Reviewerly/exHarmony huggingface dataset:
CCMMI: A class center-based multiple mixed imputation method

Abstract Missing data is ubiquitous in the real society. Multiple imputation (MI) reflects the uncertainty of missing data and makes full use of existing data. It is an effective method to deal with missing data. However, when MI considers the imputation model, the optimal single model is usually selected from candidate ones to impute multiple times, thus ignoring the uncertainty of model selection. Therefore, this paper introduces a class center-based multiple mixed imputation method (CCMMI), which takes both data and model selection uncertainties into account to obtain better imputation results than MI method of other single models. It mainly determines an imputation threshold by measuring the Euclidean distance between the class center and each class of data samples, and then uses multiple single models to generate multiple different imputation datasets. Finally, it uses the imputation threshold to determine the optimal interpolation in the imputation datasets. The experimental results on numerical, categorical and mixed datasets with different missing rates show that the performance of CCMMI method is better than that of other single models with missing rates ranging from 10–50%.
Low-cost Relevance Generation and Evaluation Metrics for Entity Resolution in AI

Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking. In this paper we propose a low cost relevance generation framework by generating features using customer implicit and explicit feedback signals. The generated relevance datasets can serve as test sets to measure ER performance. We also introduce a set of metrics that accurately measures the performance of ER systems in various dimensions. They provide great interpretability to deep dive and identifying root cause of ER issues, whether the problem is in relevance generation or ranking.
MASTERING METHODOLOGICAL TECHNIQUES ORGANIZATIONS OF JOINT EDUCATIONAL ACTIVITIES STUDENTS - ONE OF THE AREAS OF PROFESSIONAL PREPARATIONS FOR FUTURE MUSIC TEACHERS

The traditional and other methodical techniques of effective nitftini. iifmn of educational activities of pupils at the music lesson at school, analyzed in the article. Gaining the experience of using these twchniques improves the professional Teachers Training activities of the future specialists.
BayesPCN: A Continually Learnable Predictive Coding Associative Memory

Associative memory plays an important role in human intelligence and its mechanisms have been linked to attention in machine learning. While the machine learning community's interest in associative memories has recently been rekindled, most work has focused on memory recall ($read$) over memory learning ($write$). In this paper, we present BayesPCN, a hierarchical associative memory capable of performing continual one-shot memory writes without meta-learning. Moreover, BayesPCN is able to gradually forget past observations ($forget$) to free its memory. Experiments show that BayesPCN can recall corrupted i.i.d. high-dimensional data observed hundreds to a thousand ``timesteps'' ago without a large drop in recall ability compared to the state-of-the-art offline-learned parametric memory models.
Representation Power of Graph Neural Networks: Improved Expressivity via Algebraic Analysis

Despite the remarkable success of Graph Neural Networks (GNNs), the common belief is that their representation power is limited and that they are at most as expressive as the Weisfeiler-Lehman (WL) algorithm. In this paper, we argue the opposite and show that standard GNNs, with anonymous inputs, produce more discriminative representations than the WL algorithm. Our novel analysis employs linear algebraic tools and characterizes the representation power of GNNs with respect to the eigenvalue decomposition of the graph operators. We prove that GNNs are able to generate distinctive outputs from white uninformative inputs, for, at least, all graphs that have different eigenvalues. We also show that simple convolutional architectures with white inputs, produce equivariant features that count the closed paths in the graph and are provably more expressive than the WL representations. Thorough experimental analysis on graph isomorphism and graph classification datasets corroborates our theoretical results and demonstrates the effectiveness of the proposed approach.
TO THE QUESTION OF FORMATION IN STUDENTS PEDAGOGICAL SPECIALTIES OF CONCEPTS PERSON-ORIENTED EDUCATION

No abstract.
A Novel Intelligent Recognition of Video Objects in Drone Routing Inspection based on Deep Learning

In the field of electric power routing inspection, the drone electric power routing inspection method can better break through the limitations of human routing inspection and manned helicopter routing inspection, and can be used for fine routing inspection, fixed-point routing inspection and range routing inspection. The existing drone routing inspection video object recognition has the problem of misjudging the recognition of the same type and the same model of equipment state in different scenes. To address the above problems, this paper proposes a histogram video image feature extraction method based on rotation-invariant HOG (Histogram of Oriented Gradients), and further realizes the accurate recognition of objects in the routing inspection process under deep convolutional neural network. The experiments have shown that the recognition accuracy rate of 14 types of objects such as even pressure ring, anti-vibration hammer, tower end of hanging hammer string, sub conductor spacer bar, damaged part of insulator, insulator string, heavy hammer and attached gold tools commonly found in power routing inspection is not less than 75%, and the recognition accuracy of some objects can reach 97%, which has good practical value.
Visual Concepts Tokenization

