Reviewerly/exHarmony huggingface dataset:
Transformación de las estrategias pedagógicas en educación infantil para favorecer el desarrollo del lenguaje oral durante y después de la pandemia

Language development is linked to children's brain maturity and the social and family environment in which they operate. In recent decades, the term emerging literacy has been taken into account by researchers and teachers in early childhood education to name a new treatment of the evolution of reading, writing and oral language development in boys and girls. But due to the Covid-19 pandemic, there has been a need to re-signify pedagogical activities so that children can continue working on their communicative dimension from home. That is why this work proposes didactic strategies that are feasible to develop through digital media; so that in case of continuing with a quarantine or in a pandemic, teachers can work specifically in favor of the development of oral and written language of the children of the Children's School El Mundo Magico de Mickey in the town of Suba, Bogota , especially the Garden and Transition grades. This proposal uses thematic workshops as a pedagogical strategy, since in early childhood education the workshop is a work modality that stimulates interest in boys and girls and is very enriching to meet their needs. They incorporate the use of ICT tools, as well as activities consistent with emerging literacy to develop pre-reading skills, such as storytelling and decoding, and taking into account the guiding activities of childhood.
COPOD: Copula-Based Outlier Detection

Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability. As a remedy, we present a novel outlier detection algorithm called COPOD, which is inspired by copulas for modeling multivariate data distribution. COPOD first constructs an empirical copula, and then uses it to predict tail probabilities of each given data point to determine its level of extremeness. Intuitively, we think of this as calculating an anomalous p-value. This makes COPOD both parameter-free, highly interpretable, and computationally efficient. In this work, we make three key contributions, 1) propose a novel, parameter-free outlier detection algorithm with both great performance and interpretability, 2) perform extensive experiments on 30 benchmark datasets to show that COPOD outperforms in most cases and is also one of the fastest algorithms, and 3) release an easy-to-use Python implementation for reproducibility.
Study of Block chain

Blockchain is a system of recording information in a way that makes it difficult or impossible to change, hack, or cheat the system. You have probably heard of Blockchain in the past few years, mostly in the context of crypto currency. However, Blockchain has grown to have several different applications. The significant part about Blockchain is that it is never under the complete control of a single entity due to being entirely consensus-driven. It can never change the data you store in the Blockchain used widely in sharing medical data in the healthcare industry. Due to the security that Blockchain provides, this data can be shared among parties seamlessly. Another application of Blockchain is in maintaining the integrity of payment systems. Blockchain-based payment systems are immune to external attacks and theft. Blockchain can also be used in tracking the status of products in a supply chain in real-time.
Centy: Scalable Server-Side Web Integrity Verification System Based on Fuzzy Hashes

Providing integrity guarantees for websites rendered on a user’s browser is a crucial security property for web applications. There are several ways to tamper with data being received or rendered on the client side, including browser hijacking, malicious plugins, cross-site scripting attacks and manipulation of data in transit. Detecting such attacks is important for content providers in order to generate alerts and prevent further attacks. Detection of website integrity is a challenging task, due to the heterogeneity of possible attacks. In this work we present an approach to detect integrity attacks that is designed to scale to millions of clients while offering high accuracy. Our approach is based on a fine grained analysis of website internal components and a clustering technique. Such clustering allows for an efficient automatic and semi-automatic classification of client-side content (such as scripts, forms, iframes, etc.). This approach is partially implemented in a productive system and is evaluated on a real-world dataset belonging to a sample of tens of thousands unique visits. We show that we can achieve up to 98.7% accuracy on real data based on a labelled prefix, and up to 99.4% compression ratio on incoming to-be-classified client-side content. To the best of our knowledge, we are the first study to show a scalable and practical clustering system for web integrity detection.
HYPERTEXT AS A CHALLENGE TO SOCIAL SPACE IN THE CONTEXT OF MEDIA REALITY

The article considers the collisions of the transformation of social space in the conditions of media reality, which determines the configuration of the existence of modern society. It turns out that hypertext is a new principle of organization of human social existence. It is emphasized that this situation leads to the emergence of new forms of interaction of the constituent elements of the system "man - society - culture". It turns out that hypertext will get new opportunities: it establishes new forms of interaction, changes the rhythm of life, value systems, and so on. The latter is no longer exclusively a component of the Internet space, it becomes life itself. As a result of this situation there is a transformation of both social space and cultural practices of the person; the gap between natural, social and technological is widening.Key words: man, society, information space, hypertext, media revolution, media virus.
Towards Understanding of User’s Immersion Using Electroencephalogram (EEG) and Activity-Adhered Biosensor in an Environment of Full-Body Game Gesture

