Reviewerly/exHarmony huggingface dataset:
Global optimization image completion processing based on generative countermeasure network

In order to improve image clarity and ensure image processing effects, a global optimization image completion method based on generative confrontation network is proposed. The defect area of the image is collected and detected, and the feature changes of the globally optimized image are analyzed, thereby effectively de-noising the image information and effectively improving the image quality. Finally, experiments show that the global optimization image completion processing method based on the generative confrontation network can better improve the image definition and has high practicability.
EXPERIMENTAL SETUP FOR EVALUATING THE TRANSPORT FUNCTIONS OF LIVING CORNEAL ENDOTHELIAL CELLS

The paper summarizes the requirements and describes an experimental setup that allows us to study the dynamics of the concentration of intracellular sodium in the endothelium of the cornea.
Automatic checkerboard corner detection combined with gray features and energy minimization (ADGE)

In the camera calibration process, the checkerboard pattern has a wide range of applications. Aiming at the current checkerboard corner detection algorithms that have missed and misdetected situations, this paper proposes an automatic checkerboard corner detection algorithm that combines gray-scale features and energy minimization(ADGE). The ADGE first uses the proposed two sets of gray-level symmetry operators to process the image separately to extract the candidate corner points, and then uses the regional block mode to dynamically search for the local maximum value of the candidate corner points. It further constructs a reaction function to linearly solve the sub-pixel coordinates, and finally extracts all checkerboard corner points and sorts them by minimizing the energy function. Experimental results show that the corner points extracted by the ADGE have no missed and false detection, and the corner reprojection accuracy is 0.1298 pixels. The ADGE can meet the requirements of camera calibration and provide it with high-precision data.
Khmer Named Entity Recognition Based on LSTM-CRF Model

Named Entity Recognition(NER), as a significant research content of information extraction, is broadly used in Natural Language Processing fields such as information retrieval, machine translation, question answering system, and so on. Named Entity Recognition in Khmer plays a supportive part in the study of Chinese–Khmer bilingual understanding. However, the differences between languages make it hard to transfer the common Chinese and English Named Entity Recognition into Khmer. Aimed at the problem that the output of long short-term memory neural network model does not consider the sequence of tags, the output of long short-term memory neural network model and Khmer entity feature is used to input features of conditional random field model to extract Khmer corpus entity. The result of experiment shows that the precision of the conditional random field model is increased by 1.32%, recall rate by 2.55% and the F1 value which is used to measure the precision and recall rate by 1.92% after using long short-term memory neural network as the inputted feature of conditional random field model, which unites the output of neural network model with the entities in Khmer. The outcome of experiment proves the effectiveness of this method.
The optimization and improvement of bridge game system

This article from the background of the bridge, improving the system analysis, experimental test three perspective, based on the idea of reinforcement learning, from two aspects of contract and scoring ability, through a large number of calculations and the original program and every time a new system of winning IMP value level to achieve the agreed order, accumulate experience, design a set of good rewards and punishment mechanism, greatly improve the efficiency of the bridge to play CARDS and intelligence, thus the bridge game system was optimized and improved. In addition, the traditional methods need to manually extract the features of poor expansibility, this paper combined with reinforcement learning algorithm, the ideas of game devised a new system, under the condition of different effective play the computer, the program has also reached a higher level of the game structure design, for the incomplete information game theory provides a reasonable method, application creates opportunities to people living in the future.
Is Face Recognition Being Abused?–A Case Study from the Perspective of Personal Privacy

With the diversified development of face recognition application, face recognition technology plays an increasingly important role in people&#x0027;s daily life. However, due to the excessive use of face recognition, individual privacy violations and data leakage incidents have begun to emerge, raising concerns that the development of artificial intelligence such as face recognition has its side effects. It seems to be a contradictory condition that a host of people not only desire to enjoy the convenience of technology upgrading, but also want better privacy protection. This paper starts with the application of face recognition in different subjects, analyzes the harm caused by the overuse of face recognition technology through different cases, and finally puts forward corresponding policy suggestions based on the current protection methods of biological information data in different countries.
Iterative Deepening Dynamically Improved Bounds Bidirectional Search

