Reviewerly/exHarmony huggingface dataset:
How much Sample Rate is actually needed? Arm Tracking in Virtual Reality

There are plenty of studies dealing with the delays and other relations between head movements and visual response on Virtual Reality setups using head mounted displays.Most of those studies also present some consequences of deviating from those values.Yet, the rest of the human body remains relatively unmapped.In this paper, we present the data found during our research about vision-arm coordination.This data can be used to help build better and more efficient humancomputer interfaces, especially those that rely on a virtual avatar with a body and have resource restriction like battery or bandwidth.We tested body tracking Sample Rates ranging from 15 Hz up to 120 Hz (corresponding to total latencies ranging from 37 ms to 95.4 ms) and found out no significant user performance differences.We did, however, find that a small percentage of users are, indeed, capable of noticing the changes in Sample Rate.Based on the found results, we advise that, if one is trying to save battery, bandwidth or processor cycles, a low body tracking Sample Rate could be used with no negative effects on user performance.
Analysis and Implementation of Brute Force Algorithm with Optimal Mismatch Algorithm in Indonesian-Sundanese Dictionary Application

A dictionary is a reference material used to find words that you want to translate into a language or term. In general, dictionaries are sorted alphabetically along with their meanings and translations. Likewise with the Indonesian-Sundanese dictionary, it is very necessary in translating Indonesian and Sundanese words. Therefore, we need a string matching algorithm that can display the results of word translation. In this study, using the Brute Force algorithm with the Optimal Mismatch algorithm to match the words you want to search for with the words in the database. The Brute Force Algorithm and the Optimal Mismatch Algorithm are algorithms that have different ways of working, so that when implemented, different search speeds will be found. It can be seen in this study which algorithm works well and quickly in the Indonesian-Sundanese dictionary application. In the test results, the Brute Force algorithm obtained an average running time of 14.991416 ms, while the Optimal Mismatch algorithm obtained an average running time of 31.468322 ms in the string matching process in the database.
EVOLUTION AND FUTURE OF BLOCKCHAIN: FROM CRYPTOCURRENCIES TO INDUSTRIAL APPLICATIONS

As is well known, in 2009 the first article on the Bitcoin concept as an electronic transaction system "A peer-to-peer Electronic Cash System" [1] appeared, which, unlike other predecessor systems, brings the great novelty of being based on a transaction history or "Ledger" following the Blockchain concept [2]. We can consider Bitcoin as digital hard money or as its white paper [1] (Nakamoto's article) says: "An electronic cash system".
Spatiotemporal Pattern Mining for Nowcasting Extreme Earthquakes in  Southern California

Geoscience and seismology have utilized the most advanced technologies and equipment to monitor seismic events globally from the past few decades. With the enormous amount of data, modern GPU-powered deep learning presents a promising approach to analyze data and discover patterns. In recent years, there are plenty of successful deep learning models for picking seismic waves. However, forecasting extreme earthquakes, which can cause disasters, is still an underdeveloped topic in history. Relevant research in spatiotemporal dynamics mining and forecasting has revealed some successful predictions, a crucial topic in many scientific research fields. Most studies of them have many successful applications of using deep neural networks. In Geology and Earth science studies, earthquake prediction is one of the world's most challenging problems, about which cutting-edge deep learning technologies may help discover some valuable patterns. In this project, we propose a deep learning modeling approach, namely \tseqpre, to mine spatiotemporal patterns from data to nowcast extreme earthquakes by discovering visual dynamics in regional coarse-grained spatial grids over time. In this modeling approach, we use synthetic deep learning neural networks with domain knowledge in geoscience and seismology to exploit earthquake patterns for prediction using convolutional long short-term memory neural networks. Our experiments show a strong correlation between location prediction and magnitude prediction for earthquakes in Southern California. Ablation studies and visualization validate the effectiveness of the proposed modeling method.
Digital Design for Image-Adaptive Watermarking using CDF 5/3 Wavelet

Paper This paper presents a hardware architecture for image-adaptive watermarking in the wavelet domain. The embedding strength factor is selected by calculating the energy present between the different frequency bands. The current algorithm is constructed on a CDF 5/3 wavelet based on the model of lossless compression JPEG 2000. Wavelet filters are implemented using a parallel architecture with a lifting scheme, which makes them more efficient in terms of speed and hardware utilization. The top module of the system is built with the combination of serial-parallel architecture to balance the speed and power consumption. The presented watermarking system is tested using hardware in the loop-testing technique. The objective is to develop an image-adaptive, real time, low power consumption and robust watermarking system, which can be incorporated into existing hardware such as digital cameras, scanners, and camcorders. The watermarking system's efficiency against different assaults has been evaluated using the StirMark software. The proposed watermarking system showed robustness against most of the geometric and non-geometric attacks.
Manajemen Monitoring Pembelajaran Berbasis E-Learning (Studi Kasus Di SMK Raden Patah Mojosari)

