Reviewerly/exHarmony huggingface dataset:
Decision Support System for Selection of Healthy Toddler using MOORA Method

Toddlers are the human stage after infancy, toddlers are one of the most important stages in the quality development of human growth. one way to develop health in toddlers is to carry out routine checks at Public Health Center. Public Health Center has not yet provided a system for selecting healthy toddlers and also requires a system for selecting healthy toddlers. Therefore a decision support system is needed to make decisions related to checking the health of toddlers. decision support system is part of a computer-based information system, which is used to make a decision, in making a decision support system the Multi Objective Optimization by Ratio Analysis (MOORA) method is used. Based on the calculations made regarding the decision to check the health of toddlers, the MOORA method uses weighting criteria and determines the type of criteria, and MOORA does not have sub-criteria weighting provisions using FMADM, FMADM weighting reduces ambiguity because the bigger the value, the better, but if the value is too large then it is not good, such as body weight. It is necessary to provide more sub-criteria so that the accuracy in ranking is better and more accurate with a scale of 0-5. The optimization value for A1 is obtained by adding the cells from A1 0.6765 + 1.0070 + 0.2626 + 0.1195 = 2.0656 and Alternatives A2, A3, A15 have the most optimal weight values ​​with optimization values ​​(2.3282).
Implications of imbalanced datasets for empirical ROC-AUC estimation in binary classification tasks

The area under the curve (AUC) is the most popular measure for summarizing a binary classifier's receiver operating characteristic (ROC) curve. Therefore, it is essential to ensure that the AUC estimation is accurate. One straightforward and popular estimation approach is to calculate the empirical AUC from the data. However, one must look closely at the behaviour of this point estimator, particularly its variance. This study demonstrates both analytically and empirically that the empirical AUC estimation could be highly volatile in many circumstances when applied to an imbalanced dataset. To be more specific, we have proved that under some frequently encountered circumstances, variances of the empirical AUC estimator increase with the imbalanced level of the dataset. Hence, under the imbalanced setting, variances could be high. Furthermore, we conduct several simulations and experiments to solidify our findings. Therefore, extra attention must be paid when the empirical ROC-AUC is used to summarize the classifier's performance, especially when the dataset presents high class imbalance.
Avoiding Optimal Mean Robust and Sparse BPCA with L1-norm Maximization

&lt;p&gt;Recently, the robust PCA/2DPCA methods have achieved great success in subspace learning. Nevertheless, most of them have a basic premise that the average of samples is zero and the optimal mean is the center of the data. Actually, this premise only applies to PCA/2DPCA methods based on L2-norm. The robust PCA/2DPCA method with L1-norm has an optimal mean deviate from zero, and the estimation of the optimal mean leads to an expensive calculation. Another shortcoming of PCA/2DPCA is that it does not pay enough attention to the instinct correlation within the part of data. To tackle these issues, we introduce the maximum variance of samples&amp;rsquo; difference into Block principal component analysis (BPCA) and propose a robust method for avoiding the optimal mean to extract orthonormal features. BPCA, which is generalized from PCA and 2DPCA, is a general PCA/2DPCA framework specialized in part learning, can makes better use of the partial correlation. However, projection features without sparsity not only have higher computational complexity, but also lack semantic properties. We integrate the elastic network into avoiding optimal mean robust BPCA to perform sparse constraints on projection features. These two BPCA methods (non-sparse and sparse) make the presumption of zero-mean data unnecessary and avoid optimal mean calculation. Experiments on reference benchmark databases indicate the usefulness of the proposed two methods in image classification and image reconstruction.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The Influence of Positivism on E. V. D e Roberti’s Socio-Philosophical Views Formation

The article discusses the socio-philosophical views of E. V. De Roberti, who was the pioneer of the neo-positivist trend in Russia. De Roberti is a recognized theorist of socio-philosophical thought, whose sociological system was based on the “biosocial hypothesis”. This hypothesis had a huge impact on philosophers and sociologists who supported the organic theory as well as on those who supported the psychological approach, thus it violated the internal unity and consistency of their teachings. The work of E. V. De Roberti is an era in the development of the social philosophy of Russia during the pre-revolutionary period. The works of the Russian scientist have not lost their historical and scientific significance even today. Moreover, they are still a brilliant example of broad erudition and a relevant source of ideas. His life is an example of how a scientist can carry out his civic duty. These all are the things that are so necessary to preserve continuity in the development of Russian philosophical thought.
Multi-Access Filtering using FOG-Enivronment

