Reviewerly/exHarmony huggingface dataset:
Monopoly Game Media as an Effort to Improve Calculation Problem Solving Ability in Elementary School Students

This development research was motivated by the information that the student's average score on the material for solving calculation problems, including those related to money, was still below the KKM (70). One of the reasons is that the teacher does not use the media to teach the material. To overcome this problem, it is necessary to develop media in accordance with the material. The data collection instrument in this study is a checklist sheet with observation and test techniques. The data analysis technique used is quantitative and descriptive qualitative data analysis. The development model for developing monopoly media refers to a development model with the ADDIE approach which includes 5 stages, namely analysis, design, development, implementation, and evaluation. The conclusion of the results of this study is that the monopoly game media is declared valid after being revised by material experts and media experts with a score of 75% which belongs to the B criteria with the Good category The effectiveness of the media in increasing the ability to solve calculation problems, including those related to money, by using the development of monopoly games for grade III students of SDN 4 Cakranegara, Mataram
Point-of-Interest Oriented Question Answering with Joint Inference of Semantic Matching and Distance Correlation

Point-of-Interest (POI) oriented question answering (QA) aims to return a list of POIs given a question issued by a user. Recent advances in intelligent virtual assistants have opened the possibility of engaging the client software more actively in the provision of location-based services, thereby showing great promise for automatic POI retrieval. Some existing QA methods can be adopted on this task such as QA similarity calculation and semantic parsing using pre-defined rules. The returned results, however, are subject to inherent limitations due to the lack of the ability for handling some important POI related information, including tags, location entities, and proximity-related terms (e.g. “nearby”, “close”). In this paper, we present a novel deep learning framework integrated with joint inference to capture both tag semantic and geographic correlation between question and POIs. One characteristic of our model is to propose a special cross attention question embedding neural network structure to obtain question-to-POI and POI-to-question information. Besides, we utilize a skewed distribution to simulate the spatial relationship between questions and POIs. By measuring the results offered by the model against existing methods, we demonstrate its robustness and practicability, and supplement our conclusions with empirical evidence.
Fast Geometry Estimation for Phase-coding Structured Light Field

Estimation scene geometry is an important and fundamental task in light field processing. In conventional light field, there exist homogeneous texture surfaces, which brings ambiguity and heavy computation load in estimating the depth. In this paper, we propose phase-coding structured light field (PSLF), which projects sinusoidal waveform patterns and the phase is assigned to every pixel as the code. With the EPI of PSLF, we propose a depth estimation method. To be specific, the cost is convex with respect to the inclination angle of the candidate line in the EPI, and we propose to iterate rotating the candidate line until it converges to the optimal one. In addition, to cope with problem that the candidate samples cover multiple depth layers, we propose a method to reject the outlier samples. Experimental results demonstrate that, compared with conventional LF, the proposed PSLF improves the depth quality with mean absolute error being 0.007 pixels. In addition, the proposed optimization-based depth estimation method improves efficiency obviously with the processing speed being about 2.71 times of the tradition method.
Quantum hashing and Fourier transform

In the paper based on the notion of small-biased sets we define the quantum transformation that describes the quantum hash function over the cyclic group. We discuss its similarity to the well-known Quantum Fourier Transform and show possible applications to constructing space-efficient algorithms in various computational scenarios, including two-party quantum communication model, simultaneous message passing model, and the model of quantum online streaming algorithms.
M4.9 Report on Fair Data Assessment Mechanisms to Develop Pragmatic Concepts for Fairness Evaluation at the Dataset Level

This report is a milestone of the FAIRsFAIR project. It includes two main results on FAIR assessment at the dataset level: The FAIRsFAIR Data Object Assessment Metrics (v0.3) specification contains 15 metrics proposed by FAIRsFAIR to evaluate the FAIRness of research data objects in Trustworthy Digital Repositories (TDRs). We improved the metrics based on a focus group's feedback and the RDA-endorsed FAIR data maturity model guidelines and specification. A total of 33 FAIR stakeholders, such as research communities, data service providers, standard bodies, and coordination fora participated in the focus group. A preprint of the journal article titled ‘From Conceptualization to Implementation: FAIR Assessment of Research Data Objects’, submitted to CODATA Data Science Journal Special collection on RDA. The article summarizes the metrics development, and its two applications: an awareness-raising self-assessment tool, and a tool for automated assessment of research data FAIRness. The article also covers the initial results of testing the tools with researchers and data repositories, and future improvements including the next steps to enable FAIR data assessment in the broader research data ecosystem.
Use of Contextualized Instructional Materials: The Case of Teaching Gas Laws in a Public Uptown High School

