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CMC-Computers, Materials & Continua

About the Journal

Computers, Materials & Continua is a peer-reviewed Open Access journal that publishes all types of academic papers 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. This journal is published monthly by Tech Science Press.

Indexing and Abstracting

SCI: 2019 Impact Factor 4.89; Scopus CiteScore (Impact per Publication 2019): 3.8; SNIP (Source Normalized Impact per Paper 2019): 4.801; Ei Compendex; Cambridge Scientific Abstracts; INSPEC Databases; Science Navigator; EBSCOhost; ProQuest Central; Zentralblatt für Mathematik; Portico, etc.

  • Edge-Computing with Graph Computation: A Novel Mechanism to Handle Network Intrusion and Address Spoofing in SDN
  • Abstract Software Defined Networking (SDN) being an emerging network control model is widely recognized as a control and management platform. This model provides efficient techniques to control and manage the enterprise network. Another emerging paradigm is edge computing in which data processing is performed at the edges of the network instead of a central controller. This data processing at the edge nodes reduces the latency and bandwidth requirements. In SDN, the controller is a single point of failure. Several security issues related to the traditional network can be solved by using SDN central management and control. Address Spoofing and Network Intrusion… More
  •   Views:1059       Downloads:643        Download PDF
  • Analysis of Feature Importance and Interpretation for Malware Classification
  • Abstract This study was conducted to enable prompt classification of malware, which was becoming increasingly sophisticated. To do this, we analyzed the important features of malware and the relative importance of selected features according to a learning model to assess how those important features were identified. Initially, the analysis features were extracted using Cuckoo Sandbox, an open-source malware analysis tool, then the features were divided into five categories using the extracted information. The 804 extracted features were reduced by 70% after selecting only the most suitable ones for malware classification using a learning model-based feature selection method called the recursive feature… More
  •   Views:487       Downloads:373        Download PDF
  • Heuristic and Bent Key Exchange Secured Energy Efficient Data Transaction for Traffic Offloading in Mobile Cloud
  • Abstract In today’s world, smart phones offer various applications namely face detection, augmented-reality, image and video processing, video gaming and speech recognition. With the increasing demand for computing resources, these applications become more complicated. Cloud Computing (CC) environment provides access to unlimited resource pool with several features, including on demand self-service, elasticity, wide network access, resource pooling, low cost, and ease of use. Mobile Cloud Computing (MCC) aimed at overcoming drawbacks of smart phone devices. The task remains in combining CC technology to the mobile devices with improved battery life and therefore resulting in significant performance. For remote execution, recent studies… More
  •   Views:316       Downloads:194        Download PDF
  • Heat Transfer in MHD Flow of Maxwell Fluid via Fractional Cattaneo-Friedrich Model: A Finite Difference Approach
  • Abstract The idea of fractional derivatives is applied to several problems of viscoelastic fluid. However, most of these problems (fluid problems), were studied analytically using different integral transform techniques, as most of these problems are linear. The idea of the above fractional derivatives is rarely applied to fluid problems governed by nonlinear partial differential equations. Most importantly, in the nonlinear problems, either the fractional models are developed by artificial replacement of the classical derivatives with fractional derivatives or simple classical problems (without developing the fractional model even using artificial replacement) are solved. These problems were mostly solved for steady-state fluid problems.… More
  •   Views:437       Downloads:232        Download PDF
  • Study on Multi-Label Classification of Medical Dispute Documents
  • Abstract The Internet of Medical Things (IoMT) will come to be of great importance in the mediation of medical disputes, as it is emerging as the core of intelligent medical treatment. First, IoMT can track the entire medical treatment process in order to provide detailed trace data in medical dispute resolution. Second, IoMT can infiltrate the ongoing treatment and provide timely intelligent decision support to medical staff. This information includes recommendation of similar historical cases, guidance for medical treatment, alerting of hired dispute profiteers etc. The multi-label classification of medical dispute documents (MDDs) plays an important role as a front-end process… More
  •   Views:250       Downloads:179        Download PDF
  • Millimeter-Wave Concurrent Beamforming: A Multi-Player Multi-Armed Bandit Approach
  • Abstract The communication in the Millimeter-wave (mmWave) band, i.e., 30~300 GHz, is characterized by short-range transmissions and the use of antenna beamforming (BF). Thus, multiple mmWave access points (APs) should be installed to fully cover a target environment with gigabits per second (Gbps) connectivity. However, inter-beam interference prevents maximizing the sum rates of the established concurrent links. In this paper, a reinforcement learning (RL) approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links. Specifically, the problem is formulated as a multiplayer multiarmed bandit (MAB), where mmWave… More
