Reviewerly/exHarmony huggingface dataset:
Development of Acmeological Competence in Rising Social Teachers

In the modern educational process, it is important to constantly develop the personality studied in the context of acmeology. The relevance of this study is determined by the issue of the development of acmeological competence in rising social educators. The purpose of the study was to study the level of development of acmeological competence and its components in students. The methodology is based on the experimental method presented by the student survey. Fisher’s exact test, Chi-squared distribution, Student’s t -test were also used in the study. This study was conducted at Al-Farabi Kazakh National University and Abay Kazakh National Pedagogical University. The sample consisted of 253 students. The results show that 22.13% of first-year respondents had low motivation, 52.96% – medium, 24.90% – high; in the fourth-year, the level of motivation changed: 10.67% of respondents had low motivation, 46.25% – medium, 43.08% – high. The average level of motivation was 15.10 ± 2.37 and 17.00 ± 1.96 among the first- and fourth-year students, respectively, and had a tendency to increase. Thus, its average values grew toward the completion of studies. It was found that 3.56% of first-year respondents were too cautious, 51.38% were moderately cautious, and 48.62% were inclined to take risks; in the context of the fourth-year students, these indicators were 2.37, 69.96, and 30.04%, respectively. Research has shown that with age, knowledge, and experience, the willingness to take risks decreases. When conducting a correlation analysis, it was found that there is an average positive correlation between the level of motivation and the level of propensity to take risks. The results of this work indicate the need for further scientific research on the issue of acmeological competence not only of social teachers, but also of other educators in order to achieve heights in personal development and improve the learning process. In the future, it is planned to study the types of motivation and methods of its increase (self-motivation, affirmation, visualization).
The Impact of Feature Extraction to Naïve Bayes Based Sentiment Analysis on Review Dataset of Indihome Services

Indihome is a product of PT Telekomunikasi Indonesia as an internet service provider or internet service provider (ISP) in Indonesia. Every product or service offered to the public certainly has its advantages and disadvantages, as well as Indihome. From the advantages and disadvantages of Indihome services, we can do a technique, namely sentiment analysis. In this study, sentiment analysis was carried out regarding public responses or reviews about IndiHome services on Twitter social media. This study uses a comparison of TF-IDF and Word2Vec feature extraction, and the classification method used is the nave Bayes classifier. The accuracy results obtained in this study were 96% using the TF-IDF feature extraction and testing was carried out using an unseen data test that was selected randomly resulting in an accuracy of 92%. While the accuracy value obtained by using the Word2Vec feature extraction is 60% by testing using unseen test data that was selected randomly resulting in an accuracy value of 44%.&#x0D;
Academic Information System Management to Improve Service Quality to Students During the Covid-19 Pandemic at Universities in Jambi Province

Higher education with standards and good academic service management quality is the goal of every university's efforts. In Jambi Province, in particular, good management applied to a university is one of the essential factors in improving the quality of service for students, the community, or the university itself. Not a few universities whose academic service quality still does not meet the satisfaction threshold in the assessment, one of which is offline-based services that will usually hinder the delivery of existing information. The universities of UNAMA (University of Stikom Dinamika Bangsa) and IAI Tebo in Jambi Province are universities whose every delivery of academic information focuses on computers and technology. The advantages and disadvantages of the information system that is run, it is necessary to conduct a study to combine each of the advantages and disadvantages of the two universities. By applying qualitative methods, it is hoped will provide helpful research results. The focus of this research is directed at (1). Planning, (2). Implementation, (3). Evaluation (4). Solutions or follow-ups in improving the quality of academic services at universities using the Deming cycle management theory. This study uses a descriptive method with a qualitative approach and data collection by conducting observations, interviews, and documentation studies, so it can be concluded that a complete management information system can be applied to other universities, especially those that are still offline.
Most Effective Sampling Scheme for Prediction of Stationary Stochastic Processes

The problem of finding optimal sampling schemes has been resolved in two models. The novelty of this study lies in its cost efficiency, specifically, for the applied problems with expensive sampling process. In discussed models, we show that some observations counteract other ones in prediction mechanism. The autocovariance function of underlying process causes mentioned result. Our interesting result is that, although removing neutralizing observations convert sampling scheme to nonredundant case, it causes to worse prediction. A simulation study confirms this matter, too.
HE-PEx: Efficient Machine Learning under Homomorphic Encryption using Pruning, Permutation and Expansion

