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An object perception and positioning method via deep perception learning object detection Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-24 Limei Xiao; Yachao Zhang; Weizhe Gao; Dayou Xu; Ce Li
One of the fundamental problems when building perception systems for robot is to be able to provide semantic information as well as positioning in three‐dimensional (3D) space. However, two‐dimensional (2D) object detectors only can provide the semantic information and pixel coordinate in 2D space. While, the depth image can reflect the relative distance, and the semantic description of the object
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Weighted ensemble networks for multiview based tiny object quality assessment Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-23 Yichao Zhou; Wanyin Wu; Jie Zou; Jianwang Qiao; Jun Cheng
As demand for intelligent manufacturing continues to grow, tiny object quality assessment (TOQA) is becoming increasingly importance in industrial automation. Recently, visual‐based TOQA has attracted an increasing attention, since the physical appearance is the foremost assessment index for evaluating the tiny object quality. It is exhausted and challenging to determine the quality of tiny object
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Distributed frameworks for detecting distributed denial of service attacks: A comprehensive review, challenges and future directions Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-23 Nilesh Vishwasrao Patil; C. Rama Krishna; Krishan Kumar
A distributed denial of service (DDoS) attack is a significant threat to web‐based applications and hindering legitimate traffic (denies access to benign users) by overwhelming the victim system or its infrastructure (service, bandwidth, networking devices, etc.) with a large volume of attack traffic. It leads to a delay in responses or sometimes a crash victim system. Even a few moments of pause in
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Modeling, simulation, and case analysis of COVID‐19 over network public opinion formation with individual internal factors and external information characteristics Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-23 Tinggui Chen; Lijuan Peng; Jianjun Yang; Guodong Cong
With the development of information technology, the Internet has become an important channel of public opinion for expressing public interests, emotion, and ideas. Public emergency usually spreads via network. Due to the temporal and spatial flexibility and the information amplification of network, the opinions from different regions and background are easy to be represented as network public opinion
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Automated segmentation algorithm with deep learning framework for early detection of glaucoma Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-22 Deepa Natarajan; Esakkirajan Sankaralingam; Keerthiveena Balraj; Veerakumar Thangaraj
Early stage of diagnosis of eye diseases through automatic analysis in the retinal image is the emerging technology in the area of retinopathy. Glaucoma is the primary reason for the loss of visibility in people around the world. The separation of the disc and the cup in the optic region is the technique used to identify glaucoma in the human retinal image. In this paper, superpixel segmentation, followed
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Blind separation of noncooperative paired carrier multiple access signals based on improved quantum‐inspired evolutionary algorithm and receding horizon optimization Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-22 Qi Deng; Shanshan Zhang; Gang Chen; Huaxiang Lu
The single‐channel blind source separation of paired carrier multiple access (PCMA) signal is a key technology in satellite communications. Due to the high‐order complexity of existing separation methods and the uncertainty of channel parameters, the processing of noncooperative PCMA signals with long memory remains a great challenge. In this article, the blind separation was solved as the joint channel
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Structural testing for communication events into loops of message‐passing parallel programs Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-21 Silvia M. D. Diaz; Paulo S. L. Souza; Simone R. S. Souza
There is a growing demand for correct parallel programs, mainly due to nowadays availability of parallel architectures. Structural testing allows identifying defects by analyzing the internal structures of a program. However, communication and synchronization in parallel programs bring new challenges to the testing activity, such as nondeterminism. Message‐passing parallel programs require structural
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Improving word embedding quality with innovative automated approaches to hyperparameters Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-21 Beytullah Yildiz; Murat Tezgider
Deep learning practices have a great impact in many areas. Big data and significant hardware developments are the main reasons behind deep learning success. Recent advances in deep learning have led to significant improvements in text analysis and classification. Progress in the quality of word representation is an important factor among these improvements. In this study, we aimed to develop word2vec
