Reviewerly/exHarmony huggingface dataset:
Spatial Dependency Parsing for 2D Document Understanding

Information Extraction (IE) for document images is often approached as a BIO tagging problem, where the model sequentially goes through and classifies each recognized input token into one of the information categories. However, such problem setup has two inherent limitations that (1) it can only extract a flat list of information and (2) it assumes that the input data is serialized, often by a simple rule-based script. Nevertheless, real-world documents often contain hierarchical information in the form of two-dimensional language data in which the serialization can be highly non-trivial. To tackle these issues, we propose SPADE$\spadesuit$ (SPatial DEpendency parser), an end-to-end spatial dependency parser that is serializer-free and capable of modeling an arbitrary number of information layers, making it suitable for parsing structure-rich documents such as receipts and multimodal documents such as name cards. We show that SPADE$\spadesuit$ outperforms the previous BIO tagging-based approach on name card parsing task and achieves comparable performance on receipt parsing task. Especially, when the receipt images have non-flat manifold representing physical distortion of receipt paper in real-world, SPADE$\spadesuit$ outperforms the tagging-based method by a large margin of 25.8% highlighting the strong performance of SPADE$\spadesuit$ over spatially complex document.
Multi-Agent System for Post-Stroke Medical Monitoring in Web-Based Platform

Stroke is an injury to blood vessels in the brain and affects their cells; this causes the person to lose the functionality of their body and autonomy. The rehabilitation involves a set of activities for the medical specialists, that is, to have a strict control in the care of the patient, which includes the diets, therapies and ingestion of drugs so that the recovery of the patient is carried out in an optimal way and can be reintegrated into their family, social and work environment. This means that achieving communication and coordination among the members of the health area represents a problem; there is no established structured control plan and physicians need to modify them to adjust to the new situation, which is in accordance with the patient clinical needs. Thus, finding a solution to this problem becomes extremely important and, in this work, we propose a multi-agent system for post-stroke monitoring aimed at medical specialists. The main objective of this research is to communicate and coordinate the follow-up of the patient for the reduction of the damages caused by the stroke using the theory of artificial intelligence agents. The results of this research include the multi-agent system in web-based platform, description of the agents and finally, the implementation of the web system oriented to medical specialists.
BIBFRAME Linked Data: A Conceptual Study on the Prevailing Content Standards and Data Model

The BIBFRAME model is designed with a high degree of flexibility in that it can accommodate any number of existing models as well as models yet to be developed within the Web environment. The model’s flexibility is intended to foster extensibility. This study discusses the relationship of BIBFRAME to the prevailing content standards and models employed by cultural heritage institutions across museums, archives, libraries, historical societies, and community centers or those in the process of being adopted by cultural heritage institutions. This is to determine the degree to which BIBFRAME, as it is currently understood, can be a viable and extensible framework for bibliographic description and exchange in the Web environment. We highlight the areas of compatibility as well as areas of incompatibility. BIBFRAME holds the promise of freeing library data from the silos of online catalogs permitting library data to interact with data both within and outside the library community. We discuss some of the challenges that need to be addressed in order to optimize the potential capabilities that the BIBFRAME model holds.
ROI based Indonesian Paper Currency Recognition Using Canny Edge Detection

Paper currency recognition is important for automatic payment system. The paper performs a nominal paper detection process using image processing with canny method implemented in python programming language. The canny method is used to find edge features in the nominal currency. By using template matching of image reference, region of interest (ROI) of nominal value is extracted so that it can be used in any orientation of paper currency image. The ROI of nominal image is processed by canny edge method and spatial transformation to strengthen the image features and being processed by template matching to decide nominal currency. The study has successfully tested nominal value of 1000, 2000, 5000, 10000, 20000, 50000, and 100000 Indonesia banknotes which then the currency value will appear in the value variable in python.
Skrelin Method for Constructing Formants for Studying Phonetic Characteristics of Vowels

This article presents the results of applying method for obtaining formant components of vowel phonemes for the corpus of professional reading in Russian. In this paper, a review of existing areas of development of methods for obtaining formant characteristics of vowels for different languages was made. A review was also made of the extent to which formant picture patterns are used in speech technologies and natural language processing. On the corpus of professional reading CORPRES, data was obtained on formant components for 351929 realizations of vowel phonemes on the material of 8 speakers. The data obtained are grouped in accordance with the symbols in the real transcription, which was performed by phoneticians within the framework of segmenting the corpus. The formant planes represent the distribution of allophones of vowels for all speakers according to the two first formants. The variability of formant characteristics in the corpus for pre-tonic and post-tonic allophones are presented for one male speaker. The article also presents the results testifying the difference between the rounded unstressed /i/ and /a/, which are perceived by both naive speakers and expert phoneticians as /u/. As an experimental material, the recordings of reading by one male announcer of specially selected sentences, which took into account various linguistic factors, were used. Analysis of the data of the formant components of these vowels showed that the values of the first formant of these vowels are close to the values of the stressed vowel /u/ for this speaker. The closure of these vowels corresponds to the closure of /u/. The second formant values in the vowels [u], which were to be realized as [i] and [a] are different. They are more advanced in comparison with /u/.
Stress Analysis of an Arbitrarily-Shaped Structure with an Irregularly-Shaped Hole using Digital Image Correlation

