Reviewerly/exHarmony huggingface dataset:
Real-Time Recognition of Transmission Line Insulators under Complex Backgrounds: A YOLOV5s Approach

As an important component of transmission lines, regular inspection of insulators can ensure the safety and stability of new energy power systems. To address the problems of complex backgrounds and variable shooting angles in insulator inspection images, this paper proposes an improved YOLOV5s algorithm to achieve accurate target detection for insulator images. Firstly, according to the different scales of insulator targets in the image, k-means++ clustering is used to reconstruct the anchor to improve the insulator localization accuracy. Secondly, the extraction capability of features in the insulator dataset is improved by embedding the CBAM attention module in the traditional YOLOV5s algorithm. Then, by improving the neck part of the YOLOV5 neural network, a weighted bidirectional feature pyramid network is constructed to replace the original path aggregation network to enhance the feature information interaction and feature fusion capability in the detection model. Finally, the insulator target detection model with the improved YOLOV5s method is analyzed by arithmetic examples. The results show that the proposed method can improve insulator target detection's average accuracy and precision in complex environments.
Design features of books with augmented reality for preschool children.

Preschool age is rightfully considered one of the most important periods in a child's life, since at this stage the formation of the psycho-emotional structure of the personality, the recognition of oneself, one's desires, the disclosure of the concept of the inner world take place. Acquaintance with a book for a child is nothing more than an opportunity to develop in a multi-faceted manner, to learn the world through immersion in a visual-graphic environment, culture. Independent study of books and electronic resources forms the skill of searching for information without the help of the environment, teaches to concentrate on one's desires, but it also has a negative aspect - the lack of primary socialization that occurs in the process of interaction between the child and the parent. Reading with the help of an adult can help a preschooler understand a lot more, both in terms of acquiring new knowledge and gaining emotional experience. The visual-graphic language, through which the “communication” of the child and the book takes place, develops the aesthetic, artistic and moral experience that a person relies on in their adulthood. In the era of digitalization, major changes are taking place in the book market, which are associated not only with new formats, but also with new needs and types of thinking of preschoolers. Introducing a child to the virtual space from an early age leads to the consumption of a large amount of information that does not have time to be remembered due to the limitations of a child's memory and cognition. The consequence of which is a superficial acquaintance with the material, the lack of knowledge as such. Reading for preschoolers seems to be a too long and complicated process, the cognitive function of the book is losing its relevance due to the lack of interest in the process. Augmented reality technologies provide the child with the opportunity to interact with the book in 4 planes at once, which greatly expands the possibilities of paper publications. A gadget (a mobile phone or a tablet) is connected to the process of "communication" between a preschooler and a book. The interactive component consists in animating illustrations that can be presented in the form of a game that has a direct connection with the text. Emphasizing clip thinking of preschoolers, the text in augmented reality books is concise, written in simple language, with an emphasis on the most important details, which greatly simplifies understanding and memorization. AR or "Augmented Reality" has great potential for children from a very young age.
Using Symmetric Group to Generate Dynamic S-box

The main purpose of utilizing s-box (static, or dynamic) is to encrypt data with/without decrypt cipher-data, s-box may be a public in the cipher method, but if it’s private and included with steps of the cipher method, and depended it on the key cipher method. This work used the integer chaos data which are generated from the tent chaos map and symmetric group Sn in order to generate the private dynamic s-box between the sender and the receiver. An initial n × m s-box constructed by utilizing the random mixed between integer chaos sequence, and key method, with random element of symmetric group. The generation of s-box depend on the sequence Ics, where Ics generated from chaos sequence called Tent map is a simple linear function T(xn) such that to convert Tent chaos sequence x1, x2, …, xn to integer sequence d1, d2, …, dn. When the proposed method analyzed provide high randomness and high sensitivity to any simple changes of the initial used values.
Comparison of G-Means Algorithms and Kohonen Network in Solving Clustering Problems

Purpose: In this paper, the question of how to improve a self-organizing neural network consisting of a bundle of clustering algorithm and a multilayer perceptron for data verification tasks in the absence of training pairs is considered. Design/methodology/approach: The most popular clustering algorithm is the Kohonen network, but today it is not the only algorithm capable of performing the task quickly and accurately. The paper compares the Kohonen network and the G-Means algorithm. The principle of operation of these two algorithms is briefly analyzed. The accuracy of these algorithms and the speed of their learning are compared. Findings: By conducting experiments, conclusions were drawn about the speed and accuracy of the algorithms. Originality/value: The relevance of this work lies in the fact that the preparation of training pairs for intelligent systems and the process of learning with a teacher is a resource-intensive task. The systems of self-learning algorithms under consideration will significantly increase the learning rate, as well as eliminate the need for manual classification of data and the creation of training pairs for the perceptron, which in turn will allow you to create a self-learning system with the ability to generalize and predict.
CloudSim: Cloud Computing Environment Modelling and Simulation as well as Resource Provisioning Algorithm Assessment

