Reviewerly/exHarmony huggingface dataset:
Aprendizado Profundo em Redes Desafiadoras: Conceitos e Aplicações

The book Short Courses of the 38th Symposium on Computer Networks and Distributed Systems comprises the short courses selected for presentation at the 38th Brazilian Symposium on Computer Networks and Distributed Systems (SBRC), held online between December 7 and 10, 2020. The SBRC Short Courses Book has traditionally been used as high quality study material by undergraduate and graduate students as well as by IT professionals who work on computer networking and distributed systems. The short-courses presentations sessions are also an important opportunity to update the knowledge of the scientific community and to complement the attendees training. The main objective of the SBRC Short Courses is to offer short-term training and updating on topics not normally covered in the curriculum and to make both the students and professionals more interested in the area. &nbsp
Emulating Cued Recall of Abstract Concepts via Regulated Activation Networks

Abstract concepts play a vital role in decision-making or recall operations because the associations among them are essential for contextual processing. Abstract concepts are complex and difficult to represent (conceptually, formally, or computationally), leading to difficulties in their comprehension and recall. This contribution reports the computational simulation of the cued recall of abstract concepts by exploiting their learned associations. The cued recall operation is realized via a novel geometric back-propagation algorithm that emulates the recall of abstract concepts learned through regulated activation network (RAN) modeling. During recall operation, another algorithm uniquely regulates the activation of concepts (nodes) by injecting excitatory, neutral, and inhibitory signals to other concepts of the same level. A Toy-data problem is considered to illustrate the RAN modeling and recall procedure. The results display how regulation enables contextual awareness among abstract nodes during the recall process. The MNIST dataset is used to show how recall operations retrieve intuitive and non-intuitive blends of abstract nodes. We show that every recall process converges to an optimal image. With more cues, better images are recalled, and every intermediate image obtained during the recall iterations corresponds to the varying cognitive states of the recognition procedure.
Ethics of interaction between Students and Professors in the context of digital transformation of Higher education

Examined and analyzed are ethical aspects of interaction between students and teachers in the context of digital transformation of higher education. The forced massive transition to distance learning format updated the direction of research, related to ethical aspects of communication between a teacher and a student in the digital space. Digitalization processes, on the one hand, are becoming a factor of competitiveness, the prestige of educational institution, determining its place in the ranking of modern university, on the other hand, creating a problem of depersonalization of communication in the process of digital support of education. As a result of a sociological questionnaire survey and expert interviews among students and teachers of universities in Sverdlovsk region, both positive aspects of distance learning and communicative ethical difficulties were discovered during the period of transition to distance learning format. In conclusion, the authors offer a set of practical recommendations for further improving the ethical and communicative culture of students and teachers in the context of digitalization of higher education.
Remote supervision relation extraction method of power safety regulations knowledge graph based on ResPCNN-ATT

In the field of electric power, there is a scarcity of data related to the entity relationship of power safety regulations, and the types of entity relationships in the text data of power safety regulations are too complicated, and the task of extracting the relationships between power safety entities is relatively complex. In response to the above problems, this paper proposes a remote supervision relationship extraction method based on deep residual learning (Res) combined with multi-level attention mechanism. Firstly, according to the word vector and word position vector as input, the PCNN model is used to extract the semantic features of the text, and the deep residual is used to learn less the influence of noise data to better extract the deep semantic features of the power safety text sentence. Secondly, the multi-level attention mechanism is used to calculate the correlation between the corresponding entity and the context word, so as to assign different weights to different entity features and reduce the weight of noise data. Finally, through the attention mechanism of the relationship layer, it automatically learns the dependency and inclusion relationships between different relationships, and uses the softmax function to predict entity relationships. The analysis of simulation examples proves the effectiveness of the method in this paper.
Sharp Threshold Rates for Random Codes.

Suppose that $\mathcal{P}$ is a property that may be satisfied by a random code $C \subset \Sigma^n$. For example, for some $p \in (0,1)$, $\mathcal{P}$ might be the property that there exist three elements of $C$ that lie in some Hamming ball of radius $pn$. We say that $R^*$ is the threshold rate for $\mathcal{P}$ if a random code of rate $R^{*} + \varepsilon$ is very likely to satisfy $\mathcal{P}$, while a random code of rate $R^{*} - \varepsilon$ is very unlikely to satisfy $\mathcal{P}$. While random codes are well-studied in coding theory, even the threshold rates for relatively simple properties like the one above are not well understood. We characterize threshold rates for a rich class of properties. These properties, like the example above, are defined by the inclusion of specific sets of codewords which are also suitably symmetric. For properties in this class, we show that the threshold rate is in fact equal to the lower bound that a simple first-moment calculation obtains. Our techniques not only pin down the threshold rate for the property $\mathcal{P}$ above, they give sharp bounds on the threshold rate for list-recovery in several parameter regimes, as well as an efficient algorithm for estimating the threshold rates for list-recovery in general.
Analysis of Krylov subspace approximation to large-scale differential Riccati equations