  •   Views:228       Downloads:161        Download PDF
  • A Systematic Molecular Dynamics Investigation on the Graphene Polymer Nanocomposites for Bulletproofing
  • Abstract In modern physics and fabrication technology, simulation of projectile and target collision is vital to improve design in some critical applications, like; bulletproofing and medical applications. Graphene, the most prominent member of two dimensional materials presents ultrahigh tensile strength and stiffness. Moreover, polydimethylsiloxane (PDMS) is one of the most important elastomeric materials with a high extensive application area, ranging from medical, fabric, and interface material. In this work we considered graphene/PDMS structures to explore the bullet resistance of resulting nanocomposites. To this aim, extensive molecular dynamic simulations were carried out to identify the penetration of bullet through the graphene and… More
  •   Views:202       Downloads:163        Download PDF
  • Exact Analysis of Non-Linear Fractionalized Jeffrey Fluid. A Novel Approach of Atangana-Baleanu Fractional Model
  • Abstract It is a very difficult task for the researchers to find the exact solutions to mathematical problems that contain non-linear terms in the equation. Therefore, this article aims to investigate the viscous dissipation (VD) effect on the fractional model of Jeffrey fluid over a heated vertical flat plate that suddenly moves in its own plane. Based on the Atangana-Baleanu operator, the fractional model is developed from the fractional constitutive equations. VD is responsible for the non-linear behavior in the problem. Upon taking the Laplace and Fourier sine transforms, exact expressions have been obtained for momentum and energy equations. The influence… More
  •   Views:233       Downloads:163        Download PDF
  • The k Nearest Neighbors Estimator of the M-Regression in Functional Statistics
  • Abstract It is well known that the nonparametric estimation of the regression function is highly sensitive to the presence of even a small proportion of outliers in the data. To solve the problem of typical observations when the covariates of the nonparametric component are functional, the robust estimates for the regression parameter and regression operator are introduced. The main propose of the paper is to consider data-driven methods of selecting the number of neighbors in order to make the proposed processes fully automatic. We use the More
  •   Views:233       Downloads:175        Download PDF
  • Adaptive Binary Coding for Scene Classification Based on Convolutional Networks
  • Abstract With the rapid development of computer technology, millions of images are produced everyday by different sources. How to efficiently process these images and accurately discern the scene in them becomes an important but tough task. In this paper, we propose a novel supervised learning framework based on proposed adaptive binary coding for scene classification. Specifically, we first extract some high-level features of images under consideration based on available models trained on public datasets. Then, we further design a binary encoding method called one-hot encoding to make the feature representation more efficient. Benefiting from the proposed adaptive binary coding, our method… More
  •   Views:232       Downloads:175        Download PDF
  • A Multi-Conditional Proxy Broadcast Re-Encryption Scheme for Sensor Networks
  • Abstract In sensor networks, it is a challenge to ensure the security of data exchange between packet switching nodes holding different private keys. In order to solve this problem, the present study proposes a scheme called multi-conditional proxy broadcast reencryption (MC-PBRE). The scheme consists of the following roles: the source node, proxy server, and the target node. If the condition is met, the proxy can convert the encrypted data of the source node into data that the target node can directly decrypt. It allows the proxy server to convert the ciphertext of the source node to a new ciphertext of the… More
  •   Views:210       Downloads:156        Download PDF
  • An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel
  • Abstract The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning is performed to develop multiple… More
  •   Views:304       Downloads:166        Download PDF
  • Lithium-Ion Battery Screening by K-Means with DBSCAN for Denoising
  • Abstract Batteries are often packed together to meet voltage and capability needs. However, due to variations in raw materials, different ages of equipment, and manual operation, there is inconsistency between batteries, which leads to reduced available capacity, variability of resistance, and premature failure. Therefore, it is crucial to pack similar batteries together. The conventional approach to screening batteries is based on their capacity, voltage and internal resistance, which disregards how batteries perform during manufacturing. In the battery discharge process, real time discharge voltage curves (DVCs) are collected as a set of unlabeled time series, which reflect how the battery voltage changes.… More
