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TITE: A transformer-based deep reinforcement learning approach for traffic engineering in hybrid SDN with dynamic traffic Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-06 Bin Lin, Yingya Guo, Huan Luo, Mingjie Ding
Hybrid Software Defined Networks (Hybrid SDNs), with a partial upgrade of legacy routers to SDN switches in traditional distributed networks, currently stand as a prevailing network architecture. Traffic Engineering (TE) in hybrid SDN requires the efficient and timely acquisition of a routing policy to adapt to dynamically changing traffic demands, which has recently become a hot topic. Ignoring the
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Revolutionizing machine learning: Blockchain-based crowdsourcing for transparent and fair labeled datasets supply Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-06 Haitao Xu, Zheng He, Dapeng Lan
With the development of 5G/6G communication networks, the industrial Internet of Things (IIoT) industry has generated a massive amount of data, presenting opportunities for advancements in the field of machine learning. The core of machine learning, labeled datasets, requires qualities such as diversity, quantity, and quality. However, the current collection of training datasets is mostly centralized
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Benchmarking parallel programming for single-board computers Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-05 Renato B. Hoffmann, Dalvan Griebler, Rodrigo da Rosa Righi, Luiz G. Fernandes
Within the computing continuum, SBCs (single-board computers) are essential in the Edge and Fog, with many featuring multiple processing cores and GPU accelerators. In this way, parallel computing plays a crucial role in enabling the full computational potential of SBCs. However, selecting the best-suited solution in this context is inherently complex due to the intricate interplay between PPI (parallel
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Investigation on cellular LTE C-V2X network serving vehicular data traffic in realistic urban scenarios Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-04 Zeeshan Hameed Mir, Nils Dreyer, Thomas Kürner, Fethi Filali
The need for reliable vehicular communication is an essential precondition of future Cooperative Intelligent Transportation Systems (C-ITS). The C-ITS applications and their diverse requirements necessitate a hybrid approach that combines ad hoc and cellular networks to support seamless and robust connectivity. While the research showed tremendous potential, the successful adaptation of the hybrid
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Enterprise architecture-based metamodel for machine learning projects and its management Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-04 Hironori Takeuchi, Jati H. Husen, Hnin Thandar Tun, Hironori Washizaki, Nobukazu Yoshioka
In this study, we consider projects for developing service systems using machine learning (ML) techniques. These projects involve collaboration between various stakeholders. Several types of models representing system architectures are introduced so that stakeholders can develop a common understanding of these projects. In addition, metamodels are constructed by combining ML service systems models
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CTF-DDI: Constrained tensor factorization for drug–drug interactions prediction Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-03 Guosheng Han, Lingzhi Peng, Aocheng Ding, Yan Zhang, Xuan Lin
Computational approaches for predicting drug–drug interactions (DDI) can significantly facilitate combination therapy and drug discovery. Existing similarity-based methods often overlook simple yet valuable structural information or ignore multiple relationships from biological entities (e.g., target proteins and enzymes). Meanwhile, matrix factorization-based methods can alleviate the inherent sparsity
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A read-efficient and write-optimized hash table for Intel Optane DC Persistent Memory Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-03 Zexuan Li, Kaixin Huang
Emerging non-volatile memory technologies are driving the next generation of storage systems and durable data structures. Among them, many hash table proposals employ NVM as the storage layer for both fast access and efficient persistence. Most of them are based on the assumption that NVM has cacheline access granularity, poor write endurance, DRAM-comparable read latency and relatively higher write
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ORION: Verification of drone trajectories via remote identification messages Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-03 Savio Sciancalepore, Filip Davidovic, Gabriele Oligeri
With the widespread adoption of drones in daily life, next-generation smart cities need to establish highways, i.e., trajectories where drones can fly and operate safely. However, due to the untrusted nature of their ecosystem, drones might misbehave and take disallowed trajectories, e.g., to reduce the time to fly to a destination, reduce energy consumption, visit unauthorized areas, or disrupt operations
