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Correction to: Analysis and implementation of reactive fault tolerance techniques in Hadoop: a comparative study J. Supercomput. (IF 2.469) Pub Date : 2021-02-11 Hassan Asghar, Babar Nazir
Due to problems during production the author information was incorrectly published.
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Distributed application provisioning over Ethereum-based private and permissioned blockchain: availability modeling, capacity, and costs planning J. Supercomput. (IF 2.469) Pub Date : 2021-02-11 Carlos Melo, Jamilson Dantas, Paulo Pereira, Paulo Maciel
Blockchain and cloud computing are two of the main topics related to the distributed computing paradigm, and in the last decade, they have seen exponential growth in their adoption. Cloud computing has long been established as the main mechanism to test, develop, and deliver new applications and services in a distributed manner across the World Wide Web. Large data centers host many services and store
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High-performance simulations of turbulent boundary layer flow using Intel Xeon Phi many-core processors J. Supercomput. (IF 2.469) Pub Date : 2021-02-11 Ji-Hoon Kang, Jinyul Hwang, Hyung Jin Sung, Hoon Ryu
Direct numerical simulations (DNS) of turbulent flows have increasing importance because they not only provide fundamental understanding of turbulent flows but also complement and extend experimental results. DNS of high Reynolds numbers, however, require huge computing cost so high-performance computing has been strongly pursued. In this study, we examine the feasibility of cost-efficient DNS on Intel
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An intelligent internet of things-based secure healthcare framework using blockchain technology with an optimal deep learning model J. Supercomput. (IF 2.469) Pub Date : 2021-02-10 T. Veeramakali, R. Siva, B. Sivakumar, P. C. Senthil Mahesh, N. Krishnaraj
Today, the internet of things (IoT) is becoming more common and finds applications in several domains, especially in the healthcare sector. Due to the rising demands of IoT, a massive quantity of sensing data gets generated from diverse sensing devices. Artificial intelligence (AI) techniques are vital for providing a scalable and precise analysis of data in real time. But the design and development
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A novel transfer learning approach for the classification of histological images of colorectal cancer J. Supercomput. (IF 2.469) Pub Date : 2021-02-10 Elene Firmeza Ohata, João Victor Souza das Chagas, Gabriel Maia Bezerra, Mohammad Mehedi Hassan, Victor Hugo Costa de Albuquerque, Pedro Pedrosa Rebouças Filho
Colorectal cancer (CRC) is the second most diagnosed cancer in the United States. It is identified by histopathological evaluations of microscopic images of the cancerous region, relying on a subjective interpretation. The Colorectal Histology dataset used in this study contains 5000 images, made available by the University Medical Center Mannheim. This approach proposes the automatic identification
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Blockchain-based solutions for security, privacy, and trust management in vehicular networks: a survey J. Supercomput. (IF 2.469) Pub Date : 2021-02-10 Branka Mikavica, Aleksandra Kostić-Ljubisavljević
Vehicular networks are considered as one of the most pertinent research topics in intelligent transportation systems and anchors for future smart city environment due to the ability to provide road safety and precautionary measures for the drivers and passengers. Due to the characteristics of vehicular networks, security, privacy, and trust management are challenging issues. Blockchain is an emerging
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Entity-level sentiment prediction in Danmaku video interaction J. Supercomput. (IF 2.469) Pub Date : 2021-02-09 Qingchun Bai, Kai Wei, Jie Zhou, Chao Xiong, Yuanbin Wu, Xin Lin, Liang He
Sentiment analysis in Danmaku video interaction aims at measuring public mood in respect of the video, which is helpful for the potential applications in behavioral science. Once these sentiments are discovered, this feedback can help video creators improve the video quality and greatly enhance online users’ watching experience. Predicting these entity-level sentiments is challenging because there
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Computation of workflow scheduling using backpropagation neural network in cloud computing: a virtual machine placement approach J. Supercomput. (IF 2.469) Pub Date : 2021-02-08 Narayani Raman, Aisha Banu Wahab, Sutherson Chandrasekaran
For measuring the efficiency of workflow scheduling, determining makespan and execution cost is essential. As estimating makespan and cost is difficult in a Cloud environment, designing an efficient computation of workflow scheduling remains a challenge. The Cloud resources are scaled up and down in accordance with user demand by following a scheduling policy. The scalability of the work environment
