Reviewerly/exHarmony huggingface dataset:
Identification and overcoming of subject deficits of students – future math teachers

Introduction. A modern teacher should be ready to solve a number of urgent professional tasks imposed by the requirements of educational standards and international initiatives. Working teachers experience certain difficulties in their professional activities caused by a number of objective and subjective factors, including the quality of training at a university. The research purpose is to identify and overcome the deficiencies in the subject-methodological training of students – future math teachers in the aspect of modern requirements. Methodology and methods. The study involved forty-eight fourth- and fifth-year students of the Krasnoyarsk State Pedagogical University named after V.P. Astafyev. The following research methods were used: analysis of scientific literature, systematization and generalization of scientific research results, pedagogical experiment, quantitative analysis of experiment results using the Pearson’s chi-squared test. Results. The diagnostics of seven subject competencies of students – future math teachers – was carried out according to three components: cognitive, axiological, praxeological ones. Difficulties associated with the ability to solve mathematical problems of functional, geometric, probabilistic content, as well as with the personal, social and practical context were revealed. These deficits were overcome by including professionally oriented tasks in the content of elective disciplines of an educational program, as well as using an online course in the discipline “Elementary Mathematics”, which allows designing students’ individual educational trajectories to overcome their difficulties. The results of repeated diagnostics after training showed an increase in the level of formation of subject competences (χ2 = 6.63; p &lt; 0.05). Conclusion. The conducted study shows the prospects of identifying and overcoming the professional deficits of a teacher at the stage of training at a university. The described experience can be used in the practice of preparing a student at a university to prevent subject deficits in the future professional activities of a math teacher. In the future, the study will be continued in the field of methodological and other deficits of a math teacher.
Identification of Molecular Subtypes of Breast Cancer Based on Multimodal Deep Learning

The identification of breast cancer subtypes plays a key role in the prognosis of breast cancer.In recent years, deep learning (DL) has shown good performance in intelligent identification of breast cancer subtypes.However, most of the traditional DL models use single-mode data, and the extracted features are limited, so the association between patient characteristics and breast cancer subtypes cannot be established stably.In order to improve the effect of recognition, this study proposes a multimodal fusion deep learning (MFDL) model.This model combined with the breast cancer gene modal data and image modal data established a multilayer perceptron network and the depth of the convolution neural network for feature extraction, and then based on the idea of weighted aggregation on the output of the two characteristics of the network integration.Finally, the fusion features were used to identify breast cancer subtypes.The experimental results show that compared with other models in AUC value, accuracy and other indicators, the MFDL model proposed in this study is more accurate and efficient in the identification of breast cancer subtypes.
Tangible Interfaces Support Young Children’s Goal Interdependence

Understanding how to contribute to group work is challenging, especially for young children. To have a productive group process, we need to know the mechanism of positive interdependence, which is a fundamental element of successful collaboration. Unfortunately, although there are many suggestions for promoting positive interdependence with tangible technologies, there are few guidelines for structuring children’s interdependent collaboration. Therefore, we designed two tangible games, UnitRry and CollabMaze, using weak and strong goal interdependent designs. We conducted two user studies with 32 children. Our investigation revealed three main findings. First, weak and strong goal interdependent interfaces had high enjoyment and interdependence. Second, tangible interfaces help young children have more idea communication and need less time to solve the tasks. Finally, young children using tangible interfaces were more engaged in the tasks. In the long run, our results can improve the design of tangible interfaces for young children’s collaboration and help them have a better collaborative experience. Furthermore, our findings showed the value of tangible technologies compared with tablet applications in facilitating children’s collaboration.
Human functional pattern recognition with focus on hand detection in egocentric vision through an artificial intelligence approach

