Future Generation Computer Systems-The International Journal of Escience最新文献

{"title":"DQVeriChain: Distributed quantum-state-verified and DID-based self-attentive large language model for criminal tracking using blockchain","authors":"Rabi Shaw ,&nbsp;Suman Majumder","doi":"10.1016/j.future.2026.108412","DOIUrl":"10.1016/j.future.2026.108412","url":null,"abstract":"<div><div>The increasing complexity of cybercrime and identity manipulation activities demands a secure, verifiable, and quantum-resilient framework for criminal identification. This study introduces ‘DQVeriChain’, a distributed quantum-state-verified and decentralized identity-based large language model (LLM) designed for criminal tracking using blockchain. The system integrates quantum principal component analysis (QPCA), GHZ state-fidelity validation, and ZZ-feature mapping to ensure high-fidelity quantum entanglement and secure feature encoding. The verification process combines both the Quantum Self-Attention Mechanism (QSAM) and the Quantum Self-Correcting Attention Mechanism (QSCAM) with LLM-driven inference for intelligent anomaly detection. In addition, validation was performed using IBM Qiskit with 10-fold cross-validation and fidelity thresholds (<em>F</em> ≥ 0.5), achieving prediction accuracy greater than 99% with minimal quantum circuit complexity (5/5 qubits). These experimental results confirm that ‘DQVeriChain’ offers an efficient, tamper-resistant, and scalable architecture suitable for real-time forensic and cybersecurity applications.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"180 ","pages":"Article 108412"},"PeriodicalIF":6.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146110192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial-temporal dual interactive graph convolutional networks for traffic flow forecasting","authors":"Wensheng Zhang ,&nbsp;Hao Cai ,&nbsp;Hongli Shi ,&nbsp;Zhenzhen Han","doi":"10.1016/j.future.2025.108298","DOIUrl":"10.1016/j.future.2025.108298","url":null,"abstract":"<div><div>Traffic flow forecasting is central to intelligent transportation systems but remains challenging due to tightly coupled spatial temporal dependencies and high-order interactions. Existing deep models often assume static or single-view spatial structure, emphasize only pairwise relations, and struggle to represent dynamic spatial-temporal interactions, leading to a persistent accuracy-efficiency trade-off. To overcome this challenge, we propose a Spatial-Temporal Dual Interactive Graph Convolutional Network (STDIGCN) built around three coordinated components: (i) an adaptive traffic graph learner with macro-micro branches that infer long- and short-term topologies; (ii) a dynamic hypergraph obtained via dual transformations and embedding-based association learning to capture high-order group interactions; and (iii) a spatial-temporal dual-graph interactive convolution module that exchanges information between the graph and hypergraph streams, aligning pairwise node dependencies with high-order edge patterns while preserving multiscale temporal structure. Extensive experiments across six benchmark traffic datasets and multiple horizons demonstrate that STDIGCN outperforms strong baselines while maintaining computational efficiency.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108298"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Software aging issues and rejuvenation strategies for a container orchestration system","authors":"Marcelo Santos ,&nbsp;Rubens Matos ,&nbsp;Marco Vieira ,&nbsp;Jean Araujo","doi":"10.1016/j.future.2025.108274","DOIUrl":"10.1016/j.future.2025.108274","url":null,"abstract":"<div><div>Software Aging and Rejuvenation (SAR) has been extensively studied due to its critical role in ensuring the reliable operation of systems. Although container orchestration is essential for efficiently managing and scaling cloud resources, the impact of SAR is not yet fully understood. This paper presents experiments conducted on two versions of Ubuntu Linux, simulating the operational scenarios of a private cloud. Each cluster includes one Main node and three Worker nodes, utilizing Containerd as the container runtime and Kubernetes as the orchestrator, across four distinct scenarios. The primary experimental conditions were maintained across all scenarios, including configurations, workloads, and test duration. Throughout each experiment, metrics such as CPU utilization, memory usage and disk utilization were monitored, considering system-wide values and observations for the Containerd and Kubelet services. The experiments also included measuring the response time of a web server for external HTTP requests submitted to the clusters. The initial scenario focused on investigating the effects of software aging, while subsequent scenarios explored the adoption of different rejuvenation strategies. Effects of software aging were observed across all scenarios, with resource leaks identified, particularly in memory usage, even when the cluster was under no load. The issues observed can lead to performance degradation and compromise reliability and availability if the system crashes due to memory exhaustion.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108274"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GPU acceleration of hybrid FETI solver for problems of transient nonlinear dynamics","authors":"Jakub Homola,&nbsp;Ondřej Meca,&nbsp;Lubomír Říha,&nbsp;Tomáš Brzobohatý","doi":"10.1016/j.future.2025.108341","DOIUrl":"10.1016/j.future.2025.108341","url":null,"abstract":"<div><div>FETI methods, which build on the Finite Element Method, are utilized for large-scale engineering simulations. They use domain decomposition techniques to divide a large domain into many smaller subdomains, which can be processed in parallel.