Reviewerly/exHarmony huggingface dataset:
Voices from Latin America: adolescents in the face of the COVID19 pandemic

This article is derived from the 2020 webinar Views of adolescents on the impact of the COVID19 organized by the (Tejiendo Redes Infancia) project in collaboration with the National School of Social Work of the National Autonomous University of Mexico (UNAM). The objective was to analyze the experiences of Latin American adolescents on the COVID19 pandemic, through a virtual meeting on the zoom platform that allowed the voices of this sector to be positioned in the context of the health crisis in the Latin American and Caribbean region. Six adolescents participated, three of each sex between 15 and 18 years old, from Mexico, Panama, Paraguay, Guatemala and Bolivia. It was developed under a qualitative methodology, the technique used by focus groups considering it the most suitable to recover ideas, information and knowledge regarding the subject, in addition to the use of images designed by them to present their arguments. The findings revealed the similarities in Latin American realities and a general feeling of concern over the absence of State strategies to address the needs of vulnerable sectors, such as low-income families and those who will not have the possibility of returning to their schools.
The Relationship between High School Physical Education Teacher's Guiding Behavior Type, Learning Attitude, and Learning Commitment

The purpose of this study is to empirically on investigate the relationship between learning attitude, learning commitment, and high school physical education teachers' instructional behavior patterns.To achieve this research objective, a population of 484 students enrolled in a male high school located in J City, Jeollabuk-do were selected by convenience sampling, excluding 72 questionnaires that were judged to have inadequate responses after sample collection.A total of 412 parts were used for the final result.The collected data processing was analyzed as follows using the statistical program SPSS Windows 21.0 Version by coding the finally selected facility.The data processing methods were frequency analysis, exploratory factor analysis, and reliability verification (Cronbach's α).Correlation analysis and multiple regression analysis were conducted to analyze the impact relationship between each variable, and the research results according to the purpose of this study are as follows.First, as for the influence of teacher-guided behavior type on learning attitude, positive rewarding behavior, democratic behavior, and authoritative behavior among the sub-factors of teacher-guided behavior type were excluded from learning attitude, and social support behavior (β=.435, p. <.001), professor and instructional behavior (β=.203, p<.001) were found to have a positive effect.Second, the influence of teacher-guided behavior type on learning commitment was excluded because positive reward behavior and authoritative behavior were not significant among the sub-factors of teacher-led behavior type for learning commitment, and social support behavior (β=.435, p<.001), teaching and instruction behavior (β=.203, p<.001), and were found to have a positive effect.Third, the influence of learning attitude type on learning commitment is autonomy (β=.365, p<.001), attention (β=.282, p<.001), interest (β=.258) for learning commitment p<.001).
On the Concept of “Pointˮ As an Ontological Category That Underpins the Idea of Transcendence

The article shows that the active development of science, in particular, physics, which deals with unvisualizable and inexplicable phenomena, in the modern world it is especially important to understand the phenomena that are on the verge of human perception. In this sense, the study of the concept of “pointˮ not only as a mathematical object, but as a universal, anticipating the idea of an ontological order, can contribute to the methodology of thinking for new interpretations of scientific discoveries and their adequate description. The author considers the concept of “pointˮ in the mainstream of metaphysical comprehension, and the inclusion in the context of the analysis of the idea of transcendence, to which this concept leads, forms a conceptual scheme for interpreting what does not lend itself to multi-faceted comprehension and explanation. The search for alternative interpretations and additional semantic connotations of familiar concepts, which are characterized by integrativity and syncretism, is especially important in modern scientific research, because allows to enrich the epistemological arsenal and open new aspects of the thematic field of philosophy and science. The purpose of the article is to substantiate the ontological status of the concept of “pointˮ in the problem-theoretical context of modern culture. The point is presented as the beginning and end of being, which is illustrated by the generally accepted cosmological model. The special nature of this concept is demonstrated, acting as a link between the spheres of the immanent and the transcendent.
A Study on Employee Engagement in a IT Company

Employees who are engaged to their job and committed to their firm gives a better competitive advantages to the firm as well as higher productivity so it has become essential for the firm to retain their highly skilled employees. This research, study the practices followed in the firm to engage employees. The objective of the study was to find the factors of engagement, level of satisfaction, organizational inputs and to identify measures for the enhancement of employee engagement. A structured questionnaire was constructed based on the objectives of the study to collect the required data.Descriptive research design was adopted for the study. Convenience sampling technique has been deployed. The findings of the study shows the factors under employee engagement such as working condition, superior support, organizational support, co-workers support, rewards and recognition and career growth. Some of the main findings include identifying the satisfaction level of employees which leads to employee retention. Further to this, recommendations have also been given by the researcher based on the main findings including how to motivate and retain employees etc. The researcher hopes that the recommendations would help the IT firms to make their employees fully engaged and committed to their firm.
Cross-Gradient Aggregation for Decentralized Learning from Non-IID Data

