Reviewerly/exHarmony huggingface dataset:
Provenance in GIServices: A Semantic Web Approach

Recent developments in Web Service and Semantic Web technologies have shown great promise for the automatic chaining of geographic information services (GIService), which can derive user-specific information and knowledge from large volumes of data in the distributed information infrastructure. In order for users to have an informed understanding of products generated automatically by distributed GIServices, provenance information must be provided to them. This paper describes a three-level conceptual view of provenance: the automatic capture of provenance in the semantic execution engine; the query and inference of provenance. The view adapts well to the three-phase procedure for automatic GIService composition and can increase understanding of the derivation history of geospatial data products. Provenance capture in the semantic execution engine fits well with the Semantic Web environment. Geospatial metadata is tracked during execution to augment provenance. A prototype system is implemented to illustrate the applicability of the approach.
Instagram usage in destination branding through sports event: Study of Mandalika 2021 World Superbike

&lt;p&gt;International sports events can be utilized by the host city or region to form destination branding. Social media become the important tools for branding activities. This article aims to analyze the social media, focus on Instagram usage in Mandalika destination branding through the 2021 World Superbike (WSBK) sports event. WSBK 2021 become the first world-class race event held in Mandalika, and there are a number of other negative issues ahead of WSBK 2021. This research uses a qualitative method with primary data is interviews with representatives from the Indonesia Tourism Development Corporation (ITDC). Research results shows ITDC use Instagram @itdc_id as the main platform to promote Mandalika to form destination branding. The theme of content uploaded on @itdc_id are about tourist attractions in Mandalika and information about circuit readiness. ITDC regulates Instagram uploads for weekdays are spots that can be used for sports activities, while for weekends information about WSBK races, for example regarding circuit readiness or rules for spectators. During the series of race days, ITDC didn't even upload content related to the races in order to avoid copyright issues. Based on secondary data from observation towards ITDC’s Instagram posted and Google Trends analytics shows the internet user pay lower attention to the sport tourism theme. Internet users more interested in race-related information. &lt;/p&gt;
Field Apps Help Small Water Utilities Get Started With Digital Maps

Geographic information system technology can help small and rural utilities start collecting asset information using smart devices in the field.
QUANTUM MACHINE LEARNING BASED ON PENNYLANE LIBRARIES ON THE EXAMPLE OF ECONOMETRIC MODELING OF QUOTATIONS OF THE USD/RUB CURRENCY PAIR

The article is devoted to econometric calculations using quantum machine learning technologies and PennyLane libraries. The features of Penny Lane integration into the Google Colab development environment are considered. An econometric model has been created to predict the behavior of the USD/RUB currency pair in order to determine its satisfaction with the values laid down in the framework of the execution of the government budget.
Research on Multi-UCAV Four-Dimensional Trajectory Planning Algorithm Based on Improved tau-J Guidance Strategy

In this paper, a 4D trajectory planning problem of a multiple unmanned combat aerial vehicle (multi-UCAV) cooperative formation based on given 4D navigation points is studied. The three degrees of freedom model of the unmanned combat aerial vehicle (UCAV) will be established first. Then, the development of current tau theory is introduced and an improved tau-j guidance strategy is proposed. The improved tau-j guidance strategy addresses the discontinuity of velocity and acceleration at the navigation point in the original tau-j theory. The speed and acceleration of the generated four-dimensional trajectory are controlled to meet the flight requirements of the UCAV. Finally, a cost function considering the length of the flight path, flight time, collision avoidance, maximum acceleration, minimum and maximum speed of UCAV is proposed. The Particle Swarm Optimization (PSO) algorithm is used to optimize the four-dimensional trajectory of the UCAV. The results of the mathematical simulation show that the four-dimensional trajectory planning algorithm of the multi-UCAV cooperative formation meets the requirements of the constraints, which proves the effectiveness of the trajectory planning algorithm.
Rotated Aerial Object Detection Based on Improved YOLOv5 Method

This paper increases the accuracy of rotated aerial objects detection under certain practical application scenario by proposed enriching the data set method. In order to reduce computing time and improve the generalization ability of the model, two selections before and after the data augmentation are designed. Different sizes of data set are used to train and the corresponding results are compared to present the influence of size of data set on the training. The data set with more rotated images achieved 67% after 100 epochs of training on YOLOv5 network with the pre-trained model YOLOv5s.pt measured by mAP@.5:.95, higher than that of the original size of data set (65.6%) and small size of data (61.5%). The small data set with half of images in the original data set has the smallest value of mAP@.5:.95. Our method is proved to be efficient to solve the detection problem of the UAV when it is circling on the altitude of 100 m and shows the potential of data augmentation on aerial object detection.
Study on Practical Ability Training of UAV Professionals Under the Background of the Intelligence

