Reviewerly/exHarmony huggingface dataset:
Voice Assisted Guide for the Blind using QR Codes and Wearable Smartphones

Visual disabilities are a recurring issue that negatively impacts many lives. Many existing public spaces are inaccessible to blind people. Although, many studies aim at assistive technology solutions, scalability of such solutions remains a challenge. Thus, the aim of this study is to develop a scalable solution to improving the accessibility of public spaces for blind people via a voice assisted guide mobile application system. The system is based on a pair of QR code reading and planning apps. The QR reader uses the built-in camera to read QR codes and output auditory cues for the blind users with one-touch operation. The QR planner allows other users to customize a given space with contextual QR code messages for guiding the blind user as they navigate the space. The flexibility of the apps' potential applications is demonstrated through case simulations along with analysis of the operating parameters that are hoped to enable wide scale deployment in various public spaces.
Resource Security Allocation Algorithm of Ecological Network Curriculum in Higher Education Based on Fuzzy Particle Swarm Optimization

Aiming at the resource allocation of ecological network courses in higher education, the corresponding allocation framework is constructed based on fuzzy particle swarm optimization. Because of the slow convergence speed of particle swarm optimization algorithm in the later stage, it is easy to converge in local optimization. Therefore, combined with the characteristics of resource allocation problem, particle swarm optimization algorithm is improved. The resource allocation model of ecological network courses in higher education is solved by using fuzzy particle swarm optimization algorithm under the constraints, and the resource allocation scheme is obtained. The results show that compared with the manual allocation scheme, the higher education ecological network curriculum resource allocation scheme obtained by the research algorithm has higher curriculum resource utilization efficiency and resource allocation efficiency, indicating the effectiveness of the research algorithm.
Prediction method of deep transfer learning performance of permanent magnet synchronous motor under the condition of few-shot

Using deep learning algorithm to establish agent model can realize rapid prediction and analysis of electrical equipment performance, but its training process requires a lot of data. However, the lack of labeled data of modern electrical equipment leads to the low accuracy of the trained deep learning model in predicting the performance of equipment, which restricts the engineering application of the algorithm. This paper establishes a deep transfer learning performance prediction method, which applies the transfer of performance prediction knowledge accumulated in the historical motor data to the performance analysis of the target motor, and completes the performance prediction task of the target motor with less labeled data and computational power consumption.
Accuracy Recommendation Algorithm of Preschool Education Distance Teaching Course Based on Improved K-Means

In the distance learning module of preschool education, the improved K-means clustering is used to process the distance learning course of preschool education. In this paper, the characteristics of pre-school distance education courses are mined, and the scoring characteristics of different pre-school distance education courses are calculated. Based on the similarity matrix of distance learning courses in preschool education, this paper predicts the user's evaluation of distance learning courses in preschool education. By introducing the user's preference for the pre-school distance learning courses, the collaborative filtering of pre-school distance learning courses is completed. According to the connection between user's interest and distance learning courses, the shortest path between keywords and user's interest in distance learning courses of pre-school education is used. According to the user's preference, the recommendation list of distance learning courses in preschool education is predicted. By recommending the most similar distance education courses to the users, the recommendation of distance education courses for preschool education is realized. Experimental results show that the proposed RMSE of this method can reach 0.95, and the recommended accuracy is greater than 80%, and even the highest accuracy is 99%
Health, Religiosity and Hatred: A Study of the Impacts of COVID-19 on World Jewry

This exploratory study examines the likely causes of the alarming global rise of antisemitism during the COVID-19 pandemic. Beginning with an appraisal of today's world Jewry, this exploratory study highlights the main social, health, and religious impacts COVID-19 has had on Jews worldwide and goes on to highlight how various Jewish communities managed and adjusted to COVID-19 public health restrictions. From this contextual backdrop, an assessment of how and why antisemitism has surged during the pandemic is presented, along with a review of what efforts are being taken to curtail this rise in hatred toward Jews. A central aim of this study is to underline the point that until meaningful, broad, and international steps are taken to curb online hate, the historic antisemitic tropes and myths suggesting Jews are the cause of disease will undoubtedly evolve and surge (especially across social media) during future pandemics and times of global crisis and unrest.
E-activities as a didactic situation for the calculation of areas

