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Design and multilevel reconstruction method of intelligent power industry control system based on digital twins Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-19 Cen Chen, Zhuo Lv, Nuannuan Li, Tao Zhang, Zheng Zhang, Hao Chang
SummaryIn order to improve the security and reliability of power industry control systems, research proposes the design of intelligent industrial control systems based on digital twins. This design combines the digital twin multilevel reconstruction method to construct an intelligent power control system structure, and proposes a system safety verification range design and evaluation indicators. The
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Detection of algal tiny objects based on morphological features Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-19 Shuai Yuan, Ningkang Peng, Ziyan Shi, Sichuan Zhao, Runcheng Li, Yanhui Gu, Huan He
SummaryThe dense and toxic blooms formed by cyanobacteria in aquatic environments pose significant threats to public health and aquatic ecosystems. Timely monitoring and prevention of cyanobacterial blooms in freshwater bodies are thus imperative. Although object detection methods have been applied in the field of algae identification, existing research faces several challenges. A primary issue is
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Swarm Intelligence with a Chaotic Leader and a Salp algorithm: HDFS optimization for reduced latency and enhanced availability Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-18 N. Jagadish Kumar, D. Dhinakaran, A. Naresh Kumar, A. V. Kalpana
SummaryThe Hadoop distributed file system (HDFS) effectively manages data by segmenting it into blocks distributed across DataNodes in its cluster. While default block sizes in Hadoop 2.x and 1.x are 128 and 64 MB, respectively, they can be customized for larger files. HDFS ensures data reliability by replicating blocks across multiple DataNodes, but this can introduce high latency in cloud storage
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Towards a robust scale‐free network in internet of health things against multiple attacks using an inter‐core based reconnection strategy Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-18 Syed Minhal Abbas, Nadeem Javaid, Nabil Alrajeh, Safdar Hussain Bouk, Soliman Alhudaithy
SummaryWireless sensor networks (WSNs) have attained a great attraction of researchers in the recent years. In these networks, many structures are considered that have different properties. This article offers a unique approach, the inter‐core based reconnection strategy (ICRS), which is intended to improve the robustness of Scale‐Free Networks (SFNs) in the setting of wireless sensor networks (WSNs)
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Prevention of sleep deprivation attack in MANET using cumulative priority based cluster head selection Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-17 Ankita Kumari, Purushottam Singh, Prashant Pranav, Sandip Dutta, Soubhik Chakraborty
SummaryIn the rapidly evolving domain of Mobile Ad‐hoc Networks (MANETs), where their deployment spans critical military operations to essential organizational communication infrastructures, the pervasive threat of security breaches casts a long shadow on the networks' operational integrity and reliability. Central among these threats are sleep deprivation attacks, a particularly insidious form of
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Online streaming feature selection based on hierarchical structure information Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-17 Shuxian Lin, Chenxi Wang, Xiehua Yu, Huirong Fang, Yaojin Lin
SummaryHierarchical classification learning aims to exploit the hierarchical relationship between data categories. The high dimensionality and dynamic of the data feature space are the main challenges of this research. Hierarchical feature selection uses a hierarchical structure to divide large‐scale tasks into multiple small tasks, which can more effectively improve the training speed and prediction
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h5bench: A unified benchmark suite for evaluating HDF5 I/O performance on pre‐exascale platforms Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-16 Jean Luca Bez, Houjun Tang, Scot Breitenfeld, Huihuo Zheng, Wei‐Keng Liao, Kaiyuan Hou, Zanhua Huang, Suren Byna
SummaryParallel I/O is a critical technique for moving data between compute and storage subsystems of supercomputers. With massive amounts of data produced or consumed by compute nodes, high‐performant parallel I/O is essential. I/O benchmarks play an important role in this process; however, there is a scarcity of I/O benchmarks representative of current workloads on HPC systems. Toward creating representative
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Research on PSO‐SVM base wine grade recognition based on Max‐Relevance and Min‐Redundancy feature selection Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-13 Jiaping Yang, Dongping Ren, Yong Liu, Hailong Zhou, Yunquan Sun
