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  • Improved Prediction and Understanding of Glass-Forming Ability Based on Random Forest Algorithm
  • Abstract As an ideal material, bulk metallic glass (MG) has a wide range of applications because of its unique properties such as structural, functional and biomedical materials. However, it is difficult to predict the glass-forming ability (GFA) even given the criteria in theory and this problem greatly limits the application of bulk MG in industrial field. In this work, the proposed model uses the random forest classification method which is one of machine learning methods to solve the GFA prediction for binary metallic alloys. Compared with the previous SVM algorithm models of all features combinations, this new model is successfully constructed…
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  • A Bi-Histogram Shifting Contrast Enhancement for Color Images
  • Abstract Recent contrast enhancement (CE) methods, with a few exceptions, predominantly focus on enhancing gray-scale images. This paper proposes a bihistogram shifting contrast enhancement for color images based on the RGB (red, green, and blue) color model. The proposed method selects the two highest bins and two lowest bins from the image histogram, performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images. The proposed method simultaneously performs both right histogram shifting (RHS) and left histogram shifting (LHS) in each histogram shifting repetition to embed and split the highest bins while…
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  • Malware Detection Based on Multidimensional Time Distribution Features
  • Abstract Language detection models based on system calls suffer from certain false negatives and detection blind spots. Hence, the normal behavior sequences of some malware applications for a short period can become malicious behavior within a certain time window. To detect such behaviors, we extract a multidimensional time distribution feature matrix on the basis of statistical analysis. This matrix mainly includes multidimensional time distribution features, multidimensional word pair correlation features, and multidimensional word frequency distribution features. A multidimensional time distribution model based on neural networks is built to detect the overall abnormal behavior within a given time window. Experimental evaluation is…
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  • New Quantum Private Comparison Using Hyperentangled GHZ State
  • Abstract In this paper, we propose a new protocol designed for quantum private comparison (QPC). This new protocol utilizes the hyperentanglement as the quantum resource and introduces a semi-honest third party (TP) to achieve the objective. This protocol’s quantum carrier is a hyperentangled three-photon GHZ state in 2 degrees of freedom (DOF), which could have 64 combinations. The TP can decide which combination to use based on the shared key information provided from a quantum key distribution (QKD) protocol. By doing so, the security of the protocol can be improved further. Decoy photon technology is also used as another means of…
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  • Dynamic Target Detection and Tracking Based on Quantum Illumination LIDAR
  • Abstract In the detection process of classic radars such as radar/lidar, the detection performance will be weakened due to the presence of background noise and loss. The quantum illumination protocol can use the spatial correlation between photon pairs to improve image quality and enhance radar detection performance, even in the presence of loss and noise. Based on this quantum illumination LIDAR, a theoretic scheme is developed for the detection and tracking of moving targets, and the trajectory of the object is analyzed. Illuminated by the quantum light source as Spontaneous Parametric Down-Conversion (SPDC), an opaque target can be identified from the…
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  • A Prediction Method of Trend-Type Capacity Index Based on Recurrent Neural Network
  • Abstract Due to the increase in the types of business and equipment in telecommunications companies, the performance index data collected in the operation and maintenance process varies greatly. The diversity of index data makes it very difficult to perform high-precision capacity prediction. In order to improve the forecasting efficiency of related indexes, this paper designs a classification method of capacity index data, which divides the capacity index data into trend type, periodic type and irregular type. Then for the prediction of trend data, it proposes a capacity index prediction model based on Recurrent Neural Network (RNN), denoted as RNN-LSTM-LSTM. This model…
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  • Analysis of Flight Punctuality Rate Based on Complex Network
  • Abstract Aviation service plays an indispensable role in the process of social and economic development. In this process, the problem of flight punctuality becomes more and more serious. Flight delay will bring a variety of implicit and explicit losses to individual passengers and airlines, so it is necessary to analyze the influencing factors of flight punctuality rate. Complex network can be used to study various objects with complex relations, obtain the relations of these objects and calculate the influence of different indicators on the objects. This article mainly has carried on the three aspects: Firstly, get the flight data from the…
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  • Flight Delay Prediction Using Gradient Boosting Machine Learning Classifiers
  • Abstract With the increasing of civil aviation business, flight delay has become a key problem in civil aviation field in recent years, which has brought a considerable economic impact to airlines and related industries. The delay prediction of specific flights is very important for airlines’ plan, airport resource allocation, insurance company strategy and personal arrangement. The influence factors of flight delay have high complexity and non-linear relationship. The different situations of various regions and airports, and even the deviation of airport or airline arrangement all have certain influence on flight delay, which makes the prediction more difficult. In view of the…
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  • Big Data of Home Energy Management in Cloud Computing
  • Abstract A smart grid is the evolved form of the power grid with the integration of sensing, communication, computing, monitoring, and control technologies. These technologies make the power grid reliable, efficient, and economical. However, the smartness boosts the volume of data in the smart grid. To obligate full benefits, big data has attractive techniques to process and analyze smart grid data. This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid. The offered framework comprises of the following four layers: (i) Data source layer, (ii) Data transmission layer, (iii) Data…
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  • A Critical Overview on Quantum Computing
  • Abstract Quantum Computing and Quantum Information Science seem very promising and developing rapidly since its inception in early 1980s by Paul Benioff with the proposal of quantum mechanical model of the Turing machine and later By Richard Feynman and Yuri Manin for the proposal of a quantum computers for simulating various problems that classical computer could not. Quantum computers have a computational advantage for some problems, over classical computers and most applications are trying to use an efficient combination of classical and quantum computers like Shor’s factoring algorithm. Other areas that are expected to be benefitted from quantum computing are Machine…
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  •   Views:841       Downloads:655        Download PDF
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