Reviewerly/exHarmony huggingface dataset:
Power entity identification method in cloud environment

The power industry is accelerating the migration of business systems to the cloud. The knowledge graph in the cloud environment can describe the concepts, entities and relationships in massive data in a structured form, and has the ability to understand, organize and manage massive amounts of information more efficiently. The recognition of named entities is a basic technology. This paper proposes an entity recognition method. This method is based on a two-way long and short-term memory neural network, learns the long-term dependence information of the word vector sequence with the help of part of speech features, and then inputs a conditional random field to generate the global optimal sequence. Experiments show that this method is better than many traditional methods when performing named entity recognition on power corpus data. This method can not only lay a foundation for further construction of knowledge graphs and data mining systems, but also has certain reference significance for researchers and engineers in other fields.
The effectiveness of using metacognitive strategy to teach online reading comprehension for English Young Learners

This research aims to identify the effectiveness of using metacognitive strategy in teaching online descriptive text. Fifty students of the second year at MA Al-Istiqomah, Mojokerto were involved in this study. This research is One-Group Pretest-Posttest Design. Students’ reading achievement based on the minimum passing score and field notes were the instruments to get the data. The present study results in the implementation of metacognitive strategy improved the students’ skill in reading online descriptive text especially in ascertaining main idea, supporting idea and topic sentence. This showed the significant improvement of the students’ reading comprehension skills of an online descriptive text when implementing metacognitive strategy on their learning process. The finding of the present study is expected to be applicable in the online based class during this pandemic Covid -19.Keywords: online reading comprehension; descriptive texts; metacognitive strategy
Neural network-based time-domain equalization without training signal in OFDM systems without CP

This paper proposes a neural network-based time-domain equalizer (TEQ) in OFDM systems. The proposed TEQ based on the minimum output energy criterion does not require the transmission of a training signal or the insertion of a cyclic prefix to suppress inter-symbol interference; thus, the proposed TEQ does not degrade bandwidth efficiency. Further, an arbitrary decision delay and multiple receive antennas are introduced to improve the bit error rate performance. By simulation, we show that the proposed TEQ is significantly superior to a conventional scheme.
Implementation of Literation Based Learning Information Technology

Basic Principles of Digital Literacy Development that digital literacy is a skill that not only involves the ability to use technological devices, information and communication, but also the ability to socialize, the ability to learn, and have the attitude, critical thinking, creative, and inspirational as digital competence. The scope of digital literacy is very broad. Digital literacy can be applied in schools, families and communities. To apply digital literacy in these various areas, different strategies are needed. After getting to know digital literacy, it is hoped that millennials can use technology wisely.
Self-Organizing Kernel-based Convolutional Echo State Network for Human Actions Recognition.

We propose a deterministic initialization of the Echo StateNetwork reservoirs to ensure that the activation of its internal echo staterepresentations reflects similar topological qualities of the input signalwhich should lead to a self-organizing reservoir. Human actions encodedas a multivariate time series signal are clustered before using the clustered nodes and interconnectivity matrices for initializing the S-ConvESNreservoirs. The capability of S-ConvESN is evaluated using several 3Dskeleton-based action recognition datasets.
Pre-Seismic Signals Recorded By The Italian RDF Network Before The Occurrence Of Some Earthquakes In Northern Italy

The causes that generate an earthquake can be of various kinds and, generally, there is talk of tectonic earthquakes.This type of earthquakes, unlike sudden events, such as collapses, explosions or meteoritic impacts, follow a seismic cyclicity.During the intermediate and presismic phase they are often associated with tectonic stress tension precursors of physical and chemical nature.In this study one of these aspects is analyzed: the electromagnetic interaction generated by the deformation of minerals subjected to stress in tectinically active areas.The recognition system, at the moment in the experimental phase, is the Radio Direction Finding (RDF) Network: a monitoring system that intercepts the frequencies and identifies its direction.The use of several monitoring stations makes it possible to make triangulation and locate the area of the potential epicenter already five days before the main shock.In this study two recent earthquakes occurred in Italy, in Milan and Verona, where the RDF system has proved to be an effective method of monitoring the crustal tensions in the presismic phase, thanks to the <continuous> electromagnetic monitoring network equipped with RDF technology from the Radio Emissions Project team.
Determinants of Agricultural Production and Agricultural Sector Output in Nigeria

