Reviewerly/exHarmony huggingface dataset:
Mathematical model for forecasting the process of electric power generation by photoelectric stations

Purpose. Improving the efficiency of photovoltaic power plants in power systems by creating a model for forecasting the amount of electricity produced in the form of a harmonic function and determining the prospects for using the selected mathematical software to develop software applications. Methodology. To determine the amount of electricity generated by photovoltaic plants per day and year, statistical methods are applied using the harmonic function which allows taking into account the main meteorological factors of power change of photomodules. A technique is proposed for taking into account the level of generation by photovoltaic stations to track changes in voltage levels in the connection nodes. Findings. Mathematical models for forecasting the electricity generation of photovoltaic stations for different time ranges are built. The influence of weather factors, the length of daylight and the structure of the local generation system on the level of electricity generated by photovoltaic plants is investigated. Necessity is conditioned to use a harmonic function for forecasting the amount of electricity produced, which improves the efficiency of calculations for new and existing power plants. Originality. The factors of the influence of daylight hours and cloudiness on the level of electricity generation by photovoltaic stations are taken into account, as well as meteorological data that make it possible to predict the value of the amount of electricity generated for a certain period of time. The dependences of the amount of generated electricity by photovoltaic stations are obtained in the form of a harmonious function with reference to a coefficient that takes into account the cloud level for predicting generation volumes. Practical value. Created mathematical models of forecasting by means of harmonic function and analysis of voltage change in nodes of local networks allow increasing the efficiency of photovoltaic stations, simplify calculation of change of levels of voltages in a electric network, the forecasted values of the generated electric power on the day ahead system on the basis of duration of the light day, meteorological data and other external factors at commissioning of photovoltaic stations.
A TENSORFLOW BASED METHOD FOR LOCAL DERIVATIVE PATTERN

Python programming language provides a very convenient environment of implementing machine learning applications. However, programmers usually faced with a poor performance compared to compiled functions when they write script based programs that demands intense computations. TensorFlow framework provides acceleration by enabling the utilization of various computing resources such as multicore CPU and GPU unit as well as including various compiled algorithms for developing machine learning applications. In this way, algorithms developed using existing TensorFlow operations can shorten computation times by using these resources indirectly without requiring parallel programming or GPU programming. In this study, Local Derivative Pattern (LDP) analysis which is one of the efficient feature extraction approaches for machine learning models was realized using a TensorFlow based algorithm. Independent pixel based operations in LDP algorithm which requires intense computations, enable developing an efficient tensor based algorithm. The performance of the TensorFlow based algorithm has been measured by comparing it with the Python script version of the same algorithm. The results obtained for various sizes and numbers of sample images show that TensorFlow operations provide significant acceleration rates for the LDP algorithm.
Improving Text-to-SQL with Schema Dependency Learning

Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on the WikiSQL benchmark. However, execution-guided decoding relies on database execution, which significantly slows down the inference process and is hence unsatisfactory for many real-world applications. In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas. The proposed model outperforms all existing methods in both the settings with or without EG. We show the schema dependency learning partially cover the benefit from EG and alleviates the need for it. SDSQL without EG significantly reduces time consumption during inference, sacrificing only a small amount of performance and provides more flexibility for downstream applications.
Characterization of Interface Debonding Behavior Utilizing an Embedded Digital Image Correlation Scheme

We present an in situ Digital Image Correlation (DIC) technique to characterize the interface debond behavior in a Sylgard 184-based particulate composite under quasi-static tensile loading. We construct tensile specimens with a single 650 μm glass bead inclusion at the center of the sample along with an embedded DIC speckle pattern at the midplane to enable measurement of the strain field in the region surrounding the embedded glass bead. The planar nature of the speckle pattern paired with the transparent binder allows for measurement of the sub-surface strain with simple camera diagnostics. Symmetry of the tensile specimen enables straightforward interpretation of the results. By capturing the evolution of the strain field from the unloaded state up to, and through, the delamination, we obtain a rich data set describing the debond process. Through repetition of the experiment, the debond behavior can be described in a statistically meaningful manner. This approach allows for calibration of a targeted constitutive model and serves as a tool to probe the effect of material or surface modification to promote adhesion.
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra

