Reviewerly/exHarmony huggingface dataset:
CHALLENGES, PROBLEMS AND PROSPECTS OF INTERNATIONALIZATION DURING FORCE MAJEURE SWITCH TO DISTANT TEACHING AND LEARNING AT HIGH SCHOOLS

Unplanned and unexpected switch to totally remote teaching and learning at high schools may result in a complete termination of university activity, modification of its content, aims and goals, birth of challenges and problems. Overcoming of these challenges and problems will initiate planning and design of the educational scheme, that is demanded and relevant to the current situation. Teaching and learning are the core aspects of all high schools, and these aspects should be sustainable in force majeure, which will minimize the risks in all aspects of university activity, involving internationalization and foreign students, in particular. New and urgent for the modern labor market professional competences should be linked with the digital ways of reaching the professional goals. Hence, the consistency in the switch to a totally remote teaching and learning can be reached if the digital competence of the teaching staff has been formed before and the learning outcomes would include digital skills. The authors propose the teaching and learning scheme for the distant learning that is focused on the learning outcomes demanded by the potential employers. In the authors opinion, the scheme will facilitate paces on internationalization of higher education in force majeure.
FEATURES OF PREPARING FUTURE TEACHERS FOR EDUCATIONAL ACTIVITY: MOTIVATIONAL-VALUE COMPONENT

Reducing the role of education is becoming an urgent problem for secondary vocational education, since the instructors of technical colleges work with one of the most difficult groups of students. This fact puts special requirements to future teachers’ vocational training for educational activities. The purpose of this article is the analysis of foreign and Russian literature and identification of the level of motivational component of the readiness of teachers for educational activities in professional educational organizations. Summarizing the theoretical and empirical results, we note that the preparation of future teachers for educational activities in professional educational organizations is one of the urgent problems the education system is facing and is a promising task for a pedagogical university. There is a need to build an effective system for preparing future teachers for educational activities in professional educational organizations, taking into account their specificity and the current economic and social situation in society.
Average-case Complexity of Teaching Convex Polytopes via Halfspace Queries

We examine the task of locating a target region among those induced by intersections of $n$ halfspaces in $\mathbb{R}^d$. This generic task connects to fundamental machine learning problems, such as training a perceptron and learning a $ϕ$-separable dichotomy. We investigate the average teaching complexity of the task, i.e., the minimal number of samples (halfspace queries) required by a teacher to help a version-space learner in locating a randomly selected target. As our main result, we show that the average-case teaching complexity is $Θ(d)$, which is in sharp contrast to the worst-case teaching complexity of $Θ(n)$. If instead, we consider the average-case learning complexity, the bounds have a dependency on $n$ as $Θ(n)$ for \tt{i.i.d.} queries and $Θ(d \log(n))$ for actively chosen queries by the learner. Our proof techniques are based on novel insights from computational geometry, which allow us to count the number of convex polytopes and faces in a Euclidean space depending on the arrangement of halfspaces. Our insights allow us to establish a tight bound on the average-case complexity for $ϕ$-separable dichotomies, which generalizes the known $\mathcal{O}(d)$ bound on the average number of "extreme patterns" in the classical computational geometry literature (Cover, 1965).
On the Analysis of EM for truncated mixtures of two Gaussians

Motivated by a recent result of Daskalakis et al. 2018, we analyze the population version of Expectation-Maximization (EM) algorithm for the case of \textit{truncated} mixtures of two Gaussians. Truncated samples from a $d$-dimensional mixture of two Gaussians $\frac{1}{2} \mathcal{N}(\vec{\mu}, \vec{\Sigma})+ \frac{1}{2} \mathcal{N}(-\vec{\mu}, \vec{\Sigma})$ means that a sample is only revealed if it falls in some subset $S \subset \mathbb{R}^d$ of positive (Lebesgue) measure. We show that for $d=1$, EM converges almost surely (under random initialization) to the true mean (variance $\sigma^2$ is known) for any measurable set $S$. Moreover, for $d>1$ we show EM almost surely converges to the true mean for any measurable set $S$ when the map of EM has only three fixed points, namely $-\vec{\mu}, \vec{0}, \vec{\mu}$ (covariance matrix $\vec{\Sigma}$ is known), and prove local convergence if there are more than three fixed points. We also provide convergence rates of our findings. Our techniques deviate from those of Daskalakis et al. 2017, which heavily depend on symmetry that the untruncated problem exhibits. For example, for an arbitrary measurable set $S$, it is impossible to compute a closed form of the update rule of EM. Moreover, arbitrarily truncating the mixture, induces further correlations among the variables. We circumvent these challenges by using techniques from dynamical systems, probability and statistics; implicit function theorem, stability analysis around the fixed points of the update rule of EM and correlation inequalities (FKG).
HERBART AND HERBARTIANISM IN EUROPEAN PEDAGOGY: HISTORY AND MODERNITY

