Reviewerly/exHarmony huggingface dataset:
Efficient Cervical Cell Lesion Recognition Method Based on Dual Path Network

Ubiquitous computing and artificial intelligence are widely used in the field of Wise Information Technology of Med. It promotes the development of intelligent diagnosis and treatment, as well as reducing the workload of doctors’ diagnosis and promoting the improvement of the diagnosis level of medical institutions in remote areas. The accurate detection of cervical cytopathy is related to the precise treatment and rehabilitation of patients. However, the low rate of accuracy in the existing cervical cytopathy image detection cannot satisfy the application needs of intelligent diagnosis. In this paper, a dual path network efficient detection method for cervical cytopathy (the proposed DSRNet50) is proposed, which is based on the deep learning method, and combines residual structure and dense connection. The proposed DSRNet50 is mainly based on residual structure, supplemented by dense connection paths, which improves the utilization of features, and maintains the ability of exploring new features by reducing feature redundancy. Meanwhile, the proposed DSRNet50 leverages packet convolution to reduce the computation burden of the network and eliminate the over fitting phenomenon of the network. The proposed DSRNet50 further exploits channel attention mechanism to recalibrate important features to suppress the propagation of irrelevant information. We use the Herlev dataset to verify the proposed DSRNet50 in the terms of the detection accuracy, parameter quantity, and computing complexity. The experiment results are presented to show the achievable performance of the proposed DSRNet50.
A Metadata-Based Approach to the Integration of Educational Resources in Ethnic Traditional Physical Education

Ethnic traditional sports are a comprehensive interdisciplinary field that draws on and integrates theories and methods from related disciplines, particularly anthropology, to reveal the laws governing the emergence and development of ethnic traditional sports. The evolution of ethnic traditional sports has historically contributed to the construction of theoretical systems, the transmission of traditional culture, the formation of distinctive fitness techniques, the intensification of socioeconomic interests, and the development of cultural self-confidence. The majority of ethnic traditional sports teaching resources, however, are unstructured data including documents, images, animations, programs, tools, and videos. They rely heavily on ethnic traditional physical education teachers for instructional design, and there is no way to create a unified integration and sharing platform. From the preparation, supplementation, and description of resource metadata specifications to the clustering, integration, and cataloging of digital resources and the implementation of retrieval and service interfaces, this paper focuses on the effective integration of high-quality digital traditional ethnic sports resources in the process of network sharing.
Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

Given an unlabeled dataset and an annotation budget, we study how to selectively label a fixed number of instances so that semi-supervised learning (SSL) on such a partially labeled dataset is most effective. We focus on selecting the right data to label, in addition to usual SSL's propagating labels from labeled data to the rest unlabeled data. This instance selection task is challenging, as without any labeled data we do not know what the objective of learning should be. Intuitively, no matter what the downstream task is, instances to be labeled must be representative and diverse: The former would facilitate label propagation to unlabeled data, whereas the latter would ensure coverage of the entire dataset. We capture this idea by selecting cluster prototypes, either in a pretrained feature space, or along with feature optimization, both without labels. Our unsupervised selective labeling consistently improves SSL methods over state-of-the-art active learning given labeled data, by 8 to 25 times in label efficiency. For example, it boosts FixMatch by 10% (14%) in accuracy on CIFAR-10 (ImageNet-1K) with 0.08% (0.2%) labeled data, demonstrating that small computation spent on selecting what data to label brings significant gain especially under a low annotation budget. Our work sets a new standard for practical and efficient SSL.
Partition and Analytic Rank are Equivalent over Large Fields

We prove that the partition rank and the analytic rank of tensors are equal up to a constant, over finite fields of any characteristic and any large enough cardinality depending on the analytic rank. Moreover, we show that a plausible improvement of our field cardinality requirement would imply that the ranks are equal up to 1+o(1) in the exponent over every finite field. At the core of the proof is a technique for lifting decompositions of multilinear polynomials in an open subset of an algebraic variety, and a technique for finding a large subvariety that retains all rational points such that at least one of these points satisfies a finite-field analogue of genericity with respect to it. Proving the equivalence between these two ranks, ideally over fixed finite fields, is a central question in additive combinatorics, and was reiterated by multiple authors. As a corollary we prove, allowing the field to depend on the value of the norm, the Polynomial Gowers Inverse Conjecture in the d vs. d-1 case.
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series data

