Reviewerly/exHarmony huggingface dataset:
Centralized Path Planning for Multi-aircraft in the Presence of Static and Moving Obstacles

This article proposes a new approach for centralized path planning of multiple aircraft in presence of the obstacle-laden environment under low flying rules. The problem considers as a unified nonlinear constraint optimization problem. The minimum time and control investigate as the cost functions and the maximum velocity and power consider as the constraints. The pseudospectral method applies as a popular and fast direct method to solve the constrained path planning problem. The three-degree-of-freedom nonlinear point mass equations of motion with realistic operational aircraft constraints consider through the simplified mathematical model. The fixed obstacle considers as a combination of spheres with different radius. Also, the moving obstacles consider as a sphere with a known radius and fly at a constant speed. The effectiveness of the proposed concept will be demonstrated by presenting four case studies with a different number of aircraft along with the static and moving obstacles in various scenarios to ensure safe and effective flights.
Depth estimation for a road scene using a monocular image sequence based on fully convolutional neural network

An advanced driving assistant system is one of the most popular topics nowadays, and depth estimation is an important cue for advanced driving assistant system. Depth prediction is a key problem in understanding the geometry of a road scene for advanced driving assistant system. In comparison to other depth estimation methods using stereo depth perception, determining depth relation using a monocular camera is considerably challenging. In this article, a fully convolutional neural network with skip connection based on a monocular video sequence is proposed. With the integration framework that combines skip connection, fully convolutional network and the consistency between consecutive frames of the input sequence, high-resolution depth maps are obtained with lightweight network training and fewer computations. The proposed method models depth estimation as a regression problem and trains the proposed network using a scale invariance optimization based on L2 loss function, which measures the relationships between points in the consecutive frames. The proposed method can be used for depth estimation of a road scene without the need for any extra information or geometric priors. Experiments on road scene data sets demonstrate that the proposed approach outperforms previous methods for monocular depth estimation in dynamic scenes. Compared with the currently proposed method, our method has achieved good results when using the Eigen split evaluation method. The obvious prominent one is that the linear root mean squared error result is 3.462 and the δ &lt; 1.25 result is 0.892.
Motivation in the use of digital platforms for teaching and learning mathematics

The integration of ICT in education and their use in effective didactic practices are favored when teachers' motivation increases.The Theory of Self-Determination (Deci and Ryan, 2000) focuses on the study of intrinsic motivation and on the types of extrinsic motivation that can be internalized in order to motivate an individual's behavior.Research has been carried out with 2 nd and 3 rd year students of the university Primary Education Degree program, analyzing the factors that favor their intrinsic motivation after carrying out activities using digital platforms for teaching and learning mathematics.The factors assessed were: the subject's satisfaction with or interest in the use of the digital platforms, his/her perception of the competence he/she had and the value or utility provided by the activity.The intrinsic motivation in both groups was high, and was higher in students that did not have previous experience with digital platforms than in students that had previous experience.
Implementation of Blockchain on Criminality Record Checker

In the current scenario, the demand for computerization and data security has been increasing to combat with the increasing rate of crimes in the society.In the existing solution for prison management system there exist substantive data symmetry among prisons.Thus, during access of any prisoner detail, system may provide inappropriate details.Our objective is to implement a novel approach that stores the prisoner's credentials and efficiently verifies the prisoner's credentials via the blockchain technology.Our proposed solution is implements in the Hyperledger framework and the experimental result shows that our proposed solution is beneficial for the existing and upcoming investigation department.
GMDH-BASED OPTIMAL SET FEATURES DETERMINATION IN DISCRIMINANT ANALYSIS

The task of searching optimum on complexity discriminant function is considered. Criteria of quality of the discriminant functions developed in the Group Method of Data Handling are described: the criterion based on a partition of observations on training and testing samples, and criterion of sliding examination. The tasks of this class belong to pattern recognition problems under the condition of structural uncertainty, which were considered by academician A.G. Ivakhnenko as long ago as 60–70-th of the last century as actual problems of an engineering cybernetics.
Modified Selfish Herd Optimizer for Function Optimization

Selfish herd optimizer (SHO) is a new optimization algorithm. However, its optimization performance is not satisfactory. The main reason for this phenomenon is the weak global search ability of SHO. In this paper, in order to increase the global search ability of SHO, we add Levy-flight distribution strategy. To verify the performance of the proposed algorithm, we use 10 benchmark functions as test cases. Experiment results show that our algorithm is more competitive.
Formation of inclusive values as development of democratic values

