Reviewerly/exHarmony huggingface dataset:
PhD data collection challenges due to the Covid-19 crisis – a report

The following report depicts the challenges I have faced in the process of collecting the necessary data for my PhD thesis titled Vocabulary learning strategies (VLSs) used by university students in an ESP context due to Covid-19 pandemics and switching to online teaching. In order to test the functionality of the VLSs qualitatively, and indicate which of them is responsible for acquisition of larger vocabulary quantities, my mentor (prof. Svetlana Dimitrova - Gyuzeleva) and I have designed an experiment which was originally intended to be fully implemented during the second semester of 2019/2020 (February to June). This experiment would provide a more detailed outline on the efficiency of different strategies use at the same time tackling maybe even a more important issue – raising learners’ awareness about their importance for individual language development. The respondents were students of Law and Finance. However, the Covid-19 pandemic interrupted the implementation of the experiment in the middle (in March) because the government introduced special measures and we were forced to convert to online teaching. Even though the teaching process was not dramatically challenged, since we were able to smoothly transfer to teaching via the platform Microsoft Teams, the experiment, or rather the results that were to be obtained with it, were significantly affected.
합성곱 신경망을 사용한 하천 수질예측 정확도 평가

The present study assessed the applicability of convolutional neural network (CNN), which showed superior performance for classification, segmentation, and natural language processing, to river water quality prediction. Monthly data was compiled from upstream and downstream water quality monitoring stations in the Hwang River over the period of January 2007 through December 2020, from which training and test sets were constructed in the ratio of 70:30. The performance of CNN consisting of single and multiple layers were evaluated separately using univariate data with single dependent variable (i.e., either chemical oxygen demand (COD) or chlorophyll-a (Chl-a) as well as multivariate data with dependent and 9 independent variables. The results showed that the prediction accuracy of the proposed CNN algorithm tested with univariate data was noticeably higher for COD than for Chl-a (in terms of target variable) as well as for multiple layers than for single layer (with respect to model architecture). In addition, the CNN algorithm evaluated with multivariate data achieved had better prediction performance than that of univariate data although its performance varied widely among data sets, and to a less extent, among stations and target variables. No measurable difference was also found in prediction performance of the CNN algorithm (for two target dependent variables) according to the number of (important) independent variables. All these results demonstrate that while the proposed CNN algorithm can be adopted to predict (monthly) water quality variables, its careful architecture design is yet required to achieve substantial performance improvement.
Question Decomposition with Dependency Graphs

QDMR is a meaning representation for complex questions, which decomposes questions into a sequence of atomic steps. While state-of-the-art QDMR parsers use the common sequence-to-sequence (seq2seq) approach, a QDMR structure fundamentally describes labeled relations between spans in the input question, and thus dependency-based approaches seem appropriate for this task. In this work, we present a QDMR parser that is based on dependency graphs (DGs), where nodes in the graph are words and edges describe logical relations that correspond to the different computation steps. We propose (a) a non-autoregressive graph parser, where all graph edges are computed simultaneously, and (b) a seq2seq parser that uses gold graph as auxiliary supervision. We find that a graph parser leads to a moderate reduction in performance (0.47 to 0.44), but to a 16x speed-up in inference time due to the non-autoregressive nature of the parser, and to improved sample complexity compared to a seq2seq model. Second, a seq2seq model trained with auxiliary graph supervision has better generalization to new domains compared to a seq2seq model, and also performs better on questions with long sequences of computation steps.
Features of Formation of Motivation for Online Learning of Students of Pedagogical Specialties

The concept of and modern theories of learning motivation are theoretically substantiated and described: expectancy-value theory, attribution theory, social-cognitive theory, goal orientation theory, self-determination theory. Teaching strategies and pedagogical conditions that motivate students to educational and cognitive activities in the online environment, taking into account the theory of self-determination, are determined. In particular, in order to succeed in mastering something new, a student must feel competent (acquire the appropriate skills to perform practical tasks), related (feel belonging and attachment to groups or individuals), autonomous (feel that he/she controls his/her behavior and is responsible for its goals, capable of self-learning).The pedagogical conditions for the formation of positive motivation and involvement of students in learning online are substantiated and determined, namely: giving a student the opportunity to be an accomplice in the learning process; structuring educational materials in accordance with learning objectives; promoting student initiative and creativity, ensuring group activities and a multidimensional assessment system.The key to fulfilling the pedagogical conditions is the teacher's motivation to implement online learning at the level of the course author, mentor and tutor for higher education students. External and internal factors of motivation of teachers for introduction of elements of distance (online) and mixed (alternation of online and offline) training by teachers in the educational process of establishments of higher education are defined.Experimentally confirmed: positive impact on the motivation of the implemented online learning strategies and pedagogical conditions; revealed a significant direct relationship between the student's ability to self-development and self-learning and his/her motivation for online learning.
The Dark Side of Financial Innovation: Deterrent for Economic Growth

