Reviewerly/exHarmony huggingface dataset:
Creatividad e innovación digital en estudiantes de nivel superior de una Universidad Tecnológica en el Sureste de México

This study focuses on identifying the level of innovation and creativity that students have for accomplishing their learning needs in the context of the pandemic COVID 19. This research had a quantitative method. The used process for data collection consisted in an instrument Questionnaire for the Study of Digital Competence in University Students (CDAES) [1], as instrument that allows to evaluate the creativity and digital innovation. The research concluded the presence of a “Medium” level of creativity and innovation in University Students, which belong to the degree of Computer Science and Information Technology, with a result of 37.0% and about a “high” level, it was 28,4%. In this way, this study suggests to implement strategies to motivate students for reaching a better creativity and innovation level.
Hardening Deep Neural Networks in Condition Monitoring Systems against Adversarial Example Attacks

Abstract Condition monitoring systems based on deep neural networks are used for system failure detection in cyber-physical production systems. However, deep neural networks are vulnerable to attacks with adversarial examples. Adversarial examples are manipulated inputs, e.g. sensor signals, are able to mislead a deep neural network into misclassification. A consequence of such an attack may be the manipulation of the physical production process of a cyber-physical production system without being recognized by the condition monitoring system. This can result in a serious threat for production systems and employees. This work introduces an approach named CyberProtect to prevent misclassification caused by adversarial example attacks. The approach generates adversarial examples for retraining a deep neural network which results in a hardened variant of the deep neural network. The hardened deep neural network sustains a significant better classification rate (82% compared to 20%) while under attack with adversarial examples, as shown by empirical results.
Pedagogical Facilitation in the Context of Professional Training of Future Foreign Language Teachers

The article considers the phenomenon of pedagogical facilitation in the context of professional training of future foreign language teachers. Analysis of research has shown that the term “pedagogical facilitation” comes from the English word facilitate and means “easy, convenient”. Based on this, a facilitator is considered to be a person who helps to make the process of communication with other people simpler and easier. Despite the existing research on the terms “facilitation” and “pedagogical facilitation”, these terms need additional research in the context of professional training of future foreign language teachers due to the fact that in today's world learning foreign languages is one of the most important places in education. It was determined that a communicative approach that is leading in the study and teaching of a foreign language and requires the future professional to have facilitation skills. It was determined that effective facilitative pedagogical interaction involves the presence of the following stages in the work of teachers, including foreign language teachers: approval; adoption; trust. The article analyzes the experience of domestic and foreign scientists who defined the concepts of “social facilitation”, “psychological facilitation”, “pedagogical facilitation”.
An Android-based Face Recognition System for Class Attendance and Malpractice Control

Over time, examination malpractice in form of students indulging in impersonation has been a serious setback to academic growth in several Educational institutions. Conventional methods of keeping student’s attendance records has proved inefficient as there are so many cases of data loss and mismanagement. Several systems developed to solve this problem are either immobile or costly to implement. In order to solve this problem of cost and rigidity as well as remove the problem of examination impersonation, a mobile system running on the Android Operating System was developed using the Viola-Jones object detection framework and Eigen faces to carry out Facial Recognition of students and take record of attendance in classes in a user-friendly and secure manner. The facial recognition ability of the system was tested with 95% accuracy while that of Facial Detection ability gave an accuracy of 78%. The optimum security performance achieved by the system was based on its strong backend and its distinct modular structure
Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming

Video streaming application such as Youtube is one of the most popular mobile applications. To adjust the quality of video for available network bandwidth, a streaming server provides multiple representations of video of which bit rate has different bandwidth requirements. A streaming client utilizes an adaptive bit rate (ABR) scheme to select a proper representation that the network can support. However, in mobile environments, incorrect decisions of an ABR scheme often cause playback stalls that significantly degrade the quality of user experience, which can easily happen due to network dynamics. In this work, we propose a control theory (Linear Quadratic Optimization)-based ABR scheme to enhance the quality of experience in mobile video streaming. Our simulation study shows that our proposed ABR scheme successfully mitigates and shortens playback stalls while preserving the similar quality of streaming video compared to the state-of-the-art ABR schemes.
Information modeling and knowledge extraction for machine learning applications in industrial production systems