The proposed article offers an assessment of J.-F. Herbart and Herbartians in the history of pedagogy of Germany, pre-revolutionary Russia, and Ukraine; the influence of the doctrine of J.-F. Herbart and Herbartians on the development of pedagogical science; borrowing ideas of these representatives in school education, namely the processes of formation, changes in directions, ideas, principles and tasks of the educational process; structural and substantive features of Herbartian pedagogy are revealed. It has been outlined the ways of popularizing Herbartianism in European countries and the significance of the theoretical heritage of Herbartians in the modern history of pedagogy.
Adversarial Validation Approach to Concept Drift Problem in User Targeting Automation Systems at Uber

In user targeting automation systems, concept drift in input data is one of the main challenges. It deteriorates model performance on new data over time. Previous research on concept drift mostly proposed model retraining after observing performance decreases. However, this approach is suboptimal because the system fixes the problem only after suffering from poor performance on new data. Here, we introduce an adversarial validation approach to concept drift problems in user targeting automation systems. With our approach, the system detects concept drift in new data before making inference, trains a model, and produces predictions adapted to the new data. We show that our approach addresses concept drift effectively with the AutoML3 Lifelong Machine Learning challenge data as well as in Uber's internal user targeting automation system, MaLTA.
Design and Research of Virtual Payment System in Colleges and Universities

With the development of mobile payment and intelligent technologies, virtual payment system has been introduced to campus to improve the campus payment environment and the degree of intelligence on campus, so that online and offline campus payment can be combined, and payment by scanning QR code and card can be simultaneously achieved to greatly facilitate the learning and life of teachers and students, meet their diverse payment needs and enhance the service level and payment efficiency.Based on summarizing the problems faced by traditional campus card system in application, functions, and services, this paper analyzes the goals of building a virtual payment system, and builds a virtual payment system integrating applications, such as virtual campus card, electronic payment, and aggregation payment.
Abstraction and Refinement in Games with Dynamic Weighted Terrain

This abstract looks at one version of the pathfinding problem in games and discusses how it motived our recent work at the AIIDE 2019 conference.
Thermal preparation of a three-mode Gaussian graph state

The hybrid circuit is proposed for implementation of continuous-variable quantum state of spin ensembles. Transmission line resonators (TLRs) and the current-biased Josephson junction (CBJJ) provide transfer of entangled field. Dissipative processes lead the system to thermal equilibrium. Squeezed microwave field can be considered as driving force for generating cluster state.
Bayesian Reinforcement Learning via Deep, Sparse Sampling

We address the problem of Bayesian reinforcement learning using efficient model-based online planning. We propose an optimism-free Bayes-adaptive algorithm to induce deeper and sparser exploration with a theoretical bound on its performance relative to the Bayes optimal as well as lower computational complexity. The main novelty is the use of a candidate policy generator, to generate long-term options in the planning tree (over beliefs), which allows us to create much sparser and deeper trees. Experimental results on different environments show that in comparison to the state-of-the-art, our algorithm is both computationally more efficient, and obtains significantly higher reward over time in discrete environments.
Pembinaan Santri Pondok Pesantren NW Kayangan Lombok Barat dalam Pembuatan Produk Olahan Jamur Tiram Berbasis Entrepreneurship

Oyster mushrooms have high economic potential because they can be processed into a variety of foods and snacks, as well as prospects for development in the community. One of the potentials that can be used by the hut to hone students' life skills is by providing training for the manufacture of oyster mushroom products. The purpose of these community service activities is to be expected later when students graduating from the NW Kayangan Islamic Boarding School can become young entrepreneurs who make them economically independent and can open new jobs for the people in their respective homes. Community service activities focus on training in processing oyster mushrooms into products in the form of nutritious satay and crispy mushrooms that are nutritious and of high economic value, targeting the students and teachers at Pondok NW Kayangan, West Lombok Regency. The activity method includes: (1) preparation phase, (2) counseling, and (3) demonstration and training on making oyster mushroom and crispy mushroom. This activity increases the knowledge and skills of students in processing oyster mushroom raw materials. This is evidenced by the results of processed oyster mushrooms in the form of mushroom satay and krispi mushroom created by teachers and students.
Artificial intelligence and the interplay between tumor and immunity