This study explores the impact of financial innovation on economic growth with a unique focus on its detrimental effects. Using data from 164 countries for 1990 to 2017, the paper uses panel OLS regression model with an exhaustive set of variables to conduct the base analysis while GMM is used to accurately study the possible endogenous relationship between financial variables and economic performance across countries. The results show that there is a significant, negative relationship between financial innovation and economic growth, thereby highlighting the dark side of financial innovation. The findings have important policy implications for regulators especially with emphasis on the role of market discipline.
Automatic recognition and representation of text in the form of audio stream

The problem of automatic speech generation from a text file is considered. An analytical review of the software has been completed. They are designed to recognize texts and convert them to an audio stream. The advantages and disadvantages of software products are estimated. Based on this, a conclusion was drawn about the relevance of developing a software for automatic generation of an audio stream from a text in Russian. Models based on artificial neural networks, which are used for speech synthesis, are analyzed. After that, a mathematical model of the created software is built. It consists of three components: a convolutional encoder, a convolutional decoder, and a transformer. The architecture of the software is designed. It includes a graphical interface, an application server, and a speech synthesis system. A number of algorithms have been developed: preprocessing text before loading it into a software, converting audio files of a training sample and training a network, generating speech based on arbitrary text files. A software has been created, which is a single-page application and has a web interface for interacting with the user. To assess the quality of the software, a metric was used that represents the average score of different opinions. As a result of the aggregation of different opinions, the metric received a sufficiently high value, on the basis of which it can be assumed that all the tasks have been solved.
Seismic stability monitoring system of building structure based on machine vision

In order to overcome the problems of low detection accuracy and efficiency existing in the traditional seismic stability monitoring system of building structure, a new seismic stability monitoring system of building structure based on machine vision is proposed in this paper. The function and structure of the system are designed on LabVIEW platform. Based on this, the hardware of the system is designed, including building structure visual image acquisition equipment and wireless transmission module. In the software part of the system, the image algorithm is used to calculate the visual image of the building structure to complete the judgment of the instability factor. The experimental results show that, compared with the traditional monitoring system, the visual feature extraction accuracy and stability monitoring accuracy of the designed system are higher, and the practical application performance is stronger.
[The value of diversifying blackboard writing in college education: taking Biochemistry teaching as an example].

Blackboard writing undertakes the dual task of knowledge transmission and classroom culture inheritance. Well-designed blackboard writing will not only help students to better memorize, understand and construct knowledge framework, but also create a serious but lively classroom atmosphere, strengthen the soul of moral education in the classroom, leading to improved quality of education. Taking the practice of blackboard writing in teaching the Biochemistry course as an example, the authors categorized the blackboard writing approaches according to the teaching objectives to be achieved, and discussed the necessity and application scope of each type of blackboard writing approach in the multimedia era. Our goal was to make blackboard writing, a conventional teaching approach, play an important role in the new era of classroom education.
Supervised-Learning for Symbol Detection in Time Varying Channels

This paper presents a learning-based symbol detection method for time-varying inter-symbol-interference channels. Under the time-varying channel environment, accurate detection of the data symbols is challenging because of incomplete knowledge of instantaneous channel state information at the receiver. Inspired by an existing data-driven joint channel estimation and symbol detection method, our detection method is to adaptively learn the channel variation using a set of previously detected symbols as new training samples for the channel estimation. Using simulations, we show that the proposed online learning based symbol detection method outperforms the existing learning based symbol detection methods under moderate mobility scenarios.
Research on a voice changed by distortion

The concept and essence of distortion are considered from a technical stand-point and criminal law perspective. The most common ways of distorting a voice and a speech are provided, as well as certain methods of detecting an intentional change in a voice and human speech by computer tools and linguistic analysis.&#x0D; Some software and hardware tools changing a speech signal both in real time and in a pre-prepared recording are analyzed.&#x0D; To solve diagnostic and identification tasks, a pressing issue in forensic video and audio analysis is studied which is addressed to forensic experts in this field more often.
An approach for cross-modality guided quality enhancement of liver image

