Reviewerly/exHarmony huggingface dataset:
Machine learning based conditional mean filter: a non-linear extension of the ensemble Kalman filter&amp;#160;

&amp;lt;p&amp;gt;Filtering is an uncertainty quantification technique that refers to the inference of the states of dynamical systems from noisy observations. This work proposes a machine learning-based filtering method for tracking the high-dimensional non-Gaussian state-space models with non-linear dynamics and sparse observations. Our filter method is based on the conditional expectation mean filter and uses machine-learning techniques to approximate the conditional mean (CM). The contribution of this work is twofolds: (i) we demonstrate theoretically that the assimilated ensembles obtained using the ensemble conditional mean filter (EnCMF) provide a correct prediction of the posterior mean and have the optimal variance, and (ii) we implement the EnCMF using artificial neural networks, which has a significant advantage in representing non-linear functions that map between high-dimensionality domains, such as the CM. We implement the machine learning-based EnCMF for tracking the states of the Lorenz-63 and 96 systems under the chaotic regime. Numerical results show that the EnCMF outperforms the ensemble Kalman filter.&amp;lt;/p&amp;gt;
El cine como recurso pedagógico para promover el engagement en la educación superior

The use of cinema in an educational context adds opportunities in an upward spiral, bringing out senses and meanings for new and exciting learnings. This article aims to analyze whether the use of cinema in an educational context promotes emotional, physical and cognitive engagement. Methodologically, the study combines a qualitative investigation, supported by the assumptions of Bardin (2010). The results reveal that the students consider that the use of cinema can provide: 1) levels of emotional, physical and cognitive engagement facilitated and enhanced, 2) sharing of more optimized knowledge, 3) expansion of vocabulary and cultural background, 4) affinity with the challenge and research, 5) appreciation of the opinion of peers, 6) improvement in self-perception, motivation and interest in deepening themes focused on the discipline, 7) immediate and optimized feedback, culminating in teacher-student satisfaction.
The development of thematic modules based on Strengthening Character Education (PPK) and Quick Response (QR) code

Abstract Less attractive and effective learning modules in thematic learning and less reinforcement in character education make students bored and do not really implement strengthening character education in the school environment and society. Therefore, alternative teaching materials are needed in thematic learning and strengthening student character education. The purpose of this research is to develop thematic modules based on PPK and QR code. This study applied the Bog &amp; Gall development research model. The data analysis technique used was quantitative and qualitative data analysis. The research instruments used were observation, questionnaires and documentation. The results of research on the development of thematic modules based on PPK and QR Code, it is known that the results of the analysis of media experts obtained a score of 92.86 percent with a very valid category, material experts obtained a score of 88.64 percent in the very valid category, linguists obtained a score of 75 percent with a fairly valid category, the response of educators obtained a score of 89.92 percent in the very practical category, the response of students obtained a score of 87.78 percent in the practical category. Based on the results of the explanation above, the thematic module based on PPK and QR Code can be used as a supplementary teaching material for the thematic book to make it easier for students to acquire the lesson content and apply characters education in schools and communities.
“Relationship Between Learning by Teaching with Teachable Chatbots and the Big 5

Learning through teaching robots has been shown in some studies to produce learning effects similar to those of teaching humans. However, it cannot be said that these learning tools, known as teachable agents, are effective for all users. This is because most of the studies on teachable agents have been conducted on younger children who tend to prefer to take care of others. We hypothesized that if adult subjects interacted with these agents, then their learning experiences would differ based on their individual personalities. Therefore, in this study, we developed a novel-style teachable chatbot and investigated the relationship between the evaluation of learning using this system and personality.
Efficient generation of rectangulations via permutation languages

A generic rectangulation is a partition of a rectangle into finitely many interior-disjoint rectangles, such that no four rectangles meet in a point. In this work we present a versatile algorithmic framework for exhaustively generating a large variety of different classes of generic rectangulations. Our algorithms work under very mild assumptions, and apply to a large number of rectangulation classes known from the literature, such as generic rectangulations, diagonal rectangulations, 1-sided/area-universal, block-aligned rectangulations, and their guillotine variants. They also apply to classes of rectangulations that are characterized by avoiding certain patterns, and in this work we initiate a systematic investigation of pattern avoidance in rectangulations. Our generation algorithms are efficient, in some cases even loopless or constant amortized time, i.e., each new rectangulation is generated in constant time in the worst case or on average, respectively. Moreover, the Gray codes we obtain are cyclic, and sometimes provably optimal, in the sense that they correspond to a Hamilton cycle on the skeleton of an underlying polytope. These results are obtained by encoding rectangulations as permutations, and by applying our recently developed permutation language framework.
Analysis of the ratio of the mean square deviations of the kernel probability density estimation in the conditions of independent and dependent random variables

