Reviewerly/exHarmony huggingface dataset:
STRATEGIES OF TEACHING SPEAKING IN ENGLISH TO DESA BAHASA SRAGEN’S STUDENTS

This research aims to find out the teachers’ strategies of teaching speaking. To reach the goal, a case study on two teachers is used in this research. The participants of this research are two teachers in Desa Bahasa Sragen . The researcher is interested to conduct the research there because it offers a program called ten days English acceleration program. It claims that the students will be able to speak English fluently after joining the program.. The researcher used and the teachers’ interview to collect the data. The finding shows that there are three strategies are used at Desa Bahasa Sragen , they are: cooperative activities, creative task and drilling. The findings of this research are expected to be beneficial for the teachers on conducting the English teaching learning process, especially in teaching speaking in formal school.
Exploring MDSplus data-acquisition software and custom devices

MDSplus is a software tool designed for data acquisition, storage, and analysis of complex scientific experiments. Over the years, MDSplus has primarily been used for data management for fusion experiments. This paper demonstrates that MDSplus can be used for a much wider variety of systems and experiments. We present a step-by-step tutorial describing how to create a simple experiment, manage the data, and analyze it using MDSplus and Python. To this end, a custom example device was developed to be used as the data source. This device was built on an open-source electronic hardware platform, and it consists of a microcontroller and two sensors. We read data from these sensors, store it in MDSplus, and use JupyterLab to visualize and process it. This project and code demo are available on the GitHub site at this URL: https://github.com/santorofer/MDSplusAndCustomeDevices
Reform of College English Teaching Mode Guided by Formative Evaluation

The mode of college English teaching oriented by formative evaluation is a kind of new teaching concept, which requires college English teachers to fully understand. Based on the author’ teaching and practice experience, this paper first introduced the connotation of formative evaluation, then analyzed the principles of formative evaluation in college English teaching, and finally discussed the strategies of college English teaching reform oriented by formative assessment in order to improve the quality of college English teaching.
LISTENING COMPREHENSION STRATEGIES EMPLOYED BY INDONESIAN EFL STUDENTS: LESSON LEARNED FROM INDONESIAN ISLAMIC HIGHER EDUCATION

The objective of this research is to know the strategies in acquiring listening comprehension used by English students in English classes of Islamic university in Bengkulu, Indonesia. The data of the research included questionnaire and interview the result of this research, there were two strategies in acquiring listening: First in direct strategies they were memory, cognitive and compensation strategies. Second in indirect strategies they are metacognitive, affective and social strategies. Especially in direct strategies, memory was the most strategy was chosen by students in the second semester. Indirect strategies, social was the most strategy was chosen by student. So the most strategies used by English students are memory and social strategy. The objective of this research is to know the strategies in acquiring listening comprehension used by English students in English classes of Islamic university in Bengkulu, Indonesia. The data of the research included questionnaire and interview the result of this research, there were two strategies in acquiring listening: First in direct strategies they were memory, cognitive and compensation strategies. Second in indirect strategies they are metacognitive, affective and social strategies. Especially in direct strategies, memory was the most strategy was chosen by students in the second semester. Indirect strategies, social was the most strategy was chosen by student. So the most strategies used by English students are memory and social strategy
Particle swarm optimization of multi-linear regression for evapotranspiration estimation model

The data demanding Food and Agricultural Organization-56 Penman-Montieth model (FPM-56) is the most accurate model in estimating evapotranspiration (ET) but it is not applicable at data scarce region. This paper evaluates the performance of conventional MLR models and improved MLR by using PSO algorithms (MLR-PSO) in estimating potential evapotranspiration (ETp) by only using 2 significant parameters affecting ETp for tropical climate. In this study, 17 meteorological stations around Peninsular Malaysia were used in this study and obtained its both MLR and MLR-PSO models. These models were compared by using root mean square error (RMSE), coefficient of determination (R2) and its accuracy (Acc). The obtained results show MLR models itself has accuracy closed to 94% against FPM-56 models. Whereas optimized MLR-PSO models has improved up to 2.95% of accuracy. Out of 4 PSO algorithm, the standard c1=c2=2.0 and w=1.0 resulted better performance in 7 stations compared to others. The results proves that MLR and MLR-PSO models both useful for estimating ETp at data scarce region as it required only 2 main parameters affecting ETp.
Supervised machine learning for region assignment of zebrafish brain nuclei based on computational assessment of cell neighborhoods