A novel approach for multimodal liver image contrast enhancement is put forward in this paper. The proposed approach utilizes MRI scan of liver as a guide to enhance the structures of CT liver. The enhancement process consists of two phases: The first phase is the transformation of MRI and CT modalities to be in the same range. Then the histogram of CT liver is adjusted to match the histogram of MRI. In the second phase, an adaptive histogram equalization technique is presented by splitting the CT histogram into two sub-histograms and replacing their cumulative distribution functions with two smooths sigmoid. The subjective and objective assessments of experimental results indicated that the proposed approach yields better results. In addition, the image contrast is effectively enhanced as well as the mean brightness and details are well preserved.
Generalized LR-Drawings of Trees.

The LR-drawing-method is a method of drawing an ordered rooted binary tree based on drawing one root-to-leaf path on a vertical line and attaching recursively obtained drawings of the subtrees on the left and right. In this paper, we study how to generalize this drawing-method to trees of higher arity. We first prove that (with some careful modifications) the proof of existence of a special root-to-leaf path transfers to trees of higher arity. Then we use such paths to obtain generalized LR-drawings of trees of arbitrary arity.
Formation of metasubject educational achievements of primary schoolchildren in the process of assessment activities

The main mission of modern education - improving the quality, cannot be realized without innovative approaches to the assessment system, without the formation of mechanisms, procedures and technologies for monitoring the educational achievements of students. Federal state educational standards of primary general education of the second generation orient teachers towards the qualitative formation of subject, meta-subject and personal results. Modern teachers successfully diagnose the level of subject and personal achievements of junior schoolchildren, evaluating these universal educational actions. Certain difficulties arise with the assessment of metasubject learning outcomes of schoolchildren. The assessment activity of students, according to the authors, acts as an effective means of forming metasubject competences of younger students, since the mechanisms for diagnosing metasubject educational achievements have not yet been worked out. This scientific work discusses not only modern approaches to assessing metasubject achievements, but also proves that a well-constructed assessment activity can become an effective means of forming metasubject competencies. The authors pay great attention to such methods of assessment activities, which, within the framework of the implementation of the state standard, ensure the flexibility of the educational process, create an opportunity not only for teachers, but also for the students themselves to control and evaluate metasubject results.
Review -video watermarking schemes

Watermarking nowadays is one of the most sought after research domain in data security.Many techniques have been implemented so as to accomplish the embedding and extraction of data bits that are used for data authentication.A non exhaustive review has been conducted so as to analyze the pros and cons of various research papers to get an insight of recent trends in domain of watermarking. The main feature of this paper is that it compares simulation results of SVD, DWT-SVD, LWT-SVD, and RWDT-SVD for video watermarking to give an insight of various evaluation parameters .Simulations have been performed on videos in presence of variants of noise attacks and comparative analysis for fidelity and robustness is obtained in terms of PSNR and CC.
Quantum Information Interface on a Photonic Crystal Chip

Quantum systems for information processing rely on the distribution of quantum information in a network. Lanthanide complexes coupled to an optical cavity can act as an interface between a stationary and a flying QuBit. Here we show that in such a system the stationary quantum information can be mapped onto a photon, paving the way for a node in photonic quantum network.
Research on Feature Fusion Method of Mine Microseismic Signal Based on Unsupervised Learning

The feature extraction of high-precision microseismic signals is an important prerequisite for multicategory recognition of microseismic signals, and it is also an important basis for intelligent sensing modules in smart mines. Aiming at the problem of unobvious feature extraction of multiclass mine microseismic signals, this paper is based on the unsupervised learning method in the deep learning method, combined with wavelet packet energy ratio and empirical modulus singular value decomposition, and proposes a method based on wavelet packet energy and empirical modulus singular value decomposition and proposes a method (M-W&amp;E) based on wavelet packet energy and empirical modulus singular value decomposition. This method firstly performs empirical modulus singular value decomposition and wavelet packet energy ratio on the microseismic signal to construct the basic feature vector and then uses the unsupervised learning algorithm to perform the unsupervised learning method feature fusion of the basic feature vector to construct the fused feature vector. After visualization by t-SNE, various distinctions in the fusion feature vector are more obvious. After testing the fusion feature classification using SVM, it is found that the recognition rate of the new feature after feature fusion is better than that of a single wavelet packet empirical energy component and singular value of empirical modulus, which basically meets the engineering needs and is a mine microseism. The signal extraction and feature enhancement fusion of multiclass samples provide a new idea.
Presence in VR Experiences—An Empirical Cost–Benefit Analysis