The rapid development of intelligent technology provides more possibilities and application scenarios for UAVs. The relevant enterprises and scientific research institutions have higher and higher requirements for the ability of UAV talents in the colleges and universities. This paper first fully analyzes the current situation in the training of UAV professionals’ practical ability, and sorts out the existing problems. The specific measures are contrapuntally put forward in terms of educational concepts, system construction, promotion paths, and content settings. Then, combined with the concrete work and experience, several thoughts that need to be paid more attention to in the process of practical ability training are proposed. The research results of this paper provide theoretical basis and method reference for the decision-making about UAV personnel training of the universities and colleges.
A Hyperparameter Quality Assessment Method for UAV Object Detection Based on IER Rule

Unmanned aerial vehicles (UAVs) have been widely used in various target tracking applications through deep learning. The choice of the super parameters on the efficiency of model training and accuracy has important influence, however, the complexity of model structure and the difference of application background, the trainer cannot give precise hyperparameters. Therefore, A hyperparameter quality assessment method is proposed based on interval evidential reasoning (IER) rule in this paper. The interval confidence distribution is used to represent the interval probability generated by the uncertainty judgment, which improves the reliability of the evaluation. The evaluation process based on IER rule is defined, and the effectiveness of the evaluation is verified by experiments.
Deep Reinforcement Learning Based on Curriculum Learning for Drone Swarm Area Defense

In a drone swarm (also known as a UAV swarm), coordination is an important research topic in swarm control. In this paper, the problem of a drone swarm defending a target area is taken as the background of research on the cooperative control of the drone swarm, and deep reinforcement learning is used to form the control policy. We construct a detailed environment model about drone swarm area defense, including intrusion policy of non-learning drones, capture process modeling, and Markov Decision Process (MDP) modeling. In addition, we propose a new reinforcement learning algorithm with curriculum learning, in which different numbers of agents performing policy communication correspond to various courses. Finally, we compare the proposed algorithm with other algorithms experimentally, and the results show that our algorithm has better policy performance.
An Autonomous Hydraulic Mobile Manipulator Control System for Steel Manufacture

In steel processing industry, dust, noise and other factors seriously threaten the health of operators. Aiming at solving these problems, an autonomous hydraulic mobile manipulator system is designed and developed in this paper. The system consists of four modules, namely kinematic modeling module, decision making and planning module, control module and virtual reconstruction module. According to the functions realized in the system, mobile manipulator can move from any initial position to the target position and complete the corresponding tasks. Moreover, the vadility and effectiveness of the autonomous hydraulic mobile manipulator system for steel manufacture are verified by experiments.
Goal Conditioned Generative Adversarial Imitation Learning Based on Dueling-DQN

Generative Adversarial Imitation Learning is a powerful and practical approach for learning sequential decision-making policies. Compared to Reinforcement Learning, Generative Adversarial Imitation Learning has the ability to lean sequential decision-making policies when the reward function is unknown. Despite the significant empirical progresses, Instability of training and a great quantity of interactions are the two main problems of generative adversarial imitation learning. These problems are more prominent in multi-task environments and high-dimensional state spaces. In order to apply this algorithm to tasks such as autonomous driving and visual navigation, researchers use other imitation learning algorithms to train generative adversarial imitation learning. We focus more attention on improving the training efficiency of the generative adversarial imitation learning algorithm to ensure that the algorithm can complete the training independently without any pre-trained step. Goal conditioned setting such as goal conditioned reward function has recently shown promising training result in the policy training on navigation and manipulator tasks. Incorporate with goal conditioned reward function, we propose a novel generative adversarial imitation learning framework replacing the PPO policy update rules with the off-policy algorithm Dueling-DQN. Only with a small amount of expert data, our proposed method achieves performance that surpasses GAIL and Dueling-DQN on UAV visual navigation and obstacle avoidance tasks in urban and wild environments without the help of other imitation learning algorithms. Furthermore, our research fills the gap of generative adversarial imitation learning in the field of UAV visual obstacle avoidance navigation.
Online Trajectory Optimization Method for Autonomous Obstacle Avoidance and Swift Maneuvering of Quadrotor UAV

