Reviewerly/exHarmony huggingface dataset:
The restrictions of human rights – COVID-19 pandemic and ethical issue

Purpose: The purpose of this article is to analyze some of the most significant ethical and human rights impacts of the COVID-19 pandemic. The analysis aims to demonstrate the failures of many political decision that lead to restriction and limitation of human rights. Design/methodology/approach: We analyze various documents, reports and news articles that provide essential information about the different governmental restrictions that may lead to controversial human rights issues. We also use some philosophical texts to support our theoretical basis for the defence of human rights. Overall, we aim to find some of the groups that were vulnerable during COVID-19 pandemic and describe some of the human rights concerns and ethical issues. Social implications: We hope that our article will impact political regulations and restrictions that can have severe human rights implications. We also hope to inspire citizens, scientists and politicians to uphold and protect human rights and dignity during COVID-19 pandemic. Findings: In our article, we have found that many countries had problems with creating rules, restriction and regulations that are upholding and protecting human rights or did not have ethical implications. We have also found that many vulnerable groups were disadvantaged because regulations did not take into account their precarious position. Originality/value: We have analyzed the ongoing ethical and human rights problems with the COVID-19 pandemic because we believe that they present some of the most fundamental challenges to our society. Our analysis tries to demonstrate some of the most fundamental human rights issues and proposes to address these issues to avoid any future failures.
Improving Monitoring and Evaluation Activities in High Education

At present, global changes are taking place in the system of high education. They are the result of, firstly, serious socio-economic transformations in society, and secondly, integration processes that have occurred in the European educational space. Under these conditions, the issue of the quality of education did not lose its relevance, but also acquired new facets. The level of students’ training in high educational institutions requires an objective assessment at high level. Since modernization of educational space is a difficult and long process, high education institutions must constantly look for new tools, methods and technologies for evaluating the results of educational activities. Professional and pedagogical competence of teachers in the framework of modern system of high education requires constant improvement. The argument for using new methods is that they comprehensively assess emerging competencies. In turn, for the most efficient use of the methods traditionally used, it is necessary to carry out a phased improvement of traditional means of control. To this end, the authors investigated the issue of improving control and appraisal activities in Nizhny Novgorod State Pedagogical University named after Kozma Minin. The aim of the work is to develop a model of teacher’s control and assessment activity in the context of competence approach implementation. The verification of the model was carried out with the help of the research of completion level of qualifying work done by students. The verification was carried out in two stages: before the introduction of the model in 2017 and after the introduction of the model in 2018. The main results and conclusions of the work can be used in development of control and assessment tools for mastering competencies of students of high educational institutions.
Remarks about the unification types of some locally tabular normal modal logics

Abstract It is already known that unifiable formulas in normal modal logic $\textbf {K}+\square ^{2}\bot $ are either finitary or unitary and unifiable formulas in normal modal logic $\textbf {Alt}_{1}+\square ^{2}\bot $ are unitary. In this paper, we prove that for all $d{\geq }3$, unifiable formulas in normal modal logic $\textbf {K}+\square ^{d}\bot $ are either finitary or unitary and unifiable formulas in normal modal logic $\textbf {Alt}_{1}+\square ^{d}\bot $ are unitary.
The Reputation Factor in Start-up Companies: A Literature Review

The paper analyzes the concept of reputation with particular reference to startup companies.The definition of 'reputation' that has inspired this work is that of a judgement of reliability expressed by a community about a social player based on the behaviour of that player over time.The peculiarity of this judgement is that only one or some of the members of the community have had direct knowledge of the player and their actions, and it is on this experience that they have based their personal judgements of value.The authors carried out a literature review in order to analyze the constituent factors of reputation.What emerges is that the reputational issue is configured with marked peculiarities in startups, in particular in the pre-revenue phase, when the startup has not yet matured its history.The reputation of the pre-revenue startup, in fact, since it cannot be the product of its activity, is the product of the activity and the behaviour of the founder and, more generally, of his team; in other words, the reputation of the latter becomes the reputation of the startup.Furthermore, it is a fundamental competitive lever for the startup, as it affects the ability to raise funds.Effective reputation management, therefore, cannot ignore the consideration of the life phase of the company and the nature of the subjects to which the reputational factors refer.
The art of deep mapping

