Reviewerly/exHarmony huggingface dataset:
DIGITAL TECHNOLOGIES IN ONLINE ENGLISH CLASS FOR RAILWAY MECHANICAL STUDENTS DURING COVID19 OUTBREAK

Online learning has opened many innovations and opportunities in developing digital technologies in classroom, especially in language learning. However, in English for Specific Purpose (ESP) like in Railway Mechanical Technology study program, the development of online applications is still limited. Therefore, there is an urgency to conduct study about digital technologies in English class for Railway Mechanical Technology. This study is aimed to describe instructional design of general English course for Railway Mechanical Technology and investigate the learning outcomes of general English course utilizing digital technologies. The data were collected during 2nd semester of 2020/2021 academic year (March – August) in Railway Mechanical Technology study program. There were 24 students participated in this study (91.67% male and 8.33% female). The data were analyzed using Paired-sample t-test, Pearson r correlation, and Spearman rho correlation. The results showed the significant improvement in online English class for Railway Mechanical Technology. Despite the correlation showed linier direction between pretest and posttest, the degree of correlation was low with weak entailment.These results shed light on course design utilizing digital technologies in general English course for Railway Mechanical Technology study program.
Analisis dan Pemetaan Mata Kuliah Bersyarat Program Studi Fisika Menggunakan BackPropagation Neural Network

This study aims to analyze and map the conditional courses at the Tadris Physics Study Program, Faculty of Tarbiyah and Teacher Training, Sulthan Thaha Saifuddin State Islamic University Jambi. This research is an applied science research, data analysis using quantitative descriptive technique. The data is in the form of documenting the value of the 2019/2020 Tadris Physics Study Program students. The research sample consisted of 11 sample subjects from 19 population subjects. The data is processed using Backpropagation Neural Network with Python programming language. Validation and accuracy of prediction results using Mean Absolute Percentage Error and determinant coefficient R Square. The prediction results of conditional courses obtained are accurate and valid with MAPE values &lt;10% (very good) and R Square values close to 1. This study shows that the mapping of prerequisite courses set by the study program is appropriate, except for Basic Physics Courses. 2 (R 0.216) and Mathematics Physics Course I (R 0.50) require additional other prerequisite courses.Keywords: mapping; conditional courses, backpropagation neural network
An empirical study on the current adoption of quantum programming

Quantum computing is no longer just a scientific curiosity; it is rapidly evolving into a commercially viable technology that has the potential to surpass the limitations of classical computation. As a result of this transition, a new discipline known as quantum software engineering has emerged, which is needed to describe unique methodologies for developing large-scale quantum applications. In the pursue of building this new body of knowledge, we undertake a mining study to elicit the purposes quantum programming is being used for, and steer further research.
ASSESSMENT AND EVALUATION IN HIGHER EDUCATION QUALITY MANAGEMENT

The process of education quality management in combination with evaluation and analytical activity is considered in this research as an activity to implement the goals and coordinate the activities of all components of the educational system, its subjects and achievement of the planned results based on management decisions, the validity and predictability of which enhance the evaluation and analytical activity. Objective assessments of learning outcomes based on the theory of pedagogical measurements have a priority role in providing information base for evaluation and analytical activities in education quality management. These measurements should be the basis for recognition, analysis, functioning, development, forecasting and improvement of education quality management systems. A systematic and prioritised approach to quality management should be developed at different levels, from the macro level to the quality management of the individual educational organisation. A sound use of information in education quality management requires the construction of a hierarchy and interconnection of information flows. This systematic approach should be comprehensive, combining dynamic, comparative, differentiating and predictive analysis of assessment-analytical data. When carrying out assessment-analytical activities based on measurement results in education quality management, a set of scientific requirements for information base characteristics should be taken into account, contextual factors in the analysis and interpretation of measurement data should be taken into account. The requirements primarily include the use of measurement models and design, high reliability and validity of the results of assessment and evaluation activities, extensive use of dynamic approach to assessment of learning quality and ensuring compatibility of assessment and evaluation results, the use of balanced indicators of educational quality, operable in measurement. A system of balanced indicators of the quality of learning achievements, structured according to the levels of education quality management, is presented.
Dilated convolutional neural network for WSD

