Reviewerly/exHarmony huggingface dataset:
Building ESexpert: The Machine Learning System for Voice Interactive Interview Score Assessment for Student Loan Funds

Scholarship applicants' interviews generally last from an hour to an entire day. Several critical situations of each student caused the interviewer's feelings, such as stress, sadness, and anxiety. Listening to and discussing negative points can cause mental health problems. In order to decrease the duration of interviewing, This research paper discusses the development of a machine learning model for evaluating Student Loan Fund interview scores and an automated voice-based interactive machine learning interview scoring system. The process consists of building a machine-learning model using the Random Forest Algorithm with an accuracy of 99.54% and the SMOTE method to adjust the data asymmetry. The software was developed as a web application based on the SDLC for a second process, running Cloud Speech-to-Text of Google Speech API. Experts in all five areas evaluated the software, and the evaluation score was high. The research also looks at the Software Development Life Cycle (SDLC) models and the use of cross-validation and bootstrapping for accuracy estimation and model selection.
6 Agile Testdokumentation

No AccessAgile TestingDec 20236 Agile Testdokumentationhttps://doi.org/10.3139/9783446478411.006SectionsAboutPDF ToolsAdd to FavoritesDownload CitationTrack CitationsCopy LTI LinkPDF key 'share (en)' returned an object instead of string.FacebookTwitterEmailLinkedIn previous chapternext chapter FiguresReferencesRelatedDetails 2023Pages: 137-152Print ISBN: 978-3-446-47767-4eISBN: 978-3-446-47841-1 PDF downloadLoading ...
Analyzing the Requirement for Module-Based Reading Intervention for Children with Reading Difficulties in Sri Lanka

Reading proficiency is a fundamental skill crucial for academic success. However, some children encounter reading difficulties stemming from decoding or linguistic comprehension challenges. This need analysis aims to identify the reading fluency module needs for junior secondary school students in Sri Lanka who have reading difficulties. The objectives are to find out the teachers' awareness of children with reading difficulties, to find out the teachers' perception of module needs for teaching reading to children with reading difficulties, and to find out the need for Smart Reading Module (SRM) development. An adapted questionnaire was used, involving 164 (59.8% female and 40.2% male) junior secondary teachers from the Puttalam South Division in Sri Lanka. SPSS version 29 facilitated robust data analysis. The surveyed teachers, 78.7% recognized reading difficulties as significant obstacles. A striking 93.9% supported teaching children with reading challenges, emphasizing the importance of reading fluency for academic success and the potential for effective intervention programs. Moreover, 85% found teaching reading to such children highly challenging. Additionally, 82.3% highlighted the necessity of a structured module for reading instruction. Furthermore, 90.9% advocated for Smart Reading Module (SRM) resources, including teacher guides with guidance and lesson plans for effective teaching.
Designing in students’ training in the field of law

According to the Ministry of Labor and Social Protection of the Russian Federation, there is an opinion among leading employers that only 8 % of professional educational organizations graduates are fully prepared to perform their job duties. Modern economy, especially in the stage of import substitution and sanctions, requires a strong and highly qualified competitive employee. Modern vocational education has special requirements, it needs transformation. Professional competencies of modern graduates continue to be the center of attention. Continuous development of techniques and technologies requires mobility, flexibility, and timely actualization from professional education. The study analyzes experience of Law project training of students as a way to increase the demand for law graduates in labor market. The analysis has been conducted on legal specialists training.
Determining university’s readiness to implement AI technologies for personalizing educational paths

The key issue of the article is to determine readiness of HEIs to implement artificial intelligence and machine learning technologies for personalization of students’ individual educational trajectories (hereinafter – IET). In case of compliance of HEIs with the proposed groups of factors, including developed information architecture, there is an opportunity to use multidimensional structured and unstructured data more technologically to improve various types of university activities. High-tech marketing analytical tools facilitate the collection of contextual data and insights into various processes within university, as well as in-depth analysis of learners’ digital footprints. Methods of machine learning, predictive analytics, and modern generative neural networks allow to create recommendation services, with the help of which individual educational trajectories are formed by machine intelligence, simultaneously considering hundreds of parameters. Beyond the tasks of IET formation, machine intelligence can successfully solve tasks for other stakeholders of the university such as professors, researchers, and administration.
Word sense disambiguation for low resource languages: setswana collocations