The lifelong ambition of computing as a utility, cloud computing, has the power to transform a significant portion of the IT sector, increasing the appeal of software as a service and influencing the development and selection of IT hardware.New hardware investments and labor costs for operating Internet services are no longer necessary for developers of innovative new invention concepts.They need not be concerned about over-or under-provisioning for a service whose demand turns out to be much higher or lower than expected, squandering expensive resources and maybe losing out on consumers and income.
Approaches for Rule Discovery in a Learning Classifier System

To fill the increasing demand for explanations of decisions made by automated prediction systems, machine learning (ML) techniques that produce inherently transparent models are directly suited.Learning Classifier Systems (LCSs), a family of rule-based learners, produce transparent models by design.However, the usefulness of such models, both for predictions and analyses, heavily depends on the placement and selection of rules (combined constituting the ML task of model selection).In this paper, we investigate a variety of techniques to efficiently place good rules within the search space based on their local prediction errors as well as their generality.This investigation is done within a specific LCS, named SupRB, where the placement of rules and the selection of good subsets of rules are strictly separated in contrast to other LCSs where these tasks sometimes blend.We compare a Random Search, (1, λ)-ES and three Novelty Search variants.We find that there is a definitive need to guide the search based on some sensible criteria, i.e. error and generality, rather than just placing rules randomly and selecting better performing ones but also find that Novelty Search variants do not beat the easier to understand (1, λ)-ES.
Classification data mining with Laplacian Smoothing on Naïve Bayes method

Data mining is a process of gathering information to find important pattern recognition in the data set in the database so that it becomes knowledge discovery. Classification is a technique of grouping data based on data attachment to sample data. Naïve Bayes is one of the techniques in data mining classification that uses the probability method and is better known as the Naïve Bayes Classifier (NBC). The main characteristic of NBC is that there is a strong assumption of independence from each condition (independent variable). By applying Naive Bayes to a data sometimes causes misclassification if the training data is only a few so that the testing data is not found in the training data and this causes the probability result to be zero and an error in the classification process. To avoid zero probability results that cause errors in the classification process, a refinement method is needed. Laplacian Smoothing is a smoothing technique that can be used in Naïve Bayes classification in an easy way. The concept is to add a small positive value to each of the existing conditional probability values to avoid zero values in the probability model. Laplacian Smoothing on NBC is examined in this article.
The Analysis of Students’ Vocabulary Mastery Who Are Using Tiktok Application

The aim of study is to examine the use of TikTok which is useful for improving students' vocabulary mastery. The research method used in this research is descriptive qualitative analysis method. This observation was carried out for 3 days starting on Sunday, April 10-13, 2022. The subjects of this study were selected into two groups, namely students who use TikTok and those who do not use TikTok, totaling 8 students as representatives of EFL students at UMN AL Washliyah Medan. Data collection techniques used were observation, interviews and tests, while data analysis used scoring techniques, then to find out the vocabulary mastery achieved by students, from all research data found 8 research subjects with 2 students actively watching review-based content with a vulnerable time where they often use their spare time to use this TikTok application. Students who were classified as active openly stated that the TikTok application really helped improve their vocabulary, to cover their difficulties in learning vocabulary. Meanwhile, students who do not use TikTok are divided into 1 student who relies on himself as a role in helping to learn vocabulary, 2 people rely on the people closest to them while 1 other student relies on a dictionary application in learning his vocabulary. This triggers them to not elaborate on the use of many applications, one of which is TikTok in covering their vocabulary learning difficulties. Some of the motivation they get in learning vocabulary is from the people closest to them such as parents, friends, teachers and even their smartphones help them improve their vocabulary, the only difference is parental control over them in using this application where active users get support use the application while those who are not users of the TikTok application do not. The results of this study show that students' vocabulary mastery is very influential on how active they are on social media, especially TikTok, it can be seen from the test scores of active users who do not use it and also parental control and their closest people are needed in the use of social media, especially TikTok. . Some of the other reasons people find it difficult to understand English is because they do not understand its meaning and rarely use it in their daily communication. Therefore, by increasing their vocabulary, it is easier for them to understand what they read, listen, write, and speak.
Handling of expert knowledge in software product lines development with usage of repertory grids method

