Reviewerly/exHarmony huggingface dataset:
Malaysian Tertiary ESL Students' Writing Errors and Their Implications on English Language Teaching

Abstract: Errors, either in writing or speaking, are common in second language production. Researchers have long been investigating English as Second Language (ESL) learners' errors because they may produce different kinds of errors than native speakers. The purpose of this study was to identify errors made by adult ESL learners and to examine the L1 interference in writing errors. Writing samples were collected from 30 students enrolled in the academic writing class in Universiti Putra Malaysia, and the errors were identified, recorded, and analyzed to determine whether they were intralingual or interlingual ones. The study results showed that most of the errors were in subject-verb agreement and verb tenses, which could be caused by first language (L1) influence. The findings of this study would shed light on the types of errors these ESL learners make and their weaknesses in using the English language. Besides, the findings could support instructors, curricula creators, and textbook writers to create and offer materials that could help learners enhance their command of the English language.&#x0D; &#x0D; Keywords: Error Analysis, Intralingual, Interlingual, writing problems, writing errors
Unsupervised Detection of Anomalies in Fetal Heart Rate Tracings using Phase Space Reconstruction

Detection of anomalies in time series is still a challenging problem. In this paper, we provide a new approach to unsupervised detection of anomalies in time series based on the concept of phase space reconstruction and manifolds. We propose a rotation-insensitive metric for quantifying the similarity of manifolds and a method that uses it for estimating the probability of an outlier. The proposed method does not rely on any features and can be used for signals with variable lengths. We tested it on both synthetic signals and real fetal heart rate tracings. The method has promising performance and can be used for interpreting the severity of fetal asphyxia.
The The Role of Customer Satisfaction on the Relationship between Service Quality, Price and Word of Mouth (WOM): The Experience of EDTL.EP in Dili, Timor-Leste

Service quality, price, customer satisfaction and word of mouth (WOM) appear to be the most important variables in today’s business market, and many researchers have focused their attentions on their relationships. This research aims to test the influence of service quality and price to customer satisfaction and WOM, as well as the mediation effect of customer satisfaction in the relationship between service quality and price with WOM. Using accidental sample method, this study collects data from 315 respondents from Timor-Leste Electricity, State-owned Entreprise namely Eletricidade de Timor Leste, Empresa Publica (EDTL.EP) customers who are based in Dili Municipality, and tests hypothesis using SMART-PLS 3.0. Result shows that relationship between both service quality, price, customer satisfaction and WOM have positive and significant effect. This study contributes to the extensive discussions related to service quality, price, customer satisfaction and WOM in different industries. It is also expected to contribute to the improvement EDTL.EP management to provide better service quality and meet customers’ expectation to encourage more positive recommendations to other customers via WOM.
Profissionais de enfermagem acometidos por COVID-19 no Brasil / Nursing professionals affected by COVID-19 In Brazil

Introduction: Nursing plays a fundamental role in the promotion, prevention, recovery and rehabilitation of individuals and society at different levels of health care.In the situation posed by the COVID-19 pandemic, the importance of the nursing professional was highlighted.Objective: To trace the conditioning factors for the high rate of contamination by COVID-19 among the nursing category.Method: Descriptive epidemiological study, which analyzed the incidence and prevalence of cases of nursing professionals infected with COVID -19 in Brazil, between the months of May and October 2020.This information came from the Epidemiological Bulletin of Ministry of Health from bulletins nº 21 to nº 36, corresponding to epidemiological weeks 27 and 42, respectively.Results and Discussion: Sixteen epidemiological bulletins were analyzed in Brazil, covering the period from June to October 2020, in which a gradual increase in contamination in nursing professionals by the new coronavirus was observed.About 4413,025 confirmed cases of COVID-19 in health professionals, 2,128.863 of which correspond to the nursing category.It is essential to highlight the work of nursing professionals on the front line in the fight against Covid-19 and the risks they are exposed to on a daily basis.Conclusion: Given the above, a significant increase in nursing professionals affected by COVID-19 was observed in Brazil compared to other countries.The most relevant indexes of identified records can be analyzed in geographic and historical clippings published weekly in epidemiological bulletins in Brazil.
Single Document Text Summarization of a Resource-Poor Language using an Unsupervised Technique

