Reviewerly/exHarmony huggingface dataset:
A NEEDS ANALYSIS FOR THE DEVELOPMENT OF E-MODULES PROJECT-BASED LEARNING

This research aims to describe the results of the needs analysis for the development of electronic module project- based learning. This research was a qualitative descriptive study by using survey methods in identifying the needs of students and teachers in learning assessment. In this research, needs analysis refers to the achievement of student learning outcomes, problems faced by students, and the media needed in the learning assessment process. This research used observation and interview methods in collecting the data. Observations and interviews were done for the students of grade X of computers and basic networks subject at SMKN 5 Padang. The results showed that: 1) completeness of student learning 2) Interactive learning media needed on computer and basic networks subject. The results of this research concluded that students need interactive learning media in learning activities. Thus, this research is used in designing electronic module project based learning.
Reviewing inquiry-based learning approaches in virtual laboratory environment for science education

In science education, students’ active involvement in experimental procedures contributes to higher performance achievements compared to traditional lecturing, as laboratories play a key role and help learners develop the necessary skills of inquiry. Inquiry-based learning, a learning method based on students’ personal involvement in the cognitive process through searching, querying and posing questions, is an educational approach where in the case of research, the learner formulates hypotheses which tests them via experimentation and observation of the experimental results. The educational value of the inquiry learning method has been verified in several studies, where the application of the inquiry learning approach resulted in more effective learning, compared to traditional teacher-centered methods. The evolution of the Information and Communication Technology (ICT) contributed towards the popularity of the inquiry-based learning, since it supports the development of educational approaches and environments which facilitate the inquiry learning process. Virtual reality laboratories give the user the opportunity to conduct the same scientific inquiry provided by hands-on experiments but with the advantages of the virtual environment. The present study focuses on the identification of the inquiry-based learning applications and methods used based on virtual laboratories in the field of science. The peer-reviewed literature was the main source of information and data. The findings of the reviewed studies indicated that virtual labs, when used in combination with hands-on labs in inquiry learning, support learners and help them gain deeper conceptual understanding of science.
Haversine Formula and RPA Algorithm for Navigation System

The system uses a geographic information system to analyze and monitor traffic congestion and use GPS data for public transport planning in Yangon, Myanmar.The system provides accurate maps for estimating traffic conditions more efficiently from GPS data, saving more time.The proposed system displays changes in the position, distance and direction of vehicles traveling on the streets of Yangon by using traffic state and routing pattern algorithm.There established centralized GPS server database infrastructure provides any kind of analysis that requires GPS traffic data stored in a distributed clientserver environment.In this system, a statement of user desired traffic jams between the source and destination is estimated and the results are presented with a Map.This system is for analyzing traffic data, avoiding traffic congestion and obtaining optimal routes with a modified A* algorithm.GPS data (current location) and user search area using the K-d tree and Haversine algorithm are required.Second, look for traffic jam data with Google's traffic layer and the routing matrix pattern algorithm.Finally, Analysis the traffic by Smart-A* and then show the result of traffic congestion statement and best optimal route.In the case, there are three main components: Data Collection, Data Extraction and Implementation.And this is Client-Server database system that storing the data and server in the cloud Virtual Machine (VM).
A Need Analysis of English Learning for the Aircraft Maintenance Students

This research aimed to find out the needs of English learning for the aircraft maintenance students at SMK Penerbangan Nusantara Ketaping.The information of the students' needs were obtained through necessities, wants and lacks.The type of the research was descriptive research.The data were collected from 8 workers and 137 students through two types of questionnaires.The results showed that in this field, the dominant English skill needed was reading and several important topics (necessities).However, there were still some gaps (lacks) between English topics that should be mastered with the topics that the students mastered.Moreover, the students had their preferences (wants) related to the English learning process.
The Contemporary Problems of Corporate Education in Russian Companies: Can There Be Learning Without Development?

