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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 : 2024-03-17 Weirong Liu, Peidong Liu, Changhong Shi, Zhiqiang Zhang, Zhijun Li, Chaorong Liu
As 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 decomposition
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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
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Multi objective optimization scheduling of unmanned warehouse handling robots based on A star algorithm Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-05 Tong Li, Ziming Wang
SummaryThe sorting efficiency of the warehouse management and handling robots (automated guided vehicles [AGVs]) in an unmanned warehouse affects the delivery and lead time of orders. Studying the collaborative sorting of multiple AGVs can significantly improve work efficiency. The goal of this paper is to minimize the overall shortest transportation path and time while ensuring the completion of all
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A deep learning model of dance generation for young children based on music rhythm and beat Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-05 Shanshan Kong
SummaryWith the development of technology, research related to music‐based dance generation models has been increasing. Some of the studies have applied algorithms to dance generation models, but these algorithms suffer from problems such as the inability to make an exact match between music and dance. To solve these problems, the research innovatively proposes a deep learning toddler dance generation
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Reviewing various feature selection techniques in machine learning-based botnet detection Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-03 Sangita Baruah, Dhruba Jyoti Borah, Vaskar Deka
Machine learning approaches are widely used for the detection and classification of emerging botnet variations due to their ability to yield more precise results compared to traditional methods. The relevancy of the features plays a major role in these detection algorithms' effectiveness. As such, the most distinctive characteristics must be extracted from a high-dimensional dataset that is used to
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Neighbor cleaning learning based cost‐sensitive ensemble learning approach for software defect prediction Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-04 Li Li, Renjia Su, Xin Zhao
SummaryThe class imbalance problem in software defect prediction datasets leads to prediction results that are biased toward the majority class, and the class overlap problem leads to fuzzy boundaries for classification decisions, both of which affect the model's prediction performance on the dataset. A neighbor cleaning learning (NCL) is an effective technique for defect prediction. To solve the class
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LSB‐XOR technique for securing captured images from disaster by UAVs in B5G networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-04 Farheen Syed, Saeed Hamood Alsamhi, Sachin Kumar Gupta, Abdu Saif
SummaryRecently, unmanned aerial vehicles (UAV) technology has been utilized to monitor and capture images from disasters to analyze, process, and take action in real‐time for a speedy recovery. UAVs represent one of the critical technologies used beyond the fifth generation (B5G) of heterogeneous networks. Due to the sensitive data captured from disaster areas by UAVs, security has become a significant
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Microservices‐based databank for Turkish hazelnut cultivars using IoT and semantic web technologies Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-04 Sahin Aydin, Dieaa Aldara
SummaryInformation and communication technologies (ICTs) can play a crucial role in facilitating access to comprehensive information on the quality standards of Turkish hazelnut cultivars. In this regard, this study introduces a Hazelnut Databank System (HDS) that utilizes the microservices architecture, an integrated software system supported by the Internet of Things (IoT) and semantic web, to categorize
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CCSFLF: Cloud‐edge‐terminal collaborative self‐adaptive federated learning framework Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-01 Tong Zhou, Yaning Yu, Haonan Yuan, Bing Liu, Hongyang Zhao, Ruijin Wang
SummaryThis article addresses the issue of ensuring model accuracy and training efficiency in a constrained federated learning environment. In an actual federated learning environment, each device's software, hardware, and network conditions are heterogeneous. Some terminal devices may not be able to undertake the work assigned by the server, resulting in poor model accuracy and slower convergence
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A recurrence for the surface area of the (n,k)$$ \left(n,k\right) $$‐star graph Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-01 Ethan Gibbons, Ke Qiu
SummaryWe present a simple recurrence for the surface area of the ‐star graph, , that is, the number of nodes at a certain distance from the identity node in the graph, an important parameter for interconnection networks in parallel computing. The family of the ‐star graphs includes several popular interconnection networks such as the star graph and the alternating group network. Previously, a surface
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FPHO: Fractional Pelican Hawks optimization based container consolidation in CaaS cloud Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-03-01 Manoj Kumar Patra, Bibhudatta Sahoo, Ashok Kumar Turuk