Automatic text summarization of a resource-poor language is a challenging task. Unsupervised extractive techniques are often preferred for such languages due to scarcity of resources. Latent Semantic Analysis (LSA) is an unsupervised technique which automatically identifies semantically important sentences from a text document. Two methods based on Latent Semantic Analysis have been evaluated on two datasets of a resource-poor language using Singular Value Decomposition (SVD) on different vector-space models. The performance of the methods is evaluated using ROUGE-L scores obtained by comparing the system generated summaries with human generated model summaries. Both the methods are found to be performing better for shorter documents than longer ones.
Detection of Aerobics Action Based on Convolutional Neural Network

To further improve the accuracy of aerobics action detection, a method of aerobics action detection based on improving multiscale characteristics is proposed. In this method, based on faster R-CNN and aiming at the problems existing in faster R-CNN, the feature pyramid network (FPN) is used to extract aerobics action image features. So, the low-level semantic information in the images can be extracted, and it can be converted into high-resolution deep-level semantic information. Finally, the target detector is constructed by the above-extracted anchor points so as to realize the detection of aerobics action. The results show that the loss function of the neural network is reduced to 0.2 by using the proposed method, and the accuracy of the proposed method can reach 96.5% compared with other methods, which proves the feasibility of this study.
Perbandingan Kinerja Algoritma K-Nearest Neighbor, Naïve Bayes Classifier dan Support Vector Machine dalam Klasifikasi Tingkah Laku Bully pada Aplikasi Whatsapp

WhatsApp is the most popular messaging application in Indonesia. This causes the emergence of cyberbullying behavior by its users. This study aims to classify WhatsApp chat to two classes, namely bully and not bully. The classification algorithms used are k-NN, NBC and SVM. The results show that the SVM algorithm is better at solving this case with an accuracy of 81.58%.
Low Cost AES Protection Against DPA Using Rolling Codes

Many block cipher algorithms like AES are known to be weak against differential power analysis attacks (DPA) if the executing unit presents certain levels of information leakage, which is a common problem in microprocessors. Internal nonlinear functions tend to generate such information leakage which can be exploited through side-channels attacks by power line monitoring or any other means. Different countermeasures are known to be effective but at the expenses of more processing effort and therefore higher cost in terms of time and power, though not completely armored against higher order attacks. Rolling code generators (RCG) have been used for many years to avoid the man-in-the-middle attacks in wireless communications. In this paper, the effectiveness of RCG as a low cost countermeasure against AES-DPA is investigated. It has been demonstrated that it is effective to that purpose and a discussion is provided regarding the possibilities that RCG can offer protection to other types of differential side-channel attacks.
Implementation of a Noise-Shaped Signaling System through Software-Defined Radio

Along with the development of electromagnetic weapons, Electronic Warfare (EW) has been rising as the future form of war. Especially in the area of wireless communications, high security defense systems such as Low Probability of Detection (LPD), Low Probability of Interception (LPI), and Low Probability of Exploitation (LPE) communication algorithms are being studied to prevent military force loss. One LPD, LPI, and LPE communication algorithm, physical-layer security, has been discussed and studied. We propose a noise signaling system, a type of physical-layer security, which modifies conventionally modulated I/Q data into a noise-like shape. To suggest the possibility of realistic implementation, we use Software-Defined Radio (SDR). Since there are certain hardware limitations, we present the limitations, requirements, and preferences of practical implementation of the noise signaling system. The proposed system uses ring-shaped signaling, and we present a ring-shaped signaling system algorithm, SDR implementation methodology, and performance evaluations of the system using the metrics of Bit Error Rate (BER) and Probability of Modulation Identification (PMI), which we obtain by using a Convolutional Neural Network (CNN) algorithm. We conclude that the ring-shaped signaling system can perform high LPI/LPE communication functioning because an eavesdropper cannot obtain the correct modulation scheme information. However, the performance can vary with the configurations of the I/Q data-modifying factors.
Adapting Entities across Languages and Cultures