In language processing, the identification of correct sense of word is a difficult one until to date. The reason behind this problem is the contribution of different meanings for a single word. A single word can give multiple meanings based on their expression and the neighboring words. This necessitates research in the field of understanding the correct sense of context. The research process has been started from the golden era to modern days using different approaches like supervised learning, unsupervised and semi-supervised learning. This paper is about the supervised learning approach for the discrimination of sense of words. The approach is based on the training of a type of Convolutional neural network called Dilated Convolutional Neural Network. The practical simulation of the proposed method is implemented using WORDNET. The proposed method produced the good results with an accuracy of 98.1% on SEMEVAL TASK 11.
THE INFLUENCE OF ENGLISH READING ATTITUDE ON STUDENTS’ READING COMPREHENSION

Attitude in reading the English texts drives students to have enjoyable or unpleasing reading practice and affects the final result. This study intended to look for the influence of English reading attitude along with its aspects on students' reading comprehension. It also attempted to figure out which aspect of reading attitude contributed to reading comprehension. This study was conducted using correlation method. The population was the tenth grade students of an Islamic Senior High School in Palembang. The total sample participated was118 students selected by using cluster random technique. The data collection technique used reading attitude questionnaire and reading comprehension test. After analyzing the data using correlation and regression analysis, it was found that there was a significant correlation between reading attitude and reading comprehension. Though the correlation was in low category, reading attitude gave contribution to reading comprehension. For the aspects of reading attitude, cognitive attitude, negative affect, and self-assessment significantly correlated to reading comprehension. However, only negative affect showed its contribution. It indicates that English reading attitude influenced reading comprehension in positive way but only in small portion. Students could recognize the importance of reading English text for their language improvement and future success. However, poor reading comprehension skill obstructed them to have more practice in reading. The negative feelings about reading materials block students to achieve comprehension on the subject matter. Keywords : reading attitude, reading comprehension
Faster crystallization during coral skeleton formation correlates with resilience to ocean acidiﬁcation

Coral skeletons are made of aragonite (CaCO 3 ) forming by attachment of amorphous particles and ions, continuously added to the skeletons' growing surface 1 .Amorphous particles are formed just outside the skeleton, in intracellular vesicles within calicoblastic cells.This observation was repeated in three diverse genera of corals: Acropora sp., Stylophora pistillata differently sensitive to ocean acidification (OA) 2 and Turbinaria peltata.Thus, particles are formed away from seawater, in a presumed intracellular calcifying fluid (ICF) in closed vesicles and not, as previously assumed, in the extracellular calcifying fluid (ECF), which, unlike ICF, is partly open to seawater.After particle attachment, the growing skeleton surface remains exposed to ECF, and, remarkably, its crystallization rate varies significantly across genera.The skeleton surface layers containing amorphous pixels vary in thickness across genera: ~2.1 μm in Acropora, 1.1 μm in Stylophora, and 0.9 μm in Turbinaria.Thus, the slowcrystallizing Acropora skeleton surface remains amorphous and soluble longer, including overnight, when the pH in the ECF drops.Increased skeleton surface solubility is consistent with Acropora's vulnerability to OA, whereas the Stylophora skeleton surface layer crystallizes faster, consistent with Stylophora's resilience to OA. Turbinaria, whose response to OA has not yet been tested, is expected to be even more resilient than Stylophora, based on the present data 3 .1 C-Y Sun, CA Stifler, RV Chopdekar, CA Schmidt,
Soft skill improvement for Seruni Foundation foster children

The lack of financial literacy, the low level of social skills, and the low self-confidence of Seruni Foundation children are the backgrounds of this service program being implemented. This program aims to provide training on developing and improving soft skills and financial skills for Seruni Foundation children. The specific target to be achieved is to be able to improve soft skills and financial skills in Seruni Foundation children such as having a spirit of competitiveness, adaptive and anticipatory, open to change, able to learn, skilled, easy to adapt to new technology, literate about finances and budgets and having a strong foundation of the ability to develop. The method used is to provide consultation and training. The parties involved in this program are consist of lecturers in the fields of finance and organizational behaviour, in collaboration with a foundation that manages the Seruni Foundation children for 14 hours divided into two days. With the training provided by the team, Seruni Foundation children have become more developed, more confident, understand how to manage finances properly and are able to compete in the business and industrial world
Towards Efficient Superconducting Quantum Processor Architecture Design

