Reviewerly/exHarmony huggingface dataset:
Pedagogical Model for the Development of the Legal Competence of a Teacher

The article presents a pedagogical model for the development of legal competence of a teacher, its intention, purpose and features. The organizational and pedagogical conditions determined by the model for the development of legal competence of a teacher in the context of legal self-education are considered.
The “Search Step” Slot as an Element of the “Missing Information” Frame (Based on Search Documents of the Late 18th — Early 19th Century)

The article discusses the features of the semantics of the “search step” slot, which was mandatory in the structure of the “missing information” frame. The study was conducted on the material of fifteen search documents of the late 18th — early 19th century from the State Budgetary Institution of the Tyumen region “State Archive in Tobolsk”. The theoretical and methodological basis of the work consists of works in the field of cognitive linguistics, formation, linguistic source studies and lexico-semantic description of archival documents. Conclusions are drawn about the mosaic structure of the slot “search step” and filling it with search semantics.
Interdisciplinary Status of the Tartu Semiotics of Culture

The paper presents the interdisciplinarity of scientific knowledge as a necessary principle of the semiotic study of culture. The arguments used are: the studies of representatives of the Tartu school, the opinions of experts in the field of modern philosophy of science, metatheoretical constructions around the Russian semiotic tradition. The interdisciplinarity of Tartu semiotics has been verified in many aspects. The article substantiated a) the relevance of interdisciplinarity in the modern scientific picture of the world, b) the need to develop a common language as a condition for interdisciplinary dialogue, c) the fundamental connection of the interdisciplinary paradigm, systemic and structural research approaches. The conclusions of the article lay the foundations for extrapolating the ideas of cybernetics and systems theory into the boundaries of metatheoretical constructions around the Tartu semiotics of culture.
The Formation of Aesthetics Orientation of a Novice Artist’s Work

The article analyses the problem of aesthetic education of young artists in the process of artistic creation, which is influenced by a paradigm shift in the development of contemporary fine arts. It is shown that the transformation of the paradigm has a direct impact on the professional training of future artists. The interaction of the emotional, aesthetic and technical components in the creative process is revealed.
The principles of extended metadata formation in RNPLS&amp;T’s Single Open Information Archive

The RNPLS&amp;T’s Single Open Information Archive (SOIA) under construction supports the functionality of open access repository. To provide interoperability, the metadata describing the SOIA objects are rendered to external information systems in various formats using OAI-PMH and SRU/SRW [ 1 , 2 ] technologies. The open access repositories support Dublin Core metadata. Besides, MARCXML, ORE, RDF metadata are also among the supported schemes for some program implementations, however, their presence is quite limited. The OAI-PMH proprietary software used in SOIA enables to expand the completeness of metadata, in particular, for integration with promising technologies, e. g. semantic networks and linked data. The authors analyze the match between SOIA subject headings and concepts of GEMET thesaurus built within SKOS model and RDF. Terms interpretation within the thesaurus is one of SKOS functions. Despite the relatively quantitatively low intersection of SOIA headings and GEMET concepts, the identified matchings enrich SