Reviewerly/exHarmony huggingface dataset:
The English Pragmatic Competence of Indonesian English Speakers

The aim of the research were (1) to examine participants’ pragmatics competence toward English formal, neutral and formal request, (2) to find evidence of the participants’ lack of pragmatics competence toward various setting of English pragmatics situations.qualitative and quantitative methods were applied to analyze the result of the findings. English grammar competence test was given to determine the subjects’ grammar competence. The English pragmatic test was given to determine the subject’ competence on English pragmatic.the subjects of the research were grouped into 3 categories; group a, subjects whose English grammar competence are at basic level, group b, subjects whose English grammar competence are at intermediate level and group c, subjects whose English grammar competence are at advance level. Each group consists of 5 individuals, who have no prior information of the test model and the aims of the research. Ielts model test is administered before the pragmatic competence test, to groups the subjects of the research. 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 willingness and unwillingness with reasons in using the utterances (3) answering appropriately to multiple questions.the result of the research shows different finding from each of the English grammar competence level. The research result unsurprisingly shows low level of pragmatics competence at intermediate and basic level of grammar competence. Different result showed at higher level whereas the relationship between grammar and pragmatic competence is strong.
View-Invariant Feature Representation for Action Recognition under Multiple Views

Robust Action Recognition under multiple views has gained a significant research interest recently.To enhance the performance of Multi-view Action Recognition, we propose a novel Feature extraction and Feature Selection mechanism that allows building a mutual relationship between the actions sequences of multiple views.The feature extraction considered multiple features which are invariant to scale and orientation.Three different features such as Intensity Features, Orientational Features and Contour Features are used to represent every action.Further, the feature selection is accomplished through self-similarity matrix and is very much helpful in the provision of a perfect discrimination between actions sequences of different views.The proposed method is validated over the standard multi-view IXMAS dataset and experimental results confirm that the proposed method outperforms the conventional approaches with respect to Recognition Accuracy.
Spectral Characteristics of Hydrothermal Alteration in Zuru, NW Nigeria

This study demonstrated the ability of a Landsat-8 OLI multispectral data to identify and delineate hydrothermal alteration zones around auriferous prospects within the crystalline basement, North-western Nigeria. Remote sensing techniques have been widely used in lithological, structural discrimination and alteration rock delineation, and in general geological studies. Several artisanal mining activities for gold deposit occur in the surrounding areas within the basement complex and the search for new possible mineralized zones have heightened in recent times. Systematic Landsat-8 OLI data processing methods such as colour composite, band ratio and minimum noise fraction were used in this study. Colour composite of band 4, 3 and 2 was displayed in Red-Green- Blue colour image to distinguish lithologies. Band ratio image displayed in red was used to highlight ferric-ion bearing minerals (hematite, goethite, jarosite) associated with hydrothermal alteration, band ratio image displayed in green was used to highlight ferrous-ion bearing minerals such as olivine, amphibole and pyroxenes, while ratio image displayed in blue was used to highlight clay minerals, micas, talc-carbonates, etc. Band rationing helped to reduce the topographic illumination effect within images. The result of this study showed the distribution of the lithological units and the hydrothermal alteration zone which can be further prospected for mineral reserves.
Penerapan Metode Waterfall Dan Webqual 4.0 Pada Pengembangan Website Dealer Asa Mandiri Motor

Abstract — Asa Mandiri Motor Dealer is one of the companies involved in the sale and purchase of used motorcycles. The problem that occurs at Asa Mandiri Motor Dealer is the decline in customer visits due to the lack of strategic location of the main dealer. The purpose of establishing a system for developing Asa Mandiri Motor dealer through the Waterfall method website is to measure the quality level using the webqual 4.0 method and the Importance Performance Analysis (IPA) to determine the assessment tests and improvements that are required to improve the service to users and customers through the website.In this study, the author also tested the waterfall method using black box testing to determine the level of functionality on the site, and using tests on the quality level of the site to make recommendations for improvements through quadrant analysis in the Webqual 4.0 method and the Importance Performance Analysis (IPA). This test was conducted on 50 respondents based on the type of respondent (client, non-client (general public), administrator). The results of the study show that the website can be accessed online, the level of website functionality that meets the design requirements, still has a gap value of -0.07, which means that the site is still not in accordance to the needs of users with relatively small scores and author has made improvements to the indicators (INF1, INF2, INF3, USA4, USA5), but no further testing has been done.&#x0D; &#x0D; Keywords— Waterfall, Webqual 4.0, Importance Analysis, Black Box Test
Mobile Learning in Suzuki Violin Lessons: A Study of QR Code Game