This paper presents a new bidirectional search algorithm to solve the shortest path problem. The new algorithm uses an iterative deepening technique with a consistent heuristic to improve lower bounds on path costs. The new algorithm contains a novel technique of filtering nodes to significantly reduce the memory requirements. Computational experiments on the pancake problem, sliding tile problem, and Rubik’s cube show that the new algorithm uses significantly less memory and executes faster than A* and other state-of-the-art bidirectional algorithms. Summary of Contribution: Quickly solving single-source shortest path problems on graphs is important for pathfinding applications and is a core problem in both artificial intelligence and operations research. This paper attempts to solve large problems that do not easily fit into the available memory of a desktop computer, such as finding the optimal shortest set of moves to solve a Rubik’s cube, and solve them faster than existing algorithms.
Four-Layer Authentication with Honeypot and Cloud Data Encryption

Data storage on the cloud has been gaining popularity nowadays as the most favoured form of storage for businesses as it abstracts technical details from the consumer, such as server geography and scaling, and provides large amounts of memory that are ready to be accessed by the consumer as and when required. However, the security of data storage on the cloud often prevents businesses to migrate from traditional storage to cloud storage. Providing safe, secure and reliable services in a cloud computing environment is of at most importance in this present day and age. This paper presents ways to securely store data using cryptography techniques and securing it from unauthorized access by giving access to duplicate files on the honeypot. To detect the authenticity of the user, a four-layer authentication system is used along with honeypot technology. This system consists of OTP, password, ReCAPTCHA and a unique RSA certification key (Pem) file verification techniques. AES-256 encryption is used for all files stored on the cloud. In case a user fails any one of the authentication steps involved, they will be redirected to a honeypot file system with irrelevant content that will be displayed to the user. The honeypot detects and counteracts the unauthorized access by generating a garbage file.
Design of the English independent learning platform based on computer technology

In recent decades, computer network technology has also been developed in various fields and attracted the attention of many experts in the English design platform. After having a thorough understanding of computer network technology, these experts and scholars connected them with the design of English independent learning platform, and they also further explored the development ideas of computer network technology in English platform design. This paper also explores the difficulties of English independent learning platform by analyzing the development mode, characteristics and form of English independent learning platform. At the same time, this article also clearly indicates the role, paths and countermeasures of computer network technology in English platform design. The indication of the role, path and countermeasures of computer network technology also enriches the ideas and methods of English platform design, and also lays a certain foundation for the development of English platform.
Formation of student culture in pedagogical skills

Promoting pedagogical skills in the family is a very difficult task, which is explained by the age of parents, educational experience, differences in professions, diversity of worldviews. The main basis of pedagogical skills is professional knowledge. The content of such knowledge includes knowledge of pedagogy and psychology of the methodology of the content of the taught science
On The Resilience of Cellular Neural Networks to Low-intensity Adversarial Attacks

Deep Neural networks are commonly used in various tasks and enabled the solution of many practical problems. These approaches usually result sufficiently high accuracy, but the robustness of these methods in critical applications is still under investigation. Adversarial attacks, in which minor perturbations can cause misclassification pose one of the most significant challenges. In case of convolutional neural networks there is ongoing research to create more resilient networks towards these attacks. In this paper I will demonstrate that multi-layered cellular neural networks in their nature are more robust and resilient to low-intensity attacks than their convolutional counterparts.
Object Detection In Power Operation Scene Based On Two-Stream Network

In power production and construction, power accidents caused by the nonstandard operation of workers will not only bring economic losses but also cause personal injury. To supervise the non-standard operation of workers, this paper proposes an object detection method based on a two-stream Faster R-CNN. The main detection in the operation, whether the power workers in accordance with the requirements of electrical safety operation regulations, correctly wear a safety helmet and uniform, in order to ensure the safety of workers can get the maximum protection. According to the outstanding color feature of workers&#x2019; uniforms and safety helmets. In this paper, a two-stream network based on Faster R-CNN is proposed, in which the first-stream is RGB stream and the second-stream is HSV stream. The purpose of introducing HSV stream is to highlight the HSV color space features of the detected object, so that the final neural network model can be more sensitive to the HSV color space features of the detected object, so as to solve the false alarm problem when using Faster R-CNN model only. Compared with the Faster R-CNN method, the accuracy of the proposed method is improved by six percentage points.
Identical Twin Face Recognition Using Gabor Filter, SVM Classifier and SURF Algorithm

Face recognition can be used to identify a person uniquely based on digital image processing. This face recognition is mainly used in security systems. The major problem here is to identify twins; in order to overcome this problem, we are using facial recognition technique algorithms such as multisvm open surf and Gabor. The facial recognition algorithms are used to identify identical twins with the help of classification. In the proposed system, the input given is the image of the person from which different features are extracted by using the Gabor algorithm, and open surf algorithm is used to find the dissimilarities and similarities between the two images. Using multi-SVM classifier, we compare the features of the two images, and then it is classified. As a result of classification, we can identify whether the persons are identical twins or not.
Research on Interior Design Method Based on Deep Learning

