Reviewerly/exHarmony huggingface dataset:
Design and implementation of UAV delivery system based on combined BeiDou/visual navigation

With the development of UAV software and hardware, the application of UAVs in the field of material delivery has become more and more extensive. However, the problem of precise positioning of UAVs is the main bottleneck limiting UAV material delivery, and it is urgent to study a reliable navigation and positioning scheme to lay the foundation for the realization of UAV material delivery. In this paper, we propose the use of combined BeiDou/visual navigation to realize UAV autonomous mount and fixed-point material delivery, and study its system model and realizability. Based on the basic mechanical structure of the quadrotor UAV, the system is designed and equipped with BeiDou navigation module, image transmission module, mechanical actuator and other systems to initially realize the task of autonomous UAV mounting and fixed-point material delivery. The UAV system design is improved and the UAV flight coefficients are revised through feedback from several actual flight results to achieve the predetermined success rate. The research results show that the combined Beidou/visual navigation has a very high construction for UAV autonomous mount and fixed-point delivery, and the use of combined Beidou/visual navigation to realize UAV autonomous mount and fixed-point delivery of materials has a broad application prospect.
Regret Minimization of Extensive Games and Its Application on Game Strategies

Game theory has been discussed by people from generation to generation. The concept of game theory has been applied to various areas. Extensive games, as a typical form of games, are able to simulate the circumstances in many other areas. Recently, more and more attention has been paid to finding a Nash equilibrium in large extensive games. In this paper, we describe a method to solve extensive games, the basis of this method is regret minimization. We then use a concrete case to demonstrate how this technique can be used to make decisions by players in the game of Hearthstone. We calculate the overall regret of all the strategies that the player can take in a given circumstance, and then choose the strategy with the least overall regret. We demonstrate through the case that by minimizing regret in extensive game models, players are able to optimize their strategies and increase the chances to win.
The study of Simulation Intelligent Analysis System of UAV Control On the basis of Computer Big Data Technology

With the advent of the era of computer big data, the application of big data is increasing. Computer big data technology is suitable for the analysis of UAV simulation intelligent system, which can clarify its practicability. After the intelligent analysis system is put into use, the accuracy of UAV control is improved. The UAV flight control system is the core part of UAV, and the performance of UAV depends on the design of its flight control system to a great degree. In this paper, the intelligent analysis system of UAV control simulation is studied. The accuracy of massive data set algorithm with adaptive selection and adjustment strategy and the complex causal relationship between data in the intelligent analysis system of UAV control simulation under computer big data technology are studied. The research results show that the global optimization of the whole process can be ensured by combining the structural characteristics and algorithms of data-to-data association algorithm, and the data is more than 58.96%. Therefore, the intelligent analysis system of airborne software simulation, which provides guarantee for the research and development of UAV control through testing, has a certain market application space and prospect.
Design of Path Planning Robot Based on Machine Vision

We established a path planning mobile robot system based on machine vision. The robot has multiple sensors to scan for obstacles. After catching the images, the robot will greyscale, process the images and find the most efficient path based on the ant colony algorithm. Finally, the robot will reach the destination with the corporation of motors and other components under the control of the motherboard. Simulations prove that the path planning mobile robot can find the most efficient way with censors and programs, and it can also reach its destination with the cooperation of the moving components.
A Novel Approach for Handling Imbalanced Data in Breast Cancer Dataset

Breast cancer is a deadliest disease which is the second most leading cause of death in the world. There are 5 types of commonly occurring cancers which include Ductal carcinoma in situ (DCIS), Invasive ductal carcinoma (IDC), Tubular carcinoma, Medullary carcinoma, and Invasive lobular carcinoma (ILC). Early detection of breast cancer creates 93% or more chances of survival. So in order to detect breast cancer, one of the important morphological feature is mitotic count—showing the rate of division of cell. In a dataset, if one class have less number of examples compared to number of examples in the other class, then the dataset is imbalanced. Since there is much abundance of non-mitotic cells over mitotic cells, the dataset has class imbalance problem. This class skewness leads to inefficiency in classification task. At present there are many models which uses data sampling methods in order to decrease the class imbalance problem. But if there is a feature set that provides the exact decision boundary, then there is no need of sampling methods for solving the class imbalance issue. Therefore in this work a model is proposed which uses autoencoders to learn a feature set which has better classification capabilities of majority class and minority class to address the class imbalance problem. In this paper, Dual stacked autoencoder is applied on imbalanced mitotic and non-mitotic dataset, which showed improvement in f-measure and also reduces the overhead of smapling the dataset. The proposed dual stack autoencoder shown f-measure of 0.82, and outperformed many of the traditional classification methods.
A <i>Labeled</i> Architecture for Low-Entropy Clouds: Theory, Practice, and Lessons