The article is devoted to modern problems of corporate education in Russian companies. It consistently solves three issues: analysis of quantitative and qualitative dynamics of corporate education system formation based on secondary information data, investigation of factors that influenced the process of corporate education system formation, critical analysis of the results of the ongoing changes from the standpoint of psychological science. A significant development and transformation of the corporate education system in Russian companies has been noted over the past three years. However, changes do not always produce the expected results. Analyzing the “resource — result” link from the position of the double loop learning concept suggested by C. Argyris, the authors come to the conclusion that there exist problems of organizational and methodological nature that affect the performance of corporate education. The causes of these problems are related to the psychological mechanisms underlying the adult learning process. Psychological and pedagogical approaches to overcoming these problems are proposed.
Interactions between Technical and Public Services: Perceptions from Three Different Librarians

The presenters described different experiences in public and technical services at their respective organizations. These experiences included a career shift, launching e-book initiatives, and the work of a solo librarian. Although each presenter described a unique experience in terms of the nature of their work and institution, they each emphasized the benefits of working toward a collaborative and cooperative culture among different library or institutional departments.
A Novel Five-Dimensional Three-Leaf Chaotic Attractor and Its Application in Image Encryption

This paper presents a novel five-dimensional three-leaf chaotic attractor and its application in image encryption. First, a new five-dimensional three-leaf chaotic system is proposed. Some basic dynamics of the chaotic system were analyzed theoretically and numerically, such as the equilibrium point, dissipative, bifurcation diagram, plane phase diagram, and three-dimensional phase diagram. Simultaneously, an analog circuit was designed to implement the chaotic attractor. The circuit simulation experiment results were consistent with the numerical simulation experiment results. Second, a convolution kernel was used to process the five chaotic sequences, respectively, and the plaintext image matrix was divided according to the row and column proportions. Lastly, each of the divided plaintext images was scrambled with five chaotic sequences that were convolved to obtain the final encrypted image. The theoretical analysis and simulation results demonstrated that the key space of the algorithm was larger than 10150 that had strong key sensitivity. It effectively resisted the attacks of statistical analysis and gray value analysis, and had a good encryption effect on the encryption of digital images.
E-assessment on the spotlight: present and future prospects

The role that technology has played in the educational domain is undeniable.In fact, it has affected various aspects of students' way of learning, not least their assessment.This change has been conducive to an online based-evaluation in line with the 21 st century era referred to as e-assessment.The present paper is a review of the literature that first defines assessment and spotlights its types, features, and importance.Then, it moves to e-assessment by highlighting its emergence, tools, and process.It also sheds light on the differences that may exist between e-assessment and other more conventional modes of evaluation and the underlying reasons behind its use.Because the computer-based assessment is seen divergently from one scholar to another, the article discusses those disagreeing opinions in the form of pro and cons from pedagogical, social, administrative, and financial considerations.Moreover, it explores the set of challenges that can come across its implementation and provides a set of recommendations.The work intends to provide an in-depth understanding about such type of evaluation and insights for future edesigner, teachers, and researchers alike on the different parameters that have to be taken into account before designing any e-assessment task in an educational setting.
Strike Activity Detection and Recognition Using Inertial Measurement Unit towards Kendo Skill Improvement Support System

In the field of sports, there are increasing opportunities to use inertial measurement units (IMUs) to enhance the training process and improve the performance of athletes.We focus on kendo, a traditional martial art using shinai (bamboo swords) in Japan, and propose methods for detecting and recognizing strike activities using IMUs towards realizing a kendo skill improvement support system.We used a sensor data set of strike activities obtained from 14 participants (seven kendo-experienced and seven inexperienced persons).We attached four IMUs to the participants' right wrist, waist, and shinai (tsuba and saki-gawa).First, to detect the strike activity, we calculated the dynamic time warping (DTW) distance between the training data and the time series data, and detected the strike activity sections.The proposed method detected strike activities with a high accuracy of 95.0%.Next, to recognize the strike activity, we recognized five types (Center-Men, Right-Men, Left-Men, Dō, and Kote).In the person-dependent (PD) case, we achieved an accuracy of 89.5% using data of the right wrist.In the person-independent (PI) case, we achieved an accuracy of 54.9% using IMUs attached to the three positions.These results clarified the points to be improved in the proposed method to realize the support system.
ANALISIS KESALAHAN BERBAHASA DI BIDANG DIKSI DALAM BUKU PANDUAN UPT PERPUSTAKAAN IAIN SURAKARTA EDISI 2018

