Reviewerly/exHarmony huggingface dataset:
Pembelajaran Bahasa Jepang Perhotelan III Berbasis Daring di Masa Pandemi Covid-19

This study aims to determine the learning process of Japanese Language Hospitality during the Pandemic Covid-19 period. This study was researching effectiveness of the online distance learning process and evaluate the weakness and problem occurred during learning process. This research was conducted on 43 students of the Faculty of Tourism at Triatma Mulya University Diploma IV Hospitality Program in Third Semester, Academic Year 2020/2021. The respondent was choosen from students who have learned Japanese Language Hospitality 3 on one semester from June to December 2020. The data collection method in this study used documentation techniques and questionnaires via google form. The online-based learning process carried out in this study is to create virtual classroom learning situations through online-based e-learning methods. Students not only listeners in class, but students are strongly directed to presentation about hotel hospitality using Japanese Language. The problem during learning process of Japanese Hospitality online are, the lack of supporting facilities in online class, less concentration and other distractions.
Towards Developing “Oops! Solver” for Elderly Care

Long-term care costs, burdens of caregivers, and their resultant shortage are prevalent concerns in an increasingly aging society. Use of information and communication technologies is one possible solution to expedite human resource development; however, there are few knowledge resources that are interpretable by computers. In this study, we present Example of structured manuals for elderly care as a knowledge resource for question-answering systems. We conducted semi-structured interviews with caregivers in a care facility and retrieved information from an open data set to collect "Oops" incidents in daily caregiving. Based on the collected incidents, we created questions worded in natural language and corresponding computer-interpretable queries. Consequently, we obtained 150 Oops incidents and 33 computer-interpretable queries and confirmed the queries can retrieve relevant knowledge from the knowledge resource.
An Advanced Dynamic Visualization Method of Global Ocean Tide with Multi-core Processor Based on OpenGL

Abstract. In terms of ocean tide visualization, to meet the requirement of both display range and operational efficiency, an advanced method is proposed, in which the tide height is rapidly computed with global tide model EOT10a, and dynamically displayed by OpenGL. Aiming at the large amounts of calculation of global tide height, the feature of multicore processor is integrated into the method. The experiment shows that, compared to a single-core processor, when using a 6-core processor, the speedup ratio is about 5.4, parallel efficiency reaches 90%, and 880 000 tide heights can be calculated per second. Eventually, the result would be output as a tide height graph by OpenGL. This method could be a useful tool for marine cartography due to the large display range and the high efficiency.
Hand Gesture-based Interface for Navigating a Virtual Space using Leap Motion and Machine Learning

In this paper, we propose a hand gesture-based interface for navigating 3D virtual space using Leap Motion and machine learning. Using the interface, the user can control an avatar in a first-person perspective by making it move in 4 directions, rotate its viewpoint, grasp/release an object, and adjust moving/rotating speed. After defining 9 types of hand gesture, we used the leap motion device to capture 150 hand skeletal datasets for each type, and converted them into feature datasets, with which we developed a heuristic algorithm and a machine learning (multilayer perceptron) model for hand gesture recognition. Experimental results showed that the machine learning model was much more accurate than the heuristic algorithm. We also implemented 3 methods for 3D virtual space navigation: the use of keyboard and mouse (KB) and the use of the leap motion device with the heuristic algorithm (HA) and the machine learning model (ML). After conducting a user study in which each of 15 subjects was guided to find a target object and move it to a start position with the methods, we found from the user study that the method ML outperformed the others in task time and in most of qualitative aspects.
Undergraduate Students of Special Education’s Readiness Towards the Use of Application Mobile in Teaching and Learning the Sign Language

This study intends to identify the perceptions of the undergraduate students of Special Education towards the use of application mobile in teaching and learning the sign language.This study involved the perception of 200 undergraduate students of Special Education from 3 particular public universities.All 3 public universities offer both Special Education and the sign language course which are compulsory for the students.This study was conducted by distributing the questionnaire that consists of students' background and demography, also the students' readiness towards the use of application mobile in teaching and learning the sign language.The data was then analyzed using Packages for Social Sciences (SPSS) version 20.0.The findings have gathered that majority of the students have high readiness and interest towards the use of application mobile in teaching and learning the sign language with the overall mean of 4.65 and standard deviation of 0.364.The overall mean has shown that the use of application mobile is preferred by majority of the students.In conclusion, it is obvious that students are ready to use technology involved materials in the teaching and learning the sign language.
Flight Off-Block Time Prediction Based on BP Neural Network Optimized by Genetic Algorithm

