private key to sign the challenge, eliminating the necessity for traditional authentication methods such as usernames and passwords. Furthermore, the scheme leverages mobile device security features like secure computing environments and biometric authentication to bolster authentication. By adding an extra layer of protection and focusing on user convenience, security is heightened without introducing unnecessary complexity. Evaluations conducted in local signing scenarios have demonstrated the scheme's effectiveness, acceptance, and potential, indicating promising results for its application in enhancing work process security.
Copyright (c) 2024 Mohammadjavad Sharifpour, Mehdi Shajari, Seyyed Amir Asghari Tochae https://creativecommons.org/licenses/by-nc-sa/4.0
Bitcoin Price Prediction Using N-BEATs ML Technique
Bitcoin is a decentralised digital currency that has been in existence for some time now. Its value has been volatile, with most of its prices fluctuating significantly, making it difficult to predict its prices when investing. This paper employed a Neural Basis Expansion Analysis Time Serie (N-BEATS) deep learning architecture to predict Bitcoin prices. The model was chosen because of its proven capabilities of modelling intricate patterns in time series data. An hourly Bitcoin price data of 729 days collected from Yahoo Finance extensively assesses the N-BEATS model's performance while comparing it with other machine learning models like the Linear Regression and the long-short-term-memory (LSTM) networks. Mean Absolute Error (MAE) and R-squared (R²) were utilised as performance evaluation metrics. N-BEATS surpassed the others by providing an R² score of 0.00240 and an MAE score of 0.9998. These findings are significant and shed light on how deep learning models can be used for financial forecasting. The result shows that the N-BEATS model is more accurate and reliable for predicting cryptocurrencies' prices, which may be very useful for investors in making decisions and managing risks.
Copyright (c) 2024 G. Asmat, K. M. Maiyama https://creativecommons.org/licenses/by-nc-sa/4.0
NOMA Assisted Energy-Efficient MEC for Environmental Severity Monitoring in Power IoT Networks
This paper proposes an energy-efficient mobile edge computing (MEC) scheme that utilizes non-orthogonal multiple access (NOMA) for environmental severity monitoring in Power Internet of Things (IoT) networks. The primary objective of the proposed approach is to optimize energy consumption while ensuring tasks are completed within their respective deadlines and meet reliability constraints. The scheme integrates NOMA's superposition coding with mobile edge computing to improve task offloading efficiency and reduce computational delays. To achieve this, an iterative water-filling (IWF) algorithm is applied to dynamically adjust the power allocation for each task based on varying channel conditions and latency requirements. The optimization problem is formulated to minimize energy consumption while respecting the given constraints, including outage probability and transmission rate. Simulation results demonstrate that the proposed IWF-based method significantly outperforms traditional schemes. For instance, under a stringent delay threshold of 10 ms, the IWF method reduces energy consumption by approximately 30\% compared to conventional approaches. Furthermore, even as the delay threshold increases, the IWF method consistently maintains a noticeable advantage, achieving up to 20\% lower energy consumption compared to other schemes.
Copyright (c) 2025 Guangmao Li, Gang Du, Hongbin Wang, Hongling Zhou, Jie Yang, Zhikai Pang https://creativecommons.org/licenses/by-nc-sa/4.0