More computational resources (i.e., more physical qubits and qubit connections) on a superconducting quantum processor not only improve the performance but also result in more complex chip architecture with lower yield rate. Optimizing both of them simultaneously is a difficult problem due to their intrinsic trade-off. Inspired by the application-specific design principle, we propose an automatic design flow to generate simplified superconducting quantum processor architecture with negligible performance loss for different quantum programs. Our architecture-design-oriented profiling method identifies program components and patterns critical to both the performance and the yield rate. A follow-up hardware design flow decomposes the complicated design procedure into three subroutines, each of which focuses on different hardware components and cooperates with corresponding profiling results and physical constraints. Experimental results show that our design methodology could outperform known general-purpose design schemes with better Pareto-optimal results.
Analysis of The Impact of Sedimentation Dredging on Headloss Repair &amp; Operating Patterns of CWP (Circulating Water Pump) PLTU Lontar

The PLTU Lontar Extension Project (1x315 MW) is a national strategic project being built by PT. PLN (Persero). The project stage is currently in the commissioning phase but is hampered by indications of sedimentation occurring at the intake mouth area of ??the Circulating Water System. There are indications of sedimentation at the intake mouth due to the high head loss between the intake mouth and the upstream box culvert which has an impact on inhibiting the flow of seawater entering the Lontar PLTU intake canal. The existence of obstacles to the flow of sea water has resulted in insufficient cooling water flow for the needs of 8 Circulating Water Pump (CWP) pumps, namely 6 CWP for 3 units of PLTU Existing and 2 CWP for 1 unit of PLTU Extension. The sea bed level on the side of the intake mouth according to the basic design is -4.5 m but after the survey (bathymetry, topography, floating sediment, currents, tides) the actual condition is currently at a depth of -0.9 m. As one solution, the dredging method is taken to restore conditions according to the basic design. To find out if there is an improvement in headloss, a simulation is carried out using the Hec-ras 2D application. After the simulation, it was found that the headloss/compression loss significantly improved in the hydraulic simulation with a flow rate of 42 m3/s at 6 pumps running, before dredging 30 cm and after dredging to 18 cm and also with a flowrate of 60 m3/s at 8 pumps running. , before dredging 55 cm and after dredging to 21 cm at the right position upstream of the box culvert. This headloss condition is predicted to propagate downstream to the forebay and CWP pump pits, so that dredging in this area is simulated to increase the performance of the Lontar PLTU circulating water intake so that 8 CWPs can operate together continuously.
Combining SDLC Method and ITIL Framework by Involving Auditors

The framework that provides best practice in IT service management is called IT Infrastructure Library (ITIL). The IT service management leads to the management of a service, this service is generated through the system development process. Each system has a different scale, characteristics, requirements, types, and properties but has the same stage in its development, that is by implementing the System Development Life Cycle (SDLC)&#x0D; Software developers on IT projects must implement SDLC by selecting a particular model process. But many IT development teams do not pay attention to software management frameworks or standards so that they will experience difficulties in planning, reporting work results, monitoring, controlling, etc., especially when all personnel in the team don’t have sufficient experience. It would be better if every software house company and IT Consultant applies the general framework and standardization so that the software development process will be more directed and produced high quality services.&#x0D; In conducting the SDLC process, an accountant is needed, in this case the auditor who has a role as an information provider in describing business processes in a company activity, the auditor also plays a role as controller that ensuring the system is in accordance with accounting regulations, and accountants also act as checker of the system so that the auditors must involved in the initial design of a system. The application of ITIL in the SDLC process will have an impact on the ease of the auditor in carrying out the duties.
Letting Spring Build Your Data Access Objects

In Chapter 6 , we saw how we can build a data access object using JDBC, configured with Spring. In this chapter, we’re going to take a look at Spring Data, which provides a bridge to other underlying persistence mechanisms – the Java Persistence API, in this case, using Hibernate as an underlying implementation.
Pelatihan Pengolahan Dan Visualisasi Data Assessment Berbasis Google Form Di Buayan

Data processing is one of the most important aspects in the assessment stage. The existence ofdata processing and visualization will make it easier to read the assessment results. Thisservice is carried out to provide knowledge and training on processing and visualizingassessment data based on google form at the MWC NU institution, Buayan District, KebumenRegency. The subject in this service is Syuriyah (supervisory board) at MWC NU Buayan.This service is carried out for two days through four stages, namely planning, preparation forimplementation/implementation, and evaluation/reflection. The results of the service showthat Syuriyah can process and visualize assessment data well. Processing and visualization ofassessment data is the key in providing recommendations from the results of monitoring andevaluation.
Sentiment Analysis and Topic Modelling of The COVID-19 Vaccine in Indonesia on Twitter Social Media Using Word Embedding