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Integrating quantum computing resources into scientific HPC ecosystems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-02 Thomas Beck, Alessandro Baroni, Ryan Bennink, Gilles Buchs, Eduardo Antonio Coello Pérez, Markus Eisenbach, Rafael Ferreira da Silva, Muralikrishnan Gopalakrishnan Meena, Kalyan Gottiparthi, Peter Groszkowski, Travis S. Humble, Ryan Landfield, Ketan Maheshwari, Sarp Oral, Michael A. Sandoval, Amir Shehata, In-Saeng Suh, Christopher Zimmer
Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era’s inherent external noise issues. This paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC)
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Distributed asynchronous rendezvous planning on the line for multi-agent systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-02 Deniz Ozsoyeller, Öznur Özkasap
Multi-agent systems have become increasingly significant in various application areas such as search-and-rescue, exploration, surveillance, and assembly. In this study, we focus on the asynchronous autonomous rendezvous planning in multi-robot (i.e. multi-agent) systems. The objective is that the robots located in linear environments to gather rapidly at a previously unknown rendezvous location. We
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Hack me if you can: Aggregating autoencoders for countering persistent access threats within highly imbalanced data Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-07-02 Sidahmed Benabderrahmane, Ngoc Hoang, Petko Valtchev, James Cheney, Talal Rahwan
Advanced Persistent Threats (APTs) are sophisticated, targeted cyberattacks designed to gain unauthorized access to systems and remain undetected for extended periods. To evade detection, APT cyberattacks deceive defense layers with breaches and exploits, thereby complicating exposure by traditional anomaly detection-based security methods. The challenge of detecting APTs with machine learning is compounded
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Anomaly-based error and intrusion detection in tabular data: No DNN outperforms tree-based classifiers Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-29 Tommaso Zoppi, Stefano Gazzini, Andrea Ceccarelli
Recent years have seen a growing involvement of researchers and practitioners in crafting Deep Neural Networks (DNNs) that seem to outperform existing machine learning approaches for solving classification problems as anomaly-based error and intrusion detection. Undoubtedly, classifiers may be very diverse among themselves, and choosing one or another is typically due to the specific task and target
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FedRFC: Federated Learning with Recursive Fuzzy Clustering for improved non-IID data training Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-29 Yuxiao Deng, Anqi Wang, Lei Zhang, Ying Lei, Beibei Li, Yizhou Li
In contemporary times, artificial intelligence is extensively applied across domains, concurrently raising concerns about privacy breaches. In response, federated learning has emerged as a promising solution that allows multiple parties to collaboratively train shared models without sharing local data. Nonetheless, the prevalence of non-IID data among clients poses challenges for traditional federated
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Special Issue on Digital Twin for Future Networks and Emerging IoT Applications (DT4IoT) Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Akram Hakiri, Sadok Ben Yahia, Aniruddha S Gokhale, Nédra Mellouli
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ARGENT: Multi-task learning model for predicting autism-related genes and drug targets using heterogeneous graph convolutional network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Xinxin Miao, Weiwei Yu
Autism Spectrum Disorder (ASD) is a multifactorial-driven neurodevelopmental disorder, the pathophysiological mechanisms of which remain largely elusive, significantly hampering the development of effective therapeutic strategies. MicroRNAs (miRNAs) are emerging as critical regulators in the molecular etiology of autism, influencing gene expression by interacting with target mRNAs involved in neural
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Prediction of drug targets related to HCC metastasis from the perspective of programmed cell death based on transformer Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Yaoguo Huang, Fang Fang, Lin Liu, Keyan Chen, Yaqi Du
Hepatocellular carcinoma (HCC) ranks as the sixth most prevalent cancer globally and the third leading cause of cancer-related deaths. The early diagnosis of HCC is challenging, and its propensity for metastasis results in generally poor prognosis. Existing studies have demonstrated a close association between HCC metastasis and programmed cell death (PCD). However, due to the high-dimensional, high-order
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Automated parallel execution of distributed task graphs with FPGA clusters Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Juan Miguel de Haro Ruiz, Carlos Álvarez Martínez, Daniel Jiménez-González, Xavier Martorell, Tomohiro Ueno, Kentaro Sano, Burkhard Ringlein, François Abel, Beat Weiss
Over the years, Field Programmable Gate Arrays (FPGA) have been gaining popularity in the High Performance Computing (HPC) field, because their reconfigurability enables very fine-grained optimizations with low energy cost. However, the different characteristics, architectures, and network topologies of the clusters have hindered the use of FPGAs at a large scale. In this work, we present an evolution
