Reviewerly/exHarmony huggingface dataset:
Using game platforms of class management in online-learning for improve the quality of educational results

Problem and purpose. Problems of social and environmental nature, requirements for providing highquality support for online education have revealed new conditions for implementation, difficulties and prospects for learning at a distance. The authors suggest using game technologies for managing information interaction in the classroom to support motivation, activate cognition, involve students in collaborative work, and control attendance. Method of research. Theoretical and methodological analysis and generalization of fundamental scientific works on the research problem, processing of the results of control measures and the content of the ClassDojo service (forms, thank-you letters, stickers, posters) are used. The authors took into account the provisions of the system-activity, personality-oriented and communicative approaches. The pedagogical experiment involved 48 students (75% of girls and 25% of boys) from the "Lyceum of Natural Sciences", Kirov. The G-signs criterion was used as a statistical processing method. Results. The features of online learning based on game technologies for managing information interaction through the ClassDojo service are formulated: the content of the educational material is focused on the peculiarities of perception of the modern schoolchild, the inclusion of mobile services activates cognitive interest, supports research and communication practice, intensifies the process of mutual assistance in the group. The empirical value of Gemp=3 less than Gcr=8 (for p=0.01) confirms that the shift towards improving the quality of educational results after using the ClassDojo game service is not accidental. Conclusion. The inclusion of game technologies to support class management in online-learning will help improve the quality of educational results while providing a set of conditions: activation of knowledge, connecting students to information interaction in new ways, expanding the classroom beyond the school walls, changing the roles of participants in the didactic process, the use of mobile applications.
AutoSampling: Search for Effective Data Sampling Schedules

Data sampling acts as a pivotal role in training deep learning models. However, an effective sampling schedule is difficult to learn due to the inherently high dimension of parameters in learning the sampling schedule. In this paper, we propose an AutoSampling method to automatically learn sampling schedules for model training, which consists of the multi-exploitation step aiming for optimal local sampling schedules and the exploration step for the ideal sampling distribution. More specifically, we achieve sampling schedule search with shortened exploitation cycle to provide enough supervision. In addition, we periodically estimate the sampling distribution from the learned sampling schedules and perturb it to search in the distribution space. The combination of two searches allows us to learn a robust sampling schedule. We apply our AutoSampling method to a variety of image classification tasks illustrating the effectiveness of the proposed method.
Development of Universal Skills at the Russian Universities: Challenges and Experience

Rapid technological development and digital transformation in all areas of life have resulted in a dramatic change in the nature of thinking and requirements to skills. In the information economy, it is not money or natural resources, or territory but people with their critical thinking and high-quality skills, who have become a valuable asset. In this regard, the educational sector advances to the forefront. However, education hardly complies with the requirements of the new economy and retains its basic features of the industrial era. The curricula and educational processes at universities fail to prepare young people for life in our complicated world, or develop critical thinking; they discourage creativity and ability to cooperate. Curricula tend to focus on the development of professional skills and neglect the development of social and cognitive skills. The study sets out to investigate the usefulness of higher social and cognitive skills in the educational process at the Faculty of Humanities in Perm National Research Polytechnic University and offers recommendations on the development of these skills in the educational process.
Isometric Propagation Network for Generalized Zero-shot Learning

Zero-shot learning (ZSL) aims to classify images of an unseen class only based on a few attributes describing that class but no access to any training sample. A popular strategy is to learn a mapping between the semantic space of class attributes and the visual space of images based on the seen classes and their data. Thus, an unseen class image can be ideally mapped to its corresponding class attributes. The key challenge is how to align the representations in the two spaces. For most ZSL settings, the attributes for each seen/unseen class are only represented by a vector while the seen-class data provide much more information. Thus, the imbalanced supervision from the semantic and the visual space can make the learned mapping easily overfitting to the seen classes. To resolve this problem, we propose Isometric Propagation Network (IPN), which learns to strengthen the relation between classes within each space and align the class dependency in the two spaces. Specifically, IPN learns to propagate the class representations on an auto-generated graph within each space. In contrast to only aligning the resulted static representation, we regularize the two dynamic propagation procedures to be isometric in terms of the two graphs' edge weights per step by minimizing a consistency loss between them. IPN achieves state-of-the-art performance on three popular ZSL benchmarks. To evaluate the generalization capability of IPN, we further build two larger benchmarks with more diverse unseen classes and demonstrate the advantages of IPN on them.
Understanding Unstable Information Systems Phenomena: A Punctuated Equilibrium Perspective

