Reviewerly/exHarmony huggingface dataset:
The Source Of Error in Translation

Based on the preliminary study, most students made error in writing abstract especially in translating Indonesian language into English language. Therefore, the aim of this research was to find out the sources of translation error and the dominant of source of error. This research used a descriptive method. In getting the data, this research applied a purposive sampling technique and the data of this research was taken secretly. The subject of this research was 61 abstracts of theses and dissertation taken from various universities in Makassar and analyzed by using error analysis technique. The result of the research showed that in the sources of translation error, the most error made by the students was Syntactic with 120 errors (62.1%) Lexical with 42 errors (21.7%), Unclassified with 17 errors (8.8%), and the least was Morphological aspect with 14 errors (7.2%). It can be concluded that the dominant source of error was the syntactic aspect.&#x0D;
A social constructivism approach to learning digital technologies for effective online teaching in Covid-19

Use of digital technologies can enable effective e-learning. In this paper the authors have compared cognitive constructivism approach and social constructivism approach to enable school teachers to use digital technologies for online teaching in Covid-19. It has been found that social constructivism is more effective than cognitive constructivism for enabling school teachers to use digital technologies for delivery of classes in the online mode. Online learning is a method of learning that makes use of the Internet and the World Wide Web. Given the large range of applications accessible on the Internet and the web, it has the ability to stimulate learning in a social constructivist paradigm. The social constructivist paradigm is related with collaborative learning and creative problem solving. The findings of qualitative research papers about barriers to efficient online learning are examined in this integrative literature review. Digital technologies provide teachers with a plethora of new opportunities, but they must constantly be utilized.
Remote system performance analysis of the virtual applications and virtual desktops by using Parallels 2X RAS technique

The fast spread of computer networks and broadband Internet access and also the development of different operating systems make possible the use of different virtualization techniques. Virtualization techniques provide efficient IT solutions in corporate and educational sectors. We present an original structural framework for which the effect of virtualization technology are measured, based on a real campus wide deployment. The Virtual Desktop Infrastructure (VDI) provides academic members access to virtual applications on and off-campus for easy convenient access to academic resources in a server in data centre. In this paper, we present, at the beginning, a comparative performance analysis of Remote applications and Desktop Virtualization based on parallels 2X RAS versus Microsoft RDS. We introduce system architecture for the two tested scenario and test environment with Experiment Setup and implementations. Also, this paper provides analysis and implementation on the perceived and categorical perspectives on the usefulness, effectiveness and values of this technology in an academic environment. The main conclusions of the paper Indicated that students and academic staff have generally improved a remote accessibility to their course documents and academic materials virtually, using their own devices of different platforms which enabled them to perform course work more effectively resulting in improved system reliability, availability and scalability.
Examining Onboarding Experience through the Lens of New Employees’ Expectations after Covid-19: An Empirical Study

Onboarding is an established process in an organization that can span from 3 days to 1 year. Onboarding entails functional induction and professional socialization of the new employee in the company’s environment. It helps the employee to learn about the behaviors and expectations required for the position. Even after that, the outcomes of Onboarding are mostly not realized. The past and recent efforts on Onboarding are mostly related to creating a successful Onboarding process and has been unidirectional. This paper addresses the expectation of new employees joining the manufacturing industry in a management role after covid-19, and how integrating new employee’s expectations with the Onboarding process will help enhance employee’s experience during Onboarding. The paper also considers the key factors that will enable a new employee to increase efficiency and reduce the turnaround time. In all, the study stitches the various best practices in the industry and employee’s expectations from Onboarding, bringing out a fruitful outcome.
Semi-supervised Learning via Conditional Rotation Angle Estimation

Self-supervised learning (SlfSL), aiming at learning feature representations through ingeniously designed pretext tasks without human annotation, has achieved compelling progress in the past few years. Very recently, SlfSL has also been identified as a promising solution for semi-supervised learning (SemSL) since it offers a new paradigm to utilize unlabeled data. This work further explores this direction by proposing to couple SlfSL with SemSL. Our insight is that the prediction target in SemSL can be modeled as the latent factor in the predictor for the SlfSL target. Marginalizing over the latent factor naturally derives a new formulation which marries the prediction targets of these two learning processes. By implementing this idea through a simple-but-effective SlfSL approach -- rotation angle prediction, we create a new SemSL approach called Conditional Rotation Angle Estimation (CRAE). Specifically, CRAE is featured by adopting a module which predicts the image rotation angle conditioned on the candidate image class. Through experimental evaluation, we show that CRAE achieves superior performance over the other existing ways of combining SlfSL and SemSL. To further boost CRAE, we propose two extensions to strengthen the coupling between SemSL target and SlfSL target in basic CRAE. We show that this leads to an improved CRAE method which can achieve the state-of-the-art SemSL performance.
A Proof of the Focusing Theorem via MALL Proof Nets

