Reviewerly/exHarmony huggingface dataset:
USED CARS PRICE PREDICTION IN DKI JAKARTA USING EXTREME GRADIENT BOOSTING AND BAYESIAN OPTIMIZATION ALGORITHM

Used cars are in demand today, and the market for used cars continues to grow. Different price variations on each website for used cars of the same type make buyers confused in determining the price according to their value, this will result in buyers getting used cars that are too expensive or overpriced from the price they should be. This research aims to predict price of a used car based on its features such as brand, model, transmission, and others. This research is expected to help prospective buyers in knowing the price of a car based on its value so when buying a car, it is not overpriced. The dataset is a list of used cars with columns model, transmission, and until price of the car. The Algorithms used in this research are Extreme Gradient Boosting (XGBoost) and Bayesian Optimization. Bayesian Optimization is used for automatic hyperparameter tuning in the XGBoost algorithm. From the results of evaluation, model machine learning is not overfitting, which was shown in RMSE metric, the value is 0,12129 on validation dataset and 0,13471 on testing dataset. The machine learning model also can predict the price of car well based on its features as indicated by R Squared metric is 0,98271 on validation dataset and 0,97870 on testing dataset.
Research on multi-traveling salesman problem based on simulated annealing algorithm

As an extension of the traveling salesman problem, the multi-traveling salesman problem is also a typical NP hard problem, but it has wider practical significance. In the past, most of the research on solving the multi-travel salesman problem was limited to the minimum sum of all travel salesman paths as the optimization standard. In this paper, based on the constraints of the minimum travel quotient, the path balance among travel agents, and the time window traversing the city, a mathematical model was established to optimize the multi-travel salesman path using the improved simulated annealing algorithm. The feasibility of the algorithm is verified by a simulation example, and it meets the requirements of real-time performance.
Research on the Construction System of Language Service Platform based on Computer Corpus

The language service platform is an online platform that provides real-time language services for enterprises and individuals. The demanders can obtain the required language services quickly and find professional language services immediately, saving time and effort. A language service platform was built based on client/server. The platform includes online, photo, manual translation and language selection. The service platform builds a corpus, provides standardized services and realizes applications with multiple interaction methods for multi-platform devices. The online translation uses Python to crawl data and implements corpus services through the. NET platform; manual translation builds an artificial translation module through the. NET platform and uses WebAPI to provide external services. The language service platform is part of the national language capacity, essential in promoting international economic and cultural exchanges.
Research on hole feature matching based on image restoration

Aiming at the problem that stains and strong light reduce the accuracy of light hole and threaded hole matching in the process of traditional template matching, a template matching method for body in white hole feature based on image restoration is proposed. Firstly, the patch match repair method is used to repair the image of stains and strong light, so as to eliminate the interference of the two to the matching. Then the light hole and thread are matched by combining the gray matching method of improved search strategy and the edge feature matching method respectively. Finally, the sub-pixel coordinate fitting is performed on the matching results. Experimental results show that the accuracy of matching smooth holes and threaded holes can reach 100 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> and 98%, and the difference between matching accuracy and commercial software matching accuracy is less than 0.3.
Fused Attention Decoder in End-to-End Model with Complex Scenes

Recognition of text in images is a practical technique that has been studied for decades. However, the recently recognition algorithms in complex scenes still have difficulties such as high computational cost and vanishing gradients. This paper proposes a fused End-to-End Attention Decoder image text recognition structure to search for the best character center points and character attention features by using fused shared features in the detection-recognition modules. Our experiments on dataset show that the proposed fused attention model could enhance the performance index of the recognizer, especially to better predict text location and content information in irregular scene images.
Low-Dose Mouse Micro-CT Image Segmentation Based on Multi-Resolution Multi-Organ Shape Prior Knowledge Model

Automatic segmentation of computed tomography (CT) images of mice is a step toward computer-assisted preclinical image analysis. Due to the low image quality of micro-CT images, fully-automatic methods may not achieve robust segmentation. For this reason, human interventions are needed to achieve higher segmentation accuracy. In this paper, we propose a human interactive segmentation method incorporating anatomical prior knowledge for multiple abdominal organs in mouse micro-CT images. The method automatically fits a multi-organ shape model to the user-sketched partial boundary contours. Segmentation accuracy is validated by comparing the proposed method against existing shape models. The robustness of our proposed method was evaluated with different users. Finally, the results suggest the proposed method generates accurate segmentation with good robustness.
Computer Technology and Artificial Intelligence Applied in Smart English Audiovisual Course System