With the rapid development of China's economy, the quality of life and living standards of the people have been greatly improved, and the living environment requirements are gradually improved, so it is more challenging for the construction industry. Due to the lack between interior design and architectural design, there will be conflicts between interior design and architectural design, which will seriously affect the living experience, fail to meet the needs of citizens, and is not conducive to improving people's living standards. According to the problems existing in interior design and architectural design, the architectural industry improves them. By improving the level of interior design and architectural design, it integrates interior design and architectural design together, so as to provide quality services and improve the living experience of citizens. Combine interior design and architectural design together, and put forward improvement Suggestions for interior design and architectural design, and provide reference for the construction industry.
The Reference Distributions of Maurer’s Universal Statistical Test and Its Improved Tests

Maurer’s universal statistical test can widely detect non-randomness of given sequences. Coron proposed an improved test, and further Yamamoto and Liu proposed a new test based on Coron’s test. These tests use normal distributions as their reference distributions, but the soundness has not been theoretically discussed so far. Additionally, Yamamoto and Liu’s test uses an experimental value as the variance of its reference distribution. In this paper, we theoretically derive the variance of the reference distribution of Yamamoto and Liu’s test and prove that the true reference distribution of Coron’s test converges to a normal distribution in some sense. We can apply the proof to the other tests with small changes.
Orthogonal matching pursuit hybrid precoding algorithm based on multi source security information

The traditional precoding algorithm lacks comprehensive processing of information, which leads to high BER in communication process. Therefore, a hybrid precoding algorithm based on multi-source security information is proposed. In the design process, a coding channel model is established. According to the principle of channel sparsity, the channel parameter phase is selected as the precoding vector, and the orthogonal matching tracking hybrid precoding is used, The association fusion of multi-source security information is realized. The simulation results show that the designed hybrid precoding algorithm based on optimal decomposition has better BER than traditional algorithm in different conditions, and has better performance in practical application.
Lexically-Aware Semi-Supervised Learning for OCR Post-Correction

Much of the existing linguistic data in many languages of the world is lockedaway in non-digitized books and documents. Optical character recognition (OCR)can be used to produce digitized text, and previous work has demonstrated theutility of neural post-correction methods that improve the results ofgeneral-purpose OCR systems on recognition of less-well-resourced languages.However, these methods rely on manually curated post-correction data, which arerelatively scarce compared to the non-annotated raw images that need to bedigitized. In this paper, we present a semi-supervised learning method that makes itpossible to utilize these raw images to improve performance, specificallythrough the use of self-training, a technique where a model is iterativelytrained on its own outputs. In addition, to enforce consistency in therecognized vocabulary, we introduce a lexically-aware decoding method thataugments the neural post-correction model with a count-based language modelconstructed from the recognized texts, implemented using weighted finite-stateautomata (WFSA) for efficient and effective decoding. Results on four endangered languages demonstrate the utility of the proposedmethod, with relative error reductions of 15-29%, where we find the combinationof self-training and lexically-aware decoding essential for achievingconsistent improvements. Data and code are available athttps://shrutirij.github.io/ocr-el/.
An Operation with Crossover and Mutation of MPSO Algorithm

Zhong, Yuxin Chen, Yuxin Yang, Chen Meng, ZhenyuAs an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems in expert systems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains an open issue. To overcome these drawbacks and strengthen the ability of PSO in solving complex optimization problems, a modified PSO using adaptive strategy called MPSO is proposed, although MPSO has achieved excellent performance, and its convergence and stability are still some defects. In this paper, we presented a new variant of MPSO algorithm which can explore the search space deeper than the previous method, and better performance can be achieved under CEC2013 test suite.
End-To-End Neural Speaker Diarization Through Step-Function

This paper presents an approach to the speaker diarization problem based on a step-wise form of speech file. There are known solutions to the diarization problem using complicated models of neural nets. Hence training of such net requires serious computation. The research goal is to construct an algorithm that uses very restricted resources during the discussion with a few persons using just a regular notebook. The time needed for training the system for work with the given persons is also minimal. This goal is attained by transforming the input signal of the net into a step-function having three values. This circumstance provides leveraging a simple model of the neural net for end-to-end diarization. For training, we use a segmented speech file where any segment belongs to one speaker. The number of speakers is known in advance. We convert each segment into a step-function applying the threshold value estimated using the developed fast algorithm. Using the end-to-end neural net, we exclude the clusterization step in the speaker diarization problem. Experiments show the acceptability of diarization quality.
Named Entity Recognition for Brazilian Portuguese Product Titles

