Reviewerly/exHarmony huggingface dataset:
Developing Interactive E-Book for Early Reading-Writing Stage at Class I Elementary School

The interactive e-book was developed to improve early reading and writing skills as log of pandemic Covid-19. The type of this research is R&amp;D with the product of this research is the E-Book Interactive reading and writing starters. The questionnaire instrument was used as a research data collection technique. The results of the product validity test show the following results. First, the appearance of the interactive E- book product has an attractive appearance, second, the selection of an attractive typeface and the presentation of animated text in the form of a video embedded in the interactive E-book. Third, to get students involved in learning activities it is necessary to have interaction in learning, interesting content and presentations combbined with learning videos to explain the material interactively
Optimising Decision Making on Communication Systems: The Federated Learning Approach

We provide a survey of different categories of communication systems to which conventional decision-making schemes which are aware of the goals and constraints of each system are enhanced Federated Learning algorithms. Formulating new Federated Learning-oriented strategies key design aspects and open research issues (e.g. resource optimization, reliability) are presented from an empirical research view. Controlled experiments at the level of simulation or real-life data sets indicate that FL-based communication schemes become context- and mobility- aware with the mission of optimizing the decisions of each design. Also, empirical research results present a goodness of fit of FL schemes into hybrid, multilayer communication systems as they are emerging in mobile-cloud networks, 5G, and IOT.
Formation of a Digital Educational Ecosystem For Lifelong Learning in the Field of Technosphere Safety

Digitalization and continuing education are the top priority of education modernization in the Russian Federation. At present, in the conditions of increased virus threats digital educational ecosystems with online courses, the educational and methodical digital technologies are becoming particularly relevant for the educational process in the field of human security of power engineering. The article is devoted to the formation of a digital educational ecosystem in the field of human security in the technosphere. The ecosystem based on the Dispace 2.0 software platform and used for continuous training in the safety of the technosphere. Empirical and theoretical methods and an expert assessment involving the Thomas L. Saaty hierarchy analysis are used. The formation of a pedagogically useful emergent digital ecosystem is the current solution to the problem of staff training for providing human safety in the energetics, corresponding to the threats of the present-day realities. This, however, needs a radical rethinking of purposes, content, technologies, and the methods of staff training. The article shows that the formed digital educational ecosystem is the best and meets the stated criteria and needs of the educational process in the field of human safety in technosphere at the Novosibirsk State Technical University (Faculty of Power Engineering). The analytical hierarchy method can be effectively used by managers and teaching staff of educational organizations for expert assessment and selection of learning management systems and other options.
A Multi-interaction Model with Cross-Branch Feature Fusion for Video-Text Retrieval

With the explosive growth of videos on the internet, video-text retrieval is receiving increasing attention. Most of the existing approaches map videos and texts into a shared latent vector space and then measure their similarities. However, for video encoding, most methods ignore the interactions of frames in a video. In addition, many works obtain features of various aspects but lack a proper module to fuse them. They use simple concatenation, gate unit, or average pooling, which possibly can not fully exploit the interactions of different features. To solve these problems, we propose the Multi-Interaction Model (MIM). Concretely, we propose a well-designed multi-scale interaction module to exploit interactions among frames. Besides, a fusion module is designed to combine representations from different branches by encoding them into various subspaces and capturing interactions among them. Furthermore, to learn more discriminative representations, we propose an improved loss function. And we design a new mining strategy, which selectively reserves informative pairs. Extensive experiments conducted on MSR-VTT, TGIF, and VATEX datasets demonstrate the effectiveness of the proposed video-text retrieval model.
DEVELOPMENT OF AN INFORMATION SYSTEM FOR PROFESSIONAL TRAINING OF STUDENTS IN DIGITAL EDUCATIONAL ENVIRONMENT

The state of professional training of students needs to improve the digital educational environment, which can ensure the effective formation of digital and research competencies of future specialists. One of the components of software and methodological support of the modern university environment is the development and implementation of information systems aimed at their professional training. The article considers the main stages of design and development of the information system for students’ professional training, the process of designing the architecture and interface of the web system is described. Prototypes of the main pages of the system were also developed. The web system component diagram, the scheme of the website, the sequence diagram of the information system, and the states diagrams of the main processes are presented. The research shows the technologies used in the development of the information system of professional training, describes and demonstrates its pages, presents the structure of the database and gives a description of its tables. Thus, CMS WordPress was used for development, which is a popular free content management system, PHP was chosen as a programming language. In the developed information system teachers have the opportunity to post educational materials and publish practical tasks; students get access to educational resources, can sample them according to a certain criterion. The peculiarity of the information system for professional training presented by the authors is the development of the parser for automatic filling the database of educational resources for students. It allows to improve the process of updating materials for students of computer specialties, because the need for manual filling of the information system by users is reduced. The article will be interesting for people involved in the didactic process at universities and for specialists involved in the design and implementation of IT systems
Behavioral Analysis of Multitenant SaaS Applications