Unlike most maps, deep maps have a particular story to tell, a point to make. Here a half dozen or so of the author’s deep maps—from a museum exhibition through maps of remembered paper routes to a neighborhood atlas—are displayed to exemplify the proposition. This story is discovered by getting out into, and living in the field, which is the very essence of field work. Living in the field leads to the discovery of the direction in which the work is heading. And, finally, to its destination. Deep mapping is a deep, highly directed, multifaceted engagement with place.
Early Usability Evaluation to Enhance User Interfaces - A Use Case on the Yggdrasil Cybersecurity Mockup

Most usability evaluation methods are designed to be applied after the implementation of a software product.As such, there is limited research on how to conduct usability evaluation procedures before the implementation of a software product, especially on cybersecurity platforms user interfaces.For this purpose, we illustrate the early usability evaluation of a cybersecurity graphical interface.In this sense, we conduct a usability evaluation procedure on the mockup of Yggdrasill, an application centered on the early identification of vulnerabilities using open-source intelligence, more specifically Tweeter posts and news websites.To achieve this, we conducted a usability inquiry procedure using the adaptation of a questionnaire designed to evaluate the attractiveness of interactive products.In addition, we conducted a usability testing procedure based on the think-aloud protocol.The results of this study enabled the timely identification of improvement opportunities for the graphical interface of the Yggdrasil graphical interface before the actual implementation.
A Survey on Evolutionary Clustering Algorithms and Applications

Evolutionary algorithms have become more popular nowadays in order to solve non-linear complex real world problems. One of these many applications includes clustering of complex data into numerous small groups, which have certain similarities. The process of clustering may be achieved using certain measures such as minimum intra-cluster variance or maximum inter-cluster variance, depending upon the kind of data at hand. For such clustering problems, population-based evolutionary algorithms can be utilized to perform a clustering process using various performance measures as an objective function. These algorithms take advantage of a random population-based stochastic approach and treats clustering as a means of determining an optimal solution such as in document clustering, where the optimum cluster formation is achieved on the given data. Based on the nature of the data, these approaches have applications in various domains, such as text document clustering, image segmentation, customer segmentation, telecommunication clustering, healthcare analysis, pollution effect analysis etc. Among the popular examples of evolutionary approaches are Genetic Algorithm, Gene Expression Programming, Differential Evolution, Particle Swarm Optimization, Gray Wolf Optimizer, Antlion Optimizer, Neuroevolutionary Algorithm etc. The chapter presents the state-of-the-art stock of various evolutionary techniques for clustering, their applications in different domains, and key advantages and disadvantages.
Deferring Decision in Multi-target Trajectory Optimization

This paper introduces a novel trajectory optimization framework referred to as deferred-decision trajectory optimization (DDTO) for handling uncertainties in the operational environment without explicitly modeling them. DDTO maximizes the duration of time for which a vehicle is able to reach multiple targets while satisfying physical constraints and ensuring an acceptable level of suboptimality with respect to metrics like fuel consumption and control effort. As a result, in the event of a contingency, the vehicle can escape to a safe location or divert to an alternate target to complete the mission with partial success. A practical solution approach for DDTO, which is grounded on results from convex optimization, is demonstrated on a double-integrator path planning example.
From Objects to a Whole Painting

Style image painting is the process of using some stylized strokes to redraw a reference image purposefully and meaningfully. It is a kind of style image generation. In recent years, the application of GAN has greatly improved the quality of generated images for style image generation. However, those methods which use GAN are usually non-serialized. To solve this problem, reinforcement learning and RNN methods are applied to the generation of style images based on strokes. To speed up training, stroke-based style image painting using CNN framework is also proposed. But none of the existing style image painting methods takes into account the content distribution in the reference image, which makes the painting process lack a clear order. We extract the feature of the object contained in the image through the content acquisition module and use the optimal transmission theory to construct a compound loss function to integrate the content information into the painting process. As more advanced macro content information is added to the painting process, our painting method can draw images in a more orderly way. We have conducted a lot of experiments to prove that our method is superior to the current state-of-the-art style image painting method.
Estimating Breast Cancer Class Using Artificial Neural Network and Logistic Regression Methods

In this study, artificial neural networks (ANN) and logistic regression (LR) prediction models for breast cancer type has been developed. The proposed models are implemented with real clinical data for breast cancer type prediction. For purpose of constructing the prediction model, there are 699 instances and 10 attributes used in training and testing for the model. The data used in the ANN and LR models are arranged in a format of 9 input parameters and an output parameter which is the class of breast cancer (benign or malignant). The evaluation was made by comparing the data obtained by the two methods. ANN and LR models have accuracy performance 94,78% and 96,18%, respectively. The LR method's accuracy rate is better than the ANN method. This is because the LR method analyzes by taking into account the form of categorical data.
Pengaruh Pembelajaran Online Terhadap Tingkat Motivasi Belajar Mahasiswa/Mahasiswi FEB Akuntansi UKSW di Masa Pandemi COVID 19