The development of software product lines (SPL) is a promising way to create competitive software for the modern IT - market. Because of perpetual changes in user needs and the appearance of new software and hardware components for SPL, maintaining a certain level of variability at all stages of their full life cycle (FLC) becomes an actual problem. One of the most important stages is requirements engineering. To support this process it is advisable to utilize the methods of expert knowledge elicitation and analysis. Based on the recognized multilevel approach to SPL engineering proposed by K. Pohl, the research goal is to develop an approach to correct specification of variability features at the initial stages of SPL FLC. The analysis of methods to extract and process the expert knowledge is carried out, their classification is presented, and the method of repertory grid (RG) is chosen for the further usage. It allows analyzing and structuring expert knowledge in a multidimensional information space and makes it possible to consider various contextual factors that affect relevant requirements for the target SPL. The essentials of the RG method are considered and its formal description is elaborated. The examples of its usage in designing ​ “Smart House” systems which allow analyzing the technical, social and economic aspects of user requirements for software and hardware variability at the conceptual design stage of these systems are considered. As the result, a possibility to automate a process of a corresponding FODA-model creation is shown. That provides variability support at the next FLC stages of such systems.
Transformation of higher education management system with regard to Covid-19 pandemic experience

The accelerated digitalization of the educational process provoked a “digital breakthrough” in higher education and drew the attention of the world community to a number of problems related to digital literacy and the conditions for using electronic educational resources in universities. The subject of this research is the modern management system of higher education. The object of the research is universities operating during the COVID-19 pandemic. The relevance is associated with the inevitable transformation of the infrastructure of universities, entailing a modification of the relationships and interdependencies of the subjects of the educational process, and is due to the uniqueness of the situation in the field of higher education in 2019-2021. The theoretical basis of the study is the work of domestic and foreign scientists, as well as specialists in the field of higher education management. The purpose of the study is to identify and assess the rationality of the relationships between the subjects of the digital educational process. The objectives of the research are to study the principles and mechanisms of university management, analyze the fundamental options for digital interaction between the teacher and the student, and formulate the prerequisites for the transformation of university management.
’A--VENUE’: ONLINE VENUE RESERVATION SYSTEM FOR RECOGNIZED STUDENTORGANIZATIONS OF ADAMSON UNIVERSITY

Adamson University has a diverse community where students are engaged in different activities in line with their academic field and extra--curricular activities. There are three types of student organizations in the university;; academic, socio--civic and religious. These organizations are collectively known as Recognized Student Organizations (RSOs), administered by the Office for Student Affairs (OSA). This office oversees all organized events together with Physical Facilities and General Services Office (PFGSO) for the venue reservation and approval. The aforementioned offices are currently using the traditional pen and paper method for event processing. The main goal of this research is to develop an Online Venue Reservation System that can be used to manage and process event data and help reduce problems encountered, such as data redundancy, data loss, and excessive paper use. The study used qualitative research wherein qualified respondents from OSA, PFGSO, and RSO’s were chosen using purposive sampling. This developed web application allows different users to request, monitor, and approve or disapprove a venue reservation request as well as generate reports when necessary. The website was evaluated using ISO 25010 Software Quality Model by the target respondents. The survey showed that the developed system was assessed as “Good” in terms of functionality suitability, performance efficiency, compatibility, usability, reliability, security, maintainability and portability. In terms of compatibility, the website was rated “Fair”. Furthermore, the respondents also evaluated the website as “Highly Feasible” in terms of technical, operational, economic and environmental considerations.
Distilling Massive Amounts of Data into Simple Visualizations: Twitter Case Studies

We present three Twitter case studies of distilling massive amounts of data into simple visualizations.
Fast Traffic Assignment by Focusing on Changing Edge Flows (Extended Abstract)

This paper presents a novel algorithm for solving the traffic assignment problem (TAP). Contrary to traditional algorithms, which use the one-to-all shortest path algorithm to solve the problem for all origin destinations (OD) pairs, this algorithm tracks the changes of the edges and (at certain iterations) solves the problem only for critical edges whose flows have changed substantially using a state-of-the-art edge p2p shortest path algorithm. When additionally, only OD pairs with larger flows are considered, this enhancement halves the time needed to optimize the solution with a very small error in a large-scale network.
The Effects of Human-like Modifications to Heuristic Action Evaluation in Video Game Pathfinding