We present a demonstration of Andreoli’s focusing theorem for proofs of linear logic (MALL) that avoids directly reasoning on sequent calculus proofs. Following Andreoli-Maieli’s strategy, exploited in the MLL case, we prove the focusing theorem as a particular sequentialization strategy for MALL proof nets that are in canonical form. Canonical proof nets satisfy the property that asynchronous links are always ready to sequentialization while synchronous focusing links represent clusters of links that are hereditarily ready to sequentialization.
Unified Embedded Fusion Sensors for Aircrafts

Current aircraft systems, which are mostly established on wired links are intricate, complex to road, heavy and more susceptible to damage as they should be. In this case most existing and perspective aircraft structures and their subsystems require periodic and scheduled inspection and maintenance functions. Hence, structural examining is vital, and it has a gigantic capacity to reduce the costs related to these processes. In this case the Kalman filter method is extremely helpful in the kinematic fusion procedure. Through extremely dynamic aircraft systems are continuous in time, the Kalman method is mainly applied. In this article the author studies the notion of integrating the magnitude into the data-fusion, as update as filtering procedure and find a developed and superior evaluation of the state. Accordingly, data update and state-propagation algorithms were used. Due to traditional inference methods for decision making or fusion does not sustain the practice of a priori data regarding the possibility of a planned assumption, however, it was found that a priori chance is considered in the Bayesian inference method. As a result, fusion sensitivity could indicate as inner explanation of the exterior nature across the aircraft. MATLAB simulation of a designed derivative-free Kalman filters for fusion shows that it could be the most important cause for its realization appealing state-space design and a prediction.
Verification of a Dataset for Korean Machine Reading Comprehension with Numerical Discrete Reasoning over Paragraphs

Numerical reasoning in machine reading comprehension (MRC) has demonstrated significant performance improvements in the past few years. However, due to the process being restricted to specific languages, low-resource languages are not considered, and MRC studies on such languages are limited. In addition, the methods that rely on existing information extracted within the span of a paragraph have limitations in responding to questions requiring actual reasoning. To overcome these shortcomings, this study establishes a dataset for learning Korean Question and Answering (QA) models that not only answer within the span of passages but also perform numerical reasoning on passages and questions. Its efficacy was verified by training the model. We recruited eight annotators to tag the ground truth label, and they annotated datasets with 920, 115, and 115 passages in the train, dev, and test, respectively. A simple yet sophisticated automatic inter-annotation tool was created by effectively reducing the possibility of inaccuracy and error entailed by humans in the data construction process. This tool used common KoBERT and KoELECTRA. We defined four general conditions, and six conditions humans must inspect and fine-tune the pre-trained language models with numerically aware architecture. The KoELECTRA and NumNet+ with KoELECTRA were fine-tuned, and experiments in identical hyperparameter settings showed that compared with other models, the performance of NumNet+ with KoELECTRA was higher by more than 1.3 points. Our research contributes to the Korean MRC research and suggests potential and insight into MRC models capable of numerical reasoning.
Blockchain-based Smart Contract for Decentralized Marketplace

The advance of information technology has a growing influence on one of the most popular social trends: online shopping. The rising popularity of online shopping among the general public, as indicated by the growth in the number of online purchasers each year, has prompted business owners to pursue online ventures. The marketplace is intrinsically tied to online buying activity that connects merchants and customers, allowing customers to search for various goods and services from various providers. However, service failures are vulnerable to centralized market systems that emerge frequently. When the company's services to customers fail to satisfy consumer expectations. A breakdown in purchasing and selling essential services, including product delivery and customer support, is referred to as service failure. As a result, not only does this harm confidence, but it may also cause clients to migrate to an alternative marketplace. The marketplace's competitiveness is based on consumer confidence. The decentralized marketplace can address this security concern. A decentralized marketplace is meant to build a system that does not require the confidence of a third party using blockchain technology and smart contracts that can record all transactions clearly and consistently, allowing them to serve as a single point of truth between distrusting entities. The findings largely support the feasibility of Ethereum Smart Contracts to construct a decentralized marketplace. However, there are some places where further study and development are needed.
The VaccineLand: An Interactive Digital Board Game to Educate Public about Vaccines