Abstract: Monitoring is the process of collecting data/information and analyzing it based on predetermined indicators regarding school programs or activities to be corrected for the improvement of school programs or activities. The purpose of this study is to 1) analyze the implementation of monitoring management on e-learning-based learning. 2) analyze the implications of monitoring management on e-learning-based learning. This study uses a qualitative research approach, with a managerial type or more generally with the term management approach. The subjects in this study were the Principal, Deputy Principal, Engineering Teachers, Students. The results of the study show that, 1) Implementation of Monitoring on E-Learning-Based Learning: (a) Planning for Learning Monitoring. First, preparation of Human Resources (HR). Second, e-learning training for all those involved in learning that is applied at SMK Raden Patah Mojosari. (b) Implementation of Learning Monitoring. First, the implementation phase of monitoring e-learning learning (Creation of lesson plans, selection of WEB-based Computer, HP and Laptop Media). Second, approach (using applications, WA, Classroom, Moodle, Zoom). Third, learning monitoring methods (direct or real-time learning and web-based or web-based indirect learning). Fourth Internet network, Fifth, Student Motivation, Sixth Facilities and infrastructure. (c) Evaluation of learning monitoring. 2) Monitoring Implications for E-Learning-Based Learning is divided into two, namely; (a) the impact of monitoring e-learning on teachers, (b) the impact of monitoring e-learning on students. The monitoring implications of e-learning-based learning are divided into two, namely; a) the impact of monitoring e-learning learning on teachers, b) the impact of monitoring e-learning on teachers.
ADVANTAGES OF USING 3D STUDIO MAX FOR CONDUCTING CHEMICAL EXPERIMENTS

The basis of a modern education system is quality and high creates a technological environment. Its creation and development technically complex, as well as training in such an environment to improve the system, information communication in the educational process serves to introduce technologies. Education in the process the use of multimedia technologies interactive lessons mode. The basis of a modern education system is quality and high creates a technological environment. Its creation and development technically complex, as well as training in such an environment to improve the system, information communication in the educational process serves to introduce technologies.
EXPLORING SOCIAL INTERACTION OF AI ROBOT ON EMPLOYEE ENGAGEMENT

Artificial intelligence (AI) can be regarded as an algorithm capable of reproducing human behaviors and solve tasks (Loureiro, Guerreiro, & Tussyadiah, 2021).For Kaplan and Haenlein (2019, p. 15) artificial intelligence is, "a system's ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation".
Description-based Label Attention Classifier for Explainable ICD-9 Classification.

ICD-9 coding is a relevant clinical billing task, where unstructured texts with information about a patient’s diagnosis and treatments are annotated with multiple ICD-9 codes. Automated ICD-9 coding is an active research field, where CNN- and RNN-based model architectures represent the state-of-the-art approaches. In this work, we propose a description-based label attention classifier to improve the model explainability when dealing with noisy texts like clinical notes.
Influence of Mindfulness-Based Self-Efficacy on Rumination: Dual Mediating Effects of Self-Compassion and Dysfunctional Attitudes

This study explored the correlation between mindfulness-based self-efficacy (MSE) and rumination in males and females in Seoul, Incheon, Gwangju, Jeonju, Gangneung, and Pusan to determine and confirm a dual mediating model of self-compassion and dysfunctional attitudes. To this end, data were collected from 172 of 180 surveys conducted. The survey consisted of the Mindfulness-Based Self-Efficacy Scale (MSES), the Korean version of Self-Compassion Scale (K-SCS), the Dysfunctional Attitudes Scale (DAS), and the Rumination Scale (RS). The results were analyzed using IBM SPSS V.22 and M-Plus 6.12 programs. To verify the average and standard deviation of variables and the influence of the mediation model of MSE on rumination through self-compassion and dysfunctional attitudes, the author used bootstrapping methods. As a result, this study presented descriptive statistics for each variable and a correlation analysis between MSE and self-compassion (β=.844, p<.001), between self-compassion and dysfunctional attitudes (β=.725, p<.001), and between dysfunctional attitudes and rumination (β=.459, p<.001) was conducted. Moreover, a correlation analysis was conducted between MSE, rumination through self-compassion, and dysfunctional attitudes (β=.281, C.I[.133~.548]) in due order.
MeetDot: Videoconferencing with Live Translation Captions.

