Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Picture Object Identification System Design and Implementation

The computer plays a significant role in computer vision to help with everyday tasks. Defense, biometrics, visual surveillance, robotics, and driver assistance are just a few of the many uses for object recognition. Lane/railroad track detection, the detection of obstacles before the vehicle/train, is part of the driver assistance system. Driver assistance systems for various modes of transportation can be improved by employing an efficient object-recognition approach (road, rail, etc.). One of the major problems with the driver assistance system is its inability to detect railroad obstacles. There has been an increase in the amount of research and development in obstacle detection for road transportation in the last few years. Despite the fact that railroads are the other primary land transportation mode, much less effort has been put into developing technologies for detecting obstacles on the rails than on the roads. Efforts to improve the recognition performance of safety inferences are ongoing. Real-time object recognition in driving situations despite the rapid development in the field of object recognition on datasets with a tremendous number of different types of objects remains extremely challenging. Autonomous driving systems (ADAS) and advanced driver assistance systems (ADAS) face a number of key difficulties in visual object recognition. Object recognition is complicated by a variety of factors, including changes in lighting conditions, the presence of shadows, and partial occlusion, for example. Changing light conditions are the most common triggers for variation. These critical realities are taken into account in this study, which aims to offer a solution to these problems.
Developing a Multilingual Stemmer for the Requirement of Text Categorization and Information Retrieval

Information retrieval IR is the process of finding information (generally documents) that matches the needs of the user.One way to improve the search effectiveness, as well as the quality of text categorization is to build an effective stemmer that helps to match users' queries with relevant documents in IR and reduce the space of textual representation in TC.This has been always an interesting research topic in IR and TC.We can define stemming as the process of reducing inflected and derived words to their reduced forms (stems or roots).Many stemmers have been developed for different languages, but there is always many weaknesses and problems.In the present work, we have developed a multilingual stemming approach, based on the extraction of the word root and that exploits the technique of n-grams of characters.Our experiments have been done on three languages which are: Arabic, English, and French.
Improving the Ability to Recognize Consonants Through Smart Box Media for Children aged 4-5 Years in Kindergarten

The ability to recognize consonants has an important role in the lives of early childhood because introducing consonants to children is the first step for children to be able to read and to make a word, consonants and vowels are needed. This study aims to determine the application and results of the "Smart Box" media towards increasing consonant recognition in children in group A. This study uses Classroom Action Research as an approach to improve children's ability to recognize consonants. It is carried out in 2 cycles which are divided into three stages, namely pre-cycle, cycle I and cycle II to obtain the expected results. In the pre-cycle the result of the percentage of action achievement value was 46.16%, while in the first cycle the percentage increased by 22.09% so that the percentage score was 69%, which means it was not successful. Then the researcher continued by giving action in the second cycle, after doing the research in the second cycle, the percentage of action achievement value became 88.58%%, so that the percentage value obtained in the first cycle and second cycle increased by 19.58%. With this it can be seen from the comparison between cycles I and II during the research. From the results obtained, there is an increase which proves that using the "Smart Box" media can improve the ability to recognize children's consonants.
Code limits

Are there limitless permutations of QR codes or are they recycled after a prescribed length of time? Bonita Ely, Sydney, Australia
Study on the Integration of Industry and Education through Deep Cooperation between Government, Industry, Universities and Enterprises

A new idea of in-depth cooperation between government, industry, schools, and enterprises is offered in response to current difficulties of development in the integration of industry and education. Universities provide technologies that the government, industry and enterprises work and operate to achieve common sharing in industry construction. The integration of industry and education oriented to serve local industries and promote talent cultivation is an important part of higher education at present. Taking the development of Zhengkang sugar industry as an example, China Agricultural University (CAU) cooperates deeply with local government, agricultural machinery enterprises and sugar mills to explore the efficient and effective way for the agricultural talent cultivation through higher education, internship, curricular practical training (CPT), and etc. With the accomplishment of innovation and the optimization of the educational system, more and more talents are being fostered to contribute in sugar industry and rural development.
Video-Based Abnormal Driving Behavior Detection and Risk Control Using Machine Learning