  •   Views:218       Downloads:175        Download PDF
  • A Numerical Gas Fracturing Model of Coupled Thermal, Flowing and Mechanical Effects
  • Abstract Gas fracturing, which overcomes the limitation of hydraulic fracturing, is a potential alternative technology for the development of unconventional gas and oil resources. However, the mechanical principle of gas fracturing has not been learned comprehensively when the fluid is injected into the borehole. In this paper, a damagebased model of coupled thermal-flowing-mechanical effects was adopted to illustrate the mechanical principle of gas fracturing. Numerical simulation tools Comsol Multiphysics and Matlab were integrated to simulate the coupled process during the gas fracturing. Besides, the damage evolution of drilling areas under several conditions was fully analyzed. Simulation results indicate that the maximum… More
  •   Views:258       Downloads:146        Download PDF
  • Research on the Freezing Phenomenon of Quantum Correlation by Machine Learning
  • Abstract Quantum correlation shows a fascinating nature of quantum mechanics and plays an important role in some physics topics, especially in the field of quantum information. Quantum correlations of the composite system can be quantified by resorting to geometric or entropy methods, and all these quantification methods exhibit the peculiar freezing phenomenon. The challenge is to find the characteristics of the quantum states that generate the freezing phenomenon, rather than only study the conditions which generate this phenomenon under a certain quantum system. In essence, this is a classification problem. Machine learning has become an effective method for researchers to study… More
  •   Views:211       Downloads:146        Download PDF
  • Abnormal Behavior Detection and Recognition Method Based on Improved ResNet Model
  • Abstract The core technology in an intelligent video surveillance system is that detecting and recognizing abnormal behaviors timely and accurately. The key breakthrough point in recognizing abnormal behaviors is how to obtain the effective features of the picture, so as to solve the problem of recognizing them. In response to this difficulty, this paper introduces an adjustable jump link coefficients model based on the residual network. The effective coefficients for each layer of the network can be set after using this model to further improving the recognition accuracy of abnormal behavior. A convolution kernel of 1×1 size is added to reduce… More
  •   Views:207       Downloads:160        Download PDF
  • Zubair Lomax Distribution: Properties and Estimation Based on Ranked Set Sampling
  • Abstract In this article, we offer a new adapted model with three parameters, called Zubair Lomax distribution. The new model can be very useful in analyzing and modeling real data and provides better fits than some others new models. Primary properties of the Zubair Lomax model are determined by moments, probability weighted moments, Renyi entropy, quantile function and stochastic ordering, among others. Maximum likelihood method is used to estimate the population parameters, owing to simple random sample and ranked set sampling schemes. The behavior of the maximum likelihood estimates for the model parameters is studied using Monte Carlo simulation. Criteria measures… More
  •   Views:187       Downloads:141        Download PDF
  • Parameter Calibration of SWMM Model Based on Optimization Algorithm
  • Abstract For the challenge of parameter calibration in the process of SWMM (storm water management model) model application, we use particle Swarm Optimization (PSO) and Sequence Quadratic Programming (SQP) in combination to calibrate the parameters and get the optimal parameter combination in this research. Then, we compare and analyze the simulation result with the other two respectively using initial parameters and parameters obtained by PSO algorithm calibration alone. The result shows that the calibration result of PSO-SQP combined algorithm has the highest accuracy and shows highly consistent with the actual situation, which provides a scientific and effective new idea for parameter… More
  •   Views:199       Downloads:144        Download PDF
  • Road Damage Detection and Classification Using Mask R-CNN with DenseNet Backbone
  • Abstract Automatic road damage detection using image processing is an important aspect of road maintenance. It is also a challenging problem due to the inhomogeneity of road damage and complicated background in the road images. In recent years, deep convolutional neural network based methods have been used to address the challenges of road damage detection and classification. In this paper, we propose a new approach to address those challenges. This approach uses densely connected convolution networks as the backbone of the Mask R-CNN to effectively extract image feature, a feature pyramid network for combining multiple scales features, a region proposal network… More
  •   Views:204       Downloads:174        Download PDF
  • Multi-Purpose Forensics of Image Manipulations Using Residual- Based Feature
