Reviewerly/exHarmony huggingface dataset:
Blockchain-based CP-ABE data sharing and privacy-preserving scheme using distributed KMS and zero-knowledge proof

Nowadays, the integration of blockchain technology with Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has drawn the researcher attention because it can provide key security auditing and transaction traceability in the context of data sharing. However, in a majority of existing blockchain-based CP-ABE schemes, private keys were still issued by one central authority that would lead to heavy computation, higher transaction costs, and restricted scalability within the decentralized system. To address these challenges, we present an enhancement approach towards utilizing distributed key management service (KMS) and zero-knowledge paradigms. In our improved novel blockchain system model, we define two types of blockchain nodes for the CP-ABE scheme through staking mechanism. Firstly, the proxy re-encryption nodes are introduced to offer secure multi-party management and distribution of the CP-ABE's master secret key, eliminating dependence on a central authority and producing proofs of re-encryption correctness. Secondly, the operator nodes can collect all transactional information in blockchain-based CP-ABE scheme and then send the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) proofs to verify the batch’s integrity via smart contract. Subsequently, we employ the staking economic incentive model with reward determination and slashing in the decentralized blockchain system to ensure network security. Finally, simulation results validate the effectiveness of our proposed scheme in achieving secure and efficient data sharing. Even amidst the pressure of 100 simultaneous transactions, the average response time for a single node remains at an approximate 28 s. Additionally, there is a notable decrease in on-chain gas consumption, with a gas reduction exceeding 61%. Comparative analyses further indicate that our blockchain-based CP-ABE scheme, in conjunction with a decentralized KMS, offers a superior balance between computational efficiency and functional capability.
Exploring explainable artificial intelligence techniques for evaluating cervical intraepithelial neoplasia (CIN) diagnosis using colposcopy images

Although artificial intelligence techniques have performed well in analysing medical images, it is essential to consider the degree of understanding or interpretation in evaluations. This is especially important when diagnostic support is involved, as the trustworthiness of machine learning predictions needs to be clarified and evaluated. For deep learning algorithms to be translated into clinical practice, they must be made less opaque. To examine the classification of cervical dysplasia-diagnosed patients, we will explore explainable artificial intelligence (XAI) approaches, by implementing seven different interpretation techniques (Saliency, XRAI, Integrated Gradients, Smooth Gradients, Smooth Integrated Gradients, Grad-CAM, Smoothgrad-CAM). These XAI techniques shall be explored in order to assess the performance of four Convolutional Neural Network models (AlexNet, VGG16, MobileNet, and InceptionNet). Our results show that saliency qualities most closely match expert annotations, and there is a moderate to strong association between a model's sensitivity and the agreement between humans and computers. We also discovered a relevant relationship between expert insights and computational learning. This highlights the potential role of human expertise in influencing effective computer learning.
Introducing teacher students to digital fabrication to support children’s mathematical learning

While digital fabrication has been closely linked to narratives of science, technology, engineering, and mathematics (STEM) education and design thinking, it can provide a gateway to reimagining mathematical teaching and deepening the understanding of both mathematical content and pedagogy. In this paper, we present a workshop series that engages teacher students in the creation of their own mathematical manipulatives using digital fabrication tools. Manipulatives are tangible objects that can be used to support children’s learning of mathematical concepts, such as numbers, fractions, or geometry. The workshops included activities for finding, adapting, creating, and sharing manipulatives (FACS) using 2D and 3D modeling and fabrication techniques. Our in-depth analysis of video recordings presents how teacher students successfully acquire digital fabrication skills and reflect on the use of customized manipulatives to support children’s mathematical learning. The findings suggest that introducing digital fabrication in teacher education programs can shift the focus from consuming ready-made manipulatives to creating customized materials that better suit the teaching context. The authors propose FACS as a specific approach for introducing digital fabrication to teacher students.
Application of cluster analysis to identify different reader groups through their engagement with a digital reading supplement

