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.

  • Data Driven Modelling of Coronavirus Spread in Spain
  • Abstract During the late months of last year, a novel coronavirus was detected in Hubei, China. The virus, since then, has spread all across the globe forcing Word Health Organization (WHO) to declare COVID-19 outbreak a pandemic. In Spain, the virus started infecting the country slowly until rapid growth of infected people occurred in Madrid, Barcelona and other major cities. The government in an attempt to stop the rapssid spread of the virus and ensure that health system will not reach its capacity, implement strict measures by putting the entire country in quarantine. The duration of these measures, depends on the… More
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  • On the Detection of COVID-19 from Chest X-Ray Images Using CNN-Based Transfer Learning
  • Abstract Coronavirus disease (COVID-19) is an extremely infectious disease and possibly causes acute respiratory distress or in severe cases may lead to death. There has already been some research in dealing with coronavirus using machine learning algorithms, but few have presented a truly comprehensive view. In this research, we show how convolutional neural network (CNN) can be useful to detect COVID-19 using chest X-ray images. We leverage the CNN-based pre-trained models as feature extractors to substantiate transfer learning and add our own classifier in detecting COVID-19. In this regard, we evaluate performance of five different pre-trained models with fine-tuning the weights… More
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  • Machine Learning and Classical Forecasting Methods Based Decision Support Systems for COVID-19
  • Abstract From late 2019 to the present day, the coronavirus outbreak tragically affected the whole world and killed tens of thousands of people. Many countries have taken very stringent measures to alleviate the effects of the coronavirus disease 2019 (COVID-19) and are still being implemented. In this study, various machine learning techniques are implemented to predict possible confirmed cases and mortality numbers for the future. According to these models, we have tried to shed light on the future in terms of possible measures to be taken or updating the current measures. Support Vector Machines (SVM), Holt-Winters, Prophet, and Long-Short Term Memory… More
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  • Mathematical Analysis of Novel Coronavirus (2019-nCov) Delay Pandemic Model
  • Abstract In this manuscript, the mathematical analysis of corona virus model with time delay effect is studied. Mathematical modelling of infectious diseases has substantial role in the different disciplines such as biological, engineering, physical, social, behavioural problems and many more. Most of infectious diseases are dreadful such as HIV/AIDS, Hepatitis and 2019-nCov. Unfortunately, due to the non-availability of vaccine for 2019- nCov around the world, the delay factors like, social distancing, quarantine, travel restrictions, holidays extension, hospitalization and isolation are used as key tools to control the pandemic of 2019-nCov. We have analysed the reproduction number RnCov of delayed model. Two… More
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  • COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
  • Abstract With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed. Establish a “Scrapy-Redis-Bloomfilter” distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the… More
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  • A Robust Watermarking Scheme Based on ROI and IWT for Remote Consultation of COVID-19
  • Abstract In the current dire situation of the corona virus COVID-19, remote consultations were proposed to avoid cross-infection and regional differences in medical resources. However, the safety of digital medical imaging in remote consultations has also attracted more and more attention from the medical industry. To ensure the integrity and security of medical images, this paper proposes a robust watermarking algorithm to authenticate and recover from the distorted medical images based on regions of interest (ROI) and integer wavelet transform (IWT). First, the medical image is divided into two different parts, regions of interest and non-interest regions. Then the integrity of… More
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  • An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM
  • Abstract New coronavirus disease (COVID-19) has constituted a global pandemic and has spread to most countries and regions in the world. Through understanding the development trend of confirmed cases in a region, the government can control the pandemic by using the corresponding policies. However, the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction, and even have large estimation errors. To address this issue, we propose an improved method for predicting confirmed cases based on LSTM (Long-Short Term Memory) neural network. This work compares the deviation between the experimental results of the improved LSTM… More
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  • Four-Step Iteration Scheme to Approximate Fixed Point for Weak Contractions
  • Abstract Fixed point theory is one of the most important subjects in the setting of metric spaces since fixed point theorems can be used to determine the existence and the uniqueness of solutions of such mathematical problems. It is known that many problems in applied sciences and engineering can be formulated as functional equations. Such equations can be transferred to fixed point theorems in an easy manner. Moreover, we use the fixed point theory to prove the existence and uniqueness of solutions of such integral and differential equations. Let X be a non-empty set. A fixed point for a self-mapping T More