The objective of this research is to analyze the enforcement of e-activities on calculation of designed areas based on the phases of a Brousseau Didactic Situation through a quasi-experimental design. We worked with two groups, group of parallel B was the experimental group to which a didactic sequence was applied with the e-activities mentioned before, while the didactic sequence of the group of parallel A that is the control group did not intent to use this type of activities. We use a structured test based on Ser Bachiller exam that is applied in Ecuador at the end of high school education, by comparing the differences between the median scores of both groups, it was possible to conclude that there is a statistically significant difference between them with p=0.02, and also verified a higher median for the experimental group.
Learning to Optimize Resource in Dynamic Wireless Environment via Meta-Gating Graph Neural Network

Generally speaking, artificial intelligent (AI) models are trained under special learning hypotheses, especially the one that statistics of the training data are static during the training stage. However, the distribution of the channel state information (CSI) is constantly changing in real wireless communication environments. Therefore, it is essential to study the dynamic deep learning (DL) technology for wireless communications. In this paper, we investigate a beamforming design problem by maximizing the weighted sum rate in episodically dynamic wireless environment, where the CSI distribution changes over periods and maintains stationary within each period. In order to effectively solve this problem, a novel framework named meta-gating network is proposed, which can achieve three important goals, i.e., seamlessly, quickly and continuously. Specifically, the proposed framework consists of an inner network and an outer network, both of them are implemented by graph neural networks (GNNs). To achieve the former two goals, we propose a training method by combining the model-agnostic meta learning (MAML) algorithm with the unsupervised training. Following this training method, the outer network can help the inner network learn good initialization and then fast adapt to the different channels. As for the goal of ‘continuously’, we design an element-wise gating operation to multiply the outputs of the inner and outer networks, aiming at the selection activation of the inner network. Simulation results demonstrate that the proposed meta-gating GNN can well achieve the three important goals compared with the existing state-of-the-art algorithms.
Optimization of Sentiment Analysis using Naive Bayes with Features Selection Chi-Square and Information Gain for Accuracy Improvement

Freedom of expression on social media is something that really challenging nowadays, where Twitter become one of the favorite social media platforms in Indonesia. There are many challenges that must be faced on this platform such as offensive language and hate speech from social media users. Based on Indonesian law, cyberbullying is a crime that violates the Law Electronic Information and Transactions (UU ITE). Therefore, the purpose of this study is to conduct a sentiment analysis that can classify sentiment tweets that have elements of cyberbullying. There are several challenges in the sentiment analysis study, such as polarity shift, binary classification, data sparsity, and accuracy. The focus of this research is to conduct experimental research on the performance of Naive Bayes methods in order to improve the accuracy of sentiment analysis. There are three methods that have been used in this study, they are original Naive Bayes without features selection, the combination of Naive Bayes and feature selection Chi-Square, and the combination of Naive Bayes and Information Gain. The results obtained in this research, that the accuracy of the Naive Bayes without feature selection obtained 85.6% accuracy, a combination of Naive Bayes and Chi-Square obtained 89.6% accuracy, and a combination Naive Bayes and Information Gain obtained 88.6% accuracy. It can be concluded, the Naive Bayes method combined with feature selection success improved the accuracy of the sentiment analysis by about 4 % from the original Naive Bayes without features selection.
Level of the dimensions of the digital competence in university students from different academic areas

The digital competence in university students has taken on greater relevance as a response to a new teaching-learning modality and for graduates, it has meant facing the demands of the current labor market that demands new digital skills. The objective of this research is to compare the level of the dimensions of the digital competence by academic areas with an instrument with proven validity. Validation was performed by expert judgment and calculation of Cronbach’s Alpha coefficient. The survey was applied to university students in the areas of Law and Business Sciences, Social Sciences, Sciences and Engineering and Health Sciences of a university in Arequipa. The instrument used for the comparative study exceeded the cut-off levels, achieving content validity. Likewise, the internal consistency of the scores has good to excellent reliability. Most students have a high to very high level of knowledge of digital skills. In five indicators there is a significant difference between the levels of knowledge of the different academic areas. We concluded that the evaluated students are prepared to face current technological requirements, keeping in mind that it is a duty of the university to ensure that all of its students satisfy said requirements.
Comparative Study Of Convolutional Neural Network And Haar Cascade Performance On Mask Detection Systems Using Matlab

