Reviewerly/exHarmony huggingface dataset:
Faster quantum concentration via Grover's search

One of the most important challenges in information networks is to gather data from a larger set of nodes to a smaller set of nodes. This can be done via the use of a concentrator architecture in the connection topology. This paper is a proof-of-concept that demonstrates a quantum-based controller in large interconnection networks can asymptotically perform this task faster. We specifically present quantum algorithms for routing concentration assignments on full capacity fat-and-slim concentrators, bounded fat-and-slim concentrators, and regular fat-and-slim concentrators. Classically, the concentration assignment takes O(n) time on all these concentrators, where n is the number of inputs. Powered by Grover's quantum search algorithm, our algorithms take O(ncln⁡c) time, where c is the capacity of the concentrator. Thus, our quantum algorithms are asymptotically faster than their classical counterparts, when cln2⁡c=o(n). In general, c=nμ, satisfies cln2⁡c=o(n), implying a time complexity of O(n0.5(1+μ)ln⁡n), for any μ,0<μ<1.
A Comprehensive Approach to Pedagogical Forms of Presenting Educational Information in Higher Education Institutions

The article discusses a systematic approach to the pedagogical technology of presenting educational information in higher education institutions. Consistency is inherent in any technology, so pedagogical technology is no exception and exhibits the properties of a system. As mentioned above, pedagogical technology consists of interrelated components that describe the educational process. This article reveals the scientific component of the pedagogical technology for presenting educational information, which will justify the pedagogical technology of visualizing educational information in higher education, as well as determine its composition and structure. The practical side of the pedagogical technology of presenting educational information includes formal-descriptive and procedural-effective components.
Classification of Mobile Interactions Based on Human Emotion

Today, smartphones have become an integral part in one’s life. There are now techniques to identify the user’s emotional state as per their usage of the mobile device. In this paper, we present an attempt to classify the various interaction types such as scroll, tap, swipe, and pinch zoom based on human emotions. We run a formative statistical study where we have collected data from 60 participants over a period of 1 month. The participants were given different situations while using social networking applications and video entertainment applications and were asked to rate on 12 emotions (5 positive, 2 neutral, and 5 negative). Through the study, we find that there is significant relationship between different interaction types with satisfaction, surprise, and entertainment in emotions (p ≤ 0.05) for social networking applications. Also, there is significant relationship between different interaction types with satisfaction (p ≤ 0.05) in emotions for video entertainment applications. This result can be used while developing an interaction system. We could also develop a questionnaire that measures interaction types and emotions. The questionnaire reliability was tested using Cronbach’s Alpha.
High winding number of topological phase in periodic quantum walks

We construct one-dimensional multi-period quantum walks to investigate the topological phases with high winding number. It is found that the maximum of winding number is determined by the number of shift operation in one-step evolution only under an appropriate time frame. For odd-period quantum walks, 0-energy edge states and π-energy edge states coexist equally on the boundary, but for even-period quantum walks, these two kinds of edge states may exist alone or together. The relations between the number of edge state and high winding number are discussed by considering coin parameter as a step function of position. The validity of the bulk-edge correspondence is confirmed. We also reveal that the occupation probability of the walker at the boundary exhibits dynamical oscillation due to the coexistence of 0-energy and π-energy edge states.
Emotional Response Generation in Multi-Turn Dialogue

Abstract The research on emotional response generation is one of the directions in dialog system research. However, most of the researches on emotional dialog generation focus on the expression of expected emotion in single round dialogue. In this paper, we propose a method to model the emotion in multi-turn dialogue. Our model bases on a proposed model: Emotional Chatting Machine, and solves the problem of ECM model that which emotion should be used to the response. We compared the effects of various models and finally chose LSTM to model emotions in multi-turn dialogue. Experiments show that the performance of the dialogue system can be effectively improved by modelling emotion in multi-turn dialogue.
Image-Quality Evaluation based on Regional Saliency Pooling

Inspired by the successful application of visual saliency in image quality evaluation, we propose an image quality metric based on regional saliency pooling. We first introduce the image saliency detection model to obtain regional saliency maps for image sub-patches. Then, the importance of each image sub-patch is calculated using a VGG16 network based on its saliency map. Such importance is referred to as the quality weight, which is also the pooling result in the proposed framework. Finally, the prediction quality is defined as the weighted sum of the Structural SIMilarity (SSIM) of all image sub-patches. In the experimental part, we choose the popular LIVE image quality database. The results show that the performance of our model is highly competitive, which indicates the effectiveness of the proposed saliency pooling strategy.
An Adaptive Layer Expansion Algorithm for Efficient Training of Deep Neural Networks