Fog computing is gaining popularity, even in traditionally conservative and delicate sectors like the Armed forces and nations.The interconnection of our community and technology breakthroughs like the IOT technology both have an impact on this. However, one of many crucial factors in fog computing adoption is guarding against privacy leaks. As a result, we present a multi-layer access filtration model in this study that safeguards privacy and was designed for such fog-based cloud environment, thus the name (FAF)fog-based access filter. The three major algorithms that make up FAF are the tuple reduction algorithm, the optimal privacy-energy-time methodology, and the access filter initialization method. The different protective aims are also distinguished using a hierarchical classification. Our experimental evaluation's conclusions show that FAF enables one to strike the perfect balance between privacy protection and computing costs.
Exhibit 11: ADEA Position Papers

Journal of Dental EducationVolume 87, Issue S2 p. S124-S139 EXHIBITS Exhibit 11: ADEA Position Papers First published: 24 July 2023 https://doi.org/10.1002/jdd.13331Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat Volume87, IssueS2Supplement: ADEA ANNUAL PROCEEDINGSJuly 2023Pages S124-S139 RelatedInformation
Ethereum Phishing Fraud Detection Based on Heterogeneous Transaction Subnets

As one of the most active blockchain platforms at present, Ethereum attracts a great deal of interest, including that of fraudsters. They exploit the anonymity of Ethereum accounts to perpetrate varieties of scams, the most common of which is phishing frauds. However, existing phishing detection work ignores the heterogeneity of Ethereum transaction edges. In fact, the activities on Ethereum include external transactions, internal transactions, and token transactions. Therefore, this paper proposes an Ethereum account phishing fraud detection method named HTSGCN. Based on heterogeneous transaction subnets, our method makes full use of the type and direction information contained in transactions. First, we collect Ethereum transaction data and construct a k-order heterogeneous subnet for each account. To aggregate the neighbor feature, we design a message propagation mechanism based on graph convolution network. Finally, we classify node representation vectors containing neighborhood and its own characteristics. Experimental results show that HTSGCN has a better effect on detecting phishing accounts than previous work which is based on homogeneous networks.
Comprehensive Review and Evaluation of Machine Learning Approaches for Solar PV Output Power Forecasting

Accurate forecasting of solar photovoltaic (PV) output power is a fundamental prerequisite to maintain the optimal management and stability of the grid. This paper presents a review and analysis of the solar photovoltaic power forecasting model based on machine learning techniques. Machine learning techniques are a subfield of artificial intelligence that is widely applied to solve different nonlinear and complex data type systems. The focus of this work is on machine learning methods, which give a thorough and critical analysis of the methodology used to predict PV power output. Furthermore, this study proposed an Ensemble (ENBG) Trees-based machine learning model to incorporate the effect of weather parameters on PV power forecasting. The performance of the proposed machine learning-based model is evaluated and compared with the help of Statistical indices such as Mean Absolute Error (MAE), Mean Square Error (MSE), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Error and found within desired limits. The proposed forecasting model is found to be more accurate, and it may be useful for smart grid applications to maintain grid stability, reliability, and power quality.
A Novel Lossless Image Compression Algorithm for Optimized Memory Storage

Data compression is a specific and special task that goes on and on as more and more data is required to be stored in the memory. For memory usage, it is always “smaller is better”. But, of course, there is always a threshold limit which may never be broken. Saving a graphical file is always challenging task as whenever we try to compress it, there is a possibility of loss of data whether in terms of colour or in terms of sharpness. The study (algorithm) presented in this paper is a methodical formation where the size of a graphical file has been cut to half without any type of loss of information. Neither the sharpness nor the colour gets affected. Through the paper, a novel algorithm has been developed which compresses an image file to half its previous size. The best part is that the complexity of algorithm is comparatively low for compressing as well as de-compressing (re-generation) purpose.
Cross-Modal Hashing for Material Surface Properties Fusion