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Enforcing group fairness in privacy-preserving Federated Learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Chaomeng Chen, Zhenhong Zhou, Peng Tang, Longzhu He, Sen Su
With the expansive application of Federated Learning (FL) in critical domains such as healthcare and financial services, ensuring group fairness in FL has become imperative. The intrinsic distributed architecture of FL, while enhancing training efficiency, introduces significant challenges in maintaining group fairness due to client data privacy concerns and heterogeneous data distribution. For this
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Enhancing campus OS community engagement through the miniOS pilot class: A nine-year journey Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-28 Jianhua Gu, Mingxuan Liu, Tianhai Zhao
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MvG-NRLMF: Multi-view graph neighborhood regularized logistic matrix factorization for identifying drug–target interaction Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-27 Yu Zhang, Qian Liao, Prayag Tiwari, Ying Chu, Yu Wang, Yi Ding, Xianyi Zhao, Jie Wan, Yijie Ding, Ke Han
Traditional methods for predicting drug–target interactions (DTIs) have significant room for improvement in terms of time period and monetary overhead. At present, machine learning-based approaches are commonly used in the drug discovery field. In this study, a multi-view graph neighborhood regularized logical matrix factorization (MvG-NRLMF) model was proposed to predict unknown DTIs. Multiple similarity
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Dynamic gradient filtering in federated learning with Byzantine failure robustness Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-27 Francesco Colosimo, Floriano De Rango
Federated Learning (FL) introduces a novel methodology with the potential to achieve enhanced privacy and security assurances compared to existing methods. This is achieved by allowing multiple users to collaboratively train a global model without revealing their individual training data. Moreover, the popularity of federated learning is on the rise in fields like wireless communications and machine
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An intelligent resource allocation strategy with slicing and auction for private edge cloud systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-26 Yuhuai Peng, Jing Wang, Xiongang Ye, Fazlullah Khan, Ali Kashif Bashir, Bandar Alshawi, Lei Liu, Marwan Omar
The convergence of transformative technologies, including the Internet of Things (IoT), Big Data, and Artificial Intelligence (AI), has driven private edge cloud systems to the forefront of research efforts. The access to massive terminals and the emergence of personalized services pose serious challenges for efficient resource management in power private edge cloud systems. To address the challenge
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Clusterslice: Slicing resources for zero-touch Kubernetes-based experimentation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-25 Lefteris Mamatas, Sotiris Skaperas, Ilias Sakellariou
ClusterSlice is an open-source solution for automated Kubernetes-centered experimentation. It introduces well-designed abstractions that reduce experimentation complexity with improved reliability and reproducibility. Its main capabilities are: (i) automated declarative operation, e.g., through declarative specifications of experimentation slices; (ii) infrastructure-as-a-service (i.e., the utilization
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Prediction of cancer drug combinations based on multidrug learning and cancer expression information injection Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-25 Shujie Ren, Lu Chen, Hongxia Hao, Liang Yu
Compared with patients with common diseases, cancer patients usually have a more fragile cellular microenvironment and more complex or varied complications. Therefore, to meet treatment needs while also keeping the body in a state of balance between resistance and protection, there is an urgent need for the large-scale accurate identification of effective cancer drug combinations. Inspired by many
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Accelerating LASG/IAP climate system ocean model version 3 for performance portability using Kokkos Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-25 Junlin Wei, Pengfei Lin, Jinrong Jiang, Hailong Liu, Lian Zhao, Yehong Zhang, Xiang Han, Feng Zhang, Jian Huang, Yuzhu Wang, Youyun Li, Yue Yu, Xuebin Chi
In this paper, the performance portability of the LASG/IAP Climate System Ocean Model version 3 (LICOM3) is demonstrated based on the C++ library Kokkos. Kokkos enables application execution in various High-Performance Computing (HPC) architectures for on-node parallelism. This study employs Kokkos to expose on-node parallelism and reuses pre-existing Message-Passing Interface (MPI) for internode parallelism
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Enabling federated learning across the computing continuum: Systems, challenges and future directions Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-24 Cédric Prigent, Alexandru Costan, Gabriel Antoniu, Loïc Cudennec