Privacy-preserving neural network (NN) inference solutions have recently gained significant traction with several solutions that provide different latency-bandwidth trade-offs. Of these, many rely on homomorphic encryption (HE), a method of performing computations over encrypted data. However, HE operations even with state-of-the-art schemes are still considerably slow compared to their plaintext counterparts. Pruning the parameters of a NN model is a well-known approach to improving inference latency. However, pruning methods that are useful in the plaintext context may lend nearly negligible improvement in the HE case, as has also been demonstrated in recent work. In this work, we propose a novel set of pruning methods that reduce the latency and memory requirement, thus bringing the effectiveness of plaintext pruning methods to HE. Crucially, our proposal employs two key techniques, viz. permutation and expansion of the packed model weights, that enable pruning significantly more ciphertexts and recuperating most of the accuracy loss, respectively. We demonstrate the advantage of our method on fully connected layers where the weights are packed using a recently proposed packing technique called tile tensors, which allows executing deep NN inference in a non-interactive mode. We evaluate our methods on various autoencoder architectures and demonstrate that for a small mean-square reconstruction loss of 1.5*10^{-5} on MNIST, we reduce the memory requirement and latency of HE-enabled inference by 60%.
A single $T$-gate makes distribution learning hard

The task of learning a probability distribution from samples is ubiquitous across the natural sciences. The output distributions of local quantum circuits form a particularly interesting class of distributions, of key importance both to quantum advantage proposals and a variety of quantum machine learning algorithms. In this work, we provide an extensive characterization of the learnability of the output distributions of local quantum circuits. Our first result yields insight into the relationship between the efficient learnability and the efficient simulatability of these distributions. Specifically, we prove that the density modelling problem associated with Clifford circuits can be efficiently solved, while for depth $d=n^{Ω(1)}$ circuits the injection of a single $T$-gate into the circuit renders this problem hard. This result shows that efficient simulatability does not imply efficient learnability. Our second set of results provides insight into the potential and limitations of quantum generative modelling algorithms. We first show that the generative modelling problem associated with depth $d=n^{Ω(1)}$ local quantum circuits is hard for any learning algorithm, classical or quantum. As a consequence, one cannot use a quantum algorithm to gain a practical advantage for this task. We then show that, for a wide variety of the most practically relevant learning algorithms -- including hybrid-quantum classical algorithms -- even the generative modelling problem associated with depth $d=ω(\log(n))$ Clifford circuits is hard. This result places limitations on the applicability of near-term hybrid quantum-classical generative modelling algorithms.
Learning from and with others: Analysis of interactions as opportunities for teacher professional development

Introduction: The emerging perspective of Learning Ecologies states that one of the key elements in the process of learning and professional development of the faculty is the interaction and relationship with other people. Relationships constitute an essential basis in the design and creation of knowledge networks, which, in turn, forms a basic strategy of ecological learning. Goals: Consequently, the following research is proposed, which aims to investigate the professional development processes of university teachers, focusing specifically on the analysis of interactions. Methods To respond to the stated objective, a qualitative study is proposed, framed in the tradition of Case Study research. The limits that define and specify our case study are essentially defined by: a) teaching excellence, b) the branch of knowledge, and c) the institution. Thus, the participants selected to be part of our study are the five best Health Sciences teachers at the University of A Coruña (Spain). As a data collection strategy, 3 semi-structured interviews were conducted with each teacher, through which we intended to assess the frequency and mode of interaction that have significantly promoted and favored their teaching development throughout their academic-professional career. The data analysis consisted of content analysis, with the support of the Atlas.ti software. Results: The results are structured according to the subcategories generated around the ecological element of interactions: professional sphere and personal sphere. Conclusions: One of the conclusions drawn indicates that the interactions most valued by the participating teachers to develop professionally are those established in the professional field (especially with students, colleagues and mentors), without denying the influence, also significant, that the interactions exert. of the most personal environment.
Left Ventricle Segmentation in Cardiac MR Images via an Improved ResUnet

