Reviewerly/exHarmony huggingface dataset:
Application of Expert System With Forward Chaining Method in Detecting Infectious Diseases in Children

Limited information about infectious diseases, especially diseases in children, is a major problem. The application of an expert system in detecting infectious diseases in children is needed in early detection in diagnosing diseases. The purpose of this study was to determine the diagnosis of infectious diseases in children based on the symptoms of the disease and the results of the diagnosis from the system for recommendations to patients with the type of disease suffered and the solutions provided. The stages of the research are collecting data and information on infectious diseases in children. The second is symptom and rule data collection. The third is making each solution from the types of diseases, and the last step is the solution for preventing infectious diseases in children, which is displayed in the system based on the analysis of disease rules. As for the variables in the application of disease tracing graphs, there are 17 rule graphs in detecting diseases in children. Analysis of the rules in the forward chaining method in disease detection for the types of symptoms of high fever (G001), sore throat and cough (G004), persistent hard cough that begins with a long breath through the mouth (G023), and diarrhea (defecation). watery more than three times per day (G030) Itching (G48), irritable and irritable (G033) The upper right abdominal area will feel pain, especially when pressed (G49). Analysis of each symptom G001, G004, G023, G030, G031, G033, G48, G49, the disease that will be detected is P009, namely Hepatitis A. The results obtained are in accordance with patient input by conducting a search using a rule model that utilizes a trace tree. forward based on tracing pediatric disease diagnoses with existing facts. The results of this study can display the results of diagnosis, disease analysis, solutions, and prevention based on the symptoms of the disease in children.
Lightweight Image Super-Resolution Based on Re-Parameterization and Self-Calibrated Convolution

Image super-resolution technique can improve image quality by increasing image clarity, bringing a better user experience in real production scenarios. However, existing convolutional neural network methods usually have very deep network layers and a large number of parameters, which causes feature information to be lost as the network deepens, and models with a large numbers of parameters are not suitable for deploying on resource-constrained mobile devices. To address the above problems, we propose a novel lightweight image super-resolution network (RepSCN) based on re-parameterization and self-calibration convolution. Specifically, to reduce the computational cost while capturing more high-frequency details, we designed a re-parameterization distillation block (RepDB) and a self-calibrated distillation block (SCDB). They can improve the reconstruction results by aggregating the local distilled feature information under different receptive fields without introducing extra parameters. On the other hand, the positional information of the image is also crucial for super-resolution reconstruction. Nevertheless, existing lightweight SR methods mainly adopt the channel attention mechanism, which ignores the importance of positional information. Therefore, we introduce a lightweight coordinate attention mechanism (CAM) at the end of RepDB and SCDB to enhance the feature representation at both spatial and channel levels. Numerous experiments have shown that our network has better reconstruction performance with reduced parameters than other classical lightweight super-resolution models.
Education and Intelligent Discourse

This chapter discusses the importance of education and rigor in the definition and use of vocabulary surrounding automation and data. More education helps individuals by enhancing their ability to understand data and data science’s growing impact, and to both contribute to and benefit from the field. A more knowledgeable public and a clear vocabulary for discourse is needed to have better communication and debate.
TABLE BOOKING BY 3D TOUR

Long wait time for tables can turn customers away or can be an uncomfortable experience. Therefore, we intend to aid in a comfortable experience by bringing in a restaurant tour through our website. This involves a complete tour of the ambiance and the tables. This tour is not just looking at normal pictures of the restaurant’s ambiance to get a glimpse; it is a complete 360- degree view that helps the customer to book a table with one click. People are always running short of time and custom table booking fits right into their hectic schedule which solves the problem of waiting time. Also, this type of feature is favorable to restaurants as it saves an ample amount of time and helps in getting more customers to visit. In conclusion, a 3D tour makes booking a table easier and convenient with easy accessibility. The use of hardware tools like an android phone and a tripod will help in capturing images; programming languages like HTML, CSS, JavaScript will be used; tools like the Google Tour platform and Google Street View app will be used for producing the 360Degree video of the restaurant; and software tools like MongoDB, NodeJS, Express JS will help in building the restaurant website using Visual Studio Code.
Emerging Trends in Multimedia