</div><div>Current trends in HPC focus on GPU-accelerated clusters. To utilize them efficiently, FETI solvers should be able to use these accelerators. Recent developments have demonstrated that the fundamental component of the FETI methods, the dual operator, can be successfully offloaded to the GPU.</div><div>In this paper, we focus on GPU acceleration of the Hybrid FETI variant. It reduces the size of the projector by using a two-level decomposition, thus allowing for a significantly higher number of compute nodes to be efficiently utilized. In turn, it allows us to split the problem into a larger number of smaller subdomains, which improves single-process performance.</div><div>We demonstrate the performance on a real-world problem of transient nonlinear dynamics that requires reassembling of the dual operator, preconditioner, and projector during each call of the solver. On the MareNostrum 5 supercomputer, using Nvidia H100 GPUs, we achieved a speedup of 2.9 for the whole Hybrid FETI solver compared to a CPU-only run.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108341"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence for Interoperability (AIFI) - FGCS Editorial summary","authors":"Luca Sciullo ,&nbsp;Ivan Zyrianoff ,&nbsp;Ronaldo C. Prati ,&nbsp;Lionel Medini","doi":"10.1016/j.future.2025.108366","DOIUrl":"10.1016/j.future.2025.108366","url":null,"abstract":"","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108366"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI-guided test-time adversarial defense for resilient YOLO detectors in Industrial Internet of Things","authors":"Ruinan Ma ,&nbsp;Zuobin Ying ,&nbsp;Wenjuan Li ,&nbsp;Dehua Zhu ,&nbsp;Wanlei Zhou ,&nbsp;Yu-An Tan ,&nbsp;Hongyi Liu","doi":"10.1016/j.future.2025.108356","DOIUrl":"10.1016/j.future.2025.108356","url":null,"abstract":"<div><div>With deep learning-based object detectors widely deployed as visual components in Industrial Internet of Things (IIoT) devices like cameras, their adversarial robustness has become paramount to the security and resilience of hyperconnected industrial systems. Existing adversarial defenses are often inadequate for the complexities of object detection, and securing already deployed detectors with a lightweight defense that avoids costly retraining remains a major challenge. In this paper, we propose XAIAD-YOLO: Explainable AI-Guided Adversarial Defense for YOLO detectors, a novel test-time defense to enable resilient YOLO detectors. XAIAD-YOLO introduces a synergistic two-stage purification framework grounded in distinct theoretical principles. Its initial stage, based on signal processing principles, filters high-frequency adversarial noise from genuine image structures. The second stage performs targeted feature destabilization; guided by our efficient XAI saliency map and grounded in the principle of differential feature stability, it precisely neutralizes fragile adversarial artifacts. Experiments show that our XAI method achieves 66.08 FPS (1.56x faster than Grad-CAM++), and our defense method significantly improves adversarial robustness, making anchor-based, anchor-free, lightweight, and non-lightweight YOLO detectors more resilient in both white-box and black-box scenarios. By uniquely integrating explainability into the defense mechanism, XAIAD-YOLO provides a practical and effective solution for enhancing the resilience and trustworthiness of AI in critical industrial applications. Our source code and datasets are available <span><span>https://anonymous.4open.science/r/XAIAD-YOLO-B0A3/here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108356"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145893755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-efficient and topology-aware scheduling algorithms in distributed stream computing systems","authors":"Hongjian Li ,&nbsp;Shuheng Wang ,&nbsp;Gangfan Tan ,&nbsp;Xiaolin Duan","doi":"10.1016/j.future.2025.108340","DOIUrl":"10.1016/j.future.2025.108340","url":null,"abstract":"<div><div>With the rapid growth of data volume and increasing real-time processing requirements, stream processing systems face challenges of execution inefficiency and excessive resource consumption. Apache Storm employs a simplistic round-robin scheduling strategy by default, neglecting node heterogeneity, task topology, and varying traffic patterns, leading to performance degradation and resource wastage. To address these limitations, this paper proposes two novel scheduling strategies: a resource-cost and topology-aware distributed method (<strong>MMO-Stream</strong>) and a resource-aware cooperative strategy (<strong>D-Storm</strong>). MMO-Stream integrates a cost-effective Quality-of-Service (QoS) model with a meta-heuristic-based multi-criteria optimization algorithm to optimize resource consumption, latency, and throughput simultaneously. D-Storm utilizes historical performance data and resource-awareness mechanisms to dynamically optimize task reallocation, mitigating performance deterioration from frequent rescheduling. Experimental results show MMO-Stream achieves cost-effective QoS (C-QoS) improvements of 41.7% and 39.5%, and latency reductions of 23.9% and 15.8%, compared to Storm’s default scheduling and Ts-Stream, respectively. D-Storm reduces latency by 23.9% and 37.5% compared to default and Ts-Stream strategies, significantly outperforming MMO-Stream. The proposed methods effectively enhance Storm’s scheduling performance and resource efficiency.