Reviewerly/exHarmony huggingface dataset:
Using of Cloud Technologies of Education in Professional Preparation in Educational Institutions

Using cloud technologies in education is one of the factors of development of creative potential and digital competence of students through the using of informative-communicative technologies. Technologies of processing information have undergone great changes with spreading of the Internet network. One of the modern and effective instruments for the decision of tasks of digitalization of educational process are cloud services, the main advantage of which is the absence of expenses, connected with buying, installing and technical support of proper software. Herewith, the great part of software products, used by cloud technologies, is free of charge or spread with great discounts for educational institutions. Using of cloud technologies for the professional preparation of students in the institutions of higher education gives a possibility to do qualitative preparation of competitive specialist. Implementation of different in educational processes gives an impetus to the increasing of informative- communicative competence of students, to understanding of effective directions of development of future profession and creates conditions for wide implementation of cloud technologies. Cloud technologies of study in professional preparation in institutions of higher education give a number of advantages: saving of finances for buying software; reduction of necessity of specialized accommodations; executing of different kinds of studying work, control and evaluation online; saving of disc space; anti-virus, ad-free, anti-hacker safety and openness of educational environment for teachers and students. Besides, the work of computer network in the educational institution is becoming simpler. It is gradually removed functions of storage informational resources and providing its safety.
Correction to: Cognitive Engineering for AI: An Octave Drawing Test for Building a Mathematical Structure of a Subconscious Mind

Correction to: Chapter “Cognitive Engineering for AI: An Octave Drawing Test for Building a Mathematical Structure of a Subconscious Mind” in:M. S. Kaiser et al. (eds.), Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering, Lecture Notes in Networks and Systems 348, https://doi.org/10.1007/978-981-16-7597-3_11
Pedagogical Conditions for the Formation of Innovative Competence of Future Primary School Teachers

The pedagogical conditions of formation of innovative competence of the future primary school teacher are investigated, in particular increase of level of motivation to mastering innovative competence by the future primary school teacher in pedagogical college; the focus of the content of training on the acquisition by the future primary school teacher of innovative competence; integral combination of traditional teaching methods with innovative ones, aimed at intensifying modeling of professional situations on the basis of innovative-activity approach. In the course of the research the author notes that certain pedagogical conditions provide a comprehensive mastery of knowledge and methods of practical activities that ensure successful activities, motivate to master innovative technologies and promote self-realization in modern society of professionals. It was proved that future primary school teachers received a thorough knowledge of innovative teaching methods and technologies; their ability to innovate in the educational process of primary school has increased in the vast majority, they have learned to successfully apply modern strategies in working with primary school students; they were interested in the possibilities of applying innovative technologies; they began to demonstrate the desire for professional self-improvement, the ability to develop innovative skills and make non-standard innovative decisions, the ability to organize their own innovative activities; increased the level of technological readiness for innovation. It is concluded that the pedagogical conditions developed by us contribute to the effective formation of innovative competence of the future primary school teacher.
Classification

With the current chapter, you are now arriving at one of the main parts of the book about machine learning, namely, classification. Classification is, next to regression and clustering, one of the three main tasks in machine learning, and they will all be covered in this book.
Engineering Software Quality

In the 1970s, when I first began designing and developing systems, the tribal knowledge at that time was that an information system should meet the following four criteria:
Experience of using audiovisual material by Spanish educators

The article identifies and outlines the main suggestions of Spanish educators Cabero, Cebriano Martinez, Salinas, Romero and Bartolomé regarding the use of audiovisual media during foreign language classes. It is noted that Spanish scholars have developed rules for the use of audiovisual materials during lessons in educational institutions of different levels. They believe that it is necessary to determine the purpose of using audiovisual materials during lessons. Psycho-emotional peculiarities of the perception of video materials by the recipients of education should be taken into account. Particular attention should be paid to guidelines for preparing beneficiaries to absorb information from audiovisual material. Recipients of education should not passively perceive information, they should be active participants in the learning process. This result is achieved through the teacher's use of a variety of methodological techniques, individualisation of teaching. The beneficiaries should be involved in interpreting fragments of the audio-visual materials, supplementing them with their own reflections, life facts and drawing conclusions. Spanish educators recommend starting a discussion, preparing a questionnaire for the recipients of education on the video, tracing interdisciplinary links, etc. A number of studies on the specificity of using audiovisual materials in the learning process in Spain have shown that if all methodological aspects are taken into account during the use of video materials, recipients of education develop competencies that contribute to their self-realization in the future. It is especially important to use audiovisual materials during the teaching of elective disciplines in foreign languages in higher education institutions, which is a promising area of research.
Research on Data Dynamic Adjustment Method Considering Security Requirements in Cloud Computing Environment

