Reviewerly/exHarmony huggingface dataset:
A Simple Approach to Image Tilt Correction with Self-Attention MobileNet for Smartphones.

The main contributions of our work are two-fold. First, we present a Self-Attention MobileNet, called SA-MobileNet Network that can model long-range dependencies between the image features instead of processing the local region as done by standard convolutional kernels. SA-MobileNet contains self-attention modules integrated with the inverted bottleneck blocks of the MobileNetV3 model which results in modeling of both channel-wise attention and spatial attention of the image features and at the same time introduce a novel self-attention architecture for low-resource devices. Secondly, we propose a novel training pipeline for the task of image tilt detection. We treat this problem in a multi-label scenario where we predict multiple angles for a tilted input image in a narrow interval of range 1-2 degrees, depending on the dataset used. This process induces an implicit correlation between labels without any computational overhead of the second or higher-order methods in multi-label learning. With the combination of our novel approach and the architecture, we present state-of-the-art results on detecting the image tilt angle on mobile devices as compared to the MobileNetV3 model. Finally, we establish that SA-MobileNet is more accurate than MobileNetV3 on SUN397, NYU-V1, and ADE20K datasets by 6.42%, 10.51%, and 9.09% points respectively, and faster by at least 4 milliseconds on Snapdragon 750 Octa-core.
소통의 측면에서 본 기이담(奇異談) 향유 - 《천예록》 기이담 향유에서 드러나는 복합적 소통의 양상과 그 의미에 대한 고찰을 중심으로 -

The purpose of this paper is to explain the enjoyment of Gi-Yi(奇異) stories as a view point of communication. In early Yadam(野談) studies, unrealistic yadam texts are treated like meaninglessness texts, and it is not treated in academic area. After changing view point about unrealistic Yadam, texts like 《Cheonyerok》 are treated in various ways. But in these studies about 《Cheonyerok》 are not focused about the difference of narrative chapter and comment chapter. So this study focused on both narrative chapter and comment chapter and try to explain the enjoyment phenomena as communication aspect. Literary acception is also text communication through literature text, and Gi-Yi stories enjoyment are can be treated under the point of communication.Yadam Gi-Yi(奇異) story enjoyment are explained as two layers, as creation layer and transmission layer. In creation layer, Gi-Yi(奇異) stories in 《Cheonyerok》 are showing self-communication and it causes the complx communication in transmission layer. In transmission layer, Gi-Yi stories show five complex communication types. The most frequent type is “to express Gi-Yi discourse effetive-true false jugement” type. It can be interpreted as culural meaning as horrible era as unrealistis sotories are treated as information resource. And it can be a meaning in communication in korean education in various aspects.
PERSONALITY TYPES OF PHYSICAL TEACHER UPBRINGING

The typological approach makes it possible to more accurately determine the subject of research, which is the system of personality traits in the aggregate of their specific and typical mental phenomena. To determine the typical personality traits of a modern physical education teacher, the typological approach of J. Holland's professional preferences was applied. This scientific theory combines the concept of personality with vocational guidance theory.This article is about the typology of personal and professional orientations of teachers of physical education.
Kompleksno vrednovanje studenata u digitalnom okruženju

The introductory part of the paper analyzes the increase of a new digital generation of students characterized by freedom, speed, and flexibility (Prensky, 2005; Berk, 2009; Tapscott 2011) optimism, team and global orientation, rationality, extreme curiosity (Jones &amp; Shao, 2011) performance of parallel processes, enjoyment current pleasures, and aspiration for networking (Berk, 2009; Tapscott, 2011). In line with the above, this research aims to determine the advantages and limitations of complex evaluation in the digital environment. A theoretical analysis of the advantages and disadvantages of computer utilisation in the complex evaluation was performed, with special reference to specific aspects (objectivity, individualization, reliability, evaluation of results, processes, and application of knowledge). The attitudes of final-year students of the Faculty of Teacher Education at the University of Belgrade were investigated. The phenomenon of depersonalisation of education was noticed and the advantages and limitations of the use of evaluation in the digital environment were pointed out. Pedagogical recommendations have been made aimed at: 1. the criteria that technology needs to meet in order to support complex evaluation; 2. development of a digitally competent teacher, who is qualified for the pedagogical use of technologies.
Virtual Reality for Enhancing Student Teachers' Science Competencies

