Reviewerly/exHarmony huggingface dataset:
A high confidence correlation filter tacker based on multi-feature adaptive weighting

Correlation filter-based trackers have achieved excellent performance in visual tracking in recent decade. Compared with traditional trackers, they show better robustness and efficiency. Most correlation filter based trackers learn the appearance model using only a single feature, like Histogram of Oriented Gradients feature, color naming feature, Gray features and so on. However, single-feature model can't maintain great robustness in various complex situations. In order to solve this problem, we propose a High confidence correlation filter tacker based on multi-feature adaptive weighting. The tracker can acquire the initial weights using the information obtained in the second frame, and adaptively adjust the weights of different feature according to the tracking results of each frame. Moreover, we use a high confidence update strategy, and exploit the high confidence tracking results to update the model, so as to reduce the probability of model drift. We experiment the algorithm on 36 sequences of OTB-50 benchmark dataset, the experimental results show that our algorithm has a better performance compared with the original algorithm on multiple sequences such as Dragonbaby, Deer and Skating1. We also experiment our algorithm on our own sequence, in which our algorithm still has a better performance.
Real Time Object Tracking System Using Ultraviolet Laser

Object tracking system is a very vast field which discovers its applications in security, defense and industrial, purposes. Path following, monitoring and pinpointing is the core of all the above mentioned examples but there are few limitations. For smooth movement and perfect results many techniques were tested but the focus was to make assembly as light weighted and cost effective as possible, whereas strong enough to drag maximum possible weight. Efficient path following, pinpointing and assembly dragging algorithm is developed and implemented using laser mesh which is spread all over the room and is responsible for all the target motion. The administrator of the secure area can view/control the complete restricted area by sitting in cabin remotely. Wireless camera is used to take the view of restricted area and the intruder. Camera is mounted on the platform that moves to and fro and also has rotating and tilting ability up to 360 degrees.
Curve Control Points-based Feature Extraction for Visual Servo with rotational pose compensation

The spline curve is commonly used for describing the contour of the target object in the field of image-based visual servo (IBVS). However, this method has the disadvantage of low sensitivity and poor precision when facing the camera's rotational motions. To solve this problem, a visual servo system with rotational pose compensation is presented in this study. The NURBS (Non Uniform Rational B-spline) curve is firstly used to obtain the curve control points by fitting the projected curve of the target contour on the image plane. And then, the control points are employed for constructing the image Jacobian matrix. The rotational compensation controller is designed to compensate the rotational motions of the robot. To verify the feasibility of this method, a set of visual servo tasks are carried out on the Puma560 platform. Experimental results demonstrate that the proposed method has high accuracy and computational efficiency.
Design and Application of Computer Games Algorithm of Checkers

In order to improve the progress of the computer games of checkers, a game system is designed based on the research of checkers of computer games. The paper provides the Min-Max search tree algorithm for serving to search the game tree. According to the Alpha-Beta pruning algorithm, the game system can get the valuation of the game tree depth and is designed the game model whose search depth can be pruned by this way. The game model solves the game formation, the valuation of the depth and the time-consuming search problem, which can achieve the search efficiency and performance. The experimental data and results show that the game search model is feasible, practical and effective finally.
Euler Diagrams for Defeasible Reasoning

We investigate Euler diagrammatic systems for defeasible reasoning by extending the usual systems for Euler and Venn diagrams corresponding to standard classical logic. To achieve this, we use the generalized quantifier “most” to formalize defeasible reasoning, as proposed by Schlechta (1995), where defeasible knowledge is represented as “Most A are B” and axioms for “most” are defined. We introduce an Euler diagrammatic system for defeasible reasoning by introducing circle mA that represents “most A” for each circle A. We show that our Euler diagrammatic system is a diagrammatic representation of the symbolic system of the generalized quantifier “most”. Furthermore, we investigate skeptical and credulous strategies in defeasible reasoning with our Euler diagrams.
The Influence of NER on the Essay Grading

Automatically evaluating essays is still a hard problem to be dealt with intelligent algorithms. The majority of approaches try to find shallow features to infer the grade such as the number of words, verbs, sentence size and so forth. Others strategies seek for more complex structures in written language to tackle this problem. This paper discusses the use of Named Entities as additional features to enhance the quality of the state-of-the art for the Portuguese language in two out of five dimensions of the evaluation criteria. These results show the importance of taking into account the named entities to evaluate the quality of the students’ arguments concerning the essay’s topic addressed.
Weighted Sparse Graph Non-negative Matrix Factorization based on L<sub>21</sub> norm

