Reviewerly/exHarmony huggingface dataset:
Internet of Drones Applications in Aviation MRO Business Services

The aim of this research is to identify the optimized outcomes to the aviation maintenance repair and overhaul (MRO) business service provider with the effective use of “DRONES” in their daily operation. Many engineering companies are using the drone services to create a pavement survey for airport pavement management. High-definition images, which were captured from the low altitude by the drone, can be used for airport maintenance to establish a repair program. The airport planner who keeps track on the pavement condition use drone to decide whether they need any replacement. In the airport sector, the drone application is mainly used for engineering and construction projects. Drones help in collecting the land survey data before they begin the project. It helps to evaluated, monitor the project stages, and document the progress of the project during the entire phase of process construction. Many airlines and other aviation stakeholder like MRO service provider have identified that drone can assist them to reduce cost and the time required on Maintenance repair overhaul activities on the components of the aircrafts. The damages, which occurred due to lighting, can be done through visual inspection. If the visual inspection is done manually, it will take nearly four to six hours. But unmanned aerial vehicle (UAVs) can finish 344this visual inspection within half an hour. These drones are used to find the problematic areas in the fuselage and other parts for the scheduled maintenance. It helps us to plan the repairs that are needed. Drone will increase the efficiency of the repair work by delivering the spare parts to the MRO personnel very quickly. Micro drones are used to inspect inside the aircraft engines. The automation of defect detection helps engineers to find the potential damage in the parts, which can be inspected visually at the later stage. This study implies that MRO Business Services require research and investment in technologies like drones, machine learning (ML) and cloud applications to support the maintenance and repair process. This will help in more effective maintenance and component defect identification, optimized technician schedule for each work plan as well as improved turnaround times for aircrafts.
Assessment on irrigation system performance of sugarcane farm using remote sensing at lower Omo basin, Ethiopia

This study was aimed at assessing the irrigation system performance at Omo Kuraz Sugar Cane Development Project using data from remote sensing and meteorological stations. To analyze the distribution of evapotranspiration over the treatment area, the SEBAL (Surface Energy Balance Algorithm) model was used to evaluate the evapotranspiration (ET) rate for sugarcane at the lower Omo River Basin. Surface energy balance algorithm input like NDVI, Land surface temperature, TOA albedo and emissivity was calculated from Land Lat 8 image using the ENVI software. The data were collected from the farm site meteorology station, and the calculated evapotranspiration rate was one of the inputs into irrigation system performance indicators model, along with actual field data gathered from irrigation delivery schedule, root depth of crop at each growth stage, soil moisture before and after irrigation, and water diverted to the field. The four pillars of irrigation system performance are over all consumed ratio, depleted fraction, evaporative fraction, and relative evapotranspiration. This study also examined system performance using four standard indicators; namely, adequacy, efficiency, reliability, and equity. These indicators were calculated using the SEBAL algorithm and data were classified based on satellite and irrigation application. The findings of this study revealed that the irrigation system performed poorly with all treatment fields being below the target performance indicator values (overall water consumption ratio, ep; depleted fraction, DF; evaporative fraction, ᴧ and relative evapotranspiration, RET). The calculated crop water requirements using the SEBAL model and satellite data were not consistent with applied water. The findings from this study also showed that irrigation system performance indicator parameters were limited due to excessive water applied to the field. The study also revealed an acceptable range of RET (0.8, 0.9); however, the irrigation system's reliability was poor according to the results of field observations at the experimental site. This observation was due to the field receiving an excessive amount of water. These results and observations suggest that the irrigation agronomist should schedule irrigation water application based on crop water requirements to manage poor irrigation system performance. Key words: Irrigation system performance, SEBAL model, Remote sensing, Landsat 8, lower Omo Basin
The Integration of Social and Emotional Education in the European Context

In the European context, social and emotional education (SEE) has been developed from social and emotional learning (SEL) and aims to be a core curriculum element across the region. However, to reach the current development in SEE, apart from the effort of individual nations, the roles of the European Union (EU) in uniting and strengthening the SEE concept across its member states is significant. This paper employs document analysis method as the main tool to accentuate the roles of the EU in this development process. It first identifies the educational context and policies articulated by the EU as the foundation for the induction and inception of SEE into the area, which are reviewed afterward. Finally, the current module of SEE adopted across the EU members is introduced as an illustration of the EU’s effort to bolster SEE in their regional area. This paper presents a good example of how a regional union unites and boosts their regional development in general and particularly educational innovation.
The Analysis of CALL, Discussion and Lecture Method Toward Students’ Speaking Skills