Resource efficiency and quality of service (QoS) are both long-pursuit goals for cloud providers over the last decade. However, hardly any cloud platform can exactly achieve them perfectly even until today. Improving resource efficiency or resource utilization often could cause complicated resource contention between colocated cloud applications on different resources, spanning from the underlying hardware to the software stack, leading to unexpected performance degradation. The low-entropy cloud proposes a new software-hardware codesigned technology stack to holistically curb performance interference from the bottom up and obtain both high resource efficiency and high quality of application performance. In this paper, we introduce a new computer architecture for the low-entropy cloud stack, called labeled von Neumann architecture (LvNA), which incorporates a set of label-powered control mechanisms to enable shared components and resources on chip to differentiate, isolate, and prioritize user-defined application requests when competing for hardware resource. With the power of these mechanisms, LvNA was able to protect the performance of certain applications, such as latency-critical applications, from disorderly resource contention while improving resource utilization. We further build and tapeout Beihai, a 1.2 GHz 8-core RISC-V processor based on the LvNA architecture. The evaluation results show that Beihai could drastically reduce the performance degradation caused by memory bandwidth contention from 82.8% to 0.4%. When improving the CPU utilization over 70%, Beihai could reduce the 99th tail latency of Redis from 115 ms to 18.1 ms. Furthermore, Beihai can realize hardware virtualization, which boots up two unmodified virtual machines concurrently without the intervention of any software hypervisor.
A Deep Learning-Based Approach with Semi-supervised Level Set Loss for Infant Brain MRI Segmentation

Nowadays, numerous deep learning techniques for segmenting medical images have been proposed, with excellent outcomes and the valuable success of machine learning. However, most models use supervised methods while others use unsupervised methods, with less effective outcomes than supervised learning methods. Therefore, this paper introduces a novel level set loss function for unsupervised tasks and incorporates it with the Active Contour loss for supervised tasks. Besides, since previously introduced deep learning models generate less accurate results due to the intensity inhomogeneity issues and the often presence of low-intensity contrast tissues in infant brain segmentation, we propose a new convolutional neural network model to alleviate this problem. Instead of binary segmentation, our proposed loss entitles our model to segment multiple classes with promising outcomes. The proposed technique is utilized to segment neonatal brain magnetic resonance images into four non-overlap regions. The iSeg-2017 challenge, which offers a collection of neonatal brain magnetic resonance images from different sites, is used to evaluate our proposed process. The experiment demonstrates that our new loss function achieves promising results among the 21 participating teams. This illustrates the effectiveness of our technique in multiclass medical image segmentation.
TECHNOLOGY OF THE DEVELOPMENT OF LYCEUM TEACHERS’ PEDAGOGICAL CULTURE IN THE SYSTEM OF METHODICAL WORK

The article considers the technology of development of professional culture of lyceum teachers in the system of methodical work. It is substantiated that the creation of a united methodological platform “Pedagogical culture of lyceum teachers” on the basis of the city scientific and methodological centre allows building a clear strategy for professional development of specialized schools teachers, organizing continuous monitoring of the process and objective evaluation of its effectiveness. It is noted that important elements of the proposed technology are permanent monitoring and feedback. Monitoring is carried out by surveys at the initial and final stages. Feedback is provided by organizing a discussion after each event, as well as discussions on social networks and messengers. The program of pedagogical culture development covers all school year, the subject is multifaceted and actual, all teachers of city lyceums are involved in it that allows to make development of pedagogical culture systematic, constant, comprehensive and general. The proposed technology provides systematic acquaintance of lyceum teachers with advanced educational experience. The proposed technology (creation of the “Pedagogical culture of lyceum teachers” platform) provides the implementation of pedagogical conditions for the development of pedagogical culture of lyceum teachers and is characterized by such features as systemic (maximum consideration of various aspects of pedagogical culture of teachers), consistency and regularity (it is extended for the next year, events take place weekly) and generality (total coverage of teachers of the city’s lyceums). The proposed technology allows overcoming the differences between the organization of methodological activities aimed at improving the professional culture of teachers in different institutions of specialized education in the city
Detection of Copy–Move Image Forgery Applying Robust Matching with K-D Tree Sorting