We consider a Krylov subspace approximation method for the symmetric differential Riccati equation Ẋ = AX + XA T + Q -XSX, X(0) = X 0 .The method we consider is based on projecting the large-scale equation onto a Krylov subspace spanned by the matrix A and the low-rank factors of X 0 and Q.We prove that the method is structure preserving in the sense that it preserves two important properties of the exact flow, namely the positivity of the exact flow and also the property of monotonicity.We provide a theoretical a priori error analysis that shows superlinear convergence of the method.Moreover, we derive an a posteriori error estimate that is shown to be effective in numerical examples.
Use of Frequency Domain for Complexity Reduction of Convolutional Neural Networks

The implementation of convolutional neural networks (CNNs) is not easy because of the high number of parameters that these networks have. Researchers have applied numerous approaches to reduce the complexity of convolutional networks. Quantization of the weights and pruning are two complexity reduction methods. A new paradigm for accelerating CNNs operations and simplification of the network is to perform all the computations in the Fourier domain. Using a fast Fourier transform (FFT) can simplify the operations by converting the convolution operation into multiplication. Different approaches can be taken for the simplification of computations in FFT. Our approach in this paper is to let the CNN operate in the FFT domain by splitting the input. There are problems in the computation of FFT using small kernels. Splitting is an effective solution for small kernels. The splitting reduces the redundancy that is caused by the overlap-and-add, and hence, the network’s efficiency is increased. Hardware implementation of the proposed FFT method and complexity analysis of the hardware demonstrate the proper performance of the proposed approach.
Visible-Thermal Pedestrian Detection via Unsupervised Transfer Learning

Recently, pedestrian detection using visible-thermal pairs plays a key role in around-the-clock applications, such as public surveillance and autonomous driving. However, the performance of a well-trained pedestrian detector may drop significantly when it is applied to a new scenario. Normally, to achieve a good performance on the new scenario, manual annotation of the dataset is necessary, while it is costly and unscalable. In this work, an unsupervised transfer learning framework is proposed for visible-thermal pedestrian detection tasks. Given well-trained detectors from a source dataset, the proposed framework utilizes an iterative process to generate and fuse training labels automatically, with the help of two auxiliary single-modality detectors (visible and thermal). To achieve label fusion, the knowledge of daytime and nighttime is adopted to assign priorities to labels according to their illumination, which improves the quality of generated training labels. After each iteration, the existing detectors are updated using new training labels. Experimental results demonstrate that the proposed method obtains state-of-the-art performance without any manual training labels on the target dataset.
Dispositivi mobili per un’educazione linguistica inclusiva App e italiano L2 per migranti

The contribution provides an overview of how mobile devices may be integrated into language education for migrants. The first paragraph focuses on the heterogeneity of the migrant population in Italy. In the following paragraph, we argue that Mobile Assisted Language Learning (MALL) may support the personalisation of learning and the hybridisation between classroom and extracurricular. Since the choice of the app may not be easy, we introduce, in the third paragraph, a reference framework for MALL, and we examine four mobile apps for Italian language. We conclude that mobile apps may support the linguistic inclusion and we try to envisage some areas of potential development for this field.
ANALISIS TEKNIKAL PERGERAKAN HARGA SAHAM DENGAN MENGGUNAKAN INDIKATOR STOCHASTIC OSCILLATOR DAN WEIGHTED MOVING AVERAGE

&lt;p&gt;&lt;em&gt;Investments in the stock market are closely related to the price movement risk. Investors used technical analysis to minimize the risk caused by changes in stock prices. Hence, investors get the right decision to buy or sell the stocks. Weighted Moving Average and Stochastic Oscillator are technical analysis indicators that investors often use due to their ease and accuracy predictions. This study combines the Stochastic Oscillator and Weight Moving Average (WMA) indicators to predict stock price movements in various industrial sectors during the 2015 to 2019 period and the first semester of the 2020 covid-19 pandemic outbreak in Indonesia. This combination aims to provide better predictive results by completing the weaknesses of each indicator. To provide recommendations for the right investment decisions for investors interested in investing their funds, especially in various industrial sectors. Using the combination of WMA and SI indicator charts from the investing.com website resulting in a better prediction of the right time to buy or sell stocks in various industry sectors. While the shares of SCCO, INDR, INDS appear stable during 2020, the movement of KLBM and KLBI's share prices seems to be affected by the Covid-19 pandemic in Indonesia.&lt;/em&gt;&lt;/p&gt;
A Novel Dynamic Hybridization Method for Best Feature Selection