Engineering structures commonly have arbitrarily-shaped geometry and contain irregularly-shaped cutouts. The integrity of these structures is significantly influenced by the boundary stresses associated with these cutouts. This paper focuses on the evaluation of the stress of a complicated-shaped structure with an irregularly-shaped hole using a single measured displacement component. Digital Image Correlation (DIC) was utilized herein to record the in-plane displacements of the loaded structure. Considering the complications in providing solely analytical solutions for finite structures or numerical solutions in case of insufficient knowledge of the boundary and loading conditions, this paper demonstrates an effective hybrid technique of experimentally stress analyzing in such situations. Processing only one component of the in-plane displacement information with a stress function provides the full-field stresses, including the edges of the plate. Without the knowledge of the external loading conditions nor the need of physically differentiating the measured displacement to obtain strains, and hence stresses, the proposed hybrid technique simultaneously smoothes the recorded displacement information and evaluates the individual stresses full-field in a strong mechanics foundation (equilibrium and compatibility). To validate and support the proposed hybrid technique based on Airy stress function, the obtained results were compared with the those obtained through thermoelastic stress analysis, finite element analysis, and strain gauges.
Target Detection Based on Simulated Image Domain Migration

Annotating a large amount of data manually for supervised learning is an indispensable and expensive part. A novel system using the simulation dataset is proposed in this paper. This framework can train the neural networks for remote sensing object detection without any manually labeled dataset. The whole system can be divided into three parts. The first part is the dataset simulator. The simulator synthesizes remote sensing images with the aircraft targets based on real remote sensing images (without any aircraft targets). In the process of data generation, the simulator automatically marks the position information of the aircraft. The second part is the image dataset domain adaptation work. We introduce the work of Cycle-GAN into this part to bridge the perceptual gap between the simulation dataset and reality dataset. Specially, we propose a multi-scale generator into the original Cycle-GAN model to achieve better domain adaptation performance. The final part is the object detection neural network. The domain adaptation quality of the remote sensing images reconstructed by our novel cycle-gan network achieves better performance both in the structural similarity measure and visual appearance. The object detection model trained with the dataset processed by our novel system can get better detection precision. The analytic experiments on the test dataset demonstrate that the object detection model trained with the dataset processed by our novel system can get better detection precision.
Discriminating nonisomorphic graphs with an experimental quantum annealer

We demonstrate experimentally the ability of a quantum annealer to distinguish between sets of non-isomorphic graphs that share the same classical Ising spectrum. Utilizing the pause-and-quench features recently introduced into D-Wave quantum annealing processors, which allow the user to probe the quantum Hamiltonian realized in the middle of an anneal, we show that obtaining thermal averages of diagonal observables of classically indistinguishable non-isomorphic graphs encoded into transverse-field Ising Hamiltonians enable their discrimination. We discuss the significance of our results in the context of the graph isomorphism problem.
Comparison of Time Series ARIMA Model and Support Vector Regression

As one of the most important and costly functions of any business, sales analytics has been the target of many studies for some time now.Knowing and tracking the sales of a business proves useful in all data-driven decisions made from inventory management to shelf layouts in a supermarket.However, forecasting sales rely heavily on data and algorithms strong enough to handle unseen data.Since sales data are in nature time series datasets one of such predictive methods is time series analytics.In this paper, the ARIMA modeling to the seasonality of the data is compared with a machine learning technique, support vector regression.These comparisons are carried out on three different and unrelated datasets and these algorithms' errors when predicting future sales are compared.The results obtained from our analysis show poor results in general due to datasets having large numbers of oscillation and outliers, but for comparison purposes these datasets and results are fine.We conclude that support vector regression produces better results in comparison with time series analytics on all datasets used in this paper.
EDUCATION SYSTEM AND ENGLISH LANGUAGE TEACHING IN THE PHILIPPINES: IMPLICATIONS FOR INDONESIAN EFL LEARNING