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  • Generalized Marshall Olkin Inverse Lindley Distribution with Applications
  • Abstract In this article, a new generalization of the inverse Lindley distribution is introduced based on Marshall-Olkin family of distributions. We call the new distribution, the generalized Marshall-Olkin inverse Lindley distribution which offers more flexibility for modeling lifetime data. The new distribution includes the inverse Lindley and the Marshall-Olkin inverse Lindley as special distributions. Essential properties of the generalized Marshall-Olkin inverse Lindley distribution are discussed and investigated including, quantile function, ordinary moments, incomplete moments, moments of residual and stochastic ordering. Maximum likelihood method of estimation is considered under complete, Type-I censoring and Type-II censoring. Maximum likelihood estimators as well as approximate… More
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  • Secure Sharing Scheme of Sensitive Data in the Precision Medicine System
  • Abstract Numerous industries, especially the medical industry, are likely to exhibit significant developments in the future. Ever since the announcement of the precision medicine initiative by the United States in 2015, interest in the field has considerably increased. The techniques of precision medicine are employed to provide optimal treatment and medical services to patients, in addition to the prevention and management of diseases via the collection and analysis of big data related to their individual genetic characteristics, occupation, living environment, and dietary habits. As this involves the accumulation and utilization of sensitive information, such as patient history, DNA, and personal details,… More
  •   Views:116       Downloads:85        Download PDF
  • Benchmarking Approach to Compare Web Applications Static Analysis Tools Detecting OWASP Top Ten Security Vulnerabilities
  • Abstract To detect security vulnerabilities in a web application, the security analyst must choose the best performance Security Analysis Static Tool (SAST) in terms of discovering the greatest number of security vulnerabilities as possible. To compare static analysis tools for web applications, an adapted benchmark to the vulnerability categories included in the known standard Open Web Application Security Project (OWASP) Top Ten project is required. The information of the security effectiveness of a commercial static analysis tool is not usually a publicly accessible research and the state of the art on static security tool analyzers shows that the different design and… More
  •   Views:91       Downloads:78        Download PDF
  • A Genetic Algorithm to Solve Capacity Assignment Problem in a Flow Network
  • Abstract Computer networks and power transmission networks are treated as capacitated flow networks. A capacitated flow network may partially fail due to maintenance. Therefore, the capacity of each edge should be optimally assigned to face critical situations—i.e., to keep the network functioning normally in the case of failure at one or more edges. The robust design problem (RDP) in a capacitated flow network is to search for the minimum capacity assignment of each edge such that the network still survived even under the edge’s failure. The RDP is known as NP-hard. Thus, capacity assignment problem subject to system reliability and total… More
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  • Gain-Enhanced Metamaterial Based Antenna for 5G Communication Standards
  • Abstract Metamaterial surfaces play a vital role to achieve the surface waves suppression and in-phase reflection, in order to improve the antenna performance. In this paper, the performance comparison of a fifth generation (5G) antenna design is analyzed and compared with a metamaterial-based antenna for 5G communication system applications. Metamaterial surface is utilized as a reflector due to its in-phase reflection characteristic and high-impedance nature to improve the gain of an antenna. As conventional conducting ground plane does not give enough surface waves suppression which affects the antenna performance in terms of efficiency and gain etc. These factors are well considered… More
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  • Better Visual Image Super-Resolution with Laplacian Pyramid of Generative Adversarial Networks
  • Abstract Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to restore finer texture details during image super-resolution reconstruction? This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the Laplacian pyramid to capture the high-frequency details of the image. By combining Laplacian pyramids and generative adversarial networks, progressive reconstruction of super-resolution images can be made, making model applications more flexible. In order to solve the problem of gradient disappearance, we introduce the Residual-in-Residual Dense… More
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  • ECG Classification Using Deep CNN Improved by Wavelet Transform