  • Abstract The multi-purpose forensics is an important tool for forge image detection. In this paper, we propose a universal feature set for the multi-purpose forensics which is capable of simultaneously identifying several typical image manipulations, including spatial low-pass Gaussian blurring, median filtering, re-sampling, and JPEG compression. To eliminate the influences caused by diverse image contents on the effectiveness and robustness of the feature, a residual group which contains several highpass filtered residuals is introduced. The partial correlation coefficient is exploited from the residual group to purely measure neighborhood correlations in a linear way. Besides that, we also combine autoregressive coefficient and… More
  •   Views:194       Downloads:152        Download PDF
  • A Modified Method for Scene Text Detection by ResNet
  • Abstract In recent years, images have played a more and more important role in our daily life and social communication. To some extent, the textual information contained in the pictures is an important factor in understanding the content of the scenes themselves. The more accurate the text detection of the natural scenes is, the more accurate our semantic understanding of the images will be. Thus, scene text detection has also become the hot spot in the domain of computer vision. In this paper, we have presented a modified text detection network which is based on further research and improvement of Connectionist… More
  •   Views:208       Downloads:139        Download PDF
  • Lightweight Mobile Clients Privacy Protection Using Trusted Execution Environments for Blockchain
  • Abstract Nowadays, as lightweight mobile clients become more powerful and widely used, more and more information is stored on lightweight mobile clients, user sensitive data privacy protection has become an urgent concern and problem to be solved. There has been a corresponding rise of security solutions proposed by researchers, however, the current security mechanisms on lightweight mobile clients are proven to be fragile. Due to the fact that this research field is immature and still unexplored in-depth, with this paper, we aim to provide a structured and comprehensive study on privacy protection using trusted execution environment (TEE) for lightweight mobile clients.… More
  •   Views:228       Downloads:147        Download PDF
  • Research on Prediction Methods of Prevalence Perception under Information Exposure
  • Abstract With the rapid development of information technology, the explosive growth of data information has become a common challenge and opportunity. Social network services represented by WeChat, Weibo and Twitter, drive a large amount of information due to the continuous spread, evolution and emergence of users through these platforms. The dynamic modeling, analysis, and network information prediction, has very important research and application value, and plays a very important role in the discovery of popular events, personalized information recommendation, and early warning of bad information. For these reasons, this paper proposes an adaptive prediction algorithm for network information transmission. A popularity… More
  •   Views:214       Downloads:142        Download PDF
  • Heterogeneous Hyperedge Convolutional Network
  • Abstract Graph convolutional networks (GCNs) have been developed as a general and powerful tool to handle various tasks related to graph data. However, current methods mainly consider homogeneous networks and ignore the rich semantics and multiple types of objects that are common in heterogeneous information networks (HINs). In this paper, we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph convolutional network architecture that operates on HINs. Specifically, we extract the rich semantics by different metastructures and adopt hyperedge to model the interactions among metastructure-based neighbors. Due to the powerful information extraction capabilities of metastructure and hyperedge, HHCN has the… More
  •   Views:212       Downloads:174        Download PDF
  • 3D Trajectory Planning of Positioning Error Correction Based on PSO-A* Algorithm
  • Abstract Aiming at the yaw problem caused by inertial navigation system errors accumulation during the navigation of an intelligent aircraft, a three-dimensional trajectory planning method based on the particle swarm optimization-A star (PSO-A*) algorithm is designed. Firstly, an environment model for aircraft error correction is established, and the trajectory is discretized to calculate the positioning error. Next, the positioning error is corrected at many preset trajectory points. The shortest trajectory and the fewest correction times are regarded as optimization goals to improve the heuristic function of A star (A*) algorithm. Finally, the index weights are continuously optimized by the particle swarm… More
  •   Views:198       Downloads:138        Download PDF
  • A Sentinel-Based Peer Assessment Mechanism for Collaborative Learning
  • Abstract This paper introduces a novel mechanism to improve the performance of peer assessment for collaborative learning. Firstly, a small set of assignments which have being pre-scored by the teacher impartially, are introduced as “sentinels”. The reliability of a reviewer can be estimated by the deviation between the sentinels’ scores judged by the reviewers and the impartial scores. Through filtering the inferior reviewers by the reliability, each score can then be subjected into mean value correction and standard deviation correction processes sequentially. Then the optimized mutual score which mitigated the influence of the subjective differences of the reviewers are obtained. We… More
  •   Views:169       Downloads:157        Download PDF
