CMC-Computers, Materials & Continua

About the Journal

Computers, Materials & Continua is a peer-reviewed, Open Access journal that publishes all types of academic paper in the areas of computer networks, artificial intelligence, big data, software engineering, multimedia, cyber security, internet of things, materials genome, integrated materials science, and data analysis, modeling, designing and manufacturing of modern functional and multifunctional materials, and is published monthly by Tech Science Press.

Indexing and Abstracting

SCI: 2018 Impact Factor 3.024; Scopus CiteScore (Impact per Publication 2018): 1.99; SNIP (Source Normalized Impact per Paper 2018): 0.915; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.

  • Adaptive Data Transmission Method According to Wireless State in Long Range Wide Area Networks
  • Abstract The Internet of Things (IoT) has enabled various intelligent services, and IoT service range has been steadily extended through long range wide area communication technologies, which enable very long distance wireless data transmission. End-nodes are connected to a gateway with a single hop. They consume very low-power, using very low data rate to deliver data. Since long transmission time is consequently needed for each data packet transmission in long range wide area networks, data transmission should be efficiently performed. Therefore, this paper proposes a multicast uplink data transmission mechanism particularly for bad network conditions. Transmission delay will be increased if… More
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  • Text Compression Based on Letter’s Prefix in the Word
  • Abstract Huffman [Huffman (1952)] encoding is one of the most known compression algorithms. In its basic use, only one encoding is given for the same letter in text to compress. In this paper, a text compression algorithm that is based on Huffman encoding is proposed. Huffman encoding is used to give different encodings for the same letter depending on the prefix preceding it in the word. A deterministic finite automaton (DFA) that recognizes the words of the text is constructed. This DFA records the frequencies for letters that label the transitions. Every state will correspond to one of the prefixes of… More
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  • A Novel Approach of Image Steganography for Secure Communication Based on LSB Substitution Technique
  • Abstract Steganography aims to hide the messages from unauthorized persons for various purposes, e.g., military correspondence, financial transaction data. Securing the data during transmission is of utmost importance these days. The confidentiality, integrity, and availability of the data are at risk because of the emerging technologies and complexity in software applications, and therefore, there is a need to secure such systems and data. There are various methodologies to deal with security issues when utilizing an open system like the Internet. This research proposes a new technique in steganography within RGB shading space to achieve enhanced security compared with existing systems. We… More
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  • Residual Correction Procedure with Bernstein Polynomials for Solving Important Systems of Ordinary Differential Equations
  • Abstract One of the most attractive subjects in applied sciences is to obtain exact or approximate solutions for different types of linear and nonlinear systems. Systems of ordinary differential equations like systems of second-order boundary value problems (BVPs), Brusselator system and stiff system are significant in science and engineering. One of the most challenge problems in applied science is to construct methods to approximate solutions of such systems of differential equations which pose great challenges for numerical simulations. Bernstein polynomials method with residual correction procedure is used to treat those challenges. The aim of this paper is to present a technique… More
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  • Stabilizing Energy Consumption in Unequal Clusters of Wireless Sensor Networks
  • Abstract In the past few decades, Energy Efficiency (EE) has been a significant challenge in Wireless Sensor Networks (WSNs). WSN requires reduced transmission delay and higher throughput with high quality services, it further pays much attention in increased energy consumption to improve the network lifetime. To collect and transmit data Clustering based routing algorithm is considered as an effective way. Cluster Head (CH) acts as an essential role in network connectivity and perform data transmission and data aggregation, where the energy consumption is superior to non-CH nodes. Conventional clustering approaches attempts to cluster nodes of same size. Moreover, owing to randomly… More
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  • Aspects of Fretting Fatigue Finite Element Modelling