Word sense disambiguation (WSD) is a critical task in natural language processing (NLP) and artificial intelligence. Supervised methods, such as decision list algorithms, are considered the most accurate machine learning algorithms for WSD. However, they are strongly influenced by knowledge acquisition bottleneck, making their efficiency dependent on the size of the tagged training set, which can be difficult, time-consuming, and costly to prepare. In this paper, we developed a hierarchical decision list algorithm for low resource languages that are morphologically rich, using a statistical method for collocation extraction from a big untagged corpus. Our approach identifies the most important collocations, which are the features used to create learning hypotheses. We manually construct the decision list based on the priority of the senses, improving the efficiency and accuracy of the algorithm. Our experimentation is based on a dataset of 800 sentences, focusing on 20 Setswana polysemous words. Using precision and recall to test our WSD system, we achieved 78% accuracy compared to 50% accuracy of the existing decision list algorithm. Our method can be used for other resource-limited languages. The system poses challenges in accurately determining the appropriate sense of a word, especially when dealing with idiomatic expressions and ambiguous contexts. The proposed approach could be further enhanced by incorporating additional NLP applications, such as a morphological analyzer.
Developing a Service for Collecting and Analyzing Electives Reviews

This article provides a review of publications on the analysis of students’ satisfaction with the educational process based on natural language processing methods. 197 student feedback on 129 elective disciplines at University of Tyumen was collected. A comparative analysis of keyword extraction methods was conducted: statistical TF-IDF, RAKE and YAKE; contextual KeyBERT; graph-based TextRank. On the collected reviews, grouped by elective disciplines, the RAKE method had the highest F1 BERTScore with 79 %. By parsing open sources, a dataset with 2210 Russian-language reviews for courses of different educational platforms was formed. Machine learning methods for sentiment analysis were described: support vector machines, logistic regression and based on Transformers, comparison on the manually marked part of the collected reviews. After fine-tuning on the rubert-base-cased model macro-averaged F1- score became 71.6 %. Classification into three classes (negative, neutral, positive) is not performed for the whole text of the review, but separately for each sentence from that text. The implementation of a database and information system for collecting and analyzing student feedback on the studied elective courses are presented. The model for sentiment analysis of the feedback is put into a separate microservice, which is communicated through an interface of the freely distributed Python framework FastAPI. The information system is designed to help students choose electives based on more qualitative data, and teachers and university administration ‑ to draw conclusions for further transformation of the educational space, taking into account students’ opinions.
Quantum entanglement between signal and frequency-up-converted idler photons

Quantum frequency up-conversion is a cutting-edge technique that leverages the interaction between photons and quantum systems to shift the frequency of single photons from a lower frequency to a higher frequency. If a photon before up-conversion was part of an entangled pair, it becomes crucial to comprehend the time-frequency entanglement after up-conversion. In this study, we present a theoretical analysis of the transformation of the time-dependent second-order quantum correlations in photon pairs and find the preservation of such correlations under fairly general conditions. Furthermore, we analyze Hong-Ou-Mandel interference between the signal and frequency-converted idler photons to gain insight into the indistinguishability of the two photons. The visibility of two-photon interference is sensitive to the magnitude of frequency conversion and improves as the frequency separation between two photons decreases.
Research and Application of Urban Road Extraction from Remote Sensing Images Based on Convolutional neural network U-net

Abstract: The urban road network is the backbone of a city, and the development speed of a city largely depends on whether the planning of the urban road network is reasonable. How to accurately obtain road distribution has profound significance for urban road planning in China, and the remote sensing image classification method for extracting urban road information utilization is a very effective method. Aiming at the problems of low recognition accuracy and weak generalization ability of traditional methods for urban roads, this paper proposes the research and application of urban road extraction methods from remote sensing images based on Convolutional neural network U-net The maximum likelihood method and the Convolutional neural network U-net method based on the depth learning method are used to extract urban roads from the experimental data. By evaluating the accuracy of urban road extraction results, it is concluded that the U-net method based on Convolutional neural network is reliable and superior for urban road extraction from remote sensing images.
La universidad pública en la pospandemia: ¿vino nuevo en odres viejos?

