Reviewerly/exHarmony huggingface dataset:
SOFTWARE AND HARDWARE SYSTEM «ARDUINO LEARNER KIT»

The difference between programming embedded systems from the classical one is that in addition to knowledge of the theory of algorithms, programming languages, it is necessary to know the hardware of the object, its physical or electrical properties. Programming for embedded systems is considered a separate area. Embedded-developer is engaged in the development, maintenance, testing of software and hardware. The article considers issues related to the peculiarities of practical training and the formation of professional skills in programming embedded-systems by students studying in the field of knowledge 12 Information Technology, 17 Electronics and Telecommunications software and hardware complex "Arduino Learner Kit" based on the platform Arduino Nano. The software-hardware system "Arduino Learner Kit" is intended for experiments on the control of LEDs, generation of color and adjustment of the brightness of RGB LED, creation of a tone signal, processing of events of clock buttons, reading of data from the potentiometer, DHT11 sensor, LM35 sensor, seven-segment display, LED dot matrix indicator, LCD-indicator, Arduino port sealing by shift register 74HC595, data exchange via SPI interface (MAX7219), and I2C (DS1307). The approach of the application of the physical and virtual stand "Arduino Learner Kit" for training on the mixed and distance form is offered. Arduino Learner Kit software and technology teaches that students study demonstration programs on each topic and get acquainted with their work on the virtual stand "Arduino Learner Kit" and explore the work on the real stand by the peculiarities of the educational process in the educational institution. Features of modeling, testing, and debugging of projects with Arduino in the Proteus 8.X environment are revealed. The list of topics for the formation of Arduino programming skills, drawing diagrams from ready-made modules and parts using the software and hardware system "Arduino Learner Kit", which are implemented in the educational process.
In-campus Way of the Insight Transfer Technology

The version of solving the relevant problem of increasing the innovative activity of engineering specialties students by organizing the academic environment is proposed. The purpose of the activity is to update future engineers and working citizens' professional competencies and ensure transparent conditions for evaluating instructional designing results. The ways of implementing the project-oriented training principles in the technical university are presented, which will allow to actively involve students in scientific and innovative activities within the educational process and the performance of qualification work. The authors have proposed a model of the information system based on the use of electronic document flow, implemented, at this stage, for the organization of the activity process. The proposed approaches to engineering education will allow to use of student youth's potential for the development of innovations by scientific-educational project teams and minimize the costs of ensuring the functioning of single information space of innovation-project activity.
Comparative study among physical models and artificial intelligence methods for PV power forecasting

The renewable energy is widely used around the word, thanks to its multiples benefits as property and durability. Upgrading the accuracy of photovoltaic (PV) power forecasting is critical for management and dispatching operations. Nevertheless, this is a difficult task due to its intermittent nature. As the photovoltaic power depends mostly on solar irradiation and ambient temperature, its prediction can be done through meteorological variables estimation. Various methodologies have been developed, including physical models and artificial intelligence techniques. This paper provides a comparison between empirical models (Perrin de Brichambaut, Liu and Jordan, Eufrat, Ghouard, Capderou and the ambient temperature distribution model) and the deep learning techniques (the adaptive neuro-fuzzy inference system (ANFIS) and the multilayer perceptron neural network (MLP)) to explore their effectiveness in PV power forecasting based on the errors percentages. The simulation results show that the artificial intelligence topologies outperform the physical models. Moreover, the ANFIS model is more accurate than the MLP network.
Enhancing Probabilistic Model Checking with Ontologies

Abstract Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of state based operational models such as Markov decision processes. Description logics (DLs) provide a well-suited formalism to describe and reason about knowledge and are used as basis for the web ontology language (OWL). We investigate how such knowledge described by DLs can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose ontologized programs as a formalism that links ontologies to behaviors specified by probabilistic guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Through DL reasoning, inconsistent states in the modeled system can be detected. We present three ways to resolve these inconsistencies, leading to different Markov decision process semantics. We analyze the computational complexity of checking whether an ontologized program is consistent under these semantics. Further, we present and implement a technique for the quantitative analysis of ontologized programs relying on standard DL reasoning and PMC tools. This way, we enable the application of PMC techniques to analyze knowledge-intensive systems.We evaluate our approach and implementation on amulti-server systemcase study,where different DL ontologies are used to provide specifications of different server platforms and situations the system is executed in.
Rider Chicken Optimization Algorithm-Based Recurrent Neural Network for Big Data Classification in Spark Architecture