We present a series of parameterizable modifications to heuristic evaluation of actions in the A* algorithm, designed to create more human-like and dexterity-robust paths through games in the 2 dimensional platformer style. We attempt to create paths at various levels of player skill by imposing constraints onto the timing and duration of actions designed to mimic human reaction times and ability. We show that these action value modifications result in the A* search algorithm producing smoother paths, taking safer routes to avoid danger, and requiring fewer actions to be performed in a given amount of game time.
English Teachers’ Perspectives of Learner Autonomy in Online Reading Comprehension in Times of the COVID-19 Pandemic

The objectives of this research were focused on English teachers’ perspectives on learner autonomy and to what extent do English teachers feel their learners are autonomous in online reading comprehension in times of the COVID-19 pandemic. These research questions were answered by using observation, interview, and Likert-scale questionnaire in the form of qualitative method. The sample of this study was 26 English teachers in 4 elementary schools and 5 junior high schools in North Sumatra, Indonesia. The results of the study showed some points on learner autonomy, such as: (1) the importance of learner autonomy, (2) the way English teachers promoted learner autonomy, (3) the challenges of English teachers to implement learner autonomy, (4) English teachers’ responsibility in online reading comprehension, (5) English teachers’ observation to learners’ autonomy in online reading comprehension, (6) learners’ activities in online reading comprehension strategy, (7) English teachers’ preparation in online reading comprehension class. This research concluded that the degree of learners’ autonomy during the pandemic was higher rather than before the COVID-19 pandemic due to some reasons.
Mineralogy and Pore Structure Characterization of Lower Oligocene to Early Miocene Formations in Parts of Assam–arakan Basin, North East India.

In this study, reservoir rocks have been characterized by pore-scale measurements and mineralogical analysis of core samples, drill cuttings or crushed samples collected from Upper Assam and Mizoram, North East India.The mineralogical composition and pore types were examined using various laboratory techniques, like petrography under transmitted light microscope, field emission scanning electron microscopy (FE-SEM), X-ray diffraction and nitrogen (N 2 ) gas adsorption.The variation in porosity and permeability was related to different factors such as rock composition, cementing, textural parameters, grain size and sorting, pores and pore throats.Mapping attributes like pore structure, surface area, pore size distribution (PSD), pore orientation, connectivity and pore volume regulated fluid flow through the pore network, which provides significant variations in the reservoir properties.Pores were analysed from the image processing of FE-SEM photomicrographs, which were used to estimate porosity and generate the topology map.This information was further used to visualize pore connectivity in a 3D pore network model.Pore characterization from N 2 adsorption analysis helped infer the pore structure, pore volume, and PSD in the reservoir rocks.The studied samples have an excellent meso to macro pore network that is also supplemented by the derived pore network.
Learning Stretch-Shrink Latent Representations With Autoencoder and K-Means for Software Defect Prediction

Detecting defective source code to localize and fix bugs is important to reduce software development efforts.Although deep learning models have made a breakthrough in this field, many issues have not been resolved, such as labeled data shortage and the small size of defective elements.Given two similar programs that differ from each other by an operator or statement, one may be clean while the other may be defective.To address these issues, this study proposes a new deep learning model to facilitate the learning of distinguishing features.The model comprises of three main components: 1) a convolutional neural network-based classifier, 2) an autoencoder, and 3) a k-means cluster.In our model, the autoencoder assists the classifier in generating program latent representations.The k-means cluster provides penalty functions to increase the distinguishability among latent representations.We evaluated the effectiveness of the model according to performance metrics and latent representation quality.The experimental results on the four defect prediction datasets show that the proposed model outperforms the baselines thanks to the generation of sophisticated features.
Investigation of Accessibility of Some Frequently Use Rapid Aplication Development Environments

The object of this article is to discuss some problems, that hav low vision developers using some frequently use rapid application development environments. Particular attention is payd to those of them, that have free versions or are use by high schools and special secondary schools. Accessibility level of main menu, submenus, settings dialog boxes, text edit tools, diagnostic and debugging addressed interface are measure. Made reference, that summary accessibility level of development environment DEV-C++ does not allow effective work by using screen readers and braille displays. As better is estimated accessibility level of Microsoft Visual Studio, but, mention may here be made of some important components (related to creating GUI and database projects) are fully inaccessible. So, blind developer can without somebody else help create console applications only. Development environment C++ Builder is closely discussed, because by working with it using screen readers and braille displays problems mentioned before does not exist. The circumstance, that this development environment is out-of-date, is not omitted, so it have not any contrivances to develop 64 bit applications and applications for Android. The article says, that software tools for developers has low level of accessibility, then applications addressed to generally use.