  • MoTransFrame: Model Transfer Framework for CNNs on Low-Resource Edge Computing Node
  • Abstract Deep learning technology has been widely used in computer vision, speech recognition, natural language processing, and other related fields. The deep learning algorithm has high precision and high reliability. However, the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing power. In this paper, we propose MoTransFrame, a general model processing framework for deep learning models. Instead of designing a model compression algorithm with a high compression ratio, MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and accurately. By the integration method,… More
  •   Views:190       Downloads:151        Download PDF
  • Multi-Index Image Retrieval Hash Algorithm Based on Multi-View Feature Coding
  • Abstract In recent years, with the massive growth of image data, how to match the image required by users quickly and efficiently becomes a challenge. Compared with single-view feature, multi-view feature is more accurate to describe image information. The advantages of hash method in reducing data storage and improving efficiency also make us study how to effectively apply to large-scale image retrieval. In this paper, a hash algorithm of multi-index image retrieval based on multi-view feature coding is proposed. By learning the data correlation between different views, this algorithm uses multi-view data with deeper level image semantics to achieve better retrieval… More
  •   Views:182       Downloads:146        Download PDF
  • New SAR Imaging Algorithm via the Optimal Time-Frequency Transform Domain
  • Abstract To address the low-resolution imaging problem in relation to traditional Range Doppler (RD) algorithm, this paper intends to propose a new algorithm based on Fractional Fourier Transform (FrFT), which proves highly advantageous in the acquisition of high-resolution Synthetic Aperture Radar (SAR) images. The expression of the optimal order of SAR range signals using FrFT is deduced in detail, and the corresponding expression of the azimuth signal is also given. Theoretical analysis shows that, the optimal order in range (azimuth) direction, which turns out to be very unique, depends on the known imaging parameters of SAR, therefore the engineering practicability of… More
  •   Views:189       Downloads:152        Download PDF
  • Data Secure Storage Mechanism of Sensor Networks Based on Blockchain
  • Abstract As the number of sensor network application scenarios continues to grow, the security problems inherent in this approach have become obstacles that hinder its wide application. However, it has attracted increasing attention from industry and academia. The blockchain is based on a distributed network and has the characteristics of nontampering and traceability of block data. It is thus naturally able to solve the security problems of the sensor networks. Accordingly, this paper first analyzes the security risks associated with data storage in the sensor networks, then proposes using blockchain technology to ensure that data storage in the sensor networks is… More
  •   Views:217       Downloads:170        Download PDF
  • A Novel Edge Computing Based Area Navigation Scheme
  • Abstract The area navigation system, discussed in this paper, is composed of ground responders and a navigation terminal and can position a high-velocity aircraft and measure its velocity. This navigation system is silent at ordinary times. It sends out a request signal when positioning is required for an aircraft, and then the ground responders send a signal for resolving the aircraft. Combining the direct sequence spread spectrum and frequency hopping, the concealed communication mode is used in the whole communication process, with short communication pulses as much as possible, so the system has strong concealment and anti-interference characteristics. As the transmission… More
  •   Views:179       Downloads:133        Download PDF
  • Image and Feature Space Based Domain Adaptation for Vehicle Detection
  • Abstract The application of deep learning in the field of object detection has experienced much progress. However, due to the domain shift problem, applying an off-the-shelf detector to another domain leads to a significant performance drop. A large number of ground truth labels are required when using another domain to train models, demanding a large amount of human and financial resources. In order to avoid excessive resource requirements and performance drop caused by domain shift, this paper proposes a new domain adaptive approach to cross-domain vehicle detection. Our approach improves the cross-domain vehicle detection model from image space and feature space.… More
  •   Views:178       Downloads:148        Download PDF
  • MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method
  • Abstract Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better… More
  •   Views:187       Downloads:144        Download PDF
  • Optimal Mode Decision Method for Interframe Prediction in H.264/AVC
  • Abstract Studies show that encoding technologies in H.264/AVC, including prediction and conversion, are essential technologies. However, these technologies are more complicated than the MPEG-4, which is a standard method and widely adopted worldwide. Therefore, the amount of calculation in H.264/AVC is significantly up-regulated compared to that of the MPEG-4. In the present study, it is intended to simplify the computational expenses in the international standard compression coding system H.264/AVC for moving images. Inter prediction refers to the most feasible compression technology, taking up to 60% of the entire encoding. In this regard, prediction error and motion vector information are proposed to… More
