Reviewerly/exHarmony huggingface dataset:
Patrones de participación y logro en un MOOC de Saberes Digitales para Docentes en servicio de México

Participation patterns and completion rates of a MOOC on Digital Knowledge for Teachers with over 8000 students were investigated in this article using descriptive statistics and cluster analysis according to age, gender and previous educational attainment. Results indicate that older and more educated participants obtained higher completion rates and employed more often the discussion forum. No gender differences were obtained. Participation in the course followed a funnel model in which there is a steep decrease of involvement after the first activity which stabilizes in the fifth one. The course rested on a basis of trust assuming that each participant voluntarily enrolled with the purpose of learning and therefore considering his/her self-perception of course performance as one element of overall appraisal is consistent with these courses.
Application to train cognitive skills according to the CHC intelligence model

This paper deals with a proposal of an application designed to train quantitative abilities of thinking according to the CHC intelligence model. This application is one of the parts in the project called “Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment”, whose aim is to develop a smart neuro-rehabilitation system to increase the effectivity of the acute phase of neuro-rehabilitation of patients with brain damage. Following the project’s requirements, the application is prepared as a web application built on the .NET technology.
Adaptive Inertia-Weighted Firefly Algorithm

Real-life optimization problems required more and more technique, which completely utilizes the search spaces to obtain the best optimal solution, so researchers have an opportunity to propose a new technique or a modified version of the existing technique. In this order, this paper is a new modified version of nature-inspired metaheuristic firefly algorithm. FA is swarm intelligence algorithm inspired by flashing pattern and behavior of fireflies. FA has a tendency to trap in local optima and shows a slow convergence for optimization problems. To overcome these problems, in the proposed variant we add an adaptive inertia weight to update the position of search agents. To validate the performance of the proposed variant, it is tested on 23 traditional benchmark functions. The static and numerical results confirm the efficacy of the proposed variant over the original algorithm.
Fragments of plane filling curves of degree q + 2 over the finite field of q elements, and of affine-plane filling curves of degree q + 1

Nonsingular plane curves over a finite field Fq of degree q+2 passing through all the Fq-points of the plane admit a representation by 3×3 matrices over Fq. We classify their degenerations by means of the matrix representation. We also discuss the similar problem for the affine-plane filling projective curves of degree q+1.
API Design Principles &amp;amp; Security Best Practices – Accelerate Your Business Without Compromising Security

APIs are a strategic necessity to give your business the agility, innovation and speed needed to succeed in today’s business environment. However, the financial incentive associated with this agility is often tempered with the fear of undue exposure of the valuable information that these APIs expose.
Teaching Topographic Surface Concepts in Augmented Reality and Virtual Reality Web Environments

This paper shows the use of web resources for the creation of Virtual Reality (VR) and Augmented Reality (AR) environments to teaching of topographic surface concepts. The mockups of the topographic surfaces were built with Sketchup software, include graphical representations of level contours, terrain cuts, topographic profiles and embankments. The terrains textures used are satellite photos available by the Digital Globe platform and contribute to improve the visualization of studied concepts. Developed environments enable students to view surfaces in AR ambient, using their webcam devices, such as smartphones, tablets, or notebooks, from a variety of points of view. Each topographic surface mockup has a link to its respective representation in VR, which allows its manipulation and detailed study of each concept. The environments presented in this work can be used in disciplines of Topography, Geography and Descriptive Geometry.
Deep symbolic regression: Recovering mathematical expressions from data via policy gradients

Discovering the underlying mathematical expressions describing a dataset is acore challenge for artificial intelligence. This is the problem of symbolicregression. Despite recent advances in training neural networks to solvecomplex tasks, deep learning approaches to symbolic regression are lacking. Wepropose a framework that combines deep learning with symbolic regression via asimple idea: use a large model to search the space of small models. Morespecifically, we use a recurrent neural network to emit a distribution overtractable mathematical expressions, and employ reinforcement learning to trainthe network to generate better-fitting expressions. Our algorithm significantlyoutperforms standard genetic programming-based symbolic regression in itsability to exactly recover symbolic expressions on a series of benchmarkproblems, both with and without added noise. More broadly, our contributionsinclude a framework that can be applied to optimize hierarchical,variable-length objects under a black-box performance metric, with the abilityto incorporate a priori constraints in situ.
Why enterprises need to adopt ‘need-to-know’ security