Understanding user’s immersion in a game environment is challenging regarding human psychology's complex nature and the game itself. While several approaches have proposed measuring user immersion, a limited study used a quantitative method. Therefore, it is necessary to propose a new quantitative method. This study aims to propose a quantitative method for measuring user’s immersion in an environment of full-body game gesture. This study investigates the potency of electroencephalogram (EEG) and activity-adhered biosensor as an alternative quantitative measurement. The first experiment investigated the participant’s brain activity, and movement activity states on the immersive and the less-immersive game. The second experiment validated the findings of the first experiment by using an interview study. Quantitative results showed that immersion might rely on the “calm” and “neutral” states of brain activity and “focus” of activity states in the immersive game. Qualitative results indicated that game challenge (less thinking process) and complex motion gestures (mimicking real-world gestures) influenced positive immersion in the immersive game. While game challenge (high thinking process) disturbed immersion, simple motion gesture did not significantly influence immersion in less immersive games. Finally, this study concludes that the proposed method is the potential for measuring the user’s immersion.
UPAYA MENINGKATKAN HASIL BELAJAR EKONOMI MELALUI METODE SNOWBALL THROWING PADA PESERTA DIDIK KELAS X SEMESTER GENAP SMA NEGERI 3 BANDAR LAMPUNG TAHUN PELAJARAN 2018/2019

The problem in this study is: How the application of the snowball throwing method can increase learning activities in even semester X class students at Bandar Lampung Negeri 3 HigH School 2018/2019 and How the application of the snowball throwing method can improve the economic learning outcomes of X semester even 3 Bandar Lampung state Senior High School 2018/2019.The purpose of this study is 1). To find out how learning method can increase economic learning activities in class X students in even semester 3 of Bandar Lampung SMA Negeri 2018/2019. 2). To find out how the snowball throwing learning method can improve economic learning outcomes in class X students in the even semester of SMA 3 Bandar Lampung 2018/2019 academic year. This research is a classroom action research (PTK) conducted in two stages of the cycle which in its application uses the snowball throwing method in the learning process. The subjects in this study were class X IPS 1 in Bandar Lampung State high School 3 which numbered 28 students. The results of this study indicate that the learning activities of students in cycle I with an average score of 2,66 while learning activities obtained by cycle II with an average score of 3,77. The average value of learning outcomes in the first cycle is 66,60 then in the second cycle has increased by 76,61. It can be seen from the first cycle and the second cycle that there is an increase in the average number of 10,01. The percentage of student learning outcomes also increased. In the first cycle of 39,28% of students who completed, in the second cycle amounted to 82,14% of students who completed. The analysis shows that learning activities by applying the snowball throwing learning method can increase learning activities and learning outcomes.
Laser scanning–based straightness measurement of precision bright steel rods at one point

Abstract In industrial manufacturing of bright steel rods, one important quality factor is the straightness or straightness deviation. Depending on the application, deviations of less than 0.1 mm per meter rod length are desired and can be reached with state-of-the-art manufacturing equipment. Such high-quality requirements can only be guaranteed with continuous quality control. Manual straightness measurements conducted offline using a dial gauge provide accurate results on single positions of the rod. We propose a contactless, optical measurement technique based on laser scanning which has the potential to be used inline during production to inspect all rods over the entire length. Only for calibration of the system the rod needs to be turned around its axis. For the measurement of straightness deviation, it is not required to turn the rod. The method is based on evaluating the intensity signal of the reflected laser radiation against the scan angle. It is shown that in combination with an accurate calibration, this signal can be used to determine the rod’s deviation from a straight rod. We explain the measurement and calibration principle as well as data evaluation. We present the experimental setup and first measurement results on a single position on several samples. For a homogeneous sample surface and neglecting laser drift, accuracy and precision were determined to be in the range of 10–20 μm. We discuss the working principle of a potential inline system.
Significance of Social Networking Tools In Promoting The Academic Library Services: A Case Study

The 21st century has witnessed that Social Networking Sites (SNSs) are one of the most dynamic online exercises to impact the advanced world. SNSs and their applications act as a powerful tool to link the gap between the library and its patrons. The COVID-19 Pandemic has proved that SNSs help in providing library services with the click of a mouse, thus expanding the services the library renders to their clients. The study highlights the use of various SNSs in promoting library services, reasons for using SNSs, to know the most preferred SNS, and frequency of SNSs used by the libraries, and the obstacles faced by the libraries. The data was collected from one hundred and thirty-eight Arts and Science college librarians all over India through an online questionnaire. The majority of the respondents used various SNSs applications in promoting the library services;WhatsApp and library websites are the most preferred SNSs and MySpace was the least preferred SNSs, the respondents agreed that they used SNSs to communicate with their users, to provide quick access, to uplift the library usage and market the library resources, most of the libraries once a week updated the SNSs, a few problems faced such as lack of security, media access, and internet connectivity and none of the respondents responded that they have low skill in using the SNSs for marketing the library resources. The study suggested that library professionals should update the SNSs regularly because of the advanced technologies these sites are the mirror to the library users and they can do further study with innovative technologies adopted by the libraries for the marketing library and information products and services. © 2021. All Rights Reserved.
Review on Chaotic Theory using DNA Encoding with Image Encryption

