Reviewerly/exHarmony huggingface dataset:
Research on the Introduction to Models used in Speech Recognition

The theoretical basis of Artificial Intelligence (AI) has been established since the middle of the last century.After 1970, after John McCarthy and others established an AI laboratory at MIT, the first batch of scholars and technologies began to emerge, and AI began to flourish.path of.Today, AI is still the focus and frontier of today's science and technology.After decades of technological precipitation, AI covers a wider range of aspects, and the knowledge system is huge and increasingly complex.Therefore, this article will introduce the basic principles and models of AI from the perspective of speech recognition.By consulting the relevant literature, this article will specifically introduce the hidden Markov model and the artificial neural network model.After the summary of these papers, we found that the hidden Markov model has very good time series modeling ability, but the classification ability is not strong.The latter has excellent classification capabilities, but cannot describe time-varying information well.Therefore, this article combines the two models.After consulting the literature, I finally found that the combination of the two models is a good choice and has great potential for development.
An Artificial Potential Field Algorithm for Path Planning of Redundant Manipulators Based on Navigation Functions

In this work, we propose a novel artificial potential field off-line path planning algorithm for robot manipulators. The potential field is defined using navigation functions, and the parameters of the navigation function are defined as design variables of an optimization problem through which the minimum length of the path of control points on the manipulator links are achieved and the planned path is free of local minima. The applicability of the proposed algorithm is evaluated by applying it on a 5 degrees-of-freedom robot manipulator representing the human upper extremity to plan the so-called arm abduction and flexion motions in the presence of known workspace obstacles.
An Analysis of Junior High School Vulnerability in Flood-Prone Areas, Grogol, Sukoharjo Regency, Indonesia

Flood is a condition in which water enters and floods areas with low river areas or drainage which results in its inability to drain and accommodate water.The impact of the flood not only resulted in the paralysis of social, economic and community activities but also resulted in the cessation of educational activities.Vulnerability itself is a condition where the community and society are powerless to overcome the threat of a disaster that occurs.This study aims to determine the level of vulnerability of junior high schools in flood-prone areas, Grogol District, Sukoharjo Regency.This study uses a quantitative descriptive approach based on the variables studied.Quantitative research is research that focuses on analyzing numerical data processed by statistical methods.The research design used is a survey design.The population and sample used in this study is the population used, namely junior high schools located in flood-prone areas in Grogol District.The sample used is three junior high schools located in KRB Flood.The sample was determined using purposive sampling based on the Sukoharjo Regency Flood KRB Map published by the Sukoharjo Regency Spatial Plan.The results of the data analysis of the three junior high schools have social vulnerabilities that are relatively vulnerable to flood disasters, seen from schools that have medium class vulnerabilities.The physical condition of the three junior high schools has a vulnerability that is considered vulnerable by having a medium class and disaster risk reduction efforts.The three junior high schools show the same class, namely Low.
Chrysippus’ Logic in a Natural Deduction Setting

In this paper, we zoom in on some important features of Chrysippus’ logic by framing them in a natural deduction system for relevant logic. Our view is alternative to other recent reconstructions of Chrysippus’ view based on the sequent calculus. We show how, adding suitable elimination rules (akin to the five indemonstrables) that are missing in the Chrysippean system, one automatically obtains classical logic as a kind of deductive upper bound.
Correction to: Definition of a Digital Tool to Create Physical Artifacts: The Case of the Gamers4Nature Project

The chapter “Definition of a Digital Tool to Create Physical Artifacts: The Case of the Gamers4Nature Project” was previously published non-open access. It has now been changed to open access under a CC BY 4.0 license and the copyright holder updated to ‘The Author(s)’. The book has also been updated with this change.
Preservation of the heritage of N. A. Rubakin in the digital environment

The multifaceted activity of the classic of librarianship, the creator of bibliopsychology and the popularizer of science N. A. Rubakin is presented. The experience of the Presidential Library B. N. Yeltsin in the formation of digital collection devoted to 160th anniversary of the birth of N. A. Rubakin is described. The composition, structure and content of the collection of personalities are characterized. The creation of the personal history collections of the Presidential Library B. N. Yeltsin pursues enlightening and educational goals. The collections contain library, archival and museum resources related to the state, professional, creative and social activities of the person; the resources can serve as the basis for the research work of specialists. The digital collection being formed will contribute to the preservation and promotion of N. A. Rubakin heritage.
Noise Reduction in CT Images in the Domain of the Hyperbolic Wavelet Transform