Dimension reduction is widely concerned because of the rapid development of all walks of life, which leads to the exponential growth of data dimension. As a basic method of dimension reduction, non-negative matrix factorization is easily affected by noise, but its improved algorithm L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> NMF is not sensitive to noise. Therefore, the paper proposes a weighted sparse graph non-negative matrix factorization based on L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> norm(LSL <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> NMF). In this method, L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> norm is used as the measure criterion, the non-negative matrix is decomposed into the summation of one error matrix and the product of two matrices non-negative, the weight sparse graph is applied to the regularization term to preserved geometrical structure of data. An efficient iterative approach is developed to solve the optimization problem of LSL <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">21</inf> NMF. On the standard noisy public dataset,the experimental results show that the method is more effective and robust than other methods.
ALEC: An Accurate, Light and Efficient Network for CAPTCHA Recognition

The CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart) is a common and effective security mechanism applied by many websites and applications. CAPTCHA recognition is an important and practical problem in text recognition research. Compared with traditional methods, DCNN (Deep Convolutional Neural Network) has achieved competitive accuracy in CAPTCHA recognition recently. However, current CAPTCHA recognition researches based on DCNN usually use conventional convolution network, which causes high computation complexity and great computing resource consumption. Aiming at the problems, we propose an Accurate, Light and Efficient network for CAPTCHA recognition (ALEC) based on the encoder-decoder structure. The ALEC can greatly reduce the computation complexity and parameters while ensuring the recognition accuracy. In this paper, standard convolutions are replaced by depthwise separable convolutions to improve computational efficiency. The architecture utilizes group convolution and convolution channels reduction to build a deep narrow network, which reduces the model parameters and improves generalization performance. Additionally, effective and efficient attention modules are applied to suppress the background noise and extract valid foreground context. Experiments demonstrate that ALEC not only has higher speed with fewer parameters but also improves the accuracy of CAPTCHA recognition. In detail, the ALEC achieves about 4 times speed up over the standard ResNet-18 while reducing 97% parameters.
Optimal time-varying Kalman filter for descriptor system with random one-step measurement delay

For the linear stochastic descriptor system with random one-step measurement delays, the optimal time-varying Kalman filtering is presented. The singular value decomposition (SVD) method, the derandomization approach and the augmented state approach are presented, which are applied to transform the descriptor system with random one-step measurement delay to the standard non-descriptor system with fictitious white noises. Applying the Kalman filtering and the relation of the new and original systems, the corresponding Kalman filter and filtering error variances are presented. A simulation example about circuits system verifies the correctness of the proposed results.
Research and Application on Internal Model Control Algorithm Based on Echo State Network

Internal model controller(IMC) is widely used due to its simple structure and flexible parameter adjustment. However, the inverse model of the object is difficult to obtain accurately, especially for some non-minimum phase systems. To solve this problem, this paper proposed an neural network IMC combining Echo State Network(ESN) and IMC method. The initial ESN inverse controller and reference model are trained offline based on input and output data. In order to detect the model mismatch, the mutual information between input and model deviation are calculated. The online update of controller and reference model are achieved based on Recursive Least Square method to enhance better robustness. Simulations and experiments shows the effectiveness of this method and it has some ability of self diagnosis and self recovery.
Research on Improved RRT Path Planning Algorithm

The traditional RRT (Rapid-exploration Random Tree) path planning algorithm has poor real-time performance and large random adjustment of algorithm parameters, because it requires a large amount of calculations to traverse the entire planning task space. In order to solve the above problems, this paper analyzes the influence on the algorithm performance of the two parameters of step size and selection probability through experiments and statistical analysis of the data. Based on above research results, the principle of algorithm parameter selection and the improved deredundant node algorithm in the process of random tree growth are put forward. Finally, experimental simulation and data collation statistics confirm that the improved RRT path planning algorithm can achieve pruning in the process of random tree growth to make the algorithm quickly converged to the target point, and improves the efficiency of algorithm path planning.
Smooth estimation of size distributions in an oriented cylinder model