Cloud Computing has received significant attention as it has changed the way of computation. Looking at the benefits of a cloud, more demand for cloud computing services and Multitenancy. Multitenancy offers various advantages including low infrastructure cost, low maintenance, and reducing the application hosting cost. This paper proposes a Multitenant Multiservice SaaS model for designing Multitenant SaaS applications. The model provides a configurable service accessed by different Tenants. The paper further evaluates the Performance implementing this model using data layer patterns as Tenant Specific and Tenant Shared and generates results using Apache JMeter. Our results show that under the Tenant Specific as a Single tenant, Response time is better than Tenant Shared for increasing load defined by the number of concurrent users. With Tenant Shared schema handling multiple tenants with a single instance, the Response time with an increasing number of tenants and concurrent users is almost stable. Since the Tenant Shared model is resource efficient as compared to Tenant Specific, its performance is affected due to shared instances instead of separate application instances for each tenant.
Computational Psychometrics: A Framework for Estimating Learners’ Knowledge, Skills and Abilities from Learning and Assessments Systems

In recent years the advances in technology provided affordances for learning and assessments opportunities. In this chapter we first describe computational psychometrics as a framework for the measurement of learners’ skills, knowledge, and abilities. We discuss the changes in educational measurement that led to the need for expanding the psychometrics toolbox and describe the properties of psychometric data. We then give an example of a class of models, the Dynamic Bayesian Models that encompass many traditional psychometric models and machine-learning algorithms. We conclude by emphasizing that model complexity and power need to be balanced with the responsibility for transparency and fairness towards stakeholders.
Introduction

Abstract The Introduction contextualises the pursuit of algorithmic decentralisation within the contemporary landscape of cryptocurrencies and blockchains. It problematises these new distributed ledgers within the literatures of economic geography, software studies, and anthropology to outline the analytical scope of the book. A “follow-the-thing” method is briefly introduced to demonstrate the multi-dimensional approach used to understand Bitcoin and to help foreground the monograph’s interventions and outline the academic contribution. This research technique is supported by an epistemological claim: in order to understand blockchains stakeholders must pay close attention to how they are constantly being (re)made and performed on a daily basis. This focus involves separating ideologies of algorithmic decentralisation from how it actually plays out in practice.
3D Reconstruction of Sequence Images Based on Improved Features

Three-dimensional reconstruction refers to the establishment of a mathematical model suitable for computer representation and processing of three-dimensional objects. It is not only the basis of processing, operating and analyzing its nature in the computer environment, but also the key technology of establishing virtual reality to express the objective world in the computer. Therefore, 3D reconstruction is a common scientific problem and core technology in the fields of computer aided geometric design (CAGD), computer graphics (CG), computer animation, computer vision, medical image processing, scientific computing and virtual reality, digital media creation and so on. In recent years, although many algorithms for 3D reconstruction of sequence images have been proposed, the accuracy for 3D reconstruction is still the main problem. In this paper, a new method for 3D reconstruction of sequence images is proposed. Firstly, the Harris feature extraction algorithm is improved to extract more accurate feature points. Then the extracted feature points are tracked in the sequence images. Lastly, the estimated sample consistency algorithm is used for 3D reconstruction after sparse and dense reconstruction. The experiments demonstrate that the accuracy of 3D reconstruction of sequence images is improved.
Attention-Based Bidirectional Long Short Term Memory Networks Combine with Phrase Convolution Layer for Relation Extraction

Relation Extraction (RE) is one of the most important tasks in Natural Language Processing (NLP). In recent years, with the development of deep learning, a variety of deep neural networks, such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory Network (LSTM), have been used in relation extraction and made significant progress. Moreover, LSTM has become the mainstream model in the field of NLP due to its better long term dependencies capture capability than CNN. However, the ability of LSTM to capture long term dependencies is still limited. In order to solve this problem, we propose a phrase convolution structure. The structure can extract the phrase-level features of the sentence, and the sentence-level features can be further extracted after the features are input into LSTM. We believe that this actually enhances the ability of LSTM to capture long term dependencies. Our experiments on SemEva1-2010 Task 8 dataset show that the performance of our model is better than most existing models.
Design and Implementation of Pulsed BPSK, QPSK, Barker Encoded, CW and Chirp Signal Generation in LabVIEW PXI 5644R

