Reviewerly/exHarmony huggingface dataset:
The Method of Spatial Suitability Assessment for Photovoltaic Development at the Municipality Scale

The aim of the study was to develop a nationally universal method for estimating the solar resources and to assess, at the municipality scale, the suitability for photovoltaic development based on the implementation of GIS&T (Geographic Information Science and Technology), multi-criteria analyses, and spatial data gathered from the National Geodetic and Cartographic Resources. The developed method is based on the use of digital topographic data and data gathered by means of modern remote sensing methods as a point cloud from airborne laser scanning. Based on studies in the literature, the formal and legal conditions applicable in Poland were discussed. The sets of constraints and factor criteria have been created as a base for evaluating the suitability of a chosen municipality for photovoltaic farms’ emplacement. Weight calculations in a multi-criteria method—an AHP method (analytic hierarchy process)—were applied. In the multi-criteria analysis, 27 constraints and 13 factors were included. Undoubtedly, a utilitarian feature of the developed method is the calculation models allowing full automation of the process of spatial assessment of municipality territories for photovoltaic farms’ development. Furthermore, calculation models can be easily adapted to some specific conditions that occur in the analyzed municipality territories. The results obtained confirm the significant usefulness of the implemented approach in the context of the spatial suitability assessment of photovoltaic development at the municipality scale.
Developing A Teaching Material Prototype for Linguistics Description of English Course

Linguistics Description of English is a new subject in the curriculum of English Education Study Program in the Faculty of Teacher Training and Education in Jambi University. Hence, the material for this subject is not yet available. Therefore, this research aims at developing a prototype of teaching materials for Linguistics Description of English course. By designing the prototype of teaching materials for this course, the researchers expected that lecturers who will teach this subject will not find difficulties in teaching this course. Besides, the use of materials which have been designed based on students’ need and linguistic lecturers’ discussion is anticipated to be able to encourage students to be more active in teaching and learning process. Thus, the choice of the topics is suited to the characteristics of the students and drew from the result of the need analysis questionnaire and forum group discussion (FGD) with the lecturers. This research utilized research and development design, based on the theory developed by Borg and Gall (1983). The result of the research is the prototype of the teaching materials for Linguistics Description of English course.
Sistem Pakar Diagnosa Hama Penyakit Tanaman Padi Dan Holtikultura Menggunakan Metode Forward Chaining Berbasis Android

The development of information and communication technology-based smartphones and mobilephone growing rapidly. It is characterized by the number of smartphone users in the community. Utilization of information and communication technology is implemented in the form of knowledge application. One of these applications on the world agricultural expert system based on mobile. Knowledge of this expert system has many sources including research or research and observations that have been practiced by experts and issued either through web resources and other sources such as agricultural books, journals and others. This expert system uses Forward Chaining method using base database organized into a table in a rule that an expert and user can draw conclusions from what has been diagnosed by this expert system. Application of expert system designed and developed as an expert tools and users in diagnosing pests and diseases of rice plants and horticulture of the analysis by an expert and analysis according to the book or internet media. This expert system will show the questions will then be selected by the user to find a solution on the diagnostics program that has been done on this expert system. In addition the expert system is equipped with the paperwork to determine how plant pest management, plant species and type of fertilizer.
Visualization and Analysis in Bank Direct Marketing Prediction

Gaining the most benefits out of a certain data set is a difficult task because it requires an in-depth investigation into its different features and their corresponding values.This task is usually achieved by presenting data in a visual format to reveal hidden patterns.In this study, several visualization techniques are applied to a bank's direct marketing data set.The data set obtained from the UCI machine learning repository website is imbalanced.Thus, some oversampling methods are used to enhance the accuracy of the prediction of a client's subscription to a term deposit.Visualization efficiency is tested with the oversampling techniques' influence on multiple classifier performance.Results show that the agglomerative hierarchical clustering technique outperforms other oversampling techniques and the Naive Bayes classifier gave the best prediction results.
Deep Pyramid Variation Learning for Image Interpolation

Previous learning-based interpolation methods do not consider multi-scale structural information, which is generally effective for image modeling. In this paper, we design a deep network based on a novel pyramid variation learning approach with multi-scale structure modeling. An image is represented as multi-dimensional features. Besides two spatial dimensions, the features include a neighboring variation dimension where every pixel is encoded as the variation to its nearest low-resolution pixel, and a scale dimension along which the feature maps generated by a gradual down-sampling process are stacked. Thus, these multi-dimensional features are constructed to model local dependency and multi-scale similarity jointly. Inspired by this feature design, we build an end-to-end trainable Recurrent Multi-Path Aggregation Network (RMPAN) for image interpolation, where the scale dimension is unfolded to form a multi-path aggregation network to apply joint filters at different scales recurrently. Location aware sampling layers are used in RMPAN to transform feature maps into different scales with only location changes in each convolution path, which aggregate the context information without resolution loss. Comprehensive experiments demonstrate that our method leads to a superior performance and offers new state-of-the-art benchmark.
Implementasi Quick Response (QR) Code Pada Dokumen Instruksi Kerja Alat Laboratorium Kimia

