Reviewerly/exHarmony huggingface dataset:
An integrative approach to the development of professional competence of masters-oligophrenopedagogues in the professional training system

The article updates the problem of developing the competence of masters-oligophrenopedagogues, whose activities involve working with children with intellectual disabilities. Unfortunately, today the number of children with such disorders in special and inclusive education institutions is increasing, and therefore there is a need for specialists capable of carrying out corrective and developmental work based on the principles of humanism, equality and partnership. Understanding the essence of this phenomenon is determined by well-founded approaches to its research, where a special role belongs to the integrative one. According to the results of the analysis of the reference and scientific and pedagogical literature, the concepts of «approach» and «methodological approach» were clarified. The article concludes that the category «methodological approach» should be understood as the basis of the investigated phenomenon or process, which combines philosophical, theoretical and methodological knowledge. It is emphasized that the combination of different approaches to the implementation of the content of education justifies the integrative approach. Various views on the issue of integration in education are summarized. It was concluded that integration in education is aimed at combining various elements and ways with the aim of forming a coherent system of ideas about the world, certain phenomena and properties. The implementation of an integrative approach will promote the existence and interaction of different opinions and ideas. The article also states that in professional education, the implementation of this methodological approach is carried out with the help of the integration of various knowledge, the use of integrated methodological support, the unity of theory and practice. The features of its use in special and inclusive education are analyzed, as well as its role in the study of the development of the professional competence of the master oligophrenopedagogue in the professional training system. It has been established that the use of an integrative approach will contribute to the solution of both subject and general tasks based on psychological, pedagogical, medical, anatomical-physiological and other knowledge, and will contribute to the formation of a competent specialist capable of providing psychological and pedagogical support, generating new ideas, striving for self-improvement.
Observable Perfect Equilibrium

While Nash equilibrium has emerged as the central game-theoretic solution concept, many important games contain several Nash equilibria and we must determine how to select between them in order to create real strategic agents. Several Nash equilibrium refinement concepts have been proposed and studied for sequential imperfect-information games, the most prominent being trembling-hand perfect equilibrium, quasi-perfect equilibrium, and recently one-sided quasi-perfect equilibrium. These concepts are robust to certain arbitrarily small mistakes, and are guaranteed to always exist; however, we argue that neither of these is the correct concept for developing strong agents in sequential games of imperfect information. We define a new equilibrium refinement concept for extensive-form games called observable perfect equilibrium in which the solution is robust over trembles in publicly-observable action probabilities (not necessarily over all action probabilities that may not be observable by opposing players). Observable perfect equilibrium correctly captures the assumption that the opponent is playing as rationally as possible given mistakes that have been observed (while previous solution concepts do not). We prove that observable perfect equilibrium is always guaranteed to exist, and demonstrate that it leads to a different solution than the prior extensive-form refinements in no-limit poker. We expect observable perfect equilibrium to be a useful equilibrium refinement concept for modeling many important imperfect-information games of interest in artificial intelligence.
Rational Broadcast Protocols Against Timid Adversaries

We present a constant-round deterministic broadcast protocol against timid adversaries in the synchronous authenticated setting. A timid adversary is a game-theoretically rational adversary who tries to attack the protocol but prefers the actions to be undetected. Our protocol is secure against such an adversary corrupting t out of n parties for any $$t < n$$ . The round complexity is 5 for timid adversaries and is at most $$t+5$$ for general malicious adversaries. Our results demonstrate that game-theoretic rationality enables us to circumvent the impossibility of constructing constant-round deterministic broadcast protocols for $$t = \omega (1)$$ .
Prediction of Criminal Activities Forecasting System and Analysis Using Machine Learning

The development of machine intelligence has resulted in automated machines that are intelligent enough to carry out their tasks with the ability to influence the outcome, leading to a considerable reduction in the need for human intervention in redundant processes. There has been a correlation between chores that can be made much simpler and more discernible by mechanization and significant technical developments. However, these machines are currently only video-producing equipment and do not have any intelligence built into them. Due to the increase in material from security cameras, there is now a demand for intelligent video feeds that can automatically recognize abnormal occurrences. The major objective of the project has been to enhance community safety through the automation of measurement assignment processes and the analysis of actual closed-circuit television (CCTV) footage of criminal activity. The responsibility of detecting illegal behavior has been delegated to a structure that is able to recognize and differentiate it for more effective monitoring, enabling the successful completion of the work. In this study, we have presented a model that can distinguish between specific offenses with a precision of more than 90% for all variables.
Sentiment Analysis Using Bi-ConvLSTM