  • Abstract Atrial fibrillation is the most common persistent form of arrhythmia. A method based on wavelet transform combined with deep convolutional neural network is applied for automatic classification of electrocardiograms. Since the ECG signal is easily inferred, the ECG signal is decomposed into 9 kinds of subsignals with different frequency scales by wavelet function, and then wavelet reconstruction is carried out after segmented filtering to eliminate the influence of noise. A 24-layer convolution neural network is used to extract the hierarchical features by convolution kernels of different sizes, and finally the softmax classifier is used to classify them. This paper applies… More
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  • Continuous-Variable Quantum Network Coding Based on Quantum Discord
  • Abstract Establishing entanglement is an essential task of quantum communication technology. Beyond entanglement, quantum discord, as a measure of quantum correlation, is a necessary prerequisite to the success of entanglement distribution. To realize efficient quantum communication based on quantum discord, in this paper, we consider the practical advantages of continuous variables and propose a feasible continuous-variable quantum network coding scheme based on quantum discord. By means of entanglement distribution by separable states, it can achieve quantum entanglement distribution from sources to targets in a butterfly network. Compared with the representative discrete-variable quantum network coding schemes, the proposed continuous-variable quantum network coding… More
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  • Impact Force Magnitude and Location Recognition of Composite Materials
  • Abstract In order to identify the location and magnitude of the impact force accurately, determine the damage range of the structure and accelerate the health monitoring of key components of the composite, this paper studies the location and magnitude of the impact force of composite plates by an inverse method. Firstly, a PZT sensor mounted on the material plate is used to collect the response signal generated by the impact force, which is from several impact locations, and establish transfer functions between the impact location and the PZT sensor. Secondly, this paper applies several forces to any location on the material… More
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  • A DRL-Based Container Placement Scheme with Auxiliary Tasks
  • Abstract Container is an emerging virtualization technology and widely adopted in the cloud to provide services because of its lightweight, flexible, isolated and highly portable properties. Cloud services are often instantiated as clusters of interconnected containers. Due to the stochastic service arrival and complicated cloud environment, it is challenging to achieve an optimal container placement (CP) scheme. We propose to leverage Deep Reinforcement Learning (DRL) for solving CP problem, which is able to learn from experience interacting with the environment and does not rely on mathematical model or prior knowledge. However, applying DRL method directly dose not lead to a satisfying… More
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  • A Secure Three-Factor Authenticated Key Agreement Scheme for Multi-Server Environment
  • Abstract Multi-server authenticated key agreement schemes have attracted great attention to both academia and industry in recent years. However, traditional authenticated key agreement schemes in the single-server environment are not suitable for the multi-server environment because the user has to register on each server when he/she wishes to log in various servers for different service. Moreover, it is unreasonable to consider all servers are trusted since the server in a multi-server environment may be a semi-trusted party. In order to overcome these difficulties, we designed a secure threefactor multi-server authenticated key agreement protocol based on elliptic curve cryptography, which needs the… More
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  • Embedding Implicit User Importance for Group Recommendation
  • Abstract Group recommendations derive from a phenomenon in which people tend to participate in activities together regardless of whether they are online or in reality, which creates real scenarios and promotes the development of group recommendation systems. Different from traditional personalized recommendation methods, which are concerned only with the accuracy of recommendations for individuals, group recommendation is expected to balance the needs of multiple users. Building a proper model for a group of users to improve the quality of a recommended list and to achieve a better recommendation has become a large challenge for group recommendation applications. Existing studies often focus… More
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  • A Cross Layer Protocol for Fast Identification of Blocked Tags in Large-Scale RFID Systems
  • Abstract Blocker tag attack is one of the denial-of-service (DoS) attacks that threatens the privacy and security of RFID systems. The attacker interferes with the blocked tag by simulating a fake tag with the same ID, thus causing a collision of message replies. In many practical scenarios, the number of blocked tags may vary, or even be small. For example, the attacker may only block the important customers or high-value items. To avoid the disclosure of privacy and economic losses, it is of great importance to fast pinpoint these blocked ones. However, existing works do not take into account the impact… More
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  • A Nonuniform Clustering Routing Algorithm Based on an Improved K-Means Algorithm
  • Abstract In a large-scale wireless sensor network (WSN), densely distributed sensor nodes process a large amount of data. The aggregation of data in a network can consume a great amount of energy. To balance and reduce the energy consumption of nodes in a WSN and extend the network life, this paper proposes a nonuniform clustering routing algorithm based on the improved K-means algorithm. The algorithm uses a clustering method to form and optimize clusters, and it selects appropriate cluster heads to balance network energy consumption and extend the life cycle of the WSN. To ensure that the cluster head (CH) selection… More
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  • Applying Feature-Weighted Gradient Decent K-Nearest Neighbor to Select Promising Projects for Scientific Funding
  • Abstract Due to its outstanding ability in processing large quantity and high-dimensional data, machine learning models have been used in many cases, such as pattern recognition, classification, spam filtering, data mining and forecasting. As an outstanding machine learning algorithm, K-Nearest Neighbor (KNN) has been widely used in different situations, yet in selecting qualified applicants for winning a funding is almost new. The major problem lies in how to accurately determine the importance of attributes. In this paper, we propose a Feature-weighted Gradient Decent K-Nearest Neighbor (FGDKNN) method to classify funding applicants in to two types: approved ones or not approved ones.… More
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  • Adversarial Attacks on Content-Based Filtering Journal Recommender Systems
  • Abstract Recommender systems are very useful for people to explore what they really need. Academic papers are important achievements for researchers and they often have a great deal of choice to submit their papers. In order to improve the efficiency of selecting the most suitable journals for publishing their works, journal recommender systems (JRS) can automatically provide a small number of candidate journals based on key information such as the title and the abstract. However, users or journal owners may attack the system for their own purposes. In this paper, we discuss about the adversarial attacks against content-based filtering JRS. We… More
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  • Directional Modulation Based on a Quantum Genetic Algorithm for a Multiple-Reflection Model
  • Abstract Directional modulation is one of the hot topics in data security researches. To fulfill the requirements of communication security in wireless environment with multiple paths, this study takes into account the factors of reflections and antenna radiation pattern for directional modulation. Unlike other previous works, a novel multiple-reflection model, which is more realistic and complex than simplified two-ray reflection models, is proposed based on two reflectors. Another focus is a quantum genetic algorithm applied to optimize antenna excitation in a phased directional modulation antenna array. The quantum approach has strengths in convergence speed and the globe searching ability for the… More
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  • Using Object Detection Network for Malware Detection and Identification in Network Traffic Packets
  • Abstract In recent years, the number of exposed vulnerabilities has grown rapidly and more and more attacks occurred to intrude on the target computers using these vulnerabilities such as different malware. Malware detection has attracted more attention and still faces severe challenges. As malware detection based traditional machine learning relies on exports’ experience to design efficient features to distinguish different malware, it causes bottleneck on feature engineer and is also time-consuming to find efficient features. Due to its promising ability in automatically proposing and selecting significant features, deep learning has gradually become a research hotspot. In this paper, aiming to detect… More
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  • Privacy Protection for Medical Images Based on DenseNet and Coverless Steganography
  • Abstract With the development of the internet of medical things (IoMT), the privacy protection problem has become more and more critical. In this paper, we propose a privacy protection scheme for medical images based on DenseNet and coverless steganography. For a given group of medical images of one patient, DenseNet is used to regroup the images based on feature similarity comparison. Then the mapping indexes can be constructed based on LBP feature and hash generation. After mapping the privacy information with the hash sequences, the corresponding mapped indexes of secret information will be packed together with the medical images group and… More
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  • High Accuracy Network Cardinalities Estimation by Step Sampling Revision on GPU
  • Abstract Host cardinality estimation is an important research field in network management and network security. The host cardinality estimation algorithm based on the linear estimator array is a common method. Existing algorithms do not take memory footprint into account when selecting the number of estimators used by each host. This paper analyzes the relationship between memory occupancy and estimation accuracy and compares the effects of different parameters on algorithm accuracy. The cardinality estimating algorithm is a kind of random algorithm, and there is a deviation between the estimated results and the actual cardinalities. The deviation is affected by some systematical factors,… More