  • Abstract Fretting fatigue is a type of failure that may affect various mechanical components, such as bolted or dovetail joints, press-fitted shafts, couplings, and ropes. Due to its importance, many researchers have carried out experimental tests and analytical and numerical modelling, so that the phenomena that govern the failure process can be understood or appropriately modelled. Consequently, the performance of systems subjected to fretting fatigue can be predicted and improved. This paper discusses different aspects related to the finite element modelling of fretting fatigue. It presents common experimental configurations and the analytical solutions for cylindrical contact. Then, it discusses aspects of… More
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  • A Novel Technique for Estimating the Numerical Error in Solving the Helmholtz Equation
  • Abstract In this study, we applied a defined auxiliary problem in a novel error estimation technique to estimate the numerical error in the method of fundamental solutions (MFS) for solving the Helmholtz equation. The defined auxiliary problem is substituted for the real problem, and its analytical solution is generated using the complementary solution set of the governing equation. By solving the auxiliary problem and comparing the solution with the quasianalytical solution, an error curve of the MFS versus the source location parameters can be obtained. Thus, the optimal location parameter can be identified. The convergent numerical solution can be obtained and… More
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  • Design of Learning Media in Mixed Reality for Lao Education
  • Abstract To improve and develop education systems, the communication between instructors and learners in a class during the learning process is of utmost importance. Currently the presentations of 3D models using mixed reality (MR) technology can be used to avoid misinterpretations of oral and 2D model presentations. As an independent concept and MR applications, MR combines the excellent of each virtual reality (VR) and augmented reality (AR). This work aims to present the descriptions of MR systems, which include its devices, applications, and literature reviews and proposes computer vision tracking using the AR Toolkit Tracking Library. The focus of this work… More
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  • A Strategy of Signal Detection for Performance Improvement in Clipping Based OFDM System
  • Abstract In this paper, the supervised Deep Neural Network (DNN) based signal detection is analyzed for combating with nonlinear distortions efficiently and improving error performances in clipping based Orthogonal Frequency Division Multiplexing (OFDM) ssystem. One of the main disadvantages for the OFDM is the high Peak to Average Power Ratio (PAPR). The clipping is a simple method for the PAPR reduction. However, an effect of the clipping is nonlinear distortion, and estimations for transmitting symbols are difficult despite a Maximum Likelihood (ML) detection at the receiver. The DNN based online signal detection uses the offline learning model where all weights and… More
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  • Pipeline Scheduling Based on Constructive Interference in Strip Wireless Sensor Networks
  • Abstract Strip Wireless Sensor Networks (SWSNs) have drawn much attention in many applications such as monitoring rivers, highways and coal mines. Packet delivery in SWSN usually requires a large number of multi-hop transmissions which leads to long transmission latency in low-duty-cycle SWSNs. Several pipeline scheduling schemes have been proposed to reduce latency. However, when communication links are unreliable, pipeline scheduling is prone to failure. In this paper, we propose a pipeline scheduling transmission protocol based on constructive interference. The protocol first divides the whole network into multiple partitions and uses a pipelined mechanism to allocate active time slots for each partition.… More
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  • Analysis of Semi-Supervised Text Clustering Algorithm on Marine Data
  • Abstract Semi-supervised clustering improves learning performance as long as it uses a small number of labeled samples to assist un-tagged samples for learning. This paper implements and compares unsupervised and semi-supervised clustering analysis of BOAArgo ocean text data. Unsupervised K-Means and Affinity Propagation (AP) are two classical clustering algorithms. The Election-AP algorithm is proposed to handle the final cluster number in AP clustering as it has proved to be difficult to control in a suitable range. Semi-supervised samples thermocline data in the BOA-Argo dataset according to the thermocline standard definition, and use this data for semi-supervised cluster analysis. Several semi-supervised clustering… More
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  • A New Sequential Image Prediction Method Based on LSTM and DCGAN
  • Abstract Image recognition technology is an important field of artificial intelligence. Combined with the development of machine learning technology in recent years, it has great researches value and commercial value. As a matter of fact, a single recognition function can no longer meet people’s needs, and accurate image prediction is the trend that people pursue. This paper is based on Long Short-Term Memory (LSTM) and Deep Convolution Generative Adversarial Networks (DCGAN), studies and implements a prediction model by using radar image data. We adopt a stack cascading strategy in designing network connection which can control of parameter convergence better. This new… More
