Reviewerly/exHarmony huggingface dataset:
The Ensemble of Unsupervised Incremental Learning Algorithm for Time Series Data

Data mining is one of the key concepts to discover hidden knowledge from available data. Along with the data mining, data analytics is a field to analyze and process data in a scientific and cognitive angle. It is more helpful to convert knowledge to actionable knowledge for accurate decision making. Data Stream Mining is another challenging area than normal Data Mining due to its dynamics. Dynamics of data in a stream includes changes in data frequency, volume and nature. This paper focuses on the behavior of data mining of machines in process/manufacturing industries. In general, such data is continuous numerical and time series data captured by various industrial sensors. By nature, equipment or machinery behaviour can change over time. It requires calibration/replacement before failure of machinery. By analyzing data, one can find the behavior or state change. To identify that, dynamic models are required to be built using data mining and data stream mining. Thus, we are following a semi-novel approach for building such models using “Ensemble of Unsupervised Incremental Learning" method. Results show how the existing methods are different from the proposed method. This method can be applied for any other domain like image/audio/video or text mining.
Word Reordering for Zero-shot Cross-lingual Structured Prediction

Adapting word order from one language to another is a key problem in cross-lingual structured prediction.Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive.Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models.This paper builds structured prediction models with bag-of-words inputs.It introduces a new reordering module to organize words following the source language order, which learns taskspecific reordering strategies from a generalpurpose order predictor model.Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language. 1
Doors in the sky: Detection, localization and classification of aerial vehicles using laser mesh

Stealth technology and Unmanned Aerial Vehicles (UAVs) are expected to dominate current and future aerial warfare. The radar systems at their maximum operating ranges, however, are not always able to detect stealth and small UAVs mainly due to their small radar cross sections and/or low altitudes. In this paper, a novel technique as an alternative to radar technology is proposed. The proposed approach is based on creating a mesh structure of laser beams initiated from aerial platforms towards the ground. The laser mesh acts as a virtual net in the sky. Any aerial vehicle disrupting the path of the laser beams are detected and subsequently localized and tracked. As an additional feature, steering of the beams can be used for increased coverage and improved localization and classification performance. A database of different types of aerial vehicles is created artificially based on Gaussian distributions. The database is used to develop several Machine Learning (ML) models using different algorithms to classify a target. Overall, we demonstrated through simulations that our proposed model achieves simultaneous detection, classification, localization, and tracking of a target.
Towards Task-Agnostic Privacy- And Utility-Preserving Models

Modern deep learning models for natural language processing rely heavily on large amounts of annotated texts.However, obtaining such texts may be difficult when they contain personal or confidential information, for example, in health or legal domains.In this work, we propose a method of de-identifying free-form text documents by carefully redacting sensitive data in them.We show that our method preserves data utility for text classification, sequence labeling, and question answering tasks.
AN ONLINE REPOSITORY OF PYTHON RESOURCE FOR TEACHING MACHINE TRANSLATION TO TRANSLATION STUDENTS

This paper presents an online repository of Python resources aimed at teaching the technical dimension of machine translation to students of translation studies programmes. The Python resources provided in this repository are Jupyter notebooks. These are web-based computational environments in which students can run commented blocks of code in order to perform MT-related tasks such as exploring word embeddings, preparing MT training data, training open-source machine translation systems or calculating automatic MT quality metrics such as BLEU, METEOR, BERTScore or COMET. The notebooks are prepared in such a way that students can interact with them even if they have had little to no prior exposure to the Python programming language. The notebooks are provided as open-source resources under the MIT License and can be used and modified by translator training institutions which intend to make their students familiar with the more technical aspects of modern machine translation technology. Institutions who would like to contribute their own Python-based teaching resources to the repository are welcome to do so. Keywords: translation technology, machine translation, natural language processing, translation didactics, Jupyter notebooks, Python programming
Multilingual Multi-Domain NMT for Indian Languages

