Reviewerly/exHarmony huggingface dataset:
The Effect of Problem-Based Learning Strategy to Students’ Ability in Writing Argumentative Text at Muhammadiyah University of Bengkulu

The question posted in this study was “Did the Problem Based – Learning Strategy give effect to the students’ ability in writing argumentative text in English Study Program of Muhammadiyah University of Bengkulu?”. The aim of this study was to examine whether problem-based learning strategies affect students' ability to write argumentative text in English Study Program of Muhammadiyah University of Bengkulu. The methodology of this research was experimental research. With 146 students, the samples for this study consisted of two classes. Class B has 20 students and class C 20 students. The researchers took courses B and C because they had similar subject-writing skills. The researcher took class B and C because they have similar skill in writing I subject.  The data were obtained from the pre-test before the treatment in three meetings and post-test after the treatment by using Problem-Based Learning Strategy in experimental class. Students’ writing product is assessed by using analytical scoring rubric. The last step was the researcher discussed and concluded the data. The result of this research showed that Problem Based – Learning Strategy can improve students' writing skills. Calculated using T-test formula, it was found that t-count is higher than t-table (2.9>2.0024) and the average score  of the two classes was increased, the experimental class got increasing as much as 38.7 points, while in control class indicate the increasing as much as 3.8 points.  So, it can be concluded that this strategy gave a positive effect to the student’s ability in writing argumentative text.
Interpreting and Improving Deep-Learning Models with Reality Checks

Abstract Recent deep-learning models have achieved impressive predictive performance by learning complex functions of many variables, often at the cost of interpretability. This chapter covers recent work aiming to interpret models by attributing importance to features and feature groups for a single prediction. Importantly, the proposed attributions assign importance to interactions between features, in addition to features in isolation. These attributions are shown to yield insights across real-world domains, including bio-imaging, cosmology image and natural-language processing. We then show how these attributions can be used to directly improve the generalization of a neural network or to distill it into a simple model. Throughout the chapter, we emphasize the use of reality checks to scrutinize the proposed interpretation techniques. (Code for all methods in this chapter is available at "Image missing"<!-- image only, no MathML or LaTex --> github.com/csinva and "Image missing"<!-- image only, no MathML or LaTex --> github.com/Yu-Group , implemented in PyTorch [54]).
Minimal Translations from Synchronous Communication to Synchronizing Locks

In order to understand the relative expressive power of larger concurrent programming languages, we analyze translations of small process calculi which model the communication and synchronization of concurrent processes.The source language SYNCSIMPLE is a minimalistic model for message passing concurrency while the target language LOCKSIMPLE is a minimalistic model for shared memory concurrency.The former is a calculus with synchronous communication of processes, while the latter has synchronizing mutable locations -called locks -that behave similarly to binary semaphores.The criteria for correctness of translations is that they preserve and reflect maytermination and must-termination of the processes.We show that there is no correct compositional translation from SYNCSIMPLE to LOCKSIMPLE that uses one or two locks, independent from the initialisation of the locks.We also show that there is a correct translation that uses three locks.Also variants of the locks are taken into account with different blocking behavior.
Regularized Intermediate Layers Attack: Adversarial Examples With High Transferability

Although convolutional neural network (CNN) models have demonstrated state-of-the-art performance, especially in many image classification and recognition tasks, the classification accuracy would significantly decreased in the adversarial image samples set by adding slight perturbations in the input images. Currently, many adversarial examples were designed for specific CNNs but they were not universal valid across different CNNs. In this paper, we proposed a new intermediate layer optimization method to ensure that the adversarial examples are effective across different CNN models. Given one image, the proposed algorithm can derive multiple adversarial examples from just one white-box adversarial example by analyzing its regularized features in the intermediate layers of the attacked CNN. The adversarial examples derived from the intermediate layers showed better transferability compared with the original white-box adversarial example. According to the experiments on multiple CNN models, our algorithm promotes the averaging transfer attacking success rate (ASR) by 10.5% and 4.81%, compared to the baseline white-box attacking methods and the recent intermediate layer based attacking method ILA respectively.
Contrastive Representations for Label Noise Require Fine-Tuning