1. Inferring ecological interactions is hard because we often lack suitable parametric representations to portray them. Neural ordinary differential equations (NODEs) provide a way of estimating interactions nonparametrically from time series data. NODEs, however, are slow to fit, and inferred interactions have not been truthed. 2. We provide a fast NODE fitting method, Bayesian neural gradient matching (BNGM), which relies on interpolating time series with neural networks, and fitting NODEs to the interpolated dynamics with Bayesian regularisation. We test the accuracy of the approach by inferring ecological interactions in time series generated by an ODE model with known interactions. We also infer interactions in experimentally replicated time series of a microcosm featuring an algae, flagellate, and rotifer population, as well as in the hare and lynx system. 3. Our BNGM approach allows us to cut down the fitting time of NODE systems to only a few seconds. The method provides accurate estimates of ecological interactions in the artificial system, as linear and nonlinear true interactions are estimated with minimal error. In the real systems, dynamics are driven by a mixture of linear and nonlinear ecological interactions, of which only the strongest are consistent across replicates. 4. Overall, NODEs alleviate the need for a mechanistic understanding of interactions, and BNGM alleviates the heavy computational cost. This is a crucial step availing quick NODE fitting, cross-validation, and uncertainty quantification, as well as more objective estimation of interactions, and complex context dependence, than parametric models.
The Role of Explanatory Value in Natural Language Processing

A key aim of science is explanation, yet the idea of explaining language phenomena has taken a backseat in mainstream Natural Language Processing (NLP) and many other areas of Artificial Intelligence. I argue that explanation of linguistic behaviour should be a main goal of NLP, and that this is not the same as making NLP models explainable. To illustrate these ideas, some recent models of human language production are compared with each other. I conclude by asking what it would mean for NLP research and institutional policies if our community took explanatory value seriously, while heeding some possible pitfalls.
Patching Weak Convolutional Neural Network Models through Modularization and Composition

Despite great success in many applications, deep neural networks are not always robust in practice. For instance, a convolutional neuron network (CNN) model for classification tasks often performs unsatisfactorily in classifying some particular classes of objects. In this work, we are concerned with patching the weak part of a CNN model instead of improving it through the costly retraining of the entire model. Inspired by the fundamental concepts of modularization and composition in software engineering, we propose a compressed modularization approach, CNNSplitter, which decomposes a strong CNN model for $N$-class classification into $N$ smaller CNN modules. Each module is a sub-model containing a part of the convolution kernels of the strong model. To patch a weak CNN model that performs unsatisfactorily on a target class (TC), we compose the weak CNN model with the corresponding module obtained from a strong CNN model. The ability of the weak CNN model to recognize the TC can thus be improved through patching. Moreover, the ability to recognize non-TCs is also improved, as the samples misclassified as TC could be classified as non-TCs correctly. Experimental results with two representative CNNs on three widely-used datasets show that the averaged improvement on the TC in terms of precision and recall are 12.54% and 2.14%, respectively. Moreover, patching improves the accuracy of non-TCs by 1.18%. The results demonstrate that CNNSplitter can patch a weak CNN model through modularization and composition, thus providing a new solution for developing robust CNN models.
Acoustic-Linguistic Features for Modeling Neurological Task Score in Alzheimer's

The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and track aging-associated disease in cognitive function among older adult populations is imperative. In particular, research related to automatic detection and evaluation of Alzheimer's disease (AD) is critical given the disease's prevalence and the cost of current methods. As AD impacts the acoustics of speech and vocabulary, natural language processing and machine learning provide promising techniques for reliably detecting AD. We compare and contrast the performance of ten linear regression models for predicting Mini-Mental Status Exam scores on the ADReSS challenge dataset. We extracted 13000+ handcrafted and learned features that capture linguistic and acoustic phenomena. Using a subset of 54 top features selected by two methods: (1) recursive elimination and (2) correlation scores, we outperform a state-of-the-art baseline for the same task. Upon scoring and evaluating the statistical significance of each of the selected subset of features for each model, we find that, for the given task, handcrafted linguistic features are more significant than acoustic and learned features.
FACTORS AFFECTING THE TIMELINESS OF DISTRIBUTION OF SCHOOL OPERATIONAL ASSISTANCE FUNDS (BOS PROGRAM) AT SMK MUHAMMADIYAH DELANGGU IN 2020/2021ACADEMIC YEAR

