Reviewerly/exHarmony huggingface dataset:
ENVIRONMENTAL EDUCATION OF YOUNGER SCHOOL CHILDREN IN OUT-OF-LESSON ACTIVITIES IN CONDITIONS OF MIXED LEARNING

Annotation. Introduction. Environmental education of primary schoolchildren in extracurricular activities in a blended learning environment will be effective provided that teachers realize its goals, content, resources and the ability to support this process by parents - full-fledged participants in modern education.The aim of the study is to compare the attitude of teachers and parents of primary schoolchildren to the extracurricular activities of children in distance learning and to determine ways to solve the problem of selecting the content, means and forms of organizing extracurricular activities aimed at the implementation of environmental education in a blended learning environment. The object of the research is the process of environmental education of primary schoolchildren in extracurricular activities in a blended learning environment. The subject of the research is the content, means and forms of organization of environmental education of primary schoolchildren in extracurricular activities.Materials and methods. The study used an anonymous questionnaire survey (N = 10) of teachers and parents (N = 292) of students in grades 1-4 who were included in the organization of extracurricular activities of younger schoolchildren on environmental education in a blended learning environment. The respondents from the Chelyabinsk, Tyumen and Sverdlovsk regions took part in the questionnaire procedure.Research results. Discrepancies were revealed in the attitude of teachers and parents of students in grades 1-4 to blended learning: 30% of teachers and 3.7% of parents have a positive attitude towards it. Parents (65.4%) identified the decline in the quality of knowledge of primary schoolchildren as the main argument for their negative attitude to the blended learning format. The survey made it possible to determine the opinion of teachers and parents about the reasons for the decline in the quality of knowledge of primary schoolchildren and their own difficulties in the transition to blended learning in extracurricular activities. Teachers noted that it is difficult for children to maintain attention throughout the entire distance lesson; their skills of independent work are poorly formed. At the same time, they believe that younger students are able to conduct independent natural-science observations and experiments under the supervision of their parents. It is most difficult for parents in blended learning to control the process of observation in nature (92.5%), reports-reflections on the results of observations in nature (96.2%), experiments and experiments (87% of respondents).Discussion and conclusion. The revealed discrepancies in the attitude of teachers and parents to blended learning made it possible to select the content of the course of extracurricular activities "Practical ecology for younger students", structure and present verbal and digital resources, systematize and describe the forms of organization of environmental education of younger students in extracurricular activities.
Multi- Objective Hybrid Algorithm for Energy Efficeincy in Task Scheduling in Cloud Computing

The on-demand facility of virtual resources is supplied as a service with the assist of virtualization even without unnecessary waiting period, and cloud computing has progressively assumed a significant role in scientific applications. For job scheduling difficulties based on a time limitation, energy usage is lowered, resulting in a decline in energy cost. In this paper, we propose an Innovated energy efficiency algorithm benefits of ACO and PSO (Particle Swarm Optimization) algorithm. It is focused on the voltage scaling factor for the reduction of energy consumption. Performance of an Innovated energy efficiency When contrasted to ACO, the algorithm is significantly improved from 50 tasks forward. Energy consumption is the same as the ACO algorithm because as the number of tasks increases (40 to 75) there is a considerable decrease in the energy consumption rate. Furthermore, we examined energy usage for numerous processes and its pace of growth - up to 7 processors, energy consumption is significantly decreased, and energy consumption likely to be stable as the count of processors increases.
An Optimized GHV-Type HE Scheme: Simpler, Faster, and More Versatile

In this paper we present an optimized variant of Gentry, Halevi and Vaikuntanathan (GHV)’s Homomorphic Encryption (HE) scheme. Our scheme is appreciably more efficient than the original GHV scheme without losing its merits of the (multi-key) homomorphic property and matrix encryption property. In this research, we first measure the density for the trapdoor pairs that are created by using Alwen and Peikert’s trapdoor generation algorithm and Micciancio and Peikert’s trapdoor generation algorithm, respectively, and use the measurement result to precisely discuss the time and space complexity of the corresponding GHV instantiations. We then propose a generic GHV-type construction with several optimizations that improve the time and space efficiency from the original GHV scheme. In particular, our scheme can achieve asymptotically optimal time complexity and avoid generating and storing the inverse of the used trapdoor. Finally, we present an instantiation that, by using a new set of (lower) bound parameters, has the smaller sizes of the key and ciphertext than the original GHV scheme.
Poisoning Attacks to Local Differential Privacy Protocols for Key-Value Data