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A new efficient multi‐task applications mapping for three‐dimensional Network‐on‐Chip based MPSoC Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-20 Khadidja Gaffour; Mohammed Kamel Benhaoua; Abou El Hassan Benyamina; Amit Kumar Singh
Three‐dimensional Network‐on‐Chip (3D NoC) is a promising solution for solving 2D NoC problems while optimizing the system's performance. Mapping applications in 3D NoC is a crucial step as it has a significant impact on overall system performance. Moreover, multi‐task supported processing elements (PEs) are needed to run multiple applications and provide more scalability. Most of the existing 3D mapping
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Gamma — General Abstract Model for Multiset mAnipulation and dynamic dataflow model: An equivalence study Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-20 Rui R. Mello; Leandro S. de Araújo; Tiago A. O. Alves; Leandro A. J. Marzulo; Gabriel A. L. Paillard; Felipe M. G. França
With the increase of the search for computational models where the expression of parallelism occurs naturally, some paradigms arise as options for the current generation of computers. In this context, dynamic dataflow and Gamma—General Abstract Model for Multiset mAnipulation—emerge as interesting computational model choices. In dynamic dataflow model, operations are performed as soon as their associated
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If you build it, promote it, and they trust you, then they will come: Diffusion strategies for science gateways and cyberinfrastructure adoption to harness big data in the science, technology, engineering, and mathematics (STEM) community Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-20 Kerk F. Kee; Bethanie Le; Kulsawasd Jitkajornwanich
In the big data era, for science gateways (SG) and cyberinfrastructure (CI) projects to have the greatest impacts, they need to be widely adopted in the scientific community. However, diffusion activities, or activities aimed to spread SG/CI in the science, technology, engineering, and mathematics community, are often an afterthought in projects. We warn against the fallacy of “If You Build It, They
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MMWD: An efficient mobile malicious webpage detection framework based on deep learning and edge cloud Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-19 Yizhi Liu; Chaoqun Zhu; Yadi Wu; Heng Xu; Jun Song
In recent years, with the rapid development of mobile social networks and services, the research of mobile malicious webpage detection has become a hot topic. Most of the existing malicious webpage detection systems are deployed on desktop systems and servers. Due to the limitation of network transmission delay and computing resources, these existing solutions fail to provide the real‐time and lightweight
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An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-18 Arash Mazidi; Mehregan Mahdavi; Fahimeh Roshanfar
Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational
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RCU‐HTM: A generic synchronization technique for highly efficient concurrent search trees Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-12 Dimitrios Siakavaras; Konstantinos Nikas; Georgios Goumas; Nectarios Koziris
Concurrent search trees (STs) are among the most widely used data structures to store and retrieve data in contemporary multithreaded applications. Despite the high amount of prior work, it still remains challenging to implement highly efficient concurrent STs. This is mainly due to the fact that both traditional synchronization methods (i.e., locks and atomic operations) and more novel ones (i.e.
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Optimizing resource scheduling based on extended particle swarm optimization in fog computing environments Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-10 Narayana Potu; Chandrashekar Jatoth; Premchand Parvataneni
Cloud computing (CC) allows on‐demand networks to access central computer resources, such as servers, databases, storage, and network services. While clouds can handle enormous amounts of data, they still encounter problems due to insufficient cloud resources. Therefore, another computing model, called fog computing, was introduced. However, the inefficient scheduling of user tasks in fog computing
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Exploiting If This Then That and Usage Control obligations for Smart Home security and management Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-10 Giacomo Giorgi; Antonio La Marra; Fabio Martinelli; Paolo Mori; Athanasios Rizos; Andrea Saracino
In this article we present an application of the Usage Control paradigm to a Smart Home infrastructure, based on a model extension and structured use of obligations. In the proposed extended model obligations are exploited to enforce two different access revocation time, namely revoke and suspend. This increases the policy expressiveness and enable to optimize the resource usage. Furthermore, obligations
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Energy‐aware and SLA‐guaranteed optimal virtual machine swap and migrate system in cloud‐Internet of Things Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-10 Ramamoorthy Karthikeyan; Venkatachalam Balamurugan