A host of different cyber attacks – including phishing and ransomware, advanced persistent threats conducted by nation-state actors, zero-day exploits and more – are forcing security professionals to bring their ‘A game’ to work every day. In addition to these attacks, information security professionals must manage information in compliance with data privacy regulations such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA) or else face stiff fines. Massive data breaches and large penalties imposed by regulatory bodies show no signs of abating and continue to make headlines across the world. To thwart new attacks and prevent breaches, you need to develop a new mindset that challenges the status quo and nurtures new ways of thinking, especially when it comes to protecting information assets. Traditional approaches are being rendered ineffective by social engineering attacks, globalisation and the Internet of Things. In the face of these and other challenges, a multipronged approach with a ‘need-to-know’-based security model at the foundation is an effective strategy to secure enterprise information and prevent a breach, explains Aaron Rangel of iManage.
Inter/intra-frame constrained vascular segmentation in X-ray angiographic image sequence

Abstract Background Automatic vascular segmentation in X-ray angiographic image sequence is of crucial interest, for instance, for better quantifying coronary arteries in diagnostic and interventional procedures. Methods A novel inter/intra-frame constrained vascular segmentation method is proposed to automatically segment vessels in coronary X-ray angiographic image sequence. First, a morphological filter operator is applied to remove structures undergoing the respiratory motion from the original image sequence. Second, an inter-frame constrained robust principal component analysis (RPCA) is utilized to remove the quasi-static structures from the image sequence. Third, an intra-frame constrained RPCA is employed to smooth the final extracted vascular sequence. Fourth, a multi-feature fusion is designed to improve the vascular contrast and the final vascular segmentation is realized by thresholding-based method. Results Experiments are conducted on 22 clinical X-ray angiographic image sequences. The global and local contrast-to-noise ratio of the proposed method are 6.6344 and 4.2882, respectively. And the precision, sensitivity and F1 value are 0.7378, 0.7960 and 0.7658, respectively. It demonstrates that our method is effective and robust for vascular segmentation from image sequence. Conclusions The proposed method is effective to remove non-vascular structures, reduce motion artefacts and other non-uniform illumination caused noises. Also, the proposed method is online which can just process one image per time without re-optimizing the model.
Law and Technology in the Dimension of Time

The relationship between law and technology in the dimension of time is a popular theme in legal and policy circles, usually recurring as a critique of outdated laws. In these debates, law is most often portrayed as falling behind technology as both travel together along the dimension of time. While such images simplify the relationship between law and technological change, they reflect some insight into the challenge faced in ensuring law remains relevant, appropriate, comprehensive, well-adapted and clear in the face of an ever-evolving socio-legal-technical landscape. Law’s struggle in the face of socio-technical change has been referred to as the pacing problem or the challenge of regulatory connection. A phenomenon that receives much less attention in these popular debates is the temporal impact of attempts to embed the law and social values into technological design. There are a variety of terms that capture ideas around design-based regulation, each with different foci and associated literature. For example, ‘value sensitive design’ focuses on the design process, while ‘compliance by design’ focuses on extracting, modelling and implementing legal requirements but both are about using architecture and processes to achieve a particular effect (respecting values or ensuring compliance with law). The idea of embedding law, values or preferences into technical design choices and business processes is rarely subjected to similar time-inspired critiques despite the fact that technology and procedures can be designed around outdated understandings of legal requirements and policy goals. Whether technology design decisions are based on technical, commercial, legal or regulatory objectives and requirements (or combinations thereof), they may come to be seen as obsolete as those objectives and requirements evolve. Therefore, the challenge of staying up-to-date or continuing to fit in an evolving world is not only a legal challenge. Technology can also fail to meet evolving legal requirements or fail to adapt seamlessly to other technical elements within systems as the broader socio-legal environment evolves. In particular, law can impose particular demands on technology, so that it is called on to catch up.
Incremental Learning of Multi-Tasking Networks for Aesthetic Radar Map Prediction

It is difficult and challenging to evaluate the aesthetics quality of images from multiple angles.Since humans' perception of images comes from many factors, the integrated image aesthetic quality assessment cannot be easily summarized by few attributes.A comprehensive evaluation is supposed to predict many aesthetic attributes across not only one dataset.This requires the model to have not only high accuracy, but also strong generalization ability, resulting in a better prediction on multiple models and datasets.Recent work shows that deep convolution neural network can be used to extract image features and further evaluate the total score of images, and the method of evaluation are lacking of sufficient detailed features.In this paper, we propose a multi-task convolution neural network with more incremental features.We show the results in the way of a hexagon map, which is called aesthetic radar map.This allows the network model to fit different attributes in various datasets better.
Detection and Control of Radon and its Progenies in a Tunnel