In this paper, we propose an adaptive layer expansion algorithm to reduce the training time of deep neural networks without noticeable loss of accuracy. Neural networks have become much larger to improve accuracy. The size of such networks makes them time-consuming to train. Hence, we propose an adaptive layer expansion algorithm that reduces training time by dynamically adding nodes where they are necessary to improve the training efficiency while not losing accuracy. We start with a smaller model of only a fraction of parameters of the original model, then train the network and add nodes to specific layers determined by the stability of gradients. The algorithm repeatedly adds nodes until it reaches a threshold and trains the model until the accuracy converges. The experiment results indicate that our algorithm only uses a quarter of computation time of a full model and achieves 64.1% accuracy on MobileNet with dataset CIFAR100, which is only 2% less than the complete model. The algorithm stops adding nodes when it has only half of the parameters of the original model. As a result, this new model provides fast inference in those environments where both the computation power and memory storage are limited, such as mobile devices.
Reading in the era of globalization and information technologies in contemporary society

We must analyze literacy in general and in particular the so-called digital literacy as a sociocultural problem linked to the formation of individuals. Every social or technological change has always demanded a profound transformation from humanity, due to the vertiginous advances that have occurred; Throughout history, man has seen the need to transform some practices. Reading has been one of the fundamental axes because the human being has required addressing and being able to face the challenges facing globalization and information technologies. The deep and rapid transformations in all fields of modern life are something that characterizes the world in which we live. The new forms of communication have accelerated, a revolution of wide scope that revolves around the transformation of the mechanisms of production, diffusion and access to information, as well as in the expressive and representative languages of culture and knowledge. In universities, they must identify the areas that need to be strengthened to support students in carrying out the readings responsibly.
Reading Comprehension and Context of the Digital Generation of Secondary Engineering Schools’ Pupils in the Czech Republic

Reading comprehension and reading context are in mutual interaction and influenced by several factors, particularly the level of reading literacy and the level of individuals’ reading strategies. Reading is part of communication applying the correct (de) coding of written information. An important factor in understanding the information is the set of knowledge and skills elements of a sociological, psychological, ethical and media nature. Language as a tool for the transmission of information and human thinking concurrently reflects the values of the individual and society. This study aims to characterise the level of reading strategies of secondary vocational school students in the Moravian-Silesian Region in connection with the findings of an international survey of reading literacy of 15-year-old students (PISA 2018), which pointed out that Czech students (especially boys) achieve a below-average level in analysed reading strategies. Fifteen-year-old pupils of the region are among the worst regional groups in the Czech Republic. The second goal of the research was to find out which factors influence the development of pupils’ reading literacy, during and after working with the (e)text, reading comprehension and the effectiveness of reading comfort. An analysis of the responses of 488 students of three secondary schools of engineering showed that students who read for knowledge prefer a particular topic when reading and that students who ask questions while reading or after reading a text also look for new information to discover a context of information. Another important finding was that reading comfort and reading context are in mutual interaction. To develop an individual’s language culture, it is significant to support reading not only as a part of school duties but as an integral part of everyday life.
Planning model of a group UAV flight in three-dimensional space

Abstract The paper describes the algorithm and model for planning group actions of unmanned aerial vehicles (UAVs) to monitor a given set of objects. A distinctive feature of the task is the shooting of objects from a predetermined height. This makes it necessary to move from considering a “flat” model to planning a flight in three-dimensional space. In addition, the authors take into account the possibility of destroying individual UAVs and the need to reschedule the flight route for the remaining UAVs in order to accomplish the assigned task.
A Matrix-Valued Inner Product for Matrix-Valued Signals and Matrix-Valued Lattices