Decentralized learning enables a group of collaborative agents to learn models using a distributed dataset without the need for a central parameter server. Recently, decentralized learning algorithms have demonstrated state-of-the-art results on benchmark data sets, comparable with centralized algorithms. However, the key assumption to achieve competitive performance is that the data is independently and identically distributed (IID) among the agents which, in real-life applications, is often not applicable. Inspired by ideas from continual learning, we propose Cross-Gradient Aggregation (CGA), a novel decentralized learning algorithm where (i) each agent aggregates cross-gradient information, i.e., derivatives of its model with respect to its neighbors' datasets, and (ii) updates its model using a projected gradient based on quadratic programming (QP). We theoretically analyze the convergence characteristics of CGA and demonstrate its efficiency on non-IID data distributions sampled from the MNIST and CIFAR-10 datasets. Our empirical comparisons show superior learning performance of CGA over existing state-of-the-art decentralized learning algorithms, as well as maintaining the improved performance under information compression to reduce peer-to-peer communication overhead. The code is available here on GitHub.
Exploring the Creative Possibilities of Infinite Photogrammetry through Spatial Computing and Extended Reality with Wave Function Collapse

Modern extended reality systems that merge virtual and augmented reality provide a unique design space for creative applications. These devices have begun to incorporate spatial computing, or methods of runtime digital photogrammetry which translate the physical world into the virtual. In this study, we examine the use of extended reality for photogrammetry, a system of mapping the physical world into a virtual experience and procedurally generating an infinite version of the scanned architecture. We explore our system through a use case of mapping a residential home for infinite photogrammetry with the Magic Leap Spatial Computing Headset, Wave Function Collapse Algorithm, and Unity Game Engine. We conclude with a discussion on the creative applications of infinite photogrammetry and considerations for future research.
Voluntary Risk disclosure Assessment in The Corporate Board Structure under uncertainty: A Case Study of Saudi Arabian Companies

The global crisis of financial and corporate scandals of governance have run to calls for well voluntary risk disclosure. Firms limit this disclosure of voluntary by the proprietary cost theory to evade the risk of opposing actions. So, this paper investigates and assessment voluntary risk disclosure in the corporate board structure. These voluntary risk disclosures contain the conflict and multiple criteria. So the concept of multi-criteria decision-making (MCDM) is used to overcome this problem. The neutrosophic sets are used to deal with uncertain information. This paper used the Analytic Network Process (ANP) and Decision-making trial and evaluation laboratory (DEMATEL) methods to assess voluntary risk disclosure. The ANP is used to calculate the weights of criteria. DEMATEL method is used to assess and show the impact of voluntary risk disclosure. This research uses Saudi Arabian companies as a case study.
GPT3-to-plan: Extracting plans from text using GPT-3

Operations in many essential industries including finance and banking are often characterized by the need to perform repetitive sequential tasks. Despite their criticality to the business, workflows are rarely fully automated or even formally specified, though there may exist a number of natural language documents describing these procedures for the employees of the company. Plan extraction methods provide us with the possibility of extracting structure plans from such natural language descriptions of the plans/workflows, which could then be leveraged by an automated system. In this paper, we investigate the utility of generalized language models in performing such extractions directly from such texts. Such models have already been shown to be quite effective in multiple translation tasks, and our initial results seem to point to their effectiveness also in the context of plan extractions. Particularly, we show that GPT-3 is able to generate plan extraction results that are comparable to many of the current state of the art plan extraction methods.
Exemplar Loss for Siamese Network in Visual Tracking

Visual tracking plays an important role in perception system, which is a crucial part of intelligent transportation. Recently, Siamese network is a hot topic for visual tracking to estimate moving targets' trajectory, due to its superior accuracy and simple framework. In general, Siamese tracking algorithms, supervised by logistic loss and triplet loss, increase the value of inner product between exemplar template and positive sample while reduce the value of inner product between exemplar template and background sample. However, the distractors from different exemplars are not considered by mentioned loss functions, which limit the feature models' discrimination. In this paper, a new exemplar loss integrated with logistic loss is proposed to enhance the feature model's discrimination by reducing inner products among exemplars. Without the bells and whistles, the proposed algorithm outperforms the methods supervised by logistic loss or triplet loss. Numerical results suggest that the newly developed algorithm achieves comparable performance in public benchmarks.
Understanding Instance-based Interpretability of Variational Auto-Encoders