The presence of radon and its progenies in the tunnel directly affects the health of the staff. Effective measures should be taken to lower the concentration of radon and its progenies in the tunnel to reduce their harm to the human body. The article first introduces the investigation results of radon and its progenies in a tunnel, analyzes the monitoring data briefly, and then analyzes the application of ventilation, shielding and adsorption three kinds of methods in the control of tunnel radon, and gives some reasonable advice.
Interactive Map Model of Flat Design for Istano Basa Pagaruyung Tourism Development

Abstract The power in conveying messages to communicants is a form of active media. There are many things and forms of media that developed in the digital era today. Of course, the development of the media to be more friendly and close to the community will make it easier to convey the message. The media is also the most important part of supporting the tourism sector. Especially in the cultural tourism sector that we are familiar with cultural heritage or historical heritage. one of them is the Rumah Gadang Istano Basa as a historical heritage of West Sumatra. The map that is part of the sign system is certainly very needed for visitors. A flat design that is appealing in a way that will certainly help increase the interest of visitors, especially this makes it easier to share information through digital technology such as smartphones. This map design method will adapt to the Waterfall design method created by Pahl and Beitz. In the design process, it will go through the requirements analysis, design, implementation, integration testing and ending with maintenance. Of course, the design of maps that use this digital application must follow a structured stage of the Waterfall method. Then, the design which becomes an important point in this design is the concept of infographics that is clear and has the power in readability.
Dealing with Hate Speech

Abstract Anti-Korean hate speech has become a serious social problem in recent years in Japan. In order to address the growing concern about the issue, Japan’s National Diet passed an anti-hate speech law in 2016. While some legal actions have been taken as a result and there has been some recent academic research into the social production of hate speech, relatively little effort has been made to investigate how the targeted group understands and deals with this form of hostility. Our aim here is to document the ways in which young Koreans who have been living in Japan for greater or lesser lengths of time perceive the rise in hate speech directed against them there and their strategies for coping with it. We explore these issues through an analysis of in-depth interviews with 14 young Koreans, for we seek to shift the focus of hate speech away from the “act” and toward its “targets.”
Deep Learning-Based Firework Video Pattern Classification

The video analytics technology has been a rapidly improving discipline in the past decade. With the recent developments in computer vision, we now have the ability to mine massive video data to obtain a clear understanding of what is happening in the world. Because of the remarkable successes of deep learning, currently, we are able to improve video analysis performance significantly than traditional statistical approaches. This study focuses on classifying the patterns of firework videos with various deep learning techniques using spatial and temporal features, beyond common types of pattern classifications. Among successful artificial neural networks, Convolutional Neural Networks (CNN) have demonstrated superiority on modeling high-level visual concepts, while Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) units have shown great talent in modeling temporal dynamics in video-based pattern classification. Our basic models consist of CNN, LSTM, and GRU and, we did experiments by fine-tuning the parameters of layers and using different dropout values with sequence LSTM and GRU models. Our experimental results demonstrated that the model with a sequence of LSTM units and double dropout layers—one for input and another for hidden layers—outperforms the other experimental models with the training accuracy of 83.05%.
Tweet and Account Based Spam Detection on Twitter

Today, with the spread of social media, the number of spam users identified as malicious users is increasing. Twitter is one of the most used social media platforms to send 500 million tweets per day. Twitter is used by every user, as well as malicious spam or fake users. One out of every 200 social media messages and 21 tweets is estimated to be spam. Spam detection is a critical issue because spam accounts are security-threatening accounts. Twitter cannot interfere with spam accounts. In this study, a method for spam detection was presented. Naive Bayes, J48 and Logistic machine learning methods were applied to obtain the accuracy of 97.2% with J48 algorithm.
Effect of bell size on sound characteristics of cymbals

Cymbals are percussion instruments that vibrate and radiate sounds when hit with a stick. A bell is the raised section in the middle of the cymbal and its size produces different sounds. In this study, we investigate the effect of bell size on the sound characteristics of cymbals. The radiated sounds and vibrations for cymbals with two different bell sizes are measured. In addition, the natural frequency and mode shape are obtained by finite element analysis and the sound radiation efficiency is calculated for each mode. The measured results indicate that the sound frequency characteristics for the large bell show three peaks with large sound pressure within the range of 1000 to 3000 Hz and the sound pressure for the small bell is larger than that for the large bell within the range of 4000 to 5000 Hz. The vibration frequency characteristics show there is no remarkable difference between the large and small bells. The sound radiation efficiencies indicate that the large bell has many modes with high radiation efficiency within the range of 1000 to 3000 Hz and their modes have a small number of nodal diameters and a large deformation at the bell. The small bell has many modes with high efficiency within the range of 4000 to 5000 Hz. This is reason for the difference in sound characteristics between the large and small bells.
Pemerolehan Bahasa Anak Periode Linguistik

