Reviewerly/exHarmony huggingface dataset:
The Techniques of Vedic Mathematics for ECC Over Weierstrass Elliptic Curve Y2 = X3 + Ax + B

An analysis is presented to the study, the proficient implementation of ancient mathematics formulae for multiplications and squares in the cryptographic system. In this approach, we have used ancient mathematics techniques and algorithms in the different projective coordinates system (Jacobian, Chudnovsky-Jacobian, Modified Jacobian coordinates system) to get minimum steps in the calculation of addition algorithm, doubling algorithm and for improving the speed of processing time in the operations of ECC (points addition, points doubling). The coding and synthesis are done in MATLAB for 16-bit digit multiplications and squares. The results proved that the Vedic mathematics-based scheme shows better performance compared to the conventional method and total delay in computation is reduced by Vedic mathematics Sutras (Urdhva-tiryagbhyam, Dvandva-yoga). The results of some AVIM techniques over ECC were obtained and discussed in the form of tables and graphs.
Latent Posterior Based Generative Adversarial Network for Imbalance Classification

The imbalance classification problem arises when certain class is underrepresented in comparison with other classes, leading to a classifier partial to the majority classes. Existing interpolation-based oversampling methods for handling this problem characteristically do not make full use of the probability distribution of data. To overcome this weakness, this study proposes latent posterior based generative adversarial network oversampling approach(LPGOS), which uses a variational encoder to obtain the posterior distribution of latent variables. In addition, giving the high correlation between generated synthetic data and original data, we introduce a transfer learning approach with weight scaling factor namely TrWSBoost in which the generated minority class samples are treated as source domain data. Visual results prove that the proposed approach LPGOS is capable of approximate high dimensional data distribution and outperform other existing oversampling techniques. The performance of binary classifiers verify the effectiveness of proposed approaches.
Research on Improved Dark Channel Dehazing Algorithm Based on Parameter Differentiating Sky Area

For the dark channel prior defogging algorithm, there are problems such as sky region distortion and darkening of the restored image. An optimization method based on parameter α to distinguish between sky and non-sky regions is discussed. First, correct the rough transmittance estimation method for the bright area of the sky. Then, discuss the effectiveness of the distinction between the sky and the sky region by α and tolerance parameter K. Estimate the rough transmittance of the sky and non-sky regions separately. And it is refined by adaptive median filtering and guided filtering. Finally, the atmospheric light values estimated by the quadtree subspace partitioning method and the variogram are respectively substituted into the atmospheric scattering model to compare the advantages and disadvantages of the restored image effect. The Experiments prove that the algorithm in this paper has the advantages of strong robustness, can cope with complex scenes, and maintain the normal color of the sky area after defogging, especially compared with the existing methods. Improved the defogging ability and visual effect.
Interactive deformation‐driven person silhouette image synthesis

Abstract This paper addresses the problem of synthesizing person silhouette images. Previous methods on human‐centric image synthesis deal with normal images taken under consistent lighting conditions. However, a silhouette image has an inconsistent lighting appearance, in which the dark subject and the bright background form a strong contrast. Therefore, it brings great challenges to person silhouette image synthesis. We present a staged method to synthesize realistic person silhouette photos with various poses. The purpose of our method is to give ordinary users the opportunities to interactively adjust the pose of a person in a silhouette photo. The method consists of four main steps: person detection, person analysis, interactive deformation, and image synthesis. To obtain the shape of the target person, we propose a silhouette image segmentation algorithm combined with person detection. Moreover, we also present an effective image inpainting approach to complement a silhouette image with an irregular hole. Experimental results show that the proposed method can generate a set of realistic person silhouette images with interactively changed poses.
Additivity in Classical-Quantum Wiretap Channels

Due to Csiszár and Körner, the capacity of classical wiretap channels has a single-letter characterization in terms of the private information. For quantum wiretap channels, however, it is known that regularization of the private information is necessary to reach the capacity. Here we study hybrid classical-quantum wiretap channels in order to resolve how much quantumness is needed to witness non-additivity phenomena in Shannon information theory. For wiretap channels with quantum inputs but classical outputs, we prove that the characterization of the capacity in terms of the private information stays single-letter. Hence, entangled input states are of no asymptotic advantage in this setting. For wiretap channels with classical inputs, we show by means of explicit examples that the private information already becomes non-additive when either one of the two receivers becomes quantum (with the other receiver staying classical). This gives non-additivity examples that are not caused by entanglement and illustrates that in the wiretap model quantum adversaries are strictly different from classical adversaries.
Echo State Network based on Phase Space Reconstruction: El Niño 3 Index Forecasting