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  • Classification for Glass Bottles Based on Improved Selective Search Algorithm
  • Abstract The recycling of glass bottles can reduce the consumption of resources and contribute to environmental protection. At present, the classification of recycled glass bottles is difficult due to the many differences in specifications and models. This paper proposes a classification algorithm for glass bottles that is divided into two stages, namely the extraction of candidate regions and the classification of classifiers. In the candidate region extraction stage, aiming at the problem of the large time overhead caused by the use of the SIFT (scale-invariant feature transform) descriptor in SS (selective search), an improved feature of HLSN (Haar-like based on SPP-Net)… More
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  • Sound Source Localization Based on SRP-PHAT Spatial Spectrum and Deep Neural Network
  • Abstract Microphone array-based sound source localization (SSL) is a challenging task in adverse acoustic scenarios. To address this, a novel SSL algorithm based on deep neural network (DNN) using steered response power-phase transform (SRP-PHAT) spatial spectrum as input feature is presented in this paper. Since the SRP-PHAT spatial power spectrum contains spatial location information, it is adopted as the input feature for sound source localization. DNN is exploited to extract the efficient location information from SRP-PHAT spatial power spectrum due to its advantage on extracting high-level features. SRP-PHAT at each steering position within a frame is arranged into a vector, which… More
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  • Programming Logic Modeling and Cross-Program Defect Detection Method for Object-Oriented Code
  • Abstract Code defects can lead to software vulnerability and even produce vulnerability risks. Existing research shows that the code detection technology with text analysis can judge whether object-oriented code files are defective to some extent. However, these detection techniques are mainly based on text features and have weak detection capabilities across programs. Compared with the uncertainty of the code and text caused by the developer’s personalization, the programming language has a stricter logical specification, which reflects the rules and requirements of the language itself and the developer’s potential way of thinking. This article replaces text analysis with programming logic modeling, breaks… More
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  • Efficient Hierarchical Multi-Server Authentication Protocol for Mobile Cloud Computing
  • Abstract With the development of communication technologies, various mobile devices and different types of mobile services became available. The emergence of these services has brought great convenience to our lives. The multi-server architecture authentication protocols for mobile cloud computing were proposed to ensure the security and availability between mobile devices and mobile services. However, most of the protocols did not consider the case of hierarchical authentication. In the existing protocol, when a mobile user once registered at the registration center, he/she can successfully authenticate with all mobile service providers that are registered at the registration center, but real application scenarios are… More
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  • Efficient Strip-Mode SAR Raw-Data Simulator of Extended Scenes Included Moving Targets Based on Reversion of Series
  • Abstract The Synthetic Aperture Radar (SAR) raw data generator is required to the evaluation of focusing algorithms, moving target analysis, and hardware design. The time-domain SAR simulator can generate the accurate raw data but it needs much time. The frequency-domain simulator not only increases the efficiency but also considers the trajectory deviations of the radar. In addition, the raw signal of the extended scene included static and moving targets can be generated by some frequency-domain simulators. However, the existing simulators concentrate on the raw signal simulation of the static extended scene and moving targets at uniform speed mostly. As for the… More
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  • An Effective Steganalysis Algorithm for Histogram-Shifting Based Reversible Data Hiding
  • Abstract To measure the security for hot searched reversible data hiding (RDH) technique, especially for the common-used histogram-shifting based RDH (denoted as HS-RDH), several steganalysis schemes are designed to detect whether some secret data has been hidden in a normal-looking image. However, conventional steganalysis schemes focused on the previous RDH algorithms, i.e., some early spatial/pixel domain-based histogram-shifting (HS) schemes, which might cause great changes in statistical characteristics and thus be easy to be detected. For recent improved methods, such as some adaptive prediction error (PE) based embedding schemes, those conventional schemes might be invalid, since those adaptive embedding mechanism would effectively… More