In the tasks of robotic interaction with its surrounding environments, it is critical to judge the properties of the surrounding objects quickly and accurately. Existing high-definition cameras and tactile sensors can collect high-precision visual and tactile information, but it cannot comprehensively describe the surface properties of objects from single-modality. Hence, how to establish the cross-modal fusion between visual and tactile plays an important role in material surface judgement. To achieve above target, we propose a Hashing Learning For Cross-Modal Material Surface Retrieval. First, we apply hashing learning to cross-modal fusion, which can reduce storage costs and improve query efficiency. Then, a self-attention network is proposed to eliminate redundant information, in other words, to extract cross-modal correlation information between different modalities. Finally, in the process of generating hashing codes, a center constraint is applied to protect intra-modal structural information. Experimental results on the publicly available data set demonstrate the effectiveness of the proposed method.
A Min-Heap-Based Accelerator for Deterministic On-the-Fly Pruning in Neural Networks

This paper addresses the design of an area and energy efficient hardware accelerator that supports on-the-fly pruning in neural networks. In a layer of N neurons, the accelerator selects the top K neurons in every timestep. As K is fixed, the runtime of the pruned network is deterministic, which is an important property in real-time systems such as hearing aids. As a first contribution, we propose to use a min-heap for the top K selection due to its efficient data structure and low time complexity. As a second contribution, we design and implement a hardware accelerator for dynamic pruning that is based on the min-heap algorithm. The heap memory storing the top K neurons and their index is realized as a 3-port standard cell-based memory implemented with latches. As a third contribution, we evaluate the energy savings from pruning of a gated recurrent unit used in a neural network for speech enhancement (regression task). Our experiments demonstrate energy savings of ~78% without degrading the SNR improvement, and up to ~93% while reducing the SNR improvement by 0.1 - 1.11 dB. Moreover, the overhead of the hardware accelerator constitutes negligible ~0.5% of the total energy. The accelerator is implemented in a 22nm CMOS process.
Formation of a Continuous Education System in the Digital Environment

The article considers the concept of continuous education, emphasizes its priority as a socially and economically significant component of social development. The factors that determine the formation of the continuous education mechanism are identified: the widespread use of information and communication technologies, the merger of financial markets, the aging of the population, and the deregulation of markets. Continuing education performs professional, social and personal functions. The study of approaches to the formation of an education system throughout life made it possible to identify the main directions for the formation of a system of lifelong education and the effects received by the adult population.
Four-level knowledge graph contrastive learning structure for smart-phone application recommendation

With the development of smart-phones, the number of smart-phone applications (apps) has exploded in recent years, making it time-consuming and labor-intensive for users to read the reviews and select the apps they really need. Therefore, it is a good solution to infer users’ preferences from user-app interactions and recommend it. However, most of the smart-phone applications are free to use and the method to evaluate users’ preferences are different from that of the subscription products. For this reason, we redefined heavy user of apps and use it to determine whether a user prefer an app or not.In the field of recommender system, one of the most popular ways is to use graph neural network (GNN) for prediction. GNN techniques have been demonstrated to be efficient in representation learning for graphs in various domains because most of the data in recommendation has essentially a graph structure. Besides, as real-life dataset always conforms a long-tail distribution, which means a small portion of classes have massive sample points but the others have only few samples. It will induce a good performance on the head samples but a poor performance on the tail samples. To enhance the representation ability of the models, one of the most accepted way is to implement contrastive learning(CL). In recent years, graph contrastive learning also achieves great success. However, there are relatively few researches implement contrastive learning on a knowledge graph. Therefore, we proposed a f our-l evel g raph c ontrastive l earning structure (FLGCL) and a structure-aware graph augmentation method for recommendation.For the task of app recommender systems, there have already been some researches that applied GNN. Although app dataset conforms a typical long-tail distribution, few researchers tested the feasibility of CL on the app dataset. To fill this research gap, we firstly proposed a novel way to model the interactions between users and apps as well as the relationships between different entities as a knowledge graph. We call it app knowledge graph(AKG). Then, we implemented FLGCL on the AKG. And in order to find out the versatility of FLGCL, we also implemented the experiments on the MovieLens Dataset. We find that FLGCL outperforms all the baseline models.
QContext: Context-Aware Decomposition for Quantum Gates

In this paper we propose QContext, a new com-piler structure that incorporates context-aware and topology- aware decompositions. Because of circuit equivalence rules and resynthesis, variants of a gate-decomposition template may exist. QContext exploits the circuit information and the hardware topology to select the gate variant that increases circuit optimization opportunities. We study the basis-gate-level context-aware decomposition for Toffoli gates and the native-gate-level context- aware decomposition for CNOT gates. Our experiments show that QContext reduces the number of gates as compared with the state-of-the-art approach, Orchestrated Trios [12].
Low Power Hardware Architecture for Sampling-free Bayesian Neural Networks inference