The quantum and electronic noise generated by the physical principles of X-ray Computed Tomography (CT) degrade the quality of the projection data obtained.These components, as well as the possible anatomical noise, are propagated as pixel noise in the obtained CT images by means of reconstruction algorithms.The different characteristics of the medium through which the X-rays pass determine the direction and the non-stationary nature of the noise in the CT slices.This determines our choice of an adaptive statistical noise reduction algorithm based on the wavelet-threshold method and the correlation between two quasi-identical images.The hyperbolic wavelet transform (HWT) is used in the present paper, because the Hilbert transform (HT) does not change the variance of a random variable.The conducted experiments show that the proposed method gives results comparable or superior to the corresponding ones obtained by similar methods.
DaLC: Domain Adaptation Learning Curve Prediction for Neural Machine Translation

Domain Adaptation (DA) of Neural MachineTranslation (NMT) model often relies on a pretrained general NMT model which is adapted to the new domain on a sample of in-domain parallel data.Without parallel data, there is no way to estimate the potential benefit of DA, nor the amount of parallel samples it would require.It is however a desirable functionality that could help MT practitioners to make an informed decision before investing resources in dataset creation.We propose a Domain adaptation Learning Curve prediction (DaLC) model that predicts prospective DA performance based on in-domain monolingual samples in the source language.Our model relies on the NMT encoder representations combined with various instance and corpus-level features.We demonstrate that instance-level is better able to distinguish between different domains compared to corpus-level frameworks proposed in previous studies (Xia et al., 2020;Kolachina et al., 2012).Finally, we perform indepth analyses of the results highlighting the limitations of our approach, and provide directions for future research.
Augmented Reality in Instagram Story Filter for Increasing Awareness through Learning Interactivity and Enjoyment

The learning process is essential for education.Interactivity and enjoyment in the learning process are crucial elements that can improve awareness.The use of technology in the learning process is overgrowing, one of which is Augmented Reality (AR).This study investigates how AR as a filter in Instagram stories can affect user awareness and attitudes in the learning process.It explores user experience and examines its effect on interaction and enjoyment in social media related to the learning process.Furthermore, quantitative methods using frequencies and correlation data analysis were used to measure user perceptions about increasing the awareness of using AR filters on interactivity and enjoyment of social media.The result of this experiment is AR as an effective tool in Instagram stories as a media platform to increase awareness in the learning process with experiential methods of interactivity and enjoyment.
Why Cauchy Membership Functions: Reliability

An important step in designing a fuzzy system is the elicitation of the membership functions for the fuzzy sets used. Often the membership functions are obtained from data in a traininglike manner. They are expected to match or be at least compatible with those obtained from experts knowledgeable of the domain and the problem being addressed. In cases when neither are possible, e.g., insufficient data or unavailability of experts, we are faced with the question of hypothesizing the membership function. We have previously argued in favor of Cauchy membership functions (thus named because their expression is similar to that of the Cauchy distributions) and supported this choice from the point of view of efficiency of training. This paper looks at the same family of membership functions from the point of view of reliability
On Computational Complexity of Transfer Learning Approaches in Facial Analysis

More practical experiments proved high performance rates due not only to recent hardware technological advancements, but also to the new proposed pipelines in preprocessing inspired from domain-specific techniques for high-speed image recognition systems, image analysis applications, increased quality of tracking systems, and closely related to human performance of some decision-based applications. The performance has been obtained, mainly, through learning from previous experiments, integrating multi-step approaches, parallel processing, and transfer learning. In this paper, we describe some computational complexity results based on practical projects in facial image analysis and recognition.
Unsupervised Preference-Aware Language Identification

Recognizing the language of ambiguous texts has become a main challenge in language identification (LID).When using multilingual applications, users have their own language preferences, which can be regarded as external knowledge for LID.Nevertheless, current studies do not consider the inter-personal variations due to the lack of user annotated training data.To fill this gap, we introduce preferenceaware LID and propose a novel unsupervised learning strategy.Concretely, we construct pseudo training set for each user by extracting training samples from a standard LID corpus according to his/her historical language distribution.Besides, we contribute the first user labeled LID test set called "U-LID".Experimental results reveal that our model can incarnate user traits and significantly outperforms existing LID systems on handling ambiguous texts.Our code and benchmark have been released.
Eevou-Net: An Ensemble of Evolutionary Deep Fully Convolutional Neural Networks for Medical Image Segmentation