Climate forecasting, especially tor the abnormal weather like El Nino and La Nina etc., is one of the most difficult tasks of time series prediction domain, because of its inherent difficulty of long-term forecasting. In this paper, we investigate whether there is a simple pattern to describe El Niño phenomenon that can be reconstructed by mathematical models and a multiple steps ahead predicting method is proposed, which is an improved echo state network (ESN) using the phase-space reconstruction (PSR). PSR is to embed a one-dimensional time series sequence in a high-dimension phase space as the input of the ESN. Specifically, the method PSR-ESN can capture seasonal characteristics of the climate data and reduce the lag of prediction as much as possible. The method is evaluated on a real Niño 3 SST index data and it outperforms the other state-of-the-art baselines from the perspective of the magnitude and the lag.
Research on Person Recognition Model of Domain Adaptive Learning

The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.
Convert Gestures of Arabic Words into Voice

Abstract Gestures are one of the best ways of communication between dumbs and other people using the expression of signs language. In this paper, we suggest an algorithm for recognizing hand gestures of Arabic words (اتمنى لك حیاة سعیدة-اقتباس) to by using dumb (through signs) and convert the sings into voice corresponding to sings words. The proposed algorithm for Convert Gestures of Arabic Words into Voice, record video of gesture (of the dumb person) then convert the video into frames (images), preprocessing for the resulted image must done by remove the noise, resize the images and increase the contrast, then calculate the distance to clustering the words by using (C4.5, k-mean, k- medoid and artificial neural network), calculate the distance (or features) by using Euclidean distance and slope where, there are eighteen features (eight features from Euclidean distance, eight features from slop, Area, and perimeter). The results in the training stage were (C4.5 gave 100%, k-mean gave 95.2% k-medoid gave 91.9% and ANN gave 91.27%). While in the testing stage we used three classifiers (Euclidian Distance, Modify of the Standardize Euclidian Distance and Correlation) and the results show that (Euclidian Distance gave 94.4%, Modify of the Standardize Euclidian Distance gave 100% and Correlation gave 94.4%) We create our database (three videos with 250 frames) for training and one video for testing.
Automated Threat Propagation Model Through a Topographical Environment Modelling

Situation Awareness area deals with an ever-growing amount of data to be processed. Decision makers need new tools to swiftly assess a situation, in spite of the huge amount of information to interpret. This reality is even truer in Crisis Analysis as military and rescue domains. Automated systems are absolutely necessary for decision makers as they enable them to save a valuable time while making decisions quickly in order to solve problems. In this paper, we will first focus on a model of a dynamic environment designed for a military use case based on the NATO military doctrine. The scenario we have chosen is that of an army attacking the borders of a country. Intelligence services identify specific zones to observe and understand the enemy’s intentions. The model provides a topographical structure representing the different zones and the potential targets. The second part of the following work is an automated threat propagation model which provides a heat map of the threat in case of an enemy attack. Thanks to the threat spreading representation, the system is able to propose the k-best strategies of the assailant to identify which target the enemy is likely to attack to set up an efficient and fast counter-measure.
A Subtopic Classification Method Based on Latent Dirichlet Allocation Model and Topic Similarity

For each topic discussed in Micro-blog, users pay attention to different aspects. So some subtopics under a given topic is formed,which provides the basis for user group division of Micro-blog topic. However, due to the difficulty in determining the appropriate number of subtopics in all existing subtopic division methods for Micro-blog, the division effect is not good enough. In order to divide the subtopics of Micro-blog topics, this paper proposes a subtopic classification method based on Latent Dirichlet Allocation model and topic similarity. First, Latent Dirichlet Allocation model and Gibbs sampling are used to obtain subtopics distribution of the sample set and the word distribution of each subtopic. Then, in each topic, some words with high probability in word distribution are selected as keywords to calculate topic similarity and fuse similar topics. Finally, we give the subtopic name according to its keywords manually. Experimental results show that the proposed model can divide a given Micro-blog topic into several subtopics well.
A Simulation Study of Integrated Maneuver Trajectory Control

Aiming at the maneuver control problem that depends on the standard maneuver library or pilots experience, a method is proposed to improve the Monte Carlo Tree Search algorithm based on Receding Horizon Control. Through establishing a search model to find the maneuver sequence, meanwhile updating the state of UAV by iteration until the maneuver target is reached. The simulation results show that maneuver missions can be completed with a high success rate in different conditions and for different targets. Moreover, it is demonstrated that the algorithm is relatively robust with respect to parameters perturbation experiment.
Optimal time-varying Kalman filter and predictor for the descriptor system with multiplicative noise and colored noises