Video-based driver abnormal driver behavior detecting and risk control are incrementally well-liked nowadays, because it is important for ensuring the safety of drivers and people within the vehicles, and it is a necessary step in realizing automatic driving at this situation. Different researchers are proceeded with the detecting abnormal behavior of driver with the help of the capturing and detecting the face and objects in the hand of a driver through image and video process, but the standard algorithms are not efficient of detecting difficult temporary characteristics of driver behavior. In this paper, we have added a step to the existing mechanism which can track the user’s abnormal behavior and thus send the information to the owner of vehicle. We have used three deep learning models to detect the behavior of the driver. These three new fusion models help in designing the model building and thus helping in risk control and help traffic department.
Research on the Optimal Strategy of "Crossing the Desert" Game Based on Dijkstra Algorithm

This article mainly analyzes the problem of "crossing the desert" game, and transforms the problem of cross-desert game into the shortest path problem in graph theory.Taking the consumable materials and consumable funds as the weights, using Dijkstra's algorithm, path planning, dynamic planning and recursive methods, the model is established in consideration of the weather, and the optimal strategy is given to maximize the remaining funds when the destination is reached.
SVR Model Used for Economic Fluctuation Analysis

The purpose of this study is to find an optimal algorithm for the prediction of market value and the analysis of economic fluctuations.We propose an ensemble learning algorithm based on SVR and apply it to market value prediction and economic fluctuation analysis.It was found that in most situations, the smaller the window value of short-term learning model is, the smaller the weight of long-term learning model is, and the better the performance of ensemble learning model is.However, with the decrease of weight value, ensemble learning model will have the problem of over-fitting, which makes the performance of the model decline.This paper proposes a market value forecasting model based on long-term and short-term ensemble learning.In the theory of SVR model, the validity and superiority of the model are verified through a large number of experiments.[1
Improved Faster R-CNN-Based Anomaly Target Detection for Truck Driving Environment

With the development of the economy and the improvement of people's living standards, the volume of goods transported is constantly on the rise.At present, the dominant position in the transport industry is still road transport, and at the same time the problem of road traffic accidents is becoming increasingly prominent.Generally speaking, the truck is relatively large, with a wide blind spot and high cab height, which makes the driver unable to have a good observation and control of the environment around the vehicle, so it is extremely easy to have all kinds of traffic accidents.In such a context, an improved Faster R-CNN algorithm is proposed for the detection of abnormal targets in the truck driving environment.First, the ResNet-50 network is chosen to replace the VGG network, which reduces the training difficulty and effectively improves the gradient disappearance problem; then, in order to enhance the feature extraction ability of the network, the Squeeze-and-Excitation attention mechanism is introduced in the residual structure to strengthen the feature extraction ability; finally, the original feature pyramid network structure is improved by adding a self Finally, the original feature pyramid structure was improved by adding a bottom-up channel enhancement route to improve the propagation of lower-level features and further enhance the propagation of feature information.The experimental results show that the improved mAP value improves by 2.05% compared to the original algorithm.
Application of Augmented Reality Technology in Immersive Experience of Bo Zhou Intangible Cultural Heritage Wuqinxi

Intangible cultural heritage is a very important form of cultural heritage, Wuqinxi as a national intangible cultural heritage, in the current social background, its development and inheritance are facing a severe test, it is urgent to explore a new path of protection.The development of digital technology, especially the rapid development of augmented reality technology, provides a new idea for the protection and inheritance of intangible cultural heritage.Wuqinxi intangible development present situation and the purpose of this paper is to investigate analysis based on facing the bottleneck, to explore how AR technology combined with the art characteristic of this kind of Wuqinxi intangible, through methods such as interactive and immersive virtual experience, as well as increase the fun, coruscate Wuqinxi new life charm, for Bozhou intangible Wuqinxi better popularity and spread both at home and abroad more beneficial exploration.
Predictive Analysis of Cryptocurrency Residual Log-returns Based on Lightgbm

Cryptocurrencies have taken an increasingly important place in the international financial markets.However, due to the complexity of the factors affecting cryptocurrencies and the high volatility of cryptocurrency prices, predicting the future trend of cryptocurrencies has been a difficult topic.In this paper, we establish a prediction model based on LightGBM, aiming to find the optimal machine learning algorithm to predict different cryptocurrencies.Firstly, we preprocessed the data to ensure data integrity and availability.Secondly, we trained the training set using the LighGBM algorithm.Then, to find the optimal LightGBM algorithm for different cryptocurrencies, we optimized the algorithm using Grid Search.Finally, we evaluated the results by Root Mean Squared Error (RMSE).It shows that the Root Mean Squared Error of the prediction results for all cryptocurrencies is below 8%, which means LightGBM can be a good choice for monitoring cryptocurrencies.
Formulation of Asteroid Mineral Resources Allocation Scheme Based on an Ideal Equity Allocation Model and Real Economic Allocation Model

