Reviewerly/exHarmony huggingface dataset:
Plan educativo aprendamos juntos en casa y sus consecuencias en el proceso de enseñanza - aprendizaje en el Ecuador 2020

The research entitled: Educational Plan “Aprendamos Juntos en Casa” and its consequences in the teaching-learning process in Ecuador 2020, its objective was to determine the consequences that generated the application of the Educational Plan “Aprendamos Juntos en Casa”, to establish its effectiveness or failure. in the teaching-learning process; For this research was considered the Le Flore's theory of constructivism (2000) based on learning in virtual environments, it was an investigation with qualitative approach with a phenomenological design; The versions of teachers, parents and students about the educational changes generated by the pandemic and how they affected them. It used the interview technique and as an instrument, the interview guide. It was obtained as a result that at the beginning the virtual study was complicated, but later the community adapted to this new modality; considering that the pandemic continued, they had to become more involved in the online system; the prospect of virtual education improved; however, they did not consider it suitable to continue with virtual classes once the pandemic was over, because they assured that virtuality has left many gaps in students; since despite being promoted to the next higher year, they do not have the necessary knowledge to face the new academic year correctly.
Trisymmetric Multiplication Formulae in Finite Fields

Multiplication is an expensive arithmetic operation, therefore there has been extensive research to find Karatsuba-like formulae reducing the number of multiplications involved when computing a bilinear map. The minimal number of multiplications in such formulae is called the bilinear complexity, and it is also of theoretical interest to asymptotically understand it. Moreover, when the bilinear maps admit some kind of invariance, it is also desirable to find formulae keeping the same invariance. In this work, we study trisymmetric, hypersymmetric, and Galois invariant multiplication formulae over finite fields, and we give an algorithm to find such formulae. We also generalize the result that the bilinear complexity and symmetric bilinear complexity of the two-variable multiplication in an extension field are linear in the degree of the extension, to trisymmetric bilinear complexity, and to the complexity of t-variable multiplication for any \(t\ge 3\).
Implementasi Particle Swarm Optimization (PSO) pada Analysis Sentiment Review Aplikasi Trafi menggunakan Algoritma Naive Bayes (NB)

Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Abstract - The development of transportation applications is now getting bigger so that many vendors compete for business in creating transportation mode applications, starting from the quality and quantity so that it is often questioned. With this, the researcher held a transportation application called Trafi to get opinions or comments on applications from people who had used the application and poured it into online media. Of the many comments reviewed to obtain a set of positive and negative forms of data from the text that the researcher will process. For classification data using Naïve Bayes (NB), NB is one of the most popular algorithms for pattern recognition. Apart from simplicity, the Naive Bayes classifier is a popular machine learning technique for text classification, Particle Swarm Optimization (PSO) which combines with the Naive Bayes classification to improve performance. Before use, optimization with PSO in the data set accuracy obtained was 69.50% and after the combination of Naive Bayes and PSO accuracy was 72.34%. Use PSO and Naïve Bayes according to the concept of text mining which aims to find patterns that exist in text, the activity carried out by text mining here is text classification.Keywords: Sentiment Analysis, Android Appstore Product Review, Naive Bayes Algorithm
EFEKTIFITAS BLENDED LEARNING DALAM PROSES PEMBELAJARAN

As the rapid development of Science and Technology, especially in the Millennial Era, this certainly has influenced the learning methods used in education. E-learning programs are increasingly encouraged to answer the challenges of accelerating the global era through programs that are perceived as fast, efficient with targets that seem to want to penetrate space and time so that slowly shifting face-to-face methods are considered by some as an effective learning process. This paper wants to examine how the e-learning method approach is compared to the face-to-face method in the learning process. Which is more efficient and effective in increasing competency for students without being limited to the level of participants in the learning process through the pedagogical approach, andragogy and the role of the facilitator, but in general we will see how organizing the education process through the two systems in question. From the author's observations supported by related literacy, these two approaches in principle have their advantages and disadvantages, so that to produce an effective approach both can be combined in the form of a blended learning system.
Enhancing Immersiveness in Paramedicine Education XR Simulation Design