SummaryIn response to the challenges inherent in Baijiu classification, characterized by ambiguity and limited methodologies, this study introduces a novel framework for comprehensive Baijiu brewing process management. By integrating mathematical models and machine learning algorithms, our aim is to standardize and enhance the accuracy of the Baijiu brewing process. Through a meticulous analysis of
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UAV path planning: Integration of grey wolf algorithm and artificial potential field Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 Yaqing Chen, Qizhou Yu, Dan Han, Hao Jiang
SummaryUnmanned aerial vehicle (UAV) path planning is an important issue in UAV applications, with the goal of finding the optimal path to meet mission requirements, while considering factors such as avoiding obstacles and optimizing flight performance. To improve the efficiency of UAV path planning and enhance the smoothness and safety of UAV operation, this paper proposes a fusion optimization algorithm
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Enhancing CNN‐LSTM neural networks using jellyfish search algorithm for pandemic modeling Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 Azade Hashemi Feriz, Mehrdad Jalali, Yahya Forghani
SummaryThis paper presents a comprehensive three‐step approach (CNN‐JSO‐LSTM) for predictive modeling using a pandemic such as COVID‐19 as a test case. Initially, a Convolutional Neural Network (CNN) is employed to extract crucial features pertinent to the pandemic. Subsequently, the Jellyfish Search Optimizer (JSO) algorithm is applied for feature selection, identifying the most relevant factors.
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Domain‐specific translation tool from structured text to C source code with code readability enhancement in programmable logic controllers Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 Bing Han, Congfei Li, Hua Deng, Guowei Liu, Ze Zheng
The Industrial Internet has emerged as a key technology in the field of industrial automation, revolutionizing traditional manufacturing processes and enabling advanced control systems by integrating machines, sensors, and software systems through network connectivity, allowing for real‐time data exchange, analysis, and decision‐making in industrial environments. Programmable Logic Controllers (PLCs)
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Predicting accurate batch queue wait times on production supercomputers by combining machine learning techniques Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 Nick Brown, Gordon Gibb, Evgenij Belikov, Rupert Nash
The ability to accurately predict when a job on a supercomputer will leave the queue and start to run is not only beneficial for providing insights to users, but can also help enable non‐traditional HPC workloads that are not necessarily suited to the batch queue style‐approach that is ubiquitous on production HPC machines. However there are numerous challenges in achieving such a prediction with high
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Early experiences evaluating the HPE/Cray ecosystem for AMD GPUs Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 Verónica G. Melesse Vergara, Reuben D. Budiardja, Wayne Joubert
SummaryThe Oak Ridge Leadership Computing Facility (OLCF) has a long history of supporting and promoting GPU‐accelerated computing starting with the deployment of the Titan supercomputer in 2021 and continuing with the Summit supercomputer which has a theoretical peak performance of approximately 200 petaflops. Because the majority of Summit's computational power comes from its 27,972 GPUs, users must
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Throughput and coverage based Base Station–Relay Station deployment for 5G cellular network Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-12 R. Ratheesh, M. Saranya Nair, P. Vetrivelan, J. Rajeswari
SummaryFifth generation cellular networks have high data rates and connectivity demands. The relaying approaches are widely used in cellular networks to improve coverage, user throughput, and capacity at low cost. The increasing data requirements of the mobile network increase energy consumption. Energy efficiency‐based approaches also impact network coverage and throughput. Therefore, developing an
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New parallelism and heuristic approaches for generating tree t‐spanners in graphs Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-11 Luís Cunha, Eriky Marciano, Anderson Moraes, Leandro Santiago, Carlos Santos
SummaryThe ‐admissibility is a min‐max problem that concerns to determine whether a graph contains a spanning tree in which the distance between any two adjacent vertices of is at most in . The stretch index of , , is the smallest for which is ‐admissible. This problem is in P for , NP‐complete for , , and remaining open for . In a very recent development, Couto et al. (Inf Process Lett, 2022; 177:
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Containerized deep learning in agriculture: Orchestrating GoogleNet with Kubernetes on high performance computing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-11 Syed Humaid Hasan, Syeda Huyam Hasan, Usman Ali Khan, Syed Hamid Hasan