In this paper we show that the combination of a Contrastive representation with a label noise-robust classification head requires fine-tuning the representation in order to achieve state-of-the-art performances. Since fine-tuned representations are shown to outperform frozen ones, one can conclude that noise-robust classification heads are indeed able to promote meaningful representations if provided with a suitable starting point. Experiments are conducted to draw a comprehensive picture of performances by featuring six methods and nine noise instances of three different kinds (none, symmetric, and asymmetric). In presence of noise the experiments show that fine tuning of Contrastive representation allows the six methods to achieve better results than end-to-end learning and represent a new reference compare to the recent state of art. Results are also remarkable stable versus the noise level.
Review of the Volume ” Studies and Current Trends in Science of Education, ICSED 2017”, Editor: Otilia Clipa, Lumen Publishing House, 2018

The volume “Studies and Current Trends in the Science of Education”, coordinated by Assoc. Prof. PhD Otilia CLIPA, published by LUMEN Publishing House, from Iași, Romania, in 2018, addresses all specialists in the educational system and readers who intend to work in this field. The volume was published following the 15th edition of the International Scientific Conference on Sciences of Education, Studies and Current Trends in Science of Education, which took place between June 9-10, 2017 in Suceava, Romania, organized by Ştefan cel Mare University of Suceava, Romania.
Projected Virtual Reality Technology In Hotel Management

Virtual Reality (VR) and Augmented Reality (AR) with its exceptional qualitiesfascinate numerous researchers and professionals to involve in develop- ment of newstandards and applications. The applications of VR and AR are pre- cisely proven theirquality exclusively during the process of planning, marketing, education, and tourist sportpreservation. Virtual Reality has been become as the important role of human computerinteractions and it has the great power to target a large amount of the applications, such asin hotel marketing management, mil- itary, medical field etc. In this project, the VR withwireless combined and ap- plied research on the virtual reality technology in the hotelmanagement. The designed VR projector model is projects the name of the dishes in thetable such as idly, dosa, coffee, etc. by simply touching on the visible light dishes namesitself, it’s considering as ordering of the touched dish items by sending the cor- respondingsync value to the kitchen with the help of buzzer sound and mentioned with table number.Such that they can prepare and make ready for the ordered dish to the respective tables.
Influence of Computer Attitude on Critical Thinking Disposition

This study was conducted to investigate the effects of computer attitudes such as anxiety, confidence, preference, usefulness, and computer literacy on their critical thinking ability, targeting college students at unspecified university universities. The period of data collection was from January 10, 2020 to January 17, 2020. A total of 213 people participated in the study. For data analysis, SPSS 18.0 was used for frequency analysis, Pearson correlation, x2 square, and simple regression analysis. As a result of the analysis, the difference of interests under the statistical significance level among general characteristics was Gender (x2=55.7***, p<0.001), Age (x2=407.5***, p<0.001), Resident (x2=127.5***, p<0.001) Family (x2=289.4***, p<0.001). In the simple regression analysis, computer liking (t=2.102, p<0.05) and computer use ability affected Truth seeking (t=-6.319, p<0.01), computer use ability was affecting analyticity (t=-5.630, p<0.01), computer use ability was affecting systematicity (t=-2.545, p<0.05), computer use ability was affecting maturity under statistical significance (t=-2.583, p<0.05). In conclusion, this study suggests that computer liking and computer use ability affect the sub-factor components of critical thinking disposition, truth seeking, analyticity, systematicity, and maturity, therefore the emphasis of this study is to emphasize that it is desirable to use the computer as a medium of learning based on computer literacy rather than worrying about excessive computer use.
A Short Introduction to the Bambuso Sonoro

Bambuso Sonoro was designed and built by Hans van Koolwijk. The instrument arose from a need to enable a single player to play more than one bamboo flute at a time. It is an unpolished instrument in which the visual element is intimately linked to the auditory. The main differences between this instrument and a pipe organ is that it works with variable wind pressure, thus maintaining many of the capabilities of mouth-blown flutes. The unruly and at times unpredictable nature of the Bambuso is a product of its chance subsidiary sounds, the various sound qualities, divergent harmonics, glissandos, interference of tones, rhythmic patterns, and so forth. The conglomeration of these qualities makes this instrument especially well suited for the playing of unconventionally notated scores where the expression of a particular sphere is more important than the reproduction of exact pitches.
Performance Comparison of Bootstrapped Statistical Taggers on Urdu Tweets