The study examined the determinant of agricultural production and agricultural sector output in Nigeria. The objective of the study is to determine the impact of agricultural production determinants on agricultural output. The study was carried out based on secondary data collected through the CBN statistical bulletin unit root test was conducted test and granger causality test were used as the main statistical tests. The findings from the study based on the OLS results shows that agricultural funding, agricultural credit/loan as well as exchange rate have positive relationship with agricultural production output. Also, the granger causality test shows that agricultural funding, agricultural credit loan as well as exchange rate impact on agricultural production output. In view of the findings, it is recommended for adequate budgetary provisions for the agricultural sector in order to provide infrastructural facilities to the rural areas where farm produce are concentrated in order to boost production. Also, provision of credit facilities to the agricultural sector through the farmers in rural areas should be encouraged
Bowtie Networks: Generative Modeling for Joint Few-Shot Recognition and Novel-View Synthesis

In this paper, we propose a novel dual-task of joint few-shot recognition and novel-view synthesis: given only one or few images of a novel object from arbitrary views with only category annotation, we aim to simultaneously learn an object classifier and generate images of that type of object from new viewpoints. While there has been increasing interest in simultaneously addressing two or more tasks, existing work mainly focuses on multi-task learning of shareable feature representations. Here, we take a different perspective --- learning a shared generative model across the dual-task. To this end, we propose bowtie networks that jointly learn 3D geometric and semantic representations with a feedback loop. Experimental evaluation on challenging fine-grained recognition datasets demonstrates that our synthesized images are realistic from multiple viewpoints and significantly improve recognition performance as ways of data augmentation, especially in the low-data regime.
The state of voice user interfaces in Latin America

This paper describes and analyzes the state of research in Voice User Interfaces (VUIs) in Latin America based on the review of scientific documents published in SCOPUS from 1999 to June 2020, through a bibliometric analysis. We analyzed 419 academic papers. Although a gradual increase is observed over the years, the number of published documents has increased considerably since 2014. Brazil (44%) and Mexico (28%) are the countries with more documents published. Co-authorship occurs between Latin American countries (Brazil, Argentina, Mexico, Ecuador, and Costa Rica). However, the mayor collaboration from Latin American countries occurs with the United States, France, Germany, Spain, Portugal, the United Kingdom, and Japan. The main researched topics are studies of automatic speech recognition, artificial intelligence, speech processing, and human-computer interaction, which have grown over the past few years. Natural language processing, conversational agents, user experience, and chatbots are keywords related to more recent studies. Our analysis reveals that the primary active research developed in the short-term future are personal assistants and assistive technology using voice user interfaces.&#x0D;
Sürdürülebilir Ağ Yönetimi için Performans Metrikleri ve İzleme Araçları

Network infrastructures comprise the backbone of our modern life, ranging in scale from relatively micro-level settings of offices or university campuses, to macro-level structures such as smart cities or national defense systems. Consequently, effective management of the quality and sustainability of provided services and network infrastructures has grown to be essential for modern service providers. Considering standard features like reliability, availability and maintainability, several questions are identified in this study for network performance management and investment levels of sustainable network infrastructures. We describe various stages of the performance management processes, along with corresponding software tools to measure network efficiency and sustainability for information and communications technologies. Fundamental key performance indicator categories and their definitions are also compiled according to their functions within the network management context. Depending on the importance of services provided on a network, it is necessary to develop different performance management systems for monitoring the infrastructure for different practitioners. The development of required management systems will be possible by integrating the comprehensive compilation of indicators and software tools presented in this study.
Visualization of Music as a Means for the Contemporary Composition to Function: Edison Denisov, “Three Pictures by Paul Klee”