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  • Prophet_TD Routing Algorithm Based on Historical Throughput and Encounter Duration
  • Abstract Opportunistic networks are self-organizing networks that do not require a complete path between the source node and the destination node as it uses encounter opportunities brought by nodes movement to achieve network communication. Opportunistic networks routing algorithms are numerous and can be roughly divided into four categories based on different forwarding strategies. The Prophet routing algorithm is an important routing algorithm in opportunistic networks. It forwards messages based on the encounter probability between nodes, and has good innovation significance and optimization potential. However, the Prophet routing algorithm does not consider the impact of the historical throughput of the node on… More
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  • Predictive Control Algorithm for Urban Rail Train Brake Control System Based on T-S Fuzzy Model
  • Abstract Urban rail transit has the advantages of large traffic capacity, high punctuality and zero congestion, and it plays an increasingly important role in modern urban life. Braking system is an important system of urban rail train, which directly affects the performance and safety of train operation and impacts passenger comfort. The braking performance of urban rail trains is directly related to the improvement of train speed and transportation capacity. Also, urban rail transit has the characteristics of high speed, short station distance, frequent starting, and frequent braking. This makes the braking control system constitute a time-varying, time-delaying and nonlinear control… More
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  • Chinese Spirits Identification Model Based on Mid-Infrared Spectrum
  • Abstract Applying computer technology to the field of food safety, and how to identify liquor quickly and accurately, is of vital importance and has become a research focus. In this paper, sparse principal component analysis (SPCA) was applied to seek sparse factors of the mid-infrared (MIR) spectra of five famous vintage year Chinese spirits. The results showed while meeting the maximum explained variance, 23 sparse principal components (PCs) were selected as features in a support vector machine (SVM) model, which obtained a 97% classification accuracy. By comparison principal component analysis (PCA) selected 10 PCs as features but only achieved an 83%… More
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  • An Efficient Bar Code Image Recognition Algorithm for Sorting System
  • Abstract In the sorting system of the production line, the object movement, fixed angle of view, light intensity and other reasons lead to obscure blurred images. It results in bar code recognition rate being low and real time being poor. Aiming at the above problems, a progressive bar code compressed recognition algorithm is proposed. First, assuming that the source image is not tilted, use the direct recognition method to quickly identify the compressed source image. Failure indicates that the compression ratio is improper or the image is skewed. Then, the source image is enhanced to identify the source image directly. Finally,… More
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  • Improving Chinese Word Representation with Conceptual Semantics
  • Abstract The meaning of a word includes a conceptual meaning and a distributive meaning. Word embedding based on distribution suffers from insufficient conceptual semantic representation caused by data sparsity, especially for low-frequency words. In knowledge bases, manually annotated semantic knowledge is stable and the essential attributes of words are accurately denoted. In this paper, we propose a Conceptual Semantics Enhanced Word Representation (CEWR) model, computing the synset embedding and hypernym embedding of Chinese words based on the Tongyici Cilin thesaurus, and aggregating it with distributed word representation to have both distributed information and the conceptual meaning encoded in the representation of… More
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  • Privacy-Preserving Decision Protocols Based on Quantum Oblivious Key Distribution
  • Abstract Oblivious key transfer (OKT) is a fundamental problem in the field of secure multi-party computation. It makes the provider send a secret key sequence to the user obliviously, i.e., the user may only get almost one bit key in the sequence which is unknown to the provider. Recently, a number of works have sought to establish the corresponding quantum oblivious key transfer model and rename it as quantum oblivious key distribution (QOKD) from the well-known expression of quantum key distribution (QKD). In this paper, a new QOKD model is firstly proposed for the provider and user with limited quantum capabilities,… More
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  • A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm
  • Abstract The prediction of particles less than 2.5 micrometers in diameter (PM2.5) in fog and haze has been paid more and more attention, but the prediction accuracy of the results is not ideal. Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze. In order to improve the effects of prediction, this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning. Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze, and deep confidence network is utilized to extract… More
