Reviewerly/exHarmony huggingface dataset:
Focusing on Both Teaching Agile Software Development and Communication

The field of Software engineering haves several roles, one of their important roles is communication. It has been found that communication helps with a better understanding of the product the stakeholder needs to be developed. If software engineers can understand and be able to express their understanding to the stakeholders or to their team half of the problems would be solved. This would lead to better utilization of resources. Thus, for educating students for the future, this study focuses on teaching agile development to students with an emphasis on communication. This study is ongoing, and the presented research includes one year of data. The same process is repeated this year.
LL-PMS8: A time efficient approach to solve planted motif search problem

Among other motif finding algorithms Planted Motif Search (PMS) or (ℓ, d) motif search algorithms are considered as next-generation motif finding algorithms. Here, n strings and two integer ℓ and d are provided as input in PMS. It gives outputs of all sequences of length M in each input string, where each occurrence varies from Min at most d points. This paper proposes a new algorithm, for the (ℓ, d) motif discovery problem in which we find all strings of length ℓ that seems in every string of a provided set of strings with at most d mismatches has been. After proper investigation of the PMS problem, it has been shown that this is an NP-hard exact algorithm. Usually, in the worst-case scenarios, all the known exact algorithms for PMS need exponential time in some of the underlying parameters. In this paper, we proposed a faster approach that reduces the searching time in the sample-driven part of the algorithm. In particular, we used dynamic programming techniques to eliminate the recalculation of the values of some common subtree and introduced some new speedup techniques like using a linked list, reduced ℓ-mers for making the algorithm faster. Due to the use of Linked List as a main speedup technique, we named our proposed algorithm as Linked List Implementation technique of PMS8 (LL-PMS8).
Trajectory planning for unmanned surface vehicles operating under wave-induced motion uncertainty in dynamic environments

We present a deliberative trajectory planning method to avoid collisions with traffic vessels. It also plans traversal across wavefields generated by these vessels and minimizes the risk of failure. Our method searches over a state-space consisting of pose and time. And, it produces collision-free and minimum-risk trajectory. It uses a lookup table to account for motion uncertainty and failure risk. We also present speed-up techniques to increase performance. Our wave-aware planner produces plans that (1) have shorter execution times and safer when compared to previously developed reactive planning schemes and (2) comply with user-defined wave-traversal constraints and Collision Regulations (COLREGs)
Extracurricular Activity Of Students In The Aspect Of The University Idea

The ways of solving the problem for organizing activities of students are considered in the article. It clarifies the role and place of this activity in formation of intellectual culture and socialization of students in the aspect of the idea of the university. Attention is focused on the fact that students most fully develop the qualities of a free, capable of critical thinking and responsible action of the personality mainly in activities. Events outside the school time to a greater extent allow students to express themselves as a person in social, scientific, cultural, sports University life. The article defines the concept of extracurricular activities of describes the main types of activities of students, analyzes the roles of the main subjects of activities: university teachers and students, identifies the principles of organization of activities of students at the University. It is shown that activities influence directly formation of intellectual culture and socialization of students which contributes to the generation of the country's intellectual potential. It is the definite expression of the University idea in the context of organization of activities for students at Togliatti State University. Using a number of examples illustrating productivity of activities of students in formation of their intellectual culture and socialization, it is concluded that this type of activity is an integral part of university education and upbringing, self-education, research and development contributing to the formation of the intellectual potential of society.
Sparse ℓ<sub>1</sub>- and ℓ<sub>2</sub>-Center Classifiers

In this article, we discuss two novel sparse versions of the classical nearest-centroid classifier. The proposed sparse classifiers are based on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{2}$ </tex-math></inline-formula> distance criteria, respectively, and perform simultaneous feature selection and classification, by detecting the features that are most relevant for the classification purpose. We formally prove that the training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) at a linear computational cost. Especially, the proposed sparse classifiers are trained in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(mn)+O(m\log k)$ </tex-math></inline-formula> operations, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> is the number of samples, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> is the total number of features, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k \leq m$ </tex-math></inline-formula> is the number of features to be retained in the classifier. Furthermore, the complexity of testing and classifying a new sample is simply <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(k)$ </tex-math></inline-formula> for both methods. The proposed models can be employed either as stand-alone sparse classifiers or fast feature-selection techniques for prefiltering the features to be later fed to other types of classifiers (e.g., SVMs). The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection and classification techniques while having a substantially lower computational cost.
A 4D Augmented Flight Management System Based on Flight Planning and Trajectory Generation Merging