The information systems (IS) literature includes different perspectives, epistemologies, and research philosophies to explore phenomena at the intersection of technologies, information, people, organizations, and processes.As studies are replicated and knowledge accumulates, researchers can develop a more in-depth understanding of how their constructs of interest interact and affect each other.IS researchers have reported mixed findings in prior research as the phenomena change.In this paper, we discuss unstable phenomena in IS and argue that conflicting findings in a variety of domains might be the result of this instability.Using examples from IS security and word processing research streams, we examine the issues surrounding unstable phenomena using a punctuated equilibrium lens and suggest research strategies and a research framework to help researchers conduct studies in this challenging environment.
Aerial Target Tracking Based on Frequency-Domain Gabor Filters and Correlation Filters under Complex Interference Environment

Aerial infrared target automatic tracking technology is the core technology of the photoelectric countermeasure system of infrared imaging missiles. Aiming at challenging conditions of complex battlefield environment, large scale change of air target, infrared interference partial occlusion and so on, we propose a new correlation filtering algorithm based on Gabor filtering features in the frequency domain for aerial infrared target tracking. This algorithm constructs a set of frequency-domain Gabor filters, which extracts frequency-domain Gabor filtering(GF) features of the target image block and reduces dimension fusion, thereby effectively suppressing background noise and highlighting target texture information. According to the characteristics and variation law of spectrum energy distribution, the eigenvectors of target spectrum scale are extracted to improve the accuracy of target scale information estimation. High-confidence patches are used to track the reliable part of the target, and the energy distribution of the tracking box is calculated to predict the occluded part of the target simultaneously. This method provides more target information and improves the robustness of the algorithm in case of reappearance of the occluded target. Compared with other tracking algorithms, the average accuracy of the proposed algorithm was improved by 15.2%, and the frame frequency reached above 110 Hz.
The concept of forecasting based on semantic analysis of text information

The main issue, which may be practically impossible to solve, is that in making a long-term forecast, all available data usually relate to the past or present, and there is not enough exact and reliable information for future issues. The following feature is observed: the larger the forecast horizon, the amount of information about the future state not only decreases, but also changes its structure. So, if all information will be presented as consistence of digital and text components, the relative amount of digital information for forecasting decreases, and the text information increases. In the limit, with a significant forecast horizon of decades, we will not have digital information at all, but only text information. The article considers the method of using text information to solve a wide range of problems in long-term forecasting. The method is based on K. Goedel's idea of semantic truth in relation to the consistency of digital and textual statements. It is theoretically justified and practically shown that from these positions textual information after the implementation of the semantic analysis procedure, as a result of which when semantic cores of truth of statements are formed, they can be used for forecasting. This approach is methodically new and does not require the use of complex software tools. An algorithm for implementing the procedure for semantic analysis of information is proposed, as a result of which, a concept map is formed that allows analyzing the object of forecasting more systematically, without ignoring its elements and interconnections between them. Using the concept map, generation of new knowledge about the future States of the forecast object can be proceed and an "array" of possible options can be created. Subsequently, the most probable forecast data is selected from this "array". The presented approach is based on using only text information without taking into account digital information, but in real forecast tasks, of course, digital information must also be taken into account. Obtained results illustrate the method and also interest wide class of long-term forecasting problems.
Formation of ICT competencies for future physics teachers

The article discusses effective ways to use IT-services in teaching future physics teachers. This reflects the mutual understanding between the teacher and the student based on the use of modern information technologies in the development of the modern educational process. Some IT-services in teaching meet modern requirements. But this does not mean the chaotic use of any platform, programs, and software applications, that is, it is necessary to take into account the specifics of the subject, the pedagogical and psychological state of students. Based on this, the article describes what digital competencies should be used in the training of future physics teachers. Video lectures and audio lectures, infographics, visual lectures made in Mindmap, and Vyond on the discipline “Methodological basis of solving physical tasks” have a great influence on the comprehensive search of students. The article analyzes the necessity and relevance of using information technologies in the educational process but also discusses ways to implement them practically. This creates skills for students to use digital competencies to expand their knowledge. There are also ways to conduct questionnaires, tests, and reflections in various formats that allow students to test their knowledge. The types of tasks created in the effective applications used in the teaching of physics are given.
Enhanced Adaptive Dense Connection Single Image Super-Resolution