Digital images contribute significantly to the field of visualization. Using stronger technology, digital image forgery is easier. The most common method of image forgery is to re-create a portion of a person’s location or to conceal a portion of an image. In our paper, we worked on detecting region duplication forgery using COMOFORD databases by utilizing the discrete cosine transform (DCT), k-dimensional tree (k-d tree) for sorting efficiently, and a robust matching method. Here, the size of the block will be 16 × 16, and it will be divided into four blocks. This study can detect forged portions for PNG images with better performance by highlighting the images with a quality factor of 0.5 and a threshold value of 10, as well as gives good results for JPEG images.
STRUCTURAL EQUATION MODELLING (SEM) PENGGUNAAN SEM – GSCA DAN APLIKASI GSCA – PRO UNTUK MAHASISWA DAN DOSEN

The Tri Dharma of Higher Education requires lecturers to carry out research, teaching and community service. Therefore as a form of lecturer's moral responsibility to the community to improve knowledge through formal and non-formal education. Our team of lecturers will carry out community service in the form of making training on the internet in the form of webminars that are very suitable to reach audiences during this pandemic. The coaching material that will be delivered by the lecturer team includes the use of SEM-GSCA and the GSCA-PRO application for research for students and lecturers (S1, S2, S3). The purpose of this training is to introduce SEM (Structural Equation Modeling) Applications Generalized Structure Component Analyst and how to use and process data through the GSCA-PRO application. So far, students and lecturers are still patterned with a regression analysis, by studying SEM students and lecturers will have the freedom in making the research model. The benefit that can be taken from training on the use of SEM-GSCA and the GSCA-PRO Application for research for students and lecturers is that there are other options offered to students and lecturers in terms of doing research.
Accelerate minimum cut calculation by tree-cut mapping with local pruning

As a fundamental problem in graph theory, the minimum cut (min-cut) problem and solutions have many applications in networking related researches. A new approximation algorithm for the problem in undirected graph is proposed that can accelerate existing method by up to 6 orders of magnitude, while the preprocessing overhead is just a limited number of depth-first traversing. In the algorithm, after checking and recording the cut values of various types of traversal trees, the good upper bound on the min-cut value between any node pair can be approximated using the minimal cut value of all related traversal trees, and the upper bound is very likely to be the exact value. The independence between different traversal passes makes it ready for further acceleration using parallelization. The traversal tree-cut mapping algorithm with local pruning (TCLP) is compared with other methods using random, scale-free and real-world graphs, the results show that even the serial implementation of TCLP can achieve far larger acceleration ratio than existing methods while obtaining exact min-cut value of 99.9% node pairs. The results also indicate that TCLP is general enough to not rely on any special local graph structure and is seldom affected by graph types, thus can be used as an effective supplement to the existing methods.
An Augmented Reality Solution for the Positive Behaviour Intervention and Support

Abstract The spread of Augmented Reality (AR) and the recent technological developments, provide innovative techniques and tools that show a growing potential in education. One of the pilots of the European Horizon 2020 project ARETE (Augmented Reality Interactive Educational System) aims to investigate and evaluate for the first time the introduction of an AR solution to support a behavioral lesson in schools where the Positive Behaviour Intervention and Support (PBIS) methodology is adopted. Specifically in this paper, we describe the architectural design and implementation of a PBIS-AR application as a component of the ARETE ecosystem. It describes the functionality of the system and the teaching process that the AR solution will support.
Sentiment Analysis of Tweets on Telangana State Government Flagship Schemes

Over the last decade, the usage of social media has evolved to a greater extent. Today, social media platforms like Twitter, facebook, snapchat are vastly used to incept the opinions of public about a particular entity. Social media has become a great source of text data. Text analytics plays a crucial role on social media data to give answers to a wide variety of questions about public feedback on many issues or topics. The primary objective of this work is to analyse the public opinion or sentiment in social media on Telangana state government welfare schemes. The purpose of sentiment analysis is to find opinions from tweets and extract sentiments from them and find their polarity, i.e., positive, neutral or negative. Here we are using twitter as it has gained much popularity and media attention. The first step is to extract the tweets on particular schemes through Twitter API and Python language followed by cleaning and pre- processing steps of the raw tweets. Then tfidf vectorizer was invoked for feature extraction and creation of bag of words and finally sentiment polarity scores were obtained by using VADER (Valence Aware Dictionary and sentiment Reasoner), lexicon and rule-based sentiment analysis tool.
Implementation of Voter by using Artificial Bee Colony Algorithm for Fault Tolerant System