A matrix-valued inner product was proposed before to construct orthonormal matrix-valued wavelets for matrix-valued signals. It introduces a weaker orthogonality for matrix-valued signals than the orthogonality of all components in a matrix that is commonly used in orthogonal multiwavelet constructions. With the weaker orthogonality, it is easier to construct orthonormal matrix-valued wavelets. In this paper, we re-study the matrix-valued inner product more from the inner product viewpoint that is more fundamental and propose a new but equivalent norm for matrix-valued signals. We show that although it is not scalar-valued, it maintains most of the scalarvalued inner product properties. We introduce a new linear independence concept for matrix-valued signals and present some related properties. We then present the Gram-Schmidt orthonormalization procedure for a set of linearly independent matrix-valued signals. Finally we define matrix-valued lattices, where the newly introduced Gram-Schmidt orthogonalization might be applied.
Stochastic control and time scheduling for irregular robots

Vehicles of different sizes are difficult to navigate in close vicinity. In this paper, we propose multi-vehicle coordination strategy by stochastic control and time scheduling to guarantee no collisions. We use contours and relative motions of vehicles to calculate collision time and use it in multi-vehicle scheduling and reduce computation burden. The proposed strategy enables the vehicles to add calculation delay when vehicles are moving towards the destinations. To avoid complicated control rule design for motion-restricted irregular vehicles, we propose stochastic control to provide satisfactory performance. By changing the frequency of control update, a modification is proposed to take congestion into account. Simulation examples are given to demonstrate the effectiveness of the proposed approach.
Neural Network Training Techniques Regularize Optimization Trajectory: An Empirical Study

Modern deep neural network (DNN) trainings utilize various training techniques, e.g., nonlinear activation functions, batch normalization, skip-connections, etc. Despite their effectiveness, it is still mysterious how they help accelerate DNN trainings in practice. In this paper, we provide an empirical study of the regularization effect of these training techniques on DNN optimization. Specifically, we find that the optimization trajectories of successful DNN trainings consistently obey a certain regularity principle that regularizes the model update direction to be aligned with the trajectory direction. Theoretically, we show that such a regularity principle leads to a convergence guarantee in nonconvex optimization and the convergence rate depends on a regularization parameter. Empirically, we find that DNN trainings that apply the training techniques achieve a fast convergence and obey the regularity principle with a large regularization parameter, implying that the model updates are well aligned with the trajectory. On the other hand, DNN trainings without the training techniques have slow convergence and obey the regularity principle with a small regularization parameter, implying that the model updates are not well aligned with the trajectory.
Mathematical Model of a Filter for Water Treatment Using Biofilms

Abstract The paper considers the principles of constructing a mathematical model of water treatment based on the use of a biologically active layer, the bacteria of which absorb harmful impurities contained in water. A system of equations is presented on the basis of which a model of water purification is constructed in the simplest element, which is a rod covered with a biofilm. The system of equations is a system that includes a parabolic equation in a three-dimensional domain and a hyperbolic equation on a part of the surface of this domain, connected to each other through a boundary condition and a potential in an equation of hyperbolic type. Next, an asymptotic analysis of this system is carried out, which allows us to reduce the model of an individual element to the solution of a simple ordinary differential equation. On this basis, a model of the entire water treatment device is proposed.
Android based maintenance information system for machining workshop in polytechnic high education

Abstract There are six state polytechnics aged 36 years old in Indonesia. The six polytechnics have machining workshops with the same number and machines. The problem that arises in Semarang State Polytechnic is the maintenance system that has not been integrated into a system, even though the electronic maintenance and repair activities have been carried out. The purpose of this study is to develop a maintenance information system that can control all of its activities well. The system is built with an object-oriented approach implemented by android. Research results include: database systems, web application systems, and android application systems that have delivered output correctly.
Enhanced Image Steganography Technique Using Cryptography for Data Hiding

The fast improvement of the Web has expanded the simplicity of sharing data to individuals around the world. In any case, this headway additionally raises a trouble about information control when the data is transferred by the sender to the beneficiary. Along these lines, data security is a significant issue in information correspondence. Steganography and cryptography assume significant jobs in the field of data security. Steganography can be applied, however, a different computerized media, for example, video, pictures, and sound to cover data in such a manner that nobody else realizes that there is a secret data. Cryptography alludes to the specialty of changing over a plaintext (message) into an ambiguous organization. Both steganography and cryptography strategies are strong. The contents obtained after doing so is secret and its existence is also hidden. This method is tested and it is observed that it prevents steganalysis too as well as parameters like PSNR and MSE are also tested which gave good results.
Deep Pose Alignment