Technology and education is rapidly expanding and cannot be separated to each other. One of them is computer-assisted language learning (CALL), which is used to learn English. CALL is a computer-aided learning media used in education that is simple to access, disseminate, and save. Following that, this study attempted to determine the impact of CALL media with lecture technique and discussion on the cadets' English-speaking skills. This study employed a quantitative approach and descriptive quantitative analysis to uncover issues that arose during English learning. By reviewing the learning process, which involved 50 cadets from both classes A and B, statistical analysis was used. The results revealed a substantial difference in the use of CALL media in the lecture style and the discussion on English speaking skill.
Priority Queue-Based Framework for Allocation of High Performance Computing Resources

In high performance computing environment using containers, the performance of an application is always a point of concern in terms of resource allocation. Cluster is formed using containers which are provisioned based on the configuration provided as per application need. The environment has its own distributed cluster using physical machines to launch these containers. The dynamic allocation of containers provides the flexibility for clusters but does not ensure the processing of multiple requests at the same time due to resource limitations. It is difficult to figure out which request has to get the resource in terms of memory, GPU and CPU and which can be avoided due to shortage of resources. This paper describes a priority queue-based framework which is internally based on an algorithm to schedule the resource allocation incoming requests to the containers required to execute high performance computing (HPC) applications. It behaves like a priority-based scheduler to schedule resources allocation. Higher-based priority request will first get the requested resources. Request with same priority will be considered based on request submission time. It is implemented to provide a high performance computing environment to have the solution for allocating the required computing resources to containers and ensure that critical applications with higher priority will get the computing resources. The proposed framework will allocate the resources to containers in a well-balanced environment. The performance has been evaluated and compared with existing algorithms and scheduler.
Application of Deep Learning for COVID Twitter Sentimental Analysis Towards Mental Depression

The coronavirus pandemic hit the worldwide population to a large extent. But, one of the subtle effects of the COVID-19 pandemic was the depletion of the mental health of the people. Social media has become an efficient platform to express oneself, and Twitter is one of the most used platforms. There has been work where machine and deep learning were employed for tweet sentimental analysis for different applications including mental depression. Most of tweets sentimental analysis were focussed on positive and negative. There has been some research where neutral tweets were taken into consideration. In this research work, we have focussed on predicting depression of people, i.e. depressed, non-depressed and neutral from tweets during lock down period by employing deep learning models like bidirection long short-term memory (Bi-LSTM), bi-directional encoder representations from transformers (BERT), and XLNET. Also, the BERT model has been modified by adding classification layer for tweet classification. In addition, the exploratory data analysis was performed for postlockdown tweets.
Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based QA Framework

Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper we meticulously create a large amount of data connected with E-manuals and develop suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40% in ROUGE-L F1 scores over the most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.
Pragmatic competence of pre-trained language models through the lens of discourse connectives

As pre-trained language models (LMs) continue to dominate NLP, it is increasingly important that we understand the depth of language capabilities in these models. In this paper, we target pre-trained LMs' competence in pragmatics, with a focus on pragmatics relating to discourse connectives. We formulate cloze-style tests using a combination of naturally-occurring data and controlled inputs drawn from psycholinguistics. We focus on testing models' ability to use pragmatic cues to predict discourse connectives, models' ability to understand implicatures relating to connectives, and the extent to which models show humanlike preferences regarding temporal dynamics of connectives. We find that although models predict connectives reasonably well in the context of naturally-occurring data, when we control contexts to isolate high-level pragmatic cues, model sensitivity is much lower. Models also do not show substantial humanlike temporal preferences. Overall, the findings suggest that at present, dominant pre-training paradigms do not result in substantial pragmatic competence in our models.
Learning Neural Set Functions Under the Optimal Subset Oracle

Learning neural set functions becomes increasingly more important in many applications like product recommendation and compound selection in AI-aided drug discovery. The majority of existing works study methodologies of set function learning under the function value oracle, which, however, requires expensive supervision signals. This renders it impractical for applications with only weak supervisions under the Optimal Subset (OS) oracle, the study of which is surprisingly overlooked. In this work, we present a principled yet practical maximum likelihood learning framework, termed as EquiVSet, that simultaneously meets the following desiderata of learning set functions under the OS oracle: i) permutation invariance of the set mass function being modeled; ii) permission of varying ground set; iii) minimum prior; and iv) scalability. The main components of our framework involve: an energy-based treatment of the set mass function, DeepSet-style architectures to handle permutation invariance, mean-field variational inference, and its amortized variants. Thanks to the elegant combination of these advanced architectures, empirical studies on three real-world applications (including Amazon product recommendation, set anomaly detection, and compound selection for virtual screening) demonstrate that EquiVSet outperforms the baselines by a large margin.
An Optimized Method for Massive Sensitive Data Classification in an Industry Environment