BOS is a fund implementation of policies to expand the distribution of access to education. The purpose of this research is to determine the factors that affect the timeliness of BOS distribution at SMK Muhammadiyah Delanggu in the Academic Year 2020/2021. This research is a qualitative descriptive study. The main data used are observation and document review also additional interviews. The data was analyzed in 3 steps, data collection, data reduction and data display. The results showed that the process of implementing, taking, distributing and using BOS at SMK Muhammadiyah Delanggu was in accordance with government instructions or JUKNIS. The distribution of BOS has also been right on target in accordance with the needs of students and schools in order to improve the quality. The distribution can also be said to be on time because it is supported by the productivity of staffs who are motivated to distribute funds on time. The workloads are seemly balanced for each staff. The used of proper facilities also increase the factor of timeliness.
PENGARUH METODE RESITASI TERHADAP MOTIVASI BELAJAR SISWA PADA MATA PELAJARAN PAI BP KELAS X DI SMKN 14 BANDUNG

This research is motivated by problems related to learning motivation in PAI BP subjects including, lack of focus during teaching and learning activities, students are not responsive or students are not active, lack of understanding of students in mastering the material provided. The objectives of this research are knowing the application of the Recitation Method on the subject of PAI BP class X at SMKN 14 Bandung, student learning motivation on PAI BP class X subjects at SMKN 14 Bandung, and the effect of the Recitation Method on students' learning motivation on the subject of PAI BP class X at SMKN 14 Bandung. This study uses a quantitative approach with descriptive analysis research methods. The total population in this study was 134 students and a sample of 40 students. The data analysis technique used is validity test, reliability test, parametric statistic prerequisite test, hypothesis test, and coefficient of determination test. Based on the results of the study, it can be seen that: the recitation method and student learning motivation, obtained an average value of 3.73 and 3.76, which are in the interval (3.41-4.20) including in the good category. Based on the data processing performed, the significance value of the recitation method in the coefficients table is 0.000, which is less than 0.05. It is concluded that Ho is rejected and H1 is accepted, which means that the recitation method has an effect on students' learning motivation. Based on the test results, the coefficient of determination has a correlation value of 0.331, the coefficient of determination is R20.109 which implies that the effect of the independent variable on the dependent variable is 10.9%.
Analisis Indeks Risiko Genangan Air di Kecamatan Tarakan Barat Kota Tarakan

Almost every year the city of Tarakan experiences problems regarding standing water during the rainy season, especially in the West Tarakan sub-district which is the center of Tarakan City. This puddle not only submerged settlements and offices but also shops and road access which caused considerable economic losses. This can be overcome by analyzing the risk of waterlogging where each area has a different level of vulnerability. The purpose of this study is to determine high, medium, and low risk areas for standing water in the Drainage Area of West Tarakan District, Tarakan City. The research implementation method includes data collection (institutional and field surveys), analyzing the index of vulnerability, vulnerability and capacity and analyzing the flood risk map. The index analysis is carried out through simulated parameter score analysis. Based on the results of the analysis of the research data, it can be concluded that the water inundation risk index in the West Tarakan District is as follows, the sub-districts that are on the highest risk index are Karang Anyar sub-district, then Karang Rejo village, kelurahan Karang balik and Kelurahan Karang Newar Pantai Nerda is at index 2 or is at a moderate risk index, then the kelurahan that has the lowest risk index is Hope Reef with a risk index of 1
Designing and implementing individual learning routes for mathematics teachers to overcome professional deficiency

Introduction. In recent years, the problem of staffing mechanisms in the field of education has been actively discussed in the global community. Teachers are required to restructure their professional position with regard for modern demands of the society and the state, to improve their skills in designing the content of education. The latter is implemented through professional development of teachers as well as creation of personalised trajectories for the development of professional competencies. The purpose of the study is to describe a scientifically grounded approach to designing individual educational routes for mathematics teachers, aimed at overcoming the existing professional deficiency and creating due conditions for their realisation. Methodology and methods. The research involved 103 teachers of mathematics of mainstream educational institutions of Krasnoyarsk and Krasnoyarsk region, Russian Federation, aged below 35, having work experience in their profession of at least one year. Open observation of the teachers’ work, interviewing and online survey was used; in addition, the results of the final state certification of 9th and 11th-year pupils of the region were taken into account. The statistical analysis of the results was made using the Pearson’s chi-squared test. Results. The model of individual educational route of mathematics teachers, aimed to overcome professional deficiency, was designed and implemented according to the following stages: identification of teachers’ subject-specific and methodological deficiency; development of technology for design of individual educational route to overcome teachers’ professional deficiency; presentation of action plan for individual educational routes. The results of subsequent diagnostic work of mathematics teachers who took part in the study showed a statistically significant increase in the performance level in all parameters (p &lt; 0.05). Conclusion. The proposed scientifically grounded approach to the design of individual educational routes makes it possible to eliminate a number of professional deficiency features characterising the work of mathematics teachers. The realisation of the author’s algorithm for designing the route contributes to professional development of a teacher. The research materials can serve as a basis for the development of personalised individual routes for teachers of any professional profile.
Nonparametric Regression Modeling Based on Spline Truncated Estimator on Simulation Data