This publication presents informational and analytic materials to a class in 7th grade of children’s music schools with commentaries and elucidations of the artistic style of German artist Paul Klee and the personal perspective of his art works by 20th century Russian composer. Examination is made of the questions of visualization as a means of impact on the perception of a contemporary classical musical composition. The central portion of the class material is taken up by an analysis of Klee’s works (the visual element) and the analysis of the musical score of Denisov’s cycle. The musical score and the illustrations of the artist’s works are projected on the screen, and the musical fl ow is superimposed on the visual depiction. Thereby, the content of Denisov’s chamber cycle is comprehended in interconnection with and through the components of the visual means of the composition’s functioning. As the result of the attempt of a synthetic perception of art, the visual element provides essential help in providing instruction at the elementary stage of musical education. The choice of the composition is stipulated by the revised content of the 7th grade textbook for children’s music schools by Olga Averyanova “Otechestvennaya muzyka XX veka” [“20th Century Russian Music”, Moscow, 2014]. The article suggests a consistent and interdependent examinations of the parameters of connections between music and painting and their refl ection in the individual, sonorous fl ow of the musical composition
Library Subscribed Electronic Resources and Research Performance among the Student-researchers in the Higher Education Institutions in Tanzania

The digital information environment has dramatically changed the way that users access and utilize information worldwide. This change has therefore led to availability of electronic resources (E-resources) in libraries of Higher Education Institutions (HEIs); however their common, proper and maximum use is a matter for discussion. This paper therefore assessed on the influence of   the library subscribed electronic resources on research performance among the student-researchers in the HEIs in Tanzania.  It specifically determines the knowledge level of research-students on subscribed e-resources; investigates the types of library’s subscribed e-resources applied by student-researchers and it examines the influence of subscribed e-resources on research performance among the research-students in the public HEIs. The study at hand assessed e-resources such as e-books and texts, e-journals, library catalogues, reference sources, statistical sources, sound recordings, research guides by subject, indexes and image databases. Data were collected using questionnaire from 300 research-students sampled using stratified simple random technique. The collected data were mainly analyzed using Multiple Linear Regressions. The findings show that, the subscribed e-resources influence positively and significantly research performance among student-researchers in the HEIs; however, they had low knowledge level on subscribed e-resources. This low knowledge and awareness made the student-researchers to apply e-resources in small extent in HEIs. The study recommends that, the library management of HEIs is advised to advocate awareness of subscribed e-resources to the users in raising their knowledge level and eventually access and utilize them in contributing research performance in HEIs in Tanzania.
Perancangan Aplikasi Kamus Online Informatika-Indonesia Beserta Fungsinya Berbasis Web Menggunakan Metode Sequential Search

In this era of digital optimization, Information and Communication Technology is growing very rapidly. This is in line with the demands and human needs that have been actualized in various fields, such as knowledge and education. To support a learning process based on SCIENCE (Science and Technology) required several supporting aspects such as online dictionary that includes vocabulary and functions of words / terms in the FIELD of IT. One of the supporting aspects is an alternative tool in the form of media that facilitates every community in learning the terms in the field of informatics. According to pie chart data from questionnaires that the authors made, as much as 50 percent of the knowledge of the term informatics of the general public (lay people) can be said to be still very low. In contrast to the academic community such as IT Lecturers and IT Students who do master the field, although it does not close the possibility there are some IT Students who do not fully understand in their fields. The expected result of the authors in this study is the increasing knowledge, especially the general public (lay people) in studying science in the field of IT especially language and informatics terms through online disses. This Online Dictionary application will be designed website-based using sequential search method where sequential search (also called linear search) is the most simple search model performed on a data set. The Online Dictionary application will run by searching for vocabulary or terms in informatics according to the keywords they are looking for, making it easier for users to learn the language of informatics.&#x0D; Keywords: Website, Vocabulary, Dictionary Online, Informatics, IT, Sequential Search, Science and Technology
Intercept Algorithm for Predicting the Position of Passing the Ball on Robot Soccer ERSOW

ERSOW is the name of a wheeled soccer robot that competes in the Kontes Robot Sepak Bola Indonesia (KRSBI). The soccer robot plays a soccer game based on the rules adapted from the human soccer game. The ERSOW team was formed in 2016. Starting in 2017, ERSOW participated in the KRSBI with the Middle Size League (MSL) type. Research in the field of wheeled soccer robots is mostly carried out on robot intelligence, such as how robots detect and look for balls, dribble, pass the ball, avoid opponents, and communicate in teams. This research focuses on the ability that the robot can pass the ball in KRSBI 2020. There are adjustments to the rules for its implementation online where the robot has to pass the ball and score as many goals as possible. The robot's ability to know the direction of ball movement and cut the ball movement or intercept is needed. By utilizing data processing from vision to obtain ball speed data and speed algorithm calculations, the passing ball method has a small chance of missing. Based on the results of experiments that have been carried out, the success of ERSOW in passing using this method is 94.7%.
Improved One-Dimensional Convolutional Neural Networks for Human Motion Recognition

Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human motion recognition method based on wearable devices is studied. We smooth the data to remove the noise caused by additional motion first. After that, the characteristic values that can distinguish the types of activities can be extracted. Then, we propose a human motion recognition method based on the improved one-dimensional convolutional neural networks(1D-CNNs). Compared with other traditional classification and recognition methods, the recognition rates of 11 human motions have been greatly improved. The average accuracy of each activity identification can reach 92.8%, while the average precision and recall can reach 98.7% and 92.8%.
Modeling the assessment of the quality of students’ professional competencies development

Vocational training of specialists in various fields is aimed at development of a complex of qualities and competencies necessary for future professional activities. The list of professional and general competencies is regulated by the State educational standard. The assessment of the level of competencies development is a measure of the quality of graduate training. The assessment of the quality of competencies development is a measure of the effectiveness of the educational institution work and its structural divisions. It is necessary for internal and external monitoring, adjustment of the pedagogical process and rating. This determines the importance of reliable and informative analysis of how developing factors influence the final result of training. The paper proposes an analytical method for determining the weight coefficient of each developing factor in the overall assessment of the level of professional competencies development. The possibility of its use in the analysis of learning problems at individual and group levels is substantiated. It is proposed to use a matrix of information representation and elements of matrix and vector analysis for processing statistical data. The author has developed a mathematical model that describes a complex process of professional competencies development in higher education institutions. Developing factors in this model are university departments. The possibilities of using the model are discussed. The ways of eliminating the problem of matrices dimensions mismatch when solving matrix equations are indicated. The paper contains examples of the proposed model use in other aspects of the education quality study. A method for determining the integral indicator of the quality of training in all disciplines is proposed. The main conclusions and results of the research can be used directly in the educational process, in the field of education management and in methodical work.
Multi-class Gaussian Process Classification with Noisy Inputs

It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is expected to perform sub-optimally. In this paper, we focus on multi-class classification problems and use Gaussian processes (GPs) as the underlying classifier. Motivated by a data set coming from the astrophysics domain, we hypothesize that the observed data may contain noise in the inputs. Therefore, we devise several multi-class GP classifiers that can account for input noise. Such classifiers can be efficiently trained using variational inference to approximate the posterior distribution of the latent variables of the model. Moreover, in some situations, the amount of noise can be known before-hand. If this is the case, it can be readily introduced in the proposed methods. This prior information is expected to lead to better performance results. We have evaluated the proposed methods by carrying out several experiments, involving synthetic and real data. These include several data sets from the UCI repository, the MNIST data set and a data set coming from astrophysics. The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noise.
A Face Recognition Framework Based on the Integration of Eigenfaces Algorithm and Image Registration Technique

Today, face recognition systems play a crucial role in many access control and automatic identification systems. However, these systems still have shortcomings that reduce their performance efficiency. In this paper, a novel face recognition framework is introduced, combining the Eigenfaces algorithm and image registration. Firstly, the collected face images are preprocessed, then the Eigenfaces algorithm is applied to them for obtaining the reference eigenvectors. After that, three test images are captured using a webcam, and the images' faces are detected using the Viola-jones algorithm. The detected faces are registered to the collected face images, and the detected face with the lowest mean square error is selected for subsequent steps. Next, the selected detected face's eigenvector and the distance between it and reference eigenvectors are calculated, respectively. The minimum distance is then compared with a manual threshold to recognize the person as an unknown or known person. If the person is recognized as a known person, the person's identity is identified as the person belongs to the minimum distance. For validating the presented method, a public and an exclusive face image database are used. The obtained results indicate that the proposed framework achieved a better performance than traditional similarity-based methods to recognize known and unknown persons and identify known persons.
Hospital Readmission Risk Prediction Using Machine Learning

Hospital readmissions result in higher risk of mortality, more difficult treatments and increased cost for the healthcare sector. Therefore, decreasing the number of readmissions are of interest. T ...