This study aims to analyze the sentiments of the Indonesian people towards the COVID-19 vaccine on Twitter. Data collection was carried out from September 2020 to June 2021 with the keyword "covid vaccine," which resulted in 262306 tweets. After filtering and cleaning, there are 83384 tweets left. The labeling process was done manually by an expert. The label composition in the data is 35209 tweets of positive sentiment, 41596 tweets of neutral sentiment, and 6579 tweets of negative sentiment. The remaining data is preprocessed using case folding, removing punctuation, stopword removal, stemming, and the application of slang words. The highest number of tweets appeared in January 2021, after Joko Widodo became the first person in Indonesia to receive a vaccine injection. The number of tweets reached 23492 tweets. At the topic modeling stage, measurements were conducted using the Coherence Score. The distribution of the optimal number of topics is 3 topics. The first topic, with a token percentage value of 51.8%, leads to positive sentiment, while the second and third topics, with token percentage values of 24.5% and 23.7%, lead to neutral sentiment. Bidirectional LSTM architecture was implemented to perform sentiment classification. Fasttext and GloVe word embedding was tested to vectorize tweet data. The test accuracy generated by Fasttext word embedding reached 75,7690%, while the test accuracy produced with GloVe word embedding reached 74.7017%. The usage of slang words could not increase the test accuracy in this study. The use of the Modelcheckpoint to monitor model performance during training could produce a model with a slightly higher test accuracy, about 1.07% (in scenario 1 and scenario 6), compared to a model whose performance was monitored using Early Stopping. In future research, it can be tried to apply a lower learning rate to produce better accuracy in a large number of epochs, or it could be by changing the dropout parameter.
Leather Product Recommendation System using Collaborative Filtering Algorithm

Currently, e-commerce and marketplaces are constantly evolving to satisfy consumers’ needs more efficiently and conveniently. Technological developments make ecommerce smarter to serve users by providing recommendations according to user needs. Various types of products are traded in the marketplace, including leather products. Therefore, this study aims to build a recommendation system for leather products. By using the Collaborative Filtering algorithm, the system will provide recommendations for leather products to users based on patterns formed from the history of rating or user ratings. This research results in a web-based recommendation system to help users find leather products by implementing Collaborative Filtering. The experimental results on 50 leather products and 644 ratings given by 30 respondents showed an average value of Mean Absolute Error (MAE) from the application of Collaborative Filtering of 1,929. This MAE value indicates that Collaborative Filtering can recommend skin products well according to user expectations.
Choosing the best projection for 360° video

This paper compares the effectiveness of different projections for different 360-degree videos and codecs, including AV1, HEVC, VP9, and AV. Content for 360 video is especially challenging because using codecs on reprojections magnifies video-stream errors. For this reason, we tested different projections to determine their stability over different encodings. Our results came from performance measurements using end-to-end metrics such as PSNR, SSIM and VMAF as well as subjective assessment of static viewports.
Blended Experience Narratives

Our work employs conceptual integration, otherwise known as blending, as a tool for designing interactions that work across and between the digital/physical divide.Theoretical approaches to blends can be difficult to navigate and a hard subject to get to grips with, particularly for novice designers.This paper offers a synthesis of the literature around this topic developed into a framework.We use speculative design as a means for iterating solutions that employ physical/digital transitions.Working collaboratively with thirteen undergraduate university students attending a twelve-day Blended Interactions workshop, we developed speculative narrative storyboards over the course of a live theatre festival ultimately presenting proposed solutions as part of the festival itself.Narratives drew on data gathered through participant observation and interviews of stakeholders with data and feedback from over 380 festival visitors.Presenting the storyboards to festival participants provided direct feedback.The student storyboards were professionally illustrated affording us a means for evaluating our framework in further work.
Semi-automatic Algorithm for Lumen Segmentation in Histological Images

In this paper we focus on a problem of lumen segmentation in histological images. A large number of annotated images are necessary for the development of diagnostic algorithms that can help to detect changes, such as lumen serration, indicating really serious health problems like cancer. We propose a semi-automatic interactive segmentation algorithm to accelerate the process of manual image annotation. The core of our annotation approach is a classical graph-cut algorithm that uses manually selected parameters. The user annotates an image with two types of scribbles corresponding to the gland lumen structure and the non-lumen area. After that, the model annotates all unlabeled pixels, providing the user with a fully annotated image based on the scribbled input data. The user can interact with the annotation algorithm and add new scribbles to adjust the result. The algorithm allows to reduce the annotation time of a typical histological image by 10 times for the PATH-DT-MSU dataset that can potentially seriously increase the number of fully annotated histological images.