The Multiple Environments Simulation Hub (MESH360) research cluster was established in 2016 to explore ways of making critical care simulation environments more authentic learning experiences for students (Cochrane et al., 2016). Since its establishment, three cycles of data collection have occurred exploring immersive mixed reality (XR) to enhance traditional clinical simulation methodologies in Paramedicine education (Aguayo et al., 2018; Cochrane et al., 2020). Using a design-based research (DBR) methodology focused on prototyping in practice to generate design principles (Cochrane et al., 2017; McKenney &amp; Reeves, 2019), along with a mixed-methods and multimodal approach to data collection and analysis in educational research (Cohen, Manion &amp; Morrison, 2011; Lahat, Adali &amp; Jutten, 2015), led the MESH360 team to develop a framework for designing immersive reality enhanced clinical simulation (Cochrane et al., 2020).&#x0D; &#x0D; Building upon this work, a set of design principles permitted to augment the immersive experience of participants through a case study involving an XR enhanced rescue helicopter simulation experience. Two key components of this enhanced simulation are: (1) a focus on combining XR design principles merging real environment elements with digital affordances (possibilities offered by digital tools and platforms) to provide a range of ‘learning points’ for different types of learners (i.e., from novice to experienced participants) (Aguayo, Eames &amp; Cochrane, 2020); and (2) a focus on the embodiment of the experience (Aguayo et al., 2018) to maximize the interactivity, authenticity, and realism of the enhanced immersive reality through a sequence of experiences including virtual reality (VR) helicopter ride, simulated manikin work, and critical environmental soundscapes.&#x0D; &#x0D; Findings from the third cycle indicate an increase in spatial and context awareness across all types of participants, in relation to the authenticity of the XR environment when compared to traditional Paramedicine simulation training. Furthermore, participants also reported an enhanced realism of the ‘emergency response’ helicopter VR ride, as the sequence of experiences permitted participants to plan their response based on audio cues and information updates while virtually ‘traveling to the scene’. This iterative research work has led the MESH360 project to validate the set of transferable design principles and implementation framework for the design of authentic critical care simulation environments in Paramedicine education. &#x0D; &#x0D; Here we present and discuss a series of implications and benefits from the third MESH360 cycle in Paramedicine education emerging from the framework for designing XR enhanced clinical simulation. Anecdotic yet relevant data in relation to participant demographics and VR anxiety has led the MESH360 team to explore culturally-responsive practice in XR simulation in Paramedicine education (see Aiello et al., 2021). Future directions and transferability to other health and medical contexts are also discussed.
Machine learning for detection of climate extremes: New approaches to uncertainty quantification

&amp;lt;div&amp;gt;Machine learning (ML) has proven to be a very powerful body of techniques for identifying rare but highly impactful weather events in huge volumes of climate model output and satellite data.&amp;amp;#160; When these events and the changes in them are studied in the context of global warming, these phenomena are known as climate extremes.&amp;amp;#160; This talk concerns the challenges in applying ML to identify climate extremes, which often center on how to provide suitable &amp;lt;span&amp;gt;training&amp;lt;/span&amp;gt;&amp;amp;#160;data to these algorithms.&amp;amp;#160; The challenges are:&amp;lt;/div&amp;gt;&amp;lt;ol&amp;gt;&amp;lt;li&amp;gt;In many cases, the official definitions for the weather events in the current climate are either ad hoc and/or subjective, leading to considerable variance in the statistics of these events even in literature concerning the historical record;&amp;amp;#160;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Operational methods for identifying these events are also typically quite ad hoc with very limited quantification of their structural and parametric uncertainties; and&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Both the generative mechanisms and physical properties of these events are both predicted to evolve due to well-understood physics, and hence the &amp;lt;span&amp;gt;training&amp;lt;/span&amp;gt; data set&amp;amp;#160; should but typically does not reflect these secular trends in the formation and statistical properties of climate extremes.&amp;amp;#160;&amp;amp;#160;&amp;lt;/li&amp;gt; &amp;lt;/ol&amp;gt;&amp;lt;div&amp;gt;We describe several approaches to addressing these issues, including:&amp;lt;/div&amp;gt;&amp;lt;ol&amp;gt;&amp;lt;li&amp;gt;The recent creation of the first labeled data set specifically designed for algorithm training on atmospheric extremes, known as ClimateNet;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Probabilistic ML algorithms that identify events based on the level of agreement across an ensemble of operational methods;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;Bayesian methods for that identify events based on the level of agreement across an ensemble of human expert-generated labels; and&amp;amp;#160;&amp;lt;/li&amp;gt; &amp;lt;li&amp;gt;The prospects for physics-based detection using fundamental properties of the fluid dynamics (i.e., conserved variables and Lyapunov exponents) and/or information-theoretic concepts.&amp;lt;/li&amp;gt; &amp;lt;/ol&amp;gt;
AN APPROACH TO ACCELERATE THE TRAINING OF CONVOLUTIONAL NEURAL NETWORKS BY TUNING THE HYPERPARAMETERS OF LEARNING