The 21st-century smart technologies have made a significant impact on all fields of life including education. The EdTech innovations are not only benefiting students’ learning but also teachers’ training. Due to the limited resources, the teacher training programs in most public sector universities of Pakistan are perceived as boring and dull by the student teachers. This inquiry aimed to introduce reforms in teacher education with the help of transformative technologies. This experimental study was conducted to train the prospective teachers with the help of Virtual Reality (VR) in a public sector university in Pakistan. A pretest-posttest control group design was followed that included 27 preservice teachers enrolled in a 4-year undergraduate program. The treatment group was taught the content of science with the help of VR while the control group teaching method remained unchanged. The intervention included watching interactive videos of science on VR headsets for an academic semester of the teacher preparation program. After a semester-long intervention, the achievement scores of both groups were recorded and contrasted. It was found that the experimental group had significantly improved their science scores by 198% as compared to the control group that showed an improvement of 58%. The study strived to bring innovation in conventional practices and introduced learning with VR. The findings suggest integrating innovative technologies including virtual reality for both teachers’ training and students’ learning.
Pengembangan Media Pembelajaran Kisah Nabi Untuk Meningkatkan Hasil Belajar Siswa Sekolah Dasar Kelas 3

This study aims to: 1) produce interactive learning media for Islamic religious lessons, the material of the prophet's story that is suitable for use; 2) knowing student responses to the use of learning media that have been developed; and 3) knowing the learning outcomes of SD Negeri Banyuangung students. 3. The method used in this study was ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The results of the study were: 1) interactive learning media was declared very feasible based on an assessment using the System Usability Scale (SUS) by media experts, namely getting marginal high, excellent, and grade A categories, and material experts with marginal high, excellent, and grade A categories, and the questionnaire scores of 15 students got an average SUS score of 76.17 which was categorized as marginal high, good, and grade C; 2) the application of the media received a positive response from students and was categorized as very good; 3) student learning outcomes are stated to be increased and categorized as very complete with the percentage of learning outcomes from calculations using the System Usability Scale (SUS), namely excellent, and grade A, and the questionnaire value of 15 students got an average value of SUS 76.17 based on the pretest score and posttest; 4) The distribution of this media is limited to teachers of SD Negeri Banyuangung 3 and grade 3 students.
THE USE OF VIDEO PLAYBACK STRATEGY TO INCREASE STUDENTS SPEAKING ABILITY AT THE FIRST GRADE OF SMAN 1 SESENAPADANG

This thesis concerned about teaching speaking skill by using Video Playback Strategy at the first grade of SMAN 1 Sesenapadang . The problem statement of the research was “Does the use of Video Playback Strategy increasing the students’ speaking skill at the first grade of SMAN 1 Sesenapadang?” and the objective of the research was “To find out whether Video Playback Strategy increases students speaking ability at the first grade of SMAN 1 Sesenapadang”.  This research used pre-experimental method with one pre-test and post-test group. The target of this research was class XMIPA of SMAN 1 Sesenapadang. The procedure of the research used six meeting. The data collected through speaking test, to find out the students score before and after using video playback  in teaching speaking skill. The total sample was 13 students and it used purposive sampling technique. The instrument was speaking test to measure the students’ speaking skill in learning process by using Video Playback Strategy. The result of the data showed that the implementation of Video Playback Strategy in increasing students’ speaking ability was effective. The findings indicated that using Video Playback as strategy in teaching speaking was able to increase the students’ speaking ability. It was proven by the mean score of students’ post-test was higher than the students’ mean score in pre-test () and the t-test value was higher than t-table value (11,421) at the level of significant 0.05 with degree of freedom was 12. Based on the result of data analysis, Video Playback Strategy can increase the students’ speaking ability and it is the effective activity to make the students active in learning speaking. It can be concluded that the implementation of Video Playback Strategy was successful.
Design Implementation of Search Engine Optimization (SEO) in MSMEs

The rapid development of web and internet technology today allows someone to make the desired website easier. But the fact is that after creating a website, many things must be done by someone so that their online business can make a profit. One of them is Search Engine Optimization. In this study, we will discuss website design that uses  Search Engine Optimization for MSMEs, where there are factors such as internal and external to maximize website design. The use of SEO can help MSMEs increase traffic to the website, helping to choose focus keywords, page titles, and meta descriptions that suit their needs. This can be seen from google analytics installed and gets data on changes from before and after the website implements SEO. Based on the study results, it can conclude that the design of the implementation of search engine optimization in MSMEs has been successfully carried out.
Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behaviour during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make finding gaze fixations challenging. Current approaches involve laborious coding of pupil positions overlaid on video. One solution is to combine eye tracking with motion tracking to generate 3D gaze vectors. When combined with tracked or known object locations, fixation detection can be automated. Here we use combined eye and motion tracking and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three data types: the performance of calibration routines were assessed using calibration data, a validation task that demands short fixations on task-relevant locations, and an object interaction task we used to bridge the gap between laboratory and the wild studies. Further, we generated and compared models using spherical and cartesian coordinate systems and monocular (Left or Right) or binocular data. Our results suggest that all calibration routines perform similarly, with the best performance (i.e., sub-centimeter errors) coming from the task (i.e., the most natural) trials when the participant is looking at an object in front of them. Further, we found that spherical coordinate systems generate more accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend recording one-minute calibration datasets, using a binocular eye tracking headset (for redundancy), a spherical coordinate system when depth is not considered, and ensuring data quality (i.e., tracker positioning) is high when recording datasets.
Аналіз нейромережевих моделей LSTM та GMDH для прогнозування криптовалюти