The influence on the approximation properties of a nonparametric probability density estimate of Rosenblatt-Parzen type of the information on the dependence of random variables is determined. The ratio of the asymptotic expressions of the mean square deviations of independent and dependent random variables is obtained. This relation for a two-dimensional random variable is considered as a quantitative assessment of the influence of information about their dependence on the approximation properties of the kernel probability density estimate. The established ratio is determined by the kind of probability density and the volumes of the initial statistical data that are used in estimating the probability densities of dependent and independent random variables. The general results obtained are considered in detail for two-dimensional linearly dependent random variables with normal distribution laws. The functional dependence of the ratio of the mean square deviations of the independent and dependent two-dimensional random variables on the correlation coefficient is determined. The dependence of the considered ratio on the volume of statistical data is analyzed. A method for estimating the functional of the second derivatives of two-dimensional random variables with normal distribution laws is developed. The results obtained are the basis for the development of modifications of “fast” procedures for optimizing kernel estimates of probability densities in conditions of large samples.
Obstacles and Areas Detection Based on Pulse Width Modulation Method for Electric Wheelchair Safety Using Ultrasound Sensors

People with disabilities find it difficult to operate wheelchairs especially those with disabilities who do not have hands and feet or are disabled. The aim of this research is to improve safety control for electric wheelchairs by detecting obstacles and detecting areas that minimize collisions against obstacles. The contribution of this research is the use of the HC-SR04 ultrasound sensors to avoid a wheelchair colliding with an obstacle using the Pulse Width Modulation (PWM) method which is set based on the distance of the obstacle and the detection of free areas and corridor areas using the PWM method which is adjusted based on following the wall. The HC-SR04 ultrasound sensor is used as input and is processed on the Arduino nanomicrocontroller series to produce a value of PWM. Measuring the HC-SR04 ultrasound sensor against obstacles obtained the largest deviation of 0.72 and average 148,6 cm at a distance of 150 cm and the smallest deviation at a distance of less than 50 cm and the wheelchair can move at slow, medium and fast speeds with the measurement results on Duty Cycles of 71%, 82% and 94%. The results showed that the final distance of the wheelchair against obstacles was less than 50 cm and the wheelchair moved at a distance of 34 - 53 cm following the wall. The results of this study can control motor speed based on obstacles that can be implemented in electric wheelchairs to improve safety and ease of operation for people with disabilities.
Human Behavior Guidance Methods in Distributed Virtual Shopping Malls

In this paper, we report a prototype service on a human behavior guidance method in distributed virtual shopping malls. The prototype service is to investigate whether it is possible to alter a customer's interest in the target store by displaying advertisements of alternative stores while guiding the customer to the target store. Behavioral economics theory suggests that people generally do not choose those stores when multiple alternative stores other than the intended target store are advertised at the same time. Therefore, when approaching the actual store, displaying the advertisement for an alternative store makes the store more attractive and increases the possibility of its selection. Then, the study examines the effects of the prototype service to validate the opportunities of virtualization technologies in our future daily lives.
An approach to adaptive filtering with variable step size based on geometric algebra

Recently, adaptive filtering algorithms have attracted much more attention in the field of signal processing. By studying the shortcoming of the traditional real-valued fixed step size adaptive filtering algorithm, this paper proposed the novel approach to adaptive filtering with variable step size based on Sigmoid function and geometric algebra (GA). First, the proposed approach to adaptive filtering with variable step size based on geometric algebra represents the multi-dimensional signal as a GA multi-vector for the vectorization process. Second, the proposed approach to adaptive filtering with variable step size based on geometric algebra solves the contradiction between the steady-state error and the convergence rate by establishing a non-linear function relationship between the step size and the error signal. Finally, the experimental results demonstrate that the proposed approach to adaptive filtering with variable step size based on geometric algebra achieves better performance than that of the existing adaptive filtering algorithms.
Multi-Frame GAN-based machine learning image restoration for degraded visual environments