Virtual reality (VR) is on the edge of getting a mainstream platform for gaming, education, and product design. The feeling of being present in the virtual world is influenced by many factors and even more intriguing a single negative influence can destroy the illusion that was created with a lot of effort by other measures. Therefore, it is crucial to have a balance between the influencing factors, know the importance of the factors and have a good estimation of how much effort it takes to bring each factor to a certain level of fidelity. This paper collects influencing factors discussed in literature, analyses the immersion of current off-the-shelf VR-solutions, and presents results from an empirical study on efforts and benefits from certain aspects influencing presence in VR experiences. It turns out, that sometimes delivering high fidelity is easier to achieve than medium fidelity and for other aspects it is worthwhile investing more effort to achieve higher fidelity to improve presence a lot.
Analyzing the Impact of ESL Learners' Reading Habits on Writing Skill

This study seeks to look at the reading habits of EFL students at KFUEIT University in Rahimyar Khan and the impact of reading habits on students' writing skills. This is quantitative research since it looks at how the dependent variable (writing skill) is affected by the independent variable (reading habit), and the results are in the form of numbers. The participants of the study were 150 ESL students from KFUEIT University. A reading habit questionnaire was used in this study to assess the quality of students' reading habits and a writing-based questionnaire to assess student's writing ability and how their reading habits impact it. The findings reveal that the ESL learners reading habits were of average quality. Meanwhile, an examination of the data reveals that ESL learners' reading habits impact their writing ability. Furthermore, the topic was one of the writing elements that were most impacted by the reading habit.
IMPLEMENTATION OF DIGITALIZATION ON THE REVENUE CYCLE TO IMPROVE THE QUALITY OF DECISION MAKING MORE TIMELY IN INDONESIA

Revenue from product sales is the most important component for a company where the role of the information system in the revenue cycle plays an important role on capturing any single information related to the market, customers, availability of supplies for sale, payment systems from customers, product delivery time to customers, and how quickly this information can be gathered to become an information that can support decision making. This researchintends to get certainty that the use of digitizing the revenue cycle process will create a very positive impact on companies with better, faster, more accurate, and safer business considerations, as decision-making materials that can be made faster at any time. The method in this research uses descriptive methods to present a complete picture of social conditions or is intended for exploration and clarification of a social phenomenon or reality, by describing a number of variables. Digitalization in the revenue cycle can be done by implementing a barcode system and adding the existing information in the company database to a cellular tablet which become a tool for the sales team, so that the system will mutually integrate and update information data after the sales execution process is carried out. Prior to the system implementation, information can only be received by decision makers within the next 2 working days after the sales execution process is carried out.
Factors affecting the students’ re-use of the electronic learning system (Els)

The COVID-19 pandemic has challenged higher education worldwide. From face-to-face sessions between teachers and students, teaching and learning have to be done online to allow students to continue learning while preventing the spread of this infectious disease. Faced with a wide range of electronic learning systems (ELS) to choose from, this study examined the factors that could affect the students’ intent to re-use the ELS. There were 135 college students in a private university in Manila, Philippines, and 177 college students from a public university in Macau, China, who were surveyed to find out the factors that would influence their intent to re-use ELS. Using structural equation modeling (SMART-PLS), this study showed that there was a significant relationship between perceived ease of use (PE) and perceived usefulness (PU), which were the constructs of the technology acceptance model (TAM). This implies that those who find the ELS easy to use will also find it useful. This study also extended TAM to include intrinsic motivation (IM) and extrinsic motivation (EM) as moderating variables between satisfaction (ST) and intent to re-use the ELS (INT). The result showed that IM and EM did not moderate the relationship between ST and INT. This suggests that Chinese and Filipino students are not motivated to use the ELS. This may be because there is a need to make the ELS enjoyable and engaging. Perceived convenience (PC) was significant to PE and PU. This shows that those who find the ELS convenient find it easy to use and useful. The overall results showed that user training (UT) was significant to PE and PU. This implies that training can stress the usefulness and ease of adopting the ELS. Satisfaction (ST) was significant to INT. The results validate existing literature that those who find the technology easy to use and those who are satisfied with the learning systems are likely to re-use it. This study is one of the few studies in the Philippines that examined the factors that affected students’ re-use of the ELS. It has significant implications for educational institutions in terms of designing an ELS that would encourage students to re-use it, especially during this time when educational institutions have developed courses to be taught in an online environment even if there are vaccines for COVID-19. © 2021 by De La Salle University.