We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS comes with a benchmark specification based on extensive literature review, prescribing representative problems, algorithms, and datasets. Second, GMS offers a carefully designed software platform for seamless testing of different fine-grained elements of graph mining algorithms, such as graph representations or algorithm subroutines. The platform includes parallel implementations of more than 40 considered baselines, and it facilitates developing complex and fast mining algorithms. High modularity is possible by harnessing set algebra operations such as set intersection and difference, which enables breaking complex graph mining algorithms into simple building blocks that can be separately experimented with. GMS is supported with a broad concurrency analysis for portability in performance insights, and a novel performance metric to assess the throughput of graph mining algorithms, enabling more insightful evaluation. As use cases, we harness GMS to rapidly redesign and accelerate state-of-the-art baselines of core graph mining problems: degeneracy reordering (by up to >2x), maximal clique listing (by up to >9x), k-clique listing (by 1.1x), and subgraph isomorphism (by up to 2.5x), also obtaining better theoretical performance bounds.
Survey of Centralized and Decentralized Access Control Models in Cloud Computing

In recent years, cloud computing has become a popular option for a number of different businesses sectors.It is a paradigm employed to deliver a range of computing services, such as sharing resources via the Internet.Security issues in cloud computing necessitates the need for a mechanism to keep the system safe and reliable.An access control mechanism is one that permits or denies access to cloud services.This paper presents a survey of access control models in Cloud Computing.Several existing surveys on access control mechanisms in cloud computing mainly focused on traditional access control models and encryption-based access control models while the others focused on applying blockchain technology in cloud access control.However, access models possess different characteristics, such as the system's reliance on a centralized cloud trusted system administrator to manage the access policy or adopting decentralized approach.This paper reviews and analyses existing access control mechanisms in cloud computing, based on centralized and decentralized access control models, provides detailed comparisons on each model's advantages and limitations, and discusses the challenges of, and future research direction for access control.
EDUCAÇÃO EM SAÚDE: NÃO PARE! EXERCÍCIO FÍSICO EM TEMPOS DE PANDEMIA, UM RELATO DE EXPERIÊNCIA / HEALTH EDUCATION: DON'T STOP! PHYSICAL EXERCISE IN PANDEMIC TIMES, AN EXPERIENCE REPORT

The change from a sedentary lifestyle to a physically active one is one of the most important ways to decrease the incidence of myocardial infarction, which is the major cause of death in the population.The objective was to offer, in the midst of social isolation, monitoring and guidance from a qualified professional in performing physical exercises, by electronic means, free of charge and accessible.For your organization, an informal invitation was made.The group was divided into levels, light, moderate and advanced to be able to start activities.The training structure consisted of three exercises.The project started with 17 members in the group and ended with 10 participants.In conclusion, the proposal to offer training under professional guidance, electronically in the midst of the coronavirus pandemic, is a viable alternative that allows the maintenance and / or the increase of an active behavior in the current situation of social isolation.
Design and Research of English-Chinese Translation Platform Based on BP Neural Network

College English translation is an important part of English curriculum. Due to the influence of many factors such as single teaching mode and outdated teaching method, there are many problems in English translation teaching mode. Nowadays, highly developed information technology has been widely used in various fields. Through the construction and algorithm of BP neural network model, this paper systematically introduces the application status of BP neural network theory in teaching, and gives the construction method of BP neural network teaching model. The combination of information technology and College English Chinese translation course teaching mode is helpful to improve teaching efficiency, create efficient classroom, and further promote the teaching reform of English Chinese translation course.
Development of combinatorial abilities of students in the process of developing compositions of mathematical problems

Well-developed combinatorial abilities are an essential component of the professionalism of a modern primary school teacher. The goal of the present study is to search for methodological techniques for lessons on the methods of teaching mathematics in primary school that would activate the interconnection of logical thinking and several non-cognitive abilities of teachers in training. Said interconnection is viewed as a means allowing diagnosing and developing the combinatorial abilities of future primary school teachers. The study implements testing methods with consecutive mathematical processing. Assessment of the non-cognitive factors (the level of general cultural development, general humanitarian training, characteristics of the microenvironment, the level of general psychosocial development, etc.) involved the methods assessing academic success and psychosocial development level, as well as expert assessment of the level of general cultural development. These data were compared with the level of development of pedagogical university students’ logical thinking determined through Raven’s Progressive Matrices. The forming stage of the study involved original methodological techniques used to stimulate university students’ interest in creating compositions of textual mathematical problems for primary school students. The proposed methodological solutions to the problems of the diagnostics and development of combinatorial thinking in future teachers understood as a combination of logical thinking and several non-cognitive factors have shown themselves to be effective. The development of compositions of textual mathematical problems for primary school students is an effective means of developing future teachers’ combinatorial abilities.
A New Approach for the DFT NIST Test Applicable for Non-Stationary Input Sequences