Abstract This paper proposes an effective classification method named Rider Chicken Optimization Algorithm-based Recurrent Neural Network (RCOA-based RNN) to perform big data classification in spark architecture. Initially, the input data are collected from the network by the master node and then forwarded to the slave node. These nodes are responsible for storing the data and performing computations. The features are effectively selected in the slave node using the proposed RCOA. The selected features are forwarded to the master node. The big data classification is achieved in the master node by using the RNN classifier, and the training of the classifier is done using the proposed RCOA algorithm, which is the integration of the Rider optimization algorithm (ROA) with the standard Chicken Swarm Optimization (CSO). The experimentation is done by using the Switzerland dataset, Cleveland dataset, Hungarian dataset and Skin disease dataset, in which the proposed RCOA-based RNN attained better performance based on the quantitative properties, such as sensitivity, accuracy and specificity with the values of 9.3E+01%, 9.4E+01% and 9.3E+01% using Hungarian dataset. The existing learning methods failed to address the complex classification problems at a reasonable time, which is overcome by the proposed method.
Micrometeorological Data Collection and Application in Internet of Things for Power Systems

Meteorology plays a key role in the reliability of power equipment and the operation of power system while the meteorological information that we get isn't accuracy. Many experts put forward different methods to solve this problem and these methods have made some achievements. Another angle is found in this paper, the system of micrometeorological data collection and application (SMDCA) is designed to solve this problem. There are three levels in the system, including micrometeorology collection, data transmission, data application. The SMDCA is based on internet of things for power systems (ITPS) in this paper. And it not only can collect micrometeorological information but also have the ITPS's function of intelligent interconnection. The SMDCA's applications in intelligent interconnection are designed and reviewed detailly. And it's confirmed that the application of SMDCA in ITPS has a good effect on the accuracy of related calculation in power system.
A Multimodal Tracking Approach For Augmented Reality Applications

Augmented reality (AR) contains a combination of real and virtual elements. The main challenge in this field of research is to achieve a sustainable, accurate registration between the real and computer-generated objects, which depends on obtaining accurate user’s viewpoint, i.e., the position and rotation, or pose, of the user’s camera. Computer vision approaches, namely by resorting to marker-based tracking, is one of the most commonly used techniques in AR to estimate the camera pose. However, it suffers from optical noise, occlusion, and fast motions of the user. In this paper, an Extended Kalman Filter (EKF) is proposed for sensor fusion, combining marker-based tracking with an inertial sensor and an external depth camera for accurate registration. The results show that the EKF can provide a combined estimated pose of the user’s viewpoint data, whether marker-based tracking occurs properly or not, mitigating the aforementioned limitations.
Formation of mixes with specified characteristics from the available small batches of multicomponent mixes, subject to their full use

Abstract Methods, decision algorithms and examples of tasks of new mixes formation with the set characteristics from the available small batches of components or mixes are given in this research. This analysis continues previous researches by the authors on the formation of mixes with specified quality characteristics. This article presents a solution technique, algorithms, and corresponding examples of tasks for the formation of new mixtures, provided that some small batches of mixes or components must be spent completely. Technique and technology solutions of these tasks can be of interest and to be demanded in the different industries of production, for example, at production of premixes for livestock production, at preparation of different food mixes, products and additives, medicines, metal powder materials, in construction, in military industry, by production of thixotropic mixes, fertilizers, household chemicals and in many other technology processes.
Performance evaluation of micro-blog marketing based on fuzzy theory

At present, the people in the micro-blogging marketing performance evaluation research is still in the weak status, based on the fuzzy theory, a comprehensive questionnaire with the method of entropy weight fuzzy comprehensive evaluation for the micro-blogging marketing performance in this study, with an enterprise’s from January 2019 to October of micro-blogging marketing performance activity, the propagation force and coverage for data collection and the analysis of simulation results. Fuzzy comprehensive evaluation is a method of comprehensively evaluating things based on the degree of membership in fuzzy mathematics theory. The method of evaluating participating units with multiple indicators can evaluate the participating units more reasonably and accurately. The entropy weight law reflects the relative variation degree of each indicator according to the amount of information provided by the observed value of each indicator, and calculates the weight data of the indicator by means of information entropy. Through the establishment of a performance evaluation index system for micro-blog marketing, the micro-blog marketing performance is researched on the three indicators of micro-blog activity, micro-blog communication power and micro-blog coverage in performance evaluation. Among the research results, 23% of people believe that micro-blog activity is more important for performance evaluation; 39% believe that micro-blog communication is more important in performance evaluation; 41% believe that microblog coverage is more important in performance evaluation. This result indicates that the marketing performance of the enterprise’s micro-blog is not only determined by whether the enterprise’s micro-blog is active or not, but also should pay attention to the spreading strength and coverage of the micro-blog. However, the influence of indicators with small weight on the marketing performance of the micro-blog should not be ignored.
Comparative Analysis of Intelligent Solutions Searching Algorithms of Particle Swarm Optimization and Ant Colony Optimization for Artificial Neural Networks Target Dataset