We present MeetDot, a videoconferencing system with live translation captions overlaid on screen. The system aims to facilitate conversation between people who speak different languages, thereby reducing communication barriers between multilingual participants. Currently, our system supports speech and captions in 4 languages and combines automatic speech recognition (ASR) and machine translation (MT) in a cascade. We use the re-translation strategy to translate the streamed speech, resulting in caption flicker. Additionally, our system has very strict latency requirements to have acceptable call quality. We implement several features to enhance user experience and reduce their cognitive load, such as smooth scrolling captions and reducing caption flicker. The modular architecture allows us to integrate different ASR and MT services in our backend. Our system provides an integrated evaluation suite to optimize key intrinsic evaluation metrics such as accuracy, latency and erasure. Finally, we present an innovative cross-lingual word-guessing game as an extrinsic evaluation metric to measure end-to-end system performance. We plan to make our system open-source for research purposes.
Similarity learning with implicit-network and explicit-network for zero-shot intent detection

Intent detection is an important component in dialog systems. Existing studies mainly focus on intent detection with sufficient labeled data. However, these methods are unable to detect intents that do not exist in training data. To tackle this problem, we propose an implicit-network and explicit-network model for zero-shot intent detection, which is capable of learning similarities between utterances and intent description from word level and sentence level. To enhance the representation of the intent, we introduce slot types as the intent description. We divide intent into explicit intent and implicit intent according to different expression ways, and construct explicit-network and implicit-network from word level and sentence level respectively. Meanwhile, in order to better combine these two parts of information, we also design a relation layer to fuse different levels of information. Experiments on two benchmark datasets show that our model significantly outperforms existing state-of-the-art models and demonstrate the effectiveness of learning similarity from word level and sentence level simultaneously.
МЕТОДИЧЕСКИЕ АСПЕКТЫ ОРГАНИЗАЦИИ МАТЕМАТИЧЕСКОГО КВН ДЛЯ СТУДЕНТОВ ЭКОНОМИЧЕСКИХ СПЕЦИАЛЬНОСТЕЙ

The article is devoted to the organization of mathematical KVN. The goals and objectives of this event are spelled out. Attention is drawn to the fact that the content of the competition for students of economic specialties has a number of features. It is concluded that it is necessary to take into account the economic orientation of the competition tasks. The KVN methodology is shown on the material of such traditional contests as Warm-up, Visiting card, Captains contest, Homework, contests for fans. The course of each competition, the purpose of its holding for the jury, for the organizers, is described in detail. In particular, a variant of organizing a warm-up competition using mathematical anecdotes and portraits of mathematicians is shown. The course of the Erudition competition for fans has been registered. Particular attention in the article is paid to home competitions. Using the example of mathematical KVN, held at the Siberian Institute of Business and Information Technologies, the technology of choosing a name for a team, a script for a greeting and a home competition is shown. The proposed scenarios are based on reasoning that violates semiotic transformations. The presence of humor is provided by a violation of the logic of mathematical transformations, which inevitably leads to incorrectness and absurdity of reasoning. The contests demonstrate the paradoxes of mathematics associated with division, multiplication and addition of numbers. The stages of creating a plot for the home competition How I Love Mathematics are detailed. The history of the emergence of a well-known mathematical anecdote called 28/7 = 13, a variant of its transformation for competitive display in an economic university is given. Among the features of the organization of this event, the following was highlighted: the presence of economic and mathematical components of the competition tasks; appeal to such forms of humor as a mathematical anecdote, paradox, sophism. It is noted that the proposed competitions can be easily adapted for conducting KVN in other disciplines, and new ones can be created on their basis.
Field Experiments of Real Time Foreign News Distribution Powered by MT

Field experiments on a foreign news distribution system using two key technologies are reported. The first technology is a summarization component, which is used for generating news headlines. This component is a transformer-based abstractive text summarization system which is trained to output headlines from the leading sentences of news articles. The second technology is machine translation (MT), which enables users to read foreign news articles in their mother language. Since the system uses MT, users can immediately access the latest foreign news. 139 Japanese LINE users participated in the field experiments for two weeks, viewing about 40,000 articles which had been translated from English to Japanese. We carried out surveys both during and after the experiments. According to the results, 79.3% of users evaluated the headlines as adequate, while 74.7% of users evaluated the automatically translated articles as intelligible. According to the post-experiment survey, 59.7% of users wished to continue using the system; 11.5% of users did not. We also report several statistics of the experiments.
Diversified Evaluation Method of Physical Education Teaching Effect based on Cloud Computing