Histological studies provide cellular insights into tissue architecture and have been central to phenotyping and biological discovery. Synchrotron X-ray micro-tomography of tissue, or “X-ray histotomography”, yields three-dimensional reconstruction of fixed and stained specimens without sectioning. These reconstructions permit the computational creation of histology-like sections in any user-defined plane and slice thickness. Furthermore, they provide an exciting new basis for volumetric, computational histological phenotyping at cellular resolution. In this paper, we demonstrate the computational characterization of the zebrafish central nervous system imaged by Synchrotron X-ray micro-CT through the classification of small cellular neighborhood volumes centered at each detected nucleus in a 3D tomographic reconstruction. First, we propose a deep learning-based nucleus detector to detect nuclear centroids. We then develop, train, and test a Convolutional Neural Network architecture for automatic classification of brain nuclei using five different neighborhood sizes, which correspond to 8, 12, 16, 20 and 24 isotropic voxel dimensions respectively. We show that even with small cell neighborhoods, our proposed model is able to characterize brain nuclei into the major tissue regions with a Jaccard score of 74.29% and F1 score of 85.34%. Using our detector and classifier, we obtained very good results for fully segmenting major zebrafish brain regions in the 3D scan through patch wise labeling of cell neighborhoods.
경제 분석을 위한 텍스트 마이닝 (Text Mining for Economic Analysis)

Korean Abstract: 본고에서는 텍스트 분석이 이루어지는 과정, 주요 방법론, 경제 분석에의 적용 사례 등을 상세히 기술하였다. 텍스트 마이닝이란 빅데이터 분석 또는 데이터 과학의 대표적인 분야이며, 머신러닝 등 전산기술을 활용하여 언어에 담긴 정보를 정형적 데이터로 추출해내는 과정이다. 언어는 다의성과 비정형성 등 고유한 특성이 있어 이로부터 원하는 정보만 추출해내기는 쉽지 않다. 텍스트 마이닝을 활용할 경우 기존 경제학적 방법으로 예측이 어려운 실시간 예측 및 불확실성 측정 등이 가능하며, 중앙은행 커뮤니케이션 분석 등 기존 모형의 적용이 어려웠던 주제들에 대한 분석도 가능하다. 전산기술의 발달과 동시에 텍스트 마이닝 기법은 빠른 속도로 발전해가고 있으며, 최근 학계와 중앙은행의 관심도 지속적으로 증가함에 따라 경제학 분야에서도 텍스트 마이닝을 활용한 연구가 주요 학술지에 게재되고 있다. 경제학적 분석에 주로 사용되는 구조화된 수치자료의 한계를 보완함과 동시에 새로운 정보의 원천으로써 텍스트 데이터의 수요는 더욱 증대될 것으로 기대된다.English Abstract: We provide detailed description of how text data analysis is done and review series of studies done through text mining. Natural language can be characterized with ambiguity and obscurity compared to structured data. It is hard to retrieve useful information from text data as it carries natural language itself. Text mining or natural language processing is a multi-discipline area of modern technic in which we can distill and obtain just what we need from text. With the development of AI and machine learning, text mining is becoming one of the high-end technology in various fields of research even in economics. We expect there will be more demand for text data analysis as it will be complementary to traditional structured data and also as a new source of information.
Signs of things to come?