Containers in cloud computing provide a logical packaging technique for applications to be isolated from the computing environment in which they actually execute, allowing for efficient sharing of memory, processor, storage, and network resources at the Operating System (OS) level. Since they are so compact, container‐based clouds have recently gained significant popularity. In order to maximize resource
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FogSec: A secure and effective mutual authentication scheme for fog computing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-29 Thankaraja Raja Sree, R. Harish, T. Veni
SummaryAs opposed to cloud servers, fog servers, and fog users may be malicious, so developing a mutual identity‐preserving authentication mechanism between them is a crucial and difficult problem in fog computing. Such a technique must conceal the user's true identity from the adversary; otherwise, the adversary will be able to determine which fog user and fog server are in communication. This article
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Joint optimization of load balancing and resource allocation in cloud environment using optimal container management strategy Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-29 Saravanan Muniswamy, Radhakrishnan Vignesh
SummaryDue to the high performance of cloud computing‐based microservices, a wide range of industries and fields rely on them. In a containerized cloud, traditional resource management strategies are typically used to allocate and migrate virtual machines. A major problem for cloud service providers is resource allocation for containers, which directly affects system performance and resource consumption
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Synergizing edge computing and blockchain for cyber‐physical systems Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-28 Payal Thakur, Vivek Kumar Sehgal
SummaryAs a foundational technology for managing decentralized systems like smart grids and healthcare systems, blockchain is attracting a lot of interest. However, owing to excessive resource requirements and low scalability with frequent‐intensive transactions, its use in resource‐constrained mobile devices is restricted. To let mobile devices offload processing tasks to cloud resources, edge computing
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AMter: An end‐to‐end model for transcriptional terminators prediction by extracting semantic feature automatically based on attention mechanism Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-27 Haotian Zhang, Jinzhe Li, Fang Hu, Haobo Lin, Jiali Ma
SummaryThe Terminator, a specific DNA sequence, provides the transcriptional termination signal to RNA polymerase, making it a critical aspect of transcriptional regulation. This article proposes AMter, the first end‐to‐end model designed for predicting transcriptional terminators, leveraging attention mechanisms. In AMter, rather than manual feature engineering, two distinct modules based on attention
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Optimization of layout for embedding half hypercube into conventional tree architectures Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-27 Paul Immanuel, A. Berin Greeni
SummaryEmbedding a graph into another graph can be utilized for structural simulation, processor allocation, and algorithm porting in the field of parallel architecture. This has the potential to enhance the physical layout of network‐on‐chip (NoC) devices as well as to investigate their virtualization possibilities. Layout is one of the many indicators of graph embedding. An optimal layout in NoC
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Ranking‐based architecture generation for surrogate‐assisted neural architecture search Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-27 Songyi Xiao, Wenjun Wang
Architectures generation optimization has been received a lot of attention in neural architecture search (NAS) since its efficiency in generating architecture. By learning the architecture representation through unsupervised learning and constructing a latent space, the prediction process of predictors is simplified, leading to improved efficiency in architecture search. However, searching for architectures
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Parameter instance learning with enhanced vision transformers for aerial person re‐identification Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-22 Houfu Peng, Xing Lu, Lili Xu, Daoxun Xia, Xiaoyao Xie
SummaryIn an agnostic space environment, aerial person re‐identification (Re‐ID) is a task that the query person may not occur in the gallery set, it is considered a subordinate task within the domain of open‐world person Re‐ID, and is a more challenging and practical application research. The aerial person images, captured by unmanned aerial vehicles, present more significant challenges such as weak
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Knowledge distillation representation and DCNMIX quality prediction‐based Web service recommendation Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-22 Buqing Cao, Hao Huang, Shanpeng Liu, Yizhi Liu, Yiping Wen, Dong Zhou, Mingdong Tang
SummaryWeb service recommendation as an emerging topic attracts increasing attention due to its important practical significance. As the number of available Web services continues to grow, users face the challenge of searching the most suitable services that meet their specific needs. Quality of service (QoS)‐based service recommendation becomes a popular approach to address this issue. However, existing
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BLOCKBOX: Blockchain based black box designing and modeling Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-17 Cagatay Korkuc, Nilay Aytas Korkmaz, Yasin Genc, Ahmet Akkoc, Erkan Afacan, Erdem Yazgan
