Reviewerly/exHarmony huggingface dataset:
Philosophy of Education in Postmetaphysical Thinking

Aim. The aim of the article is to define the possibilities of education in the concept of postmetaphysical thinking. The definition takes into account the need to understand education as an open process of developing an authentic personality. The new understanding of education helps to orient the educational process in the family and school towards the person of the pupil and the development of his real possibilities. Concept. The article is built around a comparison of metaphysical and post-metaphysical thought. It describes the fundamental differences between both types of thinking, with a focus on the importance of others and openness in the interpretation of fundamental pedagogical categories. Attention is paid to the understanding of the pupil, the teacher, the process of education, and the goal of education. The pupil is interpreted as a person who has individual and original abilities. The process of education is understood as the development of the individual abilities of the pupil. The objective of education is determined on the basis of the pupil's abilities. The aim of upbringing is not predetermined. The openness of pedagogical practice necessitates the openness to continuous updating of educational and training methods. Results and conclusion. Creation of a basic framework for the innovation of pedagogical practices. The proposed framework views pedagogical success as a means of finding a sense of self, leaves room for alternative solutions, and emphasises the importance of students' individual abilities. It allows for pedagogy based on acceptance of otherness. Through the pedagogy thus interpreted, it creates a path for creative solutions to educational problems while developing not only the individual abilities of students but also their capacity for tolerance, which is a prerequisite for the functioning of a democratic society, through the pedagogy thus interpreted. Cognitive value. The article presents problems that arise when using classical pedagogy, i.e. pedagogy based on metaphysical thinking. The aim of classical pedagogy is the passive passing of knowledge and the production of graduates with the same abilities and knowledge. Pedagogy based on a postmetaphysical approach educates pupils whose value is based on human nature. Each pupil is accepted according to his individual abilities. He is an original and authentic human being.
Optimizing Ensemble Trees for Big Data Healthcare Fraud Detection

We show the importance of maximum tree depth in classifying publicly available Big Data with ensemble learners. To the best of our knowledge, this is the largest dataset used in any study on the impact of maximum tree depth on classification of Big Data with multiple ensemble learners. We present classification results for two popular, open source Machine Learning Algorithms, XGBoost and Random Forest. These learners come from different families of algorithms. XG Boost is a boosting method, whereas Random Forest is a bagging method. We find increasing maximum tree depth has a profound impact on their performance. Our contribution is to show how important the maximum tree depth is, when working with imbalanced Big Data with high-cardinality categorical features. For the data we use in this study, our results show that one should increase maximum tree depth; otherwise, classification scores may be suboptimal. XGBoost's mean AUC scores increase from 0.75633 with default maximum tree depth of 6, to 0.97273 with maximum tree depth of 24. Similarly, Random Forest's mean AUC score increases from 0.80821 with its default maximum tree depth of 16, to a mean AUC of 0.96996 with maximum tree depth set to 32.
CHECKING DIGITAL COMPETENCE FORMATION OF FOREIGN LANGUAGE FUTURE TEACHERS USING GAME SIMULATORS

The article substantiates the need for the formation of digital competence of future teachers of foreign languages (here and thereafter - teachers) as an important component of their professional competence. Taking into account the theoretical works of domestic and foreign scientists on the problem under study and the peculiarities of training teachers for professional activities in the context of the requirements of the New Ukrainian school, the concept of "digital competence of future teachers of a foreign language" is specified, its main structural components (motivational, content, operational) activity and personal-reflexive) are defined. Among the means, providing the formation of digital competence of teachers, the game simulators, considered in the context of obtaining professional experience, skills, and abilities. To determine the level of formation of digital competence of teachers, criteria (motivational, cognitive, operational-activational, reflexive-corrective) and indicators were developed, by which the levels of its formation (low, average, sufficient, high) were determined. To increase the level of formation of digital competence of teachers, game simulators, expedient for use in the process of their professional and pedagogical training, the content of the disciplines "Methods of teaching a foreign language in secondary school", "Innovative approaches to teaching foreign languages in high school", "Modern approaches to teaching foreign languages" was selected; forms, methods and ways of using game simulators in training teachers were developed and introduced. Based on the analysis of the results of the pedagogical experiment, the difference in the levels of formation of digital competence of teachers using game simulators in the experimental and control groups was proved, which confirms the effectiveness of formation of digital competence of teachers by game simulators.
On GNN explainability with activation rules