A high-precision online trajectory optimization method based on convex optimization method and Radau pseudospectral method is proposed for the trajectory optimization problem of quadrotor UAV autonomous obstacle avoidance and swift manoeuvring. First, the quadrotor dynamics and related constraint models are established to form an optimal control problem for the optimization of quadrotor autonomous obstacle avoidance trajectories with the shortest flight time as the performance index. Then, the free time variables are considered as augmented control variables, the trajectory optimization problem is sequence convexified, the corresponding convex subproblem model is derived, and a suitable sequential convexification algorithm is designed. Finally, a trajectory optimization program based on C language + ECOS solver was implemented and compared with Matlab + GPOPS-II solution results to verify the correctness and effectiveness of the sequential convexification algorithm.
An Image SVD Compression Algorithm for UAV Based on Region of Interest

In order to solve the problem of image blur identification caused by high compression ratio in unmanned aerial vehicle (UAV) airborne video images, a singular value decomposition (SVD) image compression method based on the region of interest (ROI) is proposed. Using regional compression method, the experiment firstly contour edge detection was carried out for images collected by UAV to help find out the key information part,and then divided the region of interest ROI on the high-frequency part, segmented it and compressed it to a small extent. Other areas were compressed according to the corresponding compression ratio according to the trans-mission bandwidth requirements, and finally the target area and background area were synthesized to obtain clear image of target with high compression ratio. A conclusion is obtained that the UAV can obtain a large peak signal to noise ratio (PSNR) with this method on the premise of guaranteeing the transmission of important information.
A Systematic Design and Optimization Approach for High-Performance Control of First-Person-View Quadcopter

First-Person-View quadcopter drone, short as FPV quadcopter, is a variant of multicopter drone which distinguishes itself from other drones for its compact size, enormous propulsion power, first-person-view monitor and control. Its compact size and powerful motors made it suitable for both indoor and outdoor purposes. Due to its unstable flight control and high training cost, the FPV quad has not been promoted and applied in the military field. In this paper, a systematic method have been applied to solve stability issues in the high-speed movement of an FPV quadcopter drone: quadcopter frame design and optimization, advanced filtering of the gyroscope, differential evolution algorithm parameter tuning of the control system, in order to increase agility and response speed, which develops prospects for military purposes.
Robust Multi-sensor Fusion via Factor Graph and Variational Bayesian Inference

In the research, we present a novel nonlinear state estimation method to solve the problems of multi-sensor fusion application in navigation systems. In a full Bayesian framework, the muti-sensor fusion is performed by estimating the maximum a posterior over the joint probability distribution function (PDF) of all state variables. In order to exploit the full sparsity of the system, the joint PDF is represented by a factor graph model. Since the outliers which represent corrupted observations could affect the accuracy of state estimation, a variational approximation scheme is applied for robust multi-sensor fusion. The proposed method is experimentally verified using the multi-sensor data that recoded by an integrated navigation system. The simulation results demonstrate that the proposed method provides a comparable performance to the traditional muti-sensor fusion method.
Investor-Induced Cryptocurrency Contagion Channels: An Empirical Evidence from Five Major Economies

Coupled with the global pandemic and hyperinflation, the innovation risks of cryptocurrency have led to significant price volatility.There is a significant effect of cryptocurrencies on traditional financial markets and contagion channels.Therefore, regulatory authorities should understand the contagion channels to analyze the input risks and take necessary measures.This paper adopts the copula model to study the contagion channels of cryptocurrencies in five major economies.Additionally, three hypotheses are tested to determine whether investor induction serves as the main contagion channel.The empirical results indicate that cryptocurrency market contagion is significant in all five economies, with "portfolio rebalancing" as an important channel of transmission.Moreover, some countries represent time-varying contagion channels.Lastly, cryptocurrencies are not only an indirect symbol of the country's integration into the global economy but also a risk indicator for new assets.
Enhanced Reinforcement Learning Method Based on AlphaGo-Zero

Based on alpha-zero’s method, we implement a gomoku agent with reinforcement learning. It appears that the model will be stuck in a bottleneck after training for two days and cannot achieve stronger level through self-playing. According to the research of openai, this problem may be caused by over-fitting to some specific strategies in the process of self-play. To fix this issue, we introduce another gomoku agent based on vct/vcf (Victory of Continuous Three/Victory of Continuous Four, is a strategy on gomoku) during training. The experiment shows that by introducing another agent with different strategy can improve agent’s ability within less than 2000 iterations. The conclusion shows that special external strategies can be artificially added to the training of reinforcement learning to enhance the generalization and adaptability of the model, so as to enhance the performance of the agent.
Research on keyword extraction based on abstract extraction