Digital pathology image analysis and deep learning can be utilized to quantify and characterize nuanced interactions between cancer and the immune system. Recent advances in deep learning and artificial intelligence in Pathomics data have led to the development of methods and techniques that augment and empower qualitative traditional diagnostic histopathologic evaluation in order to substantially accelerate cancer research. Emerging digital pathology and deep learning applications can (1) stratify patient management through data-driven insights into cancer, (2) identify relevant biomarkers to predict clinical outcomes and treatment response, (3) enhance our collective understanding of cancer biology to motivate the utilization of novel therapeutic approaches. This chapter introduces and describes a selected set of novel Pathomics-based deep learning methods that have been developed to classify and reproducibly quantify the interplay between tumor cells and the immune response in the tumor microenvironment.
Etnografía cuantitativa. Revitalización lingüística y difusión de las tecnologías digitales en municipios de Oaxaca, México

This article’s objective is to analyze the relationship between indigenous languages and internet access in Oaxaca. Using quantitative methodology, we estimate the probability for Oaxacan municipalities to have internet access. We calculate the correlation between being an indigenous language-speaker and social media user. The results of the analysis present a positive correlation between the number of mobile phones in a municipality and internet access. Likewise, social media usage is correlated positively with being an indigenous language-speaker.
Between-Domain Instance Transition Via the Process of Gibbs Sampling in RBM

In this paper, we present a new idea for Transfer Learning (TL) based on Gibbs Sampling. Gibbs sampling is an algorithm in which instances are likely to transfer to a new state with a higher possibility with respect to a probability distribution. We find that such an algorithm can be employed to transfer instances between domains. Restricted Boltzmann Machine (RBM) is an energy based model that is very feasible for being trained to represent a data distribution and also for performing Gibbs sampling. We used RBM to capture data distribution of the source domain and use it in order to cast target instances into new data with a distribution similar to the distribution of source data. Using datasets that are commonly used for evaluation of TL methods, we show that our method can successfully enhance target classification by a considerable ratio. Additionally, the proposed method has the advantage over common DA methods that it needs no target data during the process of training of models.
Multi-Tiered Strictly Local Functions

Tier-based Strictly Local functions, as they have so far been defined, are equipped with just a single tier.In light of this fact, they are currently incapable of modelling simultaneous phonological processes that would require different tiers.In this paper we consider whether and how we can allow a single function to operate over more than one tier.We conclude that multiple tiers can and should be permitted, but that the relationships between them must be restricted in some way to avoid overgeneration.The particular restriction that we propose comes in two parts.First, each input element is associated with a set of tiers that on their own can fully determine what the element is mapped to.Second, the set of tiers associated to a given input element must form a strict superset-subset hierarchy.In this way, we can track multiple, related sources of information when deciding how to process a particular input element.We demonstrate that doing so enables simple and intuitive analyses to otherwise challenging phonological phenomena.
DHPs: Dependency Hearst’s Patterns for Hypernym Relation Extraction

Hearst’s patterns are lexico-syntactic patterns that have been extensively used to extract hypernym relations from texts. They are defined as regular expressions based on lexical and syntactical information of each word. Here, we propose a new formulation of Hearst’s patterns using dependency parser, called Dependency Hearst’s Patterns (DHPs). They are defined as dependency patterns based on dependency relations between words. This formulation allows us to define more generic Hearst’s patterns that match better complex or ambiguous sentences. To evaluate our proposal, we have compared the performance of Dependency Hearst’s patterns to lexico-syntactic patterns: Hearst’s patterns and an extended set of Hearst’s patterns applied on two corpora: Music and English. Dependency Hearst’s patterns yield to a considerable improve in term of recall and a slight decrease in term of precision.
Vision skeleton trajectory based motion assessment system for healthcare rehabilitation

Most existing methods of motion assessment system used the contact sensor, infrared sensor, and depth sensor, and few works provided the solution of digital camera. To solve this problem, the authors propose a new motion assessment system based on light camera. In this work, the motion assessment was regarded as pattern regression problem of skeleton joint trajectory. Firstly, the system uses the camera to capture the image sequences. The pose estimation method is used to obtain body skeleton from image. Secondly, due to the difference of motion frequency of each person, the length of the image sequences is different and the length of each joint trajectory also will be different. Fourier transform is applied to normalise the trajectory and use the coefficients of Fourier transform as the joint trajectory feature. Finally, the regression model is built to assess the motion quality. Some experimental results and discussion on action video data are used to verify the effectiveness of the system.
SPROOF: A Decentralized Platform for Attribute-Based Authentication