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A firefly algorithm for power management in wireless sensor networks (WSNs) J. Supercomput. (IF 2.469) Pub Date : 2021-02-08 Hossein Pakdel, Reza Fotohi
In wireless sensor networks (WSNs), designing a stable, low-power routing protocol is a major challenge because successive changes in links or breakdowns destabilize the network topology. Therefore, choosing the right route in this type of network due to resource constraints and their operating environment is one of the most important challenges in these networks. Therefore, the main purpose of these
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Implementation of a high-accuracy phase unwrapping algorithm using parallel-hybrid programming approach for displacement sensing using self-mixing interferometry J. Supercomput. (IF 2.469) Pub Date : 2021-02-08 Tassadaq Hussain, Saqib Amin, Usman Zabit, Eduard Ayguadé
Phase unwrapping is an integral part of multiple algorithms with diverse applications. Detailed phase unwrapping is also necessary for achieving high-accuracy metric sensing using laser feedback-based self-mixing interferometry (SMI). Among SMI specific phase unwrapping approaches, a technique called Improved Phase Unwrapping Method (IPUM) provides the highest accuracy. However, due to its complex
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Enhanced traffic-adaptive slotted MAC for IoT-based smart monitoring grid J. Supercomput. (IF 2.469) Pub Date : 2021-02-05 Tanvi Sood, Kanika Sharma
The implementation of a wireless sensor network has been utilized in many applications of urban and rural areas. One such application is smart-grid Internet of Things, (IoT)-based monitoring systems that work on real-time communication. They require an intelligent scheduling mechanism to scrap delay in dynamic traffic flows without undergoing any efficiency loss. This study presents a data-driven TDMA-based
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A cloud-based monitoring system for performance analysis in IoT industry J. Supercomput. (IF 2.469) Pub Date : 2021-02-05 Yong Peng, I.-C. Wu
As enterprise information systems grow in scale and computing resources remain limited, some computing system services run into occasional abnormalities such as degraded stability and the failure to respond in time. Fewer monitoring tools mean that system maintenance managers may not be notified when abnormal events occur. It becomes difficult to diagnose and manage the problems promptly, to decrease
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Mapping techniques in multicore processors: current and future trends J. Supercomput. (IF 2.469) Pub Date : 2021-02-05 Manjari Gupta, Lava Bhargava, S. Indu
Multicore systems are in demand due to their high performance thus making application mapping an important research area in this field. Breaking an application into multiple parallel tasks efficiently and task-core assignment decisions can drastically influence system performance. This has created an urgency to find potent mapping techniques which can handle the complexity of these systems. Task assignment
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Parallel modeling of wildfires using efficient solvers for ill-conditioned linear systems J. Supercomput. (IF 2.469) Pub Date : 2021-02-05 Oleg Bessonov, Sofiane Meradji
Numerical simulation of multi-physical processes requires a lot of processor time, especially when solving ill-conditional linear systems arising in fluid dynamics problems. This paper is devoted to the development of efficient parallel methods for such systems for FireStar3D wildfire modeling code. Two alternative approaches are discussed and analyzed, based on the MILU-preconditioned conjugate gradient
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Analysis of clinical features of large-cell neuroendocrine carcinoma patients guided by chest CT image under deep learning J. Supercomput. (IF 2.469) Pub Date : 2021-02-05 Chunfeng Zheng, Xiaoting Wang, Haiyun Zhou, Juan Li, Zhongtao Zhang
This work aimed to explore chest computed tomography (CT) image segmentation of patients with large-cell neuroendocrine carcinoma (LCNEC) based on deep learning, as well as the clinical manifestations and imaging and pathological features of LCNEC patients. Clinical data of 40 patients with LCNEC confirmed by pathological examination in the X Hospital from December 2015 to December 2017 were retrospectively
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A systematic study of load balancing approaches in the fog computing environment J. Supercomput. (IF 2.469) Pub Date : 2021-02-04 Mandeep Kaur, Rajni Aron
Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal
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Human behavioral pattern analysis-based anomaly detection system in residential space J. Supercomput. (IF 2.469) Pub Date : 2021-02-04 Seunghyun Choi, Changgyun Kim, Yong-Shin Kang, Sekyoung Youm
Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system.