The focus of this study is the identification of reader profiles that differ in performance and progression in an educational literacy app. A total of 19,830 students in Grade 2 from 347 Elementary schools located in 30 different districts in the United States played the app from 2020 to 2021. Our aim was to identify unique groups of readers using an unsupervised statistical learning technique - cluster analysis. Six indicators generated from the students’ log files were included to provide insights into engagement and learning across four different reading-related skills: phonological awareness, early decoding, vocabulary, and comprehension processes. A key aim was to evaluate the implementation and performance of Gaussian mixture models, k-means, k-medoids, clustering large applications and hierarchical clustering, alongside provision of detailed guidance that can benefit researchers in the field. K-means algorithm performed the best and identified nine groups of readers. Children with low initial reading ability showed greater engagement with code-related games (phonological awareness, early decoding) and took longer to master these games, whereas children with higher initial ability showed more engagement with meaning-related games (vocabulary, comprehension processes). Our findings can inform further research that aims to understand individual differences in learning behaviour within digital environments both over time and across various cohorts of children.
Batch Active Learning of Reward Functions from Human Preferences

Data generation and labeling are often expensive in robot learning. Preference-based learning is a concept that enables reliable labeling by querying users with preference questions. Active querying methods are commonly employed in preference-based learning to generate more informative data at the expense of parallelization and computation time. In this paper, we develop a set of novel algorithms, batch active preference-based learning methods, that enable efficient learning of reward functions using as few data samples as possible while still having short query generation times and also retaining parallelizability. We introduce a method based on determinantal point processes (DPP) for active batch generation and several heuristic-based alternatives. Finally, we present our experimental results for a variety of robotics tasks in simulation. Our results suggest that our batch active learning algorithm requires only a few queries that are computed in a short amount of time. We showcase one of our algorithms in a study to learn human users’ preferences.
MHGCN+: Multiplex Heterogeneous Graph Convolutional Network

Heterogeneous graph convolutional networks have gained great popularity in tackling various network analytical tasks on heterogeneous graph data, ranging from link prediction to node classification. However, most existing works ignore the relation heterogeneity with multiplex networks between multi-typed nodes and the different importance of relations in meta-paths for node embedding, which can hardly capture the heterogeneous structure signals across different relations. To tackle this challenge, this work proposes a M ultiplex H eterogeneous G raph C onvolutional N etwork (MHGCN+) for multiplex heterogeneous network embedding. Our MHGCN+ can automatically learn the useful heterogeneous meta-path interactions of different lengths with different importance in multiplex heterogeneous networks through multi-layer convolution aggregation. Additionally, we effectively integrate both multi-relation structural signals and attribute semantics into the learned node embeddings with both unsupervised and semi-supervised learning paradigms. Extensive experiments on seven real-world datasets with various network analytical tasks demonstrate the significant superiority of MHGCN+ against state-of-the-art embedding baselines in terms of all evaluation metrics. The source code of our method is available at: https://github.com/FuChF/MHGCN-plus.
Can using a pre-trained deep learning model as the feature extractor in the bag-of-deep-visual-words model always improve image classification accuracy?

This article investigates whether higher classification accuracy can always be achieved by utilizing a pre-trained deep learning model as the feature extractor in the Bag-of-Deep-Visual-Words (BoDVW) classification model, as opposed to directly using the new classification layer of the pre-trained model for classification. Considering the multiple factors related to the feature extractor -such as model architecture, fine-tuning strategy, number of training samples, feature extraction method, and feature encoding method-we investigate these factors through experiments and then provide detailed answers to the question. In our experiments, we use five feature encoding methods: hard-voting, soft-voting, locally constrained linear coding, super vector coding, and fisher vector (FV). We also employ two popular feature extraction methods: one (denoted as Ext-DFs(CP)) uses a convolutional or non-global pooling layer, and another (denoted as Ext-DFs(FC)) uses a fully-connected or global pooling layer. Three pre-trained models-VGGNet-16, ResNext-50(32×4d), and Swin-B-are utilized as feature extractors. Experimental results on six datasets (15-Scenes, TF-Flowers, MIT Indoor-67, COVID-19 CXR, NWPU-RESISC45, and Caltech-101) reveal that compared to using the pre-trained model with only the new classification layer re-trained for classification, employing it as the feature extractor in the BoDVW model improves the accuracy in 35 out of 36 experiments when using FV. With Ext-DFs(CP), the accuracy increases by 0.13% to 8.43% (averaged at 3.11%), and with Ext-DFs(FC), it increases by 1.06% to 14.63% (averaged at 5.66%). Furthermore, when all layers of the pre-trained model are fine-tuned and used as the feature extractor, the results vary depending on the methods used. If FV and Ext-DFs(FC) are used, the accuracy increases by 0.21% to 5.65% (averaged at 1.58%) in 14 out of 18 experiments. Our results suggest that while using a pre-trained deep learning model as the feature extractor does not always improve classification accuracy, it holds great potential as an accuracy improvement technique.
Base-extension semantics for modal logic

