Reviewerly/exHarmony huggingface dataset:
What Risk Resilience Measures Can I Use?

The question “What resilience measures can I use?” addresses how to reduce the impact and consequences of successful cyberattacks. The chapter begins with a case study analyzing how Capital One recovered after being hacked and highlighting how your organization can use planning to facilitate cyber resilience. It illuminates the technical means for enabling resilience from an attack, including virtualization and maintaining backups. It defines a ten-step process for responding to cyberattacks: prevention, planning, preparation, detection, analysis, containment, communication, eradication, recovery, and post-event analysis. The chapter explains how an organization can build a computer security incident response team (CSIRT) to facilitate this process, and what role a cyber crisis communication plan should play. The chapter concludes with Rosenbach’s Embedded Endurance strategy experience supporting the White House in crafting a national cyberattack resilience and response plan.
High Probability Complexity Bounds for Line Search Based on Stochastic Oracles

We consider a line-search method for continuous optimization under a stochastic setting where the function values and gradients are available only through inexact probabilistic zeroth and first-order oracles. These oracles capture multiple standard settings including expected loss minimization and zeroth-order optimization. Moreover, our framework is very general and allows the function and gradient estimates to be biased. The proposed algorithm is simple to describe, easy to implement, and uses these oracles in a similar way as the standard deterministic line search uses exact function and gradient values. Under fairly general conditions on the oracles, we derive a high probability tail bound on the iteration complexity of the algorithm when applied to non-convex smooth functions. These results are stronger than those for other existing stochastic line search methods and apply in more general settings.
Back-Training excels Self-Training at Unsupervised Domain Adaptation of Question Generation and Passage Retrieval

In this work, we introduce back-training, an alternative to self-training for unsupervised domain adaptation (UDA). While self-training generates synthetic training data where natural inputs are aligned with noisy outputs, back-training results in natural outputs aligned with noisy inputs. This significantly reduces the gap between target domain and synthetic data distribution, and reduces model overfitting to source domain. We run UDA experiments on question generation and passage retrieval from the Natural Questions domain to machine learning and biomedical domains. We find that back-training vastly outperforms self-training by a mean improvement of 7.8 BLEU-4 points on generation, and 17.6% top-20 retrieval accuracy across both domains. We further propose consistency filters to remove low-quality synthetic data before training. We also release a new domain-adaptation dataset - MLQuestions containing 35K unaligned questions, 50K unaligned passages, and 3K aligned question-passage pairs.
Differences in Emotional Display Rules and Emotional Labor Strategies according to Dual Role of Physical Education Teacher

Purpose: The purpose of this study is to investigate the effect of dual role (physical education teacher vs. athletic director) on emotional display rules (positive and negative) and emotional labor strategies (surface acting, deep acting, and genuine expression) among physical education teachers.<BR> Methods: 250 survey questionnaires were distributed to PE teachers experiencing dual roles in the region of Youngnam. Out of the 250 questionnaires distributed, a total of 229 questionnaires were chosen for final data analysis after excluding 21 incomplete questionnaires.<BR> Results: Results of chi-square tests indicated that participants were more likely to display positive emotions in the role of PE teacher than in the role of athletic director. On the other hand, participants were more likely to display negative emotions in the role of athletic director than in the role of PE teacher. Results of the paired t-tests indicated that participants utilized more surface acting in the role of PE teacher than in the role of athletic director, while they utilized more genuine expression in the role of athletic director than in the role of PE teacher.<BR> Conclusion: The overall findings of this study provide some meaningful and practical implications for dual role PE teachers regarding the use of emotional display rules and emotional labor strategies according to the types of their roles.
Argentina

This chapter explores the institutional setting and identifies the critical determinants of parliamentary speechgiving in the Argentine Chamber of Deputies between 2001 and 2016. We understand and describe access to the parliamentary floor as a complex interplay between formal and informal regulations, which govern the distribution of floor time as a scarce resource between, and its allocation within, political parties. The empirical analysis combines textual data on plenary session speeches with both individual and institutional characteristics of over 1000 Argentine MPs. The analysis results indicate that participation in parliamentary debate is driven by a combination of individual traits such as seniority and MPs’ ability to hold institutionally powerful leadership positions such as that of the parliamentary bloc. At the same time, we observe that the government–opposition divide plays no significant role in parliamentary floor access, which speaks to the Argentine Chamber of Deputies’ general characterization as a consensual institution.
Domain Informed - a better approach to regularization and semi-supervised learning for seismic event analysis.

system, and provide specific examples from its use in a radiological analysis assistant system. The goal of the research described in this paper is to provide a foundation from which dependability analysis of systems using semi-supervised, multi-modal learning can be conducted. The methods presented provide a first step towards that overall goal.
Distributed Identity in Music