The library guide book certainly has a significant role in the learning process for new students. All forms of direction contained in this book are important to consider in terms of language. This study aimed to describe language errors in diction and spelling found in the guide book. The purpose of this study was to describe language errors in the diction and spelling. The research method used was descriptive qualitative, using error analysis framework. Data collection technique was done qualitatively. The data analysis was done in the following stages: processing and preparing data to be analyzed, reading and describing the entire data, recording all data obtained, analyzing more details and describing errors in each data, classifying each data in spelling or diction fields, and interpreting all data according to relevant. The results of the analysis indicated that there were 25 errors on diction, consisting of 3 prepositional writing errors, 17 standard word writing errors, and 5 italics writing data. In addition, there were 10 errors the spelling field, consisting of 2 data on punctuation, and 8 errors in the use of foreign words.
SUPERVISED NEURAL NETWORK CONTROL OF REAL-TIME TWO WHEEL INVERTED PENDULUM

This research paper investigates an intelligent control technique stabilizing a real-time model ofthe two-wheel inverted pendulum. The TWIP model is a highly non-linear, open-loop, and unstable system which makes control a challenge. Initially,a state-feedback controller that uses the dynamical system states and control signals to construct the precise control decision is used to stabilize the system. Later, Supervised Feed-Forward Neural Networks (SFFNN) based on back propagation Levenberg-Marquardt optimization algorithm are trained by using real-time measurements of system states and motors control signals from state-feedback controller stabilization. SFFNN control the two-wheel inverted pendulum better than a state-feedback controller.
The Training Complex as a Component of the Readiness Formation of Students of Technical Specialties for Professional Activity

The article discusses a set of theoretical and practical issues related to the problem of forming the professional readiness of students of technical specialties for future professional activity.Suggestions are made to improve the organization and conduct of psychological training of students during their studies at universities.Practical classes in psychological preparation differ from other classes in a closer combination of theory and practice, the presentation, repetition, and strengthening of precisely that psychological knowledge that ensures the conscious execution of exercises.The level of preparedness of a person for professional activity is always associated with the characteristics of their personality, the totality of which characterizes the degree of development of abilities for this profession.Based on this, the main task becomes the introduction of innovative technologies in the educational process, which, of course, include educational and developing training complexes that contribute to the formation of professional and psychological readiness as a combination of psychophysiological, moral, professional and socio-psychological characteristics, properties and qualities that correspond to the characteristics of the profession and that are acting as the internal condition for its successful implementation.An experimental study was conducted to confirm the hypothesis about the effectiveness of introducing precisely training technologies into the educational process of students of technical specialties.We selected 298 students of Odessa National Polytechnic University, with whom testing was conducted in order to clarify the initial psychological profile.After conducting the full volume of the training complex with students from the experimental group, we can ascertain the direct influence of educational and developmental training through the targeted formation of cognitive qualities on professionally significant personal qualities.This indicates the relevance of our study, which is focused on the problem of optimization and intensification of the professionally significant qualities formation of specialists in technical specialties in the process of their preparation.
GRAHIES: Multi-Scale Graph Representation Learning with Latent Hierarchical Structure

A wide variety of deep neural network models for graph-structured data have been proposed to solve tasks like node/graph classification and link prediction. By effectively learning low-dimensional embeddings of graph nodes, they have shown state-of-the-art performance. However, most existing models learn node embeddings by exploring flat information propagation across the edges within the local neighborhood of each node. We argue that incorporating hierarchical node embeddings can capture the inherently hierarchical topological features of many realistic graphs such as social networks, biological network and World Wide Web. In this paper we propose GRAHIES, a general framework for graph neural networks to learn node representations that preserve hierarchical graph information at higher-orders. GRAHIES adaptively learns a multi-level hierarchical structure of the input graph, which consists of successively coarser (smaller) graphs that preserve the global structure of the original graphs at different levels. By combining the graph representations from different levels of the graph hierarchy, the final node representation captures the inherent global hierarchical structure of the original graph. Our experiments show that applying GRAHIES's hierarchical paradigm yields improved accuracy for existing graph neural networks on the node classification tasks.
WESgraph: a graph database for the wind farm domain