Abstract Kernel estimators are proposed for estimating the cumulative distribution functions and the probability density functions of several quantities of interest in a stereological oriented cylinder model. This oriented cylinder model was developed to represent anisotropic microstructural features in materials. The asymptotic properties of these estimators are studied, and the estimators are applied to two banded dual phase steel microstructures. The estimation method is quite general and can also be applied to distributions of other univariate quantities of interest.
Encryption and Decryption for Network Security Using Reverse Context-Free Grammar Productions

An enhanced symmetric key cryptographic algorithm is presented in this paper. This new technique uses context-free grammar as it presents a unique property of cryptography which states that one can generate strings from a given grammar; but one cannot classify a grammar if provided with only the strings generated by it. The planned idea consists of four modules: encode, encrypt, decrypt, and decode. Firstly, the file is encoded by making use of the encoding techniques to obtain an intermediate text. The intermediate text is then encrypted to get the ciphertext file using context-free grammar along with the secret key. The secret key is generated by using random number generation algorithm. At the receiver’s side, the ciphertext file is then decrypted by making use of context-free grammar followed by decoding techniques to obtain the original file.
Shaping of a writing researcher’s identity

Abstract The article highlights the problem of writing for publication research purposes and its impact on a beginning scientist’s identity. The author points out two aspects of research writing and publication in international indexed journals. The first aspect is the developed skills of academic writing in English. The second aspect is the knowledge of research methodology and scientific publication design. The paper views the use of the two mentioned skillsets through the lens of English for Scientific Research module. During the module, the doctorate students of Northern (Arctic) Federal University were offered a survey consisting of three blocks of questions. The students’ answers were further developed through Socratic questioning technique in order to investigate the students’ identity formation. The findings reveal a severe lack of confidence among beginning scientists in relation to the research publication process, particularly to the methodology of research. The author concludes that the challenges of the research publication process impact the identity of a junior scientist in a negative key. The study demonstrates that beginning writing scientists need a thought-through professional assistance in the area of publication research, especially research methodology so that they could build a strong identity, necessary for their development in the international scientific community.
Trigonometric Neural Networks Lp , p<1 Approximation

Abstract Many researchers studied the approximation by neural networks approximation. However only using first or second modulus, that is with low speed approaching zero. Here we define a neural network. Then we use it to approximate functions from L p− quasi normed spaces we prove upper and lower bounds trigonometric neural networks estimations using the modulus of smoothness of order k.
Constructing Multi-level Safety System of Gas IoT which is Characterized by Cloud, Management and Terminals

Beijing Gas Group used the Internet of things technology to build its Internet of things cloud platform. In the field of intelligent gas metering management and intelligent gas pipe network management, it has created the first complete NB-IoT intelligent application case in the domestic urban gas industry. The multi-layer defense system constructed by the information security design from the intelligent terminal to the application system effectively guarantees the data acquisition and remote control process of the massive NB-IoT gas intelligent terminal. The Internet of things cloud platform as a whole refers to the technical requirements of three levels of national information security protection. The security management subsystem of the Internet of things supports hardware encryption on security chips and software encryption. It can flexibly combine symmetric and asymmetric algorithms that meet the national security standards according to the needs of various business scenarios, and realize the encryption, decryption and signature verification processing of various data.
Influencing the Velocity of Central Bank Digital Currencies

The advent of stablecoins offers new and innovative ways to improve financial inclusion, reduce transaction costs, and increase the efficiency of the global financial system. The following paper explores the assets and process necessary for creating a central bank digital currency (CBDC) on the Celo platform, as well as the potential impact on the financial system. Perhaps most importantly, the paper also introduces the idea that current technological advancements allow for a better understanding of the velocity of money, and may afford central banks the ability to influence money velocity, thus potentially creating a new transmission channel for monetary policy.
Obstacle Avoidance with Energy Efficiency and Distance Deviation Using KNN Algorithm for Quadcopter

The following topics are dealt with: photovoltaic power systems; power engineering computing; mobile robots; collision avoidance; learning (artificial intelligence); neural nets; diseases; medical signal processing; multi-robot systems; autonomous aerial vehicles.
Starting flipped classroom method with iPad and Apple Pencil in the Analog Electronics Course

The present work describes several methodologies carried out for basic teaching inverted-classes or “flipped classroom” whiches can be applied to Analog Electronics subjects. Thus, the guidelines that have been used to adapt to this type of teaching are described, as well as the technology applied in the classroom and for the creation of teaching, both in written or audiovisual content forms. In order to know the degree of success of the suggested approach, satisfaction inquiries have been made to the students, as well as a comparative study between the grades obtained in the current course with respect to the previous course.
Research on the Assistant Decision-making of Gas Regulator Based on Case-based Reasoning