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Optical wireless communication using camera and RGB display J. Supercomput. (IF 2.469) Pub Date : 2021-02-03 Anil L. Pereira
In this paper, a theoretical framework for Optical Wireless Communication using RGB color model with computer monitor display and digital camera is proposed. The motivation is to find a cheaper alternative to physical network switches and wired and optical cables in communication networks that can be leveraged for computer clusters, thus reducing time and costs for purchase, setup, maintenance, power
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Optimal TAL-based registration with cell-based central policy in mobile cellular networks: a semi-Markov process approach J. Supercomput. (IF 2.469) Pub Date : 2021-02-03 Hee-Seon Jang, Jang Hyun Baek
LTE networks consist of tracking areas (TAs) or a group of cells, while several TAs constitute a TA list (TAL). The LTE network adopts TAL-based registration, where, if the user equipment (UE) enters a TA that is not in its current TAL, the UE registers the TA to inform the network of its new location. A central policy for TAL allocation, known as a TA-based central policy, was proposed for TAL-based
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Deep mixed precision for hyperspectral image classification J. Supercomput. (IF 2.469) Pub Date : 2021-02-03 M. E. Paoletti, X. Tao, J. M. Haut, S. Moreno-Álvarez, A. Plaza
Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this high-dimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges
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An efficient binary chaotic symbiotic organisms search algorithm approaches for feature selection problems J. Supercomput. (IF 2.469) Pub Date : 2021-02-03 Hekmat Mohmmadzadeh, Farhad Soleimanian Gharehchopogh
Feature selection is one of the main steps in preprocessing data in machine learning, and its goal is to reduce features by removing additional and noisy features. Feature selection methods and feature reduction in a dataset must consider the accuracy of the classifying algorithms. Meta-heuristic algorithms serve as the most successful and promising methods to solve this problem. Symbiotic Organisms
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MapReduce distributed parallel computing framework for diagnosis and treatment of knee joint Kashin-Beck disease J. Supercomput. (IF 2.469) Pub Date : 2021-02-02 Chenpo Dang, Guirong Yi, Zhaomin Zhu, Peng Zhou, Hongbin Shao, Yanbin Yao, Maosheng Zhao, Lintao Li, Shensong Li
To improve the accuracy and computational efficiency of the MapReduce distributed parallel computing framework, thereby mining the diagnosis and treatment data of Kashin-Beck Disease (KBD) of the knee joint. Based on the shortcomings of the traditional K-means Clustering Algorithm (KCA), a simplified method for distance calculation was proposed. The Manhattan distance was used instead of Euclidean
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A cellular automata rule placing a maximal number of dominoes in the square and diamond J. Supercomput. (IF 2.469) Pub Date : 2021-02-02 Rolf Hoffmann, Dominique Désérable, Franciszek Seredyński
The objective is to demonstrate that a probabilistic cellular automata rule can place reliably a maximal number of dominoes in different active area shapes, exemplarily evaluated for the square and diamond. The basic rule forms domino patterns, but the number of dominoes is not necessarily maximal and the patterns are not always stable. It works with templates derived from domino tiles. The first proposed
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Like a rainbow in the dark: metadata annotation for HPC applications in the age of dark data J. Supercomput. (IF 2.469) Pub Date : 2021-02-01 Björn Schembera
The deluge of dark data is about to happen. Lacking data management capabilities, especially in the field of supercomputing, and missing data documentation (i.e., missing metadata annotation) constitute a major source of dark data. The present work contributes to addressing this challenge by presenting ExtractIng, a generic automated metadata extraction toolkit. Existing metadata information of simulation
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A deep learning-based CEP rule extraction framework for IoT data J. Supercomput. (IF 2.469) Pub Date : 2021-01-22 Mehmet Ulvi Simsek, Feyza Yildirim Okay, Suat Ozdemir
With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously increased. By employing complex event processing (CEP) systems, valuable information can be extracted from raw data and used for further applications. CEP is a stream processing technology that matches atomic events to complex events via predefined
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A novel vector-space-based lightweight privacy-preserving RFID authentication protocol for IoT environment J. Supercomput. (IF 2.469) Pub Date : 2021-01-22 Mohd Shariq, Karan Singh
Internet of Things (IoT) is a novel paradigm that connects several physical devices and the cyber world over the Internet. IoT technology is growing rapidly and soon will have an enormous innovation in our daily lives. With the increasing number of connected IoT devices making our daily lives more convenient, it puts personal data at serious risk too. Radio frequency identification (RFID) contributes
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A CUDA-powered method for the feature extraction and unsupervised analysis of medical images J. Supercomput. (IF 2.469) Pub Date : 2021-01-21 Leonardo Rundo, Andrea Tangherloni, Paolo Cazzaniga, Matteo Mistri, Simone Galimberti, Ramona Woitek, Evis Sala, Giancarlo Mauri, Marco S. Nobile