Paper documents are still very common for all types of records of personal achievements, ID cards and many other types documents issued to an individual or a company. These paper documents, however, often come at the cost of expensive printing and issuing, loss of data or malicious counterfeits. The origin and integrity is often hard or even impossible to be verified. Digital signatures solve some of these issues, however, this still requires centralized trusted infrastructures and still does not allow for easy verification or recovery of lost documents. Furthermore, attribute-based authentication is not possible with traditional signature schemes. In this paper, we present a decentralized platform for signing and verifying digital documents that is based on the previously presented SPROOF platform and additionally supports attribute-based authentication. This platform allows for issuing, managing and verifying digital documents in a public blockchain. In the proposed approach, all data needed for verification of documents and issuers is stored decentralized, transparent, and integrity protected. The platform is permissionless and thus no access restrictions apply. Rather, following principles of the Web of Trust, issuers can confirm each other in a decentralized way. Additionally, scalability and privacy issues are taken into consideration.
A pragmatic view of hashtags: the case of impoliteness and offensive verbal behavior in the Brazilian Twitter

Studies on impoliteness have encompassed a wide range of social relations and scenarios, including some in which the means for achieving impoliteness is solely, or at least, more saliently linguistic. Among these scenarios, Digital Communication (DC) media, such as Twitter and blogs may incite linguistic manifestations of impoliteness. Since hashtags are ubiquitous in the interpersonal spaces of Twitter, the aim of this study is to investigate how hashtags were used to express offensive behavior and to convey impoliteness in the Brazilian Twitter. We compiled a corpus of 512 hashtags extracted from Brazilian Twitter in 2017 and 2018. Hashtags were manually collected from Twitter ‘trending topics’, and from 3 media sources, written in Brazilian Portuguese. Hashtags associated with offense, including derogatory language, taboo words and mockery were analyzed. Our findings suggest that hashtags served as strategies of impoliteness, since they intensified the contents of the tweets and framed the interpretation of verbal aggressiveness. While they did that, they also helped maximize face attacks in that they inscribed the tweets in a bounded communicative sphere of aggressiveness, mockery and derogatory language. Verbal attacks were mainly intended to politicians and to Brazilian public figures in general. While this permissiveness allowed for greater spontaneity and agility in the exchanges, it also encouraged outrageous uses, which would not be otherwise accomplished were it not for the transient and ephemeral framework of DC. This study shed light on the Pragmatics of DC, showing how ‘new’ features of the medium can be used to convey impoliteness.
A random walk on Area Restricted Search

These notes from a graduate class at the Unuversidad Autonoma de Madrid analyze a search behavior known as Area Resticted Search (ARS), widespread in the animal kingdom, and optimal when the resources that one is after are "patchy". In the first section we study the importance of the behavior in animal and its dependence on the dopamine as a indicator of reward. In the second section we put together a genetic algorithm to determine the optimality of ARS and its characteristics. Finally, we relate ARS to a type of random walks known as "Levy Walks", in which the probability of jumping at a distance d from the current location follows a power law distribution.
Safe-Bayesian Generalized Linear Regression

We study generalized Bayesian inference under misspecification, i.e. when the model is ‘wrong but useful’. Generalized Bayes equips the likelihood with a learning rate η. We show that for generalized linear models (GLMs), η-generalized Bayes concentrates around the best approximation of the truth within the model for specific ηeq1, even under severely misspecified noise, as long as the tails of the true distribution are exponential. We derive MCMC samplers for generalized Bayesian lasso and logistic regression and give examples of both simulated and real-world data in which generalized Bayes substantially outperforms standard Bayes.
Modeling and Specification of Bootstrapping and Registration Design Patterns for IoT Applications

The architectures of software systems are becoming more complex, large, and dynamic. The design of these architectures allows architects to master building complex software systems. But, their informal description, may give rise to ambiguity, their understanding becomes more and more difficult and leads to the incorrect implementation of these software systems. There are many solutions allowing software architecture design. In this paper, we use software design patterns as a solution. This is due to their reusable software elements. Our principal objective is to propose other alternatives to the informal visual description of software architectures. In past work, we have studied Service Oriented Architectures. We used SOA design patterns with standard formal notations. This work is a continuation to the past one. We apply our approach on design patterns for the Internet of Things. We introduce a refinement-based approach for modeling IoT design patterns. It takes advantage of graphical modeling and formal method. It is organized around two main axes. The first axis is to provide modeling solutions in conformance with the UML standard language. The second axis covers the general specification of design pattern models with the Event-B method. As a result, we propose a design support tool for IoT architectures based on IoT design patterns. It allows modeling of correct-by-design software systems.