Abstract In proof-theoretic semantics, meaning is based on inference. It may seen as the mathematical expression of the inferentialist interpretation of logic. Much recent work has focused on base-extension semantics, in which the validity of formulas is given by an inductive definition generated by provability in a ‘base’ of atomic rules. Base-extension semantics for classical and intuitionistic propositional logic have been explored by several authors. In this paper, we develop base-extension semantics for the classical propositional modal systems $K$, $KT$, $K4$ and $S4$, with $\square $ as the primary modal operator. We establish appropriate soundness and completeness theorems and establish the duality between $\square $ and a natural presentation of $\lozenge $. We also show that our semantics is in its current form not complete with respect to euclidean modal logics. Our formulation makes essential use of relational structures on bases.
An NLP Technique on Sentiment Analysis

We have to structure the data which was given to us from the Twitter social media for accurate analysis and make something outof it. We will be finding the sentiment behind the given comment by a user on twitter so that we can sort out the meaning of the text. To get the negative emotions of the text, we will be using different algorithms to find the intention behind it. Fathom this kind of issue, estimation investigation and profound learning methods are two combining methods. We are using Naive Bayes algorithms, SVM (Support Vector Machine) and otherclassification algorithms to get our required output.These are known deep learning /Machine Learning ways to extract the feelings in sentences. At the end of the result we will get the desired output and we will check the accuracy of our output accordingly.
The Formal Analysis on Negative Information Selections for Privacy Protection in Data Publishing

Negative information selection is an approach to protect the privacy by using negative information to replace original information. In this paper, we prove some bounds for negative information selection. Those bounds reveal the privacy protection strength of quantitative probability analysis. We also analyzed the reconstruction probability of original information from available negative information. The formal analysis can specify the bound on the strength of security and utility for negative information selection. Besides, we simulate brute force attacks under different data leakage ratios. Specifically, we calculate the attacker’s guess times before and after the data leakage. Experimental results indicate that the data leakage of over 30% can put the original information in a dangerous situation. Furthermore, we found that the leakage possibility has little relevance to the number of elements in the full set, but it is influenced by the ratio of the leaked information.
Semiparametric constant false alarm rate method for radar and sonar images

Abstract This work proposed a novel constant false alarm rate detection method based on Gaussian mixture model (GMM). The main contribution contains two aspects. First, the Gabor wavelet is used to determine the GMM component number. Second, the expression of probability of false alarm is derived based on the GMM parameter.
Periodization of the sociocultural genesis of Russian private school education (the 11th century – the 20s of the 21st century)

The study aims to present the periodization of the sociocultural genesis of private school education in Russia as an integrated process, combining historical and modern periods. The article addresses the comprehensive sociocultural study of the processes of historical genesis and modern development of the Russian non-state school as a pedagogical phenomenon to propose the periodization of its evolution. The scientific novelty lies in: the systematization of the historical and modern processes of private school education development, presented in the form of periodization, with the key sociocultural factor of influence, taken as its criterion; creating a holistic representation of the Russian private school genesis by combining in the proposed periodization all the sociocultural stages of its formation and development, starting from its inception in the 11th century and including the current period of its development; identifying the pedagogical specifics of the modern period of private schools development based on a theoretical analysis of the latest scientific and pedagogical publications devoted to their educational activities; introducing new information into the scientific discourse of pedagogical research, reflecting the analytics and statistics of modern private school education. As a result of the study, according to the major factor of sociocultural influence specified in the research, five periods of historical and pedagogical development of the Russian private school were defined, with all the stages of its historical genesis and modern development covered. The author has concluded that the complex socio-cultural influence of the society and state is the major factor determining the private school evolution and its pedagogical specifics in the modern period.
Artificial intelligence as a tool to optimize the work of a higher school teacher

The aim of the article is to justify the possibility of using artificial intelligence technologies – neural networks – to optimize the activities of a higher education teacher. The article discusses the educational capabilities of artificial intelligence for use in the higher education process. Procedural characteristics of artificial intelligence are presented in order to identify and describe its functions for use in higher education. The scientific novelty of the research is in identifying the potential of artificial intelligence technologies – neural networks – in relation to the educational process and optimizing the work of a higher education teacher. The results of the study include the following indicators: 1) artificial intelligence has a wide range of applications in the educational process; 2) neural networks have demonstrated the potential of their capabilities to optimize the work of a higher education teacher.
Identification and segregation of genes with improved recurrent neural network trained with optimal gene level and mutation level features