Over the last decade, a set of machine learning algorithms called deep learning has led to significant improvements in computer vision, natural language recognition and processing. This has led to the widespread use of a variety of commercial, learning-based products in various fields of human activity. Despite this success, the use of deep neural networks remains a black box. Today, the process of setting hyperparameters and designing a network architecture requires experience and a lot of trial and error and is based more on chance than on a scientific approach. At the same time, the task of simplifying deep learning is extremely urgent. To date, no simple ways have been invented to establish the optimal values of learning hyperparameters, namely learning speed, sample size, data set, learning pulse, and weight loss. Grid search and random search of hyperparameter space are extremely resource intensive. The choice of hyperparameters is critical for the training time and the final result. In addition, experts often choose one of the standard architectures (for example, ResNets and ready-made sets of hyperparameters. However, such kits are usually suboptimal for specific practical tasks. The presented work offers an approach to finding the optimal set of hyperparameters of learning ZNM. An integrated approach to all hyperparameters is valuable because there is an interdependence between them. The aim of the work is to develop an approach for setting a set of hyperparameters, which will reduce the time spent during the design of ZNM and ensure the efficiency of its work. In recent decades, the introduction of deep learning methods, in particular convolutional neural networks (CNNs), has led to impressive success in image and video processing. However, the training of CNN has been commonly mostly based on the employment of quasi-optimal hyperparameters. Such an approach usually requires huge computational and time costs to train the network and does not guarantee a satisfactory result. However, hyperparameters play a crucial role in the effectiveness of CNN, as diverse hyperparameters lead to models with significantly different characteristics. Poorly selected hyperparameters generally lead to low model performance. The issue of choosing optimal hyperparameters for CNN has not been resolved yet. The presented work proposes several practical approaches to setting hyperparameters, which allows reducing training time and increasing the accuracy of the model. The article considers the function of training validation loss during underfitting and overfitting. There are guidelines in the end to reach the optimization point. The paper also considers the regulation of learning rate and momentum to accelerate network training. All experiments are based on the widespread CIFAR-10 and CIFAR-100 datasets.
Efficient Multi-task based Facial Landmark and Gesture Detection in Monocular Images

The communication between persons includes several channels to exchange information between individuals.The non-verbal communication contains valuable information about the context of the conversation and it is a key element to understand the entire interaction.The facial expressions are a representative example of this kind of non-verbal communication and a valuable element to improve human-machine interaction interfaces.Using images captured by a monocular camera, automatic facial analysis systems can extract facial expressions to improve human-machine interactions.However, there are several technical factors to consider, including possible computational limitations (e.g.autonomous robots), or data throughput (e.g.centralized computation server).Considering the possible limitations, this work presents an efficient method to detect a set of 68 facial feature points and a set of key facial gestures at the same time.The output of this method includes valuable information to understand the context of communication and improve the response of automatic human-machine interaction systems.
Unsupervised Domain Adaptation via Minimized Joint Error

Unsupervised domain adaptation transfers knowledge from learned source domain to a different (but related) target distribution, for which only few or no labeled data is available. Some researchers proposed upper bounds for the target error when transferring the knowledge, i.e.,Ben-David et al. (2010) established a theory based on minimizing the source error and distance between marginal distributions simultaneously. However, in most works the joint error is usually ignored. In this paper, we argue that the joint error is essential for the domain adaptation problem, in particular if the samples from different classes in source/target are closely aligned when matching the marginal distributions. To tackle this problem, we propose a novel upper bound that includes the joint error. Moreover, we utilize a constrained hypothesis space to further tighten up this bound. Furthermore, we propose a novel cross margin discrepancy to measure the dissimilarity between hypotheses. We show in this paper that the new cross margin discrepancy is able to alleviate instability during adversarial learning. In addition, we present extensive empirical evidence that shows that our proposal outperforms related approaches in image classification error rates on standard domain adaptation benchmarks.
FUZZY LOGIC MENENTUKAN KEPUASAN MASYARAKAT TERHADAP KINERJA PEGAWAI KECAMTAN DENGAN METODE MAMDANI