Instance-based interpretation methods have been widely studied for supervised learning methods as they help explain how black box neural networks predict. However, instance-based interpretations remain ill-understood in the context of unsupervised learning. In this paper, we investigate influence functions [Koh and Liang, 2017], a popular instance-based interpretation method, for a class of deep generative models called variational auto-encoders (VAE). We formally frame the counter-factual question answered by influence functions in this setting, and through theoretical analysis, examine what they reveal about the impact of training samples on classical unsupervised learning methods. We then introduce VAE- TracIn, a computationally efficient and theoretically sound solution based on Pruthi et al. [2020], for VAEs. Finally, we evaluate VAE-TracIn on several real world datasets with extensive quantitative and qualitative analysis.
Node Classification Meets Link Prediction on Knowledge Graphs

Node classification and link prediction are widely studied in graph representation learning. While both transductive node classification and link prediction operate over a single input graph, they have so far been studied separately. Node classification models take an input graph with node features and incomplete node labels, and implicitly assume that the graph is relationally complete, i.e., no edges are missing. By contrast, link prediction models are solely motivated by relational incompleteness of the input graphs, and do not typically leverage node features or classes. We propose a unifying perspective and study the problems of (i) transductive node classification over incomplete graphs and (ii) link prediction over graphs with node features, introduce a new dataset for this setting, WikiAlumni, and conduct an extensive benchmarking study.
Security Professional Skills Representation in Bug Bounty Programs and Processes

The ever-increasing amount of security vulnerabilities discovered and reported in recent years are significantly raising the concerns of organizations and businesses regarding the potential risks of data breaches and attacks that may affect their assets (e.g. the cases of Yahoo and Equifax). Consequently, organizations, particularly those suffering from these attacks are relying on the job of security professionals. Unfortunately, due to a wide range of cyber-attacks, the identification of such skilled security professional is a challenging task. One such reason is the “skill gap” problem, a mismatch between the security professionals’ skills and the skills required for the job (vulnerability discovery in our case). In this work, we focus on platforms and processes for crowdsourced security vulnerability discovery (bug bounty programs) and present a framework for the representation of security professional skills. More specifically, we propose an embedding-based clustering approach that exploits multiple and rich information available across the web (e.g. job postings, vulnerability discovery reports) to translate the security professional skills into a set of relevant skills using clustering information in a semantic vector space. The effectiveness of this approach is demonstrated through experiments, and the results show that our approach works better than baseline solutions in selecting the appropriate security professionals.
A Case For the AI-Driven Public Domain

Artificial intelligence (AI) technology is becoming more advanced and capable by the day. In recent years, there has been discourse across industries and disciplines about AI and the role it is and will increasingly play in innovation in the arts and sciences. AI is already being used to create original artwork, play games, and drive cars, among many other things. Given the continuous advancements in AI, it is important that a decision is made regarding the treatment of AI-driven creation under the United States intellectual property (IP) regimes, most notably under copyright law. A major argument among legal scholars and technologists is that not granting copyright for these AI-driven works will disincentive innovation and investment in the AI space and is therefore not in our country’s best interest. Others argue that AI-driven creation is incompatible with the economic and policy considerations behind copyright law and that including AI-driven creations would, at best, require a fundamental redefinition of copyright and, at worst, destroy the incentives for human-driven creation that spurred the creation of copyright law in the first place.This paper explores the legal standards and practicality of protecting AI-created works under copyright law. Copyright law seeks to promote innovation in art and culture by granting limited monopoly protections for “original works of authorship.” This paper explores the values, intentions, legal standards, and practical application of copyright law and examines the tensions that exist when applying them to AI-created works. I conclude that the public domain is the most appropriate place for AI-created works to be in the copyright regime.
UCB Momentum Q-learning: Correcting the bias without forgetting