Language acquisition of human being was started when children learned to talk. Children who are able to speak as well as adults can be called children on linguistic period namely language acquisition period from 1 to 5 years old. Specifically, this study aims to describe language acquisition of children on linguistic period. The subject of this study was children aged 3 years old and 5 years old. Research methodology applied qualitative descriptive research by Miles and Huberman. The researchers did some activities in this study such as: (1) collected the data of language acquisition from children in linguistic period; (2) obtained the data through observation, interview and documentation; (3) analyzed the data based on the theory of children’s language acquisition period and factors which affect language acquisition. Based on the data, it can concluded that children aged 3 years old and 5 years old had language development period  namely two-word speech stage, word inflection, question and refuse sentences, rare and complex stages of construction. Meanwhile, factors which affect children language acquisition aged 3 years and 5 years old were heredity include intelligence and personality factors, social background factors include family relationships, social status and surrounding environment.
An Improved Bidirectional Shift-Based Reversible Data Hiding Scheme Using Double-Way Prediction Strategy

Reversible data hiding (RDH) is a method that allows a cover image to be completely recovered from its corresponding stego image without distortion after the embedded secret messages have been extracted. Prediction-error expansion (PEE), as a classic RDH scheme, has been studied extensively due to its high quality of stego images. Based on prediction errors, threshold values, and the relative distances between each bin and zero bin, we present a bidirectional shift and double-way prediction strategy to solve the multiple embedding problem. Compared with the original algorithm, this scheme only takes a little more time and reduces the PSNR slightly, but it improves the embedding capacity significantly and allows for reversible data hiding. When both threshold values of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>T</mml:mi><mml:mi>H</mml:mi></mml:mrow></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>T</mml:mi><mml:msup><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∗</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math> are equal to 2, the average ER value of 108 test images is 1.2 bpp which is ideal for medium data payload. At the same time, the PSNR is above 30 dB, making embedded information visually imperceptible. These data, together with other experimental results, show that the method proposed in this paper has obvious advantages in image quality and embedding capacity.
CULTURAL EDUCATION OF SCHOOL STUDENTS: A NEW PROJECT

Purpose of the study: The author addresses the problem of cultural education, the search for the most effective forms and conditions for the implementation of study programs and new elective study courses.&#x0D; Methodology: The author highlights the following principles, on which the teacher should rely on the educational process: conceptual, civilizational, cultural and historical, religious, ethnic and national, regional and territorial, artistic and aesthetic.&#x0D; Main Findings: The content of the article is built on the disclosure of cultural education from the standpoint of a wide range of research by scientists and teachers, as well as the requirements for the results of mastering the basic educational program of general education. As an example, the author highlights the integral structure of the general culture of the individual, in which she (conditionally) identifies important components. The article reveals the essential characteristics of the basic principles of pedagogical organization of conditions for the realization of cultural education.&#x0D; Applications of this study: The article shows that the introduction of a new elective course "Cultural and educational tourism" contributes to the expansion of students' artistic needs and interests.&#x0D; Novelty/Originality of this study: The author reveals the importance of the interdisciplinary nature of training, as well as the application of the excursion method of knowledge, which helps to attract various information sources that reveal historical, geographical and cultural resources, as well as artistic attractions.
Segmentation and Recognition of Handwritten Lontara Characters Using Convolutional Neural Network

This study presents a technique to recognize handwritten Lontara characters. Lontara character is Indonesia's traditional character which is used mostly in the southern area of Sulawesi during the kingdom era. The work consists of two stages. First, character segmentation of each character in images is achieved with a combination of contour feature and sliding window technique to create a boundary and extract character segments. Second, a Convolutional Neural Network (CNN) is used to recognize or classify the segmented characters. The dataset contains 23 Lontara characters with five combinations of diacritics and one special character, that falls into 139 classes. The result of the conducted experiments on the dataset shows that CNN provides good results - obtaining 96% of accuracy. Also, the result shows a promising result in a combination of segmentation and recognition.