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  • Influence Diffusion Model in Multiplex Networks
  • Abstract The problem of influence maximizing in social networks refers to obtaining a set of nodes of a specified size under a specific propagation model so that the aggregation of the node-set in the network has the greatest influence. Up to now, most of the research has tended to focus on monolayer network rather than on multiplex networks. But in the real world, most individuals usually exist in multiplex networks. Multiplex networks are substantially different as compared with those of a monolayer network. In this paper, we integrate the multi-relationship of agents in multiplex networks by considering the existing and relevant… More
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  • Authentication of Vehicles and Road Side Units in Intelligent Transportation System
  • Abstract Security threats to smart and autonomous vehicles cause potential consequences such as traffic accidents, economically damaging traffic jams, hijacking, motivating to wrong routes, and financial losses for businesses and governments. Smart and autonomous vehicles are connected wirelessly, which are more attracted for attackers due to the open nature of wireless communication. One of the problems is the rogue attack, in which the attacker pretends to be a legitimate user or access point by utilizing fake identity. To figure out the problem of a rogue attack, we propose a reinforcement learning algorithm to identify rogue nodes by exploiting the channel state… More
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  • A Revised Satellite Cloud-Derived Wind Inversion Algorithm Based on Computer Cluster
  • Abstract In view of the satellite cloud-derived wind inversion has the characteristics of large scale, intensive computing and time-consuming serial inversion algorithm is very difficult to break through the bottleneck of efficiency. We proposed a parallel acceleration scheme of cloud-derived wind inversion algorithm based on MPI cluster parallel technique in this paper. The divide-and-conquer idea, assigning winds vector inversion tasks to each computing unit, is identified according to a certain strategy. Each computing unit executes the assigned tasks in parallel, namely divide-and-rule the inversion task, so as to reduce the efficiency bottleneck of long inversion time caused by serial time accumulation.… More
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  • A Survey of Error Analysis and Calibration Methods for MEMS Triaxial Accelerometers
  • Abstract MEMS accelerometers are widely used in various fields due to their small size and low cost, and have good application prospects. However, the low accuracy limits its range of applications. To ensure data accuracy and safety we need to calibrate MEMS accelerometers. Many authors have improved accelerometer accuracy by calculating calibration parameters, and a large number of published calibration methods have been confusing. In this context, this paper introduces these techniques and methods, analyzes and summarizes the main error models and calibration procedures, and provides useful suggestions. Finally, the content of the accelerometer calibration method needs to be overcome. More
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  • Quantum Generative Adversarial Network: A Survey
  • Abstract Generative adversarial network (GAN) is one of the most promising methods for unsupervised learning in recent years. GAN works via adversarial training concept and has shown excellent performance in the fields image synthesis, image super-resolution, video generation, image translation, etc. Compared with classical algorithms, quantum algorithms have their unique advantages in dealing with complex tasks, quantum machine learning (QML) is one of the most promising quantum algorithms with the rapid development of quantum technology. Specifically, Quantum generative adversarial network (QGAN) has shown the potential exponential quantum speedups in terms of performance. Meanwhile, QGAN also exhibits some problems, such as barren… More
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  • A Safe and Reliable Routing Mechanism of LEO Satellite Based on SDN
  • Abstract Satellite networks have high requirements for security and data processing speed. In order to improve the reliability of the network, software-defined network (SDN) technology is introduced and a central controller is set in the network. Due to the characteristics of global perspective, control data separation, and centralized control of SDN, the idea of SDN is introduced to the design of the satellite network model. As a result, satellite nodes are only responsible for data transmission, while the maintenance of the links and the calculation of routes are implemented by the controller. For the massive LEO satellite network based on SDN,… More
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  • A Phrase Topic Model Based on Distributed Representation