Image texture extraction and analysis are fundamental steps in computer vision. In particular, considering the biomedical field, quantitative imaging methods are increasingly gaining importance because they convey scientifically and clinically relevant information for prediction, prognosis, and treatment response assessment. In this context, radiomic approaches are fostering large-scale studies that
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Structure-preserving NPR framework for image abstraction and stylization J. Supercomput. (IF 2.469) Pub Date : 2021-01-21 M. P. Pavan Kumar, B. Poornima, H. S. Nagendraswamy, C. Manjunath
This work presents a structure-preserving non-photorealistic rendering (NPR) framework that can produce an effective structure-preserving abstracted and stylized output by manipulating visual features from 2D color image. The proposed framework distills the prominent structural features, dominant edges, medium-scale details, curved discontinued edges, silhouette, dendritic structures and curved boundaries
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Optimal fault-tolerant quantum comparators for image binarization J. Supercomput. (IF 2.469) Pub Date : 2021-01-21 F. Orts, G. Ortega, A. C. Cucura, E. Filatovas, E. M. Garzón
Quantum image processing focuses on the use of quantum computing in the field of digital image processing. In the last few years, this technique has emerged since the properties inherent to quantum mechanics would provide the computing power required to solve hard problems much faster than classical computers. Binarization is often recognized to be one of the most important steps in image processing
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A dynamic variability management approach working with agile product line engineering practices for reusing features J. Supercomput. (IF 2.469) Pub Date : 2021-01-20 Azaz Ahmed Kiani, Yaser Hafeez, Muhammad Imran, Sadia Ali
Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both methodologies promise similar benefits, they are based on different foundations. SPL encourages systematic reuse that exploits the commonalities of various products belonging to a common domain and manages their variations systematically. In
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Evaluating the performance of FFT library implementations on modern hybrid computing systems J. Supercomput. (IF 2.469) Pub Date : 2021-01-20 Sergey I. Malkovsky, Aleksei A. Sorokin, Georgiy I. Tsoy, Sergey P. Korolev, Sergey I. Smagin, Vadim A. Kondrashev
Fast Fourier transform is widely used to solve numerous scientific and engineering problems. In particular, this transform is behind the software dealing with speech and image recognition, signal analysis, modeling of properties of new materials and substances, etc. Newly emerging high-performance hybrid computing systems, as well as systems with alternative architectures, require research on discrete
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Gait analysis in patients with neurological disorders using ankle-worn accelerometers J. Supercomput. (IF 2.469) Pub Date : 2021-01-20 Jung-Yeon Kim, Suhwan Lee, Hee Bum Lee, Byeong-Gwon Kang, Soo-Bin Im, Yunyoung Nam
The purpose of this study is to investigate gait in patients with neurological disorders using accelerometers. Accelerometers were placed on both ankles of participants undergoing gait analysis. Data were collected during the 10-min walk test from healthy participants (n = 20) and patients with neurological deficits (n = 22) scheduled for surgery. Additional data were obtained after surgery for comparison
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Absolute rotary encoder system based on optical sensor for angular measurement J. Supercomput. (IF 2.469) Pub Date : 2021-01-20 Wen-Yen Lin, Ching-Wen Huang
A goniometer is a device used to measure the angular range of motion of various human joints and muscle groups, such as the elbow, knee or waist. However, the majority of mechanical goniometers are heavy, provide inaccurate measurements and are affected by dust or stains. Electronic and optical fiber-based goniometers demonstrate better capability than their mechanical equivalents; however, due to
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Investigating multiple defects on a new fault-tolerant three-input QCA majority gate J. Supercomput. (IF 2.469) Pub Date : 2021-01-19 Seyed Amir Hossein Foroutan, Reza Sabbaghi-Nadooshan, Majid Mohammadi, Mohammad Bagher Tavakoli
Quantum-dot cellular automata (QCA) are a new technology used to fabricate digital circuits on the nanoscale in place of CMOS technology, which has limitations in device density. QCA devices are low in power consumption and high in speed due to their structure. Although some defects may occur during chemical fabrication, QCA gates and circuits can be designed to be fault-tolerant. The majority gate
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Provably secure lightweight client authentication scheme with anonymity for TMIS using chaotic hash function J. Supercomput. (IF 2.469) Pub Date : 2021-01-19 Vishesh P. Gaikwad, Jitendra V. Tembhurne, Chandrashekhar Meshram, Cheng-Chi Lee
Telecare medicine information system (TMIS) is recognized as an important tool for improving the quality and protection of healthcare services. In addition to protecting the privacy of patients, many authentication techniques are being introduced in TMIS. After investigations, it is observed that many authentication techniques have security breaches. In this article, we propose an efficient, secure