Developing a Deep Convolutional Neural Network (DCNN) is a challenging research topic which needs extensive efforts and computation to find a proper network structure and a precise set of hyper–parameters. The problem is that in most of the cases the achieved network works well on the specific application or dataset, and a small to significant changes are required to adapt it for a new one. Besides, the limited number of available labelled images and the required computational infrastructure, make this task even more challenging. Therefore, developing an automatic method that is able to find a network structure and its parameters, while using minimum computation, seems necessary. Evolutionary computation is an optimisation technique that is applicable to alleviate the above difficulties. This paper proposes an evolutionary–based framework to find a set of precise and small networks for medical image segmentation, and also, an ensemble model to improve the quality of segmentation. To the best of our knowledge, EEvoU–Net is the first ensemble method that utilises a set of evolutionary U–Net–based deep networks for medical image segmentation. In the proposed model, a Genetic Algorithm (GA) is applied to design a set of optimal network structures, along with their parameters, using a new fixed–length encoding strategy to create variable length networks, for medical image segmentation. The proposed model is evaluated using five different, publicly available medical image segmentation datasets. The best found evolutionary networks, outperformed U–Net, ResU–Net, DenseU–Net, NAS U– Net, AdaResU–Net, and EvoU–Net using considerable less trainable parameters. Furthermore, EEvoU–Net as an ensemble of evolutionary networks, has also substantially improved over those previous results.
Dim Wihl Gat Tun: The Case for Linguistic Expertise in NLP for Under-Documented Languages

Recent progress in NLP is driven by pretrained models leveraging massive datasets and has predominantly benefited the world's political and economic superpowers.Technologically underserved languages are left behind because they lack such resources.Hundreds of underserved languages, nevertheless, have available data sources in the form of interlinear glossed text (IGT) from language documentation efforts.IGT remains underutilized in NLP work, perhaps because its annotations are only semistructured and often language-specific.With this paper, we make the case that IGT data can be leveraged successfully provided that target language expertise is available.We specifically advocate for collaboration with documentary linguists.Our paper provides a roadmap for successful projects utilizing IGT data: (1) It is essential to define which NLP tasks can be accomplished with the given IGT data and how these will benefit the speech community.(2) Great care and target language expertise is required when converting the data into structured formats commonly employed in NLP.(3) Task-specific and user-specific evaluation can help to ascertain that the tools which are created benefit the target language speech community.We illustrate each step through a case study on developing a morphological reinflection system for the Tsimchianic language Gitksan.
Text classification method based on LSTM

With the rapid development of Internet technology and the explosive growth of social media, a large amount of information continues to be generated, of which the amount of text information is the largest.The main feature of various Chinese short text messages such as news headlines and instant messages is sparsity, which is only composed of a few to dozens of words, and the content of effective information packets is very small.As a result, the samples with sparse features and high feature set dimensions are difficult to provide key and accurate features for text classification learning.This paper mainly studies the application of deep learning in the field of Chinese text classification, and proposes a text classification model based on word level and character level mixed features.
Application and Comparison of Markowitz Model and Index Model in Hong Kong Stock Market

This paper aims to study the differences between the index model and the Markowitz model in the Hong Kong stock market and American stock market and explore the advantages and disadvantages of these two models, to provide an investment suggestion for investors who want to enter the Hong Kong stock market.This paper uses the price data of 15 stocks in the recent two years to calculate their return, standard deviation, beta, and other parameters.Using the index model and the Markowitz model to build the portfolio through these parameters.The research results are similar to some research results of applying the index model and the Markowitz model to the American stock market.The portfolios constructed by the two models are almost the same, but the result of the index model will be slightly better than that of the Markowitz model, and the amount of calculation required to construct the index model is much less than that of the Markowitz model.Therefore, this paper recommends investors who want to enter the Hong Kong stock market use the index model when building their optimal portfolio.
Online Reading and Viewing in Higher Education: The Case of University Students in the Philippines

With the recent global pandemic brought by the COVID-19 virus, many educational institutions have transitioned to complete remote instruction to sustain students' learning, specifically on their online reading and viewing engagements. However, university students have different views about its usefulness and practicality. While many studies have dealt with students' reading attitudes, not much has been explored on university students' attitudes toward online reading and viewing during the COVID-19 pandemic. This study, therefore, aims to describe the attitudes toward online reading and viewing of university students in the Philippines.
Grants and Acquisitions