Data storage is widely used as a basic service of cloud computing. How to improve the efficiency of data use under the premise of ensuring data security is one of the current research hotspots in the cloud computing environment of multi-copy storage. In view of the problem of storage location adjustment caused by the dynamic change of data resources during the operation of cloud computing, this paper first presents a data request habit detection strategy RT to detect the rationality of the data storage scheme in the current environment in real time. By using the optimization ability of genetic algorithm, users and related applications can obtain data safely and efficiently. Experiments show that this method can improve the security of data storage and the efficiency of data acquisition under the changeable data request mode.
The Base as an Indispensable Didactic Instrument in Remote and Classroom-Based Formats of Teaching

The article is devoted to study of the encumbrances arising in the remote format of preparing students of pedagogic higher educational institutions, as well as teaching pupils of village schools, according to the state mission of the Ministry of Enlightenment of the Russian Federation, as well as the proposed direction of search and experimental approbation of didactic means designed for overcoming the examined encumbrances. The article includes visual materials, which help form and shape the direction of the search for new means for the remote format of teaching, – namely, visual didactic bases in the form of logical-semantic models. The posed problem is solved in the following logical sequence: from the encumbrances arising in the remote format of teaching to the demands directed towards decreasing the formulated encumbrances, and, finally – towards a concrete fulfillment of the demands by means of didactics. Among the didactic means used is the method of logical-semantic modeling of knowledge presented in the language of the education, a package of universal teaching activities of the analytical type used during the logical-semantic modeling, as well as a symmetrical eight-channel foundation of a figurative character for placing the results of the logical-semantic modeling. An important distinction of the examined direction is the universality of the developments and tables brought in the article applicable for use while teaching various tutorial disciplines, including music.
Efficient Final Exponentiation for Cyclotomic Families of Pairing-Friendly Elliptic Curves with Any Prime Embedding Degrees

Pairings on elliptic curves consisting of the Miller loop and final exponentiation are used for innovative protocols such as ID-based encryption and group signature authentication. As the recent progress of attacks for the discrete logarithm problem in finite fields in which pairings are defined, the importance of the use of curves with prime embedding degrees k has been increased. In this paper, the authors provide formulas to construct algorithms for computing the final exponentiation for cyclotomic families of curves with any prime k. Since the formulas give rise to one of the same exponents given by a lattice-based method for the small cases of k, it is expected that the proposed algorithms are efficient enough for the cases of any prime k. At least for the curves with k = 13 and 19 for the pairing at the 128-bit security level, the proposed algorithms can achieve current state-of-the-art computations.
Reducing the Label Space a Predefined Ratio for a More Efficient Multilabel Classification

The multi-label classification task has been widely used to solve problems where each of the instances may be related not only to one class but to many of them simultaneously. Many of these problems usually comprise a high number of labels in the output space, so learning a predictive model from such datasets may turn into a challenging task since the computational complexity of most algorithms depends on the number of labels. In this paper, we propose a methodology to reduce the label space a predefined ratio of labels, aiming to improve the runtime of the multi-label classification algorithms. Obviously, such reduction should be done without producing a significant drop in their final predictive performance. The experimental analysis carried out over 25 well-known multi-label datasets, demonstrates a drastic reduction in the runtime. Besides, it is statistically proven that reducing 20% the number of labels does not lead to a decrease in the predictive performance of the multi-label algorithms using four well-known evaluation measures. Even more, in many cases, although reductions of up to 50% of the output space are made, the predictive performance of the algorithms is not significantly different from using the whole set of labels.
Applying Natural Language Processing Neural Network Architectures to Augment Appointment Request Review of Self-Referred Patients to an Academic Medical Center.

Selecting appropriate consultations for self-referred patients to tertiary medical centers is a time and resource intensive task. Deep learning with natural language processing can potentially augment this task and reduce clinician workload. Appointment request forms for 8168 patients self-referred to General Internal Medicine were reviewed and recommended downstream appointments from manual triage were tabulated. This paper describes the development and performance of thirty-nine deep learning algorithms for multi-label text classification: including convolutional neural networks, recurrent neural networks, and pretrained language models with transformer and reformer architectures implemented using Pytorch and trained on a single graphic processing unit. A model with multiple convolutional neural networks with various kernel sizes (1-7 words) and 300 dimensional FastText word embeddings performed best (AUC 0.949, MCC 0.734, F1 0.775). Generally, models with convolutional networks were highest performers. Highly performing models may be candidates for implementation to augment clinician workflow.
Improving the Quality of Suggestions for Medical Text Simplification Tools.