Since the outbreak of the COVID-19 virus, various technologies have developed as an alternative to preventing the spread of the COVID-19 virus; one of them is face mask detection. Many methods are used, such as Convolutional Neural Network, Haar cascade classifier, and more. This paper discusses how the system will work with face mask detection and the performance result while running the system against the parameters that can occur during training or direct testing by comparing several different methods. The test results display in the form of a line graph, and the Haar Cascade Classifier method will be displayed in tabular form, with the highest accuracy in the CNN method being 93%, while the Haar Cascade Classifier method is 96%
Combining Dynamic K-Means and Binary Search Centroid to Optimize Clustering Results on Home Industry Datasets

This study aims to improve the Dynamic k-means algorithm for determining the optimal number of clusters and iteration. Unfortunately, the dynamic k-means algorithm has limitations in determining the centroid point, whereas the binary search centroid algorithm can determine the cluster's centroid point but has limitations in determining the number of clusters. In the case of home industry datasets, it is proposed to optimize clustering results using a combination of Dynamic k-means algorithm and binary search centroid based on the advantages and disadvantages of each algorithm. 3,466 home industries were used as the sample data. The validity of both algorithms was assessed using the Davies Bouldin Index (DBI) methods. In clustering home industry datasets, the minimum value of DBI Cluster algorithm combination of Dynamic k-means and binary search centroid K-means (DK-means with BSCK-means) is the smallest, which is 0.1845, when compared to only Dynamic K-means algorithm. The final result of home industry clustering using a combination of Dynamic K-means and the Binary Search Centroid algorithm yields an optimal value in the fifth iteration, with the acquisition of the beginner cluster C1 is 3224, the developing cluster C2 is 160, and the advanced cluster C3 is 82.
Analysis of teaching Experimental Physics in Guayaquil using online education

During the COVID-19 pandemic, teaching of Physics at the high school level in the city of Guayaquil was carried out virtually. Consequently, the study that is presented below aims to explore the status and condition of teaching Experimental Physics in Guayaquil at the high school level during the two school years in which the education was carried out virtually (2020-2022) due to the COVID – 19 pandemic. The main results aim at the “rejection” of teachers to carry out virtual laboratory practices and teacher training.
Extraction of Event Sentence Information in the Covid-19 Distribution Location Detection System based on the Indonesian Language Corpus

The procedure for obtaining data on people affected by the COVID-19 disease is carried out through a manual data collection mechanism through health centers, government and private hospitals, health clinics spread throughout Indonesia, and rapid tests carried out at certain times and locations. Such a surveillance system requires time, a lot of health personnel, and is expensive. In addition, the geographical condition of the Indonesian state, which consists of many large and small islands and the vast territory of Indonesia requires another strategy to find out and make it easier to complete data on people affected by COVID-19, such as the use of information technology. The use of the Twitter dataset to detect the spread of disease in a region or country has been widely carried out by researchers. Sentence extraction is a process that must be done to facilitate the analysis of a short or very long news sentence to get the meaning and essence of the news contained in the sentence. The primary information can be identified based on keywords generated using extraction and abstraction techniques. The initial stage of the research focused on building a corpus of twitter data and a corpus of vocabulary. The first process that will be carried out is to collect natural language datasets from Indonesian-language Tweets on Twitter. Next, carry out the process of extracting incident sentence information with steps, namely making standard sentence formats, simplifying sentences, identifying essensial words, and determining input and target words in sentences.
Development of a biometric authentication platform using voice recognition

Nowadays, several areas have turned to speech recognition, as they have witnessed its efficiency in maintaining the privacy of users and facilitating access and use of numerous applications, systems even establishments. Our work revolves around recognizing a form of speech from a single word, where the main objective is to identify as accurately as possible a set of predefined words from brief audio clips. a Speech Commands dataset consisting of 65 000 one-second-long statements of 30 short term, is applied. We use a Convolutional Neural Network (CNN) to classify representatives with two-dimensional convolutions on the audio waveform. Unlike many classic techniques with critical feature engineering, we benefit from the power of deep learning to comprehend the feature representation while training. The prototype achieves an acceptable accuracy rate on the validation set. Our model has provided satisfactory results during training with high accuracy compared to its pre-descendants. Still, room for errors exists in words exceeding the range of the training data and especially noisy samples.
For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability

In this paper, we study kernel ridge-less regression, including the case of interpolating solutions. We prove that maximizing the leave-one-out ([Formula: see text]) stability minimizes the expected error. Further, we also prove that the minimum norm solution — to which gradient algorithms are known to converge — is the most stable solution. More precisely, we show that the minimum norm interpolating solution minimizes a bound on [Formula: see text] stability, which in turn is controlled by the smallest singular value, hence the condition number, of the empirical kernel matrix. These quantities can be characterized in the asymptotic regime where both the dimension ([Formula: see text]) and cardinality ([Formula: see text]) of the data go to infinity (with [Formula: see text] as [Formula: see text]). Our results suggest that the property of [Formula: see text] stability of the learning algorithm with respect to perturbations of the training set may provide a more general framework than the classical theory of Empirical Risk Minimization (ERM). While ERM was developed to deal with the classical regime in which the architecture of the learning network is fixed and [Formula: see text], the modern regime focuses on interpolating regressors and overparameterized models, when both [Formula: see text] and [Formula: see text] go to infinity. Since the stability framework is known to be equivalent to the classical theory in the classical regime, our results here suggest that it may be interesting to extend it beyond kernel regression to other overparameterized algorithms such as deep networks.
Design of a Mobile Application to encourage Women’s participation in STEM careers

STEM careers are constantly facing challenges and changes, but currently, the main problem lies in the low participation of women worldwide. From the above mentioned, the purpose of this project was born, which seeks to solve this problem through a mobile application that motivates and develops skills and knowledge about STEM. This application has two interactive modules to obtain a more significant attraction in girls. The first is called “Motivate yourself”, which contains videos and motivational phrases to publicize the experiences of successful and empowered women who have studied STEM. On the other hand, the second module, “Learn while playing”, provides us with various interactive games that test their knowledge to enrich the girls’ learning about STEM-related topics. These modules were created based on the M-Learning method that focuses on learning through digital platforms. While for the application’s architecture, the Scrum methodology is used, which is based on fulfilling tasks with a development team for short periods called “Sprints,” using the open-source software React Native with its JavaScript programming language. After developing this application, a pilot test was implemented where the design and content were evaluated, and corrections and improvements were implemented according to the results. Finally, it was possible to create a motivational tool for adolescent girls who are often afraid of studying careers of this type, either because of a lack of knowledge or because they do not feel they have the skills to carry them out.
A One-shot Lung 4D-CT Image Registration Method with Temporal-spatial Features

For lungs 4DCT registration methods, conventional pairwise methods ignore the temporal information of the periodic motion of lungs which could have been useful for registration and tracking. Although some groupwise methods take that into consideration, a clear approach has not yet emerged. We proposed a novel temporal-spatial method for lung 4D-CT registration. Through the hybrid architecture of CNN and ConvLSTM, we realize modeling of images’ temporal motion. And the periodic motion constraint was applied into the temporal model with an effectively dual-stream way. We train model in one-shot style on DIRLAB dataset. In comparison, proposed method has achieved better registration accuracy in terms of TRE than current deep learning-based unsupervised methods and is comparable with supervised methods.
Impact of Virtuality on Students’ perception of University Quality

The purpose of this paper is to identify the factors that influence the student satisfaction under the e-learning modality at Universidad Católica de Santa María, Perú, based on our own model according to the context. 1695 enrolled students took part of the sample. The study measured academic aspect, tools and connectivity, participation and evaluation, research and university social responsibility, assistance and support, and information technologies. The results will allow us to evaluate whether the methodology has preserved the quality of the service, the information technologies have held the quality of the system, and the contents have mantain the quality of the information, by measuring user satisfaction.
Semantic segmentation of breast cancer histopathology images using deep learning

Breast cancer is one of the most prevalent cancers. Before initiating treatment, the phase of breast histopathology images' segmentation is crucial for obtaining an accurate diagnosis. The effectiveness of segmentation is frequently dependent on enormous training datasets accompanied by high-quality human annotations. However, the annotation process is labor-intensive, costly, and consumes much time. This paper proposes a novel color-detection-based method for automatically annotating breast cancer histopathology images. We also build a semantic segmentation model for breast cancer histopathology images based on deep learning using the UNet architecture allowing the pathologist to make immediate and accurate diagnoses.