The Private Academic Library Network of Indiana (PALNI) has received an American Rescue Plan Act (ARPA) grant of $225,000 from the U.S. Institute of Museum and Library Services, administered by the Indiana State Library. The planning grant will enable PALNI to identify ways of expanding current infrastructure to support open bibliographic data management tools, which library staff use to manage and share collections. The project aims to significantly enhance financial sustainability and access to information, allowing libraries to further deliver on their critical role in student success. PALNI’s project will explore innovative alternatives that promote open infrastructure—using open systems to encourage new ways of understanding collections through community building, sharing staff and metadata about library materials to meet the evolving needs of library patrons.
The analysis of high school students’ errors of word order in Recount text writing

The objectives of this research are to investigate the kind of errors that students frequently made in the use of word order in recount text and investigate the reason why students make errors in the use of word order in writing recount text. This study takes a qualitative approach, with 15 high school students who lived in LKSA Budi Utomo Metro, Indonesia as the data sources. The instruments used in this research were writing test, interview guideline, and recorder to collect the data. The data results showed that 119 errors appear frequently in the writing test which then were classified into two types such as in sentence structure which there were 97 errors or 81% out of total and noun phrase were 22 errors or 18,5%. Additionally, the students’ interview response showed that first language interference and carelessness became the reasons why they made errors.
Goals for Reading and Self-Efficacy among College Students

The purpose of this quantitative non-experimental descriptive-correlational study was to determine the significant influence of goals for reading towards self-efficacy of college students on campuses of a particular university. Researcher-made questionnaires on the two variables validated by the experts and subjected to pilot testing for reliability tests were conducted among college students in a specific private college. Then these questionnaires were administered considering a stratified random sample of 339 students. Data analysis tools used were weighted mean, Pearson-r, and regression analysis to test the hypotheses. The results showed that all reading goals, curiosity, involvement, importance, recognition, grades, and competition are all at a high level. Moreover, all the indicators of self-efficacy, to wit, identifying learning needs and setting goals, selecting learning resources and materials, seeking social assistance, organizing the learning process and environment, monitoring, evaluating, and transferring acquired skills or information to other contexts are also in high level. Further, it was found that there was a significant relationship between goals for reading and self-efficacy. In contrast, domains of goals for reading that significantly influence self-efficacy are importance and competition, while the rest of the domains do not considerably influence self-efficacy. Through this research, the reading goals, as it influenced students’ reading self-efficacy, which in turn resulted in greater reading milestones and academic achievement and would help educators enhanced their instructional management abilities.
TECHNOLOGY OF USING STARPAT IN THE PROFESSIONAL TRAINING OF PEDAGOGICAL UNIVERSITY STUDENTS

In the conditions of modernization and informatization of the educational system, many different approaches, technologies and practices appear in the pedagogical environment that contribute to improving the quality of modern education and reflect the needs of modern society. One of such innovative technologies is the technology of using a startup. This technology is aimed not only at developing financial literacy among students, but also contributes to the formation of many vital personal qualities in future specialists. Among them it should be noted: the development of creative abilities, critical thinking, skills of working with information sources. The startup in the educational environment has been used relatively recently. Startups are especially rarely used in pedagogical universities. However, recently they have increasingly attracted the attention of university management, teachers, students, as well as business. This article provides information reflecting the possibility of using the technology of startups with students of the pedagogical University, created using information and communication technologies. Student startups can be carried out by them as preparation of final qualifying works (diplomas, bachelor's works and master's theses).
Visualization of Modern Warfare with Chinese Chess

Some models visualize different warfare, such as dynamic images online, and real combat models.Although there are people comparing warfare and international relation with a game of chess, rarely have models appear.International relations are just like chess, where nations compete on a huge platform by moving forces and troops, says Trump, the former President of the United States of America.This paper will try to demonstrate and analyze events in the past and prove the hypothesis that Chinese Chess can be used as a model of international affairs and combats.First of all, the pieces in Chinese Chess are going to be defined as a part of modern warfare, then, how each part of a Chessboard is going to be introduced.Secondly, the rules of a chess game and the metaphors of each piece are explained.Lastly, the process that takes to the consequence is shown with pieces on a Chinese Chessboard, and the conclusion is made.Case studies about the Cuban Missile Crisis and the results of the calculation of the model may give some possible references and explanations that help with speculations and decisions later on.