Text continues to be an important medium for communicating health-related information. We have built a text simplification tool that gives concrete suggestions on how to simplify health and medical texts. An important component of the tool identifies difficult words and suggests simpler synonyms based on pre-existing resources (WordNet and UMLS). These candidate substitutions are not always appropriate in all contexts. In this paper, we introduce a filtering algorithm that utilizes semantic similarity based on word embeddings to determine if the candidate substitution is appropriate in the context of the text. We provide an analysis of our approach on a new dataset of 788 labeled substitution examples. The filtering algorithm is particularly helpful at removing obvious examples and can improve the precision by 3% at a recall level of 95%.
Accumulated Error Reduction of Linear Motor Mover Position Measurement Based on SKLM

Cumulative measurement error is the most critical factor affecting the accuracy of long-stroke displacement measurement. Based on the 1-D image gradient method and smooth Kalman filter algorithm, this article proposes a long-stroke linear motor displacement cumulative error reduction and high-precision measurement methods. First, according to the motion characteristics of the linear motor and the principle of image measurement, a 1-D speckle target image is generated, and the 1-D Barron gradient algorithm is used to calculate the displacement of adjacent frames quickly. Second, according to the reasons for the accumulated error of long-stroke displacement measurement, the smooth Kalman algorithm for optimized autoregressive data processing is introduced to optimally estimate the measured displacement of adjacent frames to realize the reduction of accumulated error. To improve the robustness of the measurement system, a wavelet soft-threshold image filter is introduced to perform noise reduction and restoration processing on the collected signal and further realize the high-precision displacement measurement of the long-stroke linear motor. Simulations and experiments show that the method presented in this article not only improves the measurement accuracy of adjacent frames but also reduces the cumulative error of long-stroke displacement measurement. And under different working conditions, compared with other methods, it has higher accuracy and anti-interference.
Adjusting the Ground Truth Annotations for Connectivity-Based Learning to Delineate

Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. To this end, we treat the annotations as active contour models that can deform themselves while preserving their topology. This enables us to jointly train the network and correct potential errors in the original annotations. The result is an approach that boosts performance of deep networks trained with potentially inaccurate annotations.
THE EFFECT OF FIRE PROTECTION FACILITIES AND DISASTER RESPONSE SIMULATIONS ON INCREASING SKILLS IN FIRE FIGHTING AT POLTEKKES KEMENKES GORONTALO

Data obtained from the National Disaster Management Agency (BNPB) in early 2014 to 2016 states that there are still many cases of fires occurring in Indonesia. Most people do not know the causes that often cause fires and the initial steps that must be taken when a fire occurs to minimize the losses caused. Practicing and building preparedness is necessary to deal with any emergency situation that is difficult to predict. Therefore, it is necessary to conduct a disaster emergency response simulation to find out the extent of knowledge and skills in fire disaster management, especially in the work area of the Health Poltekkes, Ministry of Health, Gorontalo. The research design used is a Quasy Experiment One Group Study (Pretest-Posttest) by measuring the independent variables (independent) consisting of fire emergency response simulations, while the dependent variable is fire disaster management knowledge and skills. The number of patient samples was calculated based on the Slovin formula obtained by 36 respondents using Univariate and Bivariate analysis methods through computer statistical programs. The time of the research will be carried out in June 2019. The results show that the fire prevention facilities in the Gorontalo Health Ministry Health Poltekkes work area are adequate but are constrained by the ability of electric power, but the knowledge and skills of employees within the Gorontalo District Health Office are adequate. Poltekkes based on statistical tests using paired sample T test obtained p = 0.000 (α = 0.05) which means that the fire emergency response simulation has an effect on increasing the knowledge and skills of employees in the Health Polytechnic of the Ministry of Health of Gorontalo in dealing with fire disasters.
Quercus Hernández - Learning Physics with Graphs

We take advantage of two inductive biases [1
Geoscience is vital for meeting Paris Agreement obligations

It’s a really exciting time for the geosciences. Earth science has been instrumental in documenting climate change and is pivotal in finding solutions that address it. Put simply, it is no longer about diagnosis, but mitigation and cure. Geoscience is vital for meeting the obligations embedded in the Paris Agreement.” John Underhill, a Professor at […]
Approximating Pandora's Box with Correlations