Encryption is a method used to ensure and shield the photos from inappropriate suggestions. Nowadays because of the electronic world, it is not hard to hack any information which is accessible in the cloud. Security is a critical issue in anyone's life, and encryption accepts a basic activity to ensure security. There exist many picture encryption counts that are prevalently established on violent vital maps. Anyway, there is drawback like minimal key-space and frail security. In this paper we have proposed a strategy that uses disarranged determined mapping and DNA encoding to encode the image, ASC II private key is used to diffuse the image. The results displayed show that encryption computation subject to violent key mapping and DNA encoding gives ideal results over scrambling just with scattered key mapping. The proposed method in like manner considers the possible parametric
CNN-based YOLOv3 comparison for underwater object detection / Mohamed Syazwan Asyraf …[et al.]

Object detection that deals with identifying and locating object is one of area that integrate from the advance- ment in machine learning and computer vision. Modern object detection which carried out supervised learning utilizes Convo- lutional Neural Network (CNN) as the backbone of the detection architecture which is significant for underwater object detection as the underwater images are usually low in quality and blurry. Single stage detection such as You Only Look Once (YOLO) is one the famous object detection model that is prominent among researchers due to high performance in accuracy and processing speed. However, YOLO has many versions where the current incremental improvement model of YOLOv3 has been widely used by researchers to solve different types of problem relatedto object detection. Therefore, there is a need to explore thetrade-off relationship between the processing speed and precisionof each YOLO model. In the study, two different open source underwater datasets were used in four different YOLOv3 modelsnamely as YOLOv3-SPP, YOLOv3-Tiny, YOLOv3-Tiny-PRN and the original YOLOv3 in order to study their performance based on metrics evaluation of precision and processing speed (FPS). The result shows that YOLOv3-SPP proved to be the best in terms of precision while YOLOv3-Tiny-PRN lead in terms of execution speed. So, this study shows that YOLOv3 model is highly significant to be implemented and able to accurately detect underwater objects with haze and low-light environment. This study can help researchers and industry in determining the best YOLOv3 model specifically for detection of the underwater images and its application.
A New Deep Learning method for Automatic Ovarian Cancer Prediction & Subtype classification

Computer-aided diagnosis and other relevant biomarkers can provide valuable advice to doctors for precise diagnosis. The traditional techniques, mainly Computer Tomography (CT) & Magnetic Resonance Imaging (MRI), are costly, time-consuming, and tedious. During the literature survey, it was learned that Deep Learning outclassed traditional CNN algorithms among various image processing algorithms. In the present work, Deep Convolutional Neural Network (DCNN) is implemented for predicting ovarian cancer and classifying its subtypes with histopathological images as input. For achieving higher accuracy, a new architecture is designed and implemented from the scratch inspiring by pre-Trained AlexNet Model. The basic AlexNet architecture consists of 5 convolutional layers, 3 Maxpooling layers, and three fully-connected layers with Rectified Linear Unit (ReLU) as the activation function. We modified this by adding a Maxpooling layer after each pair of convolutional layers, four such iterations, four fully connected layers, replaced ReLU with Exponential Linear Unit (ELU), and modified architectural parameters. The original architecture considers kernel sizes of 11x11 and 5x5, which we modified to make it uniform at 3x3 as the kernel size. The network was trained using 24,742 augmented images. The accuracy of predicting Ovarian Cancer and its subtypes classification improved to 83.93% with the help of 43,94,533 parameters compared with previous studies that achieved 78%. We also trained the model with both the datasets, before and after augmentation. We concluded that the augmentation increased the accuracy from 70% to 83.93%. This new model can be considered as a benchmark.
An Analysis of Effective Methods for Cultivating Applied Undergraduate Talents in the Intelligent Era

Under the background of the intelligent age, new challenges are raised for undergraduate talent training, requiring undergraduate talent training to become more technical and application-oriented, and to build a characteristic undergraduate talent training model with application-oriented as the core goal. Therefore, this article focuses on the current undergraduate talent training. Starting from the lack of talent training, we will analyze the effective methods and paths of application-oriented undergraduate talent training under the background of the intelligent age for reference.
Learning with Little Data: Industry Challenges and Innovations

