Reviewerly/exHarmony huggingface dataset:
Multi-view and Multi-target Association Algorithm for Unmanned Aerial Vehicle Clusters Based on Siamese Network

The multi-target association algorithm of unmanned aerial vehicles (UAV) clusters in unknown environments is used to solve the problem of multiple UAVs, which can correctly determine whether the data from different perspectives contain the same target and eliminate redundant targets. This problem is different from that of multi-target tracking under the same perspective. However, the inherent relationship and differences of data from various perspectives of UAV clusters contribute to the problem of inaccurate target positioning information caused by low-cost detectors and inertial measurement units of UAVs and the constraints of limited communication capability between UAV clusters. Thus, using multi-perspective data is the key to improving the accuracy of multi-perspective situational awareness. This paper proposes a multi-view and multi-target association algorithm for UAV clusters based on the Siamese network. This algorithm can realise the problem of multi-view and multi-target identity judgement with high accuracy, achieve the requirements of high accuracy and high generalisation performance with the depth model based on convolution neural networks, extract the depth features from the optical image and map the original data to the depth feature space. Thus, the same target is matched by comparing the distance measurement of multi-view data in the feature space. The algorithm is verified by an aircraft cluster flight test, which proves the feasibility and accuracy of the algorithm.
Parameter Optimization of Dynamic Windows Approach Cost Function Based on Variable Compass Operator PIO

Dynamic Windows Approach (DWA) is a common local trajectory planning algorithm, and the parameters of cost function directly affect the speed of the vehicle. To improve the speed characteristic of DWA, this paper proposes an optimization method of DWA cost function parameters based on variable compass operator pigeon-inspired optimization (PIO). Firstly, a trajectory planning environment model including starting point, obstacles and target point is established. Secondly, a multi-objective optimization cost function is obtained based on direction, safety and speed. Finally, three weight coefficients of DWA cost function are automatically optimized based on the variable compass operator pigeon-inspired optimization and simulated by Matlab. The result shows that the DWA cost function with optimized parameters has better time response characteristic. Besides, the optimized parameters are also effective for the new map with random obstacles.
Design and Implementation of a Ground Control Station for Multiple Fixed-Wing UAVs

In recent years, Unmanned Aerial Vehicles (UAVs) have been used in many fields by virtue of their flexible design, low cost and zero casualties. With the deepening application of UAVs, the tasks they undertake are gradually becoming more complex and can no longer be accomplished by a single UAV alone, so multiple UAVs are needed to collaborate in order to perform the tasks. In this paper, a UAV ground control station software system is designed for the characteristics of multiple UAVs to perform tasks. The system is designed with the key requirement of reducing the operator’s workload in mind, while taking into account the differences in control and surveillance between multiple UAVs and a single UAV. After defining the functional requirements of the ground control station, the development and implementation of the ground station software is carried out, and finally, an application example of the ground station software is presented. The experiments show that the proposed ground station has some engineering value.
Multi-sensor Data Consistency and Fusion Based on Jensen-Shannon Divergence

In this paper, we propose a consistency determination and fusion algorithm based on Jensen-Shannon divergence of multi-sensor data. For less calculation, the Bootstrap strategy is used to sample the data obtained by each sensor multiple times, and the empirical distribution function is calculated for each set of the sampled data. Afterward, to determine the consistency of the data of multiple sensors, the Jensen-Shannon divergence between the empirical distribution functions of the sampled data from different sensors is calculated, and sequentially, for a more accuracy performance, a novel weighted fusion rule according to it is designed. Finally, via numerical examples with the position and attitude data interfered by Gaussian and non-Gaussian noises provided by measuring multiple inertial measurement units, the superiority of the algorithm proposed in this paper is verified. Simulation results show that the proposed algorithm is not affected by the types of noise and has a better fusion effect, meanwhile, it has the advantages of reducing the computational complexity, improving the fusion accuracy and increasing computational efficiency.
Relation Adaptation of the Grouped Multiagent Network in Task Allocation

In a large-scale multiagent system, agents are usually organized into groups by the network and participate in tasks in units of groups. However, according to the environment and task requirements, the original network relation is not necessarily optimal, and an adaptive evolution of this relation is needed. Therefore, a network relation adaptation method for grouped multiagent systems based on Q-learning is proposed in this paper. Each group decides whether to change neighbors and which neighbor to replace by evaluating local and global factors, such as task completion rate, task participation rate, and average revenue. With iterative learning, groups can converge at the optimal action strategy, and the optimal network structure in the current environment can also be formed. The simulation results show that compared with the network without relation adaptation, the method presented in this paper can effectively improve the task allocation success rate of grouped multiagent systems.
A Developmental Anti-collision Architecture for the Brain-Like Event-Related Potential Cycle in UAVs