The application of neural network models for the problem of cryptocurrency price forecasting is investigated. Unlike classical statistical methods of financial and economic series analysis, which are based on multidimensional linear regression analysis, a memory model and an adaptive polynomial model are proposed. Models are being tested on cryptocurrency markets due to their high volatility and low correlation with traditional assets. GMDH and LSTM neural networks are used for forecasting. The advantage of the polynomial regression model GMDH is proved by the criterion of speed-accuracy forecasting. Figs.: 5. Tabl.: 1. Refs.: 22 titles.Keywords: polynomial regression model; neural network; prognostication; cryptocurrency.
MouseView.js: Reliable and valid attention tracking in web-based experiments using a cursor-directed aperture.

Psychological research is increasingly moving online, where web-based studies allow for data collection at scale. Behavioural researchers are well supported by existing tools for participant recruitment, and for building and running experiments with decent timing. However, not all techniques are portable to the Internet: While eye tracking works in tightly controlled lab conditions, webcam-based eye tracking suffers from high attrition and poorer quality due to basic limitations like webcam availability, poor image quality, and reflections on glasses and the cornea. Here we present MouseView.js, an alternative to eye tracking that can be employed in web-based research. Inspired by the visual system, MouseView.js blurs the display to mimic peripheral vision, but allows participants to move a sharp aperture that is roughly the size of the fovea. Like eye gaze, the aperture can be directed to fixate on stimuli of interest. We validated MouseView.js in an online replication (N = 165) of an established free viewing task (N = 83 existing eye-tracking datasets), and in an in-lab direct comparison with eye tracking in the same participants (﻿N = 50). Mouseview.js proved as reliable as gaze, and produced the same pattern of dwell time results. In addition, dwell time differences from MouseView.js and from eye tracking correlated highly, and related to self-report measures in similar ways. The tool is open-source, implemented in JavaScript, and usable as a standalone library, or within Gorilla, jsPsych, and PsychoJS. In sum, MouseView.js is a freely available instrument for attention-tracking that is both reliable and valid, and that can replace eye tracking in certain web-based psychological experiments.
Generalization and locality in the AlphaZero algorithm: A study in single agent, fully observable, deterministic environments

Recently, the AlphaGo algorithm has managed to defeat the top level human player in the game of Go. Achieving professional level performance in the game of Go has long been considered as an AI milestone. The challenging properties of high state-space complexity, long reward horizon and high action branching factor in the game of Go are also shared by many other complex planning problems, such as robotics applications. This makes the algorithmic solutions of AlphaGo particularly interesting for further research. One of the key innovations in the algorithm is the combination of Monte Carlo tree search (MCTS) with deep learning. The main hypothesis of the thesis is that the success of the algorithm can be attributed to the combination of the generalization capacity of deep neural networks and the local information of tree search. This hypothesis is evaluated through the application of the AlphaZero algorithm (extension of Alphago) in single-player, deterministic and fully-observable reinforcement learning environments. The thesis presents answers to two research questions. First, what changes need to be made to transfer the AlphaZero algorithm to these environments. The changes in the reward support in these new environments can cause failure of learning, since assumption in the MCTS algorithm are violated. The thesis offers solutions that deal with this problem, including adaptive return normalization. The second research question examines what is the relative importance of the locality and generalization in the performance of the AlphaZero algorithm. This research question is answered by comparing the performance of search trees of varying sizes in several RL environments under fixed time budgets. While building small trees support generalization, through allowing more frequent training of the neural network, building larger trees provide more accurate estimates. This creates a trade-off between improved generalization capacity and more accurate local information under a fixed time budget. The experiment results show that mid-size trees achieve the best performance, which suggests that balancing local information and generalization is key to the success of the algorithm. Based on this results, possible extensions to the algorithm are proposed. At last, the thesis also highlights the relevance of the two-component system from a broader perspective and discusses the possible future impact of the algorithm.
NOTMAD: Estimating Bayesian Networks with Sample-Specific Structures and Parameters