Network SecurityVol. 2020, No. 2 RegularsSigns of things to come?Kate MacMillanKate MacMillansecurity analystSearch for more papers by this authorKate MacMillanPublished Online:3 Nov 2021https://doi.org/10.1016/S1353-4858(20)30022-2AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 February 2020Volume 2020Issue 2ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 3 November 2021 Published in print 1 February 2020 Information© MA Business Ltd.PDF download
Innovative mentorship programmes for female academics in two African Universities of Technology via ubuntu: An exploratory study

Universities are places for knowledge and skills dissemination; unfortunately, the career in the academia is male dominated. Few female academics often occupy lower positions in the career. The need for some affirmative action in the form of pro-female mentorship programme becomes crucial to empower women to compete equally with their male counterparts. This study employed qualitative method of interviews to explore the effects of pro-female academic mentorship programmes based on the African philosophy of Ubuntu (humanness) in two African Universities of Technology. The study revealed that mentorship programme underpinned by African values of compassion, cooperation and love can empower female academics with better knowledge and skills to compete equally with their male counterparts. The study recommended that African Universities of Technology should innovate and Africanise their mentorship programmes for female academics to empower them with skills and knowledge for better career prospects in the academia.Keywords: Expert; Female Academics; Innovation; Mentorship; Novice; Retention; Ubuntu
A Study on Cloud Migration Models and Security Issues in Cloud Migration

Cloud migration is the process of transferring the data, applications, operating systems and all other IT resources to cloud or a physical server. Cloud migration can also defined as migration of organizational infrastructure, computing resources, physical storage, Operating Systems, platform services and software are moved to the cloud. It is often involves combining an on-site IT infrastructure with a hybrid cloud, that may be accessed over the Internet for a nominal fee. This paper is to study and understand Different cloud migration Models and all security concerns to be taken into account when the user wants to go for Cloud Migration. Even though the advantages of usage the cloud seems to be attractive, the enterprise decision-makers should also consider the security threats associated with cloud-service migration, thereafter have to take necessary precautions to overcome or to eliminate those security issues and perform a threat free cloud migration to ensure data confidentiality and completeness on the new Virtual Environment.
Accelerating an On-Line Approximation Mining for Large Closed Itemsets

In this paper, we study acceleration methods for on-line stream mining of all frequent closed itemsets under a minimal-size restriction. The algorithm LC-K-CloStream [3] can perform an 6-approximation on-line mining based on incremental intersection of transactions. We first integrate LC-K-CloStream with an extended FP-tree with skipping in order to effectively compress a huge number of mined closed itemsets. Next, we introduce novel pruning methods for rejecting a hopeless intersection computation by using look-ahead maximal-size estimation. We show, through experimental evaluations, that the proposed methods have a great performance for mining a large set of closed itemsets in dense data sets.
Use of the Principle of Language Courage in Middle School Students

The effectiveness of teachers in speaking using polite language in learning Indonesian to junior high school students by referring to the theory of politeness principles according to Leech (1983), Lakoff (1972), Brown and Lavinson (1978), Fraser (1978). This study uses descriptive qualitative methods to obtain naturalistic data, data collection using competent and free listening techniques and record techniques. The data analysis technique uses the interactive model of Miles and Huberman (2014) which has three components of analysis: data reduction, data presentation, and drawing conclusions or verification. The results of the study found 4 forms of politeness, namely: 1) maxim of wisdom, 2) maxim of generosity, 3) maxim of agreement, 4) maxim of modesty, while forms of violation of politeness principle are 2, namely: 1) maxim of generosity.
FOREST FIRE PREDICTION WITH MACHINE LEARNING

Data mining brings understandings, outlines, and descriptive and predictive representations from the large amounts of data available today in many organizations. We have many tools for data mining. One of those tools is rattle which runs on R programming language. In this paper, we used rattle to analyze forest fires which occurred in different regions during different periods of time. We can study whether rattle is an efficient tool by analyzing its time taken for delivering the result, error rate and much more.
CFA Model and Service Quality, Customer Satisfaction and Loyalty: The Example of Four and Five Star Hotel Restaurants in Jordan