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  • Research on Vehicle Routing Problem with Soft Time Windows Based on Hybrid Tabu Search and Scatter Search Algorithm
  • Abstract With the expansion of the application scope of social computing problems, many path problems in real life have evolved from pure path optimization problems to social computing problems that take into account various social attributes, cultures, and the emotional needs of customers. The actual soft time window vehicle routing problem, speeding up the response of customer needs, improving distribution efficiency, and reducing operating costs is the focus of current social computing problems. Therefore, designing fast and effective algorithms to solve this problem has certain theoretical and practical significance. In this paper, considering the time delay problem of customer demand, the… More
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  • A Recommendation Method for Highly Sparse Dataset Based on Teaching Recommendation Factorization Machines
  • Abstract There is no reasonable scientific basis for selecting the excellent teachers of the school’s courses. To solve the practical problem, we firstly give a series of normalization models for defining the key attributes of teachers’ professional foundation, course difficulty coefficient, and comprehensive evaluation of teaching. Then, we define a partial weight function to calculate the key attributes, and obtain the partial recommendation values. Next, we construct a highly sparse Teaching Recommendation Factorization Machines (TRFMs) model, which takes the 5-tuples relation including teacher, course, teachers’ professional foundation, course difficulty, teaching evaluation as the feature vector, and take partial recommendation value as… More
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  • Image Super-Resolution Based on Generative Adversarial Networks: A Brief Review
  • Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More
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  • A Recommendation Approach Based on Bayesian Networks for Clone Refactor
  • Abstract Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are… More
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  • A Distributed Covert Channel of the Packet Ordering Enhancement Model Based on Data Compression
  • Abstract Covert channel of the packet ordering is a hot research topic. Encryption technology is not enough to protect the security of both sides of communication. Covert channel needs to hide the transmission data and protect content of communication. The traditional methods are usually to use proxy technology such as tor anonymous tracking technology to achieve hiding from the communicator. However, because the establishment of proxy communication needs to consume traffic, the communication capacity will be reduced, and in recent years, the tor technology often has vulnerabilities that led to the leakage of secret information. In this paper, the covert channel… More
  •   Views:133       Downloads:105        Download PDF
  • Bilateral Collaborative Optimization for Cloud Manufacturing Service
  • Abstract Manufacturing service composition of the supply side and scheduling of the demand side are two important components of Cloud Manufacturing, which directly affect the quality of Cloud Manufacturing services. However, the previous studies on the two components are carried out independently and thus ignoring the internal relations and mutual constraints. Considering the two components on both sides of the supply and the demand of Cloud Manufacturing services at the same time, a Bilateral Collaborative Optimization Model of Cloud Manufacturing (BCOM-CMfg) is constructed in this paper. In BCOM-CMfg, to solve the manufacturing service scheduling problem on the supply side, a new… More
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  • Identification of Weather Phenomena Based on Lightweight Convolutional Neural Networks
  • Abstract Weather phenomenon recognition plays an important role in the field of meteorology. Nowadays, weather radars and weathers sensor have been widely used for weather recognition. However, given the high cost in deploying and maintaining the devices, it is difficult to apply them to intensive weather phenomenon recognition. Moreover, advanced machine learning models such as Convolutional Neural Networks (CNNs) have shown a lot of promise in meteorology, but these models also require intensive computation and large memory, which make it difficult to use them in reality. In practice, lightweight models are often used to solve such problems. However, lightweight models often… More
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  • Information Classification and Extraction on Official Web Pages of Organizations
  • Abstract As a real-time and authoritative source, the official Web pages of organizations contain a large amount of information. The diversity of Web content and format makes it essential for pre-processing to get the unified attributed data, which has the value of organizational analysis and mining. The existing research on dealing with multiple Web scenarios and accuracy performance is insufficient. This paper aims to propose a method to transform organizational official Web pages into the data with attributes. After locating the active blocks in the Web pages, the structural and content features are proposed to classify information with the specific model.… More
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