The optimizations approaches of ant colony optimization (ACO) and particle swarm optimization (PSO) were targeted at improving the outcomes of artificial neural networks for finding best solution in the space. Both ACO and PSO are derived from the artificial intelligence concept that imitate the natural behaviors of animals in finding best path to foodstuff location relative and back their nest. The artificial neural networks (ANNs) are reliant on estimated research scheme in which models are generated for unspecified function in order find suitable interrelationships in input and output datasets. These are not without challenges including large time of computation, expansive hidden layer size, and poor accuracy. This paper examines the effects of pretraining dataset with ACO and PSO prior training process of ANN in order to overcome the aforementioned problems of speed and accuracy through optimization of the local and global minima. The outcomes of the study revealed that the ACO outperformed PSO in conjunction with ANN in terms of RAE, MSE, RMSE, and MAPE utilized. The error rates of ANN pretrained with ACO and PSO distinctively are 62 and 73% accordingly. Benchmarking the results against the solution optimization studies, ACO and PSO algorithms are most preferred in finding the best solution or nearest-optimal in search spaces.
Eye Tracking Calibration on Mobile Devices

Eye tracking has been widely used in psychology, human-computer interaction and many other fields. Recently, eye tracking based on off-the-shelf cameras has produced promising results, compared to the traditional eye tracking devices. This presents an opportunity to introduce eye tracking on mobile devices. However, eye tracking on mobile devices face many challenges, including occlusion of faces and unstable and changing distance between face and camera. This research project aims to obtain stable and accurate calibration of front-camera based eye tracking in dynamic contexts through the construction of real-world eye-movement datasets, the introduction of novel context-awareness models and improved gaze estimation methods that can be adapted to partial faces.
Modified Poisson estimators for grouped and right-censored counts

Grouped and right-censored (GRC) count data are widely adopted to study some sensitive topics or to collect information from less cognitive respondents in many research fields, such as psychology, sociology, and criminology. However, theoretical analysis of GRC counts is involved due to the co-existence of grouping schemes and right-censoring schemes. Recently, a modified Poisson regression model has been proposed to analyze GRC count data under the framework of maximum likelihood estimation. In this paper, I study the asymptotic properties of the maximum likelihood estimators of GRC counts that can cover the modified Poisson estimator. Existing results on modified Poisson estimators for GRC counts are only applicable to stochastic regressors with strictly positive definite Fisher information matrices. Results in this paper are derived under a milder condition that the information matrix of observations is divergent, which can cover the results for the stochastic case in the almost sure sense. Real data simulations are provided to investigate drug use in America.
The Multifractal Characteristics of Seismic Activities in China

Abstract In recent years, with the frequent occurrence of geological disasters, the prediction and analysis of earthquakes has gradually become a hot topic. This paper studies the multifractal spatio-temporal characteristics of China seismic energy. The study data derives from the seismic activities in the Mainland China from 1970 to 2013. In this study, it comes to a comprehensive conclusion of spatial and temporal multifractal characteristics of earthquake activities in China. The study area is divided into five regions: The Xinjiang region, the Qinghai-Tibetan Plateau region, the Northern China, the Northeast China and the Southern China. In this study, we analyze the spectrum curves of the seismic releasing energy time series to each region, and compare the differences. Then the differences of the multifractal characteristics in the time dimension can be pointed out based on the curve, and the law of earthquake occurrence is analyzed from the sequence diagrams of time intervals among various tectonic areas. In the spatial dimension, it discrete seismic data points for each region through rectangular square grid at different resolutions as the input, then derive the multifractal spectrum curve of spatial characteristic to each region. By analyzing the multifractal dimension, it explores the similarities and differences of the spatial and temporal characteristics of the earthquake among five regions.
Deep Image Captioning Survey: A Resource Availability Perspective