An emerging cloud‐Internet of Things (IoT) is a novel paradigm that brings potential benefits over a variety of applications. The pervasive use of IoT devices often struggle to meet resource requirements in cloud environment. The abundant wastage or inappropriate usage of resources leads to consume larger amount of energy and delayed response. Optimization algorithms are more popular for optimal selection
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Design of time‐interleaved data acquisition system based on Network on Chip Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-07 Jiangwei Zhao; Chuanpei Xu
In order to solve the existing problems of time‐interleaved data acquisition system's poor scalability, limited acquisition channels, and complicated clock system based on System on Chip(SoC), this work presents a novel method of high‐speed data acquisition based on Network on Chip (NoC) communication architecture and time‐interleaved principle. Six analog‐to‐digital data acquisition resource nodes
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Assessing engagement levels in a non‐face‐to‐face learning environment through facial expression analysis Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-06 Pyoung Won Kim
This study proposes a method for analyzing the level of student engagement in a non‐face‐to‐face learning situation by processing facial expressions. In previous studies, the level of learning engagement was determined through biosignals, such as galvanic skin response feedback. The method proposed in this study assesses engagement levels in an individual education situation by processing emotions
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A hierarchical neural model for target‐based sentiment analysis Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-06 Ke Chen; Wende Ke
A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment
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Proactive load balancing fault tolerance algorithm in cloud computing Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-06 Salma M.A. Attallah; Magda B. Fayek; Salwa M. Nassar; Elsayed E. Hemayed
As the demand for cloud computing infrastructure increased, availability and reliability have become more important as they are major features in real‐time computing systems. The chances of failure in a cloud computing system increase compared to other computing systems. To achieve high reliability and availability for the cloud computing infrastructure fault tolerance is one of the most essential
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Comparison of workload consolidation algorithms for cloud data centers Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-05 René Ponto; Gábor Kecskeméti; Zoltán Á. Mann
Workload consolidation is an important method for the efficient operation of cloud data centers, impacting important quality attributes such as resource utilization and power consumption. Many different approaches have been proposed for workload consolidation, but few comparative studies were executed to date. Therefore, it is unclear which of the proposed approaches work best in which situation. In
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Outsourced attribute‐based signatures with perfect privacy for circuits in cloud computing Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-05 Zhenjie Huang; Zhiwei Lin; Qunshan Chen; Yuping Zhou; Hui Huang
Attribute‐based signature (ABS) is a widely used cryptographic primitive, that provides both data integrity and fine‐grained privacy protection. However, the signing overhead of ABS is usually too large, making it unsuitable for resource‐constrained devices. Using cloud computing technology, the notion of outsourced attribute‐based signatures (OABS) was introduced to overcome this drawback. In OABS
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FATM: A failure‐aware adaptive fault tolerance model for distributed stream processing systems Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-04 Syed Muhammad Abrar Akber; Hanhua Chen; Hai Jin
Distributed Stream Processing Systems (DSPS) are very popular to process unbounded data streams in real‐time. Low processing latency is a fundamental requirement for DSPS applications to maintain the real‐time response. This requirement of low processing latency for DSPS is badly affected due to inevitable failures in computing systems. Generally, DSPS grapple with these inevitable failures by triggering
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SEV‐Net: Residual network embedded with attention mechanism for plant disease severity detection Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2021-01-03 Yun Zhao; Jiagui Chen; Xing Xu; Jingsheng Lei; Wujie Zhou
Early and accurate assessment of plant disease severity is key to preventing disease attack. Traditional detection methods rely on manual vision to distinguish between types of disease infection, but this is time consuming, laborious and inaccurate. To address this problem, this paper proposes a deep learning‐based attentional network model (SEV‐Net) for plant disease severity identification and classification
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The Science Library: Curation and visualization of a science gateway repository Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-30 Dave Braines; Jane Stockdill‐Mander; Eunjin Lee
