Reviewerly/exHarmony huggingface dataset:
SENSORY DESIGN STUDY OF MUSEUM BANK INDONESIA IN ACCOMMODATING VISUALLY IMPAIRED VISITORS

Abstract- Museum as an architectural object with its function as an educational and recreational facility is open to public and must be able to be used by all people. Therefore, the design of museums must be accessible for all people, including those with special needs. One of the museums in Indonesia which is considered to be a good work of architecture is the Museum of Bank Indonesia. It receives many visitors from various circles, including people with visual impairments. To accommodate visitors with visual impairments, the Museum of Bank Indonesia must have design features that help them to do activities, despite their limited sense of sight. Through this study, the research aims to find out how physical elements in the Museum of Bank Indonesia accommodates the orientation and mobility of visitors with visual impairments. &#x0D; This is a qualitative research, with a descriptive discussion. The collected data are the data on physical elements in the Museum of Bank Indonesia and the data on the behavior of visually impaired visitors while doing orientation and mobility in the Museum of Bank Indonesia exhibition area. The data are collected through observation and interview, then analyzed by using a table which elaborates physical elements from technical requirements aspect and sensory design aspect, and how they accommodate visually impaired visitors. &#x0D; Research shows that some physical elements in the Museum of Bank Indonesia are already adequate in accommodating visually impaired visitors. They able to provide assistance for visually impaired visitors while doing orientation and mobility, as well as gathering information on the museum objects. On the other hand, there are also some physical elements that hinder visually impaired visitors because they don’t technically meet the user’s needs. Therefore, improvement in the physical elements design is needed, so it can accommodate visitors with visual impairments better. &#x0D; &#x0D; Key Words: museum, visual impairment, Bank Indonesia, universal design, sensory design
Design of filter coefficients for optimal factored truncated cascade FIR filter using optimization algorithm

Signal filtering acts as one of the basic requirement of communication networks for the removal of unwanted features from the signal. The design of appropriate digital filter requires the selection of optimal filter coefficients for the generation of desired frequency response with reduced hardware complexity. This paper proposes a hybrid optimization algorithm named as Brain Storm- Grey Wolf Optimizer (BSGWO) algorithm for the selection of filter coefficients in the design of factored truncated cascade FIR filter. The proposed algorithm is the hybridization of the optimization algorithms, namely Brain Storm Optimization (BSO) and Grey Wolf Optimizer (GWO). The input signal is interpolated initially for the formation of an intermediate signal using the FIR filter. Then, the factored truncated cascade filter is developed for the interpolation of the signal. After designing the filter coefficients, the optimal selection of the filter coefficients is performed using the proposed BSGWO algorithm. The original filter is developed with the use of the least square estimation and the new filter is developed using the proposed algorithm that tunes the filter coefficients. The performance of the proposed system is analyzed using the metrics, such as fitness, Mean Absolute Error (MAE), magnitude, and the number of components. The proposed method produces minimum fitness, MAE, magnitude and number of components of 0.05, 0.0155, − 96.0 dB and 3372, respectively that shows the effectiveness of the proposed method.
Relation Classification Based on Vietnamese Covid-19 Information Using BERT Model with Typed Entity Markers

This paper presents a study using the Bidirectional Encoder Representations from Transformers (BERT) base model to classifying relations based on Vietnamese Covid-19 information. The study applies two BERT-base models: R-BERT and BERT with entity start. In this work, instead of using entity markers for input, typed entity markers are used. The typed entities include the patient with name, the patient with age, the patient with the job, patient with gender, patient with symptom and disease, patient with transportation. A Vietnamese dataset is labeled manually and the final Bert base model to classify Covid-19 relation is slightly better than the model applied entity marked.
Investigating Negation in Pre-trained Vision-and-language Models

Pre-trained vision-and-language models have achieved impressive results on a variety of tasks, including ones that require complex reasoning beyond object recognition.However, little is known about how they achieve these results or what their limitations are.In this paper, we focus on a particular linguistic capability, namely the understanding of negation.We borrow techniques from the analysis of language models to investigate the ability of pretrained vision-and-language models to handle negation.We find that these models severely underperform in the presence of negation.
Enhancing Model Robustness and Fairness with Causality: A Regularization Approach