The National Institute of Standards and Technology (NIST) document is a list of fifteen tests for estimating the probability of signal randomness degree.Test number six in the NIST document is the Discrete Fourier Transform (DFT) test suitable for stationary incoming sequences.But, for cases where the input sequence is not stationary, the DFT test provides inaccurate results.For these cases, test number seven and eight (the Non-overlapping Template Matching Test and the Overlapping Template Matching Test) of the NIST document were designed to classify those non-stationary sequences.But, even with test number seven and eight of the NIST document, the results are not always accurate.Thus, the NIST test does not give a proper answer for the non-stationary input sequence case.In this paper, we offer a new algorithm or test, which may replace the NIST tests number six, seven and eight.The proposed test is applicable also for non-stationary sequences and supplies more accurate results than the existing tests (NIST tests number six, seven and eight), for non-stationary sequences.The new proposed test is based on the Wigner function and on the Generalized Gaussian Distribution (GGD).In addition, this new proposed algorithm alarms and indicates on suspicious places of cyclic sections in the tested sequence.Thus, it gives us the option to repair or to remove the suspicious places of cyclic sections (this part is beyond the scope of this paper), so that after that, the repaired or the shortened sequence (original sequence with removed sections) will result as a sequence with high probability of random degree.
Refining the system of principles in teaching methodical disciplines in conditions of higher education digital transformation

The purpose of the article is to refine the system of principles in teaching disciplines with methodological direction in conditions of higher pedagogic education digitalisation. The authors note that in the modern practice of teaching methodological disciplines, such laws can be distinguished that are not presented in the existing system of general ideological principles, principles of higher education and principles of teaching using digital support, and propose to supplement the existing list with the principles of operationalisation, interiorisation and integration. The principle of operationalisation of professional actions in teaching methodology in conditions of digital transformation of education provides for the need to include methodological materials course into the digital content where professional actions would be for students in the form of a sequence of elementary operations. The dedicated sequence of operations must meet the requirements of efficiency, mass, discreteness, clarity. The principle of consistent interiorisation of methodological skills consists in the implementation, using digital technologies, of a phased formation of an indicative basis for the professional actions of a future teacher, due to the strengthening of students’ independence in constructing an indicative basis for professional actions in the course study process. The principle of integration of in-class and digital methodological tasks implies a purposeful organisation of mutual influence between students’ in-class and online work. The article concludes with proposals on the structure, selection of the online course content with methodological direction; conditions for upholding the proposed principles are considered.
Mobile Phones' Video Recording Tool

Foreign language anxiety (FLA) has a debilitating influence on the oral performance of EFL speakers. Over the past decade, most research in MALL has emphasized the use of mobile applications on improving language skills, but little research has explored the impact of using video recording tool to decrease foreign language speaking anxiety (FLSA). Hence, this study examines the effect of using video recording tool on students' EFL speaking anxiety. Twenty-eight pre-intermediate freshmen at Van Lang University, Vietnam, participated in a seven-week project. In this research, mixed method was used in combination with three data collection instruments: questionnaire, observation notes, semi-structured interview. The results indicated that video recording tool significantly lowers EFL speaking anxiety. Therefore, this study makes a major contribution to research on FLA by exploring freshmen's anxiety causes and its impacts on learners' oral performance and offering a technique to help them overcome that phobia: mobile phone video recording.
Prediction of Sunspots using Fuzzy Logic: A Triangular Membership Function-based Fuzzy C-Means Approach

Fuzzy logic is an algorithm that works on "degree of truth", instead of the conventional crisp logic where the possible answer can be 1 or 0. Fuzzy logic resembles human thinking as it considers all the possible outcomes between 1 and 0 and it tries to reflect reality.Generation of membership functions is the key factor of fuzzy logic.An approach for generating fuzzy gaussian and triangular membership function using fuzzy cmeans is considered in this research.The problem related to sunspot prediction is considered and its accuracy is calculated.It is evident from the results that the proposed technique of generating membership functions using fuzzy c-means can be adopted for predicting sunspots.
Time-Series Analysis