In the era of big data, data is endowed with higher potential value. However, new challenges are also brought to data security, especially for the sensitive data in an industrial environment. Nowadays, with the development of industrial internet, enterprises connect each other, under which a slight carelessness may lead to the leakage of sensitive data, which will bring inestimable losses to enterprises. Hence, sensitive data classification is required as a secure way to avoid such situation. This paper presents a sensitive data classification method based on an improved ID3 decision algorithm. Firstly, we introduce the idea of attribute weighting to optimize the basic structure of traditional ID3. Secondly, we use the weighted information gain to select nodes during tree construction, which improves multi-value bias defect compared with the traditional algorithm. Experimental results show that we can achieve branching accuracy up to 97.38%.
PENGARUH PENGGUNAAN LEMBAR KERJA SISWA (LKS) TERHADAP HASIL BELAJAR SELAMA NEW NORMAL PADA MATA PELAJARAN INFORMATIKA

This research is motivated by the results of learning Informatics in class X MAN 2 Agam during the current New Normal where learning outcomes are under the Minimum Completeness Criteria (KKM) that have been determined. Problems found, problem-based learning strategies are needed to help teachers in the delivery of learning materials, namely by asking students to solve a problem related to the subject matter so that students can be active in the learning process. The type of research used is Quasi Experiment. The population in this study were students of class X MAN 2 Agam for the 2020/2021 academic year. Sampling is done by purposive sampling technique, namely taking samples with certain considerations. The population of class X is 114 students. Based on the results of the daily test (initial) the average class X IPA was made the Experiment class, while some students from class X IPS/IPK were used as the control class. The data of this study were obtained by a final test in the form of objective questions as many as 25 items that have been tested. Because the value of tcount &gt; ttable, it means that H0 is rejected and H1 is accepted. Based on the above calculations, it can be concluded that tcount &gt; ttable (0.203 &gt; 1.68) where H0 (No Effect of Using Student Worksheets (LKS) on Learning Outcomes During New Normal In Informatics Subjects in MAN 2 Agam) is rejected, then Ha (There is an Influence of Using Student Worksheets (LKS) on Learning Outcomes During New Normal In Informatics Subjects at MAN 2 Agam) is accepted.
Technologies in Two Academic Libraries During the Covid-19 Pandemic The Case of the Alma Jordan Library and the Open Campus Libraries and Information Services, The University of the West Indies

The global COVID-19 pandemic has ushered in a new environment described as the ‘new normal,’ changing the way people live, learn, work, and communicate. Academic libraries have followed the lead of other sectors – business, government, health, and education – in adopting Fourth Industrial Revolution (4IR) technologies to re-engineer operations and re-imagine services which are supportive of teaching, learning, and research. This chapter presents case studies that highlight existing and potential applications of 4IR technologies at two academic libraries in the Caribbean, the Alma Jordan Library (AJL) and Open Campus Libraries and Information Services (OCLIS) at The University of the West Indies (UWI). The chapter discusses the short-term implementation of an artificial intelligence-driven digital assistant responsive to first-line reference and research queries at the AJL and explores the future potential deployment of 4IR technologies such as augmented and virtual reality, robotics, the internet of things, and 3D printing in academic libraries to enhance experiential teaching and learning experiences. The chapter concludes by showing how academic libraries’ adoption of a best practice model can enable the seamless integration of 4IR technologies into programmes, products, and services.
A STUDY ON ENGLISH GRAMMAR AND PRAGMATICS COMPETENCE OF INDONESIAN SPEAKERS