SummaryWe introduce the concept of a distributed identity in musical performance. This is about the construction of a musical identity that is issued from a collaboration, where eventually the music starts playing you and not you the music, a higher axis of musical identification in the making of musical reality and in particular: time.
Flexible Compositional Learning of Structured Visual Concepts

Author(s): Zhou, Yanli; Lake, Brenden | Abstract: Humans are highly efficient learners, with the ability to grasp the meaning of a new concept from just a few examples. Unlike popular computer vision systems, humans can flexibly leverage the compositional structure of the visual world, understanding new concepts as combinations of existing concepts. In the current paper, we study how people learn different types of visual compositions, using abstract visual forms with rich relational structure. We find that people can make meaningful compositional generalizations from just a few examples in a variety of scenarios, and we develop a Bayesian program induction model that provides a close fit to the behavioral data. Unlike past work examining special cases of compositionality, our work shows how a single computational approach can account for many distinct types of compositional generalization.
An Efficient Object Detection technique in Realtime and Noisy Environments

During the recent years there has been a great progress in Imaging technology. Image and video capturing devices are becoming cheaper and on the other hand computing power significantly increased with much more efficiency. The arena of Parallel Computing moved through different phases like multi-core processing, Graphical processing unit (GPU), Tensor processing units (TPU) and so on. Several proficient algorithms made the object detection process fine tuned. Also at the same time the processing power involves much cost. As a result, object detection has a high computational cost. We presented an object detection technique that may be executed on a standard CPU in this study. Due to this the computational cost drastically decreases. And at the same time we maintained an effective rate of FPS besides the time consumption to be low. Although we implemented the process computable at CPU level we did not let the accuracy to get deviated.
Todo início tem um antes (2021)

Todo início tem um antes (2021) Daisy Serena Click for larger view View full resolution Daisy Serena, Todo início tem um antes, 2021. Colagem digital. 21x29cm. Image courtesy of the artist. [End Page 6] Copyright © 2021 Daisy Serena
The Development of a Centralized Platform for Finding Tutor

Nowadays,students are having problem in having online classes due to limitedaccess for learning material and assistance by friends or lecturers. Even the application of seeking tutors was established before, few problems still occur. The first problem is the existing applications offer limited features for the users such as the students are unable to find the nearby tutor. The second problem is students have time constraint to contact their lecturers because of there are many procedures to do in order forthem to have personal virtual meeting. The third problem is the existing learning material that students received from their lecturers sometimes are not sufficient. This leads to them to seek the reference on the internet which in result they must pay for it in order to download or view. Hence, the proposed system will be developedwhich can be an aid for the students and even the tutors to conduct the personal online class. This system will help them to search the tutors based on location and course or subject. Once they find a suitable tutor, they will be able to make a booking by sending a request that will be notified to the tutor by the admin. Besides that, students can give feedback and rating for the tutors right after the lecture session end. The rate will be recorded as a report for the admin to monitor the process.
An in-depth look at Euclidean disk embeddings for structure preserving parsing

Preserving the structural properties of trees or graphs when embedding them into a metric space allows for a high degree of interpretability, and has been shown beneficial for downstream tasks (e.g., hypernym detection, natural language inference, multimodal retrieval).However, whereas the majority of prior work looks at using structure-preserving embeddings when encoding a structure given as input, e.g., WordNet (Fellbaum, 1998), there is little exploration on how to use such embeddings when predicting one.We address this gap for two structure generation tasks, namely dependency and semantic parsing.We test the applicability of disk embeddings (Suzuki et al., 2019) that has been proposed for embedding Directed Acyclic Graphs (DAGs) but has not been tested on tasks that generate such structures.Our experimental results show that for both tasks the original disk embedding formulation leads to much worse performance when compared to nonstructure-preserving baselines.We propose enhancements to this formulation and show that they almost close the performance gap for dependency parsing.However, the gap still remains notable for semantic parsing due to the complexity of meaning representation graphs, suggesting a challenge for generating interpretable semantic parse representations.
The structure of scientific controversies: Thomas Kuhn’s social epistemology