Hybrid metaheuristics has received a lot of attention lately to solve combinatorial optimization problems. The purpose of hybridization is to create a cooperation between metaheuristics for better solutions. Most proposed works were interested in static hybridization. The objective of this work is to propose a novel dynamic hybridization method (GPBD) that generates the most suitable sequential hybridization between GA, PSO, BAT, and DE metaheuristics, according to each problem. The authors choose to test this approach for solving the best feature selection problem in a wrapper tactic, performed on face image recognition datasets, with the k-nearest neighbor (KNN) learning algorithm. The comparative study of the metaheuristics and their hybridization GPBD shows that the proposed approach achieved the best results. It was definitely competitive with other filter approaches proposed in the literature. It achieved a perfect accuracy score of 100% for Orl10P, Pix10P, and PIE10P datasets.
CyberSecurity Challenges: Serious Games for Awareness Training in Industrial Environments

Awareness of cybersecurity topics, e.g., related to secure coding guidelines, enables software developers to write secure code. This awareness is vital in industrial environments for the products and services in critical infrastructures. In this work, we introduce and discuss a new serious game designed for software developers in the industry. This game addresses software developers' needs and is shown to be well suited for raising secure coding awareness of software developers in the industry. Our work results from the experience of the authors gained in conducting more than ten CyberSecurity Challenges in the industry. The presented game design, which is shown to be well accepted by software developers, is a novel alternative to traditional classroom training. We hope to make a positive impact in the industry by improving the cybersecurity of products at their early production stages.
Automation of Archival Documents Meta Tagging

The main goal of this project is to create a corpus of documents from the «Mikhailovsky stanichny ataman» archival fund. The methods of corpus linguistics seem to be the most optimal in this case, since they involve the processing of a large number of texts in order to solve a wide variety of linguistic problems. Our group joined the team of philologists to provide the technical and software part of the project. The main task for us is to create a document corpus engine, that is, software that solves the tasks of storing a database of marked-up texts, executing queries to this database, and also providing users with a convenient interface for work that does not require special qualifications in the field of information technology. However, it is necessary to prepare documents for inclusion in the corpus: all texts must undergo special markup. There are many types of markup, and in the previous publications [6; 9] our group has already described the solution to the problem of morphological tagging. This article is about meta tagging. Meta tagging refers to the assignment of certain descriptive attributes to text. In the case of office documents, these are such parameters as the type of document (genre), author (compiler), addressee, date and place of creation. Meta tagging is necessary for the implementation of the corpus search features, so that the researchers can receive text samples with specified external parameters: for example, texts of a certain type, created at a certain period, addressed to a certain addressee, etc. The archives of the «Mikhailovsky stanichny ataman» fund mainly contain documents from the Chanceries of the Don Army from the mid-18th to the first third of the 19th century, that’s why there are not so many varieties of these documents. Moreover, these are mostly official documents, and they were written up according to certain templates, forms, the parameters of which can be relatively easily extracted from documents through preliminary analysis. This work is also carried out by the team of philologists from VolSU under the guidance of Professor O.A. Gorban. The result of their systematization of documents was the description of special speech markers of genre parameters for all document types in the archive. Thus, in our case, there is no need for heavy methods of statistical analysis or machine learning, it is enough to search for certain markers in the document. Moreover, the main marker in all reviewed documents is a direct indication of their type. Other markers are auxiliary elements of meta tagging. The paper is devoted to the description of the created application for determining the type of a document and its meta tagging by searching the text for certain regular expressions derived from the markers.
Regular matching and inclusion on compressed tree patterns with constrained context variables

We study the complexity of regular matching and inclusion for compressed tree patterns with context variables subject to regular constraints. Context variables with regular constraints permit to properly generalize on unranked tree patterns with hedge variables. Regular inclusion on unranked tree patterns is relevant to certain query answering on Xml streams with references. We show that regular matching and inclusion with regular constraints can be reduced in polynomial time to the corresponding problem without regular constraints.
Multivariate adaptive regression spline (MARS) methods with application to multi drug-resistant tuberculosis (MDR-TB) prevalence