Work instructions laboratory equipment is one of the documents that must be available in the laboratory. This document provides information on the operation steps of the device correctly. A laboratory manager usually prints the document on A4 size paper and installs it on a part of the tool to facilitate access to the document. The treatment makes the work instruction document break down quickly and is not suitable to be applied to small sized tools. Therefore, it is necessary to manage the work instruction document for easy access, efficiency in the use of documents, and to improve the security of document storage. In this case, the QR Code can be used as a solution to the problem. QR Code is able to store documents and URL links in a small barcode image so that it can be use more efficient. Making a QR Code is preceded by storing documents at https://drive.google.com. The URL link at https://drive.google.com is entered into the QR-Code Studio 1.0 application and converted into a barcode. Barcodes are then printed and installed on each laboratory instrument. Scanning QR Code using a smartphone application in the form of a QR-Code Scanner. The results of the barcode scanned are the form of scripts for laboratory work instructions
Digitalization On Students Scoring System of SMPN 18 Bekasi

Information technology has been supporting the development of school services in the world. But there are still many schools does not using the information technology at all - specially in Indonesia, for example at SMPN 18 Bekasi. As usually like another school they only using Ms. Word and Ms. Excel applications. That is make many differences output in format scoring and mistakes while filling score on the students report format. The application of academic information system in this research have developed using PHP, HTML and MySQL as programming language. It named SIADHEL, means Eighteen Academic Information System (Sistem Informasi Akademik Delapan Belas) . The aims of this project is to provide a good tools for students or their parents to receive the exactly, fast and accurate informations of their students scoring. Teachers can use an integrated and accurate tools as facility to provide data for the Principal to make new policies. This application could be opened by every browser platform, so it will make easier for the users to access the program wherever and anytime.
Action Recognition in Real Homes using Low Resolution Depth Video Data

We report work in progress from interdisciplinary research on Assisted Living Technology in smart homes for older adults with mild cognitive impairments or dementia. We present our field trial, the set-up for collecting and storing data from real homes, and preliminary results on action recognition using low resolution depth video cameras. The data have been collected from seven apartments with one resident each over a period of two weeks. We propose a pre-processing of the depth videos by applying an Infinite Response Filter (IIR) for extracting the movements in the frames prior to classification. In this work we classify four actions: TV interaction (turn it on/ off and switch over), standing up, sitting down, and no movement. Our first results indicate that using the IIR filter for movement information extraction improves accuracy and can be an efficient method for recognizing actions. Our current implementation uses a convolutional long short-term memory (ConvLSTM) neural network, and achieved an average peak accuracy of 86%.
A Graph-Based Analysis of Anton Chekhov’s Uncle Vanya

We analyze the famous Anton Chekhov play Uncle Vanya by means of graph theory.Moreover, we make the 'brave' suggestion that Chekhov might have used graphs to represent the plot of the play and the relationships between characters.Our analysis also includes the analysis of a specific performance of the play held in Bielsko-Biała which differs slightly from the original script.The differences between the two versions are traced via graph-based analyses.When a first round of clique assignments did not give much insight we transformed them via a sequence of operations on consecutive graphs.The final graphs obtained this way from the two versions of the play differ only by one loop, which disturbs the initially intended symmetry.We explore some of the unexpected consequences.
TEACHERS BELIEFS AND PRACTICES IN TEACHING READING: A SOCIO-COGNITIVE PERSPECTIVE.

Teachers' beliefs are thought to have a great influence on their classroom practices.Due to the great influence, teachers' beliefs and practices in teaching reading have been recently gaining increased research momentum.This case study examined the teachers' beliefs and practices in teaching reading viewed from a socio-cognitive perspective.To achieve the stated purposes, three experienced Indonesian EFL teachers at Islamic Secondary School Madrasah Aliyah were interviewed and observed in their classrooms.The collected data were analyzed using constant comparative method.The findings of the study showed that what the teachers believed was articulated in what they practiced in the classroom.However, there were still some deviances that appeared in the classroom.It can be concluded that the teachers' practices were not always consistent with their beliefs.They were affected by some factors both from teachers and students.
Remote Management of Boundary Protection Devices with Information Restrictions