Scientific publications from a group or consortium often form a coherent larger body of work with underlying threads and relationships. Rich social, structural, and topical networks between authors and organizations can be identified, and to convey these we have created the publicly available “Science Library” as a user‐centric, interactive portal. A key consideration in this endeavor is rapid and
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Unmanned surface vessel obstacle avoidance with prior knowledge‐based reward shaping Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-29 Wei Wang; Xiangfeng Luo; Yang Li; Shaorong Xie
Autonomous obstacle avoidance control of unmanned surface vessels (USVs) in complex marine environments is always fundamental for its scientific search and detection. Traditional methods usually model USV motion and environments in a mathematical way that needs perceptual information. Unfortunately, it is difficult to provide sufficient perceptual information due to complex marine environments, resulting
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Linear time algorithm for computing min‐max movement of sink‐based mobile sensors for line barrier coverage Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-29 Wenjie Zou; Longkun Guo; Peihuang Huang; Geng Lin; Hengquan Mei
Witnessing broad energy‐critical applications of barrier coverage in mobile and wireless sensor networks, emerging practical applications have recently brought a new barrier coverage model which uses sink‐based mobile sensors for covering a given barrier with the aim of prolonging the lifespan of the coverage. In the model, a set of sink stations were distributed on the plane in which each sink can
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Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-28 Maawya Chellouf; Tarek Hamrouni
In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known
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A deep learning‐based indoor‐positioning approach using received strength signal indication and carrying mode information Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-28 Szu‐Yin Lin; Fang‐Yie Leu; Chia‐Yin Ko; Ming‐Chien Shih
Indoor smartphone positioning is one of the key information and cummunication technology techniques enabling new opportunities for indoor navigation and mobile location‐based services to enrich our everyday lives. Generally, the development of an indoor positioning system heavily relies on wireless sensor network. Since wireless sensors can estimate the probable distance between radio source and the
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Survey on psychotherapy chatbots Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-28 Bei Xu; Ziyuan Zhuang
This survey aims to investigate, analyze, and compare the state‐of‐the‐art chatbots' feasibility and defects for psychotherapy. The survey points out a series of tasks necessary for future psychotherapy chatbots. We searched about 1200 related literature in public databases and selected five typical and state‐of‐the‐art psychotherapy chatbots. Most of the state‐of‐the‐art psychotherapy chatbots use
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An uncertain future: Predicting events using conditional event evolutionary graph Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-27 Jianqi Gao; Xiangfeng Luo; Hao Wang
Event evolutionary graph (EEG) reflects sequential and causal relations between events, which is of great value for event prediction. However, lacking event context in the EEG raises the problems of direction uncertainty and low accuracy when making predictions. In this article, we propose a conditional event evolutionary graph (CEEG) to deal with these problems. CEEG extends EEG with an additional
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A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-27 Zeeshan Ahmad Arfeen; Usman Ullah Sheikh; Mehreen Kausar Azam; Rabia Hassan; Hafiz Muhammad Faisal Shehzad; Shahzad Ashraf; Md Pauzi Abdullah; Lubna Aziz
Microgrids have drawn substantial consideration due to high quality and reliable mix sources of electricity. This paper articulates the implication of innovative algorithms for cognitive microgrid. It perceived the algorithms that are backed by artificial intelligence (AI) are quite efficient due to the precision, convergence speed, and less computation time as compared to the conventional heuristic
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Publicly verifiable threshold secret sharing based on three‐dimensional‐cellular automata Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-27 Rosemary Koikara; Eun‐Jun Yoon; Anand Paul
Secret sharing schemes are being widely used to distribute a secret between various participants so that an authorized subset of participants belonging to appropriate access structures can reconstruct this secret. However, a dealer might get corrupted by adversaries and may influence this secret sharing or the reconstruction process. Verifiable secret sharing (VSS) overcomes this issue by adding a
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An efficient framework for data aggregation in smart agriculture Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-26 Jiangjun Yuan; Weinan Liu; Jie Wang; Jiawen Shi; Ling Miao
With the advanced development of smart devices and network technique, Internet of Things has seen a large number of popular applications, among which, smart agriculture is a good example. The sensor nodes collect some parameters in the greenhouse, and send them to the control center. Then the control center can conduct some operations according to the analysis of the collected parameters. In this paper