The aim of the research were (1) to examine and explain the role of English Grammar Competence toward English Pragmatic competence, (2) to find evidence of the conjunction of pragmatic and Grammar competence of Indonesian English Speakers. Qualitative and quantitative methods were applied to analyze the result of the findings. English Grammar competence test (IELTS model test) was given to determine the subjects of the research Grammar competence. The English pragmatic test was given to determine the subject of the research competence on English pragmatic. The subjects of the research were grouped into 3 (three) categories; group A which consist of subjects whose English Grammar competence are at basic level (IELTS score 5 to 6), group B consists of subjects whose English Grammar competence are at intermediate level (IELTS score 6 – 7) and group C consists of subjects whose English Grammar competence are at Advance level (IELTS score above 7). Each group consists of 5 individuals, who have no prior information of the test model and the aims of the research. The English Grammar competence (IELTS model) is administered before the pragmatic competence test, to group the subjects of the research. The grouping can also be fulfilled by subjects’ official IELTS statement of result. There are 3 parts of the English Pragmatic Competence which researcher applied to determine the result of the research, (1) identifying formal, informal and neutral request utterances, (2) Expressing subjects willingness and unwillingness with reasons in using the utterances (3) Answering appropriately to multiple questions. The result of the research shows various result from the three levels examined of the English Grammar competence level. The research result also showed the strong conjunction between Grammar and pragmatic competence at higher level or at the third group where the students with higher competence at grammar has higher score on the pragmatics competence as well. The research result surprisingly shows low level of pragmatics competence at the second or those who has intermediate grammar competence and low levels of pragmatics competence to the first group, those who has basic Grammar competence
The Effectiveness of Android-Based Tajwid Learning Media on Qur'an Living Literature Activities in Increasing Student Tajwid Learning Outcomes

ABSTRACK This study aims to: (1) find out the procedures for developing Android -based Tajweed learning media in improving student learning outcomes at the University of Muhammadiyah Malang, and (2) determine the effectiveness of Android -based Tajweed learning media in improving student learning outcomes at the University of Muhammadiyah Malang. To achieve the above objectives, researchers use a type of research and development model of qualitative descriptive and quantitative descriptive approaches. Data collection techniques used were observation, interviews, questionnaires and documentation. The product design used is the Borg and Gall model which consists of 7 stages, namely (1) needs analysis, (2) product design, (3) development process, (4) expert validation, (5) product revision, (6) field trials, (7) final products. Results from Design Experts reach 100% (Very Valid). Researchers conducted trials through two stages: limited trials reaching a percentage of 98.86% (Very Valid) and field trials reaching a percentage of 93.5% (Very Valid). This study shows that an increase in student learning outcomes at the University of Muhammadiyah Malang. From the control group and the experimental group the t value in the control group was 3.056 and the t value in the experimental group was 4.879. All Ho was rejected because the value of t&gt; t table = 0.2785 with a ratio of 0.736 was more effective using the Android -based Tajweed learning media that researchers developed.
Scalable Preparation and Measurement of 2N-dimensional Time-bin States in (2N+1) Mutually Unbiased Bases

We propose (2 N +1) mutually unbiased basis measurement for 2 N -dimensional time-bin states, where the number of devices scales logarithmically with the dimension. We observed average error of only 3.8% for four-dimensional phase bases.
ENRICHING THE ENGLISH STUDENTS’ SPEAKING ABILITY BY APPLYING NARRATIVE STORY TELLING WITH GUIDED QUESTIONS

Applying appropriate technique to expand the students speaking ability is the English lecturer’s responsibility in STKIP YPUP Makassar to create the situation. Narrative Storytelling with Guided Question is a technique in generating speaking class more enjoyable, fun and memorable for the students. This research aims at: (1) finding out whether or not narrative storytelling technique with guided questions enrich the students speaking ability and (2) knowing the students' attitude toward the use of narrative storytelling technique with guided questions in learning speaking.This research employed quasi experimental design. The population of this research was the students of English department at STKIP YPUP Makassar in academic year 2017/2018. The sample of this research consisted of 60 students that belong to two groups; 30 students in control group and 30 students in experimental group. The research data was collected by using speaking test through interview and questionnaire which were analyzed descriptively and by inferential statistic through SPSS 22.0 program. The students’ result of posttest of experimental group is significantly enrichd than the students’ result of posttest of control group by the mean score 71.45 &gt; 59.74. The difference of both scores is statistically significant based on the t-test value at significant level 0.05 in which the probability value is lower than the significant level (0.00 &lt; 0.05). The mean score of the students’ attitude is 79.10 and it is categorized as positive attitude. It can be concluded that narrative storytelling technique with guided questions is effective to enrich the students’ speaking ability, and the students give a positive attitude in learning speaking by applying narrative storytelling technique with guided questions.