Increasing model size often results in improved performance on super-resolution reconstruction. However, at some point large model cannot SR huge images due to GPU/TPU memory limitations. In this paper, to address this problem, we present Block-Reconstruction(BR) strategy to improve the reconstruction quality of large images, which lower memory consumption. Meanwhile, we propose an enhanced adaptive dense connection super resolution reconstruction network(EDCSR) that has 89M parameters. In AIM2020 Real Image Super-Resolution Challenge, we won the second place in Track 1 and Track 2, and the third place in Track 3.
Research on location and similar comments in point-of-interest recommendation system for users

Abstract By combining deep learning algorithms and potential probabilistic topic models, this paper proposes a new modeling strategy for individual users and constructs an effective point of interest recommendation system. First, Doc2vec is used to vectorize the user’s comments, and the Bow model is used to match the similarity of the points of interest, and then the potential probability model is used to cluster the positions of the points of interest. Finally, relying on the user’s historical comment data as a basis, the Top-K ranking recommendation scheme is obtained. The experimental results show that for large-scale location-based social network data sets, the algorithm in this paper has an ideal final effect on users’ personalized recommendations. The recommendation model for user location information and similar comment points of interest proposed in this paper is more subjectively and objectively in line with user behavior needs. On the premise of ensuring the real-time performance of recommendation, the model proposed in this paper effectively improves the accuracy of recommendation compared with traditional point-of-interest recommendation algorithms.
A Plug-in Method for Representation Factorization in Connectionist Models

In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the original trained models. Particularly, we propose factors’ decomposer-entangler network (FDEN) that learns to decompose a latent representation into mutually independent factors. Given a latent representation, the proposed framework draws a set of interpretable factors, each aligned to independent factors of variations by minimizing their total correlation in an information-theoretic means. As a plug-in method, we have applied our proposed FDEN to the existing networks of adversarially learned inference and pioneer network and performed computer vision tasks of image-to-image translation in semantic ways, e.g., changing styles, while keeping the identity of a subject, and object classification in a few-shot learning scheme. We have also validated the effectiveness of the proposed method with various ablation studies in the qualitative, quantitative, and statistical examination.
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools. In this paper, we introduce Neural Network Libraries (https://nnabla.org), a deep learning framework designed from engineer's perspective, with emphasis on usability and compatibility as its core design principles. We elaborate on each of our design principles and its merits, and validate our attempts via experiments.
HAS Based Empirical QoE Study over TCP and QUIC on Diverse Networks

HTTP adaptive streaming (HAS) is the de-facto standard for video services over the Internet delivering increased Quality of Experience (QoE) as a function of the network status. Such adaptive streaming atop HTTP relies predominantly on TCP as the reliable transport protocol. Recently, QUIC, an alternative of TCP transport, has emerged to overcome TCP’s native shortcomings and improve the HTTP-based applications QoE. This paper investigates three strategies (Rate, Buffer, and Hybrid) based adaptive bitrate streaming (ABS) algorithms behavioral performance over the traditional TCP and QUIC transport protocol. For this purpose, we experimentally evaluate different cellular network traces in a high-fidelity emulated testbed and compare the performance of ABS algorithms considering QoE metrics over TCP and QUIC. Our empirical results show that each ABS algorithm’s (Conventional, BBA, and Arbiter) QoE performance is biased for TCP. As a result, QUIC suffers the ineffectiveness of traditional state-of-art ABS algorithms to improve video streaming performance without specific changes.
Classical simulation of bosonic linear-optical random circuits beyond linear light cone

Sampling from probability distributions of quantum circuits is a fundamentally and practically important task which can be used to demonstrate quantum supremacy using noisy intermediate-scale quantum devices. In the present work, we examine classical simulability of sampling from the output photon-number distribution of linear-optical circuits composed of random beam splitters with equally distributed squeezed vacuum states and single-photon states input. We provide efficient classical algorithms to simulate linear-optical random circuits and show that the algorithms' error is exponentially small up to a depth less than quadratic in the distance between sources using a classical random walk behavior of random linear-optical circuits. Notably, the average-case depth allowing an efficient classical simulation is larger than the worst-case depth limit, which is linear in the distance. Besides, our results together with the hardness of boson sampling give a lower-bound on the depth for constituting global Haar-random unitary circuits.
Analysis of feature learning in weight-tied autoencoders via the mean field lens