IN order to solve the problems of UAVs’ anti-collision, a humanoid anti-collision method is proposed by referring to human’s behavior. First the overall introduction of humans’ development in the environment with multiple obstacles is presented, then a developmental anti-collision architecture for ERP cycle in humans is discussed. Regarding the ERP cycle, a brain-like ERP model for autonomous anti-collision of UAVs is proposed and the respective module is explained by constructing algorithms and sketch map. Finally, the simulation results show the efficiency and reliability of ERP method, which provides a mean for the human-like anti-collision for UAVs.
An Event-Based Angle Measurement Method

Complex scenes and fast movement of objects can lead to information redundancy and motion blur in camera images, causing low speed and failure of visual measurement algorithms. Event cameras are bio-inspired cameras that captures the change of light intensity, and has low delay and less motion blur. A new method to angle measurement for an inverted pendulum is proposed using event frames from event cameras. By using appropriate event frame reconstruction algorithm and angle measurement algorithm, the system can quickly complete the angle measurement of the pendulum rod, and the system remains effective when it moves fast.
Design of Airborne Distributed Service Component Platform Based on FACE

In order to solve the problems of DIMA applications’ difficulty in calling each other and poor reusability, this paper designs an airborne distributed service component platform based on FACE in VxWorks653 system by referring to the SOA architecture proposed by The Open Group. The interface of the platform is designed according to FACE technology standard to ensure the decoupling of software and hardware platform. At the same time, components are encapsulated as services through design interfaces to decouple software from operating system and improve the reusability of components. The communication middleware based on FC-DDS is developed on the platform to provide data-centered subscription and publishing communication for services. By designing corresponding system calls and partition service control center, the platform can support remote call and dynamic loading of components. This design builds a general basic platform for the airborne service components, supports the realization of service-oriented avionics system architecture, and lays a foundation for exploring the future open avionics system architecture.
Gaussian Adaptive Mutation Pigeon-Inspired Optimized Backstepping Controller for Aerial Manipulation Trajectory Tracking

For aerial manipulation, there are many challenging problems such as coupled force control and compensation of uncertain external disturbance. In this study, the backstepping controller utilizing a full coupled dynamic model is developed to control the Unmanned Aerial Vehicle (UAV) with a manipulator. In addition, the entire system is required to track the desired trajectory when the end-effector of the manipulator is disturbed by a lightweight object. Considering the precision requirement, the control problem is transformed into a controller parameter tuning problem and as well as makes full use of the heuristic optimization algorithm to search for a set of suitable parameters. Furthermore, in view of the difficulty of parameter tuning of the backstepping controller, a Gaussian Adaptive Mutation Pigeon-Inspired Optimization (GAMPIO) algorithm is proposed. Finally, a series of simulations, compared with other optimization algorithms, are conducted to demonstrate the GAMPIO’s superiority. The results verify that GAMPIO has faster convergence and better exploration capability to make end-effector performances better in trajectory tracking.
Weighting Strategy of Multi-camera System for Pose Measurement

In a multi-camera system, based on Binocular vision, choosing the best camera combination is one of the effective ways to improve measurement accuracy. When multi-cameras layout and intrinsic parameters are fixed, the angle and the distance from cameras to object are two main factors influencing the pose measurement result. From the perspective of object’s observability and image resolution, the mathematic relation between measurement error and the angle, distance from cameras to object is derived which forms the weighting formula whose high value corresponds to the best camera combination. Comprehensive simulation is conducted, proving that the proposed method can decrease the measurement error by 55% compared with former methods, and even by 94% in some occasions.
Research of Pedestrian Tracking Algorithm in Complex Environment Based on YOLOv5 and TransT

Aiming at the problem that difficulty of automatically identifying the pedestrian being followed in complex environment by eliminating interfering pedestrians, and reducing the impact of the tracking performance in the tracking process caused by pedestrian interference, deformation or frequent in-and-out, a pedestrian detection and tracking algorithm in complex environment based on YOLOv5 and TransT is proposed. Firstly, the pedestrian being followed is identified by YOLOv5 algorithm and the interference pedestrians is eliminated by multiple exclusion mechanism. Then, the template features of pedestrian being followed and the features of search region are extracted by TransT algorithm, both of them are fused by the method which can preserve semantic information and aggregate global information. Finally, the pedestrian being followed is tracked. Experimental results show that the proposed algorithm which has good tracking performance in the case of pedestrian interference, deformation and frequent in-and-out can automatically, rapidly and properly identify the pedestrian being followed in complex environments.
Using Plot of the Prose as The Model of Writing Narrative Text