The world nowadays is currently being faced with the outbreak of the COVID 19 virus. This clearly has a significant effect on various fields, especially education. Therefore, it takes motivation to learn as a form of critical attitude in balancing the currently worrisome educational situation. Motivation to learn is an essential factor in the field of education. This research was purposed to see the effect of online learning on the learning motivation of SWCU Accounting FEB students. This study uses a quantitative approach, by distributing questionnaires to accounting FEB students. The samples taken from this study were students of class 2017, 2018, and 2019 with a total of 63 respondents. The analysis technique used in this study is a simple linear regression analysis technique. This research was also conducted by testing the validity and reliability, data normality test, linearity test, hypothesis testing and determinant coefficient. The results showed that the significance value of the hypothesis testing was 0.000 less than 0.005. It means that online learning has a positive effect on the learning motivation of SWCU accounting FEB students. In addition, the value of the determinant coefficient or r square is 0.248, which means that online learning contributes to learning motivation by 0.248 or 24.8% and the rest is influenced by other variables.
Social Media Moderation

Until social media content moderation exploded as a public concern in the latter half of the 2010s, it was one of the tech world’s best-kept secrets. Only a handful of writers had paid attention to the hidden, mostly human labour of screening the internet for illicit content. While greater public awareness has forced tech companies to talk more openly about content moderation, its particularities are still somewhat of a mystery. In this chapter, I lift the lid on social media content moderation, giving readers an overview of what the work entails, why we need it, how the practice works on both human and technical levels, and what the future of content moderation may hold. This chapter combines academic writing with a range of contemporary examples, including livestreamed suicides, pole-dancing and make-up tutorials, eating disorder recovery communities, and the COVID-19 pandemic.
Haptic Sensory Perception and Customer Experience in a 360-Virtual Store: Does Time Spent Play a Role?

Building on stimulus(S)-organism(O)-response(R)theory, this research examines the effects of haptic sensory perception on user experience and satisfaction in a 360-virtual store.We postulate that time spent in a 360-virtual store reinforces the effects in the S-O-R model.The results support the theory that haptic sensory perception triggered by a 360-virtual store (stimulus) improves customer experience (organism) and that the experience further enhances virtual store satisfaction (response).We find that the time spent in a virtual store reinforces the former effect, but not the latter effect.The results of an experiment involving 587 respondents further suggest that this finding only holds true to users who are merely browsing, but not to users tasked with searching for a specific product in the 360virtual store.We encourage management to create sensory cues in virtual stores to improve user experience and satisfaction, and virtual in-store stimuli to increase time spent in the store.
The CCL-K11 on-going key comparison. Final report for the period 2018 to 2020

Main text To reach the main text of this paper, click on Final Report . Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/ . The final report has been peer-reviewed and approved for publication by the CCL, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).
APLIKASI GAME EDUKASI MATEMATIKA TINGKAT DASAR BERBASIS ANDROID

Game is one of the applications that are favored by children. Not a few parents who allow their children to use a smartphone with various reasons such as so that children do not interfere with work, so that children do not neglect, and various other reasons. However, sometimes smartphones are misused only for playing games by children, so the time that should be used for learning is instead used for playing games only. So that the game does not only waste the child's time in vain, then built a game application for children based on the genre of fighter and education in the game of hero against monsters accompanied by mathematics learning. The features provided in the application are 4 types of questions such as addition, subtraction, multiplication, and division as well as 6 levels of problem difficulty level. By this basic level math game it can be an alternative in learning while playing games through smartphones. This game is able to be a fun learning media in learning mathematics for game users.
Recognition of Emotions through Cartoon Facial Expressions in Preschoolers

Objective: Recognition of children's emotions expressed on their faces and the faces of others is very important in children's social activities, including communicating the child's emotions. This study aims to develop a research instrument that can represent or mediate children in communicating their emotions. Materials and Methods: This exploratory research begins with item development, expert review, and validation study. Data analysis was carried out through the Pearson validity test, Kuder-Richardson 20 reliability test and test-retest analysis, and MANOVA to see the main and interaction effects of the variables of age and sex of children. Results: One hundred ninety-seven children aged 3-6 years were involved in a study that tested seven cartoon-like facial expressions. These seven items show valid (rxy=0.28-0.74, p<0.05) and reliable (r11=0.912, p<0.05) items. Conclusion: This instrument is recommended for researchers to use for assessing children's emotions in a certain setting.
L2 learning online: Self-directed learning and gender influence in Indonesian university students