SummarySmart Farming has become a cornerstone of modern agriculture, offering data‐driven insights and automation that optimize resource utilization and increase crop yields. The use of cutting‐edge technologies in agriculture has given rise to Smart Farming, which has transformed traditional farming practices into efficient, data‐driven operations. This paper explores the synergy between high‐performance
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Blockchain‐cloud integration: Comprehensive survey and open research issues Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-11 Houaida Ghanmi, Nasreddine Hajlaoui, Haifa Touati, Mohamed Hadded, Paul Muhlethaler, Saadi Boudjit
SummaryCloud computing has attracted great interest in various scientific and technical fields recently as one of the widely adopted networking technologies. Despite their many benefits and applications, it still faces many security and trust challenges, including managing and controlling services, privacy, data integrity in distributed databases, data backup, and synchronization. Moreover, due to
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Towards achieving geo‐indistinguishability for 3D GPS location: A 3D Laplace mechanism approach Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-11 Yan Yan, Pengbin Yan, Adnan Mahmood, Fei Xu, Quan Z. Sheng
SummaryAs the scope of human exploration continues to expand from land to space and the oceans, location‐based data analysis and services are facing unprecedented opportunities and challenges. The wide application of various services based on spatial location in the fields of medical, transportation, financial, social and so forth not only provides great convenience but also exposes the disadvantages
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Social network based link correlation using graph neural network with deep learning architectures for feature vectors prediction and classification Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-11 Nagaraju Sonti, Rukmini Mulpuri Santhi Sri, Venkatappa Reddy Pamulapati
SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending
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Structural testing for CUDA programming model Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-09 Helder J. F. Luz, Paulo S. L. Souza, Simone R. S. Souza
SummaryGraphic processors offer an accessible solution for high‐performance computing, addressing challenges across various fields. The Compute Unified Device Architecture (CUDA) programming model has emerged to enhance the performance of general‐purpose applications on graphic processors. However, developing CUDA programs is far from straightforward, and developers' lack of experience in parallel
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Hybrid one‐dimensional residual autoencoder and ensemble of gradient boosting for cloud IDS Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-08 Nupa Ram Chauhan, Rakesh Kumar Dwivedi
SummaryThe distributed and decentralized architecture of cloud computing is important for a number of industries, including business, government, entertainment, education, and information technology. It facilitates a wide aspect of information technology, where the computing model is vulnerable to attacks or intrusion. For detecting malicious activities, a novel intrusion detection system (IDS) is
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Medical education and artificial intelligence: Question answering for medical questions based on intelligent interaction Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-08 Lei Chen
SummaryComputer assisted medical diagnosis technology is widely used in the field of medical assistance to assist doctors in making diagnostic decisions. But as the number of patients increases, the diagnostic pressure on doctors gradually increases, and more efficient computer‐aided medical diagnosis technology is needed to improve the accuracy of doctors' diagnosis. Today's computer‐based medically
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Cloud menu: Cloud based network analysis for disease‐diet associations and recommendations Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-08 Rashmeet Toor, Inderveer Chana
SummaryFood is one of the most underrated entities with respect to diseases. Food or Diet plays a vital role in healing or recovery of a patient which brings forth the need of analyzing disease and diets associations. The study of such associations is crucial for recommending appropriate diets to patients but is an arduous task due to the complex interdependencies as is evident in literature. Thus
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A lightweight performance proxy for deep‐learning model training on Amazon SageMaker Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-08 Rafael Keller Tesser, Alvaro Marques, Edson Borin
SummaryCloud computing has become popular for training deep‐learning (DL) models, avoiding the costs of acquiring and maintaining on‐premise systems. SageMaker is a cloud service that automates the execution of DL workloads. Its features include automatic hyperparameter optimization and use of spot instances. Nonetheless, it does not assist in selecting the right instance type for a workload. In public