Recent work has raised concerns on the risk of spurious correlations and unintended biases in statistical machine learning models that threaten model robustness and fairness. In this paper, we propose a simple and intuitive regularization approach to integrate causal knowledge during model training and build a robust and fair model by emphasizing causal features and de-emphasizing spurious features. Specifically, we first manually identify causal and spurious features with principles inspired from the counterfactual framework of causal inference. Then, we propose a regularization approach to penalize causal and spurious features separately. By adjusting the strength of the penalty for each type of feature, we build a predictive model that relies more on causal features and less on non-causal features. We conduct experiments to evaluate model robustness and fairness on three datasets with multiple metrics. Empirical results show that the new models built with causal awareness significantly improve model robustness with respect to counterfactual texts and model fairness with respect to sensitive attributes.
On Doctrines and Cartesian Bicategories

We study the relationship between cartesian bicategories and a specialisation of Lawvere's hyperdoctrines, namely elementary existential doctrines. Both provide different ways of abstracting the structural properties of logical systems: the former in algebraic terms based on a string diagrammatic calculus, the latter in universal terms using the fundamental notion of adjoint functor. We prove that these two approaches are related by an adjunction, which can be strengthened to an equivalence by imposing further constraints on doctrines.
Current global development trends and their impact on the educational and economic systems

We live in a period of rapid transformations of public institutions caused by the greater momentum of many social, economic, political and technological processes. In this paper, both general and special scientific research methods are used. An expert approach, a content analysis method, a sociological survey, and statistical tools for processing the results were used as special methods. We also conducted an analysis of empirical materials based on the results of a survey of expert groups: university students and lecturers, business representatives and civil servants. Results: The contradictions in the development of the world economy were identified. Along with globalization, there is a tendency towards anti-globalization as cultural differences create greater distance between nations. Another reason for the disintegration of the world's educational and economic systems is the coronavirus. All the abovementioned conditions have led to an increasing role of digital technologies in education and the economy. However, there are certain contradictions in this process as well. Distance education allows for the exchange of knowledge regardless of geographic location and national boundaries.
SOLQ: Segmenting Objects by Learning Queries

In this paper, we propose an end-to-end framework for instance segmentation. Based on the recently introduced DETR [1], our method, termed SOLQ, segments objects by learning unified queries. In SOLQ, each query represents one object and has multiple representations: class, location and mask. The object queries learned perform classification, box regression and mask encoding simultaneously in an unified vector form. During training phase, the mask vectors encoded are supervised by the compression coding of raw spatial masks. In inference time, mask vectors produced can be directly transformed to spatial masks by the inverse process of compression coding. Experimental results show that SOLQ can achieve state-of-the-art performance, surpassing most of existing approaches. Moreover, the joint learning of unified query representation can greatly improve the detection performance of original DETR. We hope our SOLQ can serve as a strong baseline for the Transformer-based instance segmentation. Code is available at this https URL.
Comparing Digital Health Strategies and COVID-19 Response in European Union Member States

Abstract Background In 2019, Digital Health was on the agenda of the European Union (EU) and its Member States (MS), and the digital health transformation of both healthcare services and Public Health has been well underway for several years. The COVID-19 pandemic crisis required an unexpectedly quick adaptation of social norms and exerted great pressure on healthcare and public health systems. Although implemented strategies and results have varied, Digital Health tools emerged as a fundamental aspect of the pandemic fight across the EU. Methods The objective of this study was to determine and compare Digital Health Strategies and COVID-19 Response in EU MS 43% had no interoperability with other apps. We analyzed the 27 MS of the EU and determined several parameters relevant before and during the pandemic: 1 - Preexisting national digital health strategy in 2019; 2 - COVID-19 General Information Website; 3 - Real time updated COVID-19 online dashboard; 4 - Mobile application for contact tracing; 4.1 - Mobile app launch date; 4.2 - Interoperability with other mobile apps; 5 - Shift to telemedicine; 6 - Official public health communication through social media; 7 - Dedicated COVID-19 Information System for Public Health Workforce. Information from publicly available sources. Results All EU MS had a preexisting digital health strategy before the pandemic, developed a dedicated COVID-19 General Information Website and a COVID-19 dashboard with real-time updates on relevant statistics. The majority of MS (79%) developed a mobile app for contact tracing (43% had no interoperability with other apps), and of those that had not developed an app as of April 30th 2021, 50% had one under development. Most apps were launched in the first trimester of 2021. Other results were heterogeneous. Conclusions In some aspects, there is a clear convergence between EU MS in the deployment of digital health tools to fight the COVID-19 pandemic. Key messages All European Union Member States used digital health tools to fight the COVID-19 pandemic. The success of national level deployment of specific digital health tools must be assessed in order to develop a more integrated digital health response plan at the European level in the future.
Agree to Disagree: Analysis of Inter-Annotator Disagreements in Human Evaluation of Machine Translation Output

