Reviewerly/exHarmony huggingface dataset:
Rationale for the Choice of a Positioning System for Mobile Agricultural Robot Movement Controlling

A correctly selected positioning system for controlling the mobile robotic means movement ensures high positioning accuracy of the robotic platform in the garden, allows to automate precise operations in the garden and systematize route planning algorithms. (Research purpose) To substantiate the rational choice of a positioning system for controlling the mobile robotic device movement. (Materials and methods) The author formulated requirements for the positioning system to perform precise operations in the garden: mechanized collection of fruits and berries, diff erentiated application of fertilizers and chemical plant protection products. The main ones were: the positioning error was no more than 5 centimetres, the stability of information transfer to the server for building traffi c maps, the movement of a robotic device along a given trajectory, equipping beacons with a mobile power source with a capacity of at least 800 milliampere-hour, information exchange between the beacon and the built-in robotic means with a microprocessor controller according to the RS-485 standard, the signal coverage area was at least 100 square meter. (Results and discussion) The six most relevant positioning systems of the following manufacturers were described: RealTrac, Rusoft CKT, Neomatic, ISBC, Avtosensor, Marvelmind. The author compared their technical and operational parameters: operating frequencies, range, data transfer interface, location accuracy and cost of ready-made kits. He showed that Marvelmind provided uninterrupted operation at frequencies of 433 and 915 megahertz with a positioning error of no more than 2 centimetres. The tests were carried out on a small robotic vehicle with the following characteristics: maximum transport speed – 30 kilometre per hour, operating weight – 500 kilograms, length 2 metres, width – 1.2 metres, height – 1.6 metres. (Conclusions) The author substantiated the choice of the most suitable and aff ordable Marvelmind positioning system and experimentally confi rmed the positioning accuracy declared by the manufacturer. When driving in a loop-free and looped turn, the positioning accuracy did not exceed 1.5 centimetres, which met the agrotechnical requirements for mechanized collection of fruits and berries, for diff erentiated application of fertilizers and chemical plant protection products
Adversarial Learning for Effective Detector Training via Synthetic Data

Current neural network-based algorithms for object detection require a huge amount of training data. Creation and annotation of specific datasets for real-life applications require significant human and time resources that are not always available. This issue substantially prevents the successful deployment of AI algorithms in industrial tasks. One possible solutions is a synthesis of train images by rendering 3D models of target objects, which allows effortless automatic annotation. However, direct use of synthetic training datasets does not usually result in an increase of the algorithms’ quality on test data due to differences in data domains. In this paper, we propose the adversarial architecture and training method for a CNN-based detector, which allows the effective use of synthesized images in case of a lack of labeled real-world data. The method was successfully tested on real data and applied for the development of unmanned aerial vehicle (UAV) detection and localization system.
Variance Reduction on Adaptive Stochastic Mirror Descent.

In this work, we study the idea of variance reduction applied to adaptive stochastic mirror descent algorithms in the nonsmooth nonconvex finite-sum optimization problems. We propose a simple yet generalized adaptive mirror descent algorithm with variance reduction named SVRAMD and provide its convergence analysis in different settings. We prove that variance reduction reduces the SFO complexity of most adaptive mirror descent algorithms and accelerates their convergence. In particular, our general theory implies that variance reduction can be applied to algorithms using time-varying step sizes and self-adaptive algorithms such as AdaGrad and RMSProp. Moreover, the convergence rates of SVRAMD recover the best existing rates of non-adaptive variance reduced mirror descent algorithms. We check the validity of our claims using experiments in deep learning.
Bridging Cost-sensitive and Neyman-Pearson Paradigms for Asymmetric Binary Classification

Asymmetric binary classification problems, in which the type I and II errors have unequal severity, are ubiquitous in real-world applications. To handle such asymmetry, researchers have developed the cost-sensitive and Neyman-Pearson paradigms for training classifiers to control the more severe type of classification error, say the type I error. The cost-sensitive paradigm is widely used and has straightforward implementations that do not require sample splitting; however, it demands an explicit specification of the costs of the type I and II errors, and an open question is what specification can guarantee a high-probability control on the population type I error. In contrast, the Neyman-Pearson paradigm can train classifiers to achieve a high-probability control of the population type I error, but it relies on sample splitting that reduces the effective training sample size. Since the two paradigms have complementary strengths, it is reasonable to combine their strengths for classifier construction. In this work, we for the first time study the methodological connections between the two paradigms, and we develop the TUBE-CS algorithm to bridge the two paradigms from the perspective of controlling the population type I error.
Non-Empirical Metrics for Ontology Visualizations Evaluation and Comparing