The problems of writing narrative text are mostly faced by students in the early semester, as they think writing narrative text as the way how they write the general model such as writing letters, news or just telling a story. The Narrative text has a certain model to stimulate the feeling of the reader to be really absorbed in the story, that’s why the plot of the narrative text must be applied similarly to the plot in the prose model. This research was done due to seeing that many novice writers in universities in Indonesia especially the College of Foreign Languages located in Malang East java made a lot of mistakes and they were weak in applying the generic structure of the narrative text. The weaknesses of the students in learning writing narrative text were not triggered by the course itself yet by the material used in the class, the method of teaching, the student in learning, and how to reward to raise the enthusiasm. the application of action research through the planning to prepare the material, acting to apply to preserve the plot method, observing to see the students respond, enthusiasm and spirit in learning, and reflecting to see the overall action in the process of research. From the action research above, it was found that most of the students’ assignments and the tests could exceed the student score or mean score in a preliminary through the statistical computation and through the questionnaire. The students felt satisfied. It can be concluded that a prose plot can improve the student’s writing capability
The Source Code Maintenance Time Classifications from Code Smell

Software maintenance is necessary for the software development process after the software deployment. The maintenance estimation time is required for repair maintenance. Many metrics have been used to estimate the maintenance time. In this paper, a maintenance estimation method is proposed to explore the possibility to accurately estimate the maintenance time using the amount of code smells. In the experiment, the amount and types of code smells were extracted by using SonarQube and the maintenance time was extracted from Github’s issue trackers. As a preliminary study, two machine learning techniques, i.e., Support Vector Machines (SVM) and Gradient Boosting Classifier (GBC) were applied to discover the relationship between three types of code smells, i.e., Bloaters, Object Orientation Abusers, and Dispensable and the median of maintenance time over Apache Flink and Apache Hive projects. The results showed that SVM and GBC could achieve 61% and 65% accuracy in terms of measurement of test accuracy calculated based on test accuracy and recall respectively. The results were shown as promising as the experiment was carried out only with small dataset and did not take any parameters. Optimization into an account, where the maximum f1 achieved from the two projects was above 0.60%. Thus, there is a possibility for develop a model better performance.
A Smart Contract-Based Framework for Value Addition in Retail Market

Recent advancement of smart contract is considered as one of the greatest innovations of the society. Blockchain is applied to many sectors like banking, healthcare, retail etc. It is providing benefits for the different applications by offering high-end security. In today’s world, smart contract plays a very vital role in many commercial sections due to its immutability property. Smart contract is bringing real transformation, so it is expected that in future many industries will leverage the advantages of blockchain to make their technology more vibrant. In this research work, the authors try to introduce smart contract in retail sector. Product management and tracking is one of the major issues in the retail sector, in that case to make the transaction more rapid and reliable, the authors propose a smart contract-based solution in retail management. In this chapter, the authors suggest an Ethereum based retail commodity management and tracking. It identifies user and supplier and track node to node delivery status of product without the help of any third-party service. Moreover, the impact of smart contract in transaction in blockchain will bring revolution in retail sector. So, authors anticipate that smart contract improves the life span of retail industry. Though it enhances time and security management but to establish quality tracking in retail sector, we need a precise model. As we all know that during the pandemic when people relied on web-based transaction at that point of time block chain played a major role in peer-to-peer communication. It not only saves the history of product delivery but also boost consumer confidence.
Accounting for Crypto Currencies

Now-a-days crypto currencies are making headlines regularly for all sorts of negative news including large frauds by cyber criminals. All these have made crypto currencies even more controversial. There is, however, another aspect of crypto currencies which is no way less controversial than the crypto currencies themselves. This is accounting for crypto currencies. The present article is a humble attempt to highlight all such debatable issues associated with recognizing, measuring and disclosing crypto currency transactions with reference to existing national and international accounting regulations..
Augmented Reality-based Worker Assistance for People with Cognitive Disabilities

This paper and the accompanying demonstration video showcase an interactive counting aid implemented with PARTAS, our personalizable, Augmented Reality-based worker assistance system. PARTAS combines contour-based instructions with a pick-by-projection approach and is particularly designed to be adaptable for people with different cognitive disabilities.
Searchable Symmetric Encryption for Private Data Protection in Cloud Environments