Even though many different approaches have been employed to address the complex mutational heterogeneity of cancer, finding driver genes is still problematic since other genomic factors cannot be fully integrated for combined analyses. This research paper presents a novel gene identification and segregation model with five key processes (a) pre-processing, (b) treatment of class imbalances, (c) feature extraction, (d) feature selection, and (e) gene classification. To increase the data quality, the gathered initial information is first pre-processed utilizing data cleaning and data normalization. This turns the raw data into something that is both useful and effective. In actuality, the sample is skewed against drivers because passenger mutation markers appear in proportionally less instances than drivers do. To address the Class Imbalance Problem, improved K-Means + SMOTE are applied to the preprocessed data. The most crucial characteristics, including those at the gene and mutation levels, are then extracted from the balanced dataset. To lessen the computational load in terms of time, the best features from the retrieved features are selected using Forensic interpretation tailored hunger food search optimization (FIHFSO). The ideal features are used to train the deep learning classifier that conducts the separation procedure. In this research, an Improved Recurrent Neural Network (I-RNN) is used to make a final decision about genes. At 90% of learning percentage, the accuracy of the proposed method achieves 0.98% of 0.83, 0.81, 0.65, 0.80, 0.92 and 0.63% which is compared to the other methods like HGS, FBIO, AOA, AO, GOA and PRO respectively.
Creative workshop as a modern form of professional development for teachers in Russian folk dance and its regional characteristics

The research objective is substantiation of the initiation of a creative workshop as a modern form of enhancing the professional skills of teachers in Russian folk dance and its regional peculiarities. The article argues for the need to ensure the process of preservation, study, and popularization of Russia's national heritage – Russian folk dance and its regional features. Russian folk dance allows forming in the younger generation a sense of love for their homeland, respect for its history, folk traditions, customs, and cultural diversity. The article emphasizes the importance of proper education on the specifics of Russian folk dance and the professional development of dance teachers. The leading forms of professional development for teachers and leaders of amateur dance groups include studying scientific and educational literature, grant activities, competitions and festivals, qualification courses, and masterclasses. The article places special emphasis on exploring the creative projects of the autonomous non-profit organization "Creative Union of Generations 'Transformation'." The modern form of enhancing the professional skills of dance teachers is presented as the cultural-educational project "All-Russian Creative Workshop on Russian Folk Dance and its Regional Features." The author provides an original definition of the essence of this new creative-pedagogical phenomenon, detailing the educational course program (from 2022 to 2024).
A Study on the Relationship Between Deep Learning and Statistical Models

Recently, deep learning has become a pervasive tool in prediction problems for structured and/or unstructured big data in various areas including science and engineering. In particular, deep neural network models (i.e. a basic core model of deep learning) can be viewed as an extension of statistical models by going through the incorporation of hidden layers. In this paper, we study the relationship between both models in terms of model structures and model learning. For this purpose, we also compare the predictive performances of both models, with two practical examples.
Cost-benefit Analysis of Adopting Blockchain Technology in the Radiology Unit of a District Hospital

Background: Blockchain technology has better security and immutable features, which are beneficial for healthcare data management. Decentralisation data management allows better sharing of medical images between units and departments. Objectives: The objectives of this study were to determine the cost of adopting the blockchain-based system for medical images and the cost-benefit analysis for five years of adoption of this technology at a district hospital in Malaysia. Methodology: Data regarding the yearly costs and workload for 2016 until 2018 were collected from a district hospital in Malaysia. The cost of blockchain implementation was obtained from a company that developed this technology. The cost-benefit analysis was done using the capital budgeting method for five years of this technology adoption. Sensitivity analysis was done to assess uncertainty conditions during this adoption. Results: The total yearly cost of Radiology Unit for 2018 was MYR490,168.96. The cost for adopting blockchain technology was MYR80,000 for the software, MYR115,000 for upgrading the assets and yearly maintenance cost was MYR16,000. The total present value of net benefits for five years of technology adoption was MYR865,295.01. Sensitivity analysis shows that consumables cost-saving is the most sensitive variable. Most pessimistic situation analyses showed positive net benefits. Conclusion: The adoption of blockchain technology in managing medical images offers numerous benefits to public hospitals in Malaysia. Investment in this technology can provide a good return to the organisation.