Sentiment analysis employs a variety of automated cognitive techniques to establish the speaker’s or author’s attitudes regarding the general emotional tendencies of an articulated object or text. Mining human sentimental tendencies has been significantly hampered in recent years by the expanding volume of opinionated content on social networks. Moreover, a single contextual representation customised for a specific activity cannot handle several sentiment analysis tasks. For this kind of issue, in this research, a hierarchical model for sentiment analysis is proposed. Initially, word embedding of natural language sentiment words into vector representations using the Skip-Gramme model. After that, extract the features from the input words using the CenterNet technique. Feature maps are derived from the input data using CenterNet’s backbone network. So, here, we employed CenterNet to effectively extract the textual information. Finally, classify the emotions into positive and negative categories using a model known as Long-Short-Term Memory with Bi-directional Convolution (Bi-ConvLSTM). The binary Stanford Sentiment Treebank (SST-2) and the Sentiment140 dataset were used in the experiments. Classification accuracy was improved as compared to other techniques.
Characterizing and Improving the Robustness of Predict-Then-Optimize Frameworks

Optimization tasks situated in incomplete information settings are often preceded by a prediction problem to estimate the missing information; past work shows the traditional predict-then-optimize (PTO) framework can be improved by training a predictive model with respect to the optimization task through a PTO paradigm called decision-focused learning. Little is known, however, about the performance of traditional PTO and decision-focused learning when exposed to adversarial label drift. We provide modifications of traditional PTO and decision-focused learning that attempt to improve robustness by anticipating label drift. When the predictive model is perfectly expressive, we cast these learning problems as Stackelberg games. With these games, we provide a necessary condition for when anticipating label drift can improve the performance of a PTO algorithm: if performance can be improved, then the downstream optimization objective must be asymmetric. We then bound the loss of decision quality in the presence of adversarial label drift to show there may exist a strict gap between the performance of the two algorithms. We verify our theoretical findings empirically in two asymmetric and two symmetric settings. These experimental results demonstrate that robustified decision-focused learning is generally more robust to adversarial label drift than both robust and traditional PTO.
Comparing Techniques for Digital Handwritten Detection Using CNN and SVM Model

The level of human dependence on robots has never been higher, affecting everything from object identification to Using deep learning and machine learning techniques, humans can add sound to silent movies and images. In a similar vein, handwritten text recognition is a substantial field of R&D with several possible consequences. Handwriting recognition (HWR), also known as handwritten text recognition (HTR), is the computer's ability to read and comprehend legible handwritten input from sources such paper documents, pictures, touchscreens, and other devices. It seems from this piece, we carried out a procedure for recognizing handwritten digits. Using MNIST and Support Vector Machines and MNIST datasets (MLP), networks are modelled by CNN and SVM. To find the most efficient digit recognition model, prior to moving on, it is important to assess how accurately and quickly the aforementioned models can be used.
Dense Convolution Neural Network for Lung Cancer Classification and Staging of the Diseases Using NSCLC Images

Lung cancer is life-threatening cancer disease which is owing to abnormal development of cells in the lung and its surrounding tissue. Hence, identification and classification of the lung tumor growth through physical examination are extremely challenging due to complex boundaries and features with high degree of intraclass variation and low degree of interclass variations. Machine learning approaches were implemented to classify the cancer on basis of the tumor representation and its features, but those models consume more computation time and produce minimized accuracy and efficiency. In order to manage those complications, deep learning architecture has been introduced as it is multiple advantageous in characterizing the lung lesions features accurately. In this article, dense Convolution Neural Network architecture is employed to lung cancer classification and staging of the disease to the NSCLC images has been proposed. Initially, Wiener filter is employed as preprocessing technique as it improves the results of segmentation. Next, gradient vector flow-based segmentation has been implemented on the images to segment the coarse appearance and lesion boundary, and segmented image has been employed to ABCD rule as feature descriptors to extract the features lesion such as diameter lesion, asymmetry, border and color. Extracted feature has been employed to the training of the dense-connected multi-constrained Convolution Neural Network which contains the dense blocks with 128 layers which is capable of producing better accuracy with reduced processing time. Furthermore, proposed model uses the hyperparameter optimization to reduce the network complexity and enhances the computation efficiency. Implementation outcome of the current approach is assessed using MATLAB software on using NSCLC dataset. Performance analysis of the proposed model on three classes of the disease as large cell carcinoma, adenocarcinoma and squamous cell carcinoma with 98.75% accuracy, 98.46 specificity and 99% sensitivity, respectively, on comparing against conventional classifiers.
Credit Card Fraud Detection and Classification Using Deep Learning with Support Vector Machine Techniques