This introduces a complex regression method, called time-series analysis. Similar to the regression method, the time series makes strong assumptions about the underlying structure of the data. We recognize time-series analysis as a parametric method because it deals with a continuous variable. Remember, we do not limit series analysis to continuous variables; we can also analyze time-dependent categorical variables.
Rotating range sensor approached for mobile robot obstacle detection and collision avoidance applications

Range finder sensors are widely used in the obstacle detection and collision avoidance applications. In this research, we propose rotating range finder sensor that provides economic and efficient solution for mobile robot applications. Rotating approach is achieved by coupling the range sensors with servomotor. In this article, rotating approach model design, main parameters, equations and limitation are described. In addition, an algorithm is developed to control the rotation angle of the range sensor, extract data from the approach and analyze it. A case study of the rotating approach by implementing ultrasonic sensor is simulated and the results are obtained. Simulation platform Gazebo and ROS are used to simulate the rotating approach.
The implementation of intelligent systems in automating vehicle detection on the road

&lt;span lang="EN-US"&gt;Indonesia is a country with a high population, especially in big cities. The road always crowded with various types of vehicles. Sometimes the growth of vehicles is not matched by road construction. During peak hours, too many vehicles can cause traffic jams on the road. The road is needed to be widened to accommodate the number of vehicles that pass each day. In order for road widening to be precise at locations that frequently occur in traffic jams, data on the number and classification of vehicles passing is required. Therefore, a system that can calculate and recognize the type of vehicle that passes is needed. The development of various studies on artificial intelligence especially about object detection can classify and calculate the type of vehicle. In this study, the authors used the you only look once (YOLO) object detection system using a convolution neural network (CNN) method to classify and count vehicles that pass automatically. The author uses a dataset of 600 images with 4 classes which are car, truck, bus, and motorbikes that pass through the road. The results showed that the YOLO object detection system can recognize objects consistently with accuracy more than 80% on CCTV video that installed on the road.&lt;/span&gt;
Big data management: Security and privacy concerns

Big data has been used by different companies to deliver simple products and provide enhanced customer insights through predictive technology such as artificial intelligence. Big data is a field that mainly deals with the extraction and systemic analysis of large data sets to help businesses discover trends. Today, many companies use Big Data to facilitate growth in different functional areas as well as expand their ability to handle large customer databases. Big data has grown the demand for information management experts such that many software companies are increasingly investing in firms that specialize in data management and analytics. Nevertheless, the issue of data protection or privacy is a threat to big data management. This article presents some of the major concerns surrounding the application and use of Big Data about challenges of security and privacy of data stored on technological devices. The paper also discusses some of the current studies being undertaken aimed at addressing security and privacy issues in Big Data.
Mobili: Development and Use of a Usability Checklist for Mobile Games and Applications

Given the expansion of the Brazilian mobile development market and its dynamic context, it is essential to use interaction design and evaluation methods that suit the industry’s needs and allow companies to design high-quality applications while continuously evolving them. The present work reports our experience of developing the Mobili checklist and applying it within a software development project. Mobili offers guidance in conducting heuristic evaluations in mobile games and applications, and it was applied to assist in identifying priority corrections and evolutions for the software products developed in our project. Thus, this paper aims to share lessons that can be useful for teams in similar contexts.
The Effect of Joining Non-Formal Education and English Achievement

The study aims to investigate the correlation between joining non-formal education and the English achievement of the second-grade students of SMA Negeri 2 Palembang. The problems of this study were: Is there any correlation between joining non-formal education and English achievement of the second-grade students of SMA Negeri 2 Palembang? And What does taking an English course gives impact toward English achievement of the second-grade students of SMA Negeri 2 Palembang? The method of this study used quantitative research. The total number of the sample was 52 students. The questionnaire and document were used to collect the data which were analyzed by using Pearson Product Moment correlation and Linear Regression analysis. The findings showed that: (1) there was a significant correlation between joining non-formal education and English achievement (r= .845), (2) there was a significant impact of taking English courses and students' English achievement (p-value= .000).