In recent years, as the boundaries of computing have expanded with the emergence of the Internet of Things (IoT) and its increasing number of devices continuously producing flows of data, it has become paramount to boost speed and to reduce latency. Recent approaches to this growing complexity and data deluge aim to integrate seamlessly and securely diverse computing tiers and data environments, spanning
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A fast and highly scalable frequent pattern mining algorithm Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-22 Wan-Shu Cheng, Yi-Ting Lin, Peng-Yu Huang, Ju-Chin Chen, Kawuu W. Lin
As the use of big data and its potential benefits become more widespread, public and private organizations around the world have realized the imperative of incorporating comprehensive and robust technologies into their business processes. In particular, companies are implementing more and more intelligent systems into their business processes, resulting in an exponential increase in the amount of data
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GRAAFE: GRaph Anomaly Anticipation Framework for Exascale HPC systems Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-21 Martin Molan, Mohsen Seyedkazemi Ardebili, Junaid Ahmed Khan, Francesco Beneventi, Daniele Cesarini, Andrea Borghesi, Andrea Bartolini
The main limitation of applying predictive tools to large-scale supercomputers is the complexity of deploying Artificial Intelligence (AI) services in production and modeling heterogeneous data sources while preserving topological information in compact models. This paper proposes GRAAFE, a framework for continuously predicting compute node failures in the Marconi100 supercomputer. The framework consists
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Blockchain empowered access control for digital twin system with attribute-based encryption Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-20 Yueyue Dai, Jian Wu, Shuqi Mao, Xiaoyang Rao, Bruce Gu, Youyang Qu, Yunlong Lu
Digital twin is a pivotal and burgeoning technique that plays a crucial role in the realms of digital transformation and intelligent advancement. To bolster diverse applications and realize digital transformation, it is imperative to share the generated device data among multiple stakeholders involved in the digital twin system product life cycle. Since the device data contains sensitive and secret
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Rapid screening of multi-point mutations for enzyme thermostability modification by utilizing computational tools Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-20 Jia Jin, Qiaozhen Meng, Min Zeng, Guihua Duan, Ercheng Wang, Fei Guo
Enzymes play an important role in industry due to their catalytic properties and environmental friendliness. For application in harsh industrial environments, enzymes are modified to obtain improved stability through simultaneous mutations at multiple sites. Contrary to experimental methods, computational methods are significantly more efficient and cost-effective for screening stabilizing mutations
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Decentralized IoT data sharing: A blockchain-based federated learning approach with joint optimizations for efficiency and privacy Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-20 Ziwen Cheng, Yi Liu, Chao Wu, Yongqi Pan, Liushun Zhao, Xin Deng, Cheng Zhu
Blockchain-based Federated Learning (BCFL) is gaining significant attention as a promising decentralized data sharing technology with privacy protection. Most existing BCFL frameworks loosely couple blockchain and Federated Learning (FL). FL transforms data sharing into model sharing, while blockchain decentralizes the model aggregation and handles security verification. However, this simplistic overlay
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A hierarchical attention-based feature selection and fusion method for credit risk assessment Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-19 Ximing Liu, Yayong Li, Cheng Dai, Hong Zhang
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Multi-resource interleaving for task scheduling in cloud-edge system by deep reinforcement learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-19 Xinglong Pei, Penghao Sun, Yuxiang Hu, Dan Li, Le Tian, Ziyong Li
Collaborative cloud–edge computing has been systematically developed to balance the efficiency and cost of computing tasks for many emerging technologies. To improve the overall performance of cloud–edge system, existing works have made progress in task scheduling by dynamically distributing the tasks with different latency thresholds to edge and cloud nodes. However, the relationship of multi-resource
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Minimum-energy virtual machine placement using embedded sensors and machine learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-18 N. Moocheet, B. Jaumard, P. Thibault, L. Eleftheriadis
Cloud data centers (DCs) consume large amounts of energy and contribute significantly to environmental concerns. Furthermore, with the advent of 5G and B5G networks, increasingly software-oriented and becoming highly dependent on cloud computing, it becomes imperative to optimize their energy consumption. Thus, in this study, we present a virtual machine placement algorithm that minimizes the energy