Obtaining the human-like perception ability of abstracting visual concepts from concrete pixels has always been a fundamental and important target in machine learning research fields such as disentangled representation learning and scene decomposition. Towards this goal, we propose an unsupervised transformer-based Visual Concepts Tokenization framework, dubbed VCT, to perceive an image into a set of disentangled visual concept tokens, with each concept token responding to one type of independent visual concept. Particularly, to obtain these concept tokens, we only use cross-attention to extract visual information from the image tokens layer by layer without self-attention between concept tokens, preventing information leakage across concept tokens. We further propose a Concept Disentangling Loss to facilitate that different concept tokens represent independent visual concepts. The cross-attention and disentangling loss play the role of induction and mutual exclusion for the concept tokens, respectively. Extensive experiments on several popular datasets verify the effectiveness of VCT on the tasks of disentangled representation learning and scene decomposition. VCT achieves the state of the art results by a large margin.
Analysis of Courses Affecting Academic Achievement in Higher Education with Association Rules Technique

Improving the quality of education is to develop humanity at its best. Therefore, this research aims at 3 goals. The 1st goal is to study the course structure that affects students' academic achievement, the 2nd goal is to study the relationships of each course that affect students' academic achievement, and the 3rd goal is to assess the patterns of each course's relationship on student achievement. Data collected for research were student data from the B.B.A. (Business Computer) at the University of Phayao from 2001 to 2020, totaling 2,017 students. The research tools are mining association rules with the Apriori algorithm, Support value, Confidence value, LaPlace value, Gain value, PS value, Lift value, and Conviction value. The research results found that it is imperative to accelerate problem-solving of learners' knowledge, skills, and abilities related to Mathematics and English knowledge in improving future curricula with the primary goal of further improving learner quality. The key findings of this research reveal that Thai students still need support and solutions in Mathematics and English for sustainable learning.
An Improved Rational Approximation of Bark Scale using Low Complexity and Low Delay Filter Banks

Abstract This paper proposes an algorithm to obtain the sampling factors to model any frequency partitioning so that it is realized using low complexity rational decimated non-uniform filter banks (RDNUFBs). The proposed algorithm is employed to approximate the Bark scale to find a rational frequency partitioning that can be realized using RDNUFBs with less approximation error. The proposed Bark frequency partitioning is found to reduce the average deviation in center frequency and bandwidth by 65.04% and 48.50% respectively, and root mean square bandwidth deviation by 54.46 % when compared to those of the existing perceptual wavelet approximation of Bark scale. In the first filter bank design approach discussed in the paper, a near perfect reconstruction partially cosine modulated filter bank is employed to obtain the improved Bark frequency partitioning. In the second design approach, the hardware complexity of the PCM based RDNUFB is reduced by deriving the channels with different sampling factors from the same prototype filter by the method of channel merging. There is a considerable reduction of 40.83 % in the number of multipliers when merging of partially cosine modulated channels is employed. It is also found that both the proposed approaches can reduce the delay by 95.17 % when compared to the existing rational tree approximation methods and hence, are suitable for real time applications.
Unblocking the Blockchain: Concept and Use Cases

In the ever-evolving world of technology, Blockchain is one of the technologies that have drawn the attention of researchers over last few years. Attributed to be the creation of unknown persons, under the pseudonym Satoshi Nakamoto, Blockchain today is a key component of many applications ranging from cryptocurrency, supply chain management to Self-Sovereign Identity. It is explored as the backbone of any application that requires a strong security layer to protect its data from any kind of tampering. This chapter discusses the basic concept behind this interesting technology and the applications where it can and is making its impact.
FORMATION OF CULTURAL TOLERANCE AND IDENTITIES IN THE EDUCATIONAL PROCESS

No abstract.
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?

Learned self-attention functions in state-of-the-art NLP models often correlate with human attention. We investigate whether self-attention in large-scale pre-trained language models is as predictive of human eye fixation patterns during task-reading as classical cognitive models of human attention. We compare attention functions across two task-specific reading datasets for sentiment analysis and relation extraction. We find the predictiveness of large-scale pre-trained self-attention for human attention depends on `what is in the tail', e.g., the syntactic nature of rare contexts. Further, we observe that task-specific fine-tuning does not increase the correlation with human task-specific reading. Through an input reduction experiment we give complementary insights on the sparsity and fidelity trade-off, showing that lower-entropy attention vectors are more faithful.
On the Prediction Instability of Graph Neural Networks