Abstract In this paper, a new information model for machine learning applications is introduced, which allows for a consistent acquisition and semantic annotation of process data, structural information and domain knowledge from industrial productions systems. The proposed information model is based on Industry 4.0 components and IEC 61360 component descriptions. To model sensor data, components of the OGC SensorThings model such as data streams and observations have been incorporated in this approach. Machine learning models can be integrated into the information model in terms of existing model serving frameworks like PMML or Tensorflowgraph. Based on the proposed information model, a tool chain for automatic knowledge extraction is introduced and the automatic classification of unstructured text is investigated as a particular application case for the proposed tool chain.
Learning Probabilistic Sentential Decision Diagrams by Sampling

Probabilistic circuits are deep probabilistic models with neural-network-like semantics capable of accurately and efficiently answering probabilistic queries without sacrificing expressiveness. Probabilistic Sentential Decision Diagrams (PSDDs) are a subclass of probabilistic circuits able to embed logical constraints to the circuit’s structure. In doing so, they obtain extra expressiveness with empirical optimal performance. Despite achieving competitive performance compared to other state-of-the-art competitors, there have been very few attempts at learning PSDDs from a combination of both data and knowledge in the form of logical formulae. Our work investigates sampling random PSDDs consistent with domain knowledge and evaluating against state-of-the-art probabilistic models. We propose a method of sampling that retains important structural constraints on the circuit’s graph that guarantee query tractability. Finally, we show that these samples are able to achieve competitive performance even on larger domains.
Computing maximum likelihood estimates for Gaussian graphical models with Macaulay2

We introduce the package "GraphicalModelsMLE" for computing the maximum likelihood estimates (MLEs) of a Gaussian graphical model in the computer algebra system Macaulay2. This package allows the computation of MLEs for the class of loopless mixed graphs. Additional functionality allows the user to explore the underlying algebraic structure of the model, such as its maximum likelihood degree and the ideal of score equations.
Lessons from Computational Modelling of Reference Production in Mandarin and English

Referring expression generation (REG) algorithms offer computational models of the production of referring expressions. In earlier work, a corpus of referring expressions (REs) in Mandarin was introduced. In the present paper, we annotate this corpus, evaluate classic REG algorithms on it, and compare the results with earlier results on the evaluation of REG for English referring expressions. Next, we offer an in-depth analysis of the corpus, focusing on issues that arise from the grammar of Mandarin. We discuss shortcomings of previous REG evaluations that came to light during our investigation and we highlight some surprising results. Perhaps most strikingly, we found a much higher proportion of under-specified expressions than previous studies had suggested, not just in Mandarin but in English as well.
Proposal of Methodology for Evaluation of Ergonomics of Teaching Materials Using Eye Tracking

Educational materials are a key component of the educational process. In this respect, it is an instrument that is essential throughout the education pro-cess and where quality plays a key role. The quality of educational materials must be assessed in terms of expertise and timeliness, and in terms of ob-servance and application of didactic and pedagogical principles. Only high-quality educational materials bring greater efficiency to the entire education process. An essential aspect of assessing the quality of educational materials is also their ergonomics. That is, assessing whether the educational material works in accordance with the author's ideas. The principle of ergonomics, which includes comprehensibility and user-friendliness, is already common-ly used in the design of web pages or advertising flyers. Their aim is also to communicate information and lead the attention of the reader. This article is devoted to the definition of the methodology by which we evaluate this er-gonomics for educational materials and based on research we are able to formulate recommendations in this area.
Uniaxial High-Speed Microscale Three-Dimensional Surface Topographical Measurements Using Fringe Projection

Abstract In situ inspection has drawn many attention in manufacturing due to the importance of quality assurance. Having an accurate and robust in situ monitoring can assist corrective actions for a closed-loop control of a manufacturing process. The fringe projection technique, as a variation of the structured light technique, has demonstrated significant potential for real-time in situ monitoring and inspection given its merits of conducting simultaneous high-speed and high-accuracy measurements. However, high-speed three-dimensional (3D) scanning methods like fringe projection technique are typically based on triangulation principle, meaning that the depth information is retrieved by analyzing the triangulation relationship between the light emitter (i.e., projector), the image receiver (i.e., camera) and the tested sample surface. Such measurement scheme cannot reconstruct 3D surfaces where large geometrical variations are present, such as a deep hole or a stair geometry. This is because large geometrical variations will block the auxiliary light used in the triangulation-based methods, which will resultantly cause a shadowed area to occur. In this paper, we propose a uniaxial fringe projection technique to address such limitation. We measured a stair model using both conventional triangulation-based fringe projection technique and the proposed method for comparison. Our experiment demonstrates that the proposed uniaxial fringe projection technique can perform high-speed 3D scanning without shadows appearing in the scene. Quantitative testing shows that an accuracy of 1.15% can be obtained using the proposed uniaxial fringe projection system.
Data Assimilation in the Latent Space of a Neural Network