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GANFAT: Robust federated adversarial learning with label distribution skew Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-18 Yayu Luo, Tongzhijun Zhu, Zediao Liu, Tenglong Mao, Ziyi Chen, Huan Pi, Ying Lin
As privacy concerns and regulatory constraints on data protection continue to grow, the distribution of collected data has become more dispersed, resembling a ”data silo” style. To harness these data effectively without exchanging raw data, federated learning has emerged as a prominent solution. However, distributions of user-generated data often exhibit imbalances between devices and labels, which
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Digital transformation with a lightweight on-premise PaaS Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-15 Din Mušić, Jernej Hribar, Carolina Fortuna
The rise of cloud computing has been enabled by advances in virtualization and containerization technology. Over the past decade, the use of cloud computing has grown rapidly and has had a significant impact on digital transformation with many enterprises migrating to public clouds. While convenient and cost efficient, such approaches are prone to certain data privacy, compliance and security risks
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Improving Hadoop MapReduce performance on heterogeneous single board computer clusters Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-15 Sooyoung Lim, Dongchul Park
Over the past decade, Apache Hadoop has become a leading framework for big data processing. Single board computer (SBC) clusters, predominantly adopting Raspberry Pi (RPi), have been employed to explore the potential of MapReduce processing in terms of low power and cost because, capital costs aside, power consumption has also become a primary concern in many industries. After building SBC clusters
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Retraction Notice to “An Enhanced Consortium Blockchain Diversity Mining Technique for IoT Metadata Aggregation” [Future Generation Computer Systems 152 (2023) 239-253] / 7046 Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-14 Premkumar Chithaluru, Fadi Al-Turjman, Raman Dugyala, Thompson Stephan, Manoj Kumar, Jagjit Singh Dhatterwal
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Quantum-empowered federated learning and 6G wireless networks for IoT security: Concept, challenges and future directions Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-13 Danish Javeed, Muhammad Shahid Saeed, Ijaz Ahmad, Muhammad Adil, Prabhat Kumar, A.K.M. Najmul Islam
The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper
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Intelligent architecture and platforms for private edge cloud systems: A review Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-13 Xiyuan Xu, Shaobo Zang, Muhammad Bilal, Xiaolong Xu, Wanchun Dou
The development of cloud, fog, and edge computing has led to great advances in reducing latency and saving bandwidth, and these methods have therefore been broadly applied in various domains, including healthcare, transportation, and the Internet of Things (IoT). Traditional edge computing solutions have proven to be insufficient in fulfilling the demanding prerequisites of low latency and high data
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Web 3.0 security: Backdoor attacks in federated learning-based automatic speaker verification systems in the 6G era Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-13 Yi Wu, Jiayi Chen, Tianbao Lei, Jiahua Yu, M. Shamim Hossain
With the advent of Next-Generation Web 3.0 and the integration of 6G technologies, digital industrial applications are undergoing unprecedented transformations. Among these, the field of intelligent voice recognition, particularly Federated Learning-based Automatic Speaker Verification (FL-ASV) systems, stands out by collaboratively training robust ASV models across systems while protecting sensitive
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The Renoir Dataflow Platform: Efficient Data Processing without Complexity Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-13 Luca De Martini, Alessandro Margara, Gianpaolo Cugola, Marco Donadoni, Edoardo Morassutto
Today, data analysis drives the decision-making process in virtually every human activity. This demands for software platforms that offer simple programming abstractions to express data analysis tasks and that can execute them in an efficient and scalable way. State-of-the-art solutions range from low-level programming primitives, which give control to the developer about communication and resource
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An efficient scheduling scheme for intelligent driving tasks in a novel vehicle-edge architecture considering mobility and load balancing Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-12 Nuanlai Wang, Shanchen Pang, Xiaofeng Ji, Haiyuan Gui, Xiao He
With the continuous popularization and evolution of 5G and 6G, mobile edge computing has achieved rapid development. This study explores the New Generation Mobile Edge Computing (NGMEC) architecture, which leverages numerous mobile nodes to provide users with enhanced computing services. Despite its advantages, NGMEC faces challenges such as high node mobility, load balancing difficulties, and incomplete