This paper proposes a new deep learning architecture that aligns human poses to be used in an exercise/rehabilitation assistant system. In short, the assistant system aims to provide users with visual feedback for their physical exercises. The feedback is generated by first extracting a user's poses while they are performing an exercise through the video feed of the session. The extracted poses are then overlaid with the correct poses and display for the user to observe and fix their errors. This paper focuses on the task of aligning the user's pose with the correct pose so that they can be overlaid on each other with minimal differences, including scales, locations, and perspectives. We design a new deep architecture to accomplish this task, and show that our methods can effectively reduce alignment errors by 70% on average.
Disease mapping of biomarkers for breast cancer in Tehran using spatial joint model: A Bayesian perspective

Breast cancer is one of the most important medical concerns that women face today. There are some biomarkers for detection of this cancer. Modeling these biomarkers, finding important factors that are associated with them and estimating the spatial pattern in disease risk across the areal units by disease mapping are the main foci of many studies. In this article, three binary biomarkers (the presence of estrogen receptors, the presence of progesterone receptors, and the absence of human epidermal growth factor receptor-2) are considered simultaneously for disease mapping of breast cancer. The association of these three biomarkers and spatial effects on them are jointly considered by using a convolution model. The proposed approach is applied to disease mapping of biomarkers of breast cancer in Tehran.
Detection of High Germ Spreading Activities

This paper discusses the preliminary process of data collection, data processing, and feature extraction and selection in applications of developing a machine learning model for activity detection, especially the germ spreading activities during the COVID-19 pandemic. In this research, a MetaWear wearable device is used to collect arm and hand motion data from a subject performing various activities. After data was collected from these different activities, the data was processed, and important time-domain features as well as frequency domain features, such as the total energy contained in different frequency bands, were extracted in respect to these different activities with the objective of differentiating between these various activities. Various features were collected to create a feature matrix and input to different Machine Learning algorithms to determine the classification accuracy of the germ spreading activities. Using the ensemble bagged tree model, a classification accuracy of 99.4% was obtained.
Computing Choice: Learning Distributions over Permutations

We discuss the question of learning distributions over permutations of a given set of choices, options or items based on partial observations. This is central to capturing the so-called “choice’’ in a variety of contexts. The question of learning distributions over permutations arises beyond capturing “choice’’ too, e.g., tracking a collection of objects using noisy cameras, or aggregating ranking of web-pages using outcomes of multiple search engines. Here we focus on learning distributions over permutations from marginal distributions of two types: first-order marginals and pair-wise comparisons. We emphasize the ability to identify the entire distribution over permutations as well as the “best ranking’’.
Conceptual Foundations of Innovative Processes in the Education System

Purpose: The valorization of learning is now key to shaping the academic and policy foundations of educational innovation. This article presents the results of a study aimed at defining the concept of modern pedagogical innovations. The goal of our research was to find the most optimal and most effective ways to improve the conceptual structure of educational institutions through the definition of pedagogical innovations. Design/methodology/approach: The analysis of the data obtained made it possible to identify seven different concepts of the concept of pedagogical innovation, formulate an updated definition and propose a cycle of pedagogical innovation concept. The article analyzes the factors that contribute to the successful introduction of innovative pedagogical practices in the education system. Findings: The issues of configuration and intensity of influence of various factors involved in the implementation of innovative pedagogical practices were considered, the links between the level of intensity of the identified factors affecting the dynamics of pedagogical innovations and the level of changes in the education system as a whole were determined. During the research, the authors attempted to define the concept of pedagogical innovations of participants in the experimental part of the study, initially taking as a basis that pedagogical innovations can be called any learning process that is carried out in a slightly different way than the traditional practice of organizing the learning process. In our opinion, any new actions aimed at improving the quality of the educational process can be equated to pedagogical innovations. Originality/value: During the research, data were obtained that made it possible to structure the main categories of the concept under study, reconstruct and streamline discourses on research issues, thereby revealing seven strategic directions of innovative processes in the education system: 1) novelty; 2) reflection (reflection); 3) change (transformation); 4) application (annex); 5) improvement (improvement); 6) the relationship between the subjects of the educational process; 7) the relationship between pedagogical technologies and pedagogy, which constitutes the novelty of research.