Cardiovascular diseases are reported as the leading cause of death around the world. Automatic segmentation of the left ventricle (LV) from magnetic resonance (MR) images is essential for an early diagnosis. An enhanced ResUnet is proposed in this paper to improve the performance of extracting LV endocardium and epicardium from MR images, improving the accuracy of the model by introducing a medium skip connection for the contracting path and a short skip connection for the residual unit. Also, a depth-wise separable convolution replaces the typical convolution operation to improve training efficiency. In the MICCAI 2009 LV segmentation challenge test dataset, the percentages of "good" contours, dice metric, and average perpendicular distance of endocardium (epicardium) are 99.12% ± 2.29%(100% ± 0%), 0.93 ± 0.02 (0.96 ± 0.01), and 1.60 ± 0.42 mm (1.37 ± 0.23 mm), respectively. Experimental results demonstrate that the proposed model obtains promising performance and outperforms state-of-the-art methods. By incorporating these various skip connections, the segmentation accuracy of the model is significantly improved, while the depth-wise separable convolution also improves the model efficiency.
The keys to the future? An examination of associative versus discriminative accounts of Serial Pattern Learning

Sequence learning is fundamental to a wide range of cognitive functions, and explaining how sequences --- and the relations between the elements they comprise --- are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine this question during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. Experiment 3 tested for `chunking' of these letters into `words'. The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: associative transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities.No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response.In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error.
Game Edukasi Introduksi Bilangan dan Operasi Aritmatika dengan Penerapan Algoritma Fisher–Yates Shuffle

The introduction of numbers and arithmetic operations is basic learning in Mathematics aimed at students at the kindergarten level, where the learning method is still conventional and tedious. The solution that can be applied is to use an android educational game using the Fisher-Yates Shuffle method. useful in obtaining random permutations of a series of questions that allow the arrangement of similar questions to be impossible to appear again while avoiding students remembering the questions and their answers. The idea of making games is to create a new impression and make it easier to learn the introduction of numbers and arithmetic operations. The technique used is Game Development Life Cycle and Android-based Unity 3D software in the development of this educational game application. The application of the concept of User-Centered Design (UCD) is used in the evaluation in measuring game excellence with a Playability testing approach in the form of 4 criteria, namely gameplay, usability, mobility, and game story. The questionnaire was filled out by 10 respondents, namely kindergarten students with the assistance of their parents. Based on the assessment, it is determined that this game gets a score where gameplay is 75%, usability is 75%, mobility is 70%, and the game story is 70%. support kindergarten students in learning number introduction and arithmetic operations.
Image Target Recognition Based on Improved Convolutional Neural Network

Convolutional neural network (CNN) algorithm is a very important branch of deep learning research, which has been widely applied in many fields and achieved excellent results, especially in computer vision, where convolutional neural network has made breakthroughs in image classification and object detection. Convolutional neural network architecture can realize more efficient network training through the final combination of different modules, and the convolutional neural network training does not need to actively extract image features and can directly carry out end-to-end training and prediction. At first, this paper analyzed some problems of the current image recognition and expounds the progress of convolution neural network in image recognition and then studied the traditional algorithm of target recognition, including traditional recognition algorithm framework of target, the target orientation, feature extraction, classifier classification, etc., and the traditional target recognition algorithm is compared with those of the target recognition algorithm of deep learning. On the basis of the above research, an improved model of CNN is proposed, which focuses on the structural design and network optimization of convolutional neural network and designs a more efficient convolutional neural network. Test experiments verify the effectiveness of the proposed model, which not only achieves lower error rate, but also greatly reduces the number of network parameters and has stronger learning ability.
A New Approach to Pedagogical Local History in the Professional Development of the Teacher of the Future: From Comprehension to Translation