In order to improve the efficiency of fault decision-making of gas surge tank, this paper proposes a case- based reasoning (CBR) for assistant decision-making of gas surge tank. On the basis of the real case of surge tank fault, the characteristic attributes of the case are extracted, and the case is represented by the frame representation method. Through analytic hierarchy process, local similarity calculation model and global similarity calculation model, the assistant decision-making method of gas surge tank based on case-based reasoning is established. Finally, the source case and target case are analyzed, and the results are proved. The application of this method in assistant decisionmaking of gas surge tank fault has practical significance.
Target tracking method based on the fusion of structured SVM and KCF algorithm

Aiming at the drift problem of traditional KCF (kernelized correlation filter) target tracking algorithm caused by scale change and fast movement, a tracking method based on the fusion of structured SVM (support vector machine) and KCF algorithm is proposed. Combined with the location and scale information of the tracking target, a structured SVM classification and prediction model is established to replace the ridge regression filtering method in KCF, train the samples collected by cyclic matrix, and solve the problem of not detailed classification. In addition, a search strategy optimization method based on tracking target motion features is proposed to reduce unnecessary search time in dense sampling, further improve the search efficiency and classifier accuracy of traditional KCF algorithm in dense sampling, improve its calculation efficiency and target tracking performance in complex environment. This can solve the problem that the target cannot be accurately tracked due to the drift caused by the change of target scale and fast movement. Experiments show that the algorithm can locate the target area more accurately when the target scale changes and fast movement. Compared with the traditional KCF tracking algorithm, it has better performance and stronger robustness.
A Method for Positioning Mark Point on Liquid Crystal Glass Based on Machine Vision

The positioning of liquid crystal glass is very important in the production process of liquid crystal glass. In order to improve the assembly efficiency of liquid crystal backlight screen, this paper proposes a liquid crystal glass positioning algorithm based on machine vision to realize fast and accurate positioning of Mark points (cross shape) on liquid crystal glasses. The algorithm first solves the problem of image fogging and uneven illumination by using the single-scale Retinex algorithm for the original image and the template image, and then determines the Region of Interest (ROI) based on gray-scale template matching. Next, perform Mean filtering, threshold segmentation and the open operation on the ROI so that the independent and complete Mark point is obtained. Finally the minimum external rotation rectangle fitting algorithm is used to fit and locate the Mark point. The experiment results show that the method in the paper can quickly and accurately locate the Mark point on the liquid crystal glass. In the sample experiment, the recognition success rate reached 98.04%, the positioning success rate reached 94.12%, and the positioning error of angle and the Mark point center point coordinate was controlled within 1 pixel unit and 0.3° respectively.
Research on Robot Tracking of Books Returning to Bookshelf Based on Particle Swarm Optimization Fuzzy PID Control

In order to avoid library intelligent returning books to bookshelf robot during the process of searching for books and bookshelves, the tracking time is long and the path deviation is large. The system uses a navigation system based on a fuzzy PID controller based on particle swarm optimization to avoid problems such as empirical parameter selection and parameter invariance in fuzzy PID control. The particle swarm optimization algorithm is used to optimize the controller parameters, enhance the controller's adaptive ability, and improve its control accuracy. Experiments show that the system improves the accuracy of the path, and has a faster response speed and higher tracking accuracy when moving.
Discriminative Weighted Non-negative Sparse Low-rank Representation Classifier for Robust Face Recognition

Aiming at the traditional face recognition method, when the training samples and test samples are in complex application scenarios, the face recognition performance is degraded by the interference factors such as light changes, pollution and occlusion. This paper proposes a robust face recognition based on discriminative weighted non-negative sparse low-rank representation classification algorithm (WDNSLRRC). Based on the non-negative sparse low rank representation classification algorithm (NSLRRC), structural inconsistency constraints and singular values with different weights are assigned by adaptive weighted kernel norms. Different classes of samples may have the same features, and structural inconsistencies may inhibit these same features while retaining independent features. Weighted kernel norm (WNNM) is a low rank algorithm for constrained matrix singular value sparsity. The superiority of the algorithm in face recognition performance is proved in different face databases.