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Multi-objective heuristics algorithm for dynamic resource scheduling in the cloud computing environment J. Supercomput. (IF 2.469) Pub Date : 2021-01-18 K. Lalitha Devi, S. Valli
Cloud infrastructure provides resources needed for tasks for resource scheduling. This work uses a genetic algorithm based on encoded chromosome (GEC-DRP) to manage dynamic resource scheduling. However, the existing scheduling algorithm estimates the number of required physical machines (PM) needed for the client in the future. This developed scheduling algorithm schedules the tasks on cloud by calculating
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HybriDroid: an empirical analysis on effective malware detection model developed using ensemble methods J. Supercomput. (IF 2.469) Pub Date : 2021-01-18 Arvind Mahindru, A. L. Sangal
Malware detection from the smartphone has become a challenging issue for academicians and researchers. In this research paper, we applied five distinct machine learning algorithms and three different ensemble methods to develop a model for detecting malware from an Android-based smartphone. In this study, we proposed a framework that helps in selecting the right sets of the feature with an aim to improve
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Application of deep learning model under improved emd in railway transportation investment benefits and national economic attribute analysis J. Supercomput. (IF 2.469) Pub Date : 2021-01-18 Jia He
The railway transportation industry is one of the essential factors to promote economic development, so research is made on improving the investment benefit of construction planning of the railway transportation industry, and on this basis, the national emergency attribute is analyzed. A prediction model of railway transportation investment benefits and national economic attributes based on EEMD-LSTM
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Recognition of food type and calorie estimation using neural network J. Supercomput. (IF 2.469) Pub Date : 2021-01-15 R. Dinesh Kumar, E. Golden Julie, Y. Harold Robinson, S. Vimal, Sanghyun Seo
Across the globe, health cognizant among the people is increasing and everyone wants to maintain a healthy and normal life. But due to the fast moving world, obesity and other related issue becomes the major health problem among the human beings. According to medical experts, a person is defined as obese when their BMI is greater than 30 kg/m2. Obesity leads to many diseases like high cholesterol,
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HSAC-ALADMM: an asynchronous lazy ADMM algorithm based on hierarchical sparse allreduce communication J. Supercomput. (IF 2.469) Pub Date : 2021-01-14 Dongxia Wang, Yongmei Lei, Jinyang Xie, Guozheng Wang
The distributed alternating direction method of multipliers (ADMM) is an effective algorithm for solving large-scale optimization problems. However, its high communication cost limits its scalability. An asynchronous lazy ADMM algorithm based on hierarchical sparse allreduce communication mode (HSAC-ALADMM) is proposed to reduce the communication cost of the distributed ADMM: firstly, this paper proposes
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A method of activity-based software maintenance cost estimation for package software J. Supercomput. (IF 2.469) Pub Date : 2021-01-14 Kyoung-ae Jang, Woo-Je Kim
This paper defines software maintenance activities and develops a model for maintenance cost estimation of package software. First, we classified software maintenance activities which were collected from the literature reviews. Second, we developed a cost structure for package software maintenance based on the identified activities. Third, the activity-based software maintenance cost estimation model
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Parallel source separation system for heart and lung sounds J. Supercomput. (IF 2.469) Pub Date : 2021-01-14 A. J. Muñoz-Montoro, D. Suarez-Dou, R. Cortina, F. J. Canadas-Quesada, E. F. Combarro
In this paper, we propose a parallel source separation system designed to extract heart and lung sounds from single-channel mixtures. The proposed system is based on a non-negative matrix factorization (NMF) approach and a clustering strategy together with a soft-masking filtering. Furthermore, we propose an offline and online implementation of the framework which can be applied in many real-time scenarios
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On protocols for increasing the uniformity of random bits generated with noisy quantum computers J. Supercomput. (IF 2.469) Pub Date : 2021-01-13 Elías F. Combarro, Federico Carminati, Sofia Vallecorsa, José Ranilla, Ignacio F. Rúa
Generating random numbers is important for many real-world applications, including cryptography, statistical sampling and Monte Carlo simulations. Quantum systems subject to a measurement produce random results via Born’s rule, and thus it is natural to study the possibility of using such systems in order to generate high-quality random numbers. However, current quantum devices are subject to errors
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Novel certificateless Chebyshev chaotic map-based key agreement protocol for advanced metering infrastructure J. Supercomput. (IF 2.469) Pub Date : 2021-01-13 Dariush Abbasinezhad-Mood, Arezou Ostad-Sharif, Morteza Nikooghadam, Sayyed Majid Mazinani