This was a qualitative research that aimed to identify the opinions of Suzuki violin students on a QR Code game designed within the scope of mobile learning. Clues about the technical and musical aspects of the pieces in the 1st book of Suzuki method, and entertaining tasks were stored in QR codes. The content analysis of observations, student interviews, student attitude forms, and expert opinions were conducted on Maxqda 12 qualitative analysis software. This provided information on the effect of the QR Code game on repeats, its compliance with technology, and opinions on white and yellow cards. Findings of the study revealed that the QR Code game allowed the students to repeat the pieces in Suzuki repertoire on different cognitive, affective, and psychomotor levels over and over. In the study, the students had a positive attitude towards the QR Code game and an active participation in the process that led to a positive and cheerful learning environment.
Hermite Convolutional Networks

Convolutional Neural Networks (CNNs) have become a fundamental methodology in Computer Vision, specifically in image classification and object detection tasks. Artificial Intelligence has focused much of its efforts in the different research areas of CNN. Recent research has demonstrated that providing CNNs with a priori knowledge helps them improve their performance while reduce the number of parameters and computing time. On the other hand, the Hermite transform is a useful mathematical tool that extracts relevant image features useful for classification task. This paper presents a novel approach to combine CNNs with the Hermite transform, namely, Hermite Convolutional Networks (HCN). Furthermore, the proposed HCNs keep the advantages of CNN while leading to a more compact deep learning model without losing a high feature representation capacity.
Factors Impacting Organizational Innovation for Managers: The Case of Jordanian Public Institutions

This study aimed to examine factors that affect organizational innovation of managers at Public institutions in Jordan. It also aimed to examine the differences of organizational variables on organizational innovation of manager's which are attributed to personal and job – related variables. The study population consisted of all managers in government institutions including (general secretaries, public managers, managers deputies and their assistants) (N = 413). Due to the smallness of the study population, the researcher considered them as the study sample in order to achieve the objectives of this study. The researcher used a questionnaire consisting of (50) items to collect data. The questionnaire was submitted to a jury to test its validity, and Cronbach-Alpha was used to test its reliability. Reliability coefficient was (0.84) The following&nbsp; were the main results of the study: There is a significant effect of for all following independent variables on organizational innovation of managers in government institutions in Jordan leadership style, corporate culture, delegating, objectives clarity, employees training, employees motivation, information technology, and communication systems. There are significant statistical differences in the effect of organizational variables on organizational creativity of managers in government departments in Jordan that are attributed to variables of age, academic Qualification, and years of Experience. There are no significant statistical differences in the effect of organizational variables on organizational creativity of managers in government departments in Jordan that are attributed to variable of gender. In the light of these results, the recommendations of&nbsp; the study were: Nurturing organizational environment that supports creativity based on flexible organizational culture that gives employees the opportunity to participate in decision making, problem solving, power delegating, simplifying work producers, and promoting innovative thinking. Increasing employees participation in suggesting ideas and solutions due to their positive effect on creative performance. Conducting more research to investigate factors affecting organizational innovation in public and private organizations.
Bayesian-imputation of urinary arsenic species below the detection limit in NHANES 2003-2016

TPS 621: Exposure to metals, Johan Friso Foyer, Floor 1, August 26, 2019, 3:00 PM - 4:30 PM Background: Epidemiological analyses of urine arsenic (As) species to evaluate inorganic As (iAs) exposure and internal dose in the US population have been limited by high analytical limits of detection (LODs) for inorganic (arsenate, As+5 and arsenite, As+3) and methylated (monomethylarsonate, MMA and dimethylarsinate, DMA) species in the National Health and Nutrition Examination Survey (NHANES). Methods: We used a Bayesian Tobit regression model to multiply impute values below the LOD for As species (As+5, As+3, MMA, DMA, and arsenobetaine) for 16,398 NHANES 2003-2016 participants. We evaluated the performance of the Bayesian-imputed distributions in comparison to previously published biomarkers of iAs internal dose via simulation and by replicating an analysis of drinking water As trends. Results: Before Bayesian-imputation, the percent of values below the LOD was 96.5% (As+5) 76.5% (As+3), 59.1% (MMA), 21.9% (DMA), and 45.3% (arsenobetaine); the median and interquartile range (IQR) for the sum of inorganic and methylated species (∑As) replacing the samples below the LOD by the LOD/square root of two (the standard NHANES method) was 5.42μg/L (3.59, 8.05). After Bayesian-imputation, the median and interquartile range (IQR) was 0.39μg/L (0.30, 0.50) for As+5, 0.47μg/L (0.27, 0.63) for As+3, 0.51μg/L (0.35, 0.97) for MMA, 3.35μg/L (1.98, 5.63) for DMA, 0.83μg/L (0.42, 4.72) for arsenobetaine, and 4.87μg/L (3.07, 7.54) for ∑As. Conclusions: Original distributions replacing values below the LOD with the LOD divided by the square root of two likely overestimate inorganic arsenic exposure at the lowest exposure levels and introduce significant bias. Bayesian-imputed distributions and statistical code are made available for public use.
Improving active learning by data balance to reduce annotation efforts

