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Strategies for particle resampling in PIC simulations Comput. Phys. Commun. (IF 3.627) Pub Date : 2021-01-07 A. Muraviev; A. Bashinov; E. Efimenko; V. Volokitin; I. Meyerov; A. Gonoskov
In particle-in-cell simulations, excessive or even unfeasible computational demands can be caused by the growth of the number of particles in the course of prolific ionization or cascaded pair production due to the effects of quantum electrodynamics. Here we discuss how one can organize a dynamic rearrangement of the ensemble to reduce the number of macroparticles, while maintaining acceptable sampling
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A new software implementation of the Oslo method with rigorous statistical uncertainty propagation Comput. Phys. Commun. (IF 3.627) Pub Date : 2021-01-13 Jørgen E. Midtbø; Fabio Zeiser; Erlend Lima; Ann-Cecilie Larsen; Gry M. Tveten; Magne Guttormsen; Frank Leonel Bello Garrote; Anders Kvellestad; Therese Renstrøm
The Oslo method comprises a set of analysis techniques designed to extract nuclear level density and average γ-decay strength function from a set of excitation-energy tagged γ-ray spectra. Here we present a new software implementation of the entire Oslo method, called OMpy. We provide a summary of the theoretical basis and derive the essential equations used in the Oslo method. In addition to the functionality
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Improving in-memory file system reading performance by fine-grained user-space cache mechanisms J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-13 Rong Gu; Chongjie Li; Haipeng Dai; Yili Luo; Xiaolong Xu; Shaohua Wan; Yihua Huang
Nowadays, as the memory capacity of servers become larger and larger, distributed in-memory file systems, which enable applications to interact with data at fast speed, have been widely used. However, the existing distributed in-memory file systems still face the problem of low data access performance in small data reading, which seriously reduce their usefulness in many important big data scenarios
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Interactive and targeted runtime verification using a debugger-based architecture J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-19 Paul Naert; Seyed Vahid Azhari; Michel Dagenais
Runtime verification of software (RV) often relies on two categories of tools : dynamic heavy-weight tools, which significantly impact performance, and lighter and more efficient but static tools, which require recompiling the binary. In this paper we propose a new framework for building efficient and targeted dynamic RV tools, bridging the gap between those two categories. This framework is separated
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Blockchain-Assisted Handover Authentication for Intelligent Telehealth in Multi-Server Edge Computing Environment J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-24 Wenming Wang; Haiping Huang; Lingyan Xue; Qi Li; Reza Malekian; Youzhi Zhang
Intelligent telehealth system (ITS) provides patients and medical institutions with a lot of convenience, medical institutions can achieve medical services for patients in time through monitored health data. However, as the scope of people's daily activities extends, the traditional single-server architecture is no longer applicable. To deal with this problem, a multi-server architecture has been proposed
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Dynamic task allocation and scheduling with contention-awareness for Network-on-Chip based multicore systems J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-23 Suraj Paul; Navonil Chatterjee; Prasun Ghosal
In recent years, Network-on-Chip (NoC) based multicore systems have become popular for executing real-time applications. Mapping and scheduling of these applications are critical for system performance. The complexity of the problem increases for dynamic scenarios with real-time applications where new applications unknown at design-time, are submitted by users at runtime. Most of the existing works
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Mitigating service-oriented attacks using context-based trust for smart cities in IoT networks J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-23 Ayesha Altaf; Haider Abbas; Faiza Iqbal; Malik Muhammad Zaki Murtaza Khan; Abdul Rauf; Tehsin Kanwal
Smart City technology is an attempt to improve the quality of life of its citizens by providing promising smart solutions for multiple applications. These applications include healthcare monitoring, resource utilization, city resource management, and various public services. Internet of Things (IoT) enables smart city applications to collect data from various sensors and process it for providing numerous
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A deductive reasoning approach for database applications using verification conditions J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-05 Md. Imran Alam; Raju Halder; Jorge Sousa Pinto
Deductive verification has gained paramount attention from both academia and industry. Although intensive research in this direction covers almost all mainstream languages, the research community has paid little attention to the verification of database applications. This paper proposes a comprehensive set of Verification Conditions (VCs) generation techniques from database programs, adapting Symbolic