Abstract. The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.
A Proposed Method for Minimizing Mining Tasks’ Data Dimensionality

Knowledge discovery techniques have heavily contributed to many fields with significant success.However, with the continuous growth of data, these techniques suffer from bottlenecks in processing these data.One of the directions to hinder this effect is reducing the data dimensionality which focuses on eliminating the attributes which have no significant effect on the discovery technique accuracy.This research proposes a novel method for reducing data dimensionality.The proposed method is based on two main pillars, the first is applying the adapted Saaty method for determining the attributes' consistency with proposing further adaptation targeting more accurate accuracy determination.The second pillar is applying the clustering techniques on the consistent attributes to eliminate the least weighted attributes in each cluster which also have the least consistent measures.The result of applying the two steps is to highlight the most significant dataset attributes.The proposed method has been successfully applied on the Gastrology dataset which attributes have been reduced from 62 attributes to 31 attributes.A set of classification techniques have been applied on the dataset to prove that the dimensionality reduction has retained the classification task accuracy, the results presented that the Random Forest algorithm had an accuracy equal 95.36% when applied to the adopted dataset.
Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis

Social media reflects the attitudes of the public towards specific events. Events are often related to persons, locations or organizations, the so-called Named Entities (NEs). This can define NEs as sentiment-bearing components. In this paper, we dive beyond NEs recognition to the exploitation of sentiment-annotated NEs in Arabic sentiment analysis. Therefore, we develop an algorithm to detect the sentiment of NEs based on the majority of attitudes towards them. This enabled tagging NEs with proper tags and, thus, including them in a sentiment analysis framework of two models: supervised and lexicon-based. Both models were applied on datasets of multi-dialectal content. The results revealed that NEs have no considerable impact on the supervised model, while employing NEs in the lexicon-based model improved the classification performance and outperformed most of the baseline systems.
Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance

In recent years there has been a growing interest in image generation through deep learning. While an important part of the evaluation of the generated images usually involves visual inspection, the inclusion of human perception as a factor in the training process is often overlooked. In this paper we propose an alternative perceptual regulariser for image-to-image translation using conditional generative adversarial networks (cGANs). To do so automatically (avoiding visual inspection), we use the Normalised Laplacian Pyramid Distance (NLPD) to measure the perceptual similarity between the generated image and the original image. The NLPD is based on the principle of normalising the value of coefficients with respect to a local estimate of mean energy at different scales and has already been successfully tested in different experiments involving human perception. We compare this regulariser with the originally proposed L1 distance and note that when using NLPD the generated images contain more realistic values for both local and global contrast. We found that using NLPD as a regulariser improves image segmentation accuracy on generated images as well as improving two no-reference image quality metrics.
Staging Location-Based Virtual Reality to Improve Immersive Experiences

Location-based virtual reality (LBVR) is promising to offer the full potential of virtual reality (VR) because it is not completely virtual, as in space independent VR, which usually relies on a head-worn display and hand-held controllers only.Therefore, LBVR can include and arrange the physical surrounding according to a particular application or include parameters, such as objects or light conditions, of the physical surrounding into the virtual environment.While LBVR is drawing a lot of attention in the creative industry and a lot of LBVR-installations are already entertaining millions of users, not much research has been employed in this direction.In this work, we offer an overview containing relevant challenges and accompanying solutions that need to be considered in an LBVR-installation.
Assessment of Soil Pollution in the Ishaqi Project Area- Salah Al-Dean Governorate, Iraq

Ten soil samples were collected from Ishaqi project area, Salah Al-Dean Governorate, and analysed for chemical elements (Fe2O3, Al2O3, CaO, K2O Na2O, Co, Zn, Cu, and Pb) to detect the pollution in the study soil using the indices of geo-accumulation (I-geo), contamination factor (CF), and pollution load index (PLI), The results of I-geo indicate that the soil of Ishaqi project area is unpolluted with Pb, Co and slightly polluted with Zn and Cu. The results of CF for Zn, Cu, and Co showed class 2 of moderate contamination and class 1 of low contamination in some samples while those for Pb demonstrated class 1 â€“of low contamination. The Pollution Load Index (PLI) values for Co, Zn, Cu, and Pb showed class 2 of local pollution.