We propose UCBMQ, Upper Confidence Bound Momentum Q-learning, a new algorithm for reinforcement learning in tabular and possibly stage-dependent, episodic Markov decision process. UCBMQ is based on Q-learning where we add a momentum term and rely on the principle of optimism in face of uncertainty to deal with exploration. Our new technical ingredient of UCBMQ is the use of momentum to correct the bias that Q-learning suffers while, at the same time, limiting the impact it has on the second-order term of the regret. For UCBMQ, we are able to guarantee a regret of at most $O(\sqrt{H^3SAT}+ H^4 S A )$ where $H$ is the length of an episode, $S$ the number of states, $A$ the number of actions, $T$ the number of episodes and ignoring terms in poly-$\log(SAHT)$. Notably, UCBMQ is the first algorithm that simultaneously matches the lower bound of $\Omega(\sqrt{H^3SAT})$ for large enough $T$ and has a second-order term (with respect to the horizon $T$) that scales only linearly with the number of states $S$.
Augmented Reality for Health Education's Model to Children with ARI (Acute Respiratory Infection) Culture Based Through Family Center Care Approach

Acute Respiratory Infection is the main problem that always consulted or hospitalization in health care facilities, especially in the child care room. This study aims to identified health education's model to children with ARI (Acute Respiratory Infection) culture based through Family Center Care approach. This method is choosen to indeep interview out at child room in Jember Balung Hospital on may - september 2020. The subjects of the study are amount of eight person involving six nurses and two person from the families of children with ARI. The instruments used by TANNAHILS MODEL. The researcher identified 4 themes as a result of the study then all the themes are explained based on the specific objectives of the study. All the themes have sub-themes with specific categories of meaning. The results from themes that is identified in the study of interviews with preventive health protection are that each parent is different. One of the results of interviews about educational media that are suitable for the prevention of ARI in children are more often done using without media or “lesan” only when certain conditions use leaflets if needed. However, along with current technological developments, educational media are modified as attractive as possible to increase children's interest, one of which is educational media in the form of Augmented Reality (AR).
Collaboration in Virtual Environments: Honouring the Métis Method of Visiting

During COVID-19, many collaborative research teams and community partners pivoted to undertake their work in a virtual way. In this discussion, we capture the mechanics, logic, and situations under which virtual methods became relevant to applied interdisciplinary work. Using a shared voice, we chart the nuances of training and research through the redesign, the reimagining of research protocols, and the nuanced cultural gaps that exist between virtual connection and in-person visiting with community partners, Métis Knowledge Keepers, and experts. Through referencing our reflexive archive of experiences, emails, fieldnotes, and meeting minutes, we address how our attempt to simulate virtual informed consent has consequently provided insights into the value of co-creation and the importance of honouring visiting as a Métis method in virtual environments.
DETECT FACEMASK IN COVID-19 PANDEMIC BY USING DEEP LEARNING AND IMAGE PROCESSING

In current days the Corona Virus (COVID 19) is causing a global health crisis for each and every part of the continent. Hence there is a great need to take preventive steps for effective protection from this virus. One among the best protection step is wearing face mask in public places, which is officially announced by the WHO (World Health Organization).All the state and central governments in and around the world impose strick lockdowns to prevent and optimize the virus transmission. But still there are lot of new cases reported and many death cases are continuously updated in every part of the world. Reports clearly indicate that those who wear facemask while they go to work or public places to buy vegetables, daily grocery items and so on, has less impact from virus transmission into their body compared with those who don’t wear any face protection mask. Hence this motivated me to design an application which can identify the person whether he is wearing mask or not by taking the help of hybrid models. As we are using hybrid model for face mask detection, this model is designed by combining several classical ML algorithms as well as deep learning algorithms for face mask detection. In order to train the model for accurate detection of face mask, we try to gather dataset from UCI repository database or KAGGLE website, which contains of with mask and without mask images; we are going to use OpenCV to do real-time face detection from a live stream via our webcam. We will use the dataset to build a COVID-19 face mask detector with computer vision using Python, OpenCV, and Tensor Flow and Keras. Our goal is to identify whether the person on image/video stream is wearing a face mask or not with the help of computer vision and deep learning.
An analysis of the development of cryptocurrency research

In this chapter, we investigate the literature on both broad-and narrow-based cryptocurrency research from a bibliometric and scientometric perspective.While Bitcoin, presented as the first every cryptocurrency by Nakamoto [2009], was established as the first piece of a decade-long expansion of academic literature based on the development of this new financial product and the associated benefits and issues contained therein.We attempt to re-trace and provide a thorough explanation of the flow of research direction during this period across all disciplines.We provide clear evidence of a growing but fragmented research area.We conclude that there is a significant difference in how researchers treat broad conceptual topics versus individual products.We finally provide a concise overview of the current topics that have been central to recent research efforts, while attempting to provide oversight key areas that have presented evidence of particular deficiency.Such recommendations will provide direction for future research synergy.