In recent decades, the helicopter has become a safe and suitable solution for future traffic, both in congested megacities and in isolated places as well as in mountainous environment where the terrain can be dangerous. Adding the third dimension to multimodal urban transport networks will improve the way we live. In order to solve the challenge of navigation towards more autonomy, it is necessary, during mission planning and flight execution, to generate effective, accurate and feasible paths. However, the major works in the literature do not address the effectiveness - flyability and controllability - of the generated trajectories for helicopters. This paper proposes an approach with continuous-curvature trajectory generation into inclined planes and proves the controllability by a simulated autopilot system designed for an Airbus Helicopters model in Matlab Simulink environment. We found that this type of trajectories is effective and controllable by an autopilot with only 3% of deviation error. The results are promising and the approach will be integrated in our works on a 4-dimensional flight management system for helicopters.
Machine Learning for Web Content Classification

Categorization of web sites is an important problem and has many practical applications. One such application is parental control for safe internet for children. Failure to classify websites by specific rules makes it difficult to access information, as well as leaving many users of different age groups with the harmful side of the Internet. Current secure internet solutions are not comprehensive or cannot be customized. Furthermore, the fact that the blocking orders issued by the courts do not cover all harmful sites and these websites change their domains so often. Thus, dynamic classification of websites using the text data is very important. In this study, using natural language processing and machine learning techniques websites are classified. Content of web sites from various languages are collected and preprocessed before applying machine learning techniques. In the study, 17 classes were used, the highest classification success was 0.8756 and this result was reached by the SVM method.
Kinematics Reconstruction of Static Calligraphic Traces from Curvilinear Shape Features

Our goal is to be able to reproduce computationally calligraphic traces, such as found in the art practices of graffiti and various forms of more traditional calligraphy, while mimicking their production process. To this end, we propose a method that allows to reconstruct kinematics solely from the geometric samples of handwritten traces in the form of parameters of the Sigma-Lognormal model. We ignore the kinematics possibly embedded in the data in order to treat online data and vector patterns with the same procedure.At the heart of our method, we develop a robust procedure to identify curvilinear shape features based on an analysis of local symmetry axes. These features determine the segmentation of a trace into circular arcs and guide an iterative reconstruction of the input kinematics and geometry in the form of Sigma-Lognormal parameters. We demonstrate how this parametrisation can be used to generate plausible kinematics for a static input trace, and how parameter variations can be exploited to generate traces that resemble the ones seen in real instances of human made calligraphy and graffiti.
Comparative Study of 16 Clear-Sky Radiative Transfer Models to Estimate Direct Normal Irradiance (DNI) in Botucatu, Brazil

Abstract The interest of the direct normal irradiation (DNI) estimation is important in the evaluation of the solar potential and, consequently, for data correction and expansion of the historical series. In this study, a review of the performance of 16 models of radiative transfer was performed. These models are used to estimate DNI on a clear day in Botucatu/SP region located in Brazil. The revised models are categorized into two classes: simple models (11 models: ASH, MAJ, ALLEN, GH, P1, HLJ, FU, KU, H1, IP, and INC) and complex models (five models: BIRD, IQ, MRM5, P2, and YANG). The evaluation methodology used here is consistent with the literature. The input parameters were estimated and a statistical analysis using relative-mean-bias-error (rMBE), root-mean-square-error (rRMSE), and mean absolute percentage error (MAPE) indicators were performed to validate those models. The results indicate that the complex models (that require more atmospheric inputs) generally performed better than simpler models. Despite the consistent limitations in the use of estimated parameters, the performance of the models can be considered satisfactory. The best performances are highlighted for models MRM5 and YANG. Simple models ASH and IP performed similar to complex models. These results were confirmed using frequency distribution and the cumulative frequency analysis. These results are important for engineers of solar systems to use the best model and select the most suitable locations for installing a small or large solar plant.
"Word cloud" as a graphical supplement to a scientific article