Recent advances in deep learning have enabled machines to see, hear, and even speak. In some cases, with the help of deep learning, machines have also outperformed humans in these complex tasks. Such improvements have reignited interest in many fields. Image captioning, which is considered an intersection between computer vision and natural language processing, has recently received significant attention. Deep learning-based image captioning models represent a great improvement on traditional methods. However, most of the work done in image captioning is based on supervised deep learning methods. Recently, unsupervised image captioning has started to gather momentum. This paper presents the first survey that focuses on unsupervised and semi-supervised image captioning techniques and methods. Additionally, the survey shows how such methods can be used with different data availability and data pairing settings, where some methods can be used with paired data, while others can be used with unpaired data. Furthermore, special cases of unpaired data, such as cross-domain and cross-lingual image captioning, are also discussed. Finally, the survey presents a discussion on challenges and future research directions of image captioning.
Remote sensing data for Geological mapping and gold prospecting of Inteet area, northern Sudan

The mineral exploration has improved in the last decades, today there are different methods of remote exploration, that are applied in mining industry worldwide, helping to discover ore minerals deposits in zones that have not access to them. The study area of this article is located about 400 km from Khartoum, characterized by low reliefs covered by sand and gravely sand. The study wants to carry out the geological mapping and the prospective zones using remote sensing and GIS techniques. Sudan is characterized by geological formations over inaccessible areas, in that way the remote sensing technique has a great value in these conditions, saving time and money. The used methodology has been divided into three phases: Pre-field office work, field work and post-field work. The processing of the satellite images includes color band composites, in order to obtain the lithological and geological features, the different types of rocks were defined by a different color. Obtaining the following rock types: High-grade gneisses and migmatites described to comprise the basement complex, ophiolitic mafic-ultramafic rocks that appear in the northern and southern sides of the area, metasediments that cover most of the study area, syn-orogenic intrusions that cut the older mafic-ultramafic units and the metavolcanosedimentary sequences, post-orogenic intrusions and gold mineralization. Based on the image analysis results of Landsat 8 OLI, 32 ore samples were collected to analysis gold and pathfinder elements obtaining high anomalies results for Au, Ag, Cu, Zn, Pb and Fe.
Analysis of the use of digital technologies in the activities of educational organizations in the conditions of distance learning (from the experience of France)

The article deals with the issues of setting up and developing distance learning technology in France on the example of the National Center for Distance Education (CNED). The organizational forms of its implementation are given, the role of digital educational resources and digital work space in realization of distance learning is shown.
Visual Information Transfer Design of Packaging Product Instructions Based on Unity Platform

With the increasing use of packaging, how to carry out green packaging has become an urgent problem. An important reason why packaging is difficult to reuse is that it is violently opened, making it difficult to recycle again. Many packaging structures on the market are cleverly designed and various packaging forms are available, but imperfect structures, materials, and information lead to violent use by users, so that the package is damaged, which is too hard to recycle. The development of augmented reality has brought a boom in information technology. We can pass the designed packaging which is combined with augmented reality technology to the user. In that way, the user can intuitively experience the use skills when using the package. We can utilize software such as Unity and Vuforia to model the packaging, import those usage methods such as opening, closing, and storage into the system, finally export the APP. Before using the package, the user can use application to identify the packaging structure on the package, obtaining the information of the 3D effect display about the packaging instructions before performing operations, which can not only protect the package from damage, but also increase the packaging utilization rate.
Exploring the Relationship Between Physical Presence, User Experience, and Task Parameters in Robotic Telepresence

This paper reports a study of pilots of a robotic telepresence system, exploring the relationship between the pilots' experience of physical presence, overall user experience, and task parameters. The pilots (N=12) performed a set of tasks characterized by different Task Focus (Process vs. Outcome) and Navigation Difficulty (Low vs. High); their experience was assessed by using a set of subjective rating scales. It was found that the experience of being physically present in a remote location positively correlated with self-reported gaming skills, objective indicators of navigation efficiency, as well as feelings of being safe and relaxed. No significant effects of task focus and navigation difficulty were found.
Comparative analysis of 3D convolutional and LSTM neural networks in the action recognition task by video data

Abstract In the present paper a comparative analysis of two architectural neural network approaches (based on 3D convolutional and LSTM) in the recognition of actions on video is made. The problem was being solved on 10 behavior classes separated from the UCF50 dataset. The original neural network architectures were developed and pre-trained. It was found that the network based on 3D convolutions has better generalization ability and is more stable in the training.