This article guides the construction of English audio-visual course system from the theory of language information system in neurocognitive linguistics. Neurocognitive linguistics believes that English audio-visual courses should give full play to the characteristics of multimedia, provide students with diversified input corpus as much as possible, and design supporting teaching activities. Teaching methods can be diversified, such as movies, TV shows, TV shows, and music TV, supplemented by other teaching materials. We establish the association between computer technology learning behavior and the brain, and deepen our understanding of the cognitive neural mechanism of computer technology platforms. On the basis of the interaction between the computer technology platform and the language, the computer technology platform intervention method that promotes language learning is cut from the English audio-visual course learning. Studies have shown that the English audio-visual course system can be well integrated with the abstract information process in neurocognitive linguistics. At the same time, the English audiovisual course system can also play a role in promoting the improvement of neurocognitive linguistics.
Prediction Model of Mortality with Respiratory Rate, Oxygen Saturation and Heart Rate using Logistic Regression

In the health context, sometimes we want to do an early warning of clinical deteriations. Because there are several incidents where people suddenly die without any noticeable symptoms. Or suddenly experience a drop without any obvious initial symptoms. Therefore, it is necessary to develop an initial study that can help to make predictions based on vital signs. But not all vital signs can be easily measured or in other words require the person to go to the hospital with complete equipment. It would be easier if people could make early warning predictions based on simple vital sign parameters that are respiratory rate, oxygen rate and heart rate. Where this vital sign parameter can be measured easily with existing tools without having to go to the hospital. This study aims to build a logistic regression model for predicting the mortality using oxygen saturation, respiratory rate and heart rate as parameters. Logistic regression is used because of the suitability of the model's advantages with the data, and the model evaluation uses F1- Macro. This study also uses Synthetic Minority Over-sampling technique and categorizing values of the variables to get a better model result. Training accuracy is 57%, while the evaluation accuracy is 55%. Although the accuracy is not yet good, this idea can be the basis for further development in developing early warning of clinical deterotions with limited parameters and can be done with measuring tools that are easily obtained without having to go to the hospital.
Research on Text Readability Based on Computer Multi-Level Comprehensive Evaluation Model and Algorithm Optimization

This paper investigates the problem of text readability. This is an important research topic in modern scientific research, and how to quantify the analysis of text difficulty through scientific methods has important practical significance. This paper makes the following research on the readability of text: First, we explore the main factors affecting the text readability. We build a four-layer index system from the perspective of text characteristics and text logic to measure the readability of text. In this indicator system, word length, sentence length, sentence length and sentence average from the perspective of text length, the total number of text syllables, the total number of single syllables, multiple syllables, the number of common words, in terms of the number of text words, which is one of the highlights of this article. The data source of this article is 319 fourth and sixth grade reading materials as well as the text in English journals, with deletion, word segmentation and other preprocessing. We built a classification statistical model of the six metrics to achieve a systematic classification statistics for the six metrics at the first two levels. Statistically in terms of the number of common words and clauses, we used the high-frequency word tables in coca (Corpus of Contemporary American English) and the common logical structure words to build a model to obtain the data. We obtained the number of emotional words to model the number of emotional words, to measure text emotional color. Building on the model, we normalized the resulting raw data so that the metrics were in the same order of magnitude.
Research on Digital Classroom Construction in the Information Age under Computer Big Data Technology

The data storage consists of three parts: SARM storage, CD-ROM and hard disk. The OLAP server is arranged, and the switch is used to realize the functional integration of front-end tools and applications. Through the personalized data analysis model, the data mining parallelization technology is adopted, the data clustering center is updated through the K-means clustering algorithm and the MapReduce parallel computing framework, the clustering performance index is minimized, and the data in the personalized data source is effectively mined. Analysis, the conclusions after analysis are regularized to generate teaching rules, which are presented to students through human-computer interaction to improve the intelligence and personalization of the teaching system.
A Study on the Construction of Traditional Chinese Medicine Multimodal Corpus under the View of Self-media