How would you explain Bill Gates to a German?He is associated with founding a company in the United States, so perhaps the German founder Carl Benz could stand in for Gates in those contexts.This type of translation is called adaptation in the translation community (Vinay and Darbelnet, 1995).Until now, this task has not been done computationally.Automatic adaptation could be used in natural language processing for machine translation and indirectly for generating new question answering datasets and education.We propose two automatic methods and compare them to human results for this novel NLP task.First, a structured knowledge base adapts named entities using their shared properties.Second, vector arithmetic and orthogonal embedding mappings identify better candidates, but at the expense of interpretable features.We evaluate our methods through a new dataset 1 of human adaptations. Wer ist Bill Gates?We define cultural adaptation and motivate its application for tasks like creating culturally-centered training data for QA.Vinay and Darbelnet (1995) define adaptation as translation in which the relationship not the literal meaning between the receiver and the content needs to be recreated.You could formulate our task as a tradi-
Recurrent Attention for the Transformer

In this work, we conduct a comprehensive investigation on one of the centerpieces of modern machine translation systems: the encoderdecoder attention mechanism.Motivated by the concept of first-order alignments, we extend the (cross-)attention mechanism by a recurrent connection, allowing direct access to previous attention/alignment decisions.We propose several ways to include such a recurrency into the attention mechanism.Verifying their performance across different translation tasks we conclude that these extensions and dependencies are not beneficial for the translation performance of the Transformer architecture.
Video Saliency Prediction via Deep Eye Movement Learning

Existing methods often utilize temporal motion information and spatial layout information in video to predict video saliency. However, the fixations are not always consistent with the moving object of interest, because human eye fixations are determined not only by the spatio-temporal information, but also by the velocity of eye movement. To address this issue, a new saliency prediction method via deep eye movement learning (EML) is proposed in this paper. Compared with previous methods that use human fixations as ground truth, our method uses the optical flow of fixations between successive frames as an extra ground truth for the purpose of eye movement learning. Experimental results on DHF1K, Hollywood2, and UCF-sports datasets show the proposed EML model achieves a promising result across a wide of metrics.
Scalable Query Answering Under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, keywords associated with studies, etc. Furthermore, there is inherent uncertainty associated with brain scans arising from the mapping between voxels-3D pixels-and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this task is that only very limited propositional query languages have been developed. In this paper we present NeuroLang, a probabilistic language based on first-order logic with existential rules, probabilistic uncertainty, ontologies integration under the open world assumption, and built-in mechanisms to guarantee tractable query answering over very large datasets. NeuroLang's primary objective is to provide a unified framework to seamlessly integrate heterogeneous data, such as ontologies, and map fine-grained cognitive domains to brain regions through a set of formal criteria, promoting shareable and highly reproducible research. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios.
Building a written corpus: what are the basics?

In this chapter we explore the key considerations attending to the compilation of written corpora. We adopt a consciously broad view of what constitutes a “written” text for this purpose, taking it to include computer-mediated and web language in addition to texts that might be considered written in a more traditional sense (e.g. handwritten texts, printed newspaper articles, etc.). The chapter is structured according to three key areas of concern in corpus compilation: 1) design (including authenticity, representativeness, balance and size); 2) ethics and copyright; and 3) text gathering and processing (including text collection, cleaning and encoding). Special attention is given to the theoretical and practical concerns associated with written texts that pose particular challenges to corpus builders, such as historical texts and computer-mediated language. However, many of the issues explored in this chapter are relevant to the compilation of corpora of any kind.
An analysis of lighting design in a football stadium