The integration of information technologies into the current power grid has raised significant security concerns for the advanced metering infrastructure (AMI). Evidently, without employing proper security measures, illegal or malicious entities could launch miscellaneous attacks. Thus, scholars have presented several key agreement schemes, which can be used by different parties in the AMI guaranteeing
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Blockchain-based federation of wireless sensor nodes J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 F. J. Haro-Olmo, J. A. Alvarez-Bermejo, A. J. Varela-Vaca, J. A. López-Ramos
Wireless sensor networks (WSNs), as an integral part of most Internet of Things (IoT) devices, are currently proliferating providing a new paradigm of emerging technologies. It is estimated that the number of globally connected products will increase exponentially in the next decade. Therefore, it is not surprising finding applications in different areas such as smart homes, smart cities, industry
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Toward efficient execution of data-intensive workflows J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 Oleg Sukhoroslov
Workflows that consume and produce large amounts of data are being widely used in modern scientific computing and data processing pipelines. Scheduling of data-intensive workflows requires a careful management of data transfers between tasks, since network contention can significantly impact the workflow execution time. The paper presents and evaluates several scheduling algorithms, data transfer strategies
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Sampling-based visual assessment computing techniques for an efficient social data clustering J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 M. Suleman Basha, S. K. Mouleeswaran, K. Rajendra Prasad
Visual methods were used for pre-cluster assessment and useful cluster partitions. Existing visual methods, such as visual assessment tendency (VAT), spectral VAT (SpecVAT), cosine-based VAT (cVAT), and multi-viewpoints cosine-based similarity VAT (MVS-VAT), effectively assess the knowledge about the number of clusters or cluster tendency. Tweets data partitioning is underlying the problem of social
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Towards design and implementation of security and privacy framework for Internet of Medical Things (IoMT) by leveraging blockchain and IPFS technology J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 Randhir Kumar, Rakesh Tripathi
The Internet of Medical Things (IoMT) is the next frontier in the digital revolution and it leverages IoT in the healthcare domain. The underlying technology has changed the current healthcare system by collecting real-time data of patients and providing a patient motioning system. But IoMT also presents a big challenge for data storage management, security, and privacy due to cloud-based storage.
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A comprehensive and holistic knowledge model for cloud privacy protection J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 Aymen Akremi, Mohsen Rouached
Although Cloud computing is gaining popularity by supporting data analysis in an outsourced and cost-effective way, it brings serious privacy issues when sending the original data to Cloud servers. Sensitive data have a significant value, and any infringement of privacy can cause great loss in terms of money and reputation. Thus, for any Cloud ecosystem to be accepted and easily adopted by different
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Traffic classification for efficient load balancing in server cluster using deep learning technique J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 V. Punitha, C. Mala
Extensive use of multimedia services and Internet Data Center applications demand distributed deployment of these applications. It is implemented using edge computing with server clusters. To increase the availability of the services, applications are deployed redundantly in server clusters. In this situation, an efficient server allocation strategy is essential to improve execution fairness in server
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Efficient design and implementation of a robust coplanar crossover and multilayer hybrid full adder–subtractor using QCA technology J. Supercomput. (IF 2.469) Pub Date : 2021-01-12 Mukesh Patidar, Namit Gupta
Quantum dot cellular automaton (QCA) is a novel emerging nanometer-scale-based circuit design using nanocomputing technology, which overcomes the limitations of complementary MOS technology in the precondition of the circuit design area, power, and latency/delay. This paper presents an efficient design of crossover single-layer (coplanar) and multilayer novel hybrid full adder–subtractor circuits by
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Fair and near-optimal coflow scheduling without prior knowledge of coflow size J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Chenghao Li, Huyin Zhang, Wenjia Ding, Tianying Zhou
Achieving the minimum average coflow completion time(CCT) and the isolation guarantees for multi-tenant, is considered a challenge in a cloud environment. This is because the minimum average CCT and isolation guarantees are two conflicting targets, and they cannot be achieved simultaneously. Prior solutions have implemented a single target either minimizing the average CCT or isolation guarantees.