  • Abstract Traditional topic models have been widely used for analyzing semantic topics from electronic documents. However, the obvious defects of topic words acquired by them are poor in readability and consistency. Only the domain experts are possible to guess their meaning. In fact, phrases are the main unit for people to express semantics. This paper presents a Distributed Representation-Phrase Latent Dirichlet Allocation (DRPhrase LDA) which is a phrase topic model. Specifically, we reasonably enhance the semantic information of phrases via distributed representation in this model. The experimental results show the topics quality acquired by our model is more readable and consistent… More
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  • KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction
  • Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis (KPCA), adaptive genetic algorithm, extreme… More
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  • Energy Efficient Resource Allocation Approach for Renewable Energy Powered Heterogeneous Cellular Networks
  • Abstract In this paper, maximizing energy efficiency (EE) through radio resource allocation for renewable energy powered heterogeneous cellular networks (HetNet) with energy sharing, is investigated. Our goal is to maximize the network EE, conquer the instability of renewable energy sources and guarantee the fairness of users during allocating resources. We define the objective function as a sum weighted EE of all links in the HetNet. We formulate the resource allocation problem in terms of subcarrier assignment, power allocation and energy sharing, as a mixed combinatorial and non-convex optimization problem. We propose an energy efficient resource allocation scheme, including a centralized resource… More
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  • Acoustic Emission Recognition Based on a Two-Streams Convolutional Neural Network
  • Abstract The Convolutional Neural Network (CNN) is a widely used deep neural network. Compared with the shallow neural network, the CNN network has better performance and faster computing in some image recognition tasks. It can effectively avoid the problem that network training falls into local extremes. At present, CNN has been applied in many different fields, including fault diagnosis, and it has improved the level and efficiency of fault diagnosis. In this paper, a two-streams convolutional neural network (TCNN) model is proposed. Based on the short-time Fourier transform (STFT) spectral and Mel Frequency Cepstrum Coefficient (MFCC) input characteristics of two-streams acoustic… More
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  • Software-Defined Space-Air-Ground Integrated Network Architecture with the Multi-Layer Satellite Backbone Network
  • Abstract Under the background of the rapid development of ground mobile communication, the advantages of high coverage, survivability, and flexibility of satellite communication provide air support to the construction of space information network. According to the requirements of the future space information communication, a software-defined Space-Air-Ground Integrated network architecture was proposed. It consisted of layered structure satellite backbone network, deep space communication network, the stratosphere communication network and the ground network. The SpaceAir-Ground Integrated network was supported by the satellite backbone network. It provided data relay for the missions such as deep space exploration and controlled the deep-space spacecraft when needed.… More
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  • Outlier Detection for Water Supply Data Based on Joint Auto-Encoder
  • Abstract With the development of science and technology, the status of the water environment has received more and more attention. In this paper, we propose a deep learning model, named a Joint Auto-Encoder network, to solve the problem of outlier detection in water supply data. The Joint Auto-Encoder network first expands the size of training data and extracts the useful features from the input data, and then reconstructs the input data effectively into an output. The outliers are detected based on the network’s reconstruction errors, with a larger reconstruction error indicating a higher rate to be an outlier. For water supply… More
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  • Personalized News Recommendation Based on the Text and Image Integration
  • Abstract The personalized news recommendation has been very popular in the news recommendation field. In most research, the picture information in the news is ignored, but the information conveyed to the users through pictures is more intuitive and more likely to affect the users’ reading interests than the one in the textual form. Therefore, in this paper, a model that combines images and texts in the news is proposed. In this model, the new tags are extracted from the images and texts in the news, and based on these new tags, an adaptive tag (AT) algorithm is proposed. The AT algorithm… More
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  • Weak Fault Diagnosis of Rolling Bearing Based on Improved Stochastic Resonance