Good health and functional ability are important for individuals to lead fulfilling mental, psychological, and social lives. The diseases such as Dementia causes irreversible damage, decline in cognition, function, and behavior which translates into difficulty in independently performing daily tasks. Studies showed that assessment of Instrumental activities of daily living(IADLs) correlate with humans' cognitive and functional status. Analysis of biomechanical markers such as hand movement/use was done with artificial intelligence(AI). We present an optimized AI algorithm for hand detection in the analysis of egocentric video recordings. This improved AI algorithm is based on a probabilistic approach where hand regions are detected in egocentric videos. They then feed the human functional pattern recognition process. To evaluate the performance of our proposal we use a dataset containing the four functional patterns organized into four classes, based on the prehensile patterns of the hands: strength-precision, and on the kinematics of the instruments: displacementhandling. This work was inspired by a previous work done by our group, where biomechanical markers were analyzed throughout the performance of IADL activities to recognize the human functional pattern. The result of our proposal yielded an accuracy of 87.5% in recognizing strength-precision and displacement-handling movement patterns when evaluating the test database with information from Segmented and Not-Segmented videos. This resulted in a single video that changed its classification ratio between the two subsets. This can be of great potential in the development of technological tools for the creation of an automated model to support the diagnosis of early Alzheimer's disease.
Intraprediction Complexity Control Algorithm Based on Visual Saliency

Intraprediction is one of the most complex parts of High-Efficiency Video Coding (HEVC), because it selects the best prediction mode by calculating the cost of every Coding Unit (CU), which provides higher complexity of intracoding. Visual saliency map can show the attention regions of the human eyes, which is generated by certain static and space-time saliency detection method. By analyzing the percentage of coding time for different size CU, and the relation of visual saliency and CU depth, an intraprediction complexity control algorithm based on visual saliency is proposed in this paper. Based on the feature of the video and the target level, the saliency threshold is adapted to determine whether the current CU in the intraprediction processing should be split into smaller CUs or the division processing should be stopped early. Three samples were compared by the proposed algorithm and other algorithm, and the proposed algorithm has better performance in PSNR, BitRate, and coding time. Experimental results show that this algorithm can effectively control the coding complexity of intraprediction with minimal visual loss and can be applied to a number of scenarios, such as real-time video coding.
Metric learning algorithms for meta learning

This chapter introduces metric-learning approaches for meta learning. The central theme is to learn a good feature embedding space where learning systems can easily classify different classes given only a few training examples. The first method is Siamese Networks, which takes a pair of samples and produces a similarity score. Matching Networks improve over Siamese Networks with more sophisticated attention-based embedding functions to make the final features dependent on the entire support set. Prototypical Networks seek an embedding space where data samples for each class form a compact cluster around a prototype. Instead of using nonparametric classifiers like previous approaches, Relation Networks aim to learn both the embedding and classification function. Finally, Graph Neural Networks can generalize metric-learning-based meta learning.
Usage of Blockchain with Machine Learning for Patient Record Management and Analysis Purposes

A machine analogously able to perform similar functions as the human brain does is called a smart machine and applied technology behind this is artificial intelligence (AI). In today's era, smart machines are used in every field like Internet of Things (network of smart things/devices), health care (smart wearable devices), robotics (application of robots), and many more. On the other side, blockchain is other renowned technology, which is purely used to maintain the record of particular data that can be related to finance, supply-chain management, smart contracts, education, medical, and many more fields where bulk record maintenance is required. As there are huge amounts of data to handle, hence security is also one of the main concern. Moreover, blockchain is the only technology that inclusively provides data storage, integrity, and security. In this chapter, amalgamation of blockchain with machine learning (ML) will be discussed. Here, we will discuss the theoretic concepts of AI, ML, and blockchain in healthcare industries with an empirical bird's eye view particularly for programmers who are enthusiastic towards the mentioned domains. Blockchain manages healthcare data securely and ML algorithms will analyze this data to aid healthcare supervisors for problem detection, second opinions, and inferences. The application of these two techniques with respective case studies are in this chapter.
Kernel dependence regularizers and Gaussian processes with applications to algorithmic fairness

Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive models are often developed using biased datasets and thus retain or even exacerbate biases in their decisions and recommendations. Removing the sensitive covariates, such as gender or race, is insufficient to remedy this issue since the biases may be retained due to other related covariates. We present a regularization approach to this problem that trades off predictive accuracy of the learned models (with respect to biased labels) for the fairness in terms of statistical parity, i.e. independence of the decisions from the sensitive covariates. In particular, we consider a general framework of regularized empirical risk minimization over reproducing kernel Hilbert spaces and impose an additional regularizer of dependence between predictors and sensitive covariates using kernel-based measures of dependence, namely the Hilbert-Schmidt Independence Criterion (HSIC) and its normalized version. This approach leads to a closed-form solution in the case of squared loss, i.e. ridge regression. We also provide statistical consistency results for both risk and fairness bound for our approach. Moreover, we show that the dependence regularizer has an interpretation as modifying the corresponding Gaussian process (GP) prior. As a consequence, a GP model with a prior that encourages fairness to sensitive variables can be derived, allowing principled hyperparameter selection and studying of the relative relevance of covariates under fairness constraints. Experimental results in synthetic examples and in real problems of income and crime prediction illustrate the potential of the approach to improve fairness of automated decisions.
Differential Privacy High-dimensional Data Publishing Method Based on Bayesian Network

Ensuring high data availability while realizing privacy protection is a research hotspot in the field of privacy-preserving data publishing. In view of the instability of data availability in the existing differential privacy high-dimensional data publishing methods based on Bayesian networks, this paper proposes an improved MEPrivBayes privacy-preserving data publishing method, which is mainly improved from two aspects. Firstly, in view of the structural instability caused by the random selection of Bayesian first nodes, this paper proposes a method of first node selection and Bayesian network construction based on the Maximum Information Coefficient Matrix. Then, this paper proposes a privacy budget elastic allocation algorithm: on the basis of pre-setting differential privacy budget coefficients for all branch nodes and all leaf nodes in Bayesian network, the influence of branch nodes on their child nodes and the average correlation degree between leaf nodes and all other nodes are calculated, then get a privacy budget strategy. The SVM multi-classifier is constructed with privacy preserving data as training data set, and the original data set is used as input to evaluate the prediction accuracy in this paper. The experimental results show that the MEPrivBayes method proposed in this paper has higher data availability than the classical PrivBayes method. Especially when the privacy budget is small (noise is large), the availability of the data published by MEPrivBayes decreases less.
Independent Component Analysis

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This second volume, Inference, builds on the foundational topics established in volume I to introduce students to techniques for inferring unknown variables and quantities, including Bayesian inference, Monte Carlo Markov Chain methods, maximum-likelihood estimation, hidden Markov models, Bayesian networks, and reinforcement learning. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 180 solved examples, almost 200 figures, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Learning, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.
Impacts of Presenting Extra Information in Short Videos via Text and Voice on User Experience

Short video is an increasingly prevalent medium in online shopping environments to present products. To cope with the great demand for short videos rising from the enormous number and the rapid update of online products, computer-supported video production is becoming a trend. The optimization of short videos considering user experience is essential. Currently, using text and voice to integrate extra information into short videos is a potential and promising approach for optimizing computer-supported video production, while the effects of these elements on user experience remain unclear. In this study, we conducted a questionnaire-based experiment including 580 participants to explore the impacts of presenting extra information in short videos via text and voice on multi-dimensional user experience. Results indicated that these two elements positively impacted user experience from different dimensions. Gender differences were also found in this study. Based on experimental results, we provided suggestions to support the use of text and voice elements in short video production considering user experience.
A web server optimization based on thread pool and epoll

The thread pool and the I/O multiplexing technology can effectively reduce the consumption of resources and enhance the concurrency of a server, which improves the capability of the system. Architects tend to use multiple threads to improve system concurrency, which has been proved to be effective in the past few years. However, the cost of creating, switching, and destroying threads remains to be an assignable problem when the system is having a concurrent peak. This paper designs and implements a server based on the technologies of the thread pool and epoll to figure out if the mixture of epoll and thread pool as optimization can help decrease thread operation costs. To deal with the system overhead, we introduce the thread pool technology based on an epoll web server to avoid the cost of frequent operations of creating or destroying threads. We build two sets of experiments to verify the effectiveness of our optimization, including a web server with thread pool and epoll as the experimental group, and a simple epoll web server without thread pool as the control group. Then, we test the system performance by altering the concurrency of each experimental and measuring the actual-time system overhead to describe the improvement we made to the multi-threaded server. The results show that the concurrent server with the technology of epoll and thread pool can achieve high performance and high concurrency.
Application of Advanced Hybrid Models to Identify the Sustainable Financial Management Clients of Long-Term Care Insurance Policy