At present, the huge prospect of asteroid mining makes the investment and benefit allocation of asteroid mining become a hot issue.The research explored how to allocate the mineral resources of asteroids fairly.Firstly, the factors influencing the ideal equity allocation were determined by putting forward a method to measure global equity.After the determined factors were given appropriate weights by entropy weight method, the grey prediction model was used to establish an ideal equity allocation model.Secondly, after analyzing the indexes that affect the fair allocation of real economy, the weights of these indexes were determined by using the fuzzy comprehensive evaluation method combined with the analytic hierarchy process.Then, the equity allocation shares of various national groups were calculated based on the established equity allocation model of real economy.The results show that the established equity allocation model is accurate and reasonable within a certain period of time.The research provides ideas for the equity allocation of asteroid mineral resources exploitation in the future.
Whale optimization algorithm based on Gaussian mutation and differential evolution

Based on the defect that whale optimization algorithm is easy to fall into local optimization and slow convergence speed, this paper proposes an improved whale optimization algorithm based on Gauss mutation and differential evolution.The algorithm initializes the population through logistic chaos, improves the diversity and randomness of the population, and enhances the global search ability of the algorithm.Gaussian mutation and differential evolution are introduced to enhance the local search ability and improve the search accuracy.The performance of the improved optimization algorithm is tested by 12 benchmark functions.The results show that compared with the basic whale algorithm, particle swarm optimization algorithm and Multi-Verse Optimizer algorithm, IWOA algorithm can improve the convergence speed of the population and improve the search accuracy and stability of the algorithm to a certain extent.
Webpage Intelligent Parsing Algorithm Based on Text and Symbol Density

Web page intelligent parsing is an inevitable part of data collection.News web pages contain a lot of information with little relevance to the topic, which makes it difficult to locate the text content directly and quickly during the data collection process.This paper proposes a web page intelligent parsing algorithm based on text and symbol density.Through empirical research on the pages of mainstream news websites in China, the algorithm can quickly and accurately extract the text of news web pages.
Evolution of Attacks on Intelligent Surveillance Systems and Effective Detection Techniques

Intelligent surveillance systems play an essential role in modern smart cities to enable situational awareness. As part of the critical infrastructure, surveillance systems are often targeted by attackers aiming to compromise the security and safety of smart cities. Manipulating the audio or video channels could create a false perception of captured events and bypass detection. This chapter presents an overview of the attack vectors designed to compromise intelligent surveillance systems and discusses existing detection techniques. With advanced machine learning (ML) models and computing resources, both attack vectors and detection techniques have evolved to use ML-based techniques more effectively, resulting in non-equilibrium dynamics. The current detection techniques vary from training a neural network to detect forgery artifacts to use the intrinsic and extrinsic environmental fingerprints for any manipulations. Therefore, studying the effectiveness of different detection techniques and their reliability against the defined attack vectors is a priority to secure the system and create a plan of action against potential threats.
Study on Mao Zedong's cadre Education Thought in Yan'an Period and its Enlightenment

Mao Zedong's cadre education thought in Yan'an period is an important part of Mao Zedong's cadre education thoughts.Its main contents include education of Marxist theory, education of serving the people wholeheartedly, education of professional knowledge and education of historical and cultural knowledge.Re-study Mao Zedong's cadre education thought in Yan 'an period,It is of great significance to the construction of cadres in the new era.
Research on the Construction of College English Blended Teaching Mode

The rapid development of online courses and its impact on the traditional classrooms of colleges and universities have prompted college educators to strive for a hybrid teaching model that combines the advantages of both traditional classrooms and online courses.Constructing and applying this brand-new teaching model in the most extensive series of college English courses can effectively improve students' learning efficiency and improve students' independent learning ability, cooperative learning ability, innovative thinking ability and other comprehensive qualities.Cultivation has a profound impact.
Research on Mixed Online and Offline Teaching Mode of College English

Mixed online and offline teaching of college oral English is helpful for students to learn oral English in different ways, stimulate students' interest in learning, and improve their oral communication ability.Based on the characteristics of mixed online and offline teaching mode and the advantages of mixed online and offline teaching mode, the paper deeply analyzes the development of mixed online and offline teaching mode.
Three dimensional structure of college entrance examination function and its new era turn