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STRmt: A state transition based model for real‐time crowd counting in a metro system Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-05 Li Sun, Juanjuan Zhao, Jun Zhang, Fan Zhang, Kejiang Ye, Chengzhong Xu
SummaryReal‐time estimation of crowd counting in underground metro systems, constrained by limited space, is crucial for managing heightened pedestrian volumes and responding promptly to emergencies. To address this challenge, we propose a passenger state transition‐based model, called STRmt, designed for the seamless and continuous monitoring of real‐time crowd movement within service areas of stations
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Research on optimal deep learning‐based formation flight positioning of UAV Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-05 Jing Lu, Yongyi Li
SummaryThe article examines the positional relationships and transmission/reception signal direction information within unmanned aerial vehicle (UAV) cluster formations. Considering the distinctive characteristics of various formation shapes (such as circle and cone), a study is conducted on the polar and Cartesian coordinate systems in neural network models to address position deviation issues arising
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EPIDL: Towards efficient and privacy‐preserving inference in deep learning Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-04 Chenfei Nie, Zhipeng Zhou, Mianxiong Dong, Kaoru Ota, Qiang Li
SummaryDeep learning has shown its great potential in real‐world applications. However, users(clients) who want to use deep learning applications need to send their data to the deep learning service provider (server), which can make the client's data leak to the server, resulting in serious privacy concerns. To address this issue, we propose a protocol named EPIDL to perform efficient and secure inference
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Fast gathering despite a linear number of weakly Byzantine agents† Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-03 Jion Hirose, Junya Nakamura, Fukuhito Ooshita, Michiko Inoue
SummaryIn this work, we study the gathering problem to make multiple agents, who are initially scattered in arbitrary networks, meet at the same node. The network has agents with unique identifiers (IDs), and of them are weakly Byzantine agents that behave arbitrarily, except for falsifying their identifiers. These agents behave in synchronous rounds, and they may start an algorithm at different rounds
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An ML‐based task clustering and placement using hybrid Jaya‐gray wolf optimization in fog‐cloud ecosystem Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-02 Rashmi Keshri, Deo Prakash Vidyarthi
SummaryThe rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important
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An improved sparrow search algorithm using chaotic opposition‐based learning and hybrid updating rules Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-02 Lian Lian
SummaryMetaheuristic algorithms have special effects in solving optimization problems in real life and have become the focus of researchers. The sparrow search algorithm (SSA) is a newly proposed swarm‐based metaheuristic algorithm that has shown excellent optimization performance. Although compared with other algorithms, SSA shows good performance, the original SSA algorithm still has problems such
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Joint optimal beam forming and resource allocation in intelligent reflecting surface aided wireless power transfer rate splitting multiple access system Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-04-02 M. Naresh, G. V. Pradeep Kumar, V. Sireesha, V. V. Satyanarayana Tallapragada
SummaryThe intelligent reflecting surface (IRS) has recently become the most promising technology to achieve maximum beam‐forming gain with a simultaneous wireless information and power transfer (SWIPT) system. Many existing studies perform a single IRS deployment or utilize space division multiple access (SDMA) and non‐orthogonal multiple access (NOMA) schemes. However, the existing schemes face high
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Multi-chaotic maps and blockchain based image encryption Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Twinkle Kumari, Damanpreet Singh, Birmohan Singh
The vast technological developments make data transmission more frequent over networks. So, the data needs to be secured and for that reason, there is a requirement to develop an effective encryption model. This article proposes a novel image encryption model using multi-chaotic maps and blockchain (MCBE). Chaotic maps have been used in encryption models as chaotic maps have the properties of randomness
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A face retrieval technique combining large models and artificial neural networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Jin Lu