With the development of technology, data has become more accessible. The storage of critical and valuable information is getting harder and harder with the increase of cyber attacks and vulnerabilities of network and internet. The centralized storage of data causes security and privacy problems. As a remedy to these problems, blockchain has occurred. The reliability, transparency, integrity and confidentiality
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Improving batch schedulers with node stealing for failed jobs Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-16 Yishu Du, Loris Marchal, Guillaume Pallez, Yves Robert
After a machine failure, batch schedulers typically re-schedule the job that failed with a high priority. This is fair for the failed job but still requires that job to re-enter the submission queue and to wait for enough resources to become available. The waiting time can be very long when the job is large and the platform highly loaded, as is the case with typical HPC platforms. We propose another
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Markov chain‐based analysis and fault tolerance technique for enhancing chain‐based routing in WSNs Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-21 Ahmad Jalili, Jafar A. Alzubi, Roghayeh Rezaei, Julian L. Webber, Christian Fernández‐Campusano, Mehdi Gheisari, Rashid Amin, Abolfazl Mehbodniya
SummaryWireless sensor networks (WSNs) are faced with the challenge of energy conservation, which makes efficient routing protocols crucial for prolonging network lifetime. In addition, delay time from sensors to the base station is critical in applications such as military, medical, and security monitoring systems. Chain‐based protocols like PEGASIS, CCBRP, and CCM have been developed to address routing
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Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-20 B. Mohammed Siyad, Ramasundaram Mohan
SummaryMost of the multilateration algorithms for object localization rely heavily on matrix multiplication for calculating the precise location of the target from all the reference points in a 3D domain. A significant disadvantage is that performing matrix multiplication can be computationally expensive, particularly for larger matrices, leading to slower performance and longer processing times. In
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Network partition detection and recovery with the integration of unmanned aerial vehicle Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-20 Aditi Zear, Virender Ranga, Kamal Kumar Gola
SummaryWireless sensor and actor networks (WSANs) consist of nodes associated in an ad hoc manner to perform sensing tasks for information gathering and acting functions on the basis of gathered information. Connectivity is an essential requirement of large‐scale wireless networks, and WSANs are supposed to stay connected. The nodes in hostile environments are prone to failures such as battery depletion
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SVM-SFL based malicious UAV detection in wireless sensor networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-15 Siyyadula Venkata Rama Vara Prasad, Pabitra Mohan Khilar
In the modern era, unmanned aerial vehicle (UAV) based wireless sensor networks (WSN) are rising technologies in wireless communication. Through UAV, the sensed data can be forwarded to the base station. However, the increase in network users leads to several malicious attacks on UAVs. Hence, it affects the performance of a WSN platform while transmitting private information through UAVs. Therefore
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Time-based DDoS attack detection through hybrid LSTM-CNN model architectures: An investigation of many-to-one and many-to-many approaches Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-14 Beenish Habib, Farida Khursheed
Internet data thefts, intrusions and DDoS attacks are some of the big concerns for the network security today. Detection of these anomalies, is gaining tremendous impetus with the development of machine learning and artificial intelligence. Even now researchers are shifting the base from machine learning to the deep neural architectures with auto-feature selection capabilities. We in this paper propose
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Distributed low-latency broadcast scheduling for multi-channel duty-cycled wireless IoT networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-14 Peng Long, Yuhang Wu, Quan Chen, Lianglun Cheng
Data broadcast is a fundamental communication pattern in wireless IoT networks, in which the messages are disseminated from a source node to the entire network. The problem of minimum latency broadcast scheduling (MLBS) which is aimed to generate a quick and conflict-free broadcast schedule has not been extensively explored in duty-cycled networks. The existing works either work in a centralized scheme
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Fused GEMMs towards an efficient GPU implementation of the ADER-DG method in SeisSol Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-13 Ravil Dorozhinskii, Gonzalo Brito Gadeschi, Michael Bader
This study shows how GPU performance of the ADER discontinuous Galerkin method in SeisSol (an earthquake simulation software) can be further improved while preserving its original design that ensures high CPU performance. We introduce a new code generator (“ChainForge”) that fuses subsequent batched matrix multiplications (“GEMMs”) into a single GPU kernel, holding intermediate results in shared memory