The Philippines is held as one of the largest English-speaking countries worldwide with most of its people possessing at least some degree of fluency in the target language. The prestige of the English language in this country has attracted the attention of many foreign students including Indonesian. In this paper, we aimed to discuss the education system and the English language teaching (ELT) in the Philippines so that practical insights can be gained to reflect on Indonesian EFL learning. To this end, we first described how the Philippine education system looks like and how the English language is taught in schools from elementary to tertiary level. We then explored some prevailing challenges surrounding the ELT, and established links relating the benefits of learning English in this country to Indonesian EFL context. Drawing on qualitative approach, we realized our research objectives by carefully selecting sources and data through critical review of articles from journals, books, websites, and other reliable sources retrieved online. Ultimately, we conducted this inquiry to find out answers so that some lessons learned can be adopted to make ELT in Indonesia better and more resilient since Indonesia remains left behind in the spectrum of English literacy.
HMCTS-OP: Hierarchical MCTS Based Online Planning in the Asymmetric Adversarial Environment

The Monte Carlo Tree Search (MCTS) has demonstrated excellent performance in solving many planning problems. However, the state space and the branching factors are huge, and the planning horizon is long in many practical applications, especially in the adversarial environment. It is computationally expensive to cover a sufficient number of rewarded states that are far away from the root in the flat non-hierarchical MCTS. Therefore, the flat non-hierarchical MCTS is inefficient for dealing with planning problems with a long planning horizon, huge state space, and branching factors. In this work, we propose a novel hierarchical MCTS-based online planning method named the HMCTS-OP to tackle this issue. The HMCTS-OP integrates the MAXQ-based task hierarchies and the hierarchical MCTS algorithms into the online planning framework. Specifically, the MAXQ-based task hierarchies reduce the search space and guide the search process. Therefore, the computational complexity is significantly reduced. Moreover, the reduction in the computational complexity enables the MCTS to perform a deeper search to find better action in a limited time. We evaluate the performance of the HMCTS-OP in the domain of online planning in the asymmetric adversarial environment. The experiment results show that the HMCTS-OP outperforms other online planning methods in this domain.
SENTIMENT ANALYSIS ON CLOSURE OF ILLEGAL MOVIE STREAMING SITES USING NAÏVE BAYES ALGORITHM

The closure of illegal movie streaming sites IndoXXI has been a trending topic on Twitter at the end of 2019. The reaction of netizens on Twitter shows positive and negative sentiments. Until now, there have been many studies in the field of Sentiment Analysis using data in the form of Tweets from Twitter users. In sentiment analysis research, there are so many method used, and Naïve Bayes is one of it, because it is very simple and efficient. The method has advantages and disadvantages. Naïve Bayes is so sensitive in feature selection. Too many features not only increase calculation time but also reduce classification accuracy. In order to solve the disadvantages and increase the performance of the Naïve Bayes classifier, this method often being combined with many kind of feature selection methods. This research aims to classify tweets into positive and negative using the Naïve Bayes classifier combined with the Genetic Algorithm. The accuracy of Naïve Bayes before using the combination of feature selection methods reaches 79.55%. While after using feature selection methods, which is the Genetic Algorithm, accuracy increased up to 88.64%. The accuracy improved by up to 9.09%.
ÇEVRİMİÇİ BANKA KARTI DOLANDIRICILIĞI: RİSK FAKTÖRLERİNİ ANLAMADA RUTİN AKTİVİTELER YAKLAŞIMI

Banking cards, including credit cards, debit card, pre-paid debit cards and ATM cards, have become the primary payment method in online transactions. However, this popularity boosted the Card-not-present (CNP) fraud victimization. Despite numerous studies exploring technological solutions to prevent CNP fraud, there is a shortage of theoretically informed research exploring the online lifestyle correlates of CNP. &#x0D; This study, which utilizes the dataset of Crime Survey for England and Wales 2014/2015, addresses this gap in the literature. Routine Activities Theory was used as the theoretical and conceptual framework in this present study. Bivariate and multivariate analyses results suggested that home users’ online lifestyle increases the risk of becoming a victim of CNP fraud. Buying goods or services, accessing online government services and online communication (email/instant messaging and chat rooms) emerged as risk factors. Illustrating the impact of technological vulnerabilities (mobile phones and public access computers) on the risk of CNP fraud victimization was another novel contribution of this study. &#x0D; Additionally, personal guardianship measures, using complex passwords and different passwords, emerged as predictors of victimization. These results provide valuable implications for situational crime prevention efforts. Practical and theoretical implications of this study are further discussed.
Radial-shearing interferometric imaging with Theon-Kepler bifocal telescope