The invention of the COVID-19 vaccines has resulted in various viewpoints and reactions from the public. The vaccines are guaranteed to be safe and clinically proven to enhance human antibodies preventing the virus from spreading. However, disseminating facts and truth about vaccines to the public is challenging due to many factors, including vaccine hesitancy and skepticism. Besides, some individuals are still concerned about vaccines' adverse effects and safety. Plus, the presence of various misleading online discussions promoted by anti-vaccine activists that spread false information about vaccines has worsened the situation. Therefore, in this paper, we proposed a digital board game, the VaccineLand, that provides information about vaccines in terms of their history, reasons for taking them, and the adverse effects on the public. Thus, VaccineLand was established based on the Game-Based Learning (GBL) model by its goal, which is to provide the public with facts and knowledge about vaccines in an interactive way. GBL is known as a type of gameplay with specific learning outcomes. Interactive GBL corresponds to the use of educational game applications for learning. This study investigates a usability evaluation consisting of four variables (usefulness, ease of use, ease of learning, and satisfaction) involving a total of 40 respondents. The finding indicates that the usability of this game was highly acceptable. We believe that VaccineLand can be a helpful tool to educate the public about vaccines
Descriptive analysis of computational methods for automating mammograms with practical applications

Mammography is a vital screening technique for early revealing and identification of breast cancer in order to assist to decrease mortality rate. Practical applications of mammograms are not limited to breast cancer revealing, identification ,but include task based lens design, image compression, image classification, content based image retrieval and a host of others. Mammography computational analysis methods are a useful tool for specialists to reveal hidden features and extract significant information in mammograms. Digital mammograms are mammography images available along with the conventional screen-film mammography to make automation of mammograms easier. In this paper, we descriptively discuss computational advancement in digital mammograms to serve as a compass for research and practice in the domain of computational mammography and related fields. The discussion focuses on research aiming at a variety of applications and automations of mammograms. It covers different perspectives on image pre-processing, feature extraction, application of mammograms, screen-film mammogram, digital mammogram and development of benchmark corpora for experimenting with digital mammograms.
Contribution au Niveau de l'Approche Indirecte à Base de Transfert dans la Traduction Automatique

In this thesis, we address several important issues concerning the morphological analysis of Arabic language applied to textual data and machine translation. First, we provided an overview on machine translation, its history and its development, then we exposed human translation techniques for eventual inspiration in machine translation, and we exposed linguistic approaches and particularly indirect transfer approaches. Finally, we presented our contributions to the resolution of morphosyntactic problems in computer linguistics as multilingual information retrieval and machine translation. As a first contribution, we developed a morphological analyzer for Arabic, and we have exploited it in the bilingual information retrieval such as a computer application of multilingual documentary. Results validation showed a statistically significant performance. In a second contribution, we proposed a list of morphosyntactic transfer rules from English to Arabic for translation in three phases: analysis, transfer, generation. We focused on the transfer phase without semantic distortion for an abstraction of English in a sufficient subset of Arabic.
Factorization and malleability of RSA modules, and counting points on elliptic curves modulo N

In this paper we address two different problems related with the factorization of an RSA module N. First we can show that factoring is equivalent in deterministic polynomial time to counting points on a pair of twisted Elliptic curves modulo N. Also we settle the malleability of factoring an RSA module, as described in [9], using the number of points of a single elliptic curve modulo N, and Coppersmith's algorithm.
Efficient Object Detection Model for Real-Time UAV Applications

Unmanned Aerial Vehicles (UAVs) especially drones, equipped with vision techniques have become very popular in recent years, with their extensive use in wide range of applications. Many of these applications require use of computer vision techniques, particularly object detection from the information captured by on-board camera. In this paper, we propose an end to end object detection model running on a UAV platform which is suitable for real-time applications. We propose a deep feature pyramid architecture which makes use of inherent properties of features extracted from Convolutional Networks by capturing more generic features in the images (such as edge, color etc.) along with the minute detailed features specific to the classes contained in our problem. We use VisDrone-18 dataset for our studies which contain different objects such as pedestrians, vehicles, bicycles etc. We provide software and hardware architecture of our platform used in this study. We implemented our model with both ResNet and MobileNet as convolutional bases. Our model combined with modified focal loss function, produced a desirable performance of 30.6 mAP for object detection with an inference time of 14 fps. We compared our results with RetinaNet-ResNet-50 and HAL-RetinaNet and shown that our model combined with MobileNet as backend feature extractor gave the best results in terms of accuracy, speed and memory efficiency and is best suitable for real time object detection with drones.
Improving online learning system by means of artificial intelligence