The article highlights the issues of professional training of future educators using innovative pedagogical approaches, as well as the formation of inclusive competency as part of professional competency. The authors present the views of scholars on such terms as “inclusive education”, “inclusive culture”, “inclusive values”, and “value-based orientations” and demonstrate the importance for teaching staff of basic knowledge and methods of working with children with special educational needs. The article highlights the effectiveness of inclusive education in the training and retraining of teachers, where the formation of inclusive values is one of the objectives. The creation of an inclusive culture in higher education institutions ensures the existence of a safe democratic society that shares the ideas of cooperation and the value of each person in common achievements. This kind of university culture creates shared inclusive values and principles that are common for all faculty and students.
Design and implementation hamming neural network with VHDL

&lt;p&gt;Hamming Neural Network is type of artificial neural network consist of two types of layers (Feed Forward Layers and Recurrent Layer). In this paper, two inputs of patterns in bianary number were used. In the first layer, two neurons and pure line function were used. In the second layer, three neurons and positive line function were used. Also applied Hamming Neural networks algorithm in three simulation methods (Logical gate method, software program coding method and instant block diagram method). In this work in VHDL software program was used and FPGA hardware used.&lt;/p&gt;
TPACK-Based E-Book for Learning Chemistry in Senior High School

chemistry learning requires the help of teaching material that can represent chemical material into concrete material in order to help students understand chemical material.Learning chemistry that is abstract and difficult to understand if only done with the lecture method will make it difficult for students to understand the learning material.The use of teaching materials and teacher's skills in teaching are essential things that can help students in overcoming difficulties faced in the chemistry learning process.This research was conducted as a foundation to describe the need for teaching materials that integrate TPACK in chemistry learning.The research method used in this study was a survey method; then, the researcher was analyzing the data obtained descriptively.Samples were taken by purposive sampling at 35 students majoring in science in high school by using a questionnaire as a data collection instrument.The survey results found that in learning chemistry, 83.9% of students need a teaching material that integrates technology, pedagogy, and content knowledge (TPACK) to achieve optimal learning.For this reason, it is necessary to develop chemical teaching materials based on TPACK (Technological Pedagogical Content Knowledge).
Особливості саморозвитку менеджерів освітніх організацій та його зв’язок із традиційними та інноваційними стилями управління

Introduction. Professional self-development plays an important role in the work of education managers and promotes the development of their innovative management styles. Aim of the study: To analyze some aspects of educational organization managers' self-development and the relationship between the levels of educational organization managers' self-development and their use of traditional and innovative management styles. Research methods. T.&nbsp;Shamova Educational Activity Barriers questionnaire, N.&nbsp;Hall Determining the Level of Emotional Intelligence, descriptive statistics and correlation analysis. Research results. The empirical study found some potentials for education managers' self-development and the factors that contribute to it, which included positive work motivation and use of various forms of professional development, promotion of innovative activities and creation of favorable working conditions in an educational institution, as well as partnership interaction between the educational institution management and teaching staff. Among the factors that impede educational organization managers' self-development the author discusses education managers' poorly organized personal life and their negative interaction with their professional environment. There was a positive statistically significant relationship between the levels of educational organization managers' self-development and their use of traditional and innovative management styles (as the level of education managers' self-development increased, their preference of innovative management styles increased, too).
Spatial Analysis Study on the Flood Impact of WalanaeCenranae River Area in Soppeng Regency South Sulawesi Province

Cabenge River is part of the WalanaeCenranae River area in Soppeng Regency. Annually, the area around the Cabenge River gets the most losses from floods, both in terms of facilities and infrastructures. This study aims to map the areas prone to flood and flood risk, based on the field that gets the most significant impact. This study uses a Geographic Information System (GIS) as a tool and ArcGis Software in determining the level of vulnerability and risk of flooding at the study site. This type of research is a combination of mapping (topographic results) and map analysis. The variables used are DEM (Digital Elevation Model) data and flood volume. The data processing technique used is the GIS-based inundation model approach using inundation height (H) based on a comparison between the volume of water (V) in inundated areas and the volume of water (Q) of flood sources. The results showed that Lilirilau District was in the high hazard class with an area of 100 km2 and had the biggest impact, while in Liliriaja District, the area that was in a high hazard class was 34 km2 and Ganra District was 21 km2.
Flsnet: Fast and Light Segmentation Network