Focus of this research includes digital design, simulation and implementation of modulated QPSK, BPSK and Barker encoded signal generation for high band frequency from 100MHz to 6Hz using LabVIEW PXI 5644R vector signal transceivers (VST). By utilizing the pulse signal generation mode of vector signal transceiver, different combinations of signals were designed and simulated in Matlab for the real time scenarios generation for radar warning receivers. Pulse width modulated radio frequency (RF) signal becomes envelope of digital pulsed signal and combination of signals are transmitted and received in receivers. QPSK and BPSK modulation models are designed in Matlab and arrays of date were fed to vector signal transceivers. Moreover, data arrays for barker encoded signal were produced in Maltab and loaded in vector signal transceivers for generation. Custom designed signal generation with varying pulses width, pulse repetitive interval and radio frequency incorporation has been done.
Intelligent Eyeball Movement Controlled Wheelchair

For people who are physically disabled, wheelchairs play a significant role. A joystick control system controls the wheelchairs that are now available. These traditional wheelchairs only work with the aid of the hand to allow the user to travel in a specific direction. It is very difficult to use such a form of device for completely paralyzed people since paralyzed people do not lift their hands. Their eye movements can allow them to step in the desired direction in such circumstances. The purpose of this work is to build a smart wheelchair that is controlled by the user’s eye movement. By processing the image of an eye using MATLAB, eye tracking can be achieved. The user’s face images are continuously recorded through a webcam The captured images undergo the technique of image processing that will be done using MATLAB. Based on the pupil direction in the chosen contour, the subsequent Instructions will be sent to the microcontroller. Therefore, the movements of the eyes regulate the directions in which the wheels initiate the motion.
Accelerating Large Sparse Neural Network Inference using GPU Task Graph Parallelism

The ever-increasing size of modern deep neural network (DNN) architectures has put increasing strain on the hardware needed to implement them. Sparsified DNNs can greatly reduce memory costs and increase throughput over standard DNNs, if the loss of accuracy can be adequately controlled. However, sparse DNNs present unique computational challenges. Efficient model or data parallelism algorithms are extremely hard to design and implement. The recent effort MIT/IEEE/Amazon HPEC Graph Challenge has drawn attention to high-performance inference methods for large sparse DNNs. In this paper, we introduce SNIG, an efficient inference engine for large sparse DNNs. SNIG develops highly optimized inference kernels and leverages the power of CUDA Graphs to enable efficient decomposition of model and data parallelisms. Our decomposition strategy is flexible and scalable to different partitions of data volumes, model sizes, and GPU numbers. We have evaluated SNIG on the official benchmarks of HPEC Sparse DNN Challenge and demonstrated its promising performance scalable from a single GPU to multiple GPUs. Compared to the champion of the 2019 HPEC Sparse DNN Challenge, SNIG can finish all inference workloads using only a single GPU. At the largest DNN, which has more than 4 billion parameters across 1920 layers each of 65536 neurons, SNIG is up to 2.3 faster than a state-of-the-art baseline under a machine of 4 GPUs. SNIG receives the Champion Award in 2020 HPEC Sparse DNN Challenge.
Resolving Anaphora using Gender and Number Agreement in Marathi text

In the area of natural language processing, there is a wide variety of text data available, but not in the standard format and therefore it is necessary to standardize it. This paper concentrates on an anaphora resolution for Marathi text because the anaphora has been resolved in various foreign as well as Indian languages like Hindi, Bengali, Kannada and many more but very less work has been done in Marathi. Anaphora often occurs written text and spoken dialogue and it is necessary to resolve an anaphora to better understanding of the discourse or the sentence because the a human being we can easily find out the anaphora refer for what antecedents but for machine it is very difficult task and hence resolving anaphora becomes a most challenging task for the researcher Here this paper concentrate to resolving the anaphora in Marathi text using the gender and the number agreement as well as the animistic knowledge. And the overall performance achieved by the system is 68.88%.
Personalized Music Teaching Mode Under the Background of Big Data

On the whole, traditional music education in colleges and universities does not pay enough attention to individual values. Emphasize the adaptability of each individual in the group, rarely explore and support the development of individuals from the perspective of human nature; pay more attention to one-way guidance and education. Paying attention to the differences and personalities of students makes them unnecessarily work in the wrong field and cannot grow better in their professional field. The advent of the era of big data has brought about major social changes in life, work and thinking. At the same time, big data can also provide useful ideas and technical support for personalized music teaching tasks. The personalized music teaching model uses big data to provide targeted and accurate services for each student, so as to cultivate outstanding and complete personalized qualities of college students. Although many methods have been proposed to improve students’ music teaching effect. However, there are still many new methods that have not yet been discovered. This article uses a combination of literature research method and survey research method to conduct research. Using the method in this article, through the combination and calculation of the past music teaching methods and the existing universal methods, a set of reasonable music teaching methods are found. According to calculations, this method can improve students’ learning ability by 14%.
System engineering as a basis for forming practices for managing complex digital transformation solutions