Multimodal sentiment analysis is a popular research direction in the field of affective computing. It extends unimodal-based sentiment analysis to the environment based on multimodal information exchange. Face retrieval is the most critical technology in the current multimodal sentiment analysis field. Traditional methods for face retrieval rely on large amounts of data to train the matching relationship
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A path planning method for unmanned aerial vehicle based on improved wolf pack algorithm Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Hao Jiang, Qizhou Yu, Dan Han, Yaqing Chen, Zejun Li
In the rapidly developing field of unmanned aerial vehicle (UVA) technology, solving local optimal problems and achieving efficient smooth planning are crucial for improving the operational efficiency and safety of UVA systems. To address these needs, our study introduces a novel optimization algorithm, called IWPA-APF, which aims to improve path planning efficiency. This algorithm is a fusion of the
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Improving model performance of shortest‐path‐based centrality measures in network models through scale space Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-27 Kenan Menguc, Alper Yilmaz
SummaryThe quality of the solution in resolving a complex network depends on either the speed or accuracy of the results. While some health studies prioritize high performance, fast algorithms are favored in scenarios requiring rapid decision‐making. A comprehensive understanding of the problem necessitates a detailed analysis of the network and its individual components. Betweenness Centrality (BC)
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MH‐ARO: Meta‐heuristic based adaptive routing for large scale opportunistic networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Soni Chaurasia, Kamal Kumar
SummaryIn wireless sensor networks, sensor nodes (SNs) are placed throughout a wide area and gather information from the surroundings. SN used to detect and send data consumes a lot of energy and dies instantly, which causes network overhead issues. Due to network overhead, network faults occur and do not cover a significant region for data transmission. A meta‐heuristic‐based adaptive routing for
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A survey on lattice‐based security and authentication schemes for smart‐grid networks in the post‐quantum era Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Hema Shekhawat, Daya Sagar Gupta
SummaryThe present scenario witnesses “the second quantum revolution,” which has enabled the development of revolutionary novel quantum tools. Quantum computing endeavors to establish higher computing standards that can potentially solve complex structures. Post‐quantum cryptography (PQC) has emerged as one of the new domains of cryptography, which is resilient to quantum attacks owing to the revolution
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FP‐JSC: Job failure prediction on supercomputers through job application sequence correlation Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-25 Gang Xian, Wenxiang Yang, Jie Yu
SummarySupercomputers are advanced computing systems interconnected through high‐speed communication networks, consisting of independent computational nodes. During the unfolding of the big data era, the potent computational capabilities of these supercomputers play a pivotal role in scientific computing. Despite executing numerous advanced computational science and engineering tasks on supercomputers
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An autonomous blockchain-based computational broker for e-science Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-18 Alper Alimoğlu, Can Özturan
Blockchain infrastructures have emerged as a disruptive technology and have led to the realization of cryptocurrencies (peer-to-peer payment systems) and smart contracts. They can have a wide range of application areas in e-Science due to their open, public nature and global accessability in a trustless manner. We propose and implement a smart contract called eBlocBroker, which is an autonomous blockchain-based
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Lc‐Stream: An elastic scheduling strategy with latency constraints in geo‐distributed stream computing environments Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-21 Dawei Sun, Yueru Wang, Jialiang Sui, Shang Gao, Jia Rong, Rajkumar Buyya
SummaryAn effective scheduling strategy is critical for achieving better performance in real‐time stream processing systems. How to quickly and efficiently process real‐time data stream is always challenging, especially when clusters are collaborating in a Geo‐Distributed computing environment. To address these challenges, we propose an elastic scheduling strategy with Latency Constraints in Geo‐Distributed
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Privacy preserving and secure robust federated learning: A survey Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-20 Qingdi Han, Siqi Lu, Wenhao Wang, Haipeng Qu, Jingsheng Li, Yang Gao