Boundary Protection Devices (BPDs) are used by US Government mission partners to regulate the flow of information across networks of differing security levels. BPDs provide several critical functions, including preventing unauthorized sharing, sanitizing information, and preventing cyber attacks. Their application in national security and critical infrastructure environments (e.g., military missions, nuclear power plants, clean water distribution systems) calls for a comprehensive load monitoring system that provides resilience and scalability, as well as an automated and vendor neutral configuration management system that can efficiently respond to security threats at machine speed. Their design as one-way traffic control systems, however, presents challenges for dynamic load adaptation techniques that require access to application server performance metrics across network boundaries. Moreover, the structured review and approval process that regulates their configuration and use presents two significant challenges: (1) Adaptation techniques that alter the configuration of BPDs must be predictable, understandable, and pre-approved by administrators, and (2) Software can be installed on BPDs only after completing a stringent accreditation process. These challenges often lead to manual configuration management practices, which are inefficient or ineffective in many cases. The Hammerhead prototype, developed as part of the SHARC project, addresses these challenges using knowledge representation, a rule-oriented adaptation bundle format, and an extensible, open-source constraint solver.
Rental Building and Event Equipment Application at the Arcadia Function Hall Web-Based

The Journal Website is one of the tools used as a means of information and communication that is able to get rid of all the limitations that exist in other information and communication tools. One of the limitations of communication and information tools is about distance, with websites we can find various information in various parts of the world. At Arcadia Function Hall the building leasing process is done manually, so that the time spent is ineffective and in the storage of rental data it becomes insecure because it is not stored in a good system. From the above problems the authors are interested in designing Web Based Applications to be able to help the building rental process performance become more effective and efficient and can store all rental data safely and can produce accurate and accurate reports.&#x0D; Keywords: Website, Storage, Application, Unifled Modeling Language (UML)
Comparing Sample-Wise Learnability across Deep Neural Network Models

Estimating the relative importance of each sample in a training set has important practical and theoretical value, such as in importance sampling or curriculum learning. This kind of focus on individual samples invokes the concept of samplewise learnability: How easy is it to correctly learn each sample (cf. PAC learnability)? In this paper, we approach the sample-wise learnability problem within a deep learning context. We propose a measure of the learnability of a sample with a given deep neural network (DNN) model. The basic idea is to train the given model on the training set, and for each sample, aggregate the hits and misses over the entire training epochs. Our experiments show that the samplewise learnability measure collected this way is highly linearly correlated across different DNN models (ResNet-20, VGG-16, and MobileNet), suggesting that such a measure can provide deep general insights on the data’s properties. We expect our method to help develop better curricula for training, and help us better understand the data itself.
Interactive Auditory Navigation in the Molecular Structures of Amino Acids: A Case Study Using Multiple Concurrent Sound Sources Representing Nearby Atoms

We are interested in sonifying the molecular structures of amino acids. This paper describes the context and the ﬁrst design choices for our approach. So far, we believe an amino acid molecule is too complex to be perceived at once. Therefore, we have designed an interactive form of soniﬁcation in which the listener navigates through the molecule over the network of carbon atoms. We describe our different approaches and discuss the topic of immediacy: the time it takes to recognize the structure surrounding the listener’s position while navigating. Furthermore, we touch upon the question how many atoms we can sonify simultaneously and the role auditory masking plays in this context. To overcome auditory masking, we propose to use irregular but easy to recognize sounds. We conclude with an interest in a three-dimensional navigation environment using general molecular structures for further research and development.
A Content Analysis of Appeals in Food and Beverage TV Commercials

This study was conducted to analyze the appeals of food and beverage TV commercials which were broadcasted on Turkey's national and international TV channels between 2011-2018.The main aim of this research was to do a reality check and try to compare different groups of food TV commercials according to their basic characteristics and appeal strategies.The research followed Aristotle's modes of persuasion, and product appeals are identified primarily from the literature review and summarized into a systematic coding.Data gathered from the broadcasted TV commercials are reached through the companies own YouTube channels.A total sample of 400 national and international food and beverage TV commercials are grouped and analyzed according to their characteristics such as dairy, beverages, foods for breakfast, oil, meat, pulse and legumes, sauces and dressings, chocolate, and confectionary.The results of the study illustrate the range of techniques used to advertise foods to consumers and the techniques do not generally differ according to the type of food advertised.The most typical product appeals were mood alteration and taste both in national and international TV commercials.
BN-invariant Sharpness Regularizes the Training Model to Better Generalization