Cloud computing arose as a suitable environment to store data with reliable and scalable remote access, but it also represents new challenges regarding data protection and privacy issues. A suitable approach to overcome these challenges is the usage of encryption, which provides the protection and privacy aspects of data. However, in general, encrypted data can not be used by the original systems, since usual functions, such as database queries and computations, need the original data. Additionally, it is very expensive to perform encryption and decryption of the database to search and get data, since these techniques have a high computational cost. In order to deal with this scenario, this paper presents a tool to protect private data in databases of cloud environments using a new approach of Searchable Symmetric Encryption (SSE), preventing problems of data leakage and privacy breaches, while enabling users to store data maintaining the functionality to search keywords in their encrypted database. Our new approach of SSE overcomes slow processing limitation, enabling a higher effectiveness when considering huge databases and dynamical data. Results from real experiments using cloud environments suggest that the proposed solution is suitable to perform the encryption.
A Novel Prediction Model for Cryptocurrency Trend Analysis Based on Time Series Data by Using Machine Learning Techniques

Bitcoin is a form of cryptocurrency that has come to be a famous inventory marketplace funding and it's been gradually growing in current years, and every now and then falling without warning, at the inventory marketplace. Because of its fluctuations, an automatic device for predicting bitcoin at the inventory marketplace is required. However, due to its volatility, traders will want a prediction device to assist them make funding selections in bitcoin or different cryptocurrencies. In this paper, Deep gaining knowledge of mechanisms like Recurrent Neural Network (RNN) and Long short-time period memory (LSTM) is proposed to broaden a version to forecast the bitcoin charge fashion withinside the marketplace. Finally, the predictions end result for the Bitcoin charge fashion are supplied over the subsequent 15, 30, and 60 days. Each version is evaluated in phrases of Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE) forecasting blunders values. The LSTM version is observed to be the higher mechanism for time-collection cryptocurrency charge prediction, however it takes longer to compile. The goal of this project is to show how a trained machine model can predict the price of a cryptocurrency if we give the right amount of data and computational power. It displays a graph with the predicted values. The most popular technology is the kind of technological solution that could help mankind predict future events. With vast amount of data being generated and recorded on a daily basis, we have finally come close to an era where predictions can be accurate and be generated based on concrete factual data. Furthermore, with the rise of the crypto digital era more heads have turned towards the digital market for investments. This gives us the opportunity to create a model capable of predicting crypto currencies primarily Bitcoin. This can be accomplished by using a series of machine learning techniques and methodologies.
Design and Research of 5 DOF Material Handling Robot

Robots have the characteristics of perception, decision-making and execution, and are widely used in manufacturing. In practical production, mobile robots are increasingly widely used as the main part of material handling robots. Based on these reasons, this paper designs the 5 DOF material handling robot. The robot is loaded with Cortex-M3 kernel, which is composed of patrol module, mobile module, multifunctional module, task setting and image recognition. The tracking differentiator and PID algorithm are used for the velocity planning and control adjustment to improve movement stability and control accuracy of the robot. The experimental results show that the robot can complete the functions of autonomous patrol, material clamping and handling, task acquisition, color recognition and information display. It has the characteristics of small size, material grabbing, high degree of automation and simple maintenance. Its diversified functions can meet the dangerous and heavy handling tasks in production, and its high automation level can improve production efficiency.
Path Planning for Service Robots

Path Planning is an essential aspect of the navigation of mobile service robots. The problem of path planning includes low time efficiency, large memory cost, local optimum, and slow speed in finding the global optimum. This paper reviews three main path planning algorithms and their extensions for the service robots. The first one is Ant Colony Optimization (ACO). The second algorithm is Particle Swarm Optimization (PSO) and some hybrid PSO-related algorithms. The third one is a conventional algorithm named Rapidly Exploring Random Tree (RRT).
Path Planning of Mobile Robot Based on Improved A* Algorithm

Path planning technology is the core part of the independent navigation of mobile robots. The problem of unscrupulous road planning and many turning point nodes need to be resolved. In response to the problem of traditional A* algorithm expansion nodes, long search time, and excessive path bending, an improvement A* algorithm that uses an inspiration function optimization and cubic Bezier curve optimization. Based on the ROS open-source system, the Gazebo physical simulation environment is built, and the practical Gmapping builds a grid map. The improved algorithm before and after the improvement is used as a global path planner plugin and applied to ROS for simulation experiments. The simulation results show that in the same simulation experimental environment, the length of the improved A* algorithm path is reduced by 17.161%, and the number of redundant turning nodes is reduced by 71.429%; More reasonable, further meet the constraints of mobile robots.