For the linear stochastic descriptor system with multiplicative noise and colored additive noises, the optimal time-varying Kalman filtering is addressed. By the singular value decomposition (SVD) method and fictitious noise approach, the descriptor system with multiplicative noise is transformed to two reduced-order non-descriptor systems without multiplicative noise. By the augmented state approach and difference transformation approach, the new augmented state space model with correlated white noise is presented. For the obtained non-descriptor standard system with white fictitious noises, the Kalman filter and predictor of the reduced-order augmented state and white process noise is presented. Further, the optimal time-varying Kalman filter and predictor of original state for the descriptor system are obtained according to the relations of the new and original systems. One simulation example verifies the correctness and effectiveness of the proposed algorithm.
Video Region Annotation with Sparse Bounding Boxes

Video analysis has been moving towards more detailed interpretation (e.g., segmentation) with encouraging progress. These tasks, however, increasingly rely on densely annotated training data both in space and time. Since such annotation is labor-intensive, few densely annotated video data with detailed region boundaries exist. This work aims to resolve this dilemma by learning to automatically generate region boundaries for all frames of a video from sparsely annotated bounding boxes of target regions. We achieve this with a Volumetric Graph Convolutional Network (VGCN), which learns to iteratively find keypoints on the region boundaries using the spatio-temporal volume of surrounding appearance and motion. We show that the global optimization of VGCN leads to more accurate annotation that generalizes better. Experimental results using three latest datasets (two real and one synthetic), including ablation studies, demonstrate the effectiveness and superiority of our method.
Toward Artificial Social Intelligence: A Semi-supervised, Split Decoder Approach to EQ in a Conversational Agent

This paper explores the idea of making text-based conversational agents more human-like by engineering emotional capabilities in them. The emotional quotient is still missing in the current generation of conversational chatbots. Since this is a broad problem, this research focuses on a key building block of such agents, i.e., ability to respond with a particular emotion given an input text embedded with a certain emotion. This work achieves this research goal by using a few innovative strategies, i.e., a layering of the system architecture, semi-supervised learning to workaround lack of labeled data, an innovative agent model based on a classical decision-maker coupled with a deep learning-based split decoder architecture, greedy training of the individual components of the model, an innovative approach for model evaluation based on emotion intensity, and finally, selection of final weights of the components of the model based on experimental results measured in terms of perplexity. The results obtained are extremely promising.
Genetic-Neuro-Fuzzy Controller for Indirect Vector-Controlled Induction Motor Drive

In this work, genetic-neuro-fuzzy controller (GA-NFC) for speed control of induction motor, which has the advantages of fuzzy logic control (FLC), genetic algorithm (GA), and artificial neural networks (ANN), is presented. Genetic algorithms are used to tune the membership functions of neuro-fuzzy controller and advance the neuro-fuzzy controller (NFC). To execute this, normalization parameters and membership functions are translated into binary bit string in order to be optimized for fitness function. Multipoint crossover, binary encoding method, and roulette wheel selection techniques are used to improve the efficiency of existing genetic algorithm. Neural networks are good at recognizing patterns and fuzzy logic is good at taking decisions. In fuzzy logic, experts should write the fuzzy rules, but in case of NFC, computer writes rules by itself. Input to proposed NFC is only speed error, but conventional NFCs use both speed error and its derivative as inputs. GA-based NFC controller for a field-oriented/vector control of induction motor is drive-simulated using MATLAB/Simulink. Simulation results indicate a great development in shortening settling time, decreased speed, and torque ripples. GA-NFC offers significant speed accuracy over a conventional NFC.
Development and application of positioning and queue transformation technology for multi-smart cars

In order to improve the situation that the technology used in entertainment equipment in the market is relatively backward and cannot meet people's consumption demand, this thesis applies wireless sensor network technology, embedded technology and UWB positioning technology to the smart car. Multi-smart car control system. According to the overall framework and the selection of the main control chip, the selection of other chips and modules in the system is determined. The hardware design of each module is carried out, and the software design of each function is carried out. The UWB-based positioning algorithm is analyzed, and a solution for multi-smart car positioning is proposed. The grid method and artificial potential field method are analyzed separately, and the combination of these two methods is given to the algorithm design of multi-smart car formation control.
Fusion Estimation for Multi-sensor Nonlinear System with Disorder and Packet Loss