GNNs are powerful models based on node representation learning that perform particularly well in many machine learning problems related to graphs. The major obstacle to the deployment of GNNs is mostly a problem of societal acceptability and trustworthiness, properties which require making explicit the internal functioning of such models. Here, we propose to mine activation rules in the hidden layers to understand how the GNNs perceive the world. The problem is not to discover activation rules that are individually highly discriminating for an output of the model. Instead, the challenge is to provide a small set of rules that cover all input graphs. To this end, we introduce the subjective activation pattern domain. We define an effective and principled algorithm to enumerate activations rules in each hidden layer. The proposed approach for quantifying the interest of these rules is rooted in information theory and is able to account for background knowledge on the input graph data. The activation rules can then be redescribed thanks to pattern languages involving interpretable features. We show that the activation rules provide insights on the characteristics used by the GNN to classify the graphs. Especially, this allows to identify the hidden features built by the GNN through its different layers. Also, these rules can subsequently be used for explaining GNN decisions. Experiments on both synthetic and real-life datasets show highly competitive performance, with up to $$200\%$$ improvement in fidelity on explaining graph classification over the SOTA methods.
On the Relationship between Self-Attention and Convolutional Layers

Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks. This raises the question: do learned attention layers operate similarly to convolutional layers? This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice. Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer. Our numerical experiments then show that self-attention layers attend to pixel-grid patterns similarly to CNN layers, corroborating our analysis. Our code is publicly available.
Implementing Fault-tolerant Entangling Gates on the Five-qubit Code and the Color Code

We compare two different implementations of fault-tolerant entangling gates on logical qubits. In one instance, a twelve-qubit trapped-ion quantum computer is used to implement a non-transversal logical CNOT gate between two five qubit codes. The operation is evaluated with varying degrees of fault tolerance, which are provided by including quantum error correction circuit primitives known as flagging and pieceable fault tolerance. In the second instance, a twenty-qubit trapped-ion quantum computer is used to implement a transversal logical CNOT gate on two [[7,1,3]] color codes. The two codes were implemented on different but similar devices, and in both instances, all of the quantum error correction primitives, including the determination of corrections via decoding, are implemented during runtime using a classical compute environment that is tightly integrated with the quantum processor. For different combinations of the primitives, logical state fidelity measurements are made after applying the gate to different input states, providing bounds on the process fidelity. We find the highest fidelity operations with the color code, with the fault-tolerant SPAM operation achieving fidelities of 0.99939(15) and 0.99959(13) when preparing eigenstates of the logical X and Z operators, which is higher than the average physical qubit SPAM fidelities of 0.9968(2) and 0.9970(1) for the physical X and Z bases, respectively. When combined with a logical transversal CNOT gate, we find the color code to perform the sequence--state preparation, CNOT, measure out--with an average fidelity bounded by [0.9957,0.9963]. The logical fidelity bounds are higher than the analogous physical-level fidelity bounds, which we find to be [0.9850,0.9903], reflecting multiple physical noise sources such as SPAM errors for two qubits, several single-qubit gates, a two-qubit gate and some amount of memory error.
On the differential properties of the power mapping <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:msup><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msup><mml:mo linebreak="badbreak" linebreakstyle="after">+</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math>

Let m be a positive integer and p a prime. In this paper, we investigate the differential properties of the power mapping xpm+2 over Fpn, where n=2m or n=2m−1. For the case n=2m, by transforming the derivative equation of xpm+2 and studying some related equations, we completely determine the differential spectrum of this power mapping. For the case n=2m−1, the derivative equation can be transformed to a polynomial of higher degree p+3. The problem is more difficult and we only obtain partial results about the differential spectrum of xpm+2.
High performance “non-local” generic face reconstruction model using the lightweight Speckle-Transformer (SpT) UNet

performance "non-local" generic face reconstruction model using the lightweight Speckle-Transformer (SpT) UNet
Dual Reversible Data Hiding Based on AMBTC Using Hamming Code and LSB Replacement