Abstract The use of deep learning for image segmentation has proven to be an efficient and accurate method, but with the complexity of the network structure, it takes up a lot of computing resources. The consumption of computing resources may be unacceptable during tasks. Aiming at this problem, a fast and light segmentation network (FLSNet) is proposed, which uses the Encoder-Decoder method to extract features. All convolutional layers use depthwise separable convolutions and the channel attention module is linked between Encoder and Decoder. Experiments are performed on the autonomous driving dataset CamVid. The results show that with a slight increase in segmentation accuracy, the model size becomes 8.65% of SegNet, the required computing resources are reduced by a dozen times, and the segmentation speed is increased by about 12%, which show that our network is efficient.
Promoting the Development of Chemistry Laboratory Teaching by Organizing Chemistry Experimental Competition

The 10 th Chemistry Experimental Competition for undergraduates was successfully held in Beijing University of Chemical Technology.This activity can test students' mastery of chemistry laboratory skills and level.It not only exercised the students' practical ability and innovative consciousness, but also fostered their rigorous and scientific attitude.Through this platform of "Chemistry Experimental Competition for undergraduates in Beijing", the experiences of chemistry laboratory teaching were exchanged between the universities，which can promote the reform of chemistry laboratory teaching and improve the teaching level simultaneously.
Moral Leadership and Job Satisfaction: The Mediating Effects of Interpersonal and Informational Justice

Job satisfaction remains an important attitude of employees for them to well execute their work roles. This paper reports the influences of moral leadership, interpersonal-, and informational justice. A total of 205 samples in this study were taken from educational staffs of high schools and universities. As expected, this study found that moral leadership was related to job satisfaction. Moral leadership was positively related to interpersonal- and informational justice. Interpersonal- and informational justice were positively related to job satisfaction.Also, this study found that the effect of moral leadership on job satisfaction mediated by interpersonal- and informational justice.Theoretical and practical implications are discussed. Remedies to some limitations of the study and future directions are also addressed.
Credit Card Fraud Detection using Classification, Unsupervised, Neural Networks Models

Nowadays online transactions have grown in large quantities.Among them, online credit card transactions hold a huge share.Therefore, there is much need for credit card fraud detection applications in bans and financial business.Credit card fraud purposes may be to obtain goods without paying or to obtain unauthorized funds from an account.With the demand for money credit card fraud events became common.This results in a huge loss in finances to the cardholder.Previously they used the most common methods like ruleinduction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), K-Nearest Neighbor algorithms to detect the fraud transaction using a credit card.From our perspective, neural networks will generate more accurate results.To increase the accuracy and precision we use the algorithms Logistic Regression, K-Means, Convolution Neural Networks.Logistic Regression is a statistical model that tries to minimize the cost of how wrong a prediction is.CNN algorithm is used, to capture the intrinsic patterns of fraud behaviors learned from labeled data.So will make use of accuracy and precision to evaluate the performance of the proposed system.
Deep manifold alignment for mid-grain sketch based image retrieval

We present an algorithm for visually searching image collections using free-hand sketched queries. Prior sketch based image retrieval (SBIR) algorithms adopt either a category-level or fine-grain (instance-level) definition of cross-domain similarity—returning images that match the sketched object class (category-level SBIR), or a specific instance of that object (fine-grain SBIR). In this paper we take the middle-ground; proposing an SBIR algorithm that returns images sharing both the object category and key visual characteristics of the sketched query without assuming photo-approximate sketches from the user. We describe a deeply learned cross-domain embedding in which ‘mid-grain’ sketch-image similarity may be measured, reporting on the efficacy of unsupervised and semi-supervised manifold alignment techniques to encourage better intra-category (mid-grain) discrimination within that embedding. We propose a new mid-grain sketch-image dataset (MidGrain65c) and demonstrate not only mid-grain discrimination, but also improved category-level discrimination using our approach.
Bottom-up simplied RefineNet for human clothing estimation

With the development of economy and the upgrading concept of people’s consumption, the market potential of fashion industry has been tapped greatly. More attention has been paid to garment analysis. However, the machine analysis of clothes is vulnerable to the influence of scale and deformation. In this paper, we present an algorithm of locating key points of women’s clothing based on human posture estimation technology. By transferring human posture estimation technology to this application, the algorithm can not only help overcome the above influences, but also promote the improvement of the effects of position alignment, local attribute recognition and automatic editing of clothing image. The innovation points of this paper are summarized as follows: firstly, in the coding stage, the bottom-up path enhancement method is adopted to improve the feature fusion ability of the coding structure of the basic network; secondly, in the decoding stage, simplifying the RefineNet structure of CPN network improves the accuracy while increasing the speed; thirdly, Integral regression method is used in this paper, which combined the advantages of heatmap and key points regression. Extensive experiment results show that the proposed algorithm can outperform CPN in COCO database.
DEVELOPMENT OF E-INFRASTRUCTURE IN THE SYSTEM OF EDUCATION AND SCIENCE OF UKRAINE