Instability of trained models, i.e., the dependence of individual node predictions on random factors, can affect reproducibility, reliability, and trust in machine learning systems. In this paper, we systematically assess the prediction instability of node classification with state-of-the-art Graph Neural Networks (GNNs). With our experiments, we establish that multiple instantiations of popular GNN models trained on the same data with the same model hyperparameters result in almost identical aggregated performance but display substantial disagreement in the predictions for individual nodes. We find that up to one third of the incorrectly classified nodes differ across algorithm runs. We identify correlations between hyperparameters, node properties, and the size of the training set with the stability of predictions. In general, maximizing model performance implicitly also reduces model instability.
HyBNN and FedHyBNN: (Federated) Hybrid Binary Neural Networks

Binary Neural Networks (BNNs), neural networks with weights and activations constrained to -1(0) and +1, are an alternative to deep neural networks which offer faster training, lower memory consumption and lightweight models, ideal for use in resource constrained devices while being able to utilize the architecture of their deep neural network counterpart. However, the input binarization step used in BNNs causes a severe accuracy loss. In this paper, we introduce a novel hybrid neural network architecture, Hybrid Binary Neural Network (HyBNN), consisting of a task-independent, general, full-precision variational autoencoder with a binary latent space and a task specific binary neural network that is able to greatly limit the accuracy loss due to input binarization by using the full precision variational autoencoder as a feature extractor. We use it to combine the state-of-the-art accuracy of deep neural networks with the much faster training time, quicker test-time inference and power efficiency of binary neural networks. We show that our proposed system is able to very significantly outperform a vanilla binary neural network with input binarization. We also introduce FedHyBNN, a highly communication efficient federated counterpart to HyBNN and demonstrate that it is able to reach the same accuracy as its non-federated equivalent. We make our source code, experimental parameters and models available at: https://anonymous.4open.science/r/HyBNN.
FORMING THE READYNESS OF FUTURE TEACHERS TO THE PROFESSIONAL EDUCATION OF SENIOR STUDENTS

The article deals with the conception of readiness for different kinds of activities and professional oriented training. The conditions which will have a positive influence on forming of prospective teacher readiness for professional oriented training of senior pupils are submitted for consideration.
Unsupervised Learning of Depth, Camera Pose and Optical Flow from Monocular Video

We propose DFPNet -- an unsupervised, joint learning system for monocular Depth, Optical Flow and egomotion (Camera Pose) estimation from monocular image sequences. Due to the nature of 3D scene geometry these three components are coupled. We leverage this fact to jointly train all the three components in an end-to-end manner. A single composite loss function -- which involves image reconstruction-based loss for depth &amp; optical flow, bidirectional consistency checks and smoothness loss components -- is used to train the network. Using hyperparameter tuning, we are able to reduce the model size to less than 5% (8.4M parameters) of state-of-the-art DFP models. Evaluation on KITTI and Cityscapes driving datasets reveals that our model achieves results comparable to state-of-the-art in all of the three tasks, even with the significantly smaller model size.
Nothing makes sense in deep learning, except in the light of evolution

Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the success of DL must look at the population of all algorithms in the field and how they have evolved over time. We argue that cultural evolution is a useful framework to explain the success of DL. In analogy to biology, we use `development' to mean the process converting the pseudocode or text description of an algorithm into a fully trained model. This includes writing the programming code, compiling and running the program, and training the model. If all parts of the process don't align well then the resultant model will be useless (if the code runs at all!). This is a constraint. A core component of evolutionary developmental biology is the concept of deconstraints -- these are modification to the developmental process that avoid complete failure by automatically accommodating changes in other components. We suggest that many important innovations in DL, from neural networks themselves to hyperparameter optimization and AutoGrad, can be seen as developmental deconstraints. These deconstraints can be very helpful to both the particular algorithm in how it handles challenges in implementation and the overall field of DL in how easy it is for new ideas to be generated. We highlight how our perspective can both advance DL and lead to new insights for evolutionary biology.
DEVELOPMENT OF SCHOOLCHILDREN'S COMPETENCE TESTING TOOLS

The author defines the notion of competence and analyses ways of developing student's competence. The advantages of test teaching technologies are examined.
THE ART OF THEATER IN EDUCATIONAL SCHOOL PROCESSES

The article deals with the problems of traditional pedagogies. The main point of its contents and the task of educational process is the aspect of school graduate personality modeling in the direction of personality forming in accordance with the given characterological qualities. To solve this problemthe author examines the theatrical performance on whose basis the school theater is formed.
Extracting a Minimal Trigger for an Efficient Backdoor Poisoning Attack Using the Activation Values of a Deep Neural Network