Changes of theories are major events in science. Two main types of questions may be asked about them: i) how do scientists choose new theories?, and ii) how is consensus formed? Generally, philosophers do not distinguish these two questions. Kuhn, on the contrary, offers very different answers to each of these questions. Theory-choice, on the one hand, is explained through the application of epistemic criteria, such as accuracy and consistency; nonetheless, because these values do not prescribe a single choice, consensus formation, on the other hand, is explained through a series of socio-epistemic mechanisms, namely: scientific pedagogy, diffusion and production of knowledge within the community (the “wave motion”), and restructuring of the scientific field. These mechanisms are the basis of Kuhn’s social epistemology, in that they are not restricted to sociology nor epistemology, encompassing both social interactions and epistemic evaluations of theories. Keywords: Thomas Kuhn, consensus formation, social epistemology.
Study on gold enrichment characteristics in Zambia: Based on 1∶1000000 geochemical mapping

The 1∶1000000 geochemical mapping of Zambia provides catchment sediment geochemical data for 58 elements including Au from 746 sediment samples at 736 sampling sites, corresponding to a sampling density of about one site per 1000 km2. Under strict quality control using field duplicates, certified reference materials, and analytical replicate samples, the Au was determined by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The detection limit of Au was 0.20 × 10−9. The 95% range (2.5%–97.5%) of Au concentrations was from 0.24 × 10−9 to 1.36 × 10−9, and the median value was 0.40 × 10−9. The most noticeable Au distribution patterns shown on the map are mainly located between Lusaka and Ndola (Lufilian Arc Belt). In addition, several high Au value areas occurred in Mansa, Muyombe, Chipata, and Livingstone. The spatial distribution patterns of Au in tectonic units, drainage basins, and geomorphological landscapes could be related to the Lufilian Arc Belt and Bangweulu Block. The Au concentrations show metallogenic belts between Muyombe and Mbala areas, between Mansa and Ndola areas, and between Lusaka and Kasempa areas.
Study on Collision avoidance of UAV with Vision-based Sensor in Outdoor Environments

This study investigates real-time path planning for UAVs (Unmanned Aerial Vehicles) to avoid obstacles in an outdoor environment in a three-dimensional space. First, we implement an algorithm to convert from Octomap to a grid map to implement a global grid map. Secondly, the ESDF (Euclidean Signed-Distance Field) algorithm is integrated into the path planning obtained through the JPS (Jump Point Search) algorithm, and the B-Spline algorithm is applied to modify the path to consider the dynamic constraint of UAVs. Finally, path planning algorithms are verified in real-time through simulation and flight testing. The simulation is based on the Gazebo environment using the open-source autopilot PX4 with the ROS. A depth camera performance is compared for an outdoor flight test to select a depth camera mounted on the drone. Moreover, the practical obstacle-avoidance performance of the proposed real-time route planning in an outdoor space is confirmed through a flight test.
Moving towards a Non-Dyadic View on Service Systems and its Operationalization – Applying the Hypergraph-based Service System Model

In today’s VUCA world, that is characterized by high volatility, uncertainty, complexity and ambiguity, service provisioning is required to realize flexible and adaptable reconfiguration of service delivery systems and its stakeholders’ resources. However, services are often embedded in greater service systems and the context information of both customer and service provider form both its boundary conditions the suitable solution service. To capture the complexity and leverage the dynamic of service systems, we propose the formal service system model (SSM) method. Following general systems theory, we define boundaries for service delivery and show SSM’s applicability for ad-hoc service operations. We show its usefulness for structuring a service system for service operations, specifically scheduling, planning, and pricing of service provisioning. We contribute to service systems engineering by applying one generalizable mathematical model for both structuring and operationalizing service systems and provide insights in-to capturing the complex relationships of its components
A Meta-Analysis of Evolution of Deep Learning Research in Medical Image Analysis

With a text mining and bibliometrics approach, we review the literature on the evolution of deep learning in medical image literature from 2012 - 2020 in order to understand the current state of the research and to identify the major research themes in image analysis to answer our research questions: RQ1: What are the learning modes that are evident in the literature? RQ2: What are the emerging learning modes in the literature? RQ3: What are the major themes in medical imaging literature? The analysis of 8704 resulting from a data collection process from peer-reviewed databases, our analysis discovered the six major themes of image segmentation studies, studies with image classification, evaluation procedures such as sensitivity and specificity, optical coherence tomography studies, MRI imaging studies, and Chest imaging studies. Additionally, we assessed the number of articles published each year, the frequent keywords, the author networks, the trending topics, and connections to other topics. We discovered that segmenting and classifying the images are the most common tasks. Transfer learning is the most researched area and cancer is the highly targeted disease and Covid-19 is the most recent research trend. © WMSCI 2021.All right reserved.
Development of critical thinking in Natural Science whit secondary school students in an Educational Institution in Pereira - Risaralda