Twitter, a social media platform has experienced substantial growth over the last few years.Thus, huge number of tweets from various communities is available and used for various NLP applications such as Opinion mining, information extraction, sentiment analysis etc.One of the key pre-processing steps in such NLP applications is Part-of-Speech (POS) tagging.POS tagging of Twitter data (also called noisy text) is different than conventional POS tagging due to informal nature and presence of Twitter specific elements.Resources for POS tagging of tweet specific data are mostly available for English.Though, availability of tagset and language independent statistical taggers do provide opportunity for resource-poor languages such as Urdu to expand coverage of NLP tools to this new domain of POS tagging for which little effort has been reported.The aim of this study is twofold.First, is to investigate how well the statistical taggers developed for POS tagging of structured text fare in the domain of tweet POS tagging.Secondly, how can these taggers be used to overcome the bottleneck of manually annotated corpus for this new domain.To this end, Stanford and MorphoDiTa taggers were trained on 500 Urdu tweet gold-standard corpus and were utilized for semi-automatic corpus annotation in bootstrapped fashion.Five bootstrapping iterations for both the taggers were performed.At the end of each iteration, the performance of taggers was evaluated against the development set and automatically tagged, manually corrected 100 tweets were added in the training set to retrain both models.Finally, at the end of last iteration, tagger performance was evaluated against test set.Stanford tagger achieved an accuracy of 93.8% Precision, 92.9% Recall and 93.3% F-Measure.Whereas, MorphoDiTa tagger achieved an accuracy of 93.5% Precision, 92.6% Recall and 93% F-Measure.A thorough error analysis on the output of both taggers is also presented.
Data Augmentation for Low-Resource Named Entity Recognition Using Backtranslation

The state of art natural language processing systems relies on sizable training datasets to achieve high performance. Lack of such datasets in the specialized low resource domains lead to suboptimal performance. In this work, we adapt backtranslation to generate high quality and linguistically diverse synthetic data for low-resource named entity recognition. We perform experiments on two datasets from the materials science (MaSciP) and biomedical domains (S800). The empirical results demonstrate the effectiveness of our proposed augmentation strategy, particularly in the low-resource scenario.
Real-Time Protocols For Communication and Collaboration Environments in Telemedical Applications

World is now moving in the era of high information and communication technology. Under the term telemedicine and tele-cooperation research series are carried out presently, intended to enable and expand a distributed, group-related cooperative work within the area of medicine. For this purpose, applications are needed, which support real-time control processes, distributed applications, communication and cooperation technology, and the representation of shared information and data. Real-Time Protocols are the basis of telemedical applications. They are needed in almost all fields of telemedicine reaching from communication transmission to reliable data transfer. In a telemedical system, how these real-time protocols are used for communications, collaboration with telemedicine applications is worth to be explored and has been discussed in this research article along with the challenges of real-time applications and their solutions by using real-time protocols.
GAN Inversion for Out-of-Range Images with Geometric Transformations

For successful semantic editing of real images, it is critical for a GAN inversion method to find an in-domain latent code that aligns with the domain of a pre-trained GAN model. Unfortunately, such in-domain latent codes can be found only for in-range images that align with the training images of a GAN model. In this paper, we propose BDInvert, a novel GAN inversion approach to semantic editing of out-of-range images that are geometrically unaligned with the training images of a GAN model. To find a latent code that is semantically editable, BDInvert inverts an input out-of-range image into an alternative latent space than the original latent space. We also propose a regularized inversion method to find a solution that supports semantic editing in the alternative space. Our experiments show that BDInvert effectively supports semantic editing of out-of-range images with geometric transformations.
Effectiveness of Mobile Assisted Language Learning (MALL) during the Covid – 19 Pandemic in Gujarat State: Charotar University of Science and Technology (CHARUSAT) Students’ Perceptions

Themajor goal of this research is todetermine the effectiveness of MALL during Covid-19in the setting of Gujarat, India, based on the perspectives of ESL students at the universitylevel. The theoretical basis for the study is Davis's Technology Acceptance Model (TAM),which he proposed in 1986. The study used the TAM framework, which focuses on twofactors: perceived usefulness (PU) and perceived ease of use (PEU) (PEOU). The purpose ofthis model in this study is to better understand students' behaviour and attitudes regardingmobile technology. Both qualitative and quantitative research designs are used in the study.Thedatawasgatheredfrom ahundredUGandPGlevelstudents(50malesand50females)at CHARUSAT's Humanities and Social Science Department. According to the students'opinionsonsmartphones, thetwovariableswerefoundtobeworkinginthecurrentstudy.
Aplikasi Sistem Informasi Pelayanan Masyarakat Rukun Tetangga 02 Kelurahan Kalibaruberbasis Website