AbstractThe rapid development of the network and devices has made multimedia technology to spread across all the aspects of the people’s life. Multimedia can be defined as the media which interactively spread the information, combining two or more media. Multimedia technology has impacted on our daily lives in various aspects like e-learning, virtual reality, journalism, Content Streaming and many more. The evolution of multimedia has been carried out in various phases over the last decade which has been further discussed under the topic Diversity in Multimedia. In this paper, we intend to explain about the various trends emerging in multimedia and the concerns related to the transmission of the user’s personal data and their privacy. The already existing technologies which are not yet fully explored such as Virtual reality, Hologram, Advanced Journalism and feedback-based content streaming and the security issues that arise with such advancement in technology has been talked about in depth in this paper.KeywordsMultimediaCommunicationAuthenticationPrivacyDeepfakeHologramVirtual reality
Textual Datasets For Portuguese-Brazilian Language Models

Advances in Natural Language Processing have generated new models that push forward the state of the art. This reached new heights in complex tasks in handling unstructured texts. Most of the new architectures and models focus on the English language. There is a lack of available datasets that can be used during the training of new models. This investigation presents four new textual datasets for language modeling in Brazilian Portuguese. Our datasets were generated from several specific methodologies that aimed to obtain data of different natures. Two of our sets were originally built from data in online web forums. We also distribute a translated version of MultiWOZ, and a clean version of BrWaC. The original datasets are made available in a structured way to facilitate their use during the training of NLP models, with questions, answers and conversations already identified.
Person re-identification from text to image based on mutual teaching

Text-based cross-modal person re-identification refers to the retrieval of the corresponding identity image in the pedestrian image set through the linguistic description of the pedestrian. Aiming at the problem that there is no interaction between the image-text feature extraction branch, we propose a text-to-image person re-identification method based on mutual teaching. The proposed method adds interactive operation in the image text feature extraction part to make the image and text information respectively guide the feature extraction of another modality, which realizes the information transfer between image and text modalities to close the distance between two modalities. The method is experimentally verified on the CUHK-PEDES dataset. The experimental results show the effectiveness and superiority of the proposed method.
Global path planning for unmanned vehicles based on improved A* algorithm

The traditional A* algorithm has many turns, long paths, and slow speed in the global path planning of unmanned vehicles. For these problems, this paper proposes an improvement scheme. Add a reverse search from the end point to the starting point and generate a straight-line path between two nodes when judging that there is no obstacle between the target nodes in both directions. In addition, redundant nodes are eliminated, and the path is smoothed based on cubic B spline curves. Simulation results show that compared with the traditional A* algorithm, the path planned by the improved A* algorithm has shorter length, fewer and smoother turns, and the planning time is reduced by 14%-75%, which achieves the optimization of the path planning of the A* algorithm.
Research on Tibetan character recognition based on model CRNN

In recent years, CRNN has been widely used in computer vision and has achieved remarkable results in the direction of text recognition. CRNN is a convolutional recurrent neural network structure, which is mainly used in image sequence recognition problems. The CRNN network model implements variable-length verification, combining CNN and RNN networks, using a bidirectional LSTM cyclic network for time series training, and then introducing a CTC loss function to recognize variable-length sequence texts. In the field of Tibetan text recognition, based on end-to-end recognition, it is usually to recognize a line of text. Due to the special structure of Tibetan syllables, the components of Tibetan characters can be split, and end-to-end recognition can be applied to the study of Tibetan single-character recognition. In this paper, a new split-based method is used for end-to-end recognition of single characters in Tibetan ancient books and single characters in Tibetan handwriting using the CRNN model, and a good recognition effect is achieved. It provides a new method for Tibetan character recognition research.
Recommendation System Using Matrix Factorization