To predict the Target Off-Block Time in time, accurately and stably, and improve the efficiency of cooperative operation of airports, airlines and air traffic controllers, a prediction method for prediction of flight off- block time based on BP neural network optimized by genetic algorithm optimization is proposed in this paper. Meanwhile, the influence of flight departure time on the prediction of Flight Off-Block Time is verified. The experimental data are divided by the departure time. The BP neural network optimized by genetic algorithm is applied to predict flight offblock time in the time before the arrival flight and in the time of flight entry. Compared with the traditional BP neural network prediction method and the empirical statistical prediction method, the experimental results show that the BP neural network optimized by genetic algorithm not only generates more stable prediction result, but also has higher prediction precision.
Old Catalan Morphosyntax: Developing an Annotated Corpus

This paper presents a full procedure for the development of a Part-of-Speech (POS) tagged corpus of Old Catalan. As an extremely low-resource language with rich inflection and frequent homographs, Old Catalan poses non-trivial problems in the development of a searchable constituency-based treebank. We demonstrate, however, that a semi- supervised method of incrementally building training data using both neural and memory-based taggers, together with the Pyrrha annotation tool is highly efficient and yields accurate results. We propose that this simple and effective method could easily be extended to other low-resource historical languages for which no NLP tools exist yet.
An improved binary artificial bee colony algorithm for solving multidimensional knapsack problem

In this article, we propose an improved binary heuristic algorithm based on artificial bee colony algorithm (ABC) for the multidimensional knapsack problem. The algorithm uses binary coding and multidimensional neighborhood search strategy to improve the search strategy of ABC algorithm. In addition, cross and mutation are introduced in the employed phase and scout phase. These measures enhance the global search capability of the algorithm, avoid the algorithm from falling into the local optimum in the iteration, and further improve the efficiency and accuracy of the algorithm. The calculation results show that the improved binary artificial bee colony algorithm in this paper can obtain high-quality solutions for various characteristic problems, and it is feasible and effective for solving multidimensional backpack problems
Application of Cart-Based Modeling in Motor Insurance Fraud

Artificial intelligence (AI) provides computer systems with the ability to automatically improve and learn from the data provided without programming it explicitly. The process of “learning” starts from the observation of data to look for patterns in the data provided. Machine learning (ML) can be categorized into supervised, unsupervised and semi-supervised learning methods. A supervised learning method is where an algorithm is used to learn a function to map the input variable (X) to the output variable. The aim here is to find a function so good that when new data (X) is set as an input, 184the model should be able to predict the output (Y) for the given data. Here, the training dataset has input data (X) which has a known corresponding output (Y). The unsupervised learning method is where for a given data its associated properties are studied. However, there are cases wherein for a given input data the response or output is not known in its entirety. Such problems are addressed using semi-supervised learning methods. Classification and regression trees (CART) based model falls under the category of supervised learning method. When the problem at hand has a discrete output variable, it is called a classification problem e.g. “fraud” or “not fraud” and when it has a continuous output variable, it is called a regression problem e.g. “temperature” or “volume”. Gradient boosting method (GBM) is one of the various ML technique which is used for classification and regression problems. Boosting is an iterative method that aims at combining many weak predictions into one powerful one. The application of such machine learning models in the insurance industry is being explored by researchers all over the world. Some potential areas where machine learning can be used in the insurance industry include underwriting and claim analytics, product pricing, claims handling, fraud detection, sales and customer experience, etc. Fraud detection in insurance using machine learning techniques is one area where a lot of research is being done. The list of some of the widely used approaches which were identified from 27 of 450 research articles and studies, by the research team in the Society of Actuaries include data mining, statistical analysis, regression, stratified sample, Monte Carlo simulation, random sampling. Most of the methods identified are machine learning methods. This indicates the fact that how extensively machine learning methods are being used in the area of fraud detection. The ability to detect fraud has various social relevance and benefits. Reduced fraud would decrease the losses faced by the insurer. This would directly or indirectly reduce the premiums charged to policyholders and increase confidence in the financial system.
Blockchain

Blockchain can be observed as a significant development in Industry 4.0, which secures all the transactions among the interconnected objects by providing a distributed ledger technology. This chapter on blockchain provides an overview to blockchain and its origin and describes the structure and working of blockchains. It also details on various types of blockchains and their evolution, various applications of blockchain technology and its benefits. The infrastructure needed to implement blockchain technology is outlined.
Modeling of constrained motion