Lighting is an essential element for human life. Based on the decree of the Minister of Health No.1405/Menkes/SK/XI/2002 Date: 19 November 2002 concerning the requirements and procedures for the implementation of healthy working environment. Indonesian National Standard 16-7062-2004 states that the quality of the inadequate lighting gives bad effects for visual function, the environment around the workplace, and psychological aspects that can be felt as fatigue, feeling less comfortable, less alertness until the effects of the most serious such as accident. The purpose of the study was to determine the intensity of the lighting, the spread of light, lighting design, type of lamp, light bulb conditions, efficiency of energy usage, and maintenance of the football stadium lights. This study employed a qualitative descriptive approach using the type of evaluation research. The subject of this research is Harapan Bangsa Football Stadium in Banda Aceh. Data analysis techniques are: data reduction, data display, and drawing conclusion. The findings of the research showed that the lighting intensity of Harapan Bangsa Football Stadium was 341 lux. Meaning that it was deficit of 458.8 lux of the standard set by PT Liga Indonesia (800 lux), deficit of 959 lux of the standard set by AFC (1.200 Lux), and deficit of 1659 of the standard set by FIFA 2000 Lux. Lighting in the Harapan Bangsa Football Stadium spread unevenly. The lighting intensity design of the stadium was 594 lux. The kind of light bulb used was mercury. The electric power needed to support all of lamps was 377. 200 Watt. The type of light bulb used was Philips trademark. Most of the light bulbs were broken and expired. The use of mercury lamps has not been efficient in energy use. The maintenance of the lamps has not maximized yet.
Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective

Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.
Object Detection Using Deep Convolutional Generative Adversarial Networks Embedded Single Shot Detector with Hyper-parameter Optimization

Itis a challenging task to identify optimal network configurations for large-scale deep neural networks with cascaded structures. In this research, we propose a hybrid end-to-end model by integrating Deep Convolutional Generative Adversarial Network (DCGAN) with Single Shot Detector (SSD), for undertaking object detection. We subsequently employ the Particle Swarm Optimization (PSO) algorithm to conduct hyperparameter identification for the DCGAN-SSD model. The detected class labels as well as salient regional features are then used as inputs for a Long Short-Term Memory (LSTM) network for image description generation. Evaluated with a video data set in the wild, the empirical results indicate the efficiency of the proposed PSO-enhanced DCGAN-SSD object detector with respect to object detection and image description generation.
Relaxation times do not capture logical qubit dynamics

Quantum error correction procedures have the potential to enable faithful operation of large-scale quantum computers. They protect information from environmental decoherence by storing it in logical qubits, built from ensembles of entangled physical qubits according to suitably tailored quantum error correcting encodings. To date, no generally accepted framework to characterise the behaviour of logical qubits as quantum memories has been developed. In this work, we show that generalisations of well-established figures of merit of physical qubits, such as relaxation times, to logical qubits fail and do not capture dynamics of logical qubits. We experimentally illustrate that, in particular, spatial noise correlations can give rise to rich and counter-intuitive dynamical behavior of logical qubits. We show that a suitable set of observables, formed by code space population and logical operators within the code space, allows one to track and characterize the dynamical behaviour of logical qubits. Awareness of these effects and the efficient characterisation tools used in this work will help to guide and benchmark experimental implementations of logical qubits.
Breast cancer segmentation using K-means clustering and optimized region-growing technique

Breast cancer is one of the major causes of death among women, and early detection may decrease the aggressiveness of the disease. The goal of this paper is to create an automated system that can classify digital mammogram images into benign and malignant. This paper presents a new detection technique of micro-calcifications in mammogram images. An automated technique for identifying breast microcalcifications (MCs) proposed utilizing two-level segmentation processes, first crop the breast area from the image using k-means clustering, then, an optimized region growing (ORG) approach has been used, where multi-seed points and thresholds are generated optimally depending on the color values of the image pixels. Then the texture features are extracted based on Haralick definitions of texture analysis. In addition, three features (cross-correlation coefficient, pearson correlation, and average area of segmented spots) are obtained from the segmented image. Support vector machine (SVM) classifier evaluate the efficiency of the system utilizing the images from the digital database for screening mammography (DDSM) dataset. The results were obtained by utilizing 355 images for training and 85 images for testing. The proposed system's sensitivity reached up to 97.05%, the specificity obtained is 98.52%, and accuracy is 98.2%.