There is an urgent need to build models to tackle Indoor Air Quality issue. Since the model should be accurate and fast, Reduced Order Modelling technique is used to reduce the dimensionality of the problem. The accuracy of the model, that represent a dynamic system, is improved integrating real data coming from sensors using Data Assimilation techniques. In this paper, we formulate a new methodology called Latent Assimilation that combines Data Assimilation and Machine Learning. We use a Convolutional neural network to reduce the dimensionality of the problem, a Long-Short-Term-Memory to build a surrogate model of the dynamic system and an Optimal Interpolated Kalman Filter to incorporate real data. Experimental results are provided for CO2 concentration within an indoor space. This methodology can be used for example to predict in real-time the load of virus, such as the SARS-COV-2, in the air by linking it to the concentration of CO2.
Student centered learning: The paradigm of modern higher education

Student centered learning implies the pedagogical approach based on constructivism and theories related to it, and it is characterized by the methods used to make a student actively contribute the learning process. It is recognized in the mindset and culture of the faculty, and focused on the needs and opinions of students. This paper presents the main elements of this approach in the context of higher education system. It starts with the perspective that placing the student at the center, the "heart" of the educational process, is a response to numerous social challenges and the reform process. The first part of the paper highlights a dichotomy resulting from the need to reconceptualize the traditional educational system in order to respond the needs of the knowledge society. It further points out the particularities and the differences between the teaching paradigm and the learning paradigm and elaborates the (im)possibility of combining them. The second part of the paper is dedicated to the analysis of the possibilities for implementing student-centered approach in higher education institutions and making it an integrative part of their climate and learning culture.
Optimization of Decision Tree Machine Learning Strategy in Data Analysis

Abstract Aiming at the problem of too coarse matching of machine learning decision tree models in the field of data mining and low prediction accuracy, the corresponding improved optimization strategies are proposed. First, the field matching degree of data is further improved by discretizing continuous attributes in multiple intervals. Then, the method makes the selection of business attributes more reasonable in the downward splitting process of the model by compensating the weight of feature attributes by business sensitivity indicators. Finally, the data classification rule transformation is used to further improve the data prediction accuracy of the model. The experimental results show that the introduction of the tree model generated by the business sensitivity index is more concise. In addition, the business pertinence and data classification capabilities are stronger. The results show that the transformed and upgraded data classification rules can effectively improve the accuracy of data prediction compared with the traditional optimization algorithm.
Key Features of the External Expansion of South Korean Protestant Organizations and Its Main Reasons

This article analyzes the process of active international expansion of South Korean Protestant organizations, continuing since the end of the 20th century. Today, based on various numerical and qualitative indicators, they could be called one of the most influential global actors in the religious sphere and partly even in international relations. On the one hand, the language and methods, which such organizations use in order to disseminate their teachings continue the centuries-old tradition of Christian missionary work, in particular the American view of it. On the other hand, they are its rethinking, that could be possible only in the conditions of modern, constantly changing, but on the whole capitalistic, deeply globalized and a democratized world, throwing new challenges to religious organizations and forcing them looking for novel ways to survive in an increasingly aggressive environment. Special attention is paid to the major reasons for the appearance of such phenomenon and its characteristic features connected with them. Based on the results of the study, the author concludes that characteristic features of South Korean Protestant organizations’ activities abroad such as interchurch competition, factionalism along with high involvement in the commercial sector and politics are directly related to the historical process of the development of this denomination on the Korean Peninsula. Therefore, despite the transnational nature of these organizations, their activities are still predominately targeted the society that gave rise to them. However, this does not prevent them from being also an instrument for expanding foreign policy influence and even an element of soft power.
Pengembangan Novelet Berbahasa Jawa Bertema Sejarah Semarang untuk Pembelajaran Sastra Jawa SMA/Sederajat