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FollowMe@LS: Electricity price and source aware resource management in geographically distributed heterogeneous datacenters J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-19 Hashim Ali; Muhammad Zakarya; Izaz Ur Rahman; Ayaz Ali Khan; Rajkumar Buyya
With rapid availability of renewable energy sources and growing interest in their use in the datacenter industry presents opportunities for service providers to reduce their energy related costs, as well as, minimize the ecological impact of their infrastructure. However, renewables are largely intermittent and can, negatively affect users’ applications and their performance, therefore, the profit
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GEML: A grammar-based evolutionary machine learning approach for design-pattern detection J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-24 Rafael Barbudo; Aurora Ramírez; Francisco Servant; José Raúl Romero
Design patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose
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Blended graphical and textual modelling for UML profiles: A proof-of-concept implementation and experiment J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-23 Lorenzo Addazi; Federico Ciccozzi
Domain-specific modelling languages defined by extending or constraining the Unified Modeling Language (UML) through the profiling mechanism have historically relied on graphical notations to maximise human understanding and facilitate communication among stakeholders. Other notations, such as text-, form-, or table-based are, however, often preferred for specific modelling purposes, due to the nature
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ProDSPL: Proactive self-adaptation based on Dynamic Software Product Lines J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-23 Inmaculada Ayala; Alessandro V. Papadopoulos; Mercedes Amor; Lidia Fuentes
Dynamic Software Product Lines (DSPLs) are a well-accepted approach to self-adaptation at runtime. In the context of DSPLs, there are plenty of reactive approaches that apply countermeasures as soon as a context change happens. In this paper we propose a proactive approach, ProDSPL, that exploits an automatically learnt model of the system, anticipates future variations of the system and generates
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Bridging the state-of-the-art and the state-of-the-practice of SaaS pricing: A multivocal literature review Inf. Softw. Technol. (IF 2.726) Pub Date : 2021-01-13 Andrey Saltan; Kari Smolander
Context Pricing is an essential element of software business strategy and tactics. Informed pricing decision-making requires the involvement of different stakeholders and comprehensive data analysis. Achieving both appears to be challenging, and pricing remains one of the most under-managed processes in the software business. Simultaneously, a coherent SaaS pricing body of knowledge and verified solutions
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Utilizing dynamic parallelism in CUDA to accelerate a 3D red-black successive over relaxation wind-field solver Environ. Model. Softw. (IF 4.807) Pub Date : 2021-01-13 Behnam Bozorgmehr; Pete Willemsen; Jeremy A. Gibbs; Rob Stoll; Jae-Jin Kim; Eric R. Pardyjak
QES-Winds is a fast-response wind modeling platform for simulating high-resolution mean wind fields for optimization and prediction. The code uses a variational analysis technique to solve the Poisson equation for Lagrange multipliers to obtain a mean wind field and GPU parallelization to accelerate the numerical solution of the Poisson equation. QES-Winds benefits from CUDA dynamic parallelism (launching
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Energy and Performance-Efficient Task Scheduling in Heterogeneous Virtualized Cloud Computing Sustain. Comput. Inform. Syst. (IF 2.798) Pub Date : 2021-01-24 Mehboob Hussain; Lian-Fu Wei; Abdullah Lakhan; Samad Wali; Soragga Ali; Abid Hussain
In virtualized cloud computing systems, energy reduction is a serious concern since it can offer many major advantages, such as reducing running costs, increasing system efficiency, and protecting the environment. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping cloud resources to user requests to achieve good performance by
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RBFNN versus GRNN Modeling Approach for Sub-Surface Evaporation Rate Prediction in Arid Region Sustain. Comput. Inform. Syst. (IF 2.798) Pub Date : 2021-01-23 Ammar Hatem Kamel; Haitham Abdulmohsin Afan; Mohsen Sherif; Ali Najah Ahmed; Ahmed El-Shafie
Evaporation from sub-surface reservoirs is a phenomenon that has drawn a considerable amount of attention, over recent years. An accurate prediction of the sub-surface evaporation rate is a vital step towards drawing better managing of the reservoir’ water system. In fact, the evaporation rate and more specifically from sub-surface is considered as highly stochastic and non-linear process that affected
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Artificial Intelligence Capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance Inf. Manag. (IF 5.155) Pub Date : 2021-01-24 Patrick Mikalef; Manjul Gupta
Artificial intelligence (AI) has been heralded by many as the next source of business value. Grounded on the resource-based theory of the firm and on recent work on AI at the organizational context, this study (1) identifies the AI-specific resources that jointly create an AI capability and provides a definition, (2) develops an instrument to capture the AI capability of the firms, and (3) examines