The paper presents a conceptual architecture for the implementation of the product approach in the aspect of systems engineering, with the aim of increasing the maturity of engineering and software activities in general. The paper presents an example of successful practice of introducing a product approach in a large IT company “Gazpromneft-Digital Solutions”. Also, the product approach is considered as a tool for the formation of a competence profile in the process of training specialists.
Unsupervised Transfer Learning For Video Prediction Based on Generative Adversarial Network

Video prediction technology offers new benefits for robots in completing a series of tasks based on self-supervised planning. In a video prediction task in a scene from the physical world, we must always attend to the authenticity of the prediction. However, the prediction model can model only the specific scene; when the scene changes, we need to train a new model from scratch to fit the domain distribution of the data, which often requires a large amount of specific data and long training time. Therefore, ways to minimize the constraints on model learning for new senses is an important issue. Aiming at its authenticity and generalization, this paper examines video prediction based on adversarial training and unsupervised transfer learning. For the interactive operation of a manipulator, a method of video prediction based on a generative adversarial structure using the transfer learning strategy is proposed. Using the Sawyer manipulator strong dataset as a guide and a UR5 manipulator dataset as target, the results of experiments verified that the prediction generator trained by the adversarial method can generate realistic results, and the transfer learning strategy has the following two advantages: (1) The number of training iterations for a new target scene is reduced, convergence is faster, overall training time is shorter, and thus efficiency is higher. (2) The prediction of interactive objects has better generalization capability.
Assessing Ambient Assisted Living Support for Patients with Mild Cognitive Impairment

According to the World Health Organization, about 50 million people worldwide suffer from dementia. Ten million new cases added every year [1]. Mild Cognitive Impairment (MCI) affects more than 15% of the population aged 65. MCI patients are mostly home-based. Ambient Assisted Living (AAL) systems provide tools for automatic registration of vital signs and other medically and socially important information. An online anonymous questionnaire for healthcare professionals was created to prove or disprove the number of interconnected hypotheses about the necessity and feasibility of the AAL system for MCI patients. The questionnaire was completed by sixty respondents. Descriptive statistics show support for the implementation of general AAL and variants for MCI patients. Comparative analysis of ordinal data groups is done with help of the non-parametric Mann–Whitney–Wilcoxon test and the Kruskal-Wallis test. Table questions results are analyzed with chi-square for frequency tables. Comparative analysis of groups demonstrated relative uniformity of responses.
Anomaly Detection with Dual Adversarial Training

Anomaly detection is of paramount importance in data mining and artificial intelligence. Deep generative models have been widely used in anomaly detection as a dominant paradigm to model complex and high-dimensional data distribution. However, developing effective and robust anomaly detection systems for complex and high-dimensional data using generative models remains a challenge. In this paper, we propose a novel Dual Adversarial Training method for Anomaly Detection (DAT-AD), which uses the adversarial training idea in Generative Adversarial Network (GAN) and the Virtual Adversarial Training (VAT) idea to improve the effectiveness and robustness of anomaly detector, respectively. In addition, we have also carefully designed the network architecture and the loss function of our method to ensure that the trained network can be utilized to the greatest extent. We demonstrate the superiority of our method by conducting various experiments on tabular and image data.
Adaptive Resource Allocation for DASH-based Video Streaming in Mobile Networks

With the rapid development of communication technology and the popularization of network equipment, there are more and more services related to high-quality mobile video services. This article provides an adaptive resource allocation method to deal with the negative impact on quality of experience (QoE) caused by the mismatch between the video bitrate quality request sent by the client and the fluctuating network conditions. We consider that the cooperative wireless resource allocation with the bitrate selection is the key to realize the truly efficient adaptive video streaming, and propose an adaptive resource allocation for DASH-based video streaming (ARA-DASH). Our method aims to minimize the number of users with longer rebuffering time, guarantee the QoE of each video user in the system, and make the wireless resource allocation adapt to the bitrate selection dynamically. In order to achieve global optimality, we use the reinforcement learning algorithm Actor-Critic(A3C) to ensure the convergence and performance of ARA-DASH. We can see from the experiment that ARA-DASH method can reduce the rebuffering events and achieve a significant optimization on user's real-time QoE.