The existence of Javanese novels in Javanese literature learning has a standard thickness of novels (more than 100 pages) with various themes. Not all novels that have been published can be used as study material in literature learning. Literary works that can be used as teaching material with the theme of Semarang history. There are various values ​​of life that can be learned, such as religion and tolerance, openness, and discipline, and hard work. There are two main objectives of the research, namely (1) the characteristics of the needs of teachers and students and (2) the draft novelet in Javanese with the theme of Semarang history. This development research was carried out with a research and development (R&amp;D) design developed by Borg and Gall (2003) and modified into two stages, namely (1) preliminary studies and (2) draft development. The characteristics of the needs of teachers and students are seen from the four aspects of book feasibility, namely material, language, presentation, and graphics. The novelet draft consists of the beginning, the content, and the end. The initial section consists of the title page, the publication page, the table of contents page, and the foreword page. The contents of the novelet draft consisted of four short novels with historical themes in the city of Semarang, such as Ki Ageng Pandanaran, Admiral Cheng Ho, the legend of Gua Kreo, and the Five Days Battle in Semarang. The final section contains the author's biography.
Implementation of Internet of Public Fitness Equipment for Health Physical Fitness Improvement

In this paper, an IoT architecture for public fitness equipment, internet of public fitness equipment, with software and hardware is proposed and implemented. We utilize reed switch and MCU with WiFi module to implement IoT hardware on public fitness equipment. Furthermore, we also implement web service, database and user interface which provide thorough individual exercise prescription and monitor service. From the experimental results, all the participants' physical fitness does improve significantly. It shows that our internet of public fitness equipment can significantly improve body health by providing an efficient exercise way.
Second-Order Neural Dependency Parsing with Message Passing and End-to-End Training

In this paper, we propose second-order graph-based neural dependency parsing using message passing and end-to-end neural networks. We empirically show that our approaches match the accuracy of very recent state-of-the-art second-order graph-based neural dependency parsers and have significantly faster speed in both training and testing. We also empirically show the advantage of second-order parsing over first-order parsing and observe that the usefulness of the head-selection structured constraint vanishes when using BERT embedding.
Trapping Sets of Quantum LDPC Codes

Iterative decoders for finite length quantum low-density parity-check (QLDPC) codes are attractive because their hardware complexity scales only linearly with the number of physical qubits. However, they are impacted by short cycles, detrimental graphical configurations known as trapping sets (TSs) present in a code graph as well as symmetric degeneracy of errors. These factors significantly degrade the decoder decoding probability performance, and cause so-called error floor. In this paper, we establish a systematic methodology by which one can identify and classify quantum trapping sets (QTSs) according to their topological structure and decoder used. Conventional definition of a TS from classical error correction is generalized to address the syndrome decoding scenario for QLDPC codes. We show that the knowledge of QTSs can be used to design better QLDPC code and decoder. Frame error rate improvements of two orders of magnitude in the error floor regime are demonstrated for some practical finite-length QLDPC codes without requiring any post-processing.
COMPARISON OF SHORT-TERM TEMPERATURE FORECASTING BETWEEN ARTIFICIAL NEURAL NETWORK AND MACHINE LEARNING.

The objective of this work was to evaluate an optimized ANN (Artificial Neural Network) model compared to the most famous basic regression models within Machine Learning, using a set of observational data collected in an anemometric tower located in the city of Mucuri, Bahia, in northeastern Brazil, within one month.To obtain results for comparison of the models, the proposed activity was to forecast the temperature in 1 hour ahead, as a contribution to the city's agricultural sector.Among all the analyzed models, the one that proved most efficient was the ANN with 4 layers (7 nodes in the input layer, 18 and 9 respectively in the 2 hidden layers, and 1 in the output layer), based on R (0.872)..
Analyzing, Designing and Implementing a Consulting Company for Management Information Systems

Nowadays, due to business diversification, globalization and growing number of different business projects, the need to support people involved in tasks related to project management is becoming increasingly important. Accurate data and time needed in the projects plans, related costs and progress are extremely important for the project managers to assure the success of the project. In this work, a Consulting Company for Management Information Systems (CCFMIS) is required to facilitate consultations in the field of MIS such as analysis of information systems, database design, development of information systems, programming and other consultation that meet their needs. The proposed CCFMIS was designed and implemented using the UML (in order to illustrate the architectural model), Microsoft Access 2016 and Visual Studio-ASP.NET programming language. In the proposed CCFMIS, the UML offers several diagrams to enable the new functions to be updated and added easily such as use cases, sequences and class diagrams, and user interfaces.