This article shows the results of research work on the development of critical thought in Natural Science with secondary school students at the Boyaca Educational Institution, conducted within the Master’s Degree program on Education and Human Development at the University of Manizales.The research was oriented toward developing critical thinking skills such as analyzing information, inferring implications, proposing alternative solutions, and arguing positions. It started with an initial assessment, followed by the design and application of activities related to the scientific method applied through a didactic intervention within the research group, and finishing with the application of the posttest focused on understanding how the development of these skills in the research group students took place.The methodology used was a descriptive quantitative approach, through a quasi-experimental method called “Pre-post design with a single group” accompanied by four phases. The aim of the research was to bring students closer to the construction of analytical, critical-reflective and argumentative skills in immediate context situations, especially in the competences of the Natural Sciences, as well as to identify hich experiences of the teaching process contributed to the critical thinking education of these students.
A genetic algorithm approach to recommender system cold start problem

Recommender systems (RS) are ubiquitous and used in many systems to augment user experience to improve usability and they achieve this by helping users discover new products to consume. They, however, suffer from cold-start problem which occurs when there is not enough information to generate recommendations to a user. Cold-start occurs when a new user enters the system that we don’t know about. We have proposed a novel algorithm to make recommendations to new users by recommending outside of their preferences. We also propose a genetic algorithm based solution to make recommendations when we lack information about user and a transitive algorithm to form neighbourhood. Altogether, we developed three algorithms and tested them using they MovieLens dataset. We have found that all of our algorithms performed well during our testing using the offline-evaluation method.
Efficient Hardware Implementation Of Elliptic Curve Diffie-Hellman Key Exchange Protocol

The aim of the present work is the hardware implementation of the elliptic curve Diffie-Hellman (ECDH) key exchange protocol on a reconfigurable circuit of type FPGA at the register-transfer level (RTL). Compared to the standard Diffie- Hellman (DH), based on exponentiation in a finite field, ECDH is known to provide equivalent level of security with lower number of bits used. Reduced bit usage implies less power and logic area are required to implement this cryptographic scheme. This is particularly important in secure embedded system, where a high level of security is required, but with low power consumption. The results show that ECDH can be implemented on FPGA with convincing performances in comparison with other published works
Adversarial Graph Augmentation to Improve Graph Contrastive Learning

Self-supervised learning of graph neural networks (GNN) is in great need because of the widespread label scarcity issue in real-world graph/network data. Graph contrastive learning (GCL), by training GNNs to maximize the correspondence between the representations of the same graph in its different augmented forms, may yield robust and transferable GNNs even without using labels. However, GNNs trained by traditional GCL often risk capturing redundant graph features and thus may be brittle and provide sub-par performance in downstream tasks. Here, we propose a novel principle, termed adversarial-GCL (AD-GCL), which enables GNNs to avoid capturing redundant information during the training by optimizing adversarial graph augmentation strategies used in GCL. We pair AD-GCL with theoretical explanations and design a practical instantiation based on trainable edge-dropping graph augmentation. We experimentally validate AD-GCL by comparing with the state-of-the-art GCL methods and achieve performance gains of up-to $14\%$ in unsupervised, $6\%$ in transfer, and $3\%$ in semi-supervised learning settings overall with 18 different benchmark datasets for the tasks of molecule property regression and classification, and social network classification.
ФОРМИРОВАНИЕ МЫШЛЕНИЯ МОЛОДЁЖИ В ПРОЦЕССЕ ОБРАЗОВАНИЯ – ВОСПИТАНИЯ

This thesis deals with the forming of youth thoughts in the process of education-training of school children. Also, paid attention to the meaning of intellect, process of intellecting, and problems of spiritual training in forming of intellect of the youth
САМООРГАНИЗАЦИЯ УЧЕБНОЙ ДЕЯТЕЛЬНОСТИ СТУДЕНТОВ СРЕДСТВАМИ RP- ТЕХНОЛОГИЙ ВЗАИМОДЕЙСТВИЯ

This article highlights the features of the modernization of Kazakhstan pedagogical education, the professional competence of the modern teacher. The study provides a rationale for the problem, proves its relevance and the need for self-organizing learning activities among students. The basis of the professional competence of a teacher is his practical readiness for self-organization of his activities, which consists of the ability to plan his activities, properly allocate his time and find the best ways to organize it. The author substantiates the problem of self-organizing educational activities of students by means of RP-interaction technologies, which is confirmed by practical examples of independent work in the discipline Art pedagogy among first-year students of the specialty Pedagogy and methods of primary education.
Virtual Desktop on ESXi

In this book so far, we have been discussing virtual machines (VMs), but there is an interesting subset of VMs that run desktop OSes instead of server OSes – virtual desktops. In this chapter, we will look at virtual desktops, discuss some of their benefits, and walk through how to create and access them.