There are numerous ontology visualization systems, however, the choice of a visualization system is non-trivial, as there is no method for evaluation and comparing them, except for empirical experiments, that are subjective and costly. In this research, we aim to develop non- empirical metrics for ontology visualizations evaluation and comparing. First, we propose several half-formal metrics that require expert evaluation. These metrics are completeness, semanticity, and conservativeness. We apply the proposed metrics to evaluate and compare VOWL and Logic Graphs visualization systems. And second, we develop a com- pletely computable measure for the complexity of ontology visualizations, based on graph theory and information theory. In particular, ontology visualizations are considered as hypergraphs and the information mea- sure is derived from the Hartley function. The usage of the proposed information measure is exemplified by the evaluation of visualizations of the sample of axioms from the DoCO ontology in Logic Graphs and Graphol. These results can be practically applied for choosing ontology visualization systems in general and regarding a particular ontology.
Unity Starts with U: A Case Study of a Counter-Hate Campaign Through the Use of Social Media Platforms

Hate has been a growing concern with hate-groups and individuals using the Internet, or more specifically, social media platforms, to globalize hate. Since these social media platforms can connect users around the world, hate-organizations are using these connections as opportunities to recruit candidates and spread their propaganda. Without opposing views, these extreme viewpoints can establish themselves as legitimate and then be used to incite hate in individuals. Thus, these extreme viewpoints must be countered by similar messages to discourage this online hate, and one such way is to use the same platforms through grassroots movements. This paper presents a case study which was conducted on a class of Criminology students who implemented a grassroots community-based campaign called Unity Starts with U (USwithU) to counter-hate in a community by using social media platforms to spread messages of inclusion and share experiences. The results from the campaign showed improvements on people’s attitude towards hate at the local community level. Based on literature and this campaign, policy recommendations are suggested for policymakers to consider when creating or making improvements on counter-narrative programs.
Introduction and purpose of the tutorial series Python for Research in Psychology

In the era of advanced computers and state-of-the-art software, there have been great strides in the domains of research and engineering.These advancements have been made possible through the popularity, accessibility, and integration of computer programming.This has made automating tasks, analysing results through complex statistical analysis, and the development of more advanced artificial intelligence possible.However, in psychology, computer programming is still being underutilized.A major factor contributing to this is the steep learning curve for programming that is compounded by a lack of tutorials and knowledge specifically geared towards psychology.Thus, the purpose of this series is to bridge this gap and lower the learning barrier for researchers.This series invites anyone to send their own tutorials and contribute towards building a comprehensive guide to programming for psychological research in one of the most versatile languages: Python!For submission details, please see the guidelines below.
Learning experience evaluation of indonesia online tutoring mobile application using heuristic for E-learning

Ruang Guru has been Indonesia's most popular online tutoring platform in 2019. This platform is intended for primary to high school students in understanding school curriculums, as well as helping with exams. However, teens may have a low success rate because of their overconfident behavior. Their undeveloped skills require simple and relatable applications. This fact raises the question of whether teenage students are able to learn through this e-learning. This study aims to find user experience problems in this e-learning application that might impact student learning success. The evaluation of the learning experience of e-learning should not only be related to usability but also instructional design aspects. This study evaluates an online tutor mobile application using the heuristic evaluation method by referring to the usability and instructional design heuristics. The results found 28 problems. Some problems were beyond determined heuristics. The mean calculation of severity level indicates minor problems that have a low improvement priority.
A Novel SIFT-SVM Approach for Prostate Cancer Detection