The study determined the effectiveness of the use of contextualized instructional materials (CIM) in teaching Gas Laws. Two groups of tenth grade students from a public uptown high school in Cebu City, Philippines participated in the study. The control group was exposed to conventional lecture method (CLM) while the experimental group was exposed to CIM for a month in the fourth quarter of the school year. Study findings revealed that the CLM group had Below Average entry and exit performances, and the CIM group had Below Average entry and Average exit performances. Both groups had significant improvement from entry to exit performances. Results also showed that students exposed to CIM had significantly higher improvement than those exposed to CLM. Gender and age did not have any relationship with the improvement of CIM’s performance. The study concluded that contextualized instructional materials were efficient medium in achieving maximum learning potential as evidenced in students’ enhanced performance in the concepts of Gas Laws in Chemistry. Workshops on making contextualized materials for teachers are recommended. DOI: http://dx.doi.org/10.17807/orbital.v12i4.1526
Accelerative and Weighted Camera Pose Estimation Based on Extended Orthogonal Iterative Algorithm

The camera pose estimation algorithm is typically more based on reference points and less on the linear information in the image.In this paper,based on the extended orthogonal iteration,an accelerative and weighted camera pose estimation algorithm based on the combination of point and line segments is proposed to ensure a good anti-interference and real-time performance of the algorithm.The algorithm considers the sum of the weighted collinearity error and the weighted coplanarity error as the error function.The weight coefficients are determined by the depth information and the re-projection error of the initial value.In addition,the algorithm accelerates the iterative process and reduces the time complexity of the iteration process dramatically from O(n)to O(1).The simulation results demonstrate that the algorithm can suppress the interference of abnormal points and reduce the calculation time.The calculation accuracy of the rotation matrix is 51.69%higher than that of the traditional orthogonal iterative algorithm,and the calculation accuracy of the translation vector is increased by 51.21%.Furthermore,the calculation time after the acceleration is reduced by 47.11%.The physical experiment shows that the algorithm can make full use of the detected reference points and reference line information,improve the calculation accuracy,reduce the cumulative error,and has high practical application value.
Quantum Classification Algorithms

This chapter covers the topic of quantum classification algorithms. You will see how quantum classification algorithms are implemented in real life. Code examples are presented for different algorithms such as quantum classifiers, support vector machines, and sparse support vector machines.
PERSONAL CREDIT RATING FORECASTING USING ML.NET

Computer technologies with intellectual analysis and calculations are on the raise.This is caused by the flow of the new ideas and approaches, formed on the intersection of computer science, artificial intelligence, statistics, and databases.The rapid growth of the software that are using newest technologies for solving new types of the tasks and bring significant economic effects are happening now.The usage of the machine learning techniques in the retail crediting provides financial institutions optimization of spending of up to 25%.The usage of machine learning for credit rating assessment and forecasting in "internet crediting" is especially relevant now.In the same time the analysis of the publications revealed the needed in the broader research in the area of economic and credit forecasting.Purpose.The purpose of the work is the design and development of the system that assesses and forecasts credit rating of the persons based on the data analysis conducted.Method.The next scientific methods of research were used in the work: abstraction, formalization, analysis and modelling.The object of the study is to forecast the assessment of the creditworthiness of individuals.The subject of the work is the possibilities of ML.NET platform to assess and forecast credit rating of the person.Results.Execution of the credit rating assessment and forecasting of a person is the binary classification task that might be solved with the use of ML.NET -free machine learning platform for C# and F# programming languages.The open dataset of the banking institution with the database of anonymized transactions was used.Different kind of transactions were used to improve quality of assessment and forecasting.Scientific novelty.The model has been built and assessment and forecasting of the credit rating of the people was completed in this work with the use of ML.NET platform.Practical importance.The software application for the dataset formation and credit rating assessment and forecasting has been developed within the course of this work.
Intangible Learning Experience Using Leap Motion Controller for Distance Mapping

Learning of classroom content with a teacher’s monologue makes it droning over time. Due to affordable options of MOOCs, social media, open blogs, etc. paying attention to classroom content in the present scenario is an option only. This may limit the kindle of inspiring thought and natural apatite towards understanding classroom content. This work focuses on designing the interactions to explore the applications of Leap Motion for a better understanding of classroom content or simple things on the sense of game and logic. The brain-body coordination for distance mapping has introduced in this work. This work is established as an intangible learning interface where a user can learn elementary logic with an immersive experience. The derived outcome of the experiments shows the significant enhancement of learning and mind-body coordination of length and measure.
Generating Instructions at Different Levels of Abstraction