Abstract: In today’s world, people are flooded with a lot of information, and no. of choices are overwhelming. For example, in any online shopping platform such as Amazon, if we search for a particular product, thousands of results appear and it becomes very difficult to select an item from vast pool of options. The growth of digital information and the number of users over the Internet has created a potential problem of information overload. The recommendation system solves this problem by searching through a large volume of data and providing personalized content to the user. This paper describes the introduction to the recommendation system, its three main types – content-based filtering, collaborative filtering, and hybrid filtering, and addresses the data sparsity problem. This paper proposed a collaborative filtering approach using matrix factorization to mitigate the sparsity problem and improve the quality of the recommendation.
Recent Activities of the IAS Education Department [Education News]

Reports on the recent activities of IAS education committees.
Exploring College Students' Perceptions Regarding Elective-centered Curriculum

이 연구의 목적은 2015 개정 교육과정을 이수한 대학생의 경험과 인식을 분석하여 학생 선택중심 교육과정의 성과를 규명하는 데 있다. 연구 목적을 달성하기 위해 G광역시에 소재한 C대학교 1, 2학년 재학생 14명을 대상으로 초점집단면접(FGI)을 실시했다. 연구 결과, 학생들은 주체성 증진, 진로 구체화 기회 제공, 요구를 반영한 선택과목 개설, 교사와의 학술적 교류 증진, 고교-대학 교육과정 연계 강화, 학업성취 만족도 제고 기여 등의 측면에서 학생 선택중심 교육과정을 긍정적으로 인식했다. 반면에 진로 미결정, 변경 가능성 등으로 인해 진로 선택이 제약될 수 있다는 점과 특정 전공계열에서만 학생 선택중심 교육과정의 성과가 나타날 수 있다는 측면에서 이를 부정적으로 인식하기도 했다. 이 같은 학생 선택중심 교육과정의 한계를 극복하기 위한 방안으로서 학생의 조기 진로 설정을 위한 교육청과 단위학교의 특별한 노력이 필요함과 선택과목 개설 확대를 위한 연계교육과정의 내실화 방안을 제언했다.The purpose of this study is to analyze the experiences and perceptions of college students to identify the outcomes of elective-centered curriculum. We conducted a focus group interview(FGI) with 14 college students who recently experienced the high school to college transition. The results revealed that the students perceived the elective-centered curriculum positively for reasons such as enhancement of identity, career-specific opportunities, the opening of demand-based elective courses, academic exchanges with teachers, curriculum transition from high school to college, and improvement of satisfaction with academic achievement. However, students had negative impressions because career choices could be limited due to undecided career paths or the possibility of career changes, and the effects of the elective-centered curriculum only appeared in some majors. Finally, an improvement plan for the elective-centered curriculum was suggested in terms of the role of the Office of Education and unit school in setting students' early career paths and the linked curriculum.
Discussion of Technology-based personalized learning: Focusing on concept and implementation in domestic and foreign literacture