Tuberculosis (TB) is the main public health problems in the world and Indonesia. The WHO report states that Indonesia is one of the countries contributing to TB in the world. If a TB patient is not successfully cured, it causes a double immunity of TB bacteria against Anti-TB Drugs (OAT), so-called multi-drug resistant (MDR). One of the Efforts to reduce the MDR-TB prevalence has been made based on information obtained from mathematical modeling, for example, using regression analysis that includes parametric, semi-parametric, and non-parametric approaches. Multivariate Adaptive Regression Spline (MARS) is one of the non-parametric regression approaches. The MARS model can overcome the problem of high dimensional data, produce an accurate prediction of response variables, and can overcome the weaknesses of recursive partition regression (RPR). The MARS has been built by a stepwise algorithm, which is a combination of forward and backward technique, according to a Generalized minimum Cross-Validation (GCV) value. The minimum GCV value 0.000015 is obtained from the best model that has a combination of Basis Function (BF) = 28, Minimum Interaction (MI) = 3, and Minimum Observation (MO) = 2. The result shows that all of the basis functions in the model have a significant effect on the response. The highest contribution of the basis function coefficient has given by BF6, which means the coefficient of BF6 will be statistically significant when the ratio of primary health facilities is more than 28.44. If the ratio of primary health facilities is more than 28.44, then the increase of one unit (other variables is considered constant) the MDR-TB prevalence increase by 0.171.
O QUE DIZEM AS FAMÍLIAS? BREVE REFLEXÃO SOBRE ENSINO REMOTO EM TEMPOS DE PANDEMIA / WHAT DO FAMILIES SAY? BRIEF REFLECTION ON REMOTE TEACHING IN TIMES OF PANDEMIC

The present article has the objective of exploring the Special Needs Education dynamics during the COVID-19 pandemic from the nuclear family optics.Individuals involved in this research attend a public school in Mesquita-RJmunicipality which chose to form a working group (GT) with special needs educators to carry on the academic year.These volunteer teachers organized the research through three perspectives: what students say; what families say; what the law says.In that way, it was possible to give a voice to those
Manajemen POAC pada Masa Pandemi Covid-19 (Studi Kasus BDR di SMP Negeri 2 Praya Barat Daya)

This study aimed to describe the implementation of learning from home (LFH) during the Covid-19 pandemic seen from the POAC (Planning, Organizing, Actuating, Controlling) management theory. The research used a qualitative approach with a case study method. The research was conducted at SMP Negeri 2 Praya Barat Daya, Central Lombok Regency, NTB. The research subjects involved the principal of the school, while the informants were teachers and students who were selected using purposive sampling technique. Data were collected through interviews and documentation. Data analysis used the Miles and Huberman model. The results showed that the principal implemented management functions in accordance with the POAC theory in the implementation of LFH, namely: 1) planning, which was identifying data of students having gadgets followed by designing online and offline LFH preparations; 2) organizing, which was dividing students into two study groups, namely the online group and the offline group; 3) actuating, which was introducing facilities and infrastructure used in online and offline learning, followed by its implementation of LFH; and 4) controlling, which was implementing the evaluation of the LFH program at the school level carried out at the end of the 2019/2020 school year.
Improved Gaussian Process Regression Solar Output Forecast with Pre-clustering Techniques

Power system operations are becoming more challenging with the increasing penetration of renewable-based resources such as photovoltaic (PV) generation. In this regard, obtaining accurate solar power output forecasts allows a deepening penetration of renewable-based resources in a secure and reliable way. In this paper, we propose a probabilistic framework to predict short-term PV output taking into account the uncertainty of weather data as well as the variability of PV output over time. To this end, we use datasets comprising of meteorological weather data such as temperature, irradiance, zenith, and azimuth and solar power output. We cluster these data in categories and train a Matern 5/2 Gaussian Process Regression model for each cluster. ´More specifically, we cluster the data into one to eight different partitions by making use of the k-means algorithm. In order to identify the optimal number of clusters we use the Elbow and Gap methods. We compare the results obtained for the different number of clusters with the (i) 5-fold cross-validation; and (ii) holding out 30 representative days as test data. The results showed that the optimal number of clusters is four, since in comparison to higher number of clusters the increase in the forecast error was marginal.
Research on Music Fountain Design based on Intelligent Water System

Abstract With the continuous development of AI intelligent technology, ornamental water resource design has been applied in various fields, and the interactive application of new music fountain and computer is also more extensive, which makes more control circuits need to be completed by computer, and it is developing towards intelligence, dispersion, synthesis and diversity. Artificial fountain music usually has two system composition, one is the control program system, main control the style and flow of water, the other is music control system, mainly through computer programming code, and according to different music, programming, so as to achieve shape synchronous music and water combined to send out a different style of artistic effect. Musical fountain landscape entertainment application scene extension is quite rich, the fountain can be designed out of different patterns, through the program programming according to a certain law to control the pattern of water, make it colorful, different shapes. Many squares include large entertainment venues, plus music fountain elements, the overall beauty can be improved, the design of an energy-saving pattern fountain controller in line with the mainstream of the times.