With the restoration of the college entrance examination system, the function of the college entrance examination has been reshaped.The college entrance examination has been continuously reformed and improved, gradually expanding to form a three-dimensional functional structure of basic functions, educational functions and social functions.Serving and selecting talents is the basic function of the college entrance examination; promoting teaching reform and promoting the all-round development of schools and disciplines is the educational function of the college entrance examination; maintaining social fairness and justice is the social function of the college entrance examination.In the new era, a series of historical and realistic factors have brought about an inevitable turn in the function of the college entrance examination, which has been further accelerated by the reform of the new college entrance examination.The function of the college entrance examination in the new era has ushered in a multi-dimensional turn and transformation, that is, the turn of the social function of cultivating diversified talents, the educational function of adaptive examination, and the value function of improving students' comprehensive quality.The evolution of the function of the college entrance examination prominently reflects the modernity of the college entrance examination and the phased characteristics of the educational reform in our country.
Construction of Professional Training System for "All-Round &amp; Application-Based" Primary Education Major in Private University

All-Round & Application-Based" primary school teachers have extensive professional knowledge and comprehensive technical capabilities, which are beneficial to balance the current allocation of primary school teachers in China and alleviate the uneven distribution of primary school teachers.In the background of a serious shortage of "All-Round & Application-Based" primary school teachers, private universities must give full play to their advantages and shoulder the mission entrusted by history.Based on the quality structure of the "All-Round & Application-Based" primary school teachers, aiming at the problems existing in the training of primary education professionals in private universities, construct personnel training system consisting of a curriculum teaching system and a practical teaching system.Among them, the curriculum teaching system includes courses in the general field, courses in the education field, courses in the subject field and courses in the extended field; the practical teaching system includes the teaching goal system, teaching content system, teaching guarantee system and teaching evaluation system.
Perfil epidemiológico das hospitalizações por Covid-19 em hospital de referência da rede pública de saúde em Barbacena

Introduction.The new coronavirus (SARS-CoV-2) is an RNA virus commonly present in the human respiratory system that can cause various types of symptoms.Objective: To understand COVID-19's epidemiological profile.Materials and Methods.An epidemiologic observational, cross-sectional and retrospective study using data from medical records of patients who tested positive for having the SARS-CoV-2 infection and were hospitalized at Hospital Policlínica e Maternidade de Barbacena -IMAIP.Results.The RT-PCR test was the most used to confirm the presence of SARS-CoV-2; dyspnea, dry cough and adynamy were the most observed symptoms; most patients received care seven days after the beginning of the symptoms; the mean age was 66.19 years; there was a predominance of males and of the comorbidities: systemic arterial hypertension (SAH), diabetes, chronic obstructive pulmonary disease (COPD) and heart diseases.The Intensive Care Unit (ICU) was necessary, at some moment of the hospitalization, for 92.5% of the patients, 42.5% of whom used mechanical ventilation and of all patients, 31.87%died.Conclusion.Male patients and those with COPD had worse outcomes.Minor values of oxygen saturation, high respiratory frequency, quantitative alterations of leukocytes, platelets, creatinine and HDL, along with the invasive use of mechanical ventilation were relevant when comparing patients with good outcomes with those who died.
Sample Importance Guided Progressive Sampling-Based Bayesian Optimization for Automatic Machine Learning

In the era of big data, machine learning-based data analysis has been integrated into almost all walks of modern life.Before applying machine learning, a machine learning algorithm with its proper hyper-parameters have to be decided, where rich machine learning knowledge and lots of practical manual iterations are required.In order to popularize machine learning and allow non-professionals to use machine learning to solve problems, automatic machine learning model selection is particularly important.Among various existing automatic machine learning model selection methods, Progressive Sampling-based Bayesian Optimization (PSBO) is one of the most efficient and effective ones.However, PSBO adopted the progressive sampling with the traditional random sampling strategy, which does not consider the importance of individual samples.Based on the idea that more important and effective samples will make the model training results better, the paper proposed a Sample Importance Guided Progressive Sampling-based Bayesian Optimization (SIG-PSBO) for automatic machine learning.SIG-PSBO defines the sample importance by the difficulty to distinguish categories in a PCA feature space.Then samples with higher sample importance are more likely to be sampled for the subsequent model training.Extensive experiment results showed that the SIG-PSBO method can significantly shorten the search time and reduce the classification error rates compared to the original PSBO method.