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Analysis of Service-Oriented Architecture and Scrum Software Development Approach for IIoT Sci. Program. (IF 0.963) Pub Date : 2021-01-23 Yanqing Cui; Islam Zada; Sara Shahzad; Shah Nazir; Shafi Ullah Khan; Naveed Hussain; Muhammad Asshad
Flexibility and change adoption are key attributes for service-oriented architecture (SOA) and agile software development processes. Although the notion of agility is quite visible on both sides, still the integration of the two diverse concepts (architectural framework and development process) should be well thought of before employing them for a software development project. For this purpose, this
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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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Adaptive Packet-size Control for Improved Throughput in Dynamic Access Networks Cluster Comput. (IF 3.458) Pub Date : 2021-01-23 Haythem Bany Salameh, Ayat Shamekh
Cognitive radio (CR) is a new intelligent wireless technology that aims at improving spectrum utilization by allowing opportunistic access to the underutilized licensed spectrum. Wireless CR operating environment is typically characterized by its unreliable and unpredictable channel conditions and time availability due to fading and the randomness of primary radio (PR) activities. In such environment
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A request aware module using CS-IDR to reduce VM level collateral damages caused by DDoS attack in cloud environment Cluster Comput. (IF 3.458) Pub Date : 2021-01-23 Priyanka Verma, Shashikala Tapaswi, W. Wilfred Godfrey
Distributed Denial of Service (DDoS) plays a significant role in threatening the cloud-based services. DDoS is a kind of attack which targets the CPU, bandwidth and other resources and makes them unavailable to benign users. The DDoS attack has an enormous impact on multi-tenant cloud network than the traditional network due to the cloud features like virtualization, load balancing, resource scaling
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A path planning method based on the particle swarm optimization trained fuzzy neural network algorithm Cluster Comput. (IF 3.458) Pub Date : 2021-01-22 Xiao-huan Liu, Degan Zhang, Jie Zhang, Ting Zhang, Haoli Zhu
The basic fuzzy neural network algorithm has slow convergence and large amount of calculation, so this paper designed a particle swarm optimization trained fuzzy neural network algorithm to solve this problem. Traditional particle swarm optimization is easy to fall into local extremes and has low efficiency, this paper designed new update rules for inertia weight and learning factors to overcome these
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The state-of-practice in requirements elicitation: an extended interview study at 12 companies Requirements Eng. (IF 1.933) Pub Date : 2021-01-23 Cristina Palomares, Xavier Franch, Carme Quer, Panagiota Chatzipetrou, Lidia López, Tony Gorschek
Requirements engineering remains a discipline that is faced with a large number of challenges, including the implementation of a requirements elicitation process in industry. Although several proposals have been suggested by researchers and academics, little is known of the practices that are actually followed in industry. Our objective is to investigate the state-of-practice with respect to requirements
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EngiO – Object-oriented framework for engineering optimization Adv. Eng. Softw. (IF 3.884) Pub Date : 2021-01-24 Ricarda Berger; Marlene Bruns; Andreas Ehrmann; Ayan Haldar; Jan Häfele; Benedikt Hofmeister; Clemens Hübler; Raimund Rolfes
This paper presents an object-oriented optimization framework for engineering optimization using the Matlab programming syntax. The novelty of the developed framework lies in its approach to remove redundancies by providing an interface for central routines of the optimization processes. Object-oriented programming is used to implement an abstract optimizer class, which controls the optimization process
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Learning based MIMO communications with imperfect channel state information for Internet of Things Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Dan Deng, Xingwang Li, Varun G. Menon
Imperfect channel state information (CSI) may seriously worsen the system performance for classical MIMO communications. In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless
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Structural textile pattern recognition and processing based on hypergraphs Inf. Retrieval J. (IF 2.209) Pub Date : 2021-01-23 Vuong M. Ngo, Sven Helmer, Nhien-An Le-Khac, M-Tahar Kechadi
The humanities, like many other areas of society, are currently undergoing major changes in the wake of digital transformation. However, in order to make collection of digitised material in this area easily accessible, we often still lack adequate search functionality. For instance, digital archives for textiles offer keyword search, which is fairly well understood, and arrange their content following
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GVSUM: generic video summarization using deep visual features Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Madhushree Basavarajaiah, Priyanka Sharma
Video Summarization is the method of producing a summary of the video content. A generic video summarization method named GVSUM is proposed in this paper. The generic summary is generated by choosing keyframes whenever a major scene change occurs in the video. All frames of the video are assigned a cluster number based on their visual features and the keyframes are extracted when the cluster number