When generating technical instructions, it is often convenient to describe complex objects in the world at different levels of abstraction.A novice user might need an object explained piece by piece, while for an expert, talking about the complex object (e. g. a wall or railing) directly may be more succinct and efficient.We show how to generate building instructions at different levels of abstraction in Minecraft.We introduce the use of hierarchical planning to this end, a method from AI planning which can capture the structure of complex objects neatly.A crowdsourcing evaluation shows that the choice of abstraction level matters to users, and that an abstraction strategy which balances low-level and high-level object descriptions compares favorably to ones which don't.
Fast and Accurate Face Alignment Algorithm Based on Deep Knowledge Distillation

Most existing face key point detection algorithms only consider improving the generalization performance of the model, while ignoring the model efficiency. However, models with good generalization performance usually contain a large number of parameters, which hinders the deployment of face key point detection algorithms on devices with limited resources. In order to overcome this problem, a Fast and Accurate Face Alignment Algorithm is proposed in this paper. First, a teacher network that stacks multiple hourglass modules is designed. Its model structure is robust to meet the positioning accuracy requirements in actual use. Then, a lightweight student network is designed, which has fewer model parameters and faster feature extraction speed, which meets the speed requirements in practical use. Finally, through the knowledge distillation strategy, the knowledge of the teacher network is transferred to the student network, which makes the student network take into account both the model inference speed and the key point positioning accuracy.
Seamless interoperability in logistics by ontology alignment

This paper is dedicated to applying ontology alignment systems to the heterogeneity problem in logistics. The primary motivation for doing so is to enable interoperability among different IT systems in logistics, all with their own database scheme. We first analyze different standards for logistics interoperability, which are implemented by XML schema definition (XSD) or Electronic Data Interchange (EDI) with implicit and inconsistent semantics across these open standards. To analyze the applicability of ontology alignment, we create two ontologies from two well-known standards that are rich in terms of semantic relations between entities. These ontologies are subjected to the state-of-the-art alignment systems, applying matching with and without logistics background knowledge. Our experimental analyses show that the alignment using background knowledge with some annotations finds better mappings between the given logistics standards and is thus applicable to real-world situations under particular conditions.
The Effects of Perspective Taking Ability on The Emotional Control Ability of Child-care Teachers

The purpose of this study is to identify the effect of perspective taking ability on the emotional control ability of the child-care teacher. The early childhood education Institutions were located in Seoul and Gyeonggi Province, A sample of 305 childhood teachers’ participated in this study. Data were analyzed for frequency, one-way variance, correlation, and multiple return analysis through SPSS 23.0. The results are the following. First, there was a significant difference in emotional control abilities in fear, excitement and joy depending on the perspective taking ability level of the child-care teacher. Second, the correlation between the perspective taking ability and the emotional control ability of the child-care teacher in the emotional situation showed that the subfactors of the perspective taking ability were all significant between self-understanding, others-understanding, decentralization, and organizational understanding. Among them, the relationship between self-understanding and others' understanding was high, and there was a high level of correlation between self-understanding and decentralization, others-understanding and organizational understanding, self-understanding and organizational understanding, and decentralization and organizational understanding. Third, the influence of self-understanding, others-understanding, decentralization, and organizational understanding on the emotional control ability was noted in situations of fear, shock and joy, but not in excited situations.
Talking to the Visuals: Exploring Ideology of Generations through Semiotic Landscape of Whatsapp Statuses

Whatsapp statuses are an example of computer-mediated communication. The current qualitative and quantitative study aims to explore the ideology of Whatsapp users and highlight the differences among the generations through the semiotic landscape of Whatsapp statuses. The analytical tools of “David Machin and Andrea Mayr” (2012) and theoretical principles of “Kress and Leeuwen” (1996) helped to analyze the multimodal discourse of WhatsApp statuses. A survey was also conducted to know the stance of Whatsapp users. Multimodal Critical Discourse Analysis (MCDA) of 630 Whatsapp statuses of 90 participants in social semiotics represented Post-Millennials, Millennials, and Generation X with clear differences in their ideologies. The differences are louder for gaze, distance, iconography, colors, vector, angle, and frames. The results of the survey show that Post-Millennial give much value to Whatsapp statuses as 92% of them display statuses daily. All three generations have quite separate reasons to use Whatsapp statuses; only informing others is the common reason between Millennials and Generation X. Basically, the users of Whatsapp statuses are the social actors who represent their cognitive meanings socially.
Solving the Linear Equation by Using Python Code