Image classification is a fundamental task in image analysis. Recent advances in deep learning have achieved promising results on many image classification benchmarks. However, in some particular tasks, especially in biomedical image analysis, preparing a large number of labelled images for the model's training is costly and unpractical. In this study, the authors aim to address the following questions: With limited effort (e.g. time, cost and manpower) for labelling, what instances should be chosen to annotate and how to train to model using limited annotated data. For that, they present an active learning algorithm combining with data balancing, making the model (e.g. convolutional neural network) fine-tuned continuously and incrementally to reduce the effort of labelling and making model's training process more robust and efficient in both binary and multi-class classification with high-performance. They have evaluated the authors’ method of both binary natural dataset and three classes biomedical dataset, demonstrating that active learning with data balancing could help models’ training more robust and broaden active learning's field to multi classification and more application scenarios. More significantly, their experiments showed that at least a half of effort in labelling could be saved for satisfied performance by their method.
A Survey and Analysis of Mobile Learning of Local Undergraduate College Students

Mobile learning is a new learning mode with the increasing popularity of multimedia technology and Internet application. It not only emphasizes the learner's subjective status and initiative, but also helps learners construct meanings through various forms of interaction (Qiu,2019). This paper investigates the basic situation of students' mobile learning based on smart phones in local colleges and universities by means of questionnaire survey and interview. It also analyses the main problems and reasons of students' mobile learning based on smart phones in local colleges and universities, and explores relevant countermeasures and suggestions.
Polycyclic aromatic hydrocarbons associated long non-coding RNAs and heart rate variability in coke oven workers

TPS 791: Occupational health 1, Exhibition Hall, Ground floor, August 26, 2019, 3:00 PM - 4:30 PM Aim: To evaluate the association between polycyclic aromatic hydrocarbon (PAH) related long non-coding RNAs (lncRNAs) and heart rate variability (HRV) indices in coke oven workers. Methods: A two-stage study was performed to identify lncRNAs associated with PAH exposures quantified using urinary monohydroxy-PAH metabolites and plasma benzo[a]pyrene-r-7, t-8,c-10-tetrahydrotetrol-albumin adducts. In discovery stage, we used Human 8X60k LncRNA Arrays to test different expression in plasma lncRNA between 12 exposed workers and 12 controls matching age, sex, and smoking. We validated associations with four selected lncRNAs in 403 coke oven workers by droplet digital polymerase chain reaction (the QX-200 system, Bio-Rad, Hercules, CA). Results: In the discovery stage, lncRNA expression profiles differed between the exposed and control groups, with 58 lncRNAs significantly down-regulated and 5 lncRNAs mildly up-regulated in the exposed group (fold change’s cutoff is 10 and false discovery rate <0.05). In the validation analysis, two lncRNAs were consistent with the chip results. In male, evaluated urinary1-hydroxynaphthalene and 2-hydroxyphenanthrene were significantly associated with lower ENST00000447277 expression (β=−0.200 and β=−0.286); while evaluated 2-hydroxyfluorene, total concentration of hydroxyfluorene, 2-hydroxyphenanthrene, total concentration of hydroxyphenanthrene and total concentration of all PAH metabolites were associated with lower NR_024564 expression (all P.< 0.05). These lncRNAs were associated with lower HRV. Moreover, the 2 lncRNAs were positively associated with the root mean of square of successive differences between adjacent normal NN intervals (β= 0.032 and β=0.036), and NR_024564 was positively related to high-frequency power (0.15 to 0.40 Hz) (β=0.077, all P< 0.05). However, in female, we only found increased urinary 4-hydroxyphenanthrene was related to lower ENST00000447277 expression (β=−0.145, P=0.034), and no significant association between ENST00000447277 and HRV. Conclusions: Associations of PAH exposures with lncRNA expression, and of lncRNA expression with HRV, suggested that lncRNAs might be a novel mechanism mediating the effects of PAH exposure on early cardiovascular damage.
CNNs and Transfer Learning for Lecture Venue Occupancy and Student Attention Monitoring

Lower student success rates in higher education might, in some case, be due to the unprecedented increase of student numbers without a comparable increase in resources and funding. This paper proposes a face-based detection system to monitor occupancy and student attention in crowded classroom using state of the art deep Convolutional Neural Networks (CNN) architectures. The aim of the proposed system is to contribute to the increase of subject success rates by monitoring attendance and attention. The system utilizes a two-phased approach: The first phase determines the number of student faces in an image frame. The Haar Cascade, LBP, HOG, Resnet CNN, TinyFace CNN, and SSD were compared to determine the algorithm best suited to the detection of faces in crowded classroom scenes. In phase two, the orientations of the faces are determined using transfer learning. Faces are classified as “right”, “left”, or at the “center”. This information is displayed on an augmented reality display to provide feedback to lecturers in semi real-time. It is hoped that this will assist lecturers to address problems related to student attention in crowded classrooms.
Persistence and Accumulation of Visual Memories for Objects in Scenes in 12-Month-Old Infants