This paper presents an investigation of modeling and solving of differential equations in the study of mechanical systems with holonomic constraints. The 2D and 3D mathematical models of constrained motion are made. The structure of the models consists of nonlinear first or second order differential equations. Cases of free movement and movement with resistance are investigated. Solutions of the Cauchy problem of obtained differential equations were obtained by Runge–Kutta method.
Decomposing Events and Storylines

Stories are typically represented as a set of events and temporal or causal relations among events. In the metro map model of storylines, participants are represented as histories and events as interactions between participant histories. The metro map model calls for a decomposition of events into what each participant does (or what happens to each participant), as well as the interactions among participants. Such a decompositional model of events has been developed in linguistic semantics. Here, we describe this decompositional model of events and how it can be combined with a metro map model of storylines.
Geostatistical and Spatial Correlation Analysis of Planetary Data

Earth and Space Science Open Archive PosterOpen AccessYou are viewing the latest version by default [v1]Geostatistical and Spatial Correlation Analysis of Planetary DataAuthorsGianlucaChiarolanzaiDGiuseppeMitriAntonioGenovaiDSee all authors Gianluca ChiarolanzaiDCorresponding Author• Submitting AuthorInternational Research School of Planetary SciencesiDhttps://orcid.org/0000-0001-6097-0859view email addressThe email was not providedcopy email addressGiuseppe MitriInternational Research School of Planetary Sciencesview email addressThe email was not providedcopy email addressAntonio GenovaiDSapienza University of RomeiDhttps://orcid.org/0000-0001-5584-492Xview email addressThe email was not providedcopy email address
The Influence Of Vocabulary and Grammar Masteryon The Students’ Writing Skill At Yogyakarta State University

This study aims to investigate the influence of (1) the students’ vocabulary mastery on their writing skills, (2) the students’ grammar mastery on their writing skill and(3) the students’ vocabulary and grammar mastery on their writing skills at Yogyakarta State University. This research was an ex-post-facto. The population comprised the third-semester students of the Pendidikan Bahasa Inggris (PBI) study program at the Languages and Arts Faculty, Yogyakarta State University in the academic year 2013/2014. Therefore, this study showed that (1) vocabulary mastery positively influenced the students’ writing skills, (2) grammar mastery positively influenced the students’ writing skills, and (3) vocabulary and grammar mastery positively influenced the students’ writing skills.
Concerning the International scientific conference “Science and Technology in the Years of Storm and Shock (to anniversaries of A. P. Karpinsky and L. S. Berg)”

The publication is devoted to the international conference, which is held annually at the S. Vavilov Institute for the History of Science and Technology of RAS in Saint-Peterburg. In 2021, the conference is dedicated to the anniversaries of A. P. Karpinsky and L. S. Berg. Within the framework of the conference, the St. Petersburg Branch of the Archive of the Russian Academy of Sciences (SPbF ARAN) traditionally organized scientific session “History of archival affairs, archival funds and collections”, the participants of which discussed a wide range of issues related to the peculiarities of the formation and use of archival documents and collections.
ARABIC TEACHER CREATIVITY TOWARDS THE USE OF INFORMATION TECHNOLOGY IN DISTANCE LEARNING

&lt;p&gt;&lt;em&gt;The main problem in this study, along with the COVID-19 pandemic, which has plagued Indonesia for more than a year, has caused various problems and has an impact on various aspects of life, especially in education. To reduce the spread of COVID-19, the government enforces distance learning (PJJ), which also impacts learning Arabic and requires teachers to develop creativity in learning. Using a qualitative field research method, this study aims to determine the creativity of Arabic language teachers in utilizing information technology for distance learning (PJJ) during the covid-19 pandemic. As for some of the media used, such as. Create video content, take advantage of social media, use video conferencing applications such as Zoom and Google Meet to take advantage of educational website services in the form of Google Classroom and Wordwall games. Based on the results of the analysis conducted by the researchers, this study proved that, first, there are various kinds of teacher creativity in learning Arabic online such as interactive multimedia, quizzes with google forms, word wall games, and so on. Second, being able to eliminate student learning boredom by developing teacher creativity based on information technology. Third, it is still necessary to conduct information technology-based creativity development training, especially in learning Arabic, because not all Arabic language teachers can develop information technology-based teaching skills. This study proved that, first, there are various kinds of teacher creativity in learning Arabic online such as interactive multimedia, quizzes with google forms, word wall games, and so on. Second, being able to eliminate student learning boredom by developing teacher creativity based on information technology. &lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: &lt;/em&gt;&lt;/strong&gt;&lt;span data-preserver-spaces="true"&gt;Creativity, Arabic Teacher, Distance Learning, Information Technology.&lt;/span&gt;&lt;/p&gt;&lt;p&gt; &lt;/p&gt;
Invariance of success probability in Grover quantum search under local  noise with memory