What impact in the medium and long term will the Pandemic have over our contemporary Society?¿and how will public universities face the current and future challenges? The answers to these two questions imply, in our perspective, a process of pedagogical innovation and in the areas of research and academic production according to the specific mission of those university systems that do not function for profit but for the wellbeing of Society and for the benefit of national and regional Latin American interest. The Centro de Estudios Generales of the Universidad Nacional of Costa Rica for many years has been pursuing a line work in education, research, outreach, and academic production guided by the principles and purposes derived from humanist though and action in our times, and the development of interdisciplinary study of complex and hypercomplex systems following some of the epistemological notions proposed by Rolando García and Jean Piaget (1982, 2000, 2006) in series of seminal works. This brief essay will cover three main sections: 1. A basic diagnosis of the global pre and post pandemic; 2. The importance of both general situations (nonetheless, emphasizing the new phenomena of the post pandemic phase) for present work in all academic areas of public universities; 3. A sketch of new humanist and interdisciplinary guidelines to contribute to our academic activity in these turbulent times.
Ice music to melt hearts

Research into the acoustics of Svalbard's glaciers and caves is fundamental to composer Erland Cooper's latest works, finds Arwa Haider
ARE GREEN CRYPTOCURRENCIES SAFE? INVESTIGATION OF THE GREEN AND NON-GREEN CRYPTOCURRENCIES

Cryptocurrencies, which started with Bitcoin, which was released differently from traditional payment and investment tools, have large transaction volumes today. In addition to the many economic benefits of cryptocurrencies, which are used both as a payment tool and as a financial investment tool, high energy consumption and a heavy carbon footprint come with them. With the owner of the automaker Tesla stating that he is worried about the increasing use of fossil fuels in Bitcoin mining and cutting its support for Bitcoin, the price of Bitcoin has fallen sharply, while green cryptocurrencies have reached historical peaks. This situation reminded the investors that they should handle risky investments carefully and also highlighted the importance of green investment tools. Understanding the relationship between green cryptocurrencies and other assets is essential for investors looking to expand their portfolios and seize emerging opportunities. In this direction, the study examined whether green cryptocurrencies are a safe haven against non-green cryptocurrencies in the period of January 2022–July 2023. In the analysis, DCC-GARCH analysis, risk, and return analyses were performed for safe haven. According to the analysis' findings, among cryptocurrencies, green cryptocurrencies are most likely to be a safe haven for investors.
Development of P-TMDRE Blended Teaching Mode on E-Commerce Foundation Course in Secondary Vocational School

This research originates from the challenges faced in the teaching of depth, systematization, and coherence in the current blended teaching mode, which is based on an innovatively constructed P-TMDRE blended teaching mode of the “E-commerce foundation course” in secondary vocational schools, which is belived to improve students’ grades, due to its emphasis of in-depth systemic learning. The sample uses the method of target sampling to select 17 experts; the course practice is aimed at 80 sophomores majoring in e-commerce, divided into 40 participants in the experimental group and 40 participants in the control group. Four rounds of expert review using the Delphi method: first round: brainstorming; second round: expert opinion review stage; third round: re-evaluation stage based on the previous round of evaluation opinions; fourth round: identify, resolve, and report Workable ideas. Through the pre-test and post-test of the quasi-experimental method, the student’s grades and deep system learning ability are tested, and the effectiveness of the P-TMDRE blended teaching mode is further confirmed. Using Delphi technology to build five modules of the P-TMDRE blended teaching mode of the “E-commerce foundation course” course. After the teaching application, the average post-test scores of the control group and the experimental group were 64.765 and 75.252, with a difference of 10.487 points, indicating that the learners’ academic performance has been significantly improved. On the motivation dimension of deep systems learning, the pre-test and post-test averages are 2.91 and 4.14. In terms of learning engagement, the mean values of the pre-test and post-test were 2.93 and 4.15. In terms of deep system learning strategies, the mean values of the pre-test and post-test of the learners in the experimental group were 2.96 and 4.17, indicating that the deep system learning ability of the learners has been significantly improved.
METRIC ALGORITHMS OF SYMBOL DETECTION FOR DETERMINING A GROUP OF SIGNIFICANT OBJECTS

This article examines the algorithm of nearest neighbors, which widely uses metric classification algorithms and the combinatorial theory of reconnection with the precedent information in solving practical recognition problems. One of the most important issues in the development of image recognition systems is that the image recognition system determines the characteristics of objects. Most pattern recognition methods are used in the process of recognizing not all objects of the training sample, but a certain part of the objects. The formation of a training sample by filtering objects according to the level of importance for metric classification algorithms is carried out by summing combinatorial estimates with the possibility of analyzing the compactness profile when choosing basic objects. One way to modify the nearest neighbor algorithm is to select a subset of a small number of base objects and recognize control objects using this subset of base objects. A criterion for the effectiveness of the use of combinatorics formulas in the implementation of adaptive control and the concept of compactness of the selection profile are proposed. The proposed algorithm is on the opposite side compared to the algorithms of the STOLP category.
Hyperspectral Image Compression Using Prediction-based Band Reordering Technique