Regression analysis is one of the statistical analysis used to estimate the pattern of the relationship between predictor variables and response variables . In general, the approach to estimating the regression function is the parametric regression, the nonparametric regression and the semiparametric regression. The approach with parametric regression is used if the shape of the regression curve is assumed to follow a certain pattern such as linear, quadratic, cubic and so on, but in fact there is an unknown pattern of relationship between predictor variables and response variables, so nonparametric regression is used. Then the combination of parametric and nonparametric regression is semiparametric regression. One of the well-known nonparametric regression estimators is the spline truncated. This study was conducted by simulating the relationship pattern of the response variable and the predictor variable that not have specific pattern by following a trigonometric function that formed a regression curve with a standard deviation of 0,05 and 0,25 which formed a different distribution of data, then will be approached with parametric regression (linear, quadratic, cubic) and nonparametric regression (spline truncated linear). Based on the coefficient of determination of each standard deviation, it will shows that the nonparametric regression approach has high flexibility so that it is able to adjust the form of regression curve estimation by itself
Document Image Binarization in JPEG Compressed Domain using Dual Discriminator Generative Adversarial Networks

Image binarization techniques are being popularly used in enhancement of noisy and/or degraded images catering different Document Image Anlaysis (DIA) applications like word spotting, document retrieval, and OCR. Most of the existing techniques focus on feeding pixel images into the Convolution Neural Networks to accomplish document binarization, which may not produce effective results when working with compressed images that need to be processed without full decompression. Therefore in this research paper, the idea of document image binarization directly using JPEG compressed stream of document images is proposed by employing Dual Discriminator Generative Adversarial Networks (DD-GANs). Here the two discriminator networks - Global and Local work on different image ratios and use focal loss as generator loss. The proposed model has been thoroughly tested with different versions of DIBCO dataset having challenges like holes, erased or smudged ink, dust, and misplaced fibres. The model proved to be highly robust, efficient both in terms of time and space complexities, and also resulted in state-of-the-art performance in JPEG compressed domain.
Picturing aesthetic futures: values and visual tools within shared decision-making

In person-centred healthcare improvement, design thinking[1][1] is being applied to multidisciplinary healthcare practices. In this paper, we will argue for design methods to operate within, and to support the integration of, everyday healthcare interactions. We draw on two specific public
A Scalable Blockchain Framework for ELA Assessment

As a popular technology in the information age, blockchain is having a profound impact on various industries, including the field of education. Blockchain technology can accelerate the modernization of education in China, adapt to the new situation of epidemic prevention and control needs, and promote the renewal of the management and training modes of college students. Blockchain technology is decentralized, tamper-proof, traceable, trackable, open and transparent, which is conducive to the realization of individual student-centered teaching and learning, as well as an open, and transparent teaching process, which can motivate the learning motivation of English majors and improve the effectiveness of the student management and training models. To address the problem of low accuracy of the traditional English proficiency classification methods, this article aims to explore the application of blockchain technology in the management and training model of English majors and its feasibility. We propose a proficiency evaluation model based on a Discrete Hopfield Neural Network (DHNN). Firstly, the hierarchical analysis method is used to construct the evaluation index system of students’ English ability, and then the ability classification indexes are divided into 5 levels.The network achieves the classification of students’ English proficiency through the associative memory of the classification criteria, and the classification results are compared with those of the BPNN model. The simulation results show that the classification accuracy of the BPNN model is 80.0% and that of the DHNN model is 100.0%. The DHNN model has improved the classification accuracy and generalization ability, and the model establishment process is simple and the results are intuitive, which verifies the effectiveness of the proposed model