Detecting credit card fraud is a critical problem online vendor’s face in the finance marketplace. Many sectors of the financial sector suffer from fraud and heavy financial losses due to the rapid and fast growing of modern technologies. The Support Vector Machine (SVM) and Multi-Layer Perceptron Learning (MLP) techniques are used to quantify the uncertainty of credit card fraud detection and classification. Through experimental analysis, the accuracy of the SVM and MLP techniques is 94.59% and 91.21%, respectively. Experimental results show that SVM and MLP techniques are classified credit fraud transactions with more than 90% accuracy.
Securing the E-records of Patient Data Using the Hybrid Encryption Model with Okamoto–Uchiyama Cryptosystem in Smart Healthcare

The term “Internet of Things” (IoT) refers to interacting with commonplace items that are networked together through wireless sensors. This fast-developing technology has the potential to enhance the standard of treatment that patients receive greatly. Many wearable devices are used in the e-healthcare system to monitor vital signs, such as a patient's blood pressure, temperature. Sensitive information is gathered through wearable gadgets. As a result, protecting this kind of private information is crucial. This study proposes EPPHS, a hybrid security solution for e-healthcare, to solve this problem. The Okamoto–Uchiyama (OU) cryptosystem's data obfuscation and encryption technologies form the basis of this suggested security method, designed to shield sensitive information from prying eyes. Therefore, this method uses a safe method of transmitting medical records. Compared to existing methods, the proposed EPPHS provides a more all-encompassing solution to the problem of securing the transfer of illness prediction data while protecting users’ privacy. In the illness model training phase, for example, we mix a super-increasing sequence with an OU cryptanalytic technique to effectively extract the indication set for each disease.
iFlow: Powering Lightweight Cross-Platform Data Pipelines

With the advent of ML applications cutting across sectors, data preprocessing for the training and proper functioning of ML models has seen a rise in importance. This research paper represents a similar attempt by proposing iFlow, a software tool for the easy creation of cross-platform data flow pipelines based on the Python programming language. The tool leverages the default file system of the user's operating system, enabling faster and real-time inflow and outflow of data for easier and more convenient data processing. The project plan emphasizes modularity and extensibility, with a focus on the automation of data pipelines, as well as the development of associated UI components for a better user experience. The paper highlights the potential applications of iFlow in the field of machine learning pipelines, positioning it as a lightweight and open-source MLOps framework for the future.
A Study Of The Reliability Of Crypto Data Provision

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Log Out or Stay Connected? Unveiling the Intention for Continuous Use in the Metaverse

Grounded on the S-O-R framework, this paper examines the impact of telepresence, interactivity, and social presence on users’ perceived enjoyment and value co-creation, which in turn, leads to continuous use intention. Moreover, the moderating effect of perceived trust is also examined in the interrelationship between perceived enjoyment, value co-creation, and continuous use intention. In this research, 400 responses were collected using a quantitative survey method with 352 valid responses, which were analyzed and interpreted. Structural equations modelling and bootstrapping were used to verify the hypotheses testing for the proposed model. The results showed that telepresence, interactivity, and social presence positively affected users’ continuous use intention in the metaverse. Perceived enjoyment and value co-creation have demonstrated a significant relationship with users’ continuous use intention in the metaverse. In addition, the moderating effect of users’ perceived trust has a significant relationship with perceived enjoyment and value co-creation to users’ continuous use intention in the metaverse. The study is one of the few that examines the impact of telepresence, interactivity, and social presence in addressing users’ perceived enjoyment and value co-creation through the metaverse. Future studies could consider using a qualitative approach to complement the quantitative findings and offer a more in-depth understanding of the continuous use intention.
Organizational Adoption of Blockchain Based Medical Supply Chain Management