Prostate cancer is a major cause of concern in male population as it is said to affect 1 in every 7 men in their lifetime.The number of cases being registered for prostate cancer and its mortality rates are increasing yearly at an alarming rate.Due to the high resolution and multidimensionality of the Magnetic Resonance Imaging (MRI) images, proper diagnostic system and tools are required.In this study, multiclass Support Vector Machines (SVM) classifier has been used, which is a well-known machine learning technique to classify the prostate images into 3 categories namely normal, benign and malign.This study has also made use of the Scale Invariant Feature Transform (SIFT) feature extraction method which is well known for its high rotation invariant nature.A SIFT-SVM approach has been introduced for the first time in prostate cancer detection.The performance of the system is computed in terms of sensitivity, specificity and accuracy.Our approach achieved high performance with an accuracy rate of about 99.95% when 40% of the training data was considered for obtaining our result.
AlexU-AUX-BERT at SemEval-2020 Task 3: Improving BERT Contextual Similarity Using Multiple Auxiliary Contexts

This paper describes the system we built for SemEval-2020 task 3.That is predicting the scores of similarity for a pair of words within two different contexts.Our system is based on both BERT embeddings and WordNet.We simply use cosine similarity to find the closest synset of the target words.Our results show that using this simple approach greatly improves the system behavior.Our model is ranked 3rd in subtask-2 for SemEval-2020 task 3.
A Recommendation System for Finding Experts in Online Scientific Communities

Online scientific communities are bases that publish books, journals, and scientific papers, and help promote knowledge. The researchers use search engines to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases due to search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. Online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. In this paper, using a dataset including bibliographic information of user’s publication, the publication venues, and other published papers provided as a way to find an expert in a particular context where experts are recommended to a user according to his records and preferences. In this way, a user request to find an expert is presented with keywords that represent a certain expertise and the system output will be a certain number of ranked suggestions for a specific user. Each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. In evaluation using IEEE database, the proposed method reached an accuracy of 71.50 percent that seems to be an acceptable result.
High-Dimensional Bayesian Optimization via Tree-Structured Additive Models

Bayesian Optimization (BO) has shown significant success in tackling expensive low-dimensional black-box optimization problems. Many optimization problems of interest are high-dimensional, and scaling BO to such settings remains an important challenge. In this paper, we consider generalized additive models in which low-dimensional functions with overlapping subsets of variables are composed to model a high-dimensional target function. Our goal is to lower the computational resources required and facilitate faster model learning by reducing the model complexity while retaining the sample-efficiency of existing methods. Specifically, we constrain the underlying dependency graphs to tree structures in order to facilitate both the structure learning and optimization of the acquisition function. For the former, we propose a hybrid graph learning algorithm based on Gibbs sampling and mutation. In addition, we propose a novel zooming-based algorithm that permits generalized additive models to be employed more efficiently in the case of continuous domains. We demonstrate and discuss the efficacy of our approach via a range of experiments on synthetic functions and real-world datasets.
Global Context Networks

The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies within an image, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by the non-local network are almost the same for different query positions. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further replace the one-layer transformation function of the non-local block by a two-layer bottleneck, which further reduces the parameter number considerably. The resulting network element, called the global context (GC) block, effectively models global context in a lightweight manner, allowing it to be applied at multiple layers of a backbone network to form a global context network (GCNet). Experiments show that GCNet generally outperforms NLNet on major benchmarks for various recognition tasks. The code and network configurations are available at https://github.com/xvjiarui/GCNet.
Breast Cancer Detection Using Supervised Machine Learning Algorithm

The most commonly causing cancer among Indian women is breast cancer and it effecting all over world with its impact.According to the medical reports of breast cancer patients in India were unable to hold the pain and about half of them are dying.In the proposed work used a machine learning algorithm to decrease the pre-processing time and to detection the symptoms and for better accuracy.The system is trained pre-processed image of fed to the system which are in the form of mammograms in common the X-ray of breast.The system which has the data segregated into the training and testing datasets analyses the input images based on the characters or the labels assigned to them done with the application of few of the algorithms which are present in the machine learning we compare the data or the image and probable output based on the character labels is obtained in the form of result.Compared to existing work and the proposed machine learning model as a serious of combination and permutations of algorithms lead to increase in the efficacy of the result and got the accuracy of 97.4% using random forests algorithm.
From Blackboard to Green Screen