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Assessing and advancing the potential of quantum computing: A NASA case study Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-12 Eleanor G. Rieffel, Ata Akbari Asanjan, M. Sohaib Alam, Namit Anand, David E. Bernal Neira, Sophie Block, Lucas T. Brady, Steve Cotton, Zoe Gonzalez Izquierdo, Shon Grabbe, Erik Gustafson, Stuart Hadfield, P. Aaron Lott, Filip B. Maciejewski, Salvatore Mandrà, Jeffrey Marshall, Gianni Mossi, Humberto Munoz Bauza, Jason Saied, Nishchay Suri, Davide Venturelli, Zhihui Wang, Rupak Biswas
Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ)
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TCN-Inception: Temporal Convolutional Network and Inception modules for sensor-based Human Activity Recognition Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Mohammed A.A. Al-qaness, Abdelghani Dahou, Nafissa Toureche Trouba, Mohamed Abd Elaziz, Ahmed M. Helmi
The field of Human Activity Recognition (HAR) has experienced a significant surge in interest due to its essential role across numerous areas, including human–computer interaction (HCI), healthcare, smart homes, and various Internet of Things (IoT) applications. The power of deep learning methods in performing various classification tasks, including HAR, has been well-demonstrated. In light of this
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CODE: Code once, deploy everywhere serverless functions in federated FaaS Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Sashko Ristov, Simon Brandacher, Mika Hautz, Michael Felderer, Ruth Breu
Infrastructure-as-Code (IaC) frameworks empower developers to swiftly define and provision their infrastructure with a single click. However, the domain-specific languages (DSLs) utilized for coding the infrastructure often lean towards provider specificity rather than being application-centric. This results in increased developer effort, as they are compelled to duplicate data when deploying serverless
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IMC-PnG: Maximizing runtime performance and timing guarantee for imprecise mixed-criticality real-time scheduling Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Jaewoo Lee, Jinkyu Lee
Mixed-Criticality (MC) systems have successfully overcome the limitation of traditional real-time systems based on pessimistic Worst-Case Execution Times (WCETs), by using different WCETs depending on different criticalities. One of the important yet unsolved problems of current MC systems is to achieve two goals (G1 and G2) for low-criticality tasks without compromising timing guarantees for high-criticality
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KRIOTA: A framework for Knowledge-management of dynamic Reference Information and Optimal Task Assignment in hybrid edge–cloud environments to support situation-aware robot-assisted operations Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Muhammad Aufeef Chauhan, Muhammad Ali Babar, Haifeng Shen
Enabling an autonomous robotic system (ARS) to be aware of its operating environment can equip the system to deal with unknown and uncertain situations. While several conceptual models have been proposed to establish the fundamental concepts of situational awareness, it remains a challenge to make an ARS situation aware, in particular using a combination of low-cost resource-constraint robots at the
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European AI and EO convergence via a novel community-driven framework for data-intensive innovation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Antonis Troumpoukis, Iraklis Klampanos, Despina-Athanasia Pantazi, Mohanad Albughdadi, Vasileios Baousis, Omar Barrilero, Alexandra Bojor, Pedro Branco, Lorenzo Bruzzone, Andreina Chietera, Philippe Fournand, Richard Hall, Michele Lazzarini, Adrian Luna, Alexandros Nousias, Christos Perentis, George Petrakis, Dharmen Punjani, David Röbl, George Stamoulis, Eleni Tsalapati, Indrė Urbanavičiūtė, Giulio
Artificial Intelligence (AI) represents a collection of tools and methodologies that have the potential to revolutionise various aspects of human activity. Earth observation (EO) data, including satellite and in-situ, are essential in a number of high impact applications, ranging from security and energy to agriculture and health. In this paper, we present the AI4Copernicus framework for bridging the
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Concurrent service auto-scaling for Knative resource quota-based serverless system Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-10 Minh-Ngoc Tran, YoungHan Kim
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Performance of algorithms for emerging ion-trap quantum hardware Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-08 Arthur Kurlej, Sam Alterman, Kevin Obenland
Quantum computing shows promise for solving problems that are intractable on classical computers. Devices that are available in the near-term will be noisy and limited in size. These type of devices have been termed: noisy intermediate-scale quantum (NISQ) to distinguish them from future fault-tolerant quantum computing platforms. As these NISQ platforms are developed, it is important to understand
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HGGN: Prediction of microRNA-Mediated drug sensitivity based on interpretable heterogeneous graph global-attention network Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-08 Junliang Liu, Xinbo Zhao, Yuran Jia, Sicong Wang, Tianyi Zhao