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SACRO : Solid state drive‐assisted chunk caching for restore optimization Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-26 Girum Dagnaw; Ke Zhou; Hua Wang
Better duplicate elimination performance causes higher fragmentation which leads to degraded restore performance. As a result, restore performance needs to be optimized either through strengthening locality by selective rewriting and/or making the best use of the limited available memory through cache optimization. In this paper, we explore SACRO, SSD Assitsted Chunk Caching for Restore Optimization
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Taming next‐generation HPC systems: Run‐time system and algorithmic advancements Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-25 Roman Wyrzykowski; Boleslaw K. Szymanski
This special issue of Concurrency and Computation: Practice and Experience contains revised and extended versions of selected papers presented at the 13th International Conference on Parallel Processing and Applied Mathematics, PPAM 2019, which was held on September 8–11, 2019 in Bialystok, Poland. PPAM 2019 was organized by the Department of Computer and Information Science of the Czestochowa University
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Practical model with strong interpretability and predictability: An explanatory model for individuals' destination prediction considering personal and crowd travel behavior Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-23 Juanjuan Zhao; Jiexia Ye; Minxian Xu; Chengzhong Xu
Real‐time individuals' destination prediction is of great significance for real‐time user tracking, service recommendation and other related applications. Traditional technology mainly used statistical methods based on the travel patterns mined from personal history travel data. However, it is not clear how to predict the destinations of individuals with only limited personal historical data. In this
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Principal component analysis, hidden Markov model, and artificial neural network inspired techniques to recognize faces Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-23 Akarsh Aggarwal; Mohammed Alshehri; Manoj Kumar; Purushottam Sharma; Osama Alfarraj; Vikas Deep
Face Recognition is a challenging task for recognizing and detecting the identity of an individual. Although, plethora of work has already been done in the field of pattern recognition still there has been lot which has not been addressed in any of the literature. In the current research, we have presented a comparative analysis using three popularly known techniques for face recognition namely, Principal
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Research on four‐wheel independent steering intelligent control strategy based on minimum load Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-23 Huipeng Chen; Sen Chen; Rougang Zhou; Xiaoyan Huang; Shaopeng Zhu
Around the new four‐wheel independent steering system, the adjustment principle of Akman angle is studied in this article. In order to improve steering stability and reduce tire wear, a new algorithm for optimal Ackerman angle allocation at full speed is proposed. Furthermore, combined with the minimum load rule, the four‐wheel rotation angle and driving force are optimized. In this process, Carsim
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Fragments‐Expert: A graphical user interface MATLAB toolbox for classification of file fragments Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-23 Mehdi Teimouri; Zahra Seyedghorban; Fatemeh Amirjani
The classification of file fragments of various file formats is an essential task in various applications such as firewalls, intrusion detection systems, antiviruses, web content filtering, and digital forensics. However, the community lacks a suitable software tool that can integrate major methods for feature extraction from file fragments and classification among various file formats. In this article
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Multiscale channel attention network for infrared and visible image fusion Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-23 Jiahui Zhu; Qingyu Dou; Lihua Jian; Kai Liu; Farhan Hussain; Xiaomin Yang
Imaging systems with different imaging sensors are widely applied to surveillance field, military field, and medicine field. Particularly, infrared imaging sensors can acquire thermal radiations emitted by different objects but lack textural details, and visible imaging sensors can capture abundant textural information but suffer from loss of scene information under poor weather conditions. The fusion
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CSS: Handling imbalanced data by improved clustering with stratified sampling Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-22 Lu Cao; Hong Shen
The traditional support vector machine technique (SVM) has drawbacks in dealing with imbalanced data. To address this issue, in this paper we propose an algorithm of improved clustering with stratified sampling technique (CSS) to improve the classification performance of SVMs on imbalanced datasets. Instead of applying a single type of sampling method as used in the literature, our algorithm treats