We revisit the classic Pandora's Box (PB) problem under correlated distributions on the box values. Recent work of arXiv:1911.01632 obtained constant approximate algorithms for a restricted class of policies for the problem that visit boxes in a fixed order. In this work, we study the complexity of approximating the optimal policy which may adaptively choose which box to visit next based on the values seen so far. Our main result establishes an approximation-preserving equivalence of PB to the well studied Uniform Decision Tree (UDT) problem from stochastic optimization and a variant of the Min-Sum Set Cover ($\text{MSSC}_f$) problem. For distributions of support $m$, UDT admits a $\log m$ approximation, and while a constant factor approximation in polynomial time is a long-standing open problem, constant factor approximations are achievable in subexponential time (arXiv:1906.11385). Our main result implies that the same properties hold for PB and $\text{MSSC}_f$. We also study the case where the distribution over values is given more succinctly as a mixture of $m$ product distributions. This problem is again related to a noisy variant of the Optimal Decision Tree which is significantly more challenging. We give a constant-factor approximation that runs in time $n^{ \tilde O( m^2/\varepsilon^2 ) }$ when the mixture components on every box are either identical or separated in TV distance by $\varepsilon$.
Layer-wise Adaptive Graph Convolution Networks Using Generalized Pagerank

We investigate adaptive layer-wise graph convolution in deep GCN models. We propose AdaGPR to learn generalized Pageranks at each layer of a GCNII network to induce adaptive convolution. We show that the generalization bound for AdaGPR is bounded by a polynomial of the eigenvalue spectrum of the normalized adjacency matrix in the order of the number of generalized Pagerank coefficients. By analysing the generalization bounds we show that oversmoothing depends on both the convolutions by the higher orders of the normalized adjacency matrix and the depth of the model. We performed evaluations on node-classification using benchmark real data and show that AdaGPR provides improved accuracies compared to existing graph convolution networks while demonstrating robustness against oversmoothing. Further, we demonstrate that analysis of coefficients of layer-wise generalized Pageranks allows us to qualitatively understand convolution at each layer enabling model interpretations.
Moving Quantum States without SWAP via Intermediate Higher Dimensional Qudits

Quantum algorithms can be realized in the form of a quantum circuit. To map quantum circuit for specific quantum algorithm to quantum hardware, qubit mapping is an imperative technique based on the qubit topology. Due to the neighbourhood constraint of qubit topology, the implementation of quantum algorithm rightly, is essential for moving information around in a quantum computer. Swapping of qubits using SWAP gate moves the quantum state between two qubits and solves the neighbourhood constraint of qubit topology. Though, one needs to decompose the SWAP gate into three CNOT gates to implement SWAP gate efficiently, but unwillingly quantum cost with respect to gate count and depth increases. In this paper, a new formalism of moving quantum states without using SWAP operation is introduced for the first time to the best of our knowledge. Moving quantum states through qubits have been attained with the adoption of temporary intermediate qudit states. This introduction of intermediate qudit states has exhibited a three times reduction in quantum cost with respect to gate count and approximately two times reduction in respect to circuit depth compared to the state-of-the-art approach of SWAP gate insertion. Further, the proposed approach is generalized to any dimensional quantum system.
Convex Combination Belief Propagation Algorithms

We present new message passing algorithms for performing inference with graphical models. Our methods are designed for the most difficult inference problems where loopy belief propagation and other heuristics fail to converge. Belief propagation is guaranteed to converge when the underlying graphical model is acyclic, but can fail to converge and is sensitive to initialization when the underlying graph has complex topology. This paper describes modifications to the standard belief propagation algorithms that lead to methods that converge to unique solutions on graphical models with arbitrary topology and potential functions.
On-site Holographic Building Construction: A Case Study of Aurora

Geometrically complex building components' reliance on high-touch implementation often results in tedious information reprocessing.Recent use of Mixed Reality (MR) in architectural practices, however, can reduce data translation and potentially increase design-to-build efficiency.This paper uses Aurora, a singlestory residential building for 2021 China's Solar Decathlon Competition, as a demonstrator to evaluate the performance of on-site holographic building construction.This paper firstly reviews recent studies of MR in architectural design and practice.It then describes an MR-aided construction process of Aurora's non-standard building envelope and rooftop mounting structure, where in-situ holographic registration, human-machine cooperation, and as-built analysis are discussed.This paper concludes by stating that MR technologies provide unskilled implementers with a handy approach to materialise complex designs.The research was guided by the UN Sustainable Development Goals, especially aligning with the GOAL 9 which seeks innovations in industry and infrastructure.