Local Differential Privacy (LDP) protocols enable an untrusted server to perform privacy-preserving, federated data analytics. Various LDP protocols have been developed for different types of data such as categorical data, numerical data, and key-value data. Due to their distributed settings, LDP protocols are fundamentally vulnerable to poisoning attacks, in which fake users manipulate the server's analytics results via sending carefully crafted data to the server. However, existing poisoning attacks focused on LDP protocols for simple data types such as categorical and numerical data, leaving the security of LDP protocols for more advanced data types such as key-value data unexplored. In this work, we aim to bridge the gap by introducing novel poisoning attacks to LDP protocols for key-value data. In such a LDP protocol, a server aims to simultaneously estimate the frequency and mean value of each key among some users, each of whom possesses a set of key-value pairs. Our poisoning attacks aim to simultaneously maximize the frequencies and mean values of some attacker-chosen target keys via sending carefully crafted data from some fake users to the sever. Specifically, since our attacks have two objectives, we formulate them as a two-objective optimization problem. Moreover, we propose a method to approximately solve the two-objective optimization problem, from which we obtain the optimal crafted data the fake users should send to the server. We demonstrate the effectiveness of our attacks to three LDP protocols for key-value data both theoretically and empirically. We also explore two defenses against our attacks, which are effective in some scenarios but have limited effectiveness in other scenarios. Our results highlight the needs for new defenses against our poisoning attacks.
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits

Stochastic gradient descent (SGD) and its variants have established themselves as the go-to algorithms for large-scale machine learning problems with independent samples due to their generalization performance and intrinsic computational advantage. However, the fact that the stochastic gradient is a biased estimator of the full gradient with correlated samples has led to the lack of theoretical understanding of how SGD behaves under correlated settings and hindered its use in such cases. In this paper, we focus on hyperparameter estimation for the Gaussian process (GP) and take a step forward towards breaking the barrier by proving minibatch SGD converges to a critical point of the full log-likelihood loss function, and recovers model hyperparameters with rate $O(\frac{1}{K})$ for $K$ iterations, up to a statistical error term depending on the minibatch size. Our theoretical guarantees hold provided that the kernel functions exhibit exponential or polynomial eigendecay which is satisfied by a wide range of kernels commonly used in GPs. Numerical studies on both simulated and real datasets demonstrate that minibatch SGD has better generalization over state-of-the-art GP methods while reducing the computational burden and opening a new, previously unexplored, data size regime for GPs.
GUIDELINES OF MOOD – THINKING – LOGIC PROFILING &amp; ANTI-HOAX FRAMEWORK: DETECTING SOMEONE'S MOTIVES ON SOCIAL MEDIA

Abstract—Social media is a lifestyle, starting from how to think and behave towards something. Understanding what is on social media requires a systematic guide to distinguish between true and false information. Therefore, this article will answer it. Two important parts of this article are discussing mood-thinking-logic which is the basis of every human's thinking, which then results in two attitudes, namely doing the right or wrong thing. This article complements the two articles that have been published. Because the problem regarding hoaxes is still an unfinished debate and still has problems finding the right formula or guide, in this article we create two concepts to solve this problem. the first concept produces guidelines of mood-thinking-logic profiling, which are concepts for understanding the layers of feelings, thoughts and logic of a person and the motives he does in social media, then the second concept is anti-hoax framework which discusses seven levels of hoaxes and solutions to overcome hoaxes. Both of these concepts will be accompanied by examples of case studies that discuss these matters, so that readers will understand the two concepts. Furthermore, this research is still being developed because it still needs a lot of refinement, and this research is part of the text mining research that we are currently doing.Keywords—Mood, Thinking, Logic, Profiling, Anti Hoax
Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes

Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning, where a Gaussian process (GP) prior is presumed on the sought value function, and instantaneous rewards are probabilistically generated based on value function evaluations at two consecutive states. Capitalizing on a random feature-based approximant of the GP prior, an online scalable (OS) approach, termed {OS-GPTD}, is developed to estimate the value function for a given policy by observing a sequence of state-reward pairs. To benchmark the performance of OS-GPTD even in an adversarial setting, where the modeling assumptions are violated, complementary worst-case analyses are performed by upper-bounding the cumulative Bellman error as well as the long-term reward prediction error, relative to their counterparts from a fixed value function estimator with the entire state-reward trajectory in hindsight. Moreover, to alleviate the limited expressiveness associated with a single fixed kernel, a weighted ensemble (E) of GP priors is employed to yield an alternative scheme, termed OS-EGPTD, that can jointly infer the value function, and select interactively the EGP kernel on-the-fly. Finally, performances of the novel OS-(E)GPTD schemes are evaluated on two benchmark problems.
PSYCHOLOGICAL READYNESS OF STUDENTS FOR SOCIAL INTERACTION AS A RESULT OF THEIR PARTICIPATION IN THE ACTIVITIES OF THE SOCIAL INSTITUTE

An important task of the modern education system is the formation of professional and personal competencies of future professionals, and, above all -the formation of psychological readiness for effective interaction with other people.The requirements of modern practice of higher education institutions require the effective formation of psychological readiness of students for social interaction, both in terms of higher education and in their further professional development.The fundamental foundations of such readiness are laid through the creation of special conditions in the training of professionals in higher education, which involves the organization of such a space in which the idea of cooperation is embedded in each center of higher education.The basic basis of psychological readiness for social interaction is the system of relations in the social environment and its concretization in the behavior and activities of the individual, the main feature of this complex internal personality-activity formation is a sign of attitude to himself as a figure in social situations.Social interaction in higher education can be reflected in the system of interdependent actions, cooperation, joint activities, interpersonal, business communication between teachers and students, students with whom their mental characteristics are manifested.The basis of students' psychological readiness for social interaction is their own model of social interaction, created by them under the influence of educational and professional activities in a higher education institution.This model contains their own generalizations about the extent to which they find common solutions, ways to implement them, and the mutual representation of actors that determine attitudes and strategic interactions.This indicates that the psychological readiness of students for social interaction is the result of "natural" adaptation of a person to the social environment in order to meet its urgent requirements and, therefore, it reproduces exactly the circumstances that are present in it.
A Bibliometric Analysis for Lebesgue Measure Integration in Optimization

In solving mathematical problems so far, Riemann's integral theory is quite adequate for solving pure mathematics and applications problems. But not all problems can be solved using this integration, such as a discontinuous function that is not Riemann's integration. Lebesgue integral is an integration concept based on measure and can solve finite and unlimited function problems and be solved in a more general set domain. One of the bases of this integration is the Lebesgues measure includes the set of real numbers, where the length of the interval is the endpoints. The alternative use of this integral is widely used in various studies such as partial differential equations, quantum mechanics, and probabilistic analysis, requiring the integration of arbitrary set functions. This paper will show a comprehensive bibliometric survey of peer-reviewed articles referring to Lebesgue measure in integration. Search results are obtained 832 papers in the google scholar database and 997 papers using Lebesgue measure integration in optimization. It can also be seen that the research have 4 clusters and 3 clusters respectively with scattered keywords for each cluster. Finally, using bibliographic data can be obtained Lebesgues measure in integration and optimization supports many of the research and provides productive citations to citing the study.
PARTICIPATION OF PEDAGOGICAL WORKERS IN THE STATE FINAL CERTIFICATION: LEGAL ISSUES

Issues of using teachers’ labor during the period of state final certification in the formats of the Unified State Exam / Main State Exam are considered. The questions of legality of teachers’ involvement in the organization of state final certification are analyzed. The conclusion about the necessity to apply the contractual form to the specified relations is made.
Temporal Feature Networks for CNN based Object Detection.