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An integrated product modularity method based on transfer network of failure mode‐recycling decision for remanufacturing Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-22 Xianfu Cheng; Minhua You; Jian Zhou; Tian Yuan; Zhihu Guo; Xiaotian Ma
The product modularity has an important influence on the whole product life cycle. In order to improve product recovery of a used product at the end‐of‐life stage, it is imperative to develop modular product architecture with remanufacturing strategies consideration. In this paper, an integrated modular design method for green remanufacturing considering hierarchical structure of the product and fuzzy
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Big data and machine learning framework for clouds and its usage for text classification Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-21 István Pintye; Eszter Kail; Péter Kacsuk; Róbert Lovas
Reference architectures for big data and machine learning include not only interconnected building blocks but important considerations (among others) for scalability, manageability and usability issues as well. Leveraging on such reference architectures, the automated deployment of distributed toolsets and frameworks on various clouds is still challenging due to the diversity of technologies and protocols
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Implementation and validation of new optimization methods by genetic algorithm for two‐parameter ridge estimator Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-20 Erkut Tekeli; Nimet Özbay; Selahattin Kaçiranlar
Two‐parameter estimators have increasing usage in the linear regression model concerning mitigating the problem of multicollinearity. In this type of biased estimators, two different parameters contribute to the solution of two different problems. Previously defined two‐parameter ridge estimator (TPRE) assures considerable merits in this context. This estimator eliminates unfavorable effects of multicollinearity
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Design and analysis of efficient neural intrusion detection for wireless sensor networks Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-17 Tarek Batiha; Pavel Krömer
Wireless sensor networks (WSNs) are important building blocks of the communication infrastructure in smart cities, intelligent transportation systems, Industry, Energy, and Agriculture 4.0, the Internet of Things, and other areas quickly adopting the concepts of fog and edge computing. Their cybernetic security is a major issue and efficient methods to improve their safety and reliability are required
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Multi‐view frontal face image generation: A survey Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-17 Xin Ning; Fangzhe Nan; Shaohui Xu; Lina Yu; Liping Zhang
Face images from different perspectives reduce the accuracy of face recognition, and the generation of frontal face images is an important research topic in the field of face recognition. To understand the development of frontal face generation models and grasp the current research hotspots and trends, existing methods based on 3D models, deep learning, and hybrid models are summarized, and the current
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Special Issue of Concurrency and Computation: Practice and experience “FPDAPP, Future Perspectives on Decentralized Applications” Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-05 Claudio Schifanella; Andrea Bracciali
Blockchain technologies make agreement among untrusted parties possible, without the need for certification authorities. Proposed frameworks have been put forward in sector as diverse as finance, health care, notary, intellectual property management, identity, provenance, international cooperation, social good, and security to cite but a few. Smart contracts, that is, self‐enforcing agreements in terms
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A novel OFDM switching algorithm for symmetric and asymmetric MIMO architectures Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-10-12 Yosra Mlayeh; Fatma Rouissi; Fethi Tlili; Adel Ghazel
In this article, we propose a new criterion for a switching algorithm to be used in MIMO‐OFDM systems. The switching algorithm, which selects between spatial multiplexing and MIMO diversity using the Demmel condition number as selection metrics, is first analyzed. Then, we show the limitation of this criterion, when increasing the receive antenna number. As a solution, we propose multiantenna processing
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Multiplatform collaborative detection resource scheduling method using K‐means clustering algorithm and Hungarian algorithm Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-15 Tianquan Ni; Yi Jiang
According to the different situation electromagnetic environment, the multiplatform collaborative detection task is planned under the condition of meeting the requirements of multiplatform performance constraints and detection and positioning accuracy. In this article, the 6‐tuples of multiple‐platform detection attributes is defined, and then a multiple‐platform cooperative detection resource scheduling
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Dealing with noise in crowdsourced GPS human trajectory logging data Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-14 Kiki Adhinugraha; Wenny Rahayu; Takahiro Hara; David Taniar