In a fault tolerant system, voting algorithm plays a major role in decision making. Inexact majority voting, majority voting, and weighted voters are most popular voting algorithms. In each of these techniques, redundant modules produce unpredictable outputs, and finding an agreement among these outputs is the functionality of voter. In this proposed system, voter is implemented using soft computing technique called ABC algorithm. The basic ABC algorithm is a very modest, adaptable and clear model. There are two categories of searchers in the hive of ABC; namely, unemployed searchers and employed searchers. Based on the intelligent performance of honey bee swarm, ABC is one of the optimization algorithms. The behaviours of bees are used to optimize weights of Triple Modular Redundant system, where each module plays an imperative role in the selection of voter. For evaluating the performance of voting algorithms, some performance measures have been defined; they are, safety and availability measures. ABC algorithm has been proposed to improve the safety and availability of the existing system.
The Fellowship of the Authors: Disambiguating Names from Social Network Context

Most NLP approaches to entity linking and coreference resolution focus on retrieving similar mentions using sparse or dense text representations. The common "Wikification" task, for instance, retrieves candidate Wikipedia articles for each entity mention. For many domains, such as bibliographic citations, authority lists with extensive textual descriptions for each entity are lacking and ambiguous named entities mostly occur in the context of other named entities. Unlike prior work, therefore, we seek to leverage the information that can be gained from looking at association networks of individuals derived from textual evidence in order to disambiguate names. We combine BERT-based mention representations with a variety of graph induction strategies and experiment with supervised and unsupervised cluster inference methods. We experiment with data consisting of lists of names from two domains: bibliographic citations from CrossRef and chains of transmission (isnads) from classical Arabic histories. We find that in-domain language model pretraining can significantly improve mention representations, especially for larger corpora, and that the availability of bibliographic information, such as publication venue or title, can also increase performance on this task. We also present a novel supervised cluster inference model which gives competitive performance for little computational effort, making it ideal for situations where individuals must be identified without relying on an exhaustive authority list.
Penggunaan Teknik Boundary Value Analysis untuk Pengujian Aplikasi Stok Barang

The application of data collection of goods in a store has many uses in running a sales business. By using a data collection application, errors will be minimized and management is better and controlled. In recording goods, business owners can monitor what items are needed when the availability of goods is running low. To minimize errors that occur testing the application is very necessary. This application will be tested in a GUI to check whether the GUI in the application has an error that causes the application to crash. For example, the price on the goods is listed with other characters that are difficult to remove, then before the test occurs, it must be done. Of the several testing methods, Black Box testing is the most commonly used. By using the Boundary Value Analysis method that is suitable for the application of the availability of goods. The method tests the maximum and minimum digits to produce valid values and is easy enough to test an application. The result of the analysis of the method used is the quality of the application that is in accordance with the function, and can be used properly by the user.
Investigating Hybridity in “AMERICANAH” by Chimamanda Ngozi Adichie

Mixing of cultures is common in the contemporary world and this phenomenon causes cultural, racial, and linguistic hybridity. Hybridity affects human beings. Migrants confront abusive behavior due to cultural hybridity. Hence, the concept of cultural hybridity is explored in this article. This study analyzes the novel "Americanah" by Chimamanda Ngozi Adichie, through the lens of the ‘Theory of Hybridity’ presented by Homi. K. Bhabha. The analyzed novel is about an Afro-American, Ifemelu, who feels hesitant to adopt both cultures. The current study displays Ifemelu’s dilemma of choosing between her friends or lover in America. It is a descriptive and text-based study and makes an attempt to answer how cultural hybridity affects the protagonist’s life. The conclusion sums up that Ifemelu’s character suffers from identity crisis and marginalization due to cultural hybridity. The study verifies the negative effects of cultural hybridity that are the core assertion of the theory of Bhabha.&#x0D; Keywords: Hybridity, mimicry, third world, Post-colonial, ambivalence, Identity crisis, Homi Bhaba
HARNESSING POTENTIALS OF SOLAR RADIATION IN LIBERIA USING ARTIFICIAL NEURAL NETWORK

The current state of energy supply in Liberia is a combination of fossil fuel and hydroelectric power generation and the cost of generating, maintaining, and distributing energy is high. On the other hand, Liberia lies within a suitable zone for solar energy utilisation for photovoltaic applications, as its climate is relatively hot all year round. This paper investigates the use of the artificial neural network to model the reliability of solar radiation in a study area in Liberia, as a necessary prerequisite for alternative power generation. Seven variables (longitude, latitude, elevation, average temperature, precipitation, wind speed and relative humidity) were used as input data (causal variables) and one parameter/factor (solar radiation) was used as output (response variable) for 2000-2018. The obtained results showed that the employed model explains all the variabilities of the response data around the mean with an overall regression value of 0.93. It was found through visualised maps that the study area is in a suitable spot for the utilisation of solar energy potentials.