In this paper we will introduce the study of solving linear equation. Gauss-elimination and Gauss-Jordan elimination by using python code. This paper aims that students can easily and quickly calculate the linear problems by using python code. Gauss-elimination and Gauss-Jordan elimination are linking the application problem such that electrical networks, traffic flow and commodity marked. By studying this theorem, they know the important characteristics of matrices, concept of vector spaces and properties of special categories of matrices. And also they can know how to use characteristics of matrix to solve a liner system of equations or study properties of a linear transformation.
INFORMATION COMPETENCE OF STUDENTS IN THE TRANSITION TO DOMESTIC SOFTWARE

The authors consider information technologies as an important component of production and economy at the present stage of society development. A graduate of a higher education institution, regardless of the field of training, must possess modern methods of working with information, including ways to ensure information security, and the skills to use information technologies in their professional activities. The article discusses the requirements for information competence of graduates in various areas of training. The author analyzes the composition of the University's software and the possibility of replacing it with domestic or freely distributed software. The article summarizes the experience of switching to domestic software at Nizhnevartovsk state University
A Web Tool for Interactive Generation and Visualization of Synthetic ShakeMaps

&amp;lt;p&amp;gt;Synthetic shaking-intensity maps provide the necessary information about the detailed shaking distribution for scenario-based seismic risk assessment as well as post-disaster rapid loss estimates. These ShakeMaps allow to identify areas heavily affected by an earthquakes and are becoming, combined with an exposure/vulnerability model, the underlying data for a risk or loss model. Such computations deliver decision makers the data for informed policy decisions for precautionary measures for increasing resilience, or, in case of post-disaster analyses, rapid estimates for disaster mitigation.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;br&amp;gt;We present a new web engine for synthetic ShakeMaps harnessing the OpenQuake engine of the Global Earthquake Model (GEM) foundation. The back-end asynchronously digests requests parameterizing earthquake sources in terms of source depth, epicentral location, moment magnitude and focal mechanism. The back-end returns shaking in user definable ground-motion measures (e.g. PGA or IMS) and can be retrieved in various formats such as ASCII, GeoJSON, among others. This tool implements an open and documented API that users and other services can query systematically and automatically. It integrates into the LEXIS framework, a Horizon 2020 funded project aiming at improving rapid loss assessments and emergency decision support systems.&amp;lt;br&amp;gt;An interactive interface allows to explore the expected shaking in the spatial domain by selecting locations of interest on a map and defining the earthquake source interactively within a web browser. Besides the interactive mode, this service now provides, through HTTP requests, a simple interface for any type of ShakeMap to be used in automated systems that require rapid ShakeMap computations without the need to run local instances of OpenQuake.&amp;lt;/p&amp;gt;
Language Models not just for Pre-training: Fast Online Neural Noisy Channel Modeling

Pre-training models on vast quantities of unlabeled data has emerged as an effective approach to improving accuracy on many NLP tasks. On the other hand, traditional machine translation has a long history of leveraging unlabeled data through noisy channel modeling. The same idea has recently been shown to achieve strong improvements for neural machine translation. Unfortunately, na ive noisy channel modeling with modern sequence to sequence models is up to an order of magnitude slower than alternatives. We address this issue by introducing efficient approximations to make inference with the noisy channel approach as fast as strong ensembles while increasing accuracy. We also show that the noisy channel approach can outperform strong pre-training results by achieving a new state of the art on WMT Romanian-English translation.
Metadata Security Measures for Protecting Confidential Information on the Cloud

In recent times, unprotected metadata resulted in many serious security breaches on a shared cloud storage, which clearly depicts the need to protect this crucial information about the original data. Encryption seems to be the obvious solution to this issue. But it makes all the processing on data infeasible, it requires us to download both the data as well as the associated metadata and perform decryption locally to further process it, which defeats the purpose of using the cloud storages. There are many instances where we need metadata information for efficient processing of data. Therefore, there is a need to develop a technique that can enable selective retrieval of this metadata information without actually revealing anything about it in the plaintext form. In this paper, the authors will present one such technique based on key-policy design framework of attribute-based encryption.
Digitization of Handwritten Attendance Entries using Structural Similarity Measurement

The aim of this research is to automatize handwritten attendance data entries in digital form with a minimal human intervention using Handwritten Character Recognition (HCR). Here, all the preprocessing techniques (Image Registration, Cropping, Erosion. Dilation, Noise removal, Line removal, Segmentation), and classification technique are implemented in the MATLAB platform. Various types of techniques for recognition of handwritten digits and characters have been implemented for different applications. Handwritten entries in the attendance sheet are classified using Structural Similarity Measurement (SSIM). The proposed system has achieved a 95.60% classification accuracy.