Context-specific Bayesian networks (i.e. directed acyclic graphs, DAGs) identify context-dependent relationships between variables, but the non-convexity induced by the acyclicity requirement makes it difficult to share information between context-specific estimators (e.g. with graph generator functions). For this reason, existing methods for inferring context-specific Bayesian networks have favored breaking datasets into subsamples, limiting statistical power and resolution, and preventing the use of multidimensional and latent contexts. To overcome this challenge, we propose NOTEARS-optimized Mixtures of Archetypal DAGs (NOTMAD). NOTMAD models context-specific Bayesian networks as the output of a function which learns to mix archetypal networks according to sample context. The archetypal networks are estimated jointly with the context-specific networks and do not require any prior knowledge. We encode the acyclicity constraint as a smooth regularization loss which is back-propagated to the mixing function; in this way, NOTMAD shares information between context-specific acyclic graphs, enabling the estimation of Bayesian network structures and parameters at even single-sample resolution. We demonstrate the utility of NOTMAD and sample-specific network inference through analysis and experiments, including patient-specific gene expression networks which correspond to morphological variation in cancer.
Implicit Surgeon Perceptions of Patient Personas: a Framework for Surgical Informed Consent Design

Geisel School of Medicine at Dartmouth College, Hanover, NH Harvard Medical School Department of Biomedical Informatics, Boston, MA Beth Israel Deaconess Medical Center, Boston, MA
Learning Debiased and Disentangled Representations for Semantic  Segmentation

Deep neural networks are susceptible to learn biased models with entangled feature representations, which may lead to subpar performances on various downstream tasks. This is particularly true for under-represented classes, where a lack of diversity in the data exacerbates the tendency. This limitation has been addressed mostly in classification tasks, but there is little study on additional challenges that may appear in more complex dense prediction problems including semantic segmentation. To this end, we propose a model-agnostic and stochastic training scheme for semantic segmentation, which facilitates the learning of debiased and disentangled representations. For each class, we first extract class-specific information from the highly entangled feature map. Then, information related to a randomly sampled class is suppressed by a feature selection process in the feature space. By randomly eliminating certain class information in each training iteration, we effectively reduce feature dependencies among classes, and the model is able to learn more debiased and disentangled feature representations. Models trained with our approach demonstrate strong results on multiple semantic segmentation benchmarks, with especially notable performance gains on under-represented classes.
Empirical Analysis of Bio-Inspired Algorithms for Medical Disease Detection: A Statistical Perspective

Medical disease detection is a vast field of image, signal and video processing that involves a large number of complex operations, which include but are not limited to data acquisition, pre-processing, segmentation, feature extraction, feature selection, classification and post-processing. The efficiency of signal classification is directly proportional to the efficiency with which these internal blocks are designed. In order to improve the efficiency of these blocks, several bio-inspired optimization algorithms are proposed by researchers. These include but are not limited to, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Neural Networks (NN), etc. Each of these algorithms can be applied to optimize individual signal processing blocks, thereby improving overall system performance. Due to a large variety of available bio-inspired algorithms, it is ambiguous for system designers to select the best possible algorithmic combination for their medical disease classification design. In order to reduce this ambiguity, the underlying text evaluates performance of some of the most efficient bio-inspired algorithms, and statistically compares them on basis of their application. These applications vary w.r.t. identified disease, type of signal being processed, etc. This comparison will assist researchers and system designers to develop highly efficient medical disease classification systems for clinical use.
Analyzing the Intricacies of Cyber Crime in India: An Overview

The Internet, which connects loosely held networks all over the world, has made data and information movement between them much easier. Security issues have been a big worry in recent years as data and information is transmitted between networks in different locations. A few people have also exploited the internet for criminal operations such as unauthorized access to other networks, frauds, and so on. Crimes are criminal behaviors that take place on the internet. It's a term we hear a lot in the news these days, thanks to the growing popularity of online activities like online banking, online shopping, and so on. As a result, Cyber was enacted in order to deter and punish cyber thieves. Law can be defined as web law, that is, it is a branch of the legal system that deals with the Internet, Cyberspace, and other legal matters such as online security and privacy. In order to provide a brief overview of what is cybercrime, the perpetrators of cybercrime-hackers and crackers, different forms of cybercrimes, and the growth of cyber laws in India, this chapter is separated into distinct sections. The chapter also explains how these laws work and the numerous preventive measures that can be employed in India to combat high-tech crime.
Python Programming Dataset

This repository contains programming data collected from 15 students during November and December of 2019 at Bielefeld University. Students were asked to implement gradient descent. Note that this data set contains only source code snapshots and neither timestamps nor personal information. All students programmed in a web environment, which is also contained in this repository.