The hyperspectral image represents various spectral properties Because it consists of broad spectral information of ground materials that can be used for various applications, These images are collected as large amounts of data that must be processed and transmitted to the ground station. These acquired images contain redundant spectral information that has to be reduced in order to reduce transmission and storage capacity. This work focuses on preserving their quality while compressing them using band reordering techniques and prediction coding. This can be accomplished by preprocessing in which sub-bands are decomposed and bands are reordered into unsequenced compression can be accomplished through using the technique of linear prediction. The report discusses the Pavia University hyperspectral image data cube, which was acquired via a sensor known as a reflected optics system imaging spectrometer (ROSIS-3) over the city of Pavia, Italy.
Design and Development of Handwritten Digit Recognition System Using Machine Learning

In any system ,the wide range of writing styles, handwritten digit recognition has proven to be a challenging task.The wide variety of writing styles has made it challenging to read handwritten numerals.. Numerous research have demonstrated the superior performance of neural networks in the classification of data. The major goal of this work is to compare various existing classification models in order to give effective and trustworthy methods for handwritten numeral recognition. As training sets, a total of 45,000 images with a pixel size of 2828 were employed. The original image was compared to the training sets and images. We also found that the classifier's accuracy increases with the amount of training data. The study's conclusion shows that K-NN maintains an accuracy of 96.7% as opposed to 96.8% while processing data at a rate nearly ten times faster than a neural network.
Research on Factors Influencing the Quality of Talent Cultivation in Music Majors in Higher Education

This study aims to assess the current status of music professional talent cultivation quality in universities. It seeks to integrate factors influencing the quality of music talent cultivation, including the interplay between quality indicators, cultivation mechanisms, and autonomy. A theoretical model for the quality of music talent cultivation is proposed based on a sample of 1988 students from 16 undergraduate universities with music majors in Shaanxi Province. The study utilizes mathematical statistics and SPSS for data analysis, focusing on questionnaire reliability. Confirmatory factor analysis uses AMOS24.0 software, and model fit testing is performed against established indicators. The research findings reveal that the theoretical model aligns with the measured data, meeting fitting standards. Descriptive analysis and correlation testing show positive correlations between variables. Univariate analysis of variance based on majors, ages, and regions indicates significant differences. The study highlights the positive impact of understanding influencing factors on practical efforts to enhance the quality of music talent cultivation in universities. It recommends innovative reforms, such as expanding major choices, developing personalized curricula, and adopting tech-supported teaching methods. Strengthening management systems, effective supervision, and enhancing quality standards are emphasized for improving music education and talent cultivation, fostering overall progress and reform in this field.
Enhancement of Real-Time Object Detection and Tracking in Collaborative Environment using AI and Mixed Reality

The area of mixed reality has had rapid growth in recent years, with a notable rise in funding. This may be attributed to the rising recognition of the potential advantages associated with the integration of virtual information into the physical environment. The majority of contemporary mixed reality apps that rely on markers use algorithms for local feature identification and tracking. This study aims to enhance the accuracy of object recognition in complicated environment and enable real-time classification operations via the introduction of a unique detection approach known as the lightweight and efficient YOLOv4 model. In the present setting, Computational vision emerges as a very valuable and engaging manifestation of artificial intelligence (AI) that finds widespread application in many aspects of daily existence. The field of computer vision is dedicated to the development of advanced artificial intelligence and computer systems that aim to replace complex elements of the human environment. In recent times, deep neural networks have emerged as a crucial component in several sectors owing to their well-established capacity to process visual input. This study presents a methodology for classifying and identifying objects using the YOLOv4 object detection algorithm. Convolutional neural networks (CNNs) have shown exceptional efficacy in the tasks of object tracking and feature extraction from pictures. Therefore, the enhanced network architecture optimizes both the precision of identification and the speed at which it operates. This research will contribute to developing mixed-reality simulations system for object detection and tracking in collaborative environment that are accessible to everyone, including users in the architectural filed. The model was evaluated in comparison to other object detection approaches. Based on the empirical results, it was observed that the YOLOv4 model exhibited a mean average precision (mAP) of 0.988, surpassing the performance of both YOLOv3 and other object identification models.