The present study was conducted to investigate Indonesian second language (L2) learners’ Self-Directed Learning (SDL) in English during the online classes due to the Covid-19 pandemic and to investigate whether there was a significant difference in SDL between female and male learners. As online learning is implemented nationwide in Indonesia due to the pandemic, it becomes paramount to investigate learners’ SDL, considered very critical in online learning settings where teachers' ability to check learners' progress is not as extensive as it is in the face-to-face mode of instruction. The participants of the study were 187 undergraduate students taking English for Academic Purposes (EAP) classes. Through the data obtained from the online questionnaire, the study found that learners, in general, reported a high level of SDL even though many of them still embraced procrastination behaviours and considered the pragmatic need to pass the EAP class more important than the actual L2 learning. It also found that there was no significant difference between female learners’ SDL and that of male learners. Based on the findings and analysis of the possible factors, possible contributions of the study are presented along with the possible limitations and suggested directions for future research in the field.&#x0D; HIGHLIGHTS:&#x0D; &#x0D; Learners, in general, reported a high level of SDL.&#x0D; Despite that, many of them still embraced procrastination behaviours and considered the pragmatic need to pass the English for Academic Purposes class more important than the actual L2 learning.&#x0D;
Algorithm for Computer Chess

This paper provides a foundation upon which to build a chess program.The game of chess models the universe, so AT Math applies.
RFID Tagged Ziplock for Drug Regimen to Track the Consumption of Pills by the Patient

Patients especially the elderly ones often lose track on the number of pills consumed and medication start time. The count of pills is often forgotten or mistaken and patients may take wrong medication or wrong dosage sometimes. This results in medication non-adherence. Consumption of pills by patients is not monitored in any of the system though. Hence, a system could be developed that incorporates RFID tagged Ziplock to keep a track on the consumption of pills by the patient.&nbsp
Doubly Robust Crowdsourcing

Large-scale labeled dataset is the indispensable fuel that ignites the AI revolution as we see today. Most such datasets are constructed using crowdsourcing services such as Amazon Mechanical Turk which provides noisy labels from non-experts at a fair price. The sheer size of such datasets mandates that it is only feasible to collect a few labels per data point. We formulate the problem of test-time label aggregation as a statistical estimation problem of inferring the expected voting score. By imitating workers with supervised learners and using them in a doubly robust estimation framework, we prove that the variance of estimation can be substantially reduced, even if the learner is a poor approximation. Synthetic and real-world experiments show that by combining the doubly robust approach with adaptive worker/item selection rules, we often need much lower label cost to achieve nearly the same accuracy as in the ideal world where all workers label all data points.
Forecast-based Multi-aspect Framework for Multivariate Time-series Anomaly Detection

Today’s cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms, as widely proven approaches, often perform sub-optimally or inconsistently across datasets. A key common issue is they strive to be one-size-fits-all but anomalies are distinctive in nature. We propose a method that tailors to such distinction. Presenting FMUAD - a Forecast-based, Multi-aspect, Unsupervised Anomaly Detection framework. FMUAD explicitly and separately captures the signature traits of anomaly types - spatial change, temporal change and correlation change - with independent modules. The modules then jointly learn an optimal feature representation, which is highly flexible and intuitive, unlike most other models in the category. Extensive experiments show our FMUAD framework consistently outperforms other state-of-the-art forecast-based anomaly detectors.
Hill Climb Game Play with Webcam Using OpenCV

Abstract: Computer vision is a process by which we can understand how the images and videos are stored and manipulated, also it helps in the process of retrieving data from either images or videos. Computer Vision is part of Artificial Intelligence. Computer-Vision plays a major role in Autonomous cars, Object detections, robotics, object tracking, etc. OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning software library. OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in the commercial products. It comes with a highly improved deep learning (dnn ) module. This module now supports a number of deep learning frameworks, including Caffe, TensorFlow, and Torch/PyTorch. This does allow us to take our models trained using dedicated deep learning libraries/tools and then efficiently use them directly inside our OpenCV scripts. MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions like Multi-hand Tracking, Face Detection, Object Detection and Tracking, etc.