본 연구는 디지털 대전환 시기에 재조명되고 있는 테크놀로지 기반 개별화 학습의 개념과 현황을 살펴보고, 우리나라 공교육에 개별화 학습을 도입하기 위해 고려해야 할 시사점을 도출하기 위해 수행되었다. 이를 위해 국내외 선행연구를 바탕으로 개별화 학습의 개념과 학습 모델을 살펴보고, 학교 단위로 개별화 학습을 적용한 연구 사례를 중심으로 개별화 학습의 효과성과 실행 측면의 이슈를 탐색하였다. 최근 테크놀로지 기반의 개별화 학습은 “선행 지식, 관심, 목표, 요구, 선호 등 학습자 특성을 반영하여 적응적으로 학습의 목표와 과정을 설계·실행하여 학습자의 참여를 유도하는 학습”으로, 전통적인 개별화 학습에 비해 의사결정이 데이터 기반으로 이루어지며, 학습 처치가 실시간으로 자동화되어 제공된다는 측면에서 적시성이 높다. 테크놀로지 기반 개별화 학습을 적용한 국내외 사례를 분석한 결과, 테크놀로지 기반 개별화 학습의 성공 여부는 학교 상황과 구성원의 의지에 따라 유동적이며, 충분한 여건이 조성되지 않을 경우 의도치 않은 교육격차를 야기할 수 있었다. 또한 개별화 교육의 도입이 교수법을 넘어선 단위 학교의 비전으로 실현되기 위해서는 개별화 학습에 대한 논의가 테크놀로지 활용에서 학습자 중심 교육 혁신으로 확대될 필요가 있다.This study examines the concept and current status of technology-based personalized learning (PL) and explores the implications for introducing PL into Korean public education. For these purposes, PL’s concept and learning model were examined based on previous studies alongside the issues regarding PL’s effectiveness and implementation focusing on research cases where it was applied at the school level. Compared to traditional PL, recent technology-based PL is “learning that encourages learner engagement by adaptively designing and implementing learning goals and processes while reflecting learner characteristics such as prior knowledge, interests, goals, needs, and preferences.” where decision-making is data-driven, providing learning treatments automatically in real-time. According to previous studies applying technology-based PL, the success of PL is flexible, relying on the school environment and members' will; if sufficient conditions are absent, it may cause an unintended educational gap. For PL to be introduced as a vision of the whole school beyond the teaching method, the discussion regarding PL must expand from technology to learner-centered education innovation.
The impact of knowledge sharing on the effectiveness of employee management in small and medium enterprises in the North Central region

Employee management in the business plays an important role in the success of an organisation. Although the management activities in small and medium enterprises (SMEs) in North Central Vietnam have had several relevant changes in recent years, their management efficiency has been still quite limited. The study uses a quantitative research method through the structural equation modelling (SEM), with a scale of 377 samples of employees in SMEs, and aims to examine the impact of knowledge sharing on the effectiveness of employee management in SMEs in North Central Vietnam. Research results show that knowledge sharing has a direct and positive impact on the effectiveness of employee management. At the same time, in the relationship between these two factors, trust and opportunistic behaviour act as mediating factors. In addition, research also indicates that trust negatively affects opportunistic behaviour. Based on the research results, the authors propose several recommendations to improve the effectiveness of employee management in businesses. The findings of this study demonstrate the importance and impact of knowledge sharing on the effectiveness of employee management. These are meaningful contributions in both theory and practice to support businesses gain more insights into knowledge sharing in its impacting relationships on the effectiveness of employee management and play a fundamental role for future research.
Soft skills training for students in the stage of Industrial Revolution 4.0

The Industrial Revolution 4.0 is placing urgent requirements on human resources. In addition to professional knowledge, students need to be equipped with the necessary soft skills to improve themselves and integrate well into the world of works. This article is based on the study of soft skills training contents of many countries around the world, proposes soft skills that universities need to pay attention to in fostering students in the current period.
Protection mechanism of the business “brand” under the law of Vietnam

Protection of “brand” (brand) aims at not only ensuring the quality of goods and services for consumers and consumption markets but also the development of enterprises for domestic and foreign markets. Brand is an intellectual property that needs to be considered, cared for and invested in because the value it brings to the success of the business is huge. The article focuses on researching legal issues of trademark protection, infringement acts and measures to protect trademarks of enterprises.
The Automation of Feature Generation with Domain Knowledge