The importance of the problem under study stems from the rising demands of the state and modern society for a teacher’s professional growth. The objective of this paper is to establish a new format of pedagogical local history as a strategic resource for professional development of the teacher of the future. The notion of pedagogical local history has been explained from the standpoint of a branch of pedagogical science that investigates the past and current situation of education in the country and its areas. Through the generalization of sources, clarification and interpretation of formulations, a new approach was proposed to understanding pedagogy as it relates to the professional readiness of the future teacher to independently manage his or her career, to determine the trajectory of profession, and to set desirable goals to promote the cultural heritage through the interaction of times, the development of a new one, and the preservation of the past. By incorporating the main principles of digitalization into pedagogical practices, regional pedagogical traditions were articulated in the context of the cultural heritage of generations of the Orenburg region. Regional factors have been found as having a novel quality to the seemingly well-known processes of professional evolution of the future teacher due to their “territorial” nature. The influence of pedagogical local history on the construction of a distinctive individual-creative style of a new evolution teacher, as well as the enrichment of historical and pedagogical heritage, was assessed analytically. The authors conclude that evaluating the strategic resources of pedagogical local history, which could play a certain reinforcing role in preparing a teacher for innovative pedagogical activities, based on the traditions and historical experience of studying the region and understanding the modern problems of its development, is of current importance. The Google Forms tool was used to conduct an online survey of 235 instructors.
An Adaptive shrinkage function for image denoising based on the neighborhood characteristics

The shrinkage function has an important effect on the image denoising results. An adaptive shrinkage function is developed in this paper to shrink the small coefficients properly for image denoising based on neighborhood characteristics. The shrinkage function is determined by the number of large coefficients near the current signal coefficients. In this way, different shrinkage functions can be adaptively used to deal with different coefficients in the process of image denoising, instead of using fixed shrinkage functions. Experimental results show that the SNR of the image processed by the adaptive shrink function algorithm is better than that processed by the soft threshold, hard threshold, and neighborhood shrink algorithm. Moreover, compared with the traditional soft threshold, hard threshold and neighborhood shrink algorithm, the PSNR of the algorithm using adaptive shrink function increases by 3.68dB, 2.28dB and 0.61dB, respectively. In addition, the proposed new algorithms, soft threshold and hard threshold, are combined with empirical Wiener filtering and shift invariant (TI) scheme to compare their image noise reduction effects. The results show that the PSNR can be improved significantly by using the adaptive shrink function algorithm combined with empirical Wiener filtering and shift invariant (TI) scheme.
Variational Nearest Neighbor Gaussian Process

Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points to form a low-rank approximation to the covariance matrix. In this work, we instead exploit a sparse approximation of the precision matrix. We propose variational nearest neighbor Gaussian process (VNNGP), which introduces a prior that only retains correlations within K nearest-neighboring observations, thereby inducing sparse precision structure. Using the variational framework, VNNGP's objective can be factorized over both observations and inducing points, enabling stochastic optimization with a time complexity of O($K^3$). Hence, we can arbitrarily scale the inducing point size, even to the point of putting inducing points at every observed location. We compare VNNGP to other scalable GPs through various experiments, and demonstrate that VNNGP (1) can dramatically outperform low-rank methods, and (2) is less prone to overfitting than other nearest neighbor methods.
Clustering Based Approach for Ground Truth Inference in Crowdsourced Data

Crowdsourcing provides a means of gathering data from the public in order to infer what the ground truth label of an unfamiliar entity is. Such data are not used for decision making in their raw form until further processing is done to infer ground truth from the crowdsourced data. This paper presents a detailed comparative analysis of the ground truth inference ability of three clustering algorithms on crowd sourced datasets with different experimental scenarios (Initializing centroids and extracting class labels). The algorithms include, the self-organizing maps, the k-means and the expectation maximization clustering algorithm. The three algorithms were experimented on different datasets. The datasets used are Adult2, weather sentiments, emotion, valence5 and employee review dataset Four possible experimental scenarios for inferring the ground truth label from the curated dataset were analysed. The first scenario makes use of the clustering algorithm alone relying on the inner workings of the algorithm to predict the ground truth, while the second scenario makes use of an extract class label mechanism where the ground truth label was inferred by performing a further analysis on the clusters provided by the algorithm. In the third scenario, the centroids of the clustering algorithm were pre-initialized by setting the maximum value in each class from the curated data as a centroid, where centroid might mean something different relative to the particular algorithm. The fourth experimental scenario is a combination of the second and third scenario. Experimental results show that the self-organizing map (SOM) performs best across all the datasets when the weights of the units in the SOM are pre-initialized. SOM had the best performance on the weather sentiments dataset recording 92.49% accuracy and ROC AUC score of 0.88. It also recorded the best overall average accuracy of 50.2% and ROC AUC score of 0.59365 across all the datasets.