An ever-greater number of journals now not only publish text content but also try to provide graphical abstracts for manuscripts. A graphical abstract is a graphical presentation of the main conclusions of the paper. The figure can be taken from the paper or specially designed. At the same time, it should represent the content of the paper as much as possible and be understandable for readers. However, it is not always possible to create a graphical abstract, for example, for review and theoretical articles in the field of social and economic sciences. The editors of the Economic Consultant journal suggested publishing a “word cloud” as a graphical supplement to a scientific paper. Since 2019, the “word cloud” has been published for each paper. A word is a visualization of word frequency in a text. The font size of each word in the cloud is determined by its prevalence in the text. The word helps readers to understand what basic keywords and terms the author uses directly in the text of the paper. To present this visualization, the editors decided to use the service https://wordscloud.pythonanywhere.com/. The layout designer can process the paper, using only its text (without the title, abstract, and references). From 2020, the editorial board decided to publish such graphical supplements not only in pdf articles but also on the pages of journal articles. However, we understand that graphical supplements cannot substitute for graphical abstracts, and the development of the latter seems to us to be a promising trend in the development of the Economic Consultant journal.
Obtaining Data from the Internet: A Guide to Data Crawling in Management Research

The increasing availability of data on the Internet opens new opportunities for management research and the method of data crawling can be used for automated large-scale data extraction. We show that data crawling has quickly gained popularity and is used for a wide variety of purposes, but has so far gained less traction in the field of management. We argue that we could use many data sets used in other disciplines for answering questions in management research and show that setting up a data crawler does not require advanced programming skills. However, a lot of pitfalls can challenge the success of using crawled data for research. We develop a guideline for crawling projects and address how many of the regularly occurring challenges can be addressed.
Satellite Imagery Noising With Generative Adversarial Networks

Using satellite imagery and remote sensing data for supervised and self-supervised learning problems can be quite challenging when parts of the underlying datasets are missing due to natural phenomena (clouds, fog, haze, mist, etc.). Solving this problem will improve remote sensing data augmentation and make use of it in a world where satellite imagery represents a great resource to exploit in any big data pipeline setup. In this paper, the authors present a generative adversarial network (GANs) model that can generate natural atmospheric noise that serves as a data augmentation preprocessing tool to produce input to supervised machine learning algorithms.
Reflexiones en torno a los colegios de bajo costo en el Perú

The privatization of education in Peru has brought with&nbsp;it the proliferation of low cost private schools. In our&nbsp;country, these types of schools are characterized by their&nbsp;low educational quality and their high segregation rate. In&nbsp;the present work, we propose a set of recommendations&nbsp;necessary to be implemented to improve the regulation and&nbsp;supervision of the low-cost educational service in Peru.
Depth-based Human Activity Recognition: vINCI Case Study

The growing aging of the world's population is leading to the need to take assistance measures and prepare health care systems for the elderly. The innovative vINCI system provides technologies and uses smart devices that can noninvasively monitor the activity of elderly, to intervene in case of alerts, to prevent possible health problems, such as falling, in the same time to keep their life independent and to improve their quality of life. Monitoring physical activity of the elderly with the help of smart cameras is important in identifying one of the most important lifestyle risk factors for many chronic conditions in the older age. In this paper there are presented the microservice-based vINCI architecture and how an Orbbec Persee camera is used to monitor the physical activity as well as to recognize the elderly. The advantages of the monitoring physical activity application consist in detecting a low level of activity or detecting health problems allowing intervention and correction of an unhealthy lifestyle.
An Object Oriented Programming on Encrypted Database System (CryptDB)

Cloud computing, share computing resources rather than having local server or personal devices to handle applications. Some of these share resources is the Database (DB), Popa create CryptDB system that encrypted and decrypted DB and saved it in cloud. Some of CryptDB system code was written in traditional way because they focus on cloud security, the goal of this paper is to create insert object (to insert new records) to insert data to multi table and multi columns, and update object (update records) and delete object (delete records), using Java language, because Java is an object oriented programming language, also Java have rich application program interface (API), and have powerful development tools such as Eclipse and NetBeans.Village.
A DEPTH IMAGE QUALITY BENCHMARK OF THREE POPULAR LOW-COST DEPTH CAMERAS