The disruptive impact of blockchain technology on transactions, contracts, networks, and supply chains is widely recognized across business sectors for its benefits of security, privacy, and transparency. However, some critical industries like healthcare and defence can generate more value from blockchain technology. To identify, categorize, and rank the determinants of blockchain-based medical Supply Chain Management (SCM) adoption in the context of the organizations, a Systematic Literature Review (SLR) and Analytic Hierarchy Process (AHP) analysis were conducted. The SLR revealed fourteen sub-factors categorized according to the Technology-Organization-Environment (TOE) framework. The AHP analysis identified the five most essential sub-factors of blockchain-based medical SCM system adoption as top management support, government support, competitive pressure, inter-organizational trust, and organizational culture. The five least significant factors were identified as complexity, standardization, IT infrastructure, perceived benefit, and financial resources. This study provides insight to all the stakeholders of the supply chain in the medical context to improve the adoption of blockchain technologies taking into account key factors that influence its success.
Are You Ready for Tapping into the Metaverse in Higher Education? Integrated by Dual PLS-SEM and ANN Approach

In the context of ever-evolving technology and the post-Covid-19 pandemic, it is essential to utilize the metaverse of modern technology to guide university students. The purpose of this study adopts the UTAUT theory and self-efficacy to investigate metaverse adoption in higher education learning. Utilizing quantitative analysis, the study find out the factors that influence the adoption of metaverse for learning. The students who possess digital devices and have experience with online learning in order to accomplish the study's objective, which is to examine the behavior of using Metaverse in education. PLS-SEM and ANN were then utilized to analyze the data. The results confirmed the objectives of proposing model UTAUT and self-efficacy to investigate the adoption of metaverse for learning, and the model explained 77% of the intention to use metaverse for learning and 69.3% of its adoption. All factors of model UTAUT have an influence on behavior in the metaverse context. In addition, the non-linear outcome of the ANN provided additional insight into the significance of social influence and self-efficacy in the adoption metaverse for learning. This study contributed to the existing literature by highlighting the initial metaverse adoption by students in higher education, particularly in developing nations such as Vietnam.
Reshaping Sport with Extended Reality in an Era of Metaverse: Insights from XR the Moroccan Association Experts

Extended reality (XR) is becoming a growing technology used by athletes, trainers, and other sports professionals. Despite the rapid growth of XR, its application in sports remains largely unexplored. This study is designed to identify and prioritize factors affecting the implementation of XR in Moroccan sports science institutes. To achieve this, the study employs the A’WOT methodology, a hybrid multi-criteria decision method combining the Strengths, Weaknesses, Opportunities, and Threats (SWOT) technique with the Analytic Hierarchy Process (AHP). Through expert group discussions, the study identifies and categorizes the factors affecting XR implementation into SWOT groups. Subsequently, the AHP methodology is employed to determine the relative importance of each factor by conducting interviews with a panel of sports and XR experts. The study’s findings, obtained through the A’WOT methodology, establish a ranking of the fundamental factors for successful XR implementation in Moroccan sports science institutes. The findings suggested that a strategic approach for implementing XR technology in Morocco needs to be driven principally by a combined approach based on the SWOT opportunities and strengths groups.
Perceived Risk of Users’ Intention to Use Cryptocurrency in Malaysia: A Multi-analytic Approach

Cryptocurrency, as the primary successful implementation of blockchain technology, has revolutionized the financial industry. These decentralized digital currencies operate without a central authority, enabling users to conduct independent and efficient transactions for goods and services. By leveraging blockchain platforms, cryptocurrencies confirm the security and legitimacy of these transactions while keeping fees low. Despite its potential, the global acceptance of cryptocurrencies, including Malaysia, has not reached desired levels. This can be attributed to consumer concerns regarding perceived ease of use, perceived usefulness, social influence, and perceived risk. Considering these factors, this research aims to investigate the users’ perceptions of accepting cryptocurrency applications from a risk perspective. The research utilises the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Perceived Risk Theory to establish and evaluate various hypotheses. The study involved a sample size of 496 individuals. The proposed model is validated via Structural Equation Modelling (SEM), and the data is utilised as inputs for an Artificial Neural Network (ANN) model to predict perceived risk factors. The outcomes show that Performance Expectancy, Effort Expectancy, Social Influence, Financial Risk, and Psychological Risk significantly influence the users’ behavioral intention to use cryptocurrency in Malaysia from a risk perspective.
Mapping Privacy Data

Throughout the global economy there exists systemic issues regarding the privacy of collected personal information. Recent regulations have attempted to curb this issue such as the European General Data Protection Directive (GDPR) and the California Consumer Privacy Protection Act (CCPA). However, it is still all too common to find that the average organization has little to no idea what data they have, why they have specific data, or how long the data has resided with the company, among many questions to be answered. A method that may be employed by organizations to begin rectifying this is to conduct data mapping. Data mapping is the process of identifying and understanding of how personal data is used in an organization’s information systems.