  •   Views:179       Downloads:152        Download PDF
  • Task-Attribute-Based Access Control Scheme for IoT via Blockchain
  • Abstract As a new form of network, the Internet of things (IoT) is becoming more widely used in people’s lives. In this paper, related theoretical research and practical applications of the IoT are explored. The security of the IoT has become a hot research topic. Access controls are methods that control reasonable allocations of data and resources and ensure the security of the IoT. However, most access control systems do not dynamically assign users’ rights. Additionally, with some access control systems, there is a risk of overstepping other user’s authority, and there may exist a central authority that is a single… More
  •   Views:185       Downloads:162        Download PDF
  • Detection of Precipitation Cloud over the Tibet Based on the Improved U-Net
  • Abstract Aiming at the problem of radar base and ground observation stations on the Tibet is sparsely distributed and cannot achieve large-scale precipitation monitoring. UNet, an advanced machine learning (ML) method, is used to develop a robust and rapid algorithm for precipitating cloud detection based on the new-generation geostationary satellite of FengYun-4A (FY-4A). First, in this algorithm, the real-time multi-band infrared brightness temperature from FY-4A combined with the data of Digital Elevation Model (DEM) has been used as predictor variables for our model. Second, the efficiency of the feature was improved by changing the traditional convolution layer serial connection method of… More
  •   Views:177       Downloads:134        Download PDF
  • An Attention-Based Friend Recommendation Model in Social Network
  • Abstract In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users and items, which were later… More
  •   Views:172       Downloads:172        Download PDF
  • A Two-Stage Vehicle Type Recognition Method Combining the Most Effective Gabor Features
  • Abstract Vehicle type recognition (VTR) is an important research topic due to its significance in intelligent transportation systems. However, recognizing vehicle type on the real-world images is challenging due to the illumination change, partial occlusion under real traffic environment. These difficulties limit the performance of current stateof-art methods, which are typically based on single-stage classification without considering feature availability. To address such difficulties, this paper proposes a twostage vehicle type recognition method combining the most effective Gabor features. The first stage leverages edge features to classify vehicles by size into big or small via a similarity k-nearest neighbor classifier (SKNNC). Further… More
  •   Views:183       Downloads:147        Download PDF
  • Internet of Things Based Solutions for Transport Network Vulnerability Assessment in Intelligent Transportation Systems
  • Abstract Intelligent Transportation System (ITS) is essential for effective identification of vulnerable units in the transport network and its stable operation. Also, it is necessary to establish an urban transport network vulnerability assessment model with solutions based on Internet of Things (IoT). Previous research on vulnerability has no congestion effect on the peak time of urban road network. The cascading failure of links or nodes is presented by IoT monitoring system, which can collect data from a wireless sensor network in the transport environment. The IoT monitoring system collects wireless data via Vehicle-to-Infrastructure (V2I) channels to simulate key segments and their… More
  •   Views:166       Downloads:136        Download PDF
  • Stacked Attention Networks for Referring Expressions Comprehension
  • Abstract Referring expressions comprehension is the task of locating the image region described by a natural language expression, which refer to the properties of the region or the relationships with other regions. Most previous work handles this problem by selecting the most relevant regions from a set of candidate regions, when there are many candidate regions in the set these methods are inefficient. Inspired by recent success of image captioning by using deep learning methods, in this paper we proposed a framework to understand the referring expressions by multiple steps of reasoning. We present a model for referring expressions comprehension by… More
  •   Views:182       Downloads:138        Download PDF
  • Research on Intelligent Mobile Commerce Transaction Security Mechanisms Based on Mobile Agent
  • Abstract In networked mobile commerce network transactions, trust is the prerequisite and key to a smooth transaction. The measurement of trust between entities involves factors such as transaction amount, transaction time, personal income of consumer entities and their risk attitude towards trust, etc., so it is difficult to accurately calculate quantitatively. In order to find out the essential characteristics of this trust relationship, based on the research background of mobile commerce in the mobile network environment, a dynamic trust mechanism is proposed through the research of trust in the mobile network environment, trust influencing factors and trust mechanism. The calculation model… More