As a crowdsourcing map platform, OpenStreetMap (OSM) relies on public contributions to enhance its dataset where the contributors can create, modify or remove features from the maps or share their trajectory trips in the repository. The majority of the data provided in a crowdsourcing platform are manually created and reviewed to suit real‐world conditions, hence human perception is the key indicator
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A scalable parallel algorithm for building web directories Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Karthick Seshadri; Aswin Maruthappan; Mukunthapriya Sundar Raman
Web directories like Wikipedia and Open Directory Mozilla facilitate efficient information retrieval (IR) of web documents from a huge web corpus. Maintenance of these web directories is understandably a difficult task that requires manual curation by human editors or semi‐automated mechanisms. Research on parallel algorithms for the automated curation of these web directories will be beneficial to
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Communication‐efficient distributed large‐scale sparse multinomial logistic regression Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Dajiang Lei; Jie Huang; Hao Chen; Jie Li; Yu Wu
Sparse multinomial logistic regression (SMLR) is widely used in image classification and text classification due to its feature selection and probabilistic output. However, the traditional SMLR algorithm cannot satisfy the memory and time needs of big data, which makes it necessary to propose a new distributed solution algorithm. The existing distributed SMLR algorithm has some shortcomings in network
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An adaptive on‐demand charging scheme for rechargeable wireless sensor networks Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Zhansheng Chen; Hong Shen; Tingmei Wang; Xiaofan Zhao
In view of the stability and reliability of energy supply, distinct from the time‐varying and uncertainty of energy harvesting systems, adopting mobile vehicles to replenish energy of sensors has become a research hotspot. While some existing studies on the mobile recharging problem ignored the limited energy capacity carried by mobile vehicle and the difference in energy consumption rates of sensors
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An efficient shortest path routing on the hypercube with blocking/faulty nodes Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Mehrdad Arabpour Niasari; Ke Qiu
We investigate fault‐tolerant shortest path problem in the hypercube between two nodes where some nodes are faulty (or blocked) and thus cannot be used in routing. Previously, several similar problems were studied where proposed algorithms are distributed and local‐information‐based, that is, each node in the network knows only its neighbor's status (faulty or not) and they also look for optimal or
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Word2Sent: A new learning sentiment‐embedding model with low dimension for sentence level sentiment classification Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Mohammed Kasri; Marouane Birjali; Abderrahim Beni‐Hssane
Word embedding models become an increasingly important method that embeds words into a high dimensional space. These models have been widely utilized to extract semantic and syntactic features for sentiment analysis. However, using word embedding models cannot be sufficient for sentiment analysis tasks because they do not contain sentiment features. Therefore, word embedding models do not adequately
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LibreSocial: A peer‐to‐peer framework for online social networks Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Kalman Graffi; Newton Masinde
Distributed online social networks (DOSNs) were first proposed to solve the problem of privacy, security, and scalability. A significant amount of research was undertaken to offer viable DOSN solutions that were capable of competing with the existing centralized OSN applications such as Facebook, LinkedIn, and Instagram. This research led to the emergence of the use of peer‐to‐peer (P2P) networks as
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A label propagation algorithm for community detection on high‐mixed networks Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-13 Qingshou Wu; Rongwang Chen; Lijin Wang; Kun Guo
Community detection on high‐mixed networks has been a challenging problem for complex network researchers. In a Lancichinetti–Fortunato–Radicchi (LFR) network with a mixing parameter mu greater than or equal to 0.5, the quality of the communities partitioned by currently available algorithms will decrease rapidly with increasing mu. To address this issue, we propose a label propagation algorithm on
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Hybrid cryptosystem in wireless body area networks using message authentication code and modified and enhanced lattice‐based cryptography (MAC‐MELBC) in healthcare applications Concurr. Comput. Pract. Exp. (IF 1.447) Pub Date : 2020-12-11 V. Anusuya Devi; V. Kalaivani
Wireless Body Area Networks (WBAN) is a network of sensor devices that are connected together located in the clothes, on the body or underneath a human's skin to monitor patient's health continuously and communicate with required resources. Recently, WBAN offers many applications like remote health monitoring, sports, military, healthcare, and so forth. In healthcare, it improves the patient's life