The aim of this research is to investigate the relationships between service quality, customer satisfaction and loyalty in a hotel restaurant context. The sample consisted of 430 respondents who stayed at four- and five-star hotels in Jordan. A structured questionnaire, with a five-point Likert scale a well as a confirmatory factor analysis (CFA) and structural equation modelling (SEM) were used to analyse the casual relationships between service quality, customer satisfaction, and customer loyalty to evaluate the hypotheses regarding relationships among model constructs. All the hypotheses developed in the study were positively confirmed, reinforcing the theory and previous research on this field. The study also reveals interesting implications in service quality, satisfaction, and customer loyalty which is useful to academics and practitioners. Managers will find this research helpful in better understanding these variables and their roles on their hotels. This study could not be generalized widely to other locations or countries.
Towards Controlling False Alarm - Miss Trade-Off in Perceptual Speaker Comparison via Non-Neutral Listening Task Framing

Speaker comparison by listening is a valuable resource, for instance, in human voice discrimination studies, and voice conversion (VC) systems evaluations. Usually, listeners are provided with application-neutral guidelines that encourage retaining overall high speaker discrimination accuracy. Nonetheless, listeners are subject to misses (declaring same-speaker trial as different-speaker) and false alarms (vice versa) with possibly non-symmetric outcomes. In automatic speaker verification (ASV) applications, the consequences of a miss and a false alarm are rarely equal, and decision making policy is adjusted towards a given application with a desired miss/false alarm trade-off. We study whether listener decisions could similarly be controlled to provoke more accept (or reject) decisions, by framing the voice comparison task in different ways. Our neutral, forensic, user-convenient bank and secure bank scenarios are played by disjoint panels (through Amazon's Mechanical Turk), all judging the same speaker trials originated from RedDots and 2018 Voice Conversion Challenge (VCC 2018) data. Our results indicate that listener decisions can be influenced by modifying the task framing. As a subjective task, the challenge is how to drive the panel decisions to the desired direction (to reduce miss or false alarm rate). Our preliminary results suggest potential for novel, application-directed speaker discrimination designs.
Extending Development Accounting

This paper extends development accounting to an environment that features imperfectly substitutable skills and cross-country variation in the skill bias of technology. We find that human capital accounts for between 50% and 65% of cross-country income gaps. This finding remains robust when we consider alternative sources of skill-biased technology variation, alternative definitions of skilled and unskilled labor, and alternative values for the elasticity of substitution between skilled and unskilled labor. We derive closed form solutions for the contribution of human capital to output gaps in terms of observable data moments. These allow us to understand precisely which features of the data are responsible for our main results.
EXPLORING CAUSES AND ALLEVIATING STRATEGIES OF ENGLISH STUDENT-TEACHERS’ WRITING ANXIETY AT PATTIMURA UNIVERSITY

There have been couples of studies reporting on English students-teachers’ writing anxiety. However, there has been little information on what factors contribute to Indonesian EFL Pre-service teachers’ writing anxiety and how they overcome it. This study aimed at figuring out the facts of related causes of anxiety and alleviating strategies that English student-teachers in English teachers at Pattimura University Ambon employ when they write in English language. The research design is both quantitative and qualitative methods. The data were collected from questionnaires and in-depth interview. The results reveal some contributing factors to participants’ writing anxiety including linguistic difficulties, time pressure, lack of writing practices, instructors’ evaluation and pressure for a perfect work. Furthermore, to overcome their problems, the participants utilize the rethorical, cognitive and socio-affective strategies. The study, then, concludes that by recognizing the causes of writing anxiety, the participants may work better on applying strategies that can reduce their level of writing anxiety.
Book Acquisition using Koha A Case study of Rabindra Granthagar, Assam University Central Library