It is arguably believed that flatter minima can generalize better. However, it has been pointed out that the usual definitions of sharpness, which consider either the maxima or the integral of loss over a delta ball of parameters around minima, cannot give consistent measurement for scale invariant neural networks, e.g., networks with batch normalization layer. In this paper, we first propose a measure of sharpness, BN-Sharpness, which gives consistent value for equivalent networks under BN. It achieves the property of scale invariance by connecting the integral diameter with the scale of parameter. Then we present a computation-efficient way to calculate the BN-sharpness approximately i.e., one dimensional integral along the "sharpest" direction. Furthermore, we use the BN-sharpness to regularize the training and design an algorithm to minimize the new regularized objective. Our algorithm achieves considerably better performance than vanilla SGD over various experiment settings.
Security assessment framework for educational ERP systems

The educational ERP systems have vulnerabilities at the different layers such as version-specific vulnerabilities, configuration level vulnerabilities and vulnerabilities of the underlying infrastructure. This research has identified security vulnerabilities in an educational ERP system with the help of automated tools; penetration testing tool and public vulnerability repositories (CVE, CCE) at all layers. The identified vulnerabilities are analyzed for any false positives and then clustered with mitigation techniques, available publicly in security vulnerability solution repository like CCE and CWE. These mitigation techniques are mapped over reported vulnerabilities using mapping algorithms. Security vulnerabilities are then prioritized based on the Common Vulnerability Scoring System (CVSS). Finally, open standards-based vulnerability mitigation recommendations are discussed.
Scribble-to-Painting Transformation with Multi-Task Generative Adversarial Networks

We propose the Dual Scribble-to-Painting Network (DSP-Net), which is able to produce artistic paintings based on user-generated scribbles. In scribble-to-painting transformation, a neural net has to infer additional details of the image, given relatively sparse information contained in the outlines of the scribble. Therefore, it is more challenging than classical image style transfer, in which the information content is reduced from photos to paintings. Inspired by the human cognitive process, we propose a multi-task generative adversarial network, which consists of two jointly trained neural nets -- one for generating artistic images and the other one for semantic segmentation. We demonstrate that joint training on these two tasks brings in additional benefit. Experimental result shows that DSP-Net outperforms state-of-the-art models both visually and quantitatively. In addition, we publish a large dataset for scribble-to-painting transformation.
Depression in Jojo Moyes’ Me Before You

This paper focuses on two subject matters of depression found in the novel. The&#x0D; subject matters of this study are the symptoms and the causes of the protagonist’s&#x0D; depression. Each matter applies different theory. For the analysis, Johnson’s and Jiwo’s&#x0D; concepts are used. The design of this study is qualitative descriptive method because&#x0D; the data are analyzed by narrative description and interpretation about the story in the&#x0D; novel. The result of this study shows that the protagonist’s depression can be seen&#x0D; from the three symptoms which are 1) he feels that no one cares about him, 2) he&#x0D; only thinks about himself or self-centeredness, 3) he always tries to end his life. Those&#x0D; symptoms appear due to two causes; 1) he starts to feel lonely and isolate himself after&#x0D; the accident which makes him disabled; and 2) he gets traumatic after the accident&#x0D; and always tries to commit suicide. Although, he tries to overcome his depression by&#x0D; keeping relationship with his lover, he finally commits suicide at the end of this story.
Assessment of Concentrations of Heavy Metals in Soils around ESPRO Asphalt Production and Quarry Site, Wasinmi, Osun State Southwest Nigeria

This work examined the concentration of heavy metals in soil samples around ESPRO Asphalt Production and Quarry Site located in Wasinmi, Irewole Local Government Area, Osun State. A total of nine (9) soil samples were collected from three points with distances of 0, 1 and 2 km from the quarry site. Atomic Absorption Spectroscopy (AAS) was used to determine the concentrations of heavy metals in the soil samples. The results revealed that the mean values metal contents in the soil samples analyzed were in the order Fe&gt;Zn&gt;Cu&gt;Cr&gt;Mn&gt;Pb&gt;Cd. The concentration of the heavy metals in the soil samples were found to decrease as the soil depths increased from 0-25 cm and also as the spatial distances of the sampled locations increased from the quarry plant.&#x0D; The study also showed that low concentrations of Lead (Pb) had a mean concentration value of 0.87 ppm and Cadmium (Cd) with a mean concentration value of 0.29 ppm. It was also observed that Iron (Fe) had the highest mean concentration value of 148.8 ppm. The study concluded that quarrying activities raised the soil heavy metal contents up to the range of about 2 km away from the quarry site. This can be attributed to the number of years of mining and excavation activities in the study area and accumulation of the heavy metal concentrations over time and this could be dangerous to the site environments, especially for farming and groundwater exploration activities in the area.