The rapid growth of the aging population and the rate of disabled people with physical and mental disorders is increasing the demand for long-term care. The decline in family care could lead to social and economic collapse. In order to reduce the burden of long-term care, long-term care insurance has become one of the most competitive products in the life insurance industry. In the previous literature review, few scholars engaged in the research on this topic with data mining technology, which was motivated to trigger the formation of this study and hoped to increase the different aspects of academic research. The purpose of this study is to develop the long-term insurance business from the original list of insurance clients, to predict whether the sustainable financial management clients will buy the long-term care insurance policies, and to establish a feasible prediction model to assist life insurance companies. This study aims to establish the classified prediction models of Models I~X, to dismantle the data with the percentage split and 10-fold cross validation, plus the application of two kinds of technology as feature selection and data discretization, for the data mining of twenty-three kinds of algorithms in seven different categories (Bayes, Function, Lazy, Meta, Misc, Rule, and Decision Tree) through the data collected from the insurance company database, and to select 20 conditional attributes and 1 decisional attribute (whether to buy the long-term insurance policy or not). The decision attribute is binary classification method for empirical data analysis. The empirical results show that: (1) the marital status, total number of policies purchased, and total amount of policies (including long-term care insurance) are found to be the three important factors affecting the decision attribute; (2) the most stable models are the advanced hybrid Models V and X; and (3) the best classifier is Decision Tree J48 algorithm for the study data used.
Memory Networks

Memory Networks are models equipped with a storage component where information can generally be written and successively retrieved for any purpose. Simple forms of memory networks like the popular recurrent neural networks (RNN), LSTMs or GRUs, have limited storage capabilities and for specific tasks. In contrast, recent works, starting from Memory Augmented Neural Networks, overcome storage and computational limitations with the addition of a controller network with an external element-wise addressable memory. This tutorial aims at providing an overview of such memory-based techniques and their applications in multimedia. It will cover an explanation of the basic concepts behind recurrent neural networks and will then delve into the advanced details of memory augmented neural networks, their structure and how such models can be trained. We target a broad audience, from beginners to experienced researchers, offering an in-depth introduction to an important crop of literature which is starting to gain interest in the multimedia, computer vision and natural language processing communities.
A port of the SuperCollider’s class library to Python

This paper presents a fully functional port of the client side SuperCollider’s class library from sclang to Python. It discusses the fundamental architectural features that make the original library unique compared to other Python clients which differ in approach or scope. An exploration of how general purpose programming languages techniques and paradigms are used to represent DSP algorithms and musical structures is made while exposing the different components of the library, technical decisions and a few needed adaptations to the resources of the target language. It also presents the implementation of a non real time mode to create OSC scores from the same code used in real time. Finally, the advantages and disadvantages of the library itself are discussed.
ResNet-based Network for Recognizing Daily and Transitional Activities based on Smartphone Sensors

In contemporary wearable computing contexts, sensor-based human activity recognition (HAR) has become a popular research topic. Investigators from the Health Applications Research Institute presented promising discoveries to promote healthcare applications, including fall detection, athletic tracking and reporting, and a monitoring scheme for senior activities in intelligent homes. In these services, ordinary and transitory human actions are captured by smartphones’ wearable sensors and analyzed as fundamental and complicated motions. Deep learning techniques demonstrated the usefulness and effectiveness of convolutional neural networks (CNNs) in extracting high-level features embedded in sensor data to develop reliable recognition models. CNN faces deterioration of gradient vanishing issues when networks require deeper convolution layers. To overcome the problem, we developed ResNet, a deep residual network for determining daily and transitory activities. Using a significant standard HAR dataset called the KU-HAR dataset that gathered smartphone sensor data of various human actions, we performed experiments to identify the most appropriate ResNet-based models. Experimental findings indicate that the ResNet-18 has the highest accuracy, at 93.54%. The acquired results surpass prior state-of-the-art models by 3.87% in terms of accuracy.