We analyze the robustness of Grover's quantum search algorithm under a local unitary noise with memory effects. For a multi-qubit register of the Grover circuit, we model the noise as originating from arbitrary but fixed unitary rotations in an arbitrary number of qubits of the register. We derive a restricted set of unitaries for which the success probability of the algorithm remains invariant with respect to the ``noise strength'' in the multi-qubit register. Precisely, we show that only when the unitary operator is either of two specific Pauli matrices, the algorithm's success probability stays unchanged when increasing or decreasing the nontrivial number of noisy qubits. When the third Pauli matrix acts as the noise unitary on an even number of noise sites, the success probability at all times will be unaltered as long as the total number of noise sites is changed to another even number. Likewise happens when the total number of noise sites stays odd. This asymmetry between the Pauli operators stems from the inherent symmetry-breaking existing within the Grover circuit. We further show that the positions of the noisy sites are irrelevant in the case of any of the Pauli matrices as noise. Our results hold true irrespective of the presence of time-correlations in the noise. The results are exemplified in a situation where the noise is Markovian-correlated in time.
The perils of being unhinged: On the accuracy of classifiers minimizing  a noise-robust convex loss

van Rooyen et al. introduced a notion of convex loss functions being robust to random classification noise, and established that the "unhinged" loss function is robust in this sense. In this note we study the accuracy of binary classifiers obtained by minimizing the unhinged loss, and observe that even for simple linearly separable data distributions, minimizing the unhinged loss may only yield a binary classifier with accuracy no better than random guessing.
Quantum Annealing for Industry Applications: Introduction and Review

Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies have enabled the development of small- and intermediate-scale quantum processors that implement the quantum annealing algorithm for programmable use. Specifically, quantum annealing processors produced by D-Wave Systems have been studied and tested extensively in both research and industrial settings across different disciplines. In this paper we provide a literature review of the theoretical motivations for quantum annealing as a heuristic quantum optimization algorithm, the software and hardware that is required to use such quantum processors, and the state-of-the-art applications and proofs-of-concepts that have been demonstrated using them. The goal of our review is to provide a centralized and condensed source regarding applications of quantum annealing technology. We identify the advantages, limitations, and potential of quantum annealing for both researchers and practitioners from various fields.
Learning to Reason from General Concepts to Fine-grained Tokens for  Discriminative Phrase Detection

Phrase detection requires methods to identify if a phrase is relevant to an image and then localize it if applicable. A key challenge in training more discriminative phrase detection models is sampling hard-negatives. This is because few phrases are annotated of the nearly infinite variations that may be applicable. To address this problem, we introduce PFP-Net, a phrase detector that differentiates between phrases through two novel methods. First, we group together phrases of related objects into coarse groups of visually coherent concepts (eg animals vs automobiles), and then train our PFP-Net to discriminate between them according to their concept membership. Second, for phrases containing fine grained mutually-exclusive tokens (eg colors), we force the model into selecting only one applicable phrase for each region. We evaluate our approach on the Flickr30K Entities and RefCOCO+ datasets, where we improve mAP over the state-of-the-art by 1-1.5 points over all phrases on this challenging task. When considering only the phrases affected by our fine-grained reasoning module, we improve by 1-4 points on both datasets.
GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization  for Deformable Image Registration

We present a Gradient Descent-based Image Registration Network (GraDIRN) for learning deformable image registration by embedding gradient-based iterative energy minimization in a deep learning framework. Traditional image registration algorithms typically use iterative energy-minimization optimization to find the optimal transformation between a pair of images, which is time-consuming when many iterations are needed. In contrast, recent learning-based methods amortize this costly iterative optimization by training deep neural networks so that registration of one pair of images can be achieved by fast network forward pass after training. Motivated by successes in image reconstruction techniques that combine deep learning with the mathematical structure of iterative variational energy optimization, we formulate a novel registration network based on multi-resolution gradient descent energy minimization. The forward pass of the network takes explicit image dissimilarity gradient steps and generalized regularization steps parameterized by Convolutional Neural Networks (CNN) for a fixed number of iterations. We use auto-differentiation to derive the forward computational graph for the explicit image dissimilarity gradient w.r.t. the transformation, so arbitrary image dissimilarity metrics and transformation models can be used without complex and error-prone gradient derivations. We demonstrate that this approach achieves state-of-the-art registration performance while using fewer learnable parameters through extensive evaluations on registration tasks using 2D cardiac MR images and 3D brain MR images.