The existing data hiding schemes conceal the data in the cover image and then communicate secretly on the channel. The weakness of these methods is that the security aspect is somewhat lacking, and there is a limit to hiding enough data. In this paper, we propose a reversible data hiding method based on dual AMBTC images. It improves security, which is a weakness of data hiding. AMBTC has strengths in low-bandwidth channel environments with simple calculations and efficient data performance. HC(7,4) and LSB replacement methods are applied to each block of AMBTC to hide secret data. After the embedding process, the two AMBTC-marked images are obtained, and these images are sent to different recipients. The recipients can extract hidden messages and restore the cover AMBTC image by using the proposed method and two marked images. Our proposed data hiding method guarantees sufficient data hiding, proper cover image quality, and restoration of the original cover image. Experimental results show that our method is efficient in terms of image quality and embedding ratio.
Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collected 346 patients diagnosed with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), each patient has 1–3 whole slide images (WSIs) and an mRNA expression file. WSIs were processed by a multi-instance deep learning model to obtain the patient-level survival risk scores; mRNA expression data were processed by weighted gene co-expression network analysis (WGCNA), and the top hub genes of each module were extracted as risk factors. Information from two modalities was integrated by Cox proportional hazard model to predict patient outcomes. The overall survival predictions of the multi-modality model (Concordance index (C-index): 0.746, 95% confidence interval (CI): ±0.077) outperformed these based on histopathology risk score or hub genes, respectively. Furthermore, in the prediction of 1-year and 3-year survival, the area under curve of the model achieved 0.816 and 0.810. In conclusion, this paper provides an effective workflow for multi-modality prognosis of HCC, the integration of histopathology and genomic information has the potential to assist clinical prognosis management.
A Spiking Neural Network Based Wind Power Forecasting Model for Neuromorphic Devices

Many authors have reported the use of deep learning techniques to model wind power forecasts. For shorter-term prediction horizons, the training and deployment of such models is hindered by their computational cost. Neuromorphic computing provides a new paradigm to overcome this barrier through the development of devices suited for applications where latency and low-energy consumption play a key role, as is the case in real-time short-term wind power forecasting. The use of biologically inspired algorithms adapted to the architecture of neuromorphic devices, such as spiking neural networks, is essential to maximize their potential. In this paper, we propose a short-term wind power forecasting model based on spiking neural networks adapted to the computational abilities of Loihi, a neuromorphic device developed by Intel. A case study is presented with real wind power generation data from Ireland to evaluate the ability of the proposed approach, reaching a normalised mean absolute error of 2.84 percent for one-step-ahead wind power forecasts. The study illustrates the plausibility of the development of neuromorphic devices aligned with the specific demands of the wind energy sector.
Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks

Deep learning (DL)-based specific emitter identification (SEI) technique can automatically extract radio frequency (RF) fingerprint features in RF signals to distinguish between legal and illegal devices and enhance the security of wireless network. However, deep neural network (DNN) can easily be fooled by adversarial examples or perturbations of the input data. If a malicious device emits signals containing a specially designed adversarial samples, will the DL-based SEI still work stably to correctly identify the malicious device? To the best of our knowledge, this research is still blank, let alone the corresponding defense methods. Therefore, this paper designs two scenarios of attack and defense and proposes the corresponding implementation methods to specializes in the robustness of DL-based SEI under adversarial attacks. On this basis, detailed experiments are carried out based on the real-world data and simulation data. The attack scenario is that the malicious device adds an adversarial perturbation signal specially designed to the original signal, misleading the original system to make a misjudgment. Experiments based on three different attack generation methods show that DL-based SEI is very vulnerability. Even if the intensity is very low, without affecting the probability density distribution of the original signal, the performance can be reduced to about 50%, and at −22 dB it is completely invalid. In the defense scenario, the adversarial training (AT) of DL-based SEI is added, which can significantly improve the system’s performance under adversarial attacks, with ≥60% improvement in the recognition rate compared to the network without AT. Further, AT has a more robust effect on white noise. This study fills the relevant gaps and provides guidance for future research. In the future research, the impact of adversarial attacks must be considered, and it is necessary to add adversarial training in the training process.
DIRECTIONS OF THE STATE POLICY OF INFRASTRUCTURE DEVELOPMENT OF EDUCATIONAL ORGANIZATIONS

The article deals with the issues of state regulation of the formation and development of the infrastructure of an educational organization. It is noted that one of the forms of state regulation and support for infrastructure development is the mechanism of public-private partnership. The authors analyzed the process of formation of the subject field of state policy in the field of education and systematization of legislative acts in the field of infrastructure development of educational organizations.
A Triple-Structure Network Model Based upon MobileNet V1 and Multi-Loss Function for Facial Expression Recognition