Autoencoders are among the earliest introduced nonlinear models for unsupervised learning. Although they are widely adopted beyond research, it has been a longstanding open problem to understand mathematically the feature extraction mechanism that trained nonlinear autoencoders provide. In this work, we make progress in this problem by analyzing a class of two-layer weight-tied nonlinear autoencoders in the mean field framework. Upon a suitable scaling, in the regime of a large number of neurons, the models trained with stochastic gradient descent are shown to admit a mean field limiting dynamics. This limiting description reveals an asymptotically precise picture of feature learning by these models: their training dynamics exhibit different phases that correspond to the learning of different principal subspaces of the data, with varying degrees of nonlinear shrinkage dependent on the $\ell_{2}$-regularization and stopping time. While we prove these results under an idealized assumption of (correlated) Gaussian data, experiments on real-life data demonstrate an interesting match with the theory. The autoencoder setup of interests poses a nontrivial mathematical challenge to proving these results. In this setup, the "Lipschitz" constants of the models grow with the data dimension $d$. Consequently an adaptation of previous analyses requires a number of neurons $N$ that is at least exponential in $d$. Our main technical contribution is a new argument which proves that the required $N$ is only polynomial in $d$. We conjecture that $N\gg d$ is sufficient and that $N$ is necessarily larger than a data-dependent intrinsic dimension, a behavior that is fundamentally different from previously studied setups.
A Digital Education Made Personal: An Examination of the Impact of Personalized Business English Education at Bayer Korea

Objectives: Many companies have transitioned into digital education, allowing learners to select a learning time, place, and curriculum that best suits them.In this case report, we share cases of individuals and businesses selecting and learning content that fit their strategies and needs, rather than taking part in a general training path.Methods: The contents of individualization and personalization are different.Individualization refers to learning only the material that one prefers; personalization is not only about one's preferred content, but it also refers to improvements in Skillsoft technology by including additional content.This study analyzes the impact of providing personalized content to office workers.Results: Continuous learning, personal satisfaction, and fulfillment of individual learners were higher when a personalized training path was provided, compared to the fixed and general business English training path.This helped learners improve their confidence in using business English for their work as they were able to apply it immediately; furthermore, it helped them save time.Conclusions: Office workers value time more than anyone else.When they make time to study business English, efficiency is an important factor.A general and broad training path does not motivate learners.On the other hand, personalized contents based on the company's global strategy, along with personal interests, allow learners to realize the value of education in their work, motivating them to invest time into learning business English and giving them a sense of satisfaction and confidence in using it.
Factsheet: Who is sending and sharing potentially harmful digital communications?

This factsheet presents findings from a quantitative study looking at adults’ experiences of sending and sharing potentially harmful digital communications in New Zealand. Typically research into harmful digital communications focuses on the experiences of those on the receiving end – the victims. However, to better address the distress and harm caused, information is needed about the people sending and sharing potentially harmful messages and posts. In this study we asked adult New Zealanders whether they had sent potentially harmful digital communications in the previous year and if so, how often they had done this, who they were sent to, the channel(s) they used, and the reason for doing this.
Improving Generalization in Meta-learning via Task Augmentation

Meta-learning has proven to be a powerful paradigm for transferring the knowledge from previous tasks to facilitate the learning of a novel task. Current dominant algorithms train a well-generalized model initialization which is adapted to each task via the support set. The crux lies in optimizing the generalization capability of the initialization, which is measured by the performance of the adapted model on the query set of each task. Unfortunately, this generalization measure, evidenced by empirical results, pushes the initialization to overfit the meta-training tasks, which significantly impairs the generalization and adaptation to novel tasks. To address this issue, we actively augment a meta-training task with "more data" when evaluating the generalization. Concretely, we propose two task augmentation methods, including MetaMix and Channel Shuffle. MetaMix linearly combines features and labels of samples from both the support and query sets. For each class of samples, Channel Shuffle randomly replaces a subset of their channels with the corresponding ones from a different class. Theoretical studies show how task augmentation improves the generalization of meta-learning. Moreover, both MetaMix and Channel Shuffle outperform state-of-the-art results by a large margin across many datasets and are compatible with existing meta-learning algorithms.