India is known as the land of many tongues and dialects.Neural machine translation (NMT) is the current state-of-the-art approach for machine translation (MT) but performs better only with large datasets which Indian languages usually lack, making this approach infeasible.So, in this paper, we address the problem of data scarcity by efficiently training multilingual and multilingual multi domain NMT systems involving languages of the Indian subcontinent.We are proposing the technique for using the joint domain and language tags in a multilingual setup.We draw three major conclusions from our experiments: (i) Training a multilingual system via exploiting lexical similarity based on language family helps in achieving an overall average improvement of 3.25 BLEU points over bilingual baselines, (ii) Technique of incorporating domain information into the language tokens helps multilingual multi-domain system in getting a significant average improvement of 6 BLEU points over the baselines, (iii) Multistage fine-tuning further helps in getting an improvement of 1-1.5 BLEU points for the language pair of interest.
Using cloud computing to develop teaching self-learning competencies among the faculty members at Jouf University

Introduction. Cloud computing is a new model of computing based on network technology where computer-related technologies are provided as services that are permanently available for use. This technology saves faculty members’ time, and increases their interaction and communication with colleagues and students. Moreover, cloud computing solutions help faculty members finish and follow up all the required courses, in addition to allowing the faculty members to store and retrieve information comprehensively and immediately. As a result, using cloud computing provides practical, interactive solutions to deal with the academic tasks which a faculty member needs to perform his/her current academic work. Aim. The current study aimed to uncover the reality of using cloud computing to develop the teaching competencies. Methodology and research methods. The study relied on applying the survey method based on a descriptive approach using two types of questionnaire as the main tools for data collection. The sample of the study includes 48 faculty members and 103 students from the College of Education at Jouf University. Results and scientific novelty. The results showed that self-assessment of using cloud computing to develop self-learning teaching competencies among faculty members is of a high level, while the teaching competencies for the application of cloud computing in self-directed learning among faculty members are of an average level according to their students. The results also demonstrated no significant relationship between these two main types of assessment. Practical significance. The current study is significant in light of the fact that it facilitates to understand the impact of utilising cloud computing to accomplish proficient and educational abilities for faculty members at Jouf University.
MultiFix: Learning to Repair Multiple Errors by Optimal Alignment Learning

We consider the problem of learning to repair erroneous C programs by learning optimal alignments with correct programs.Since the previous approaches fix a single error in a line, it is inevitable to iterate the fixing process until no errors remain.In this work, we propose a novel sequence-to-sequence learning framework for fixing multiple program errors at a time.We introduce the edit-distancebased data labeling approach for program error correction.Instead of labeling a program repair example by pairing an erroneous program with a line fix, we label the example by paring an erroneous program with an optimal alignment to the corresponding correct program produced by the edit-distance computation.We evaluate our proposed approach on a publicly available dataset (DeepFix dataset) that consists of erroneous C programs submitted by novice programming students.On a set of 6,975 erroneous C programs from the Deep-Fix dataset, our approach achieves the stateof-the-art result in terms of full repair rate on the DeepFix dataset (without extra data such as compiler error message or additional source codes for pre-training).
Face Mask Detection using Deep Learning to Manage Pandemic Guidelines

The field of Computer Vision is a branch of science of the computers and systems of software in which one can visualize and as well as comprehend the images and scenes given in the input. This field is consisting of numerous aspects for example image recognition, the detection of objects, generation of images, image super-resolution and more others. Object detection is broadly utilized for the detection of faces, the detection of vehicles, counting of pedestrians on a certain street, images displayed on the web, security systems and cars with the feature of self-driving. This process also encompasses the precision of every technique for recognizing the objects. The detection of objects is a crucial task; however, it is also a very challenging vision task. It is an analytical subdivide of various applications such as searching of images, image auto-annotation or scene understanding and tracking of various objects. The tracking of objects in motion of video image sequence was one of the most important subjects in computer vision.
Analysis of The Characters’ Concern of The Natural Environmental Problems in Japanase Animation <i>Miyori</i> no Mori Directed by Nizo Yamamoto