A depth camera outputs an image in which each pixel depicts the distance between the camera plane and the corresponding point on the image plane.Low-cost depth cameras are becoming commonplace and given their applications in the field of machine vision, one must carefully select the right device according to the environment in which the camera will be used given the accuracy these cameras can be associated with factors such as distance from the target, luminosity of the environment, etc.This paper aims to compare three depth cameras currently available in the market, Intel RealSense D435, which uses stereo vision to compute depth at pixels, ASUS Xtion and Microsoft Kinect 2 represent Time of flight-based depth cameras.The comparison will be based on how the cameras perform at different distances from a flat surface and we will check if the colour of the surface affects the depth image quality.
Analisis Faktor yang Mempengaruhi Perokok Beralih ke Produk Alternatif Tembakau (VAPE) menggunakan Metode K-Means Clustering

This research was conducted based on the emergence of a trend in Indonesia, namely the use of alternative tobacco products (vape). It is known that the use of vape can make smokers reduce smoking activities or even make smokers quit smoking because of many factors to switch to vaping. The purpose of this study was to obtain the results of the analysis of factors that influence smokers to switch to alternative tobacco products (vape). This study uses the K-means method and uses the factors of age, income, duration of smoking, and time period of using vape as parameters. Data collection techniques are in the form of data collection by questionnaire. The subject of this research is the vape community that is part of the Facebook forum (JBDJ-Jual Beli Depok Jaksel).Keywords:Clustering, Data mining, K-means, Vape.
Stochastic local search and parameters recommendation: a case study on flowshop problems

Abstract The Algorithm Selection Problem (ASP) considers the use of previous knowledge regarding problem features and algorithm performance to recommend the best strategy to solve a previously unseen problem. In the application context, the usual ASP for optimization considers recommending the best heuristics, whenever it faces a new similar problem instance, also known as the Per‐Instance ASP. Although ASP for heuristic recommendation is not new, selecting heuristics and also their parameters, or the Per‐instance Algorithm Configuration Problem, is still considered a challenging task. This paper investigates the use of meta‐learning to recommend six different stochastic local searches and their parameters to solve several instances of permutation flowshop problems. The proposed approach uses several problem features, including fitness landscape metrics, builds the performance database using irace , and trains different multi‐label recommendation models on a data set with more than 6000 flowshop problem instances. Experiments show that decision tree‐based machine learning models achieve good performance, and the quality of the recommendations is capable of outperforming the state‐of‐the‐art algorithm with tuned configuration.
Static and Dynamic Analysis on Convergence Network : Focused on Patent Analysis of Government-funded Research Institutes

As convergence technologies have been regarded as key elements for securing global competitiveness, the Korean government has increased funding for research on convergence technologies. Nonetheless, systematic analysis on the impact of the investment has yet to be provided. The purpose of this paper is to identify the patterns in the overall shape of convergence networks of government-funded research in Korea and interpret hidden implications within them. In this paper, a Social Network Analysis (SNA) method was used to analyze the convergence technology of 19 government-funded research institutes (GFRIs) in Korea. Based on convergence network of GFRIs, a quadrant matrix showing convergence characteristics has been proposed and the trends of structural changes have been analyzed. This paper shows that government investment on convergence has made impact on structure of convergence research. It also suggests that both characteristics of the network and dynamic status should be carefully considered when promoting convergence technology.
Implementation of Data Mining Algorithms for Grouping Poverty Lines by District/City in North Sumatra

The poverty line is useful as an economic tool that can be used to measure the poor and consider socio-economic reforms, such as welfare programs and unemployment insurance to reduce poverty. Therefore, this study aims to classify poverty lines according to regencies/cities in North Sumatra Province, so that it is known which districts/cities have high or low poverty lines. The grouping algorithm used is K-Means data mining. By using this algorithm, the data will be grouped into several parts, where the process of implementing K-Means data mining uses Rapid Miner. The data used is the poverty line data by district/city (rupiah/capita/month) in the province of North Sumatra in 2017-2019. Data sourced from the North Sumatra Central Statistics Agency. The grouping is divided into 3 clusters: high category poverty line, medium category poverty line, and the low category poverty line. The results for the high category consisted of 5 districts/cities, the medium category consisted of 18 districts/cities and the medium category consisted of 10 districts/cities. This can provide input and information for the North Sumatra government to further maximize efforts to overcome the poverty line in the area.