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Open-domain event schema induction via weighted attentive hypergraph neural network Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-13 Wei Qin, Hao Wang, Xiangfeng Luo
Event schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high-order
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Real-time XFEL data analysis at SLAC and NERSC: A trial run of nascent exascale experimental data analysis Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-13 Johannes P. Blaschke, Aaron S. Brewster, Daniel W. Paley, Derek Mendez, Asmit Bhowmick, Nicholas K. Sauter, Wilko Kröger, Murali Shankar, Bjoern Enders, Deborah Bard
X-ray scattering experiments using free electron lasers (XFELs) are a powerful tool to determine the molecular structure and function of unknown samples (such as COVID-19 viral proteins). XFEL experiments are a challenge to computing in two ways: (i) due to the high cost of running XFELs, a fast turnaround time from data acquisition to data analysis is essential to make informed decisions on experimental
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Advances into exascale computing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-08 Roman Wyrzykowski, Boleslaw K. Szymanski
1 INTRODUCTION The landscape of high-performance computing (HPC) has been expanding with new technologies and increased system complexity. For hardware, this trend is driven by technological inventions increasing computing power capabilities while taming cost metrics. For applications, we are witnessing increasing growth in algorithms' complexity to accommodate constantly expanding data sizes and take
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Research on denoising and enhancing methods of medical images based on convolutional neural networks Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-07 Wu Beibei
In the process of modern medical diagnosis, medical image-assisted diagnosis plays a very important role. However, the process of medical image acquisition, will be affected by various types and degrees of noise, and there will be a certain probability of producing strip artifacts, which will interfere with the doctor's diagnosis, analysis, and treatment of diseases to a certain extent. However, the
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LoHDP: Adaptive local differential privacy for high-dimensional data publishing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-07 Guohua Shen, Mengnan Cai, Zhiqiu Huang, Yang Yang, Feifei Guo, Linlin Wei
The increasing availability of high-dimensional data collected from numerous users has led to the need for multi-dimensional data publishing methods that protect individual privacy. In this paper, we investigate the use of local differential privacy for such purposes. Existing solutions calculate pairwise attribute marginals to construct probabilistic graphical models for generating attribute clusters
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Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-02-05 Qi Wang
The continuous expansion of the domestic tourism market has driven the development of the hotel related industry. It is a key focus of the hotel industry to provide accurate supporting services to tourists based on their preferences. However, the current recommendation model mainly considers tourist factors and lacks consideration for hotel factors. In order to address the concerns of recommendation
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AE-Integrated: Real-time network intrusion detection with Apache Kafka and autoencoder Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-31 Khushnaseeb Roshan, Aasim Zafar
Unknown cyber-attack detection in network traffic streams is challenging but crucial to ensure network security. It is observed that new security threats occur on a daily basis and make cyberspace vulnerable. In the literature, machine learning and deep learning-based network intrusion detection systems have gained a lot of success but still face many challenges in detecting new security threats and
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Decision tree-based task offloading in vehicle edge computing Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-30 Muhammet Tay, Arafat Şentürk
There are significant developments in the Internet of Vehicles (IoV) field, and the requirements needed in this area are increasing rapidly. When these needs are examined in the near future, it appears that the demand for connected, autonomous, shared, and electric vehicles will increase. Therefore, fundamental problems such as big data flow and storage will arise in the IoV field. Another problem
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Detection of phishing addresses and pages with a data set balancing approach by generative adversarial network (GAN) and convolutional neural network (CNN) optimized with swarm intelligence Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-29 Somayyeh Jafari, Nasrin Aghaee-Maybodi
Phishing attacks have a remarkable ability to steal user information by using simple techniques. Phishing attacks steal valuable information, such as user names and passwords. The loss caused by phishing attacks is significant, and every year, millions of dollars are lost by internet users and companies through phishing attacks. Deep learning methods such as CNN neural network are one approach to detecting
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Improvement of amorphous localization algorithm in WSN using ALO and GWO Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-29 Pujasuman Tripathy, P.M. Khilar
The process of node identification is referred to as localization, and it is rapidly gaining popularity in the field of WSN. Different node identification processes have different findings, benefits, challenges, costs, effectiveness, and applications. In this work, the position error of the Amorphous algorithm is minimized by optimizing the hop size. For optimization of the hop size of the Amorphous