From the perspective of corpus construction, this paper discusses the collection and annotation methods of traditional Chinese medicine multimodal language materials on the self-media and the building technical software-ELAN. This research establishes a multimodal corpus of traditional Chinese medicine by collecting videos related to traditional Chinese medicine in a representative self-media and using Elan for transcription and multi-level annotation. Based on the significant advantages of the multimodal corpus, it aims to reflect the image of traditional Chinese medicine on the self-media platform, and provide some empirical support for the development of traditional Chinese medicine.
Research on Interactive Practice System of Mathematics Online Classroom Under Computer Artificial Intelligence Technology

Aiming at the problem that the image of binary function in advanced mathematics teaching is difficult to visually present its morphological characteristics and the geometric meaning of related knowledge points in the traditional way, this paper designs and implements a three-dimensional dynamic auxiliary teaching system for advanced mathematics based on computer artificial intelligence technology. The system developed basic experimental teaching, comprehensive experimental teaching and display WebApp templates. At the same time, the system realizes the deep integration of online teaching, experimental practice and interactive demonstration. This system effectively solves the problem of high interactive demand of virtual experiment teaching, and fully enhances the intelligent interactive learning experience of learners.
An Evaluation of Automation on Misogyny Identification(AMI) and Deep-Learning Approaches for Hate Speech -Highlight on Graph Convolutional Networks and Neural Networks

The increasing misogynistic perspectives and the popularized abusive languages toward women appeared on social media worldwide in recent decades. People took advantages from the anonymous accounts spreading hatred and linguistic violence toward women, especially marginalized female groups. To indent this issue from a holistic lens and detect misogynistic aggression during this pandemic recession period, researchers specifically categorized abusive languages into several stratified groups, and utilized deep learning techniques to distinguish the differences between normal languages and unfriendly languages, with the creation of data-sets. To clarify the target, the paper will strictly target Spanish, English, and Hindi languages, and the social media users are concentrated on twitter and Facebook. Most of the research relies on systems of misogyny identification (AMI) and Natural Languages Processing technology (NLP). Besides, this paper will address external explanation and demonstration on Graph attention networks and Semi-supervised classification with graph convolutional networks. To interpret to what extent the misogyny issue can be explained by deep learning and relevant subfields, this paper will further provide a more insightful comprehension for the audience with understanding in depth.
Used Car Price Prediction in Surabaya Using Random Forest Regressor Algorithms

The use of private vehicles during the Covid-19 pandemic has increased because private vehicles, especially cars, are considered as the safest mode of transportation to maintain distance and prevent transmission of the Covid-19 virus. Based on data from two different Indonesian secondary car market place, a comparison of a price sample of Car X in the city of Surabaya with the specifications for the 2015 to 2018 car years with car milage under 1000 kilometers, the used cars have a variety of prices hence a used car price prediction system is needed so that people can find out the average price of used cars sold in the market. In this study the author will use the Random Forest Regressor as a machine learning algorithm to predict the price of a used car with a dataset from the AtapData website. The reason for choosing the Random Forest Regressor is because the algorithm has the power to handle large amounts of data with high dimensions with categorical and numerical data types. The evaluation method used in this study is the Root Mean Absolute Error which produces a value of 0.55612 for validation data and 0.56638 for testing data, while the evaluation proceed with Mean Absolute Error produces a value of 0.45208 for validation data and 0.47576 for testing data.
Anomaly Prediction and Remaining Useful Life for Alternator Health Monitoring System

An alternator plays a vital role in conversion of mechanical energy into electrical energy in a generator. An effective model of Machine Health Monitoring System (MHMS) is paramount in providing insightful information for the plant operators as preventive measures against any failure. Several studies have shown the effectiveness of the data-driven model, over the physics-based model in the development of MHMS. This is due to the robustness and sensitivity of the data-driven model based on deep learning or machine learning in detecting the small changes of the data which is especially useful when there are changes in the working state of the machine. In this project, we will investigate two approaches in MHMS namely Remaining Useful Life (RUL) and anomaly prediction using deep learning techniques. Among the deep learning techniques, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) for RUL prediction and anomaly prediction are chosen to be further analysed. In particular, the performance of CNN-based MHMS will be presented in comparison with the LSTM-based MHMS. The proposed RUL model will be used to identify the total number of cycles left for the generator before a major breakdown. On the other hand, anomaly prediction based on LSTM would be developed based on the multivariate forecasting method. In short, CNN model works better for the RUL prediction with the lowest RMSE value of 41.66 and accuracy up to 99.89% as compared to LSTM model whereas for the anomaly prediction through multivariate forecasting, singlelayered LSTM has a better performance as compared to stacked LSTM with accuracy up to 94.54% and RMSE value as low as 0.113
Enhancements to PERCLOS Algorithm for Determining Eye Closures