By using the method of literature, this paper analyzes the development of physical education teaching evaluation, and focuses on the diversified trend of the development of physical education teaching evaluation. The pluralistic trend of PE teaching evaluation is mainly reflected in the following aspects: the pluralistic function of PE teaching evaluation, the pluralistic evaluation standard, the pluralistic evaluation content, the pluralistic evaluation subject, the pluralistic evaluation method and the pluralistic evaluation form. In view of the current situation of unified evaluation of school physical education curriculum, this paper puts forward the diversified evaluation method of physical education teaching effect based on cloud computing, and standardizes the evaluation index optimization evaluation algorithm, in order to better provide reference for the development of physical education teaching.
Cyber-Attacks Measurement for Public Companies: An Empirical Analysis

In this paper, we investigate the likelihood of cyber-attacks targeted on publicly listed companies. By reviewing the complete financial statements of the attacked companies through CompStat North America and information of cyber-attack incidents through Privacy Rights Clearinghouse, we process hundreds of the financial features in the statements to analyze the likelihood of cyber-attacks to the cohort of listed large public companies. After assessing multiple modified training models by using logistic regression, the patterns between financial features and likelihood of cyber-attacks are established. Key financial features that are highly correlated to cyber-attacks are identified.
Deep Conditional Gaussian Mixture Model for Constrained Clustering

Constrained clustering has gained significant attention in the field of machine learning as it can leverage prior information on a growing amount of only partially labeled data. Following recent advances in deep generative models, we propose a novel framework for constrained clustering that is intuitive, interpretable, and can be trained efficiently in the framework of stochastic gradient variational inference. By explicitly integrating domain knowledge in the form of probabilistic relations, our proposed model (DC-GMM) uncovers the underlying distribution of data conditioned on prior clustering preferences, expressed as pairwise constraints. These constraints guide the clustering process towards a desirable partition of the data by indicating which samples should or should not belong to the same cluster. We provide extensive experiments to demonstrate that DC-GMM shows superior clustering performances and robustness compared to state-of-the-art deep constrained clustering methods on a wide range of data sets. We further demonstrate the usefulness of our approach on two challenging real-world applications.
Active Learning for Argument Strength Estimation

High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.
Didactic Potential of Bookstagram as a Kind of Creolized Text

The article investigates the method of working with a bookstagram in lessons of Ukrainian literature. bookstagram is considered as an element of modern educational discourse. The peculiarities of the bookstagram as a kind of creolized text that promotes creative comprehension of a work of art are analyzed. Creolized texts are now considered as an effective didactic tool, because they are easier to accept by modern pupils. The role of bookstagram in the development of creative thinking of pupils is traced, in particular, bookstagram is considered as a form of co-creation of the reader with the author of the work, a tool for modernizing Ukrainian literary classics and the school study of literature. The methodical bases of bookstagram application as a means of formation of reading culture and activation of interest of pupils to reading of the Ukrainian fiction are defined. The main functions of the book are the popularization of reading and the formation of reading competence of pupils, attracting attention to Ukrainian fiction, involving students in the constant use of library resources, the implementation of innovative forms of teaching Ukrainian literature at school. The article analyzes samples of bookstagrams created by pupils under the guidance of future teachers of Ukrainian language and literature during internships in secondary schools. Such joint activities of pupils and students-practitioners proved to be useful, because it helped to create an atmosphere of trust and increase motivation to read.
CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age

The process of manually searching for relevant instances in, and extracting information from, clinical databases underpin a multitude of clinical tasks. Such tasks include disease diagnosis, clinical trial recruitment, and continuing medical education. This manual search-and-extract process, however, has been hampered by the growth of large-scale clinical databases and the increased prevalence of unlabelled instances. To address this challenge, we propose a supervised contrastive learning framework, CROCS, where representations of cardiac signals associated with a setof patient-specific attributes (e.g., disease class, sex, age) are attracted to learnable embeddings entitled clinical prototypes. We exploit such prototypes for both the clustering and retrieval of unlabelled cardiac signals based on multiple patient attributes. We show that CROCS outperforms the state-of-the-art method, DTC,when clustering and also retrieves relevant cardiac signals from a large database. We also show that clinical prototypes adopt a semantically meaningful arrangement based on patient attributes and thus confer a high degree of interpretability.