Visual memory for objects has been studied extensively in infants over the past twenty years, however, little is known about how they are formed when objects are embedded in naturalistic scenes. In adults, memory for objects in scenes show information accumulation over time as well as persistence despite interruptions (Melcher, 2001, 2006). In the present study, eye-tracking was used to investigate these two processes in 12-month-old infants (N = 19) measuring: (1) whether longer encoding time can improve memory performance (accumulation), and (2) whether multiple shorter exposures to a scene are equivalent to a single exposure of the same total duration (persistence). A control group of adults was also tested in a closely matched paradigm (N = 23). We found that increasing exposure time led to gains in memory performance in both groups. Infants were found to be successful in remembering objects with continuous exposures to a scene, but unlike adults, were not able to perform better than chance when interrupted. However, infants’ scan patterns showed evidence of memory as they continued the exploration of the scene in a strategic way following the interruption. Our findings provide insight into how infants are able to build representations of their visual environment by accumulating information about objects embedded in scenes.
Building Recognition in Static Images

In this paper we address the problem of recognizing buildings in static images. The method described is intended to be used to identify touristic landmarks in a set of vacation photos, while using few resources, and without the aid of consecrated, online solutions. For the task, we adapt feature extraction and feature description algorithms, while employing a low level, geometrical approach in descriptor matching. We present and comment our results, pointing out advantages and disadvantages.
Hybrid Cryptography security in public cloud using TwoFish and ECC algorithm

&lt;p id="docs-internal-guid-6a0c8b63-7fff-9eae-d67f-17fb2c33e518" dir="ltr"&gt;&lt;span&gt;Cloud computing is a structure for rendering service to the user for free or paid basis through internet facility where we can access to a bulk of shared resources which results in saving managing cost and time for large companies, The data which are stored in the data center may incur various security, damage and threat issues which may result in data leakage, insecure interface and inside attacks. This paper will demonstrate the implementation of hybrid cryptography security in public cloud by a combination of Elliptical Curve Cryptography and Twofish algorithm, which provides an innovative solution to enhance the security features of the cloud so that we can improve the service thus results in increasing the trust over the technology.&lt;/span&gt;&lt;/p&gt;&lt;p dir="ltr"&gt;&lt;span&gt; &lt;/span&gt;&lt;/p&gt;
Risk Assets Management, Liquidity Management and Sustainable Performance in Nigeria Deposit Money Banks

The banking industry is an active participant in the Nigeria Financial markets in building the economy through the mobilization of funds from the surplus sector to the deficit sector.Hence, the banking industry has been facing the challenge of risk asset management which culminated into some debts becoming hardcore, irrecoverable and write-off against income, added to the challenge of mismatch of funds leading to liquidity challenge in the face of risks assets becoming non-performing.The study evaluated the effect of risk asset and liquidity management on the sustainable performance of Deposit Money Banks in Nigeria.The study adopted both the ex-post factor and survey research methods.The population of this study comprised the Deposit Money Banks operating in the banking industry while the samples were three banks in addition to Central Bank of Nigeria as sample representatives.Primary data were used to obtain opinions of respondents while secondary data were used to analyze the actions taken by the managers.Both descriptive statistics and regression analysis were used for the analyses with the aid of Statistical Package of Social Sciences (SPSS).All analyses were based on level of significance 0.05., and four hypotheses were tested.The findings showed that there are strong relationships between risk asset management, liquidity management and sustainable performance in Nigeria Deposit Money Banks.With R2 of 0.738, t-statistics of 2.526 and p-value of 0.000, non-performing loans have significant negative impact on the assets of Deposit Money Banks in Nigeria; With R2 of 0.807, t-statistics of 14.755 and p-value of 0.000,low cash deposit has a significant negative impact on the capital of Deposit Money Banks in Nigeria; With R2 of 0.725, t-statistics 11.718 and p-value 0.000,non-compliance with CBN's stipulated cash balance requirement has a significant negative impact on the profitability of Nigeria Deposit Money Banks; and with R2 of 0.671, t-statistics of 10.303 and p-value of 0.000, inadequate liquidity management has a significant negative impact on the dividend payment of Nigeria Deposit Money Banks.The study concluded that effective risk asset management and liquidity management remain the nuclear of the banking industry to maintain sustainable performance.The study recommended that the regulatory authority should enforce compliance with monetary policies; that banks should institute effective and quality risk asset and liquidity management in order to maintain financial stability and sustainability.