A backdoor poisoning attack is an approach that threatens the security of artificial intelligence by injecting a predefined backdoor trigger into a training dataset to induce misbehavior in the classification model. In this paper, we discuss an approach to extract the critical regions of the backdoor trigger for a more efficient backdoor poisoning attack. Through this approach, an attacker can attempt a more invisible and powerful attack and minimize image falsification. We first describe how to detect neurons, which are more affected by the backdoor trigger in the classification model. Then, we discuss how to iteratively update the noises using the activation values of the neurons. Here, the difference between the activation values of the critical neurons affected by the backdoor trigger and those by the fully updated noises is minimized and the noises may be used as a minimal trigger. In the future, we will draw new insights for the backdoor poisoning attack using our proposed approach.
PROBLEM SOLVING IN THE ARMY IS THE BASIS SOLUTIONS TO SOFTWARE PROBLEMS MORAL AND PSYCHOLOGICAL TRAINING MILITARY SERVICE IN SCHOOLS

The article deals with the moral and psychological training of youth for military service.
Intelligent Recognition Translation Optimization Algorithm Based on Machine Vision

At this stage, machine vision (MV) technology is developing rapidly, and good results have been achieved in the fields of text recognition, image processing, and natural language processing. Intelligent recognition translation, as an efficient tool, can perform equivalent conversions between different languages, while still maintaining the original semantics, which has important practical significance. Based on this, the purpose of this article is to study the MV - based intelligent recognition translation optimization algorithm. This article proposes a text recognition method based entirely on convolutional neural networks, incorporates attention mechanism, introduces LSTM with "forgetting" function, and introduces the specific structure of a fully convolutional sequence decoder. This article designs an intelligent recognition and translation system, and introduces the end-to-end recognition and translation module. Experimental data shows that the BLUE values of the method in this paper are 25.57, 25.63, and 25.71, respectively. This shows that the method in this paper has a good effect in text recognition and translation.
ON THE QUESTION OF THE PRINCIPLES OF THE ORGANIZATION INDIVIDUAL TRAINING IN THE SYSTEM MUSIC TEACHER TRAINING

In the article are given modern principles of organization o f the individual approach in the system of personal-oriented education of the teaches of music. &#x0D; The author investigates the content of principals of musical and general pedagogic, the ways o f their realization in musical-performing preparing of the teacher of music in the secondary school.&#x0D; The author reveals the content of musical-performing preparing of the future teacher of music, which is organized on the basic of principals o f professional-oriented education
FEATURES OF THE FORMS OF HUMANIST ASSISTANCE TO STUDENTS OF BOARDING SCHOOLS IN THE PROCESS OF THEIR SELF-DETERMINATION

The question is specificity of the humanistic help the pupil of schools - boarding schools (in particular to schoolboys with natural the slowed down rate of intellectual development), directed on liquidation of blanks in knowledge, skills of such children, their correction negative behavior, increase of a level o f their adaptation in existing vital conditions o f schools - boarding schools and to a social environment.
Service Quality, Customer Satisfaction, Consumer Behavior and Business Performance of Health and Fitness Center in Kuwait, UAE

This study investigated the quality of service of the health and fitness center as assessed by the clients in terms of reliability, responsiveness, assurance, empathy, and tangibles; the clients’ level of customers satisfaction on the services of the health and fitness center; the clients’ consumer behavior towards the services of the health and fitness center; and the business performance of the health and fitness center in terms of customer loyalty and customer retention rate. Further, it probed the relationship between the quality of service and clients’ level of customer satisfaction on the health and fitness center’s services; between the clients’ level of customer satisfaction and consumer behavior towards the health and fitness center’s services; and between the quality of service and business performance of the health and fitness center. It also ascertained whether service quality, level of customer satisfaction and level of consumer behavior could predict the business performance of the health and fitness center. This study utilized the descriptive-correlational research design, which involved 300 randomly selected clients of the Oxygen Gym Health and Fitness Center, in Kuwait, UAE. Findings revealed that the respondent-clients ‘strongly agree’ with an overall average of 3.48 to the quality of service of the health and fitness center, the Oxygen Gym, as to its services. The respondent-clients’ level of satisfaction on the services of health and fitness center was ‘very high’ with an overall weighted mean of 3.51. The respondent-clients ‘strongly agree’ with an average weighted mean of 3.50 to all the indicators of customer behavior, such as psychological, personal, social and cultural factors, towards the services of the health and fitness center. Results also revealed that the degree of customer loyalty as a qualitative measure of the Oxygen Gym’s business performance was very high. Results also shown that there was a significant relationship between level of customer satisfaction and consumer behavior towards the health and fitness center’s services