Improving the interaction between consumers and marketplaces, focusing on reaching higher conversion rates is one of the main goals of e-commerce companies. Offering better results for user queries is mandatory to improve user experience and convert it into purchases. This paper investigates how named entity recognition can extract relevant attributes from product titles to derive better filters for user queries. We conducted several experiments based on MITIE and BERT applied to smartphones/cellphones product titles from the largest Brazilian retail e-commerce. Both of our strategies achieve outstanding results with a general F1 score of around 95%. We concluded that using a classical machine learning pipeline is still more useful than relying on large pre-trained language models, considering the model’s throughput and efficiency. Future work may focus on evaluating the scalability and reusability capacity of both approaches.
Dataset Weighting Features Using Gain Ratio To Improve Method Accuracy Naïve Bayesian Classification

Abstract The Naïve Bayes method is proven to have a high speed when applied to large datasets, but the Naïve Bayes method has weaknesses when selecting attributes because Naïve Bayes is a statistical classification method that is only based on the Bayes theorem so that it can only be used to predict the probability of the class membership of a class independently. Independent without being able to do the selection of attributes that have a high correlation and correlation between one attribute with other attributes so that it can affect the value of accuracy. Naïve Bayesian Weight has been able to provide better accuracy than conventional Naïve Bayesian. Where an increase in the highest accuracy value obtained from the Water Quality dataset is equal to 88.57% in the Weight Naïve Bayesian classification model, while the lowest accuracy value is obtained from the Haberman dataset which is 78.95% in the conventional Naïve Bayesian classification model. The increase in accuracy of the Weight Naïve Bayesian classification model in the Water Quality dataset is 2.9%. While the increase in accuracy value in the Haberman dataset is 1.8%. If done the average accuracy of each dataset using the Weight Naïve Bayesian classification model is 2.35%. Based on the testing that has been done on all test data, it can be said that the Weight Naïve Bayesian classification model can provide better accuracy values than those produced by the conventional Naïve Bayesian classification model.
E-LS on The Subject of Temperature: The First Work of a Science-Physics Teacher at Siti Aminah Junior High School Surabaya

Abstract The effect of COVID-19 pandemic in Indonesia requires learning that is usually done face-to-face has turned into online learning. For teachers, the implementation of online learning requires engaging media so that learning becomes more various and exciting. This study aims to analyse the validity of the science literacy-based e-book, which will later be called E-LS. The first work of the Science-Physics teacher at Siti Aminah Junior High School Surabaya can train students’ scientific literacy skills. This study using a research method with the DDD-E design (Decide, Design, Develop, Evaluate). Based on the data analysis that has been done, we obtained descriptive-quantitatively using the Likert scale percentage score criteria. The results show that the percentage of validity in the aspect of Curriculum Conformity of 91.30 % (very valid). The material aspect is 93.45 % (very valid). The language aspect is 91.67 % (very valid). The media design aspect of 91.91 % (very valid). Scientific literacy aspect of 87.50 % (very valid). Overall the level of validity of the E-LS has an average of 91.17 % (very valid). It can be concluded that the E-LS media (temperature topic) is very suitable for use as a science-physics learning medium to train science literacy for junior high school students.
Design of UHV Transmission Line Selection Line

In order to solve the problems of long optimization time and high optimization cost in traditional UHV transmission line selection optimization method, an optimization method based on ant colony algorithm for UHV transmission line selection design is proposed. In the UHV transmission line selection design mode, the author uses cost-type wire selection line quantitative index eigenvalue membership degree and benefit type line selection quantitative index eigenvalue membership degree, establishes a line selection model for UHV transmission lines and gives the UHV transmission line selection evaluation index weight vector objective function. The objective function of the UHV transmission line is optimized by the crossover of the genetic algorithm, and the fitness function of the line selection design is obtained. The optimal solution of the objective function is obtained by using the ant colony algorithm to optimize the design mode of the UHV transmission line. The experimental results show that the optimized time is reduced by 0.58 min compared with the optimized one, and the optimization cost is less than other optimization methods, indicating that the optimization result of this method is ideal.