Employee performance is the level of achievement or work results of a person from the goals that must be achieved or implemented must be in accordance with their respective responsibilities in a certain period of time. Performance satisfaction is the level of one's feelings after comparing the performance or the results he feels compared to his expectations. Sagulung Subdistrict is one of the densely populated sub-districts in Batam city, based on the results of community interviews about the performance of employees in Sagulung sub-district, the results show that the quality of services they receive varies. Hospitality of employees when serving the community is also different between one employee and another employee, besides that community satisfaction with employee performance also varies so that the community's satisfaction with the performance of employees in Sagulung sub-district cannot be concluded. Fuzzy logic using the mamdani method is one method to determine community satisfaction with employee performance in Sagulung sub-district. Based on the results of testing of the data that has been processed, the results of manual calculations are 68.4162 while for the system testing using matlab the results are 68.7. Based on the results of these tests, it can be concluded that fuzzy logic meetode mamdani can be used to determine community satisfaction with employee performance in Sagulung sub-district.
Gradient Boosting Machine with Partially Randomized Decision Trees

The gradient boosting machine is a powerful ensemble-based machine learning method for solving regression problems. However, one of the difficulties of its using is a possible discontinuity of the regression function, which arises when regions of training data are not densely covered by training points. In order to overcome this difficulty and to reduce the computational complexity of the gradient boosting machine, we propose to apply the partially randomized trees which can be regarded as a special case of the extremely randomized trees applied to the gradient boosting. The gradient boosting machine with the partially randomized trees is illustrated by means of many numerical examples using synthetic and real data.
VAW-GAN for Singing Voice Conversion with Non-parallel Training Data

Singing voice conversion aims to convert singer’s voice from source to target without changing singing content. Parallel training data is typically required for the training of singing voice conversion system, that is however not practical in real-life applications. Recent encoder-decoder structures, such as variational autoencoding Wasserstein generative adversarial network (VAW-GAN), provide an effective way to learn a mapping through non-parallel training data. In this paper, we propose a singing voice conversion framework that is based on VAW-GAN. We train an encoder to disentangle singer identity and singing prosody (F0 contour) from phonetic content. By conditioning on singer identity and F0, the decoder generates output spectral features with unseen target singer identity, and improves F0 rendering. Experimental results show that the proposed framework achieves better performance than the baseline frameworks.
Formalizing Relations in Type Theory

Type theory plays an important role in the foundations of mathematics as a framework for formalizing mathematics and a base for proof assistants providing semi-automatic proof checking and construction.Derivation of each theorem in type theory results in a formal term encapsulating the whole proof process.This study uses a variant of type theory, namely the Calculus of Constructions with Definitions, to formalize the standard theory of binary relations.This includes basic operations on relations, criteria for special properties of relations, invariance of these properties under the basic operations, equivalence relation, well-ordering, and transfinite induction.Definitions and proofs are presented as flag-style derivations.
Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

We consider the problem of the stability of saliency-based explanations of Neural Network predictions under adversarial attacks in a classification task. Saliency interpretations of deterministic Neural Networks are remarkably brittle even when the attacks fail, i.e. for attacks that do not change the classification label. We empirically show that interpretations provided by Bayesian Neural Networks are considerably more stable under adversarial perturbations of the inputs and even under direct attacks to the explanations. By leveraging recent results, we also provide a theoretical explanation of this result in terms of the geometry of the data manifold. Additionally, we discuss the stability of the interpretations of high level representations of the inputs in the internal layers of a Network. Our results demonstrate that Bayesian methods, in addition to being more robust to adversarial attacks, have the potential to provide more stable and interpretable assessments of Neural Network predictions.
Application of HBM2 Data Storage in Time and Frequency Hopping Network Communication System

In the receiving link of time and frequency hopping communication system, it is necessary to search multiple frequency points and time intervals according to a variety of time-frequency patterns at the same time, then complete blind data acquisition and effective information extraction. A large amount of data storage is required. This paper calculates and explains the data storage amount and storage speed of a certain time and frequency hopping communication system, compares and analyzes the advantages and disadvantages of the three storage methods and the applicable scenarios. Finally, the data storage method based on HBM2 in Stratix series FPGA is introduced in detail, and the practical application in the time and frequency hopping communication system verifies the storage speed and storage capacity of the method whether meets the system requirements.