Every member of the community, especially those in the neighborhood area 002, Kalibaru Village, expects a letter of introduction to the neighborhood that can be obtained quickly and accurately. However, until now it has not materialized because it is still using a manual system such as writing on a piece of paper. Often when residents want to take care of the Cover Letter, it must take days, because the schedule of the Head of the Neighborhood Association is not always at home. The purpose of this community service outreach activity is to improve the service quality of the neighborhood Cover Letter for the community who need it. The specific target of this community service counseling is the creation of a website-based Cover Letter service system and this letter-making system can be implemented directly for the surrounding community. Based on the results of this community service outreach activity, it can be concluded that the website-based application system for the Neighborhood Association Letter is an effective and efficient administrative service solution for the community. Because this system can overcome community problems in making the Cover Letter, that is, without having to meet face-to-face with the Head of the Neighborhood Association and also not waiting for the cover letter to be made. Without realizing it, there are also weaknesses of this system. Such as the decline in the socialization of the Head of the Neighborhood Association because the residents can make an Introduction Letter without having to meet directly with the Head of the Neighborhood Association.
Triangulation between Bernoulli Distribution and Laplacian Autoregressive Model to Predict Probability of Increase in Stock Price

Stocks are one of the aspects that affect the economy in the world.In the stock market, the price of a stock changes every time.Investors who are able to predict the increase in stock prices will tend to get profit, but investors who are unable to predict the increase in stock prices will tend to experience losses.This study aims to find statistical models and use them to predict the probability of an increase in the price of a stock in the stock market.The method used in this study is a literature study regarding stochastic models and selected suitable stochastic models.This study finds stochastic models and uses them to predict the probability of an increase in stock prices.The novelty in this research lies in the stochastic model triangulation.The increase in stock prices is predicted using two stochastic models, namely: Bernoulli and autoregressive.The decision on stock prediction is determined by the results of the triangulation of the two methods.The proposed method in this study has the advantage that decision making is based on more than one stochastic model.The results of this study can be applied to the financial sector, especially in the stock market.
Comparative Analysis On Various Workload Balancing Algorithms With The Proposed Time Based Load Balancer (Tblb) Algorithm For Efficient Resource Management In An Academic Cloud Computing Environment

Resource Management plays a vital role in Cloud Computing. Especially in an academic scenario, the usage of cloud must be in an optimum manner. Effective usage of cloud resources helps to avoid the underutilization and overutilization of the virtual machines at an IAAS (Infrastructure as a Service) level.Workload must be analysed and need to be distributed evenly among all virtual machines available on host machines. Proper balancing and distribution of workload helps to attain the effective resource management in an Academic Cloud Environment. In an academic scenario, usage of resources are predefined. Hence allocation of resources could be done in an effective manner.This paper deals with the analysis of various load balancing algorithms that is used to load and schedule a workload in an academic cloud environment. Based on the performance metrics, analysis of various load balancing algorithms carried out using Cloud Analyst Simulator. Various performance metrics of each load balancing algorithm is discussed and analysed in a simulated environment.
Streaming and Learning the Personal Context

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.
Expert System for Diagnosing Human Skin Diseases Using Web-Based Naïve Bayes Method

Skin disease is a disease that often found in tropical countries like Indonesia. According to the survey, skin disease is the third of the ten most outpatient diseases. Lack of public knowledge about skin diseases and how to prevent and treat them can cause a person to develop acute skin diseases. The purpose of this research is to create an expert system application for diagnosis of human skin diseases using the web-based naïve Bayes method. With expert system, it hoped that human skin diseases can be detected early and can minimize the occurrence of more dangerous diseases. The calculation in this expert system uses the naïve Bayes method. This expert system makes diagnosis by analyzing input of symptoms experienced by patient and then processing it using certain rules according the expert knowledge that has been stored in the knowledge base. The result of this research is to build an expert system for diagnosing human skin diseases using website-based naïve Bayes. The results of the system trial of 20 respondents were the website could provide diagnosis results based on the inputted rules and could diagnose skin diseases properly. This website can used as an alternative use of technology so it can be used to diagnose skin diseases quickly, precisely and accurately. So in the future the handling of skin diseases can be faster and more efficient.