AutoML appeared in the last few years as the ultimate challenge in the field of machine learning and data science. However, despite the advances on hyper-parameter optimization, the data preparation step continues to face great difficulties, mainly due to the inability to incorporate human expertise on variables reengineering. In this paper, we present an algorithm able to automate the trivial preparation tasks and to generate features using domain knowledge, represented through entity-relationship (ER) diagrams. Along with the algorithm, we define a set of operators that can be applied to distinct kinds of data, with small human intervention. The algorithm is evaluated over a small set of public datasets, for which we designed basic ER models. The new method shows results comparable to the ones achieved with other automation tools, such as AutoSklearn [4], but with much lower processing times.
INDIRECT SELF-PRESENTATION OF PEOPLE WITH A HIGH LEVEL OF MACHIAVELLIANISM IN A SITUATION OF SUCCESS AND FAILURE

The possibilities of using indirect self-presentation in the diagnosis of a person’s personality traits on the example of people with a high level of Machiavellianism are discussed in the article. This approach expands the possibilities in diagnosing personality traits, since in this case there is no need to focus on selecting a situation for analyzing selfpresentation. In this case, it is no longer the content aspect of the narrative that is important, but how it is built. To demonstrate this possibility, two groups of respondents were selected: Machiavellian and non-Machiavellian, who were asked to create and tell stories that were not personally related to them. The cards from the board game „Imaginarium” were used as the basis for the plot. It was shown that self-presentation techniques can be identified not only in situations of direct self-presentation, but also in their stories on abstract topics too. The main features of the Machiavellian presentation were also highlighted. Their stories are about a strong hero who is opposed to the world. There may be other characters in the stories, but their role is insignificant, or they are ignored by the main character. If we are talking about failures, these situations are described as a certain combination of circumstances.
Assessment of Autofocus Techniques for Isolated Cellulose Microfiber 3D Imaging by Digital Holography

Studying the three-dimensional structure of paper fibrous networks is an important step towards understanding the relationship between manufacturing conditions and the resulting microstructural and mechanical properties. Digital holography is a promising three-dimensional imaging technique enabling quantitative phase evaluation of micro and nano-fibres. One of the advantages of this high-resolution method is the ability to perform numerical refocusing at several depths from a single shot acquisition. In this work, the suitability of 22 focusing functions to accurately identify the positions of cellulose microfibers, using digital holography, was inspected. The best performing metrics were identified and the trade-off between metric accuracy and robustness to variable conditions and their computational complexity were discussed.&#x0D; Keywords: digital holography, autofocus, cellulose microfibre, morphology, tomography
Application of spike neural network for stabilizing pendulum in the nonlinear formulation

The article describes the solution to the problem of stabilizing a nonlinear system using machine learning methods. Neural networks are one of the promising directions in this area. The article describes a model of spiking neural network, which differs from previous generations of networks by its similarity to biological neurons. A pendulum on an elastic foundation was chosen as a dynamic system for the study. The input layer of the neural network is the so-called sensory neuron, and information about the deviation of the pendulum from the equilibrium position was received on it. The Leaky Integrate-and-Fire model of the spiking neural network was used. The article shows the process of stabilization of a pendulum on an elastic foundation. The closed system was built and a method for a numerical solution was implemented. Two configurations of control functions have been considered. It is shown that the time required to bring the system into a steady equilibrium state depends on the choice of the control function.
Determination of Efficient Algorithm for Factoring Semiprime Number

The difficulty of factoring big semiprime numbers is fundamental to public key cryptography. It is critical to discover circumstances in which semiprime factoriza-tion can be performed efficiently. Our objective is to find use cases where semi prime factorization algorithm can be run efficiently. The mathematical approach for semiprime factorization is introduced in this article. Hopefully, it will aid research-ers in closing further gaps and making public key cryptography safer.
MIDI and Composing in the Digital Age

This chapter is all about the ubiquitous and well-liked Musical Instruments Digital Interface, or MIDI, which is extensively used for writing in every genre of music in a variety of contexts, including video games. Having been briefly mentioned previously in this book, we'll now go in depth with this wonderful standard. You'll also learn essential terms used in the art of video game soundtrack composition. We'll also touch on some fundamentals of music theory.