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Energy-efficient cluster head selection through relay approach for WSN J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Pramod Singh Rathore, Jyotir Moy Chatterjee, Abhishek Kumar, R. Sujatha
In the clustering method, the cluster head node loses much energy between transmissions to the base station, confirming that determining the cluster heads is crucial. A robust determination protocol needs to pick cluster heads depending on the node's region and its residual energy. We proposed an innovative approach to the method of selecting cluster heads in this work. The target of the cluster head
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A variable action set cellular learning automata-based algorithm for link prediction in online social networks J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Mozhdeh Khaksar Manshad, Mohammad Reza Meybodi, Afshin Salajegheh
Link prediction (LP) is a crucial issue in the online social network (OSN) evolution analysis. Since OSNs are growing in size on a daily basis, a growing need for scalable LP algorithms is being felt. OSNs are innately evolutionary, such that the characteristics, behavior, and activities of their components (including nodes and links) change over time. In analyzing social networks which are based on
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A blockchain-based intelligent anti-switch package in tracing logistics system J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Chin-Ling Chen, Yong-Yuan Deng, Wei Weng, Ming Zhou, Hongyu Sun
In recent years, with the rapid development of e-commerce and network technologies, many people do online shopping through the Internet. If a physical product is bought by a client, the store will entrust their logistics to deliver the goods to the client. However, there have been many cases of switched goods which were purchased by clients recently. Some scholars proposed a security mechanism with
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A new efficient approach for detecting single and multiple black hole attacks J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Rachid Khalladi, Mohammed Rebbah, Omar Smail
Mobile Ad hoc NETworks (MANET) are networks without infrastructure. The communication range among nodes is limited, where several hops are needed to transmit a packet from the source to the destination. These networks have a constantly changing topology due to its mobile nodes and their arbitrary connections, which make it vulnerable for different attacks. One of the most important attacks in MANET
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CamNav: a computer-vision indoor navigation system J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Abdel Ghani Karkar, Somaya Al-Maadeed, Jayakanth Kunhoth, Ahmed Bouridane
We present CamNav, a vision-based navigation system that provides users with indoor navigation services. CamNav captures images in real time while the user is walking to recognize their current location. It does not require any installation of indoor localization devices. In this paper, we describe the techniques of our system that improve the recognition accuracy of an existing system that uses oriented
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Joint energy optimization on the server and network sides for geo-distributed data centers J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Yang Qin, Wuji Han, Yuanyuan Yang, Weihong Yang
Energy optimization has become an emerging concern for cloud service providers. Existing methods focus on reducing the energy consumption of either server inside the data center or data transmission between data centers. Moreover, most of the works are based on assumptions that servers and workloads are homogeneous. This is not in accordance with the fact that modern data centers are built from various
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Design and implementation of an academic expert system through big data analysis J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Dojin Choi, Hyeonbyeong Lee, Kyoungsoo Bok, Jaesoo Yoo
Most researchers establish research directions in their study of new fields by providing expert advice or publishing expert papers. The existing academic search services display papers by field but do not provide experts by field. Therefore, researchers are left to judge experts in each field by analyzing the papers for themselves. In this paper, we design and implement an expert search system based
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Leveraging deep learning with audio analytics to predict the success of crowdfunding projects J. Supercomput. (IF 2.469) Pub Date : 2021-01-08 Jiatong Shi, Kunlin Yang, Wei Xu, Mingming Wang
In the social Web era, crowdfunding has become an increasingly important channel for entrepreneurs to raise funds from the crowd for their start-up projects. Previous studies have examined various factors, such as textual information of projects and social capital of investors. However, multimedia information on projects such as audio information was rarely studied for analysing crowdfunding successes
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