This work describes an analysis of interannotator disagreements in human evaluation of machine translation output.The errors in the analysed texts were marked by multiple annotators under guidance of different quality criteria: adequacy, comprehension, and an unspecified generic mixture of adequacy and fluency.Our results show that different criteria result in different disagreements, and indicate that a clear definition of quality criterion can improve the inter-annotator agreement.Furthermore, our results show that for certain linguistic phenomena which are not limited to one or two words (such as word ambiguity or gender) but span over several words or even entire phrases (such as negation or relative clause), disagreements do not necessarily represent "errors" or "noise" but are rather inherent to the evaluation process.On the other hand, for some other phenomena (such as omission or verb forms) agreement can be easily improved by providing more precise and detailed instructions to the evaluators.
New approach to greedy vector quantization

We extend some rate of convergence results of greedy quantization sequences already investigated in 2015. We show, for a more general class of distributions satisfying a certain control, that the quantization error of these sequences has an optimal rate of convergence and that the distortion mismatch property is satisfied. We will give some non-asymptotic Pierce type estimates. The recursive character of greedy vector quantization allows some improvements to the algorithm of computation of these sequences and the implementation of a recursive formula to quantization-based numerical integration. Furthermore, we establish further properties of sub-optimality of greedy quantization sequences.
Efficient protocols for oblivious linear function evaluation from ring-LWE1

An oblivious linear function evaluation protocol, or OLE, is a two-party protocol for the function f ( x ) = a x + b, where a sender inputs the field elements a, b, and a receiver inputs x and learns f ( x ). OLE can be used to build secret-shared multiplication, and is an essential component of many secure computation applications including general-purpose multi-party computation, private set intersection and more. In this work, we present several efficient OLE protocols from the ring learning with errors (RLWE) assumption. Technically, we build two new passively secure protocols, which build upon recent advances in homomorphic secret sharing from (R)LWE (Boyle et al. in: EUROCRYPT 2019, Part II (2019) 3–33 Springer), with optimizations tailored to the setting of OLE. We upgrade these to active security using efficient amortized zero-knowledge techniques for lattice relations (Baum et al. in: CRYPTO 2018, Part II (2018) 669–699 Springer), and design new variants of zero-knowledge arguments that are necessary for some of our constructions. Our protocols offer several advantages over existing constructions. Firstly, they have the lowest communication complexity amongst previous, practical protocols from RLWE and other assumptions; secondly, they are conceptually very simple, and have just one round of interaction for the case of OLE where b is randomly chosen. We demonstrate this with an implementation of one of our passively secure protocols, which can perform more than 1 million OLEs per second over the ring Z m , for a 120-bit modulus m, on standard hardware.
Clustering of Primary and Secondary School in Indonesia Using The Fuzzy C-Means Method Based on School Self-Evaluation With Imputation Data

The National Education Standard is one of the government's efforts to achieve equitable quality education. National Education Standards include eight outcomes, namely Graduation Competency Standards, Content Standards, Process Standards, Assessment Standards, Educators and Educators Standards, Facilities and Infrastructure Standards, Management Standards, and Financing Standards. This research was conducted to classify elementary and junior high schools in Indonesia based on SNP using the Fuzzy C-Means method. Prior to the cluster analysis, the missing value imputation was carried out using regression. The variables that have the lowest median and average value are the standard variables of educators and education personnel, while those with the highest value are the process standards. Based on the results of grouping using C-Means, the optimum number of clusters is four clusters with the most members being cluster 1 (the best cluster). Keywords: Education; Clustering; Fuzzy C-Means; imputation missing value
Preview-Review Bilingual Instructional Tools Development with Discovery Learning Model Setting to Enhancing Student’s Conceptual Understanding and Speaking Ability

This study aimed to analyze the validity categories and the effectiveness of preview-review bilingual instructional tools with discovery learning model setting in enhancing conceptual understanding and speaking ability. This study was research and development (R &amp; D) using ADDIE developmental model consisting of analyzing, design, develop, implementation, and evaluation. The data collection was done through non-test techniques using product validation sheets, learning observational sheet, and observational sheet for speaking ability and test techniques using an assessment sheet for measuring conceptual understanding. The validity of preview-review bilingual instructional tools with discovery learning model setting was analyzed qualitatively based on 5-point scale conversion table. The effectiveness of preview-review bilingual instructional tools with discovery learning model setting in the implementation stage was analyzed based on the average of the gain score. The result showed that the validity of preview-review bilingual instructional tools with discovery learning model setting was 3.60 for the content and 3.55 for the language which is in very good category and the developed of preview-review bilingual instructional tools with discovery learning model setting was effective in enhancing conceptual understanding and speaking ability based on average gain score.
Non-coherent change detection in real SAR satellite imagery with usage of the CFAR algorithms