Although single-frame machine learning image restoration techniques have been shown to be effective, the proposed multi-frame approach takes advantage of both spatial and temporal information to resolve high-resolution and high-dynamic-range images. The proposed algorithm is an extension of the previously proposed algorithm DeblurGAN-C and aims to further improve the capabilities of image restoration in degraded visual environments. The main contributions of the proposed techniques include: 1) Development of an effective framework to generate a multi-frame training dataset typical of degraded visual environments; 2) Adopting a multi-frame image restoration framework that generates a single restored image as the output; 3) Conducting substantial experiments against the generated multi-frame training dataset and demonstrate the effectiveness of the proposed multi-frame image enhancement algorithm.
Virtual Reality: A New Dimension for Academic Libraries

Recent articles in the news media have triggered interest in how virtual reality technologies can be applied to academic libraries. Specifically, virtual reality technologies have the potential to be applied to triage reference, reference services, and archives. It is discussed how library users can use virtual reality headsets as well as how virtual reality headsets can be used for reference services and to inform library users about archival exhibits. It is noted that academic libraries do not currently use virtual reality technologies and that additional usability studies are needed to assess user and library staff perceptions of virtual reality technologies.
CRITICAL THINKING IN THE CONTEXT OF THE WORLD VUCA: ITS ROLE AND DEFINITION

The article attempts to clarify the nature of critical thinking and identify its relationship with other styles of thinking. It has been found that this phenomenon appears as multidimensional and complex in its essence. Critical thinking is primarily reflexive and independent, which is self-regulating, and includes the elements of nonlinear and creative. In a sense, one can argue about its synthetic or integrative nature. Among its main cognitive skills, most experts name interpretation, analysis, assessment, conclusion, explanations, and self-regulation.
Creative and Innovative Thinking in the Holistic Development of Students’ Potentials through ‘Sekolah Bitara’ Visual Art Program

UPSI-Sekolah Bitara Visual Art Program was a smart, collaborative project involving Universiti Pendidikan Sultan Idris (UPSI) and SekolahKebangsaanDato’ Kamaruddin, BehrangStesen, Perak. The UPSI’s Sekolah Bitara Visual Art Program was a smart collaborative project held at the SekolahKebangsaanDato’ Kamaruddin, BehrangStesen, Perak. This program was implemented based on the concept of sharing and exchange of the knowledge of visual art involving several teachers and students, aged 8-12. This ‘visual art expression’ program was carried out in several series, which began in 2014 and ended in 2018, consisting of six (6) visual art projects as follows: batik craft, print art, ceramic art, fine metal art, wall murals, and textile art.The program involving art appreciation, textile art, and legacy of art helped exposed students and provided a rich experience for such participants in learning visual art through activities that enhanced their higher-order thinking skills. This six-series project produced 180 visual art works in various disciplines of art, which were put on display at the UPSI’s Art Gallery. In addition, students’ creative wall murals were displayed on the walls of the school building. Surely, such accomplishments signify their innovative and creative thinking that can help develop students holistically with strong intellectual, emotional, spiritual, and physical attributes, which are in line with the aspirations of the National Education Philosophy. Surely, more efforts of similar nature are required in the future to further enhance such a program to develop students who are highly creative, innovative, and competitive.
Spartans@LT-EDI-EACL2021: Inclusive Speech Detection using Pretrained Language Models

We describe our system that ranked first in Hope Speech Detection (HSD) shared task and fourth in Offensive Language Identification (OLI) shared task, both in Tamil language. The goal of HSD and OLI is to identify if a code-mixed comment or post contains hope speech or offensive content respectively. We pre-train a transformer-based model RoBERTa using synthetically generated code-mixed data and use it in an ensemble along with their pre-trained ULMFiT model available from iNLTK.
Multi-modal 3D Human Pose Estimation for Human-Robot Collaborative Applications

We propose a multi-modal 3D human pose estimation approach which combines a 2D human pose estimation network utilizing RGB data with a 3D human pose estimation network utilizing the 2D pose estimation results and depth information, in order to predict 3D human poses. We improve upon the state-of-the-art by proposing the use of a more accurate 2D human pose estimation network, as well as by introducing squeeze-excite blocks into the architecture of the 3D pose estimation network. More importantly, we focused on the challenging application of 3D human pose estimation during collaborative tasks. In that direction, we selected appropriate sub-sets that address collaborative tasks from a large-scale multi-view RGB-D dataset and generated a novel one-view RGB-D dataset for training and testing respectively. We achieved above state-of-the-art performance among RGB-D approaches when tested on a novel benchmark RGB-D dataset on collaborative assembly that we have created and made publicly available.
Oracle detection and recognition based on improved Tiny-YOLOv4