In e-commerce applications, customers search and discover one or more products using queries. Some of these queries are broad and diverse, with multiple intents. Therefore, relying purely on the anonymized and aggregated customer historical behavioral data is not sufficient to train machine learned models. For example, customers may click and purchase a galaxy charger for a "samsung galaxy s9" query. The item is not an exact match for the customer query. However, it serves as a complement to the original query and may be purchased. To address these potential mismatches from surfacing in search results, e-commerce systems rely on machine learned models trained on human- annotated data. There are two challenges in collecting human annotated data. First, the human annotation process does not scale and it is hard to obtain large volumes of annotations in multiple languages. Second, annotators must query existing systems to obtain samples for auditing, resulting in very few mismatched examples (data skewness) and counterfactual biases. In this talk, we address these challenges using two recent advances in deep learning. To address the data skewness, we generate hard negative examples using positive examples. The key idea here is to generate synthetic data using a Variational Encoder Decoder (VED) architecture. We show how a modified loss function with a novel combiner (to combine VED with the classifier) can avoid policy-based gradients and other heuristics. To address the sparsity of data in less popular languages, we combine data across all languages using language-agnostic representation learning. The side information we use aligns the items across languages in the same latent space. We show that our approaches significantly improve upon state of the art baselines, by over 25% in F1 score for the variational model, and over 20% in F1 score for the multilingual model.
Blockchain is dead! Long live Blockchain!

A decade on since Satoshi's Bitcoin paper, Blockchain is now considered to be sliding into the trough of Gartner's hype cycle. Claims in regard to Blockchain and Cryptocurrencies being dead are on the rise, whilst at the same time many claim the contrary. The vague statement encapsulates many different aspects and perspectives of a myriad of use cases, technology and platforms including both the technique as a whole and as individual instantiations.In this paper, we unpack the statement, break it down and investigate objectively concrete factors which provide indication in regard to whether Blockchain is dead. We examine metrics including budgets and investment; company registries and data; community engagement, projects and source code repositories; academic research and programmes; social media posts; and public interest. We individually demonstrate metrics that indicate the respective measures' healthy activity and come to the conclusion that the collective statement 'Blockchain is dead' does not hold. A clear message extracted from the work proposed herein is that success is achieved where the community comes together rather than works in isolation.
Managing E-Voting Process Through Blockchain

Conducting fair and transparent elections are the integral part of any democratic country. This exercise of conducting elections is a vital task for the election agencies of the countries along with the law enforcement agencies. Still now many countries are in the world is depending on ballot paper pattern for conducting the elections more efficiently. But this is not always the smart idea of conducting elections as using of ballot paper for conducting the elections may add more financial burden as it includes the more manpower in the process of conduction of elections, post voting security and counting of the same ballet papers. However, this is not the case in electronic voting machines, where all the process right from the conduction of elections, providing security and counting all the process is seamlessly conduct without much more hassle. The biggest democratic countries like India also rely on Electronic voting machines, providing the physical security of the voting machines is having the same burden as of ballot papers. Electronic voting machines are always having a threat of data tampering in mass numbers. So securing these votes in the electronic voting machine is a most important task in conduction of the elections. Blockchains are the trending technology for securing the data in the distributed paradigm more efficiently. So this research article provides a secured way to provide security for the post voting data in a controlled simulated environment using Blockchain. And the whole process is powered by the 16 byte hash keys obtained by processing the SHA 256 hashing algorithm and Bit mapping technology.
UPAYA MENINGKATKATKAN HASIL BELAJAR EKONOMI MELALUI MODEL PEMBELAJARAN TREFFINGER PADA PESERTA DIDIK KELAS X IPS

Problems in this study are learning activities and low economic learning outcomes in class x students of Surya Dharma 2 High School Bandar Lampung. The purpose of this study is 1. (to find out whether the application of the Treffinger learning model can improve the learning activities of class X students in the even semester of Surya Dharma High School 2018/2019. 2. (to find out whether the application of the Treffinger learning model can improve student economic learning outcomes) class X Even semester of Surya Dharma High School 2018/2019 academic year. This researcher is a class action research (PTK) using the Treffinger model. In the learning process which is the subject of this study is class X Surya Dharma High School. Average learning activity scores in the cycle first 2.77 and the second cycle 3.96. The average value of the first cycle of 70 results and the second cycle 78.42 The learning outcomes in the first cycle 63% and the second cycle 84% From the analysis, shows that learning activities with applying the Treffinger learning model can improve learning activities and learning outcomes.