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A secure and robust video steganography scheme for covert communication in H.264/AVC Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Mukesh Dalal, Mamta Juneja
The proposed scheme utilized H.264/AVC video format for steganography which is the most common video standard at present. The scheme employed Discrete Wavelet Transform (DWT) on the Region of Interest (ROI) based on multiple moving objects tracking. After tracking multiple objects, each object is embedded with different secret images to improve the capacity. Multiple object tracking helps in achieving
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Deep transfer learning for alzheimer neurological disorder detection Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Abida Ashraf, Saeeda Naz, Syed Hamad Shirazi, Imran Razzak, Mukesh Parsad
Alzheimer’s disease is becoming common in the world with the time. It is an irreversible and progressive brain disorder that slowly destroys the memory and thinking skills and, eventually, the ability to perform the simplest tasks. It becomes severe before the noticeable symptoms appear and causes brain disorder which cannot be cured by any medicines and therapies, however its progression can be slow
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Weakly supervised fine-grained recognition based on spatial-channel aware attention filters Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Nannan Yu, Lei Huang, Zhiqiang Wei, Wenfeng Zhang, Bin Wang
Fine-grained recognition is a very challenging issue, since it is difficulty to mine discriminative and subtle feature for objects with similar visual appearance. Because massive manual annotations (e.g., bounding box for discriminative regions) are time-consuming and labor-consuming, existing methods designed single form of attention model outputted discriminative regions in a weakly supervised way
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Enhanced network optimized generative adversarial network for image enhancement Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Lingyu Yan, Jiarun Fu, Chunzhi Wang, Zhiwei Ye, Hongwei Chen, Hefei Ling
With the development of image recognition technology, face, body shape, and other factors have been widely used as identification labels, which provide a lot of convenience for our daily life. However, image recognition has much higher requirements for image conditions than traditional identification methods like a password. Therefore, image enhancement plays an important role in the process of image
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A novel high precision mosaic method for sonar video sequence Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 Zhijie Tang, Zhihang Luo, Lizhou Jiang, Gaoqian Ma
The mosaic of sonar images is more difficult than the mosaic of traditional optical images due to their poor quality and the difficulty in extracting feature points. The existing mosaic methods of sonar images have a series of problems, such as low correct matching rate, large cumulative errors and high requirements for the quality of collected sonar images. In this paper, we proposed a high precision
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Exploration of sentiment analysis and legitimate artistry for opinion mining Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-23 R. Satheesh Kumar, A. Francis Saviour Devaraj, M. Rajeswari, E. Golden Julie, Y. Harold Robinson, Vimal Shanmuganathan
Sentiment analysis/opinion mining is a technique that analyzes people’s opinions, evaluations, sentiments, attitudes, appraisals and emotions to entities like products, organizations, services, issues, individuals, topics, events and their attributes. It is a massive problem space. People tend to express their opinions on anything, such as, a product, service, topic, individual, organization, or an
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Recommending prescription via tongue image to assist clinician Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-22 Guihua Wen, Kewen Wang, Huihui Li, Yuhua Huang, Shijun Zhang
Traditional Chinese Medicine often use the prescription composed of herbs to cure the disease, which requires doctors with the rich professional knowledge and experience. It is much expected that the prescription can be generated automatically to assist doctors in prescribing using such as machine learning on the tongue images. However, it is confronted with two challenges. First, there is not a larger
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Phenology based classification index method for land cover mapping from hyperspectral imagery Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-22 KR. Sivabalan, E. Ramaraj
Remote sensing imagery classification contributes assistance to real-time applications for comfort and secures the society. The imagery of satellites entirely depends on the sensor type in satellites. Phenology reflection varies based on the land cover type, which absorbs external energy. Multispectral high-resolution imagery has the maximum details about the earth’s surface. This research work defines
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A hybrid convolutional architecture for accurate image manipulation localization at the pixel-level Multimed. Tools Appl. (IF 2.313) Pub Date : 2021-01-22 Yixuan Zhang, Jiguang Zhang, Shibiao Xu