Existing facial expression recognition methods have some drawbacks. For example, it becomes difficult for network learning on cross-dataset facial expressions, multi-region learning on an image did not extract the overall image information, and a frequency multiplication network did not take into account the inter-class and intra-class features in image classification. In order to deal with the above problems, in our current research, we raise a symmetric mode to extract the inter-class features and intra-class diversity features, and then propose a triple-structure network model based upon MobileNet V1, which is trained via a new multi-branch loss function. Such a proposed network consists of triple structures, viz., a global branch network, an attention mechanism branch network, and a diversified feature learning branch network. To begin with, the global branch network is used to extract the global features of the facial expression images. Furthermore, an attention mechanism branch network concentrates to extract inter-class features. In addition, the diversified feature learning branch network is utilized to extract intra-class diverse features. The network training is performed by using multiple loss functions to decrease intra-class differences and inter-class similarities. Finally, through ablation experiments and visualization, the intrinsic mechanism of our triple-structure network model is proved to be very reasonable. Experiments on the KDEF, MMI, and CK+ datasets show that the accuracy of facial expression recognition using the proposed model is 1.224%, 13.051%, and 3.085% higher than that using MC-loss (VGG16), respectively. In addition, related comparison tests and analyses proved that our raised triple-structure network model reaches better performance than dozens of state-of-the-art methods.
Marketing in the Metaverse: A Sceptical Viewpoint of Opportunities and Future Research Directions

Metaverse can be a difficult concept to comprehend because of its nature which includes richness and complexity. Researchers, managers, and opinion leaders anticipate that the Metaverse might actually change how customers engage with products and services. On the other hand, much technical staff and business managers are still unfamiliar with the Metaverse as a new business concept. By taking a sceptical viewpoint, the current paper may assist managers and scholars in making better decisions concerning the concept of the Metaverse in a marketing context. The paper begins by describing the metaverse notion in broad and in relation to marketing. Following this, we adopt a sceptical approach towards four critical categories: customer experiences, customer trust, customer engagement, and advertising in the Metaverse. Lastly, we propose future research directions, including defining what marketing in the Metaverse is and describing its narratives while minimising the hype and exaggerated claims surrounding it.
The Impact of Internal Control Practices on Fraud Prevention: The Case of Lebanese Small-Medium Enterprises

When compared to large corporations, Small and Medium Enterprises (SMEs) are far more likely to suffer from employee losses and much less likely to be able to absorb these losses. Small and Medium Enterprises (SMEs) are better able to handle internal control than large corporations. It appears that the people of Lebanon view this issue with sensitivity, it is rarely brought up in casual conversation. Since internal control is the main issue facing Lebanese businesses, they would rather not address it. Only 308 of 400 responders filled out the survey, and the results were evaluated with the use of the SPSS statistical package. Findings showed that businesses in the modern era urgently require monitoring systems, as new evidence shows that monitoring frameworks affect performance evaluations. The environment in which it operates shapes a company's management mindset. The monitoring setup provides the backbone upon which further layers of internal control may be built. Consequently, the study's subjects include the organizations' cultures and working styles and the ethics and ethical beliefs of the employees who create and execute controls. Many factors interact to maintain a stable ecosystem. A subset of the executive board and representatives from each board committee have been tasked with gathering the information necessary to complete this report at the request of the board of directors.
Blockchain in Supply Chain Management

For the past few years, the market has changed a lot and it has become dynamic and demanding which has put the market into a competitive environment. The supply chain plays a crucial role to adapt the business to the dynamic environment as it is very reliant on collaboration integration as well as flexibility. The applications related to the supply chain have gotten the attention of many business owners and to improve the flow control of the supply chain many specialized applications are implemented. One of the most important new technological applications in the supply chain is blockchain technology which has garnered the attention of many business owners as it can be quickly adapted to dynamic market conditions and in the business environment. One upon reading this will get to know about the effect of blockchain technology utilization on this field. The results of the research paper recommend that companies invest in blockchain technology so that the supply chain becomes more transparent, flexible, and secure. There is no doubt in the fact that blockchain technology plays an important role in developing trust with the stakeholder of the supply chain. In the end, the research paper has also given some considerations on the implications that are positive as well as the potential of the blockchain in the field of collaboration and integration.