SummaryFederated learning (FL) has emerged as a promising solution to address the challenges posed by data silos and the need for global data fusion. It offers a distributed machine learning framework with privacy‐preserving features, allowing model training without the need to collect user data. However, FL also presents significant security and privacy threats that hinder its widespread adoption
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Cross‐network service recommendation in smart cities Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-19 Haithem Mezni, Mokhtar Sellami, Amal Al‐Rasheed, Hela Elmannai
SummaryNowadays, Internet of Things, artificial intelligence, cloud computing, and other revolutionary technologies (e.g., edge and fog computing) have become the pillar of smart cities. These latter make users' lives easier, thanks to a wide variety of smart services offered in different dimensions (e.g., smart living, smart mobility, smart economy, smart governance). However, the rapid adoption of
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Stacked ensemble modeling for improved tuberculosis treatment outcome prediction in pediatric cases Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-18 Yildiran Yilmaz
SummaryThe promising results of ML (machine learning) methods in various disciplines have led to the frequent use of these methods in health fields such as disease diagnosis, personalized medicine, medical image‐based diagnosis, and predicting the number of deaths and cases in a pandemic. However, a neglected area in the field of healthcare is the lack of study with ML to predict treatment outcomes
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Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-18 Medha Kirti, Ashish Kumar Maurya, Rama Shankar Yadav
Fault tolerance is crucial in ensuring smooth working of distributed and cloud computing. It is challenging to implement because of the constantly changing infrastructure and complex configurations in distributed and cloud computing. Implementation of various fault tolerance methods require domain‐specific knowledge as well as in‐depth understanding of the existing techniques and approaches. Recent
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TDLC: Tensor decomposition‐based direct learning‐compression algorithm for DNN model compression Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-18 Weirong Liu, Peidong Liu, Changhong Shi, Zhiqiang Zhang, Zhijun Li, Chaorong Liu
SummaryAs a deep neural networks (DNNs) model compression method, learning‐compression (LC) algorithm based on pre‐trained models and matrix decomposition increases training time and ignores the structural information of models. In this manuscript, a tensor decomposition‐based direct LC (TDLC) algorithm without pre‐trained models is proposed. In TDLC, the pre‐trained model is eliminated, and tensor
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Energy‐efficient reliability‐aware offloading for delay‐sensitive tasks in collaborative edge computing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-15 Zengpeng Li, Huiqun Yu, Guisheng Fan, Jiayin Zhang, Jin Xu
SummaryAs a burgeoning paradigm, collaborative mobile edge computing (C‐MEC) can cater to growing computation demand of mobile devices (MDs). However, there are great challenges for joint task offloading and resource allocation. In addition, failures on both MDs and edge servers greatly affect reliable task execution. This paper investigates the joint optimization problem of offloading decision, power
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A spatio‐temporal graph convolutional approach to real‐time load forecasting in an edge‐enabled distributed Internet of Smart Grids energy system Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-13 Qi Liu, Li Pan, Xuefei Cao, Jixiang Gan, Xianming Huang, Xiaodong Liu
SummaryAs the edge nodes of the Internet of Smart Grids (IoSG), smart sockets enable all kinds of power load data to be analyzed at the edge, which create conditions for edge calculation and real‐time (RT) load forecasting. In this article, an edge‐cloud computing analysis energy system is proposed to collect and analyze power load data, and a combination of graph convolutional network (GCN) with LSTM
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Deep code search efficiency based on clustering Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-13 Kun Liu, Jianxun Liu, Haize Hu
The deep‐learning based code search model mainly takes accuracy as the only target for judging the performance of the model, ignoring the efficiency of code search. This article proposes a clustering‐based code search model (C‐DCS). C‐DCS uses the K‐Means to divide the code vector base into K clusters and obtains the center vectors of K clusters. While searching, C‐DCS first matches the query vector
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EPREKM: ElGamal proxy re‐encryption‐based key management scheme with constant rekeying cost and linear public bulletin size Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-12 Payal Sharma, Purushothama B. R.