Advanced image processing techniques can easily edit images without leaving any visible traces, making manipulation detection and localization for forensics analysis a challenging task. Few studies can simultaneously locate tampered objects accurately and refine contours of tampered regions effectively. In this study, we propose an effective and novel hybrid architecture, named Pixel-level Image Tampering
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Identity Authentication with Association Behavior Sequence in Machine-to-Machine Mobile Terminals Mobile Netw. Appl. (IF 2.602) Pub Date : 2021-01-24 Congcong Shi, Miao Du, Weidong Lu, Xiaoming He, Sanglu Lu
With the rapid development of machine-to-machine (M2M) mobile smart terminals, M2M services can be used in a wide range of industries, including such as tele-medicine, remote meter reading and public security. Since different industries and enterprise users have different requirements for M2M specific applications, the security identity authentication of M2M mobile terminals is particularly worthy
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A Virtual Element Method coupled with a Boundary Integral Non Reflecting condition for 2D exterior Helmholtz problems Comput. Math. Appl. (IF 3.37) Pub Date : 2021-01-23 L. Desiderio; S. Falletta; L. Scuderi
We present a new numerical approach to solve 2D exterior Helmholtz problems defined in unbounded domains. This consists in reducing the infinite region to a finite computational one Ω, by the introduction of an artificial boundary B, and by applying in Ω a Virtual Element Method (VEM). The latter is coupled with a Boundary Integral Non Reflecting Condition defined on B (in short BI-NRBC), discretized
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Fast Analytical Motion Blur with Transparency Comput. Graph. (IF 1.351) Pub Date : 2021-01-23 Mads J.L. Rønnow; Ulf Assarsson; Marco Fratarcangeli
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SlidAR+: Gravity-Aware 3D Object Manipulation for Handheld Augmented Reality Comput. Graph. (IF 1.351) Pub Date : 2021-01-23 Varunyu Fuvattanasilp; Yuichiro Fujimoto; Alexander Plopski; Takafumi Taketomi; Christian Sandor; Masayuki Kanbara; Hirokazu Kato
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Automatic Portrait Image Pixelization Comput. Graph. (IF 1.351) Pub Date : 2021-01-23 Yunyi Shang; Hon-Cheng Wong
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TITIPy: A Python tool for the calculation and mapping of topside ionosphere turbulence indices Comput. Geosci. (IF 2.991) Pub Date : 2021-01-13 Alessio Pignalberi
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Machine learning in ground motion prediction Comput. Geosci. (IF 2.991) Pub Date : 2021-01-21 Farid Khosravikia; Patricia Clayton
This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. Typically, linear regression-based models with predefined equations and coefficients are used in ground motion prediction. However, restrictions of the linear regression models may
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On a new statistical wave generator based on atmospheric circulation patterns and it’s applications to coastal shoreline evolution Comput. Geosci. (IF 2.991) Pub Date : 2021-01-23 J. Pringle; D.D. Stretch
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Hierarchical Dynamic Time Warping methodology for aggregating multiple geological time series Comput. Geosci. (IF 2.991) Pub Date : 2021-01-23 Yuval Burstyn; Asaf Gazit; Omri Dvir
Coherent investigation of palaeo-records relies on the interpretation of multiple time series of climate proxies. This requires the application of signal matching techniques between separate records and within different proxy measurements inside a single record. However, current methods, such as correlation matrices or manual tuning using prominent signal features, result in considerable signal manipulation
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PolyFrame, Efficient Computation for 3D Graphic Statics Comput. Aided Des. (IF 3.156) Pub Date : 2021-01-22 Andrei Nejur; Masoud Akbarzadeh
In this paper, we introduce a structural form finding plugin called PolyFrame for the Rhinoceros software. This plugin is developed based on the methods of 3D Graphic Statics and Polyhedral Reciprocal Diagrams. The computational framework of this plugin uses new robust and efficient algorithms for the creation and modification of complex funicular, compression-only structural forms and is freely available
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An Overview of Procedures and Tools for Designing Nonstandard Beam-Based Compliant Mechanisms Comput. Aided Des. (IF 3.156) Pub Date : 2021-01-21 Pietro Bilancia; Giovanni Berselli
Beam-based Compliant Mechanisms (CMs) are increasingly studied and implemented in precision engineering. Straight beams with uniform cross section are the basic modules in several design concepts, which can be deemed as standard CMs. Their behavioral analysis can be addressed with a large variety of techniques, including the Euler–Bernoulli beam theory, the Pseudo-Rigid Body (PRB) method, the beam
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GPU-acceleration of the ELPA2 distributed eigensolver for dense symmetric and hermitian eigenproblems Comput. Phys. Commun. (IF 3.627) Pub Date : 2020-12-31 Victor Wen-zhe Yu; Jonathan Moussa; Pavel Kůs; Andreas Marek; Peter Messmer; Mina Yoon; Hermann Lederer; Volker Blum