  •   Views:187       Downloads:156        Download PDF
  • A Novel System for Recognizing Recording Devices from Recorded Speech Signals
  • Abstract The field of digital audio forensics aims to detect threats and fraud in audio signals. Contemporary audio forensic techniques use digital signal processing to detect the authenticity of recorded speech, recognize speakers, and recognize recording devices. User-generated audio recordings from mobile phones are very helpful in a number of forensic applications. This article proposed a novel method for recognizing recording devices based on recorded audio signals. First, a database of the features of various recording devices was constructed using 32 recording devices (20 mobile phones of different brands and 12 kinds of recording pens) in various environments. Second, the audio… More
  •   Views:200       Downloads:165        Download PDF
  • Bandwidth-Efficient Transmission Method for User View-Oriented Video Services
  • Abstract The trend in video viewing has been evolving beyond simply providing a multiview option. Recently, a function that allows selection and viewing of a clip from a multiview service that captures a specific range or object has been added. In particular, the freeview service is an extended concept of multi-view and provides a freer viewpoint. However, since numerous videos and additional data are required for its construction, all of the clips constituting the content cannot be simultaneously provided. Only certain clips are selected and provided to the user. If the video is not the preferred video, change request is made,… More
  •   Views:215       Downloads:158        Download PDF
  • IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19
  • Abstract The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables… More
  •   Views:285       Downloads:212        Download PDF
  • Upper Bound Limit Analysis of Anisotropic Soils
  • Abstract In this paper, a novel discretization method in σ-τ space is developed to calculate the upper bound limit loads and failure modes of anisotropic Mohr-Coulomb materials. To achieve this objective, the Mohr-Coulomb yield criterion is linearized in σ-τ space, which allows for upper bound solution of soils whose cohesion and friction coefficient varying with direction. The finite element upper limit analysis formulation using the modified anisotropic yield criterion is then developed. Several examples are given to illustrate the capability and effectiveness of the proposed numerical procedure for computing rigorous upper bounds for anisotropic soils. More
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  • Reversible Data Hiding in Encrypted Images Based on Prediction and Adaptive Classification Scrambling
  • Abstract Reversible data hiding in encrypted images (RDH-EI) technology is widely used in cloud storage for image privacy protection. In order to improve the embedding capacity of the RDH-EI algorithm and the security of the encrypted images, we proposed a reversible data hiding algorithm for encrypted images based on prediction and adaptive classification scrambling. First, the prediction error image is obtained by a novel prediction method before encryption. Then, the image pixel values are divided into two categories by the threshold range, which is selected adaptively according to the image content. Multiple high-significant bits of pixels within the threshold range are… More
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  • An Efficient Mechanism for Product Data Extraction from E-Commerce Websites
  • Abstract A large amount of data is present on the web which can be used for useful purposes like a product recommendation, price comparison and demand forecasting for a particular product. Websites are designed for human understanding and not for machines. Therefore, to make data machine-readable, it requires techniques to grab data from web pages. Researchers have addressed the problem using two approaches, i.e., knowledge engineering and machine learning. State of the art knowledge engineering approaches use the structure of documents, visual cues, clustering of attributes of data records and text processing techniques to identify data records on a web page.… More
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  • A Differential Privacy Based (k-Ψ)-Anonymity Method for Trajectory Data Publishing
  • Abstract In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided… More
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  • Information Flow Security Models for Cloud Computing
  • Abstract Cloud computing provides services to users through Internet. This open mode not only facilitates the access by users, but also brings potential security risks. In cloud computing, the risk of data leakage exists between users and virtual machines. Whether direct or indirect data leakage, it can be regarded as illegal information flow. Methods, such as access control models can control the information flow, but not the covert information flow. Therefore, it needs to use the noninterference models to detect the existence of illegal information flow in cloud computing architecture. Typical noninterference models are not suitable to certificate information flow in… More
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