  • Abstract Stochastic resonance can use noise to enhance weak signals, effectively reducing the effect of noise signals on feature extraction. In order to improve the early fault recognition rate of rolling bearings, and to overcome the shortcomings of lack of interaction in the selection of SR (Stochastic Resonance) method parameters and the lack of validation of the extracted features, an adaptive genetic random resonance early fault diagnosis method for rolling bearings was proposed. compared with the existing methods, the AGSR (Adaptive Genetic Stochastic Resonance) method uses genetic algorithms to optimize the system parameters, and further optimizes the parameters while considering the… More
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  • Generalized Array Architecture with Multiple Sub-Arrays and Hole-Repair Algorithm for DOA Estimation
  • Abstract Arranging multiple identical sub-arrays in a special way can enhance degrees of freedom (DOFs) and obtain a hole-free difference co-array (DCA). In this paper, by adjusting the interval of adjacent sub-arrays, a kind of generalized array architecture with larger aperture is proposed. Although some holes may exist in the DCA of the proposed array, they are distributed uniformly. Utilizing the partial continuity of the DCA, an extended covariance matrix can be constructed. Singular value decomposition (SVD) is used to obtain an extended signal sub-space, by which the direction-of-arrival (DOA) estimation algorithm for quasi-stationary signals is given. In order to eliminating… More
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  • Fine-Grained Binary Analysis Method for Privacy Leakage Detection on the Cloud Platform
  • Abstract Nowadays cloud architecture is widely applied on the internet. New malware aiming at the privacy data stealing or crypto currency mining is threatening the security of cloud platforms. In view of the problems with existing application behavior monitoring methods such as coarse-grained analysis, high performance overhead and lack of applicability, this paper proposes a new fine-grained binary program monitoring and analysis method based on multiple system level components, which is used to detect the possible privacy leakage of applications installed on cloud platforms. It can be used online in cloud platform environments for fine-grained automated analysis of target programs, ensuring… More
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  • A Fast Two-Stage Black-Box Deep Learning Network Attacking Method Based on Cross-Correlation
  • Abstract Deep learning networks are widely used in various systems that require classification. However, deep learning networks are vulnerable to adversarial attacks. The study on adversarial attacks plays an important role in defense. Black-box attacks require less knowledge about target models than white-box attacks do, which means black-box attacks are easier to launch and more valuable. However, the state-of-arts black-box attacks still suffer in low success rates and large visual distances between generative adversarial images and original images. This paper proposes a kind of fast black-box attack based on the cross-correlation (FBACC) method. The attack is carried out in two stages.… More
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  • Corpus Augmentation for Improving Neural Machine Translation
  • Abstract The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the performance of NMT is greatly reduced, and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model. However, not all languages have large-scale and high-quality bilingual corpus resources available. In these cases, improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results. This paper proposes a new method to improve the quality of data by… More
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  • Research on Efficient Seismic Data Acquisition Methods Based on Sparsity Constraint
  • Abstract In actual exploration, the demand for 3D seismic data collection is increasing, and the requirements for data are becoming higher and higher. Accordingly, the collection cost and data volume also increase. Aiming at this problem, we make use of the nature of data sparse expression, based on the theory of compressed sensing, to carry out the research on the efficient collection method of seismic data. It combines the collection of seismic data and the compression in data processing in practical work, breaking through the limitation of the traditional sampling frequency, and the sparse characteristics of the seismic signal are utilized… More
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  • Single Failure Routing Protection Algorithm in the Hybrid SDN Network
  • Abstract Loop free alternate (LFA) is a routing protection scheme that is currently deployed in commercial routers. However, LFA cannot handle all single network component failure scenarios in traditional networks. As Internet service providers have begun to deploy software defined network (SDN) technology, the Internet will be in a hybrid SDN network where traditional and SDN devices coexist for a long time. Therefore, this study aims to deploy the LFA scheme in hybrid SDN network architecture to handle all possible single network component failure scenarios. First, the deployment of LFA scheme in a hybrid SDN network is described as a 0-1… More
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