Currently, many literary works discuss environmental problems. Animation is a modern literary work that often discusses environmental issues. One of which is an animation by director Nizo Yamamoto entitled Miyori no Mori. This animation is full of criticism related to environmental issues. Through literary ecocritical research methodology, this study discusses the problems of the natural environment and the manifestations of the characters' concern for the natural environment to save the natural environment. From the research results, it is known that the main problems in this animation are: the efforts of evil humans who want to drown the forest and the village where the main character lives into a dam. The forest located near this village is the source of life for the surrounding community. With various efforts made, the main character struggled to thwart the government's efforts to sink forests and villages. The efforts made by the characters in this animation are a manifestation of their concern in protecting and protecting the natural environment from the damage caused by evil humans who want to submerge forests and their surrounding areas into the dam.
The Flow in Periciliary Layer in Human Lungs with Navier-Stokes-Brinkman Equations

In the human respiratory tract, air breathed in is often contaminated with strange particles such as dust and chemical spray, which may cause people respiratory diseases.&#x0D; However, the human body has an innate immune system that helps to trap the debris by secreting mucus to catch the foreign particles, which are removed from the body by the movement of tiny hairs lining on the surface of the epithelial cells in the immune system.&#x0D; The layer containing the tiny hairs or cilia is called Periciliary Layer (PCL). In this research, we find the velocity of the fluid in the PCL moved by a ciliary beating by using the Navier-Stokes-Brinkman equations.&#x0D; We apply the Galerkin finite element method to determine numerical solutions. For the steady linear case of the equation, the numerical result is in good agreement with an exact solution.&#x0D; Including the time derivative and nonlinear terms, we show that the velocity of the liquid is affected by the velocity of the solid, which follows the physical meaning of the fluid flow.&#x0D; The result can be applied as a bottom boundary condition of the mucous layer to be able to find the velocity of mucus in the human lungs.
Zero-Shot Clinical Questionnaire Filling From Human-Machine Interactions

In clinical studies, chatbots mimicking doctorpatient interactions are used for collecting information about the patient's health state.Later, this information needs to be processed and structured for the doctor.One way to organize it is by automatically filling the questionnaires from the human-bot conversation.It would help the doctor to spot the possible issues.Since there is no such dataset available for this task and its collection is costly and sensitive, we explore the capacities of state-of-theart zero-shot models for question answering, textual inference, and text classification.We provide with a detailed analysis of the results and propose further directions for clinical questionnaire filling.
The Effect of Weighting Data on the Goodness of Fit Indicators of the Six Sigma Structural Equation Modeling

The main core of Structural Equation Modeling (SEM) is the parameter estimation process. This process implies a variance-covariance matrix Σ that is close as possible to the sample variance-covariance matrix of data input (S). The six Sigma survey uses ordinal (rank) values from 1 to 5. There are several weighted correlation coefficients that overcome the problems of assigning equal weights to each rank and provide a locally most powerful rank test. This paper extends the SEM estimation method by adding the ordinal weighted techniques to enhance the goodness of fit indicators. A two data sets of the Six Sigma model with different statistics properties are used to investigate this idea. The weight 1.3 enhances the goodness of fit indicators with data set that has a negative skewness, and the weight 0.7 enhances the goodness of fit indicators with data set that has a positive skewness through treating the top-rankings.
Event Prominence Extraction Combining a Knowledge-Based Syntactic Parser and a BERT Classifier for Dutch

A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types.In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system.Current typology-based event detection approaches fail to handle the variety of events expressed in real-world situations.To overcome this, we aim to perform event salience classification and explore whether a transformer model is capable of classifying new information into less and more general prominence classes.After comparing a Support Vector Machine (SVM) baseline and our transformer-based classifier performances on several event span formats, we conceived multi-word event spans as syntactic clauses.Those are fed into our prominence classifier which is fine-tuned on pretrained Dutch BERT word embeddings.On top of that we outperform a pipeline of a Conditional Random Field (CRF) approach to event-trigger word detection and the BERTbased classifier.To the best of our knowledge we present the first event extraction approach that combines an expert-based syntactic parser with a transformer-based classifier for Dutch.
Design and implementation of a digital textbook about physical agents as a learning resource in the training process of Chilean physical therapy students in the context of the COVID-19 pandemic