Change detection in synthetic aperture radar (SAR) imagery is a demanding task due to the intrinsic speckle-noise accompanying the images. In general, it can be divided onto coherent (CCD) and non-coherent (NCCD) change detection methods. In the case of coherent methods, we have an increased sensitivity to changes (due to taking into account phase information) at the expense of incorporating phase noise, which in turn increases the level of false detections. In the case of incoherent methods, we do not take into account signal phase information but only its amplitude (or power), which results in a lower sensitivity to changes and also much lower level of false alarms. Therefore, incoherent detectors often demonstrates better operating performance (ROC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> curves) than coherent ones - while being simpler, more reliable and easier to handle. In this article, author will focus on SAR imagery non-coherent change detection based on constant false alarm rate (CFAR) algorithms.
The Development of E-Book-Problem Based Learning Media

Revolution 4.0 requires college students to be able to have skills in solving problems.The rapid development of technology and the outbreak of the COVID-19 pandemic require the learning process is done online.This is certainly very different from the face-to-face learning process before the pandemic era.The object of this research is Learrning Planning e-book based PBL.E-book developed using the Kvisof Flipbook Maker application.E-books are compiled and developed with regard to the material and media aspects.The validation results of the developed e-book viewed from the material and media aspects.For the material, material expert gives a value of 94.14 % and categorized as very good.For the media, media expert gives an assessment of 88.5 % and considered very good.
Real-Time Human Detection Using Spherical Camera for Web Browser-Based Telecommunications

In this study, we propose a method to apply the real-time object recognition using 360° spherical camera to web browser-based telecommunications. The main problems associated with the images captured by the spherical camera are as follows: i) the large geometric distortions of the objects near the poles in panoramic images projected by the equirectangular method and ii) the location of the object in the spherical image cannot be determined immediately. In addition, in conventional object detection methods, the browser-based, real-time object recognition method based on the use of a client-server system has not been established thus far. In this study, we addressed these problems by i) increasing the equirectangular panoramic images dataset, ii) training artificially distorted images of objects, and iii) adapting the object recognition model to Web Real-Time Communication. In our model, the range of object detection in 360° panoramic images expanded by approximately 25% compared with previous methods. Further, by transforming the results detected in a planar image into spherical coordinates, we have developed a system that can track a specific object in spherical images in real time. Our proposed system is not limited to any location as it can be utilized by users by simply inputting the image on the browser.
proScript: Partially Ordered Scripts Generation

Scripts – prototypical event sequences describing everyday activities – have been shown to help understand narratives by providing expectations, resolving ambiguity, and filling in unstated information. However, to date they have proved hard to author or extract from text. In this work, we demonstrate for the first time that pre-trained neural language models can be finetuned to generate high-quality scripts, at varying levels of granularity, for a wide range of everyday scenarios (e.g., bake a cake). To do this, we collect a large (6.4k) crowdsourced partially ordered scripts (named proScript), that is substantially larger than prior datasets, and develop models that generate scripts by combining language generation and graph structure prediction. We define two complementary tasks: (i) edge prediction: given a scenario and unordered events, organize the events into a valid (possibly partial-order) script, and (ii) script generation: given only a scenario, generate events and organize them into a (possibly partial-order) script. Our experiments show that our models perform well (e.g., F1=75.7 on task (i)), illustrating a new approach to overcoming previous barriers to script collection. We also show that there is still significant room for improvement toward human level performance. Together, our tasks, dataset, and models offer a new research direction for learning script knowledge.
Panoptic 3D Scene Reconstruction From a Single RGB Image

Understanding 3D scenes from a single image is fundamental to a wide variety of tasks, such as for robotics, motion planning, or augmented reality. Existing works in 3D perception from a single RGB image tend to focus on geometric reconstruction only, or geometric reconstruction with semantic segmentation or instance segmentation. Inspired by 2D panoptic segmentation, we propose to unify the tasks of geometric reconstruction, 3D semantic segmentation, and 3D instance segmentation into the task of panoptic 3D scene reconstruction - from a single RGB image, predicting the complete geometric reconstruction of the scene in the camera frustum of the image, along with semantic and instance segmentations. We thus propose a new approach for holistic 3D scene understanding from a single RGB image which learns to lift and propagate 2D features from an input image to a 3D volumetric scene representation. We demonstrate that this holistic view of joint scene reconstruction, semantic, and instance segmentation is beneficial over treating the tasks independently, thus outperforming alternative approaches.