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MF-DLB: Multimetric forwarding and directed acyclic graph-based load balancing for geographic routing in FANETs Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-28 Vikramjit Singh, Krishna Pal Sharma, Harsh Kumar Verma
Flying ad hoc network (FANET) comprising unmanned aerial vehicles (UAVs) emerges as a promising solution for numerous military and civil applications. Transferring data collected from the environment to the ground station (GS) is a primary concern for meeting the communication demands of most of these applications. However, the highly mobile UAVs with limited communication range, resulting in frequent
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Identification of influential vertices in complex networks: A hitting time-based approach Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-24 Pankaj Kumar, Anurag Singh, Ajay K Sharma
Unravelling the dynamics of network vertices is pivotal, and traditional centrality measures have limitations in adapting to structural changes, directed and weighted networks, and temporal analyses. This paper introduces a ground breaking approach - hitting time-based centrality. Utilizing network matrix notations and a random walk model on a connected network G $$ G $$ , we establish a Markov chain
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Efficient asynchronous federated learning with sparsification and quantization Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-24 Juncheng Jia, Ji Liu, Chendi Zhou, Hao Tian, Mianxiong Dong, Dejing Dou
While data is distributed in multiple edge devices, federated learning (FL) is attracting more and more attention to collaboratively train a machine learning model without transferring raw data. FL generally exploits a parameter server and a large number of edge devices during the whole process of the model training, while several devices are selected in each round. However, straggler devices may slow
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DDoS attack detection in SDN: Enhancing entropy-based detection with machine learning Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-23 Marcos J. Santos-Neto, Jacir L. Bordim, Eduardo A. P. Alchieri, Edison Ishikawa
Software defined network (SDN) has emerged as a new paradigm in terms of network architecture, providing flexibility, agility, and programmability to network management. These benefits boosted the SDN adoption, bringing new challenges mainly related to security, in particular, those related to Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. The detection, prevention, and mitigation
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Squirrel Henry Gas Solubility Optimization driven Deep Maxout Network with multi-texture feature descriptors for digital image forgery detection Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-24 G. Nirmala Priya, K. Suresh Kumar, N. Suganthi, Satish Muppidi
The ability to detect image forgeries is a crucial component in solving many problems, especially social ones that arise in legal proceedings, on Facebook, and so forth. Generally, forgery detection attempts to predict the artifacts through evaluating the differences in texture properties of an image. One division of an image is copied in a comparable image, typically at a different position, in a
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Local feature matching and tracking optimization of machine VSLAM based on AG Sinkhorn Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-22 Pan Pan
At present, the visual Simultaneous localization and mapping method has a wide range of applications in mobile robots and unmanned driving. However, when the environment changes, the accuracy of the system drops sharply. Aiming at the low matching accuracy of local feature matching and tracking optimization in current visual simultaneous localization and mapping methods, the attention map neural network
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Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-23 Madhavarapu Chandan, S. G. Santhi, T. Srinivasa Rao
Attacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly-based intrusion detection systems (AIDS) have a high risk of false positives. This article
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A novel approach for constructing privacy-aware architecture utilizing Shannon's entropy Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-21 Pankaj Prasad Dwivedi, Dilip Kumar Sharma
The right to privacy refers to an individual's decision about how personal information can be gathered, utilized, and disseminated. Individual consent and openness are the most important foundations for gaining consumers' confidence, and this pushes businesses to use privacy-enhancing techniques while developing systems. The purpose of a privacy-aware design is to safeguard data in such a manner that
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Virtual reality modeling application based on multi perspective and deep learning in the new media presentation and brand building of Dongguan City memory Concurr. Comput. Pract. Exp. (IF 2.0) Pub Date : 2024-01-18 Yunfeng Ye, Huifang Liu, Wenhui Kuang, Wenxuan Chen
To help the new media presentation and brand building of Dongguan City memory, a virtual reality modeling model based on multi-perspective and deep learning is proposed. First, in order to address the issue of imbalanced input and output information in depth map prediction tasks, as well as poor accuracy of predicted depth map boundaries, a depth map prediction model based on multiple perspectives