This study presents an algorithm that can detect people’s facial features being studied and then applied mainly on daily basis activities, as an example in driving which is detection of driver drowsiness. In this study, the algorithm named ‘PERCLOS’ which stands for ‘percentage of eye closure’ was tested to detect face by using two face landmark detectors, that are pre-trained model and library Dlib’s 68-points facial landmark and 468 3D face landmarks detector from MediaPipe by Google as an alternative and detects the condition of a person’s eye based on Eye Aspect Ratio (EAR). Initial assessment of the Dlib’s solution on 151,537 frames (about 84 minutes) of one of tested subjects revealed that 98.66% of eye states were properly identified, resulting in 378 blinks to be recorded. Despite having rather good accuracy, the algorithm produced 166 more blinks than the 212 blinks that were expected. As for MediaPipe, with 264 blinks and only 52 additional blinks, the MediaPipe Face Mesh solution was able to categorize the identical subject with a classification accuracy of 99.87%. Additionally, adaptive thresholds for different subjects were applied in order to investigate a way to improve the studied algorithm. Surprisingly, the adaptive threshold method being studied resulted in decreasing accuracy and precision for some of the subjects. For one of tested subject, the resulted precision of studied algorithm somehow drops from 100% to 98.60%.
Application of Computer Artificial Intelligence Online Video Tutoring Technology in University Practical Course Education

Nowadays, various types of educational websites, online schools, and online education colleges are booming, and online teaching has become a useful supplement to traditional teaching. It not only effectively improves the allocation of teaching resources, but also solves the problem that traditional teaching is limited by time and space. This paper puts forward the design idea of using J2EE technology system as the structural framework to develop network courses, and describes the process of system development using J2EE technology system in detail. This system is a network application software developed by JSPServlet, bean, JDBC and other related Java technologies according to the J2EE standard. The development of the system is based on the multi-layer architecture of Web server, application server and database server. This multi-layer structure can separate the business logic, user interface, content display and other parts of data processing, so that the business logic of the system is concentrated in the Application level. Using the reusability and cross-platform nature of JavaBean components, complex and large-scale platform applications can be rapidly developed and re-developed.
Research and System Implementation of Automatic English Synonymy Selection in the Internet Environment

Taking English synonym terminology as an example, three effective technical means for automatically acquiring Internet terminology resources are proposed, including self-learning of grammatical patterns, extraction of online synonym dictionaries, and crawling of static synonym terminology categories. Based on this, a prototype system of Web Synonym Searcher is designed and implemented. Experimental tests show that automatic acquisition of synonym terms from the Internet is a very promising way.
Analysis on Password Attack Model and Password Generation

Identity authentication is an important line of defense of network security, password has long been the mainstream way of identity authentication, password attack plays an important role in password security research. Probabilistic Context Free Grammar (PCFG) and the Markov chain-based model are the two most effective password attack methods. They can effectively model and describe the password from the substructure composition level and the character dependence level, respectively. This paper first analyzes the various encryption algorithms for cryptographic database, which are currently mainstream, and describes the comparison and strength of these algorithms. Moreover, this paper analyzes the current cipher structure within the password database. At last, this paper briefly describes the current method of password attacks and focuses on three most important methods, including Markov chain, OMEN algorithm and context-independent, to crack the password. This paper discusses the development of these three algorithms in current academic field, including the Recursive-OMEN algorithm based on structure partition and string reorganization, Adam algorithm based on dictionary attack, and genetic algorithm.
AUV Path Planning in Strong Current Fields with Less Computing Time and Improving Incompleteness Problem

This paper solves the time-optimal path planning for autonomous underwater vehicles (AUVs) in ocean environment with cluttered currents. In this problem, the planner may not find a path in the search space with discrete motion directions, because it leads to a decrease in the available direction. But the search space with the continuous motion directions will greatly increase the computation. To avoid this, the paper presents an approach to improve the lack of discrete motion model by placing Steiner points on each edge. Combining with the ant colony algorithm, the path planner finds the time-optimal path in search space. The effectiveness is verified through simulations using a set of randomly generated current fields.