Introduction. Online learning is applied in the educational system more and more actively every year. The bulk of learning activities in many programmes and courses is shifting from the classroom towards individual work. The interaction between the student and the teacher is decreasing, which negatively affects the quality of education. The aim of the research is the development of methods and algorithms for assessing the psycho-emotional state of learners and their involvement in the teaching process in online education, as well as evaluation of their impact on the efficiency of training. Materials and methods. More than 100 students of Russian State Agrarian University – Moscow Timiryazev Agricultural Academy and Financial University took part in the survey. The following characteristics were measured during the online teaching: involvement in the educational process, students’ psycho-emotional state. In order to build the training algorithms assessing students’ engagement in online learning, the modernised dataset DAiSEE was used; for assessing the psycho-emotional state – the dataset fer2013. Convolutional neural networks were used as model algorithms. The ROC curve and accuracy parameters were used as metrics for model training quality. The Farrar-Glauber test was used for the analysis of multicollinearity of parameters. To prove the efficiency of the developed methods, the statistical criteria – Fisher’s F-test and Student’s t-test – were used. Results and discussion. The developed models demonstrated an excellent quality of training. The accuracy of recognising student engagement in the learning process exceeded 90%, the accuracy of identifying the emotions in online learning was over 90%. The inculcation of the said algorithms in the online learning system showed due efficiency. It was proved that the control group (23 students) and the experimental group (25 students) differed significantly in statistical terms (temp = 2.53; p &lt; 0.05). The learners’ involvement, their emotions and the actual knowledge of the subject area did not show any strong correlation with each other (FGemp=3.61 &lt; FGcrit=7.81). Conclusion. It was proved that the introduction of artificial intelligence systems in the learning process made it possible to adjust the online learning system with regard for the obtained recommendations on the student’s psycho-emotional state and his/her involvement in the training process. Thus, the interaction between the student and the teacher, although weakened in comparison with face-to-face training, nevertheless remains at a sufficiently high level and allows influencing the students’ motivation and mood, which leads to improved quality of education.
Scaling Data Association for Hypothesis-Oriented MHT

Multi-hypothesis tracking is a flexible and intuitive approach to tracking multiple nearby objects. However, the original formulation of its data association step is widely thought to scale poorly with the number of tracked objects. We propose enhancements including handling undetected objects and false measurements without inflating the size of the problem, early stopping during solution calculation, and providing for sparse or gated input. These changes collectively improve the computational time and space requirements of data association so that hundreds or thousands of hypotheses over hundreds of objects may be considered in real time. A multi-sensor simulation demonstrates that scaling up the hypothesis count can significantly improve performance in some applications.
Lexical Simplification Benchmarks for English, Portuguese, and Spanish

Even in highly-developed countries, as many as 15-30\% of the population can only understand texts written using a basic vocabulary. Their understanding of everyday texts is limited, which prevents them from taking an active role in society and making informed decisions regarding healthcare, legal representation, or democratic choice. Lexical simplification is a natural language processing task that aims to make text understandable to everyone by replacing complex vocabulary and expressions with simpler ones, while preserving the original meaning. It has attracted considerable attention in the last 20 years, and fully automatic lexical simplification systems have been proposed for various languages. The main obstacle for the progress of the field is the absence of high-quality datasets for building and evaluating lexical simplification systems. We present a new benchmark dataset for lexical simplification in English, Spanish, and (Brazilian) Portuguese, and provide details about data selection and annotation procedures. This is the first dataset that offers a direct comparison of lexical simplification systems for three languages. To showcase the usability of the dataset, we adapt two state-of-the-art lexical simplification systems with differing architectures (neural vs.\ non-neural) to all three languages (English, Spanish, and Brazilian Portuguese) and evaluate their performances on our new dataset. For a fairer comparison, we use several evaluation measures which capture varied aspects of the systems' efficacy, and discuss their strengths and weaknesses. We find a state-of-the-art neural lexical simplification system outperforms a state-of-the-art non-neural lexical simplification system in all three languages. More importantly, we find that the state-of-the-art neural lexical simplification systems perform significantly better for English than for Spanish and Portuguese.