For a multi-sensor nonlinear system with disorder and packet loss, a fusion estimation algorithm is presented in this paper. Firstly, for equally spaced measurement sampling system, the disorder measurement is sorted by using measurement prediction in the group. Then, for the packet loss case, the lasted measurement is used for filtering. Combining the above proposed algorithm with the centralized Cubature Kalman filter (CKF), a fusion estimation algorithm for multi-sensor nonlinear system with disorder and packet loss algorithm is presented. In the simulation, the proposed algorithm is applied to an indoor positioning system based on ultra-wideband (UWB) with three sensors. The results show the effectiveness of the proposed algorithm.
Implementation of Data Mining Method for Classifying Company Application Data

Applications or software are necessary in undertaking a business company. The more advanced a company, the more applications are used. Therefore, these applications should be checked and analyzed every time to avoid stacking applications or providing information about the lack of existing applications. Data mining methods have been widely applied in companies especially to handle large data cases. In the classification problem, data mining method works very well. Algorithms that we use to classify and predict company applications are naive bayes, decision tree, random forest and k-nearest neighbors. From the research result, we found that decision tree method with J48 algorithm is the best method with the highest accuracy value and also has the fastest time compared to other methods. The accuracy value and the execution time of decision tree method are 99.92% and 0.71 seconds respectively.
Review of Different Combinations of Facial Expression Recognition System

Abstract The facial expression recognition (FER) system is a classifier system that attempts to recognize facial expressions based on the analysis of emotion behaviour on the face. The FER system can be implemented by using one classifier or combining multi feature extraction and/or multi classifiers. In general, FER is used with one classifier system to find the best label. Although a classification system is commonly used to find the most likely facial expression, it still produces substantial numbers of errors due to several factors that influence the FER result, such as data quantity, and environmental conditions (i.e. illumination and noise). Therefore, combined multi feature extraction methods and/or multi classifier systems are useful to avoid the single classifier errors. Multi feature extraction or a multi classifier system combination are used to take advantage of different system hypotheses to find an accurate result. This paper is a survey of the latest system combination techniques being used to enhance the classification performance in the FER system; the most recent studies are presented.
Comparing the Value of Control with Characteristic of Prediction Conveyed in Mathematics Textbooks in Malaysia and Australia

This study aimed to compare the value of control with characteristics of prediction conveyed in Year 10 secondary school mathematics textbooks from Victoria, Australia and Malaysia.A mathematical textbook was analyzed from each country, namely the TVA Math textbook from Australia and TMA Math textbook from Malaysia.Two chapters were selected for analysis from both textbooks with two equivalent chapters, namely Linear Equation and Trigonometric.Content analysis was used to analyze the data.In general, the TVA Math textbook conveyed the value of control with the characteristics of prediction more significantly, compared to the TMA Math textbook.The analysis of both of the Math textbooks show that, the value of control with characteristics of prediction was conveyed through three situations, namely learning objective, note and problem solving.The recommendation was that both textbooks, especially the Malaysian mathematics textbook, should be given more opportunities to instill more mathematical tasks related to the value of control, specifically the characteristic of prediction.Hence the mathematical textbooks could become a more comprehensive learning resources, if it focused not only on the cognitive aspect, but also on the affective aspects, particularly the value of control.
Freight Analysis Using YOLOv2

Monitoring traffic of India and calculating the peak hours and density count in a single day helps to develop a required travel and traffic volume estimates, which is required for satisfying all the needs in the planning of roads, its construction, its maintenance and overall administration of the state. Vehicle counting is an important aspect to understand the traffic load and optimize the traffic signals. Detection of vehicles is expected to be more efficient and robust in number of sceneries. Due to improvement in various algorithms and research work, detection mechanism of traffic data analysis has made a significant improvement over traditional methods. Traditional machine learning algorithms and computer vision for object detection now running under slow response time. This problem can be solved by modern architectures and algorithms based on ANN (Artificial Neural Network), like YOLO (You Only Look Once) without any major losses. YOLO and its versions achieved a jaw-dropping performance in computer vision and had achieved a great success in object detection and classification. In this paper, we are presenting vehicle counting, detection and classification based on YOLOv2. Some video sequences have been taken and tested with the planned algorithm. The results can be a solution for planning of new roads or any other diversions for heavy vehicles can be considered during the peak time. A detection mechanism through YOLOv2 differs from other roadway sensors, such as radar or inductive loops, which provide data only regarding traffic flow and density, and do not provide information about the type of the vehicle in real time.