The COVID-19 pandemic has had a strong impact on education, forcing the implementation of distance learning methodologies using information and communication technologies (ICT). The digital textbook is a didactic resource used to guide the training process of students; besides, its interactivity allows systematic work, favoring metacognition and the construction of self-learning. The aim of this paper is to describe, through a descriptive cross-sectional design, the design, review, digitalization and implementation phases of a digital textbook about physical agents as a learning resource to support distance education in the training process of physical therapy students enrolled in the physical agents course offered by Universidad Andres Bello in Santiago, Chile, in the context of the human mobility restriction measures implemented due to the COVID-19 pandemic. The textbook consists of thirteen thematic sections and once it was reviewed and validated by peers and by the editorial committee of the university it was digitalized and indexed into the digital resource database of the university, where it was made available to the entire cohort of students enrolled in the physical agents course during the first semester of 2020 (n=115).The digital textbook was successfully implemented as a didactic resource to guide the autonomous and distance learning of students during the time human mobility restriction measures were established in Chile due to the pandemic. Future studies should focus on reporting the students’ satisfaction and perception of this tool and its effect on learning outcomes in order to obtain effective feedback.
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives

Discourse parsing, which involves understanding the structure, information flow, and modeling the coherence of a given text, is an important task in natural language processing.It forms the basis of several natural language processing tasks such as question-answering, text summarization, and sentiment analysis.Discourse unit segmentation is one of the fundamental tasks in discourse parsing and refers to identifying the elementary units of text that combine to form a coherent text.In this paper, we present a transformer based approach towards the automated identification of discourse unit segments and connectives.Early approaches towards segmentation relied on rule-based systems using POS tags and other syntactic information to identify discourse segments.Recently, transformer based neural systems have shown promising results in this domain.Our system, SegFormers, employs this transformer based approach to perform multilingual discourse segmentation and connective identification across 16 datasets encompassing 11 languages and 3 different annotation frameworks.We evaluate the system based on F1 scores for both tasks, with the best system reporting the highest F1 score of 97.02% for the treebanked English RST-DT dataset.
A Statistical Study into Network Security Issues of IT Companies in Accra

Wireless Sensor Network (WSN) is a rising technology that offers a great assurance towards a variety of revolutionary applications such as military and public. As wireless sensor networks continue to develop, there is a high importance in security mechanisms. As sensor networks works with responsive data and operate in antagonistic environments, it is crucial to address the security issues. The sensing technology united with wireless communication and processing power makes it rewarding. Due to these computing and inherent constraints in resource, sensor network security has special challenges. The low cost and collaborative nature of the wireless networks (WNs) offers significant advantages upon the conventional communication techniques. The wireless communication technology has several kinds of security threats. The spotlight of this paper is towards addressing the security issues and challenges of WSNs. Here the idea is to identify the threats and security mechanism of wireless sensor networks among WSNs companies in Accra. This paper will contribute to the literature of Wireless sensor networks security issues and challenges in society especially in Ghana. It is concluded that wireless sensor networks have security impact and challenges on information security in IT companies in Accra.
A 3D Compensation Method for the Systematic Errors of Kinect V2

To reduce the 3D systematic error of the RGB-D camera and improve the measurement accuracy, this paper is the first to propose a new 3D compensation method for the systematic error of a Kinect V2 in a 3D calibration field. The processing of the method is as follows. First, the coordinate system between the RGB-D camera and 3D calibration field is transformed using 3D corresponding points. Second, the inliers are obtained using the Bayes SAmple Consensus (BaySAC) algorithm to eliminate gross errors (i.e., outliers). Third, the parameters of the 3D registration model are calculated by the iteration method with variable weights that can further control the error. Fourth, three systematic error compensation models are established and solved by the stepwise regression method. Finally, the optimal model is selected to calibrate the RGB-D camera. The experimental results show the following: (1) the BaySAC algorithm can effectively eliminate gross errors; (2) the iteration method with variable weights could better control slightly larger accidental errors; and (3) the 3D compensation method can compensate 91.19% and 61.58% of the systematic error of the RGB-D camera in the depth and 3D directions, respectively, in the 3D control field, which is superior to the 2D compensation method. The proposed method can control three types of errors (i.e., gross errors, accidental errors and systematic errors) and model errors and can effectively improve the accuracy of depth data.