Oracle bone inscriptions, as the earliest writing system in China, are of great cultural and academic value. In recent years, deep learning method has been widely used in the process of oracle bone character examination and interpretation, and has achieved certain effect in oracle bone character recognition. However, little progress has been made in the unknown oracle bone character recognition. To solve these problems, this paper collects four types of characters: oracle bone script, jinwen, Seal script and official script as the data set, and uses the similarity between the characters and the depth network model to build the oracle bone character detection and recognition model. Then, this paper improved the Tiny-YOLOv4 model to realize the function of unknown oracular character detection and enhanced the detection speed by changing the NMS (non-maximum Suppression). The experiment proved that the detection and recognition accuracy of oracle bone characters reached 67.28%, and the detection speed of 2.281ms was improved by using NMS strategy.
An improved and heuristic-based iterative DBSCAN clustering algorithm

In recent years, the clustering of multi-density data has been a research hotspot. As a widely applied clustering algorithm, density-based spatial clustering of application with noise (DBSCAN) algorithm can avoid the interference of noise data, and has the ability to find clusters of any shape. However, the DBSCAN algorithm used hyperparameter (e.g., eps, MinPts), which makes the clusters of different densities cannot be identified. Furthermore, when the algorithm is applied to different datasets, the clustering quality would decrease severely. To cope with the above problems, we propose an improved and heuristic-based iterative DBSCAN clustering algorithm. The new algorithm realizes flexible clustering of the data with different densities by MinPts. In particular, we first estimate the values of MinPts according to the user area densities, then the new clustering is carried out based on the MinPts. This method avoids too dense clusters. In the experiments, Silhouette Coefficient and purity are adopted to evaluate the clustering effect of the improved DBSCAN algorithm. It is demonstrated that the proposed algorithm can effectively cluster multi-density data, and has greater adaptability and clustering performance to all kinds of data.
Research on Application of Genetic Algorithms in Corporal Portal Search Engines

This paper introduces the genetic algorithm followed by implementation steps of it. We began our work with researching and summarizing the application of genetic algorithm in Search Engine Optimization (SEO) of enterprise portal, which shows the effectiveness of genetic algorithm deployed in solving the query optimization problem. Later on, we gave the implementation steps and system framework of the query optimization method based on genetic algorithm. Related studies have proved that genetic algorithm applied in SEO have the potential to optimize the search engine of enterprise portal and achieve fair results.
The design of a letter archiving application using the Model View Controller (MVC) concept

Abstract Letters are an essential means of communication in an institution or organization, and they are closely related to the times’ progress that cannot be abandoned, including today’s digital era. Based on observations, letter archiving at Hamzanwadi University is still conventional and not digitally recorded. So this research aims to design a letter archiving application by applying the Model View Controller (MVC) concept with a development model using ADDIE (Analysis, Design, Development, Implementation, and Evaluation). This Design consists of system architecture, flowchart, data flow diagram, and ERD.
The Development of Semiotic Based Contextual Mathematics Learning Videos to Support Learning from Home

This study aims to develop a contextual mathematics learning video based on semiotics that supports learning from home. This type of research is development research using a 4-D model, which is modified into three stages, namely define, design, and develop. The research subjects were grade 6th elementary school students in Tuban, East Java. In the defined stage, a needs analysis is carried out as a reference for development. The design stage produces research instruments and instructional video designs. The development stage states that based on the results of the validation of media experts by 85% and material experts by 87%, each of which comes from 21 points of assessment indicators, it shows that it meets the valid criteria as well as positive responses of 85.3% of 28 students. The right-hand t-test obtained t-count t-table, namely 3.886 1.675. It indicates that the learning outcomes of students who use semiotic-based contextual mathematics learning videos on the perimeter of circle material are better than students who do not use them. This learning video has reached effective criteria.
Application of Information Transmission Protocol in Communication Transmission Network System

Abstract The biggest advantage of the information transmission protocol is that it has a large-granularity cross-scheduling capability, and uses the information transmission protocol optical add-drop multiplexing equipment (FIROADM) to realize a multi-channel network. Based on the optical cross information transmission protocol network, wavelength-level cross scheduling and signal transmission are realized at the optical layer with the help of G709 standard encapsulation procedure mapping. The information transmission protocol can perform cross-scheduling of end-to-end services at the wavelength level, and the dispatching capability is stronger than that of electrical cross-connections. Moreover, services can run in the optical layer without processing in the electrical layer, and optical cross-connections can achieve flexible networking and support mesh networks. In addition, multiple optical layer protection methods such as optical channels and multiple paths can be constructed as well.