The solution of eigenproblems is often a key computational bottleneck that limits the tractable system size of numerical algorithms, among them electronic structure theory in chemistry and in condensed matter physics. Large eigenproblems can easily exceed the capacity of a single compute node, thus must be solved on distributed-memory parallel computers. We here present GPU-oriented optimizations of
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Wang–Landau sampling for estimation of the reliability of physical networks Comput. Phys. Commun. (IF 3.627) Pub Date : 2021-01-14 Wanyok Atisattapong; Pasin Marupanthorn
Modern physical networks, for example in communication and transportation, can be interpreted as directed graphs. Network models are used to identify the probability that given nodes are connected, and therefore the effect of a failure at a given link. This is essential for network design, optimization, and reliability. In this study, we investigated three alternative ensembles for estimating network
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FaVAD: A software workflow for characterization and visualizing of defects in crystalline structures Comput. Phys. Commun. (IF 3.627) Pub Date : 2020-12-31 Udo von Toussaint; F.J. Domínguez-Gutiérrez; Michele Compostella; Markus Rampp
The analysis of defects and defect dynamics in crystalline materials is important for fundamental science and for a wide range of applied engineering. With increasing system size the analysis of molecular-dynamics simulation data becomes non-trivial. Here, we present a workflow for semi-automatic identification and classification of defects in crystalline structures, combining a new approach for defect
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SparkNoC: An energy-efficiency FPGA-based accelerator using optimized lightweight CNN for edge computing J. Syst. Archit. (IF 2.552) Pub Date : 2021-01-14 Ming Xia; Zunkai Huang; Li Tian; Hui Wang; Chang Victor; Yongxin Zhu; Songlin Feng
Over the past few years, Convolution Neural Networks (CNN) have been extensively adopted in broad AI applications and have achieved noticeable effect. Deploying the feedforward inference of CNN on edge devices has now been considered a research hotspot in Edge Computing. In terms of the mobile embedded devices that exhibit constrained resources and power budget, the considerable parameters and computational
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FaaSten your decisions: A classification framework and technology review of function-as-a-Service platforms J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-09 Vladimir Yussupov; Jacopo Soldani; Uwe Breitenbücher; Antonio Brogi; Frank Leymann
Function-as-a-Service (FaaS) is a cloud service model enabling developers to offload event-driven executable snippets of code. The execution and management of such functions becomes a FaaS provider’s responsibility, therein included their on-demand provisioning and automatic scaling. Key enablers for this cloud service model are FaaS platforms, e.g., AWS Lambda, Microsoft Azure Functions, or OpenFaaS
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A ground-truth dataset and classification model for detecting bots in GitHub issue and PR comments J. Syst. Softw. (IF 2.45) Pub Date : 2021-01-22 Mehdi Golzadeh; Alexandre Decan; Damien Legay; Tom Mens
Bots are frequently used in Github repositories to automate repetitive activities that are part of the distributed software development process. They communicate with human actors through comments. While detecting their presence is important for many reasons, no large and representative ground-truth dataset is available, nor are classification models to detect and validate bots on the basis of such
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Improving high-impact bug report prediction with combination of interactive machine learning and active learning Inf. Softw. Technol. (IF 2.726) Pub Date : 2021-01-20 Xiaoxue Wu; Wei Zheng; Xiang Chen; Yu Zhao; Tingting Yu; Dejun Mu
Context: Bug reports record issues found during software development and maintenance. A high-impact bug report (HBR) describes an issue that can cause severe damage once occurred after deployment. Identifying HBRs from the bug repository as early as possible is crucial for guaranteeing software quality. Objective: In recent years, many machine learning-based approaches have been proposed for HBR prediction
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A fixed-points based framework for compliance of behavioural contracts J. Log. Algebr. Methods Program. (IF 0.685) Pub Date : 2021-01-19 Maurizio Murgia
We study compliance relations between behavioural contracts in a syntax independent setting based on Labelled Transition Systems. We introduce a fixed-point based family of compliance relations, and show that many compliance relations appearing in literature belong to this family. We then study fix-compliance in the context of synchronous and asynchronous session contracts.
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An Information Dissemination Influence Model for Mobile Social Network under Multi-Role View Int. J. Uncertain. Fuzziness Knowl. Based Syst. (IF 1.375) Pub Date : 2021-01-20 Jianfeng Li; Fangshuo Li; Wenxiang Wang; Jun Zhai
Mobile social networks are dominating in our society’s daily life because of fast advancements of information technologies. To further exploit benefits from the ubiquitous service, studying the influence of information dissemination in this kind of social network becomes a necessity. This paper proposes a mobile social network influence model with regard to multiple roles. In the model, the concept
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