Standard NNs should not be employed mindlessly in critical applications due to their incapability to express the uncertainty of their predictions. On the other hand, Bayesian Neural Networks (BNNs) can measure the uncertainty of their predictions. There are two methods for BNN inference, the Monte Carlo-based method, which requires the sampling of weights distributions and multiple inference iterations, and moment propagation, where the mean and variance of a normal distribution are propagated through the BNN. Hardware implementations of moment propagation BNN inference consume less power than Monte Carlo because they complete the inference in a single forward pass. However, because the propagation of distribution moments through nonlinear activation functions leads to large hardware designs, these functions are usually approximated by polynomials. Hardware implementations of moment propagation have been studied solely for fully-connected neural networks while lacking optimal accuracy due to the approximation of the ReLU activation function with a single polynomial term. Therefore, in this work, we add one more polynomial term in the approximation of ReLU, providing better accuracy with negligible additional hardware. We also propose a polynomial approximation for another common activation function, tanh, and extend the hardware implementation to Convolutional Neural Networks (CNNs). Experimental results demonstrated that the proposed approximation of ReLU outperforms the previously suggested single-term polynomial by achieving up to 5.9% higher accuracy with merely up to 0.029 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$W$</tex> power overhead.
Secure and Privacy-Preserving E-mobility Service Based on Blockchain and Hybrid Smart Contracts

The e-mobility infrastructure faces several challenges that hinder the general adoption of electric vehicles (EV). Indeed, the management system requires multiple actors to jointly act on different interdependent processes which makes it complex, time-consuming and inefficient. Moreover, the current state of the infrastructure raises several security and privacy concerns that render it non-compliant with security regulations and privacy laws. In this work, we propose a blockchain-based solution that allows a more efficient, secure and privacy-preserving management of the EV infrastructure. We used hybrid smart contracts and blockchain oracles to feed data to the blockchain in a secure and trusted manner.
In-material reservoir implementation of reservoir-based convolution

This study aims to implement a reservoir-based convolutional neural network (CNN) on physical reservoir computing (RC) to develop an efficient image recognition system for edge AI. Therefore, we propose a novel reservoir-based convolution circuit system that uses in-material reservoir computing, a type of physical RC made from a sulfonated polyaniline network. The experimental results demonstrate that the proposed circuit system extracts image features in the same way as the original CNN and that a reservoir-based CNN on the in-material RC achieves an accuracy rate of 81.7% in an image classification task while an echo state network-based CNN achieves 87.7%.
How can Moodle Quiz help teachers and researchers with complex questionnaires?

In this paper different tools with extended features for organizing questionnaires are analysed. The existing Moodle tools do not allow to implement questionnaire questions with non-standard functionality: checking of entered text (in short answers), auto-completing data from user profile, s election of cards, post-processing of question execution, for example, saving information in a separate database. To overcome these disadvantages a methodology of creation plugins for new questions and their behaviour using Moodle Quiz is presented. Comparing to another solutions the advantage of the methodology proposed is that it is possible to create a combination of quizzes and surveys (questionnaires). We present the development of a conference support system as another complex application. Also, there is an example of a mixed questionnaire test-like system for collection of a feedback from users about a connected test. In such applications it is necessary not only to collect data, but also to make sure that a person understands how to input it correctly. A system of 8 developed plugins is described.
Toward Reliable Mobile CrowdSensing Data Collection: Image Splicing Localization Overview

With the advancement of technology, the collection of data used in Intelligent Transportation Systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in Intelligent Transportation Systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.
Using the Capabilities of an Electronic Textbook in the Educational Process of Bachelors

The article gives the author’s definition of an electronic textbook; From the standpoint of the algorithmic approach, its structure is described, including stages and modules. On the example of author’s electronic textbooks, the possibilities of their use in the educational process of bachelors are revealed. The criteria for the assimilation of knowledge and universal educational actions (motivational, cognitive, practical and operational, contact) are determined; The characteristics of the levels of their formation are given. With the help of Fisher’s angular transformation, the effectiveness of the use of electronic textbooks in the educational process of higher education is proved.