A method of radial-shearing interferometric imaging based on the Theon–Kepler bifocal telescope is proposed. The Theon–Kepler bifocal telescope system consists of two identical Theon photon sieves with bifocal spots. The short focal length of the first photon sieve coincides with the long focal length of the second photon sieve. At the same time, the first focal length coincides with the second short focal length. This setup naturally constitutes two sets of 4f systems. When a plane wave is incident on the bifocal telescope, two plane waves of different sizes are emitted. These two beams undergo radial-shearing interference. The common-path setup of this new type of radial-shearing interferometer is simple, with low requirements for the experimental environment and strong anti-interference ability.
Extracting and Transferring Hierarchical Knowledge to Robots using Virtual Reality

We study the knowledge transfer problem by training a task of folding clothes in the virtual world using an Oculus Headset and validating with a physical Baxter robot. We argue such complex transfer is realizable if an abstract graph-based knowledge representation is adopted to facilitate the process. An And-Or-Graph (AOG) grammar model is introduced to represent the knowledge, which can be learned from the human demonstrations performed in the Virtual Reality (VR), followed by the case analysis of folding clothes represented and learned by the AOG grammar model. In the experiment, the learned knowledge from the given six virtual scenarios is implemented on a physical robot platform, demonstrating that the grammar-based knowledge is an effective representation.
WEC: Weighted Ensemble of Text Classifiers

Text classification is one of the most important tasks in the field of Natural Language Processing. There are many approaches that focus on two main aspects: generating an effective representation; and selecting and refining algorithms to build the classification model. Traditional machine learning methods represent documents in vector space using features such as term frequencies, which have limitations in handling the order and semantics of words. Meanwhile, although achieving many successes, deep learning classifiers require substantial resources in terms of labelled data and computational complexity. In this work, a weighted ensemble of classifiers (WEC) is introduced to address the text classification problem. Instead of using majority vote as the combining method, we propose to associate each classifier's prediction with a different weight when combining classifiers. The optimal weights are obtained by minimising a loss function on the training data with the Particle Swarm Optimisation algorithm. We conducted experiments on 5 popular datasets and report classification performance of algorithms with classification accuracy and macro F1 score. WEC was run with several different combinations of traditional machine learning and deep learning classifiers to show its flexibility and robustness. Experimental results confirm the advantage of WEC, especially on smaller datasets.
Priority Areas of Education Development in the Conditions of Digital Transformation and the Information Society

The rapid development of digital technologies has led to the emergence of a new milestone in human civilization, the digital era, which is characterized by the appearance of virtual human extensions, that is, the Internet, as well as various gadgets and devices that make it possible to constantly be online and combine existence in the real physical world with "life" in the digital (virtual) universe.The formation of a new social reality that arises as a result of complementing reality with a virtual (digital) component, where online and offline being are connected together, is the main challenge of our time and determines the direction of actual transformations in almost all spheres of human life.It is becoming impossible to live actively outside the development and quality use of the potential capabilities of modern information technologies, especially in the field of educational services.Thus, a special function of education is crystallized: to teach a person to use information resources, to form appropriate competencies, to educate the ability to live in a new information space.For its implementation, modern education should significantly change its own contours, adopt and move to a new philosophical development paradigm, the information.Informational reloads determine the essence of the reform of modern education.In connection with the above, the article is devoted to the substantiation of priority areas for the development of education in the context of digital transformation and the information society.
SISTEM PAKAR IDENTIFIKASI JENIS KULIT WAJAH DENGAN METODE CERTAINTY FACTOR

The skin has many functions, including as a protective body, as a sense of touch or communication tools, and as a temperature regulator. The desire of most people, especially women, have white, healthy, clean and well-taken facial skin. However, the treatment does not pay attention to the type of skin, causing new problems such as acne, dry skin and others. To do skin care requires sufficient knowledge. This study aims to help provide identification of the type of facial skin using an expert system. The method used is certainty factor. The results obtained in this study are that this system has been able to identify the type of facial skin with an accuracy of 91%. Thus, an expert system for identifying the type of facial skin can be used to assist users in carrying out treatments.
Application of Improved Simplex Quadrature Cubature Kalman Filter in Nonlinear Dynamic System

A novel spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is proposed to improve the estimation accuracy of nonlinear dynamic system. The nonlinear Gaussian weighted integral has been approximately evaluated using the spherical simplex rule and the arbitrary order Gauss–Laguerre quadrature rule. Thus, a spherical simplex Gauss–Laguerre cubature quadrature rule is developed, from which the general computing method of the simplex cubature quadrature points and the corresponding weights are obtained. Then, under the nonlinear Kalman filtering framework, the spherical simplex Gauss–Laguerre quadrature cubature Kalman filter is derived. A high-dimensional nonlinear state estimation problem and a target tracking problem are utilized to demonstrate the effectiveness of the proposed spherical simplex Gauss–Laguerre cubature quadrature rule to improve the performance.