In order to improve the accuracy of text keyword extraction, this paper combined with the relevant methods of abstract extraction, aiming to extract key sentences through the abstract extraction method, and then optimize the effect of keyword extraction.The experimental results show that the effect of the keyword extraction algorithm on the text after abstract extraction is improved by 6.92 percentage points.
Research on Flight Control Method of Micro Co-axial Rotor UAV Based on Reinforcement Learning

The micro co-axial rotor UAV has the advantages of high hovering efficiency and compact structure in various applications, but its attitude control system has the characteristics of strong nonlinearity and coupling, and the design of the controller is the most difficult point in the design of this type of aircraft. In this paper, a control system is designed based on reinforcement learning algorithm for a micro co-axial rotor UAV that uses aerodynamic rudder surface for attitude control. Firstly, according to the reinforcement learning theory and the characteristics of the UAV, the main formulas of the control system is designed. Secondly, digital model of the UAV and training environment are established, the controller basically meets the control requirements after the training experiments were conducted. Judging by the performance of the reinforcement learning controller, it effectively improved the control accuracy and efficiency comparing with the existing PID controller. This research implements the combination of reinforcement learning algorithm and the controller of micro co-axial rotor UAV, and provides an idea to build an intelligent controller for complex micro-aircraft.
Educational management: Managing the process of developing an electronic educational resource based on the university’s quality management system

The article deals with the issues of improving educational management related to the use of quality management system (QMS) tools in the development of electronic educational resources (EER). The relevance of the article is determined by the transformations taking place in Russia, according to which new requirements are imposed on the quality of education, focused on the professional work of graduates in the digital economy. The purpose of the article is to develop a documented procedure for managing the EER development process in accordance with the requirements of the QMS standard. The article shows that it is possible to improve the quality of educational activities by rational organization of University’s processes in the conditions of the information and educational environment of the University on the basis of its information system (IS). The process of development of EER that describes the content structure of its blocks, applied process approach of QMS to design of EER, created documented procedure, QMS-regulated institution, which defines the input and output parameters of the process, identified all its relations, established responsible at each stage of the process, identified internal and external customers of the process. The proposed approach should be used within the framework of inter-departmental research teams (IDRT) consisting of qualified subject teachers who is a customers and task managers, and it specialists of the University who is a developers, whose joint activity is to develop, pilot operation, adjust and implement EER in the educational process of the University. If the University trains future programmers, it is quite appropriate to involve them in developing an EER. Such activities will strengthen interdisciplinary relations and activate the scientific work of both customer departments and developer departments.
Feedback Neural Network and Symmetrical Connection Network in Human Environment

Human survival and development are constantly responding to the environment, and human participation has changed the original appearance of the entire natural environment to a certain extent.In recent years, pollution has intensified, and many countries have paid more attention to and actively controlled pollution, including air pollution, water pollution, solid waste and urban noise.Although pollution control has improved in recent years, urban environmental quality has improved significantly and developed steadily, many problems need further management to improve environmental quality.Effective environmental monitoring measures can provide a good basis for decision-making and implementation of environmental management, which is an effective measure for scientific evaluation of environmental management work of governments at all levels.According to the requirements of modern management, relevant government environmental monitoring agencies must change their monitoring strategies, actively introduce the concepts and methods of supervision and control, and improve the overall monitoring efficiency.In order to better carry out environmental monitoring, this paper uses symmetric connection network and feedback neural network to carry out environmental monitoring research.This paper constructs the structure of the Internet of Things for environmental monitoring, introduces the node structure and network system framework of wireless sensor networks, and classifies environmental images using symmetric connection networks.In this paper, feedback neural network is used for data fusion of the Internet of Things for environmental monitoring.In the experimental part, the environmental monitoring system is used for air monitoring experiment.The experimental results show that the monitoring system has good environmental monitoring effect, and the accuracy rate of air quality monitoring is 86.7%, with a high accuracy rate.
Introduction: XML and JSON

This chapter introduces basic concepts about XML and JSON. At the end of this chapter, there is an exercise for the environment setup.
Short-Term Load Forecasting of Power System Based on Support Vector Machine Theory

The short-term load forecasting of the power system is an important basis for the safe and economical operation of the power system. With the introduction of the price competition mechanism into the power system to form the power market, higher requirements are put forward for the accuracy and response speed of short-term load forecasting. The key problem of power system load forecasting is to establish a corresponding mathematical model according to the historical data of the forecast object to describe its development law. Support vector machine theory (SVM) can better solve practical problems such as small samples, nonlinearity, high dimensionality and local minima, and can be used to establish a relatively complete load forecasting model. The research shows that the application of SVM for load forecasting of power system has the advantages of high accuracy and fast speed, which significantly improves the effect of load forecasting.