For reliable environment perception, the use of temporal information is essential in some situations. Especially for object detection, sometimes a situation can only be understood in the right perspective through temporal information. Since image-based object detectors are currently based almost exclusively on CNN architectures, an extension of their feature extraction with temporal features seems promising. Within this work we investigate different architectural components for a CNN-based temporal information extraction. We present a Temporal Feature Network which is based on the insights gained from our architectural investigations. This network is trained from scratch without any ImageNet information based pre-training as these images are not available with temporal information. The object detector based on this network is evaluated against the non-temporal counterpart as baseline and achieves competitive results in an evaluation on the KITTI object detection dataset.
Interoperable digital skills for foreign languages education in the COVID-19 paradigm

Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the COVID-19 pandemic measures have imposed elaborate interdisciplinary and interoperable demands on the marketability of Liberal Arts university graduates' skills and competences, upon entering the workforce. The study is focused on the in-depth diagnostics of the development of interoperable orientation, universality and interdisciplinarity of digital skillsets for students of European (English, Spanish, French, Italian, German) and Oriental (Mandarin Chinese, Japanese) Languages major programs through the span of educational activities in the time-frame of COVID-19 quarantine measures of March 2020 to April 2021. The study introduces a model of interoperable digital skills for foreign languages education and professional application in different social spheres. The survey analysis is used to evaluate the dimensions of interdisciplinarity, informed by the interoperability of soft skills, professional linguistic and communication skills, and digital skills for Foreign Languages Education across contrasting frameworks of e-competence for foreign languages acquisition, professional digital communication and pre-service teacher training. © 2021 15th International Multi-Conference on Society, Cybernetics and Informatics, IMSCI 2021. All rights reserved.
La estrategia de inducción: satisfacción y aprendizaje de los estudiantes en programas virtuales

The offer of graduate programs under virtual methodologies is promising in Colombia, and that is why it is necessary to investigate the satisfaction that students have with these kinds of offers, especially when they begin their learning process, how to know, in other word: scenarios of induction. The objective of the study was to determine the satisfaction of the students on the induction strategy (how to know) in relation to their adaptation and learning in two virtual universities, in Colombia. The Lavin (2007) questionnaire was used; within the findings, it was found that 50.8% of the students are satisfied and 43.5% are very satisfied with the institutional induction strategy used, and positive correlations greater than 0.65 were found between the satisfaction variable and the components addressed in said strategy. If the student is satisfied with our educational services, their adaptation, permanence and academic success are made possible.
Rethinking Dilated Convolution for Real-time Semantic Segmentation

The field-of-view is an important metric when designing a model for semantic segmentation. To obtain a large field-of-view, previous approaches generally choose to rapidly downsample the resolution, usually with average poolings or stride 2 convolutions. We take a different approach by using dilated convolutions with large dilation rates throughout the backbone, allowing the backbone to easily tune its field-of-view by adjusting its dilation rates, and show that it's competitive with existing approaches. To effectively use the dilated convolution, we show a simple upper bound on the dilation rate in order to not leave gaps in between the convolutional weights, and design an SE-ResNeXt inspired block structure that uses two parallel $3\times 3$ convolutions with different dilation rates to preserve the local details. Manually tuning the dilation rates for every block can be difficult, so we also introduce a differentiable neural architecture search method that uses gradient descent to optimize the dilation rates. In addition, we propose a lightweight decoder that restores local information better than common alternatives. To demonstrate the effectiveness of our approach, our model RegSeg achieves competitive results on real-time Cityscapes and CamVid datasets. Using a T4 GPU with mixed precision, RegSeg achieves 78.3 mIOU on Cityscapes test set at $37$ FPS, and 80.9 mIOU on CamVid test set at $112$ FPS, both without ImageNet pretraining.
Routing with Self-Attention for Multimodal Capsule Networks

The task of multimodal learning has seen a growing interest recently as it allows for training neural architectures based on different modalities such as vision, text, and audio. One challenge in training such models is that they need to jointly learn semantic concepts and their relationships across different input representations. Capsule networks have been shown to perform well in context of capturing the relation between low-level input features and higher-level concepts. However, capsules have so far mainly been used only in small-scale fully supervised settings due to the resource demand of conventional routing algorithms. We present a new multimodal capsule network that allows us to leverage the strength of capsules in the context of a multimodal learning framework on large amounts of video data. To adapt the capsules to large-scale input data, we propose a novel routing by self-attention mechanism that selects relevant capsules which are then used to generate a final joint multimodal feature representation. This allows not only for robust training with noisy video data, but also to scale up the size of the capsule network compared to traditional routing methods while still being computationally efficient. We evaluate the proposed architecture by pretraining it on a large-scale multimodal video dataset and applying it on four datasets in two challenging downstream tasks. Results show that the proposed multimodal capsule network is not only able to improve results compared to other routing techniques, but also achieves competitive performance on the task of multimodal learning.