SummaryA vast body of literature is filled with many key management schemes constructed using different cryptographic primitives. They aim toward either security goals or improvement in performance efficiency. However, the key management schemes based on proxy re‐encryption suffer from massive communication and computational costs. We propose an ElGamal proxy re‐encryption‐based construction for the
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An optimized crypto‐based routing protocol for secure routing in wireless sensor networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-12 Khaleel‐Ur‐Rahman Khan, Mohammed Abdul Azeem
SummaryIn Wireless Sensor Networks (WSN), energy‐efficient, reliable routing is the core objective for the data transmission process. Anomaly nodes in the communication environment can affect reliable routing and network efficiency. Therefore, the present research created a novel Arithmetic Optimization‐based Rumor Routing Protocol with SKINNY Crypto mechanism (AORRP‐SCrypt) for secure Routing in WSN
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Blockchain assisted blind signature algorithm with data integrity verification scheme Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-11 Pranav Shrivastava, Bashir Alam, Mansaf Alam
SummaryAs the demand for cloud storage systems increases, ensuring the security and integrity of cloud data becomes a challenge. Data uploaded to cloud systems are vulnerable to numerous sorts of assaults, which must be handled appropriately to avoid data tampering issues. In addition, quantum computers are expected to be introduced soon, which may face multiple security issues by destroying all traditional
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Corrigendum Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-11
This article corrects the following: Hybrid intelligent intrusion detection system for multiple Wi-Fi attacks in wireless networks using stacked restricted Boltzmann machine and deep belief networks Nivaashini Mathappan, Suganya Elavarasan, Sountharrajan Sehar Volume 35, Issue 23, Concurrency Computat Pract Exper|e7769| https://doi.org/10.1002/cpe.7769 First Published online: May 18, 2023 In the original
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Computation offloading techniques in edge computing: A systematic review based on energy, QoS and authentication Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-08 Kanupriya, Inderveer Chana, Raman Kumar Goyal
In today's era, Internet of Things (IoT) devices generate a vast amount of data, which is typically stored in the cloud environment and can be accessed by edge and IoT devices. The data generated by these devices are offloaded through computation offloading (CO) techniques in an edge/cloud computing environment. This paper conducts a systematic literature review (SLR) to review the state-of-the-art
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Sampling business process event logs with guarantees Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-09 Xuan Su, Cong Liu, Shuaipeng Zhang, Qingtian Zeng
SummaryEvent log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high‐quality sample logs. This paper introduces a novel framework to support efficient event log sampling
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Early experiences on the OLCF Frontier system with AthenaPK and Parthenon-Hydro Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-05 John K. Holmen, Philipp Grete, Verónica G. Melesse Vergara
The Oak Ridge Leadership Computing Facility (OLCF) has been preparing the nation's first exascale system, Frontier, for production and end users. Frontier is based on HPE Cray's new EX architecture and Slingshot interconnect and features 74 cabinets of optimized 3rd Gen AMD EPYC CPUs for HPC and AI and AMD Instinct 250X accelerators. As a part of this preparation, “real-world” user codes have been
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Toward social media forensics through development of iOS analyzers for evidence collection and analysis Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-06 Muhammad Faraz Hyder, Saadia Arshad, Tasbiha Fatima
SummarySocial media usage in mobile phones has increased substantially in recent times, and they are a critically important source of a forensics investigation. In this paper, we have developed Python‐based forensic analyzers that are integrated with the open‐source tool Autopsy. The proposed analyzers find forensic artifacts from the three most widely used social media messaging applications, that
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Transforming data‐intensive workflows: A cutting‐edge multi‐layer security and quality aware security framework Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-06 Maha Aljohani
SummaryThe data‐intensive workflow application executes tasks on edge servers and cloud platforms in a heterogeneous big‐data computing environment. Cloud and edge servers are vulnerable to node attacks and malicious links due to their wireless connections. Thus, detecting and mitigating rogue nodes in edge server communication environments during workflow execution is crucial. In today's workflow