Drug sensitivity significantly influences therapeutic outcomes. Recent discoveries have highlighted the pivotal role of miRNAs in regulating drug sensitivity by modulating genes associated with drug metabolism and action. As crucial regulators of gene expression, miRNAs have emerged as influential factors in determining an individual’s response to pharmaceutical interventions. However, current methods
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Scalable I/O aggregation for asynchronous multi-level checkpointing Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-07 Mikaila J. Gossman, Bogdan Nicolae, Jon C. Calhoun
Checkpointing distributed HPC applications is a common I/O pattern with many use cases: resilience, job management, reproducibility, revisiting previous intermediate results, etc. This is a difficult pattern for a large number of processes that need to capture massive data sizes and write them persistently to shared storage (e.g., parallel file system), which is subject to I/O bottlenecks due to limited
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ACVPred: Enhanced prediction of anti-coronavirus peptides by transfer learning combined with data augmentation Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-07 Yi Xu, Tianyuan Liu, Yu Yang, Juanjuan Kang, Liping Ren, Hui Ding, Yang Zhang
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Parallel quantum computing simulations via quantum accelerator platform virtualization Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-07 Daniel Claudino, Dmitry I. Lyakh, Alexander J. McCaskey
Quantum circuit execution is a central task in quantum computation. Due to inherent quantum-mechanical constraints, quantum computing workflows often involve a considerable number of independent measurements over a large set of slightly different quantum circuits. Here we discuss a simple model for parallelizing such quantum circuit executions that is based on introducing a large array of virtual quantum
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DAPM-CDR: A domain adaptation prompting model for drug response prediction Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-06 Youhan Sun, Guanyu Qiao, Bo Gao, Yang Li
The rising incidence and mortality rates of cancer present significant challenges to global health. Variations in tumor growth rates and treatment responses have revealed the limitations of traditional therapies, highlighting the urgent need for predictive models for cancer drug responses based on computational methods. Current drug response prediction methods often fall short in accurately predicting
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Feature selection using metaheuristics made easy: Open source MAFESE library in Python Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-06 Nguyen Van Thieu, Ngoc Hung Nguyen, Ali Asghar Heidari
Artificial intelligence (AI) often relies on feature selection (FS) to recognize and highlight the most relevant and major features in a dataset. The procedure of training and optimizing AI systems with key data points is decisive for its development and efficacy. To address this challenge, the present study introduces MAFESE, an open-source Python library that employs metaheuristic algorithms for
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Batched sparse and mixed-precision linear algebra interface for efficient use of GPU hardware accelerators in scientific applications Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-06 Piotr Luszczek, Ahmad Abdelfattah, Hartwig Anzt, Atsushi Suzuki, Stanimire Tomov
Batched Sparse Linear Algebra has become an emergent processing mode on modern hardware accelerators based on Graphics Processing Units (GPUs) developed over the years to serve as the main compute devices in the largest computing clusters and supercomputers. We propose a set solver interface designs for batched sparse numerical solvers on these hardware accelerators. We motivate our specific designs
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An efficient privacy-preserving and verifiable scheme for federated learning Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-06 Xue Yang, Minjie Ma, Xiaohu Tang
As one of the most important methods of privacy computing, federated learning has attracted much attention as it makes data available but invisible (i.e., uploading gradients instead of raw data). However, adversaries may still recover some private information such as tabs, memberships or even training data, from gradients. Additionally, the malicious server may return the incorrect or forged aggregated
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Online learning and continuous model upgrading with data streams through the Kafka-ML framework Future Gener. Comput. Syst. (IF 6.2) Pub Date : 2024-06-06 Alejandro Carnero, Cristian Martín, Gwanggil Jeon, Manuel Díaz
A pipeline of constant data streams is being built by the Internet of Things (IoT) to monitor information about the physical environment. In parallel, Artificial Intelligence (AI) is constantly developing and enhancing industrial, economic, and academic endeavors as well as quality of life thanks to these IoT data. In streaming contexts, Kafka-ML is our open-source framework that enables the management