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108340"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885750","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clean up the mess: Addressing data pollution in cryptocurrency abuse reporting services","authors":"Gibran Gomez ,&nbsp;Kevin van Liebergen ,&nbsp;Davide Sanvito ,&nbsp;Giuseppe Siracusano ,&nbsp;Roberto Gonzalez ,&nbsp;Juan Caballero","doi":"10.1016/j.future.2025.108313","DOIUrl":"10.1016/j.future.2025.108313","url":null,"abstract":"<div><div>Cryptocurrency abuse reporting services are a valuable data source about abusive blockchain addresses, prevalent types of cryptocurrency abuse, and their financial impact on victims. However, they may suffer data pollution due to their crowd-sourced nature. This work analyzes the extent and impact of data pollution in cryptocurrency abuse reporting services and proposes a novel LLM-based defense to address the pollution. We collect 289K abuse reports submitted over 6 years to two popular services and use them to answer three research questions. RQ1 analyzes the extent and impact of pollution. We show that spam reports will eventually flood unchecked abuse reporting services, with BitcoinAbuse receiving 75 % of spam before stopping operations. We build a public dataset of 19,443 abuse reports labeled with 19 popular abuse types and use it to reveal the inaccuracy of user-reported abuse types. We identified 91 (0.1 %) benign addresses reported, responsible for 60 % of all the received funds. RQ2 examines whether we can automate identifying valid reports and their classification into abuse types. We propose an unsupervised LLM-based classifier that achieves an F1 score of 0.95 when classifying reports, an F1 of 0.89 when classifying out-of-distribution data, and an F1 of 0.99 when identifying spam reports. Our unsupervised LLM-based classifier clearly outperforms two baselines: a supervised classifier and a naive usage of the LLM. Finally, RQ3 demonstrates the usefulness of our LLM-based classifier for quantifying the financial impact of different cryptocurrency abuse types. We show that victim-reported losses heavily underestimate cybercriminal revenue by estimating a 29 times higher revenue from deposit transactions. We identified that investment scams have the highest financial impact and that extortions have lower conversion rates but compensate for them with massive email campaigns.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108313"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interference modeling and scheduling for compute-intensive batch applications","authors":"Chennian Xiong ,&nbsp;Weiwei Lin ,&nbsp;Huikang Huang ,&nbsp;Jianpeng Lin ,&nbsp;Keqin Li","doi":"10.1016/j.future.2025.108355","DOIUrl":"10.1016/j.future.2025.108355","url":null,"abstract":"<div><div>Cloud computing and virtualization technologies have significantly improved resource utilization in data centers. However, performance interference caused by resource contention remains a major challenge, particularly for compute-intensive batch applications, which are vital for large-scale data processing and task scheduling. Addressing performance interference in the modeling and scheduling of such applications still requires improvement. Existing interference models often rely on stereotypical metrics and average values, ignoring the impact of temporal fluctuations, while conventional scheduling algorithms overlook interference dynamics, leading to suboptimal scheduling results. To overcome these limitations, this article investigates the key factors influencing the performance of compute-intensive workloads and introduces a novel performance interference model that incorporates temporal fluctuations. Furthermore, we propose a historical-data-driven scheduling method that accounts for both temporal dynamics and batch application interference characteristics. Experimental results demonstrate that the proposed performance interference model achieves higher accuracy and robustness against overfitting compared to existing models that neglect temporal variations. Additionally, our interference-aware scheduling algorithm significantly outperforms traditional methods in throughput, scheduling efficiency, and server load balancing, providing an effective solution to mitigate performance interference in cloud environments.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108355"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145845125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSE-LDN: Linear decomposition networks under multi-mode spatial embedding for traffic prediction","authors":"Helong Wang,&nbsp;Changchang Che,&nbsp;Haiyuan Xu","doi":"10.1016/j.future.2025.108329","DOIUrl":"10.1016/j.future.2025.108329","url":null,"abstract":"<div><div>Traffic forecasting is fundamental to intelligent transportation systems.However, existing traffic prediction models struggle to balance modeling ability and computational efficiency. Complex graph-based or attention-based models effectively capture spatio-temporal dependencies but incur high computational costs that hinder practical deployment. To address this, we propose a linear decomposition network incorporating multi-mode spatial embedding. This embedding strategy replaces traditional graph convolution or attention mechanisms by adaptively learning distinct traffic patterns to capture dynamic spatial dependencies. The network utilizes linear blocks to decompose time series into periodic and residual terms for separate modeling. A gating mechanism subsequently fuses these components to generate predictions. Additionally, we introduce PEMS06, a new dataset reflecting recent traffic characteristics. Extensive experiments on five datasets prove our model achieves superior performance and efficiency, as well as strong generalization ability.</div></div>","PeriodicalId":55132,"journal":{"name":"Future Generation Computer Systems-The International Journal of Escience","volume":"179 ","pages":"Article 108329"},"PeriodicalIF":6.2,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145785030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}