We report on the conversion of two advanced database courses from their classical in-lecture-hall setup into an all-digital remote format that was delivered via YouTube. While the course contents were not turned on their heads, throughout the semester we adopted a video style that has been popularized by the live coding community. This new focus on the live interaction with the underlying database systems, led us (1) to adopt the idea of SQL probe queries that are specifically crafted to reveal database internals and (2) a study of database-supported computation that treats SQL like a true programming language. We are happy to share videos, slides, and code with anyone who is interested.
THE IMPROVEMENT OF NTRUENCRYPT PUBLIC KEY CRYPTOSYSTEM: DESIGN AND PERFORMANCE EVALUATION

Today cryptographic systems provide secure communication between users. In the present paper we describe existing cryptographic systems such as: systems based on the complexity of factorization of a large integer (RSA); systems based on the complexity of solving a discrete logarithm in finite Galois field (eigamal, DSA); systems based on the complexity of solving a discrete logarithm in a group of points of an elliptic curve (ECC); lattice-based systems (NTRU). Authors focus their attention on ntruencrypt encryption and decryption algorithm. The ntruencrypt public key cryptosystem guarantees the integrity and confidentiality of information when transmitting, storing and processing information messages in modern computer systems and networks. The conducted studies of public key cryptosystem made it possible to determine the way of the improve ntruencrypt public key cryptosystem. In this paper, we present improved ntruencrypt public key cryptosystem which is based on the correct selection of parameters p and q. The authors concluded that, to reduce the difference between the length of ciphertext and plaintext, it is necessary to take p and q closer to each other. At the same time it is necessary to consider that at too close values p and q the cryptosystem can be weakened. The main difference between the proposed schemes was the reducing the size of ciphertext which can minimizes the time for software encryption and decryption operations. As a result is a software implementation of the procedure for the encryption and decryption of the improve ntruencrypt public key cryptosystem using a programming language Ruby 1.9.3 was obtained using the cryptolib library. Improved algorithm will be a perfect tool for ensuring the confidentiality of information, using “cloud” computing, because protecting information from unauthorized access is one of the most pressing problems. The authors further plan a number of scientific and technical solutions to develop and implement effective methods, tools to meet the requirements, principles and approaches to cyber security and cryptosystems for provide integrity and confidentiality of information in experimental computer systems and networks.
Design and Evaluation of a Mobile Application for an Educational Card Game

Educational games can provide players with rich learning and socializing experiences through different interac-tion paradigms, such as board games, card games, and, more recently, hybrid (physical-digital) games. However, the process of making, maintaining, and evolving an educational analog game is not a trivial task. Balancing the game mechanics and dynamics to provide a pleasant and educational gaming experience can be very difficult to achieve. Furthermore, adding a digital component in the gameplay can disturb the experience of the game and learning ob-jectives, and this kind of insertion should be evaluated. This work then aims to report the process of developing and evaluating a mobile application for helping the gameplay of a card game that focuses on on teaching software testing concepts. Our primary concern during the development of the application was whether its insertion would compromise the learning process or the social experience of the card game. The developed application has the fol-lowing functionalities: point counter, dice scrolling, timer, and a summary of the rules. We designed and developed a first version of the application. Then, we evaluated the impact of its insertion on gameplay by applying the game assisted with the application with students from Computer Science and Computer Engineering courses and, after the end of the game, the participants answered questionnaires about the players’ experience and their impressions about the application. Based on the results, we perceived that the use of the application provided benefits to the players’ experience, although the evaluation highlighted some opportunities for improvement. Thus, we evolve the mobile application based on the comments gathered in this evaluation. This new version has improvements on user’s interface, aiming to provide a better user experience, and new functionalities. Furthermore, we assessed the second version and compared both versions of the mobile app in order to collect evidence regarding improvements in the game experience.
Motives and Values of Students - Future Teachers

The study aims to determine correlation between a person’s value system and motives for choosing the teaching profession. The article provides theoretical rationale and analysis of scientific papers on the issue of correlation between motives and values in choosing the teaching profession, presents results of an empirical study of the motivational-value sphere of teacher education students’ personalities. Scientific novelty of the study lies in identifying specific features of the personality structure of young people who have chosen the teaching profession. As a result of the research, it is proved that there is a high degree of correlation between motives for choosing the teaching profession and a person’s values in orientation towards interaction with people and orientation towards self.