The present paper projects the status of library automation using acquisition module of Koha-an open source library integrated software in Rabindra Library of Assam University, Silchar. Technology has overpowered the manual systems of various house-keeping operations of the libraries. With the advent of smartphones or android phones, librarians are forced to provide the notifications of the libraries on the android phones of the patrons-be it catalogue, any new service or transaction details or otherwise. Like other service sectors such as Bank, Airlines, etc. libraries are forced to adopt new technologies and serve their users in a better way by making use of the technology. Libraries are forced to adopt the required transformation in their look, services in a better positive way. Modern techniques have become more indispensible for quick and prompt services as users are very demanding and their patience level is very low. A properly computerized library helps in expeditious retrieval and dissemination of information as well as other library services in smoother and more efficient way. Acquisition section is one of the most important sections of the library which deals with the procurement of documents and their efficiency and promptness reflects the related activities of the libraries. By implementing acquisition module of Koha in the section, Rabindra library could integrate all the activities of the library more effectively and efficiently and make its patrons more satisfied. This paper mainly projects a case study of acquisition module of Koha and its implementation in Rabindra Library, Assam University, Silchar.
Student Alumni Networking

This lightning talk describes a pair of on-campus events and their possible effects on student retention and graduate employment rate. The Physics Department at St. Mary's College of Maryland has built a program to help its students prepare for the job market. This fall I replicated one part of this program in the computer science department with a two-hour networking event at which alumni answered questions as part of a panel and then participated in a speed dating type meet and greet with current students. In the future I would like to hold similar events while collecting data on how student's understanding of career opportunities change as a result of the event, and in the long term the difference in career type and employment rate. I would also like to test the effect of these events on student perception of diversity in the field of computer science. Faculty who might be interested in holding similar events and collecting data on their campus' or collaborating with the proposer to develop the study should email the proposer
Gender Estimation from a Hybrid of Face, Upper and Full Body Images at Varying Body Poses

High gender classification accuracies have been recorded with high-resolution faces under controlled conditions. However, real-life scenarios are faced with challenges not limited to high pose variations in subjects, poor visibility, occlusion, and distance from camera. These have led to the current trend in estimating gender from full body images, notwithstanding the challenges posed by partial body images in a typical life scenario. We demonstrate that there are certain sections in a body image, the face, upper or lower body that are useful for recognition at near or far distances. Given the challenges of body captured at far distance or partially showing body in a photo, we therefore propose a combination of three classifiers for gender estimation from face; upper and full body from single-shot image. Our results in far compared to near distance images suggest that gender is best estimated from a hybrid of face; upper and full body images under challenging conditions.
Does Test Type Influence False Recognition in the DRM Paradigm? Comparison of the Yes/No Recognition Test and Two-Alternative Forced-Choice Test

Abstract Studies attempting to identify the mechanisms that are responsible for the level of false recognition in the DRM paradigm usually apply a manipulation during encoding. The main aim of the studies reported here was to investigate the within- and between-participant effects of a testing method on memory performance using a standard yes/no recognition test and a 2-alternative forced-choice recognition test (2FC). To allow a direct comparison of the 2 testing methods, a 2FC test containing similar items as a typical yes/no test in the DRM paradigm was elaborated on in the pilot study. Moreover, 2 methods of data calculation were used: comparing rates of hit and false alarms with critical lures and with unstudied and unrelated items between the 2 tests and comparing measures of sensitivity (d’) derived from signal detection theory. Both experiments showed a lower false alarm rate to critical lures and higher hit rate in a 2FC test as compared with a yes/no test, depicting a typical mirror effect. A within-participant design (Experiment 1) also showed that this increased accuracy of a 2FC test diminished when memory performance was expressed in terms of a sensitivity measure, which may suggest that similar processes are used during these 2 retrieval methods. A similar analysis performed for a between-participants design (Experiment 2) revealed that a 2FC test was less susceptible to associative memory distortions, but the quality of memory (“remember/know” judgments) remained similar for both tests.