A transfer of grid technologies that were developed for the needs of the scientific community for use them in the educational environment is proposed. Specific areas of application and innovative prospects for the development of e-infrastructure in the educational environment of modern university are considered. To solve these tasks the article proposed to use the best practices of the Grid Enabled Remote instrumentation with Distributed Control and Computation project. The purpose of the project was to use the abilities of grid for the safe and remote joint work of the research teams for ensure monitoring and control of the instruments and transfer of experimental data that are generated and stored on the distributed scientific equipment in grid. The key element of the future research e-infrastructure become the instrument Element that consists of the connected set of services that provide all the necessary functionality for configuring, managing and monitoring the measuring devices like sensor, dummy and smart instruments or the complex scientific facilities. instrument Element ensures the interaction the devices with external grid. it is shown that grid provides all the technical capabilities for integrating educational and research environment of the modern universities. The use of grid technologies in distance learning form is aimed on students gaining practical skills in working with equipment of physical, chemical, biological and other educational and research laboratories. The aim of our research is to analyze the technical and socio-economic factors that influence the development of the research e-infrastructure of Ukraine. Using the principle of Responsible Research and innovation that distinguishes socially important innovations and research and approaches like design thinking we have proposed the own concept of building of national research e-infrastructure that must start from formation of educational e-infrastructure. Analysis shows that such consistency should become impetus for integration of the existing e-infrastructures of domestic science and education and will also contribute to the effective integration of future research e-infrastructure of Ukraine into European and world research e-infrastructures.
"Fault Prediction of Online Power Metering Equipment Based on Hierarchical Bayesian Network"

The failure rate assessment of online metering equipment is significan t for power metering. For traditional methods, the performance of the model is not satisfactory especially in the case of small samples. In this paper, a n online power measuring equipment fault evaluation method based on Weibull parameter hierarchical Bayesian model is proposed. Firstly, the z-score method is used to eliminate outliers in the raw failure data. Then, a generalized linear function with variable intercept is established according to the characteristics of failure data. The information of each region is merged using the characteristics of multi-layer Bayesian network uncertainty reasoning. The model parameters are updated based on the Markov chain Monte Carlo method. Thereafter, the trend of failure rate is provided with time-dependent. Finally, the proposed method is verified by the failure samples of the online measurement equipment in three typical environmental areas. The accuracy and validity of the hierarchical Bayesian model is verified by a series of experiments
Development of a Model Architecture for Job Scheduling

Job scheduling has a long history.Job schedulers have been one of the major component of IT infrastructure since the early mainframe system, at first, stacks of punched cards were processed one after the other, hence the term batch processing.The aim of a job scheduler was to arrange, control and optimize work and workload in a production process.The study aimed at achieving a job scheduler that can assign jobs to available resources in such a way that workload leveling can be achieved.The objectives of the study included to; ascertain the existing schedule system, design a model platform for job scheduling, implement the model platform for job scheduling, and test and deploy the model platform for job scheduling.The study adopted the spiral development model.The proposed system was developed using java script which is a client scripting language which is used for creating web and windows application and it has almost all online application since the system is an online system.The developed application was tested with field Meta data and the outcome was according to specification and of desired output.The application was hosted on three different servers, one server for the frontend, another server for the backend and the third server for the database, hosting on the three different servers did the magic by making the system faster and easy to optimize.
Complexity and Accuracy Analysis of Convolutional Neural Networks in Scene Text Detection

Machine vision, deep learning and convolutional neural networks have been applied to scene text detection by more and more scholars, whom proposed many excellent methods like Faster R-CNN, Connectionist Text Proposal Network (CTPN) and Arbitrary-Oriented Scene Text Detection via Rotation Proposals (RRPN). This article analyzes the various indicators and detection accuracy of these three methods. On the network architecture, Faster R-CNN is more complex than RRPN and CTPN. In terms of detection accuracy, the F-measure of RRPN is 0.18 higher than Faster R-CNN; the F-measure of CTPN is 0.15 higher than Faster R-CNN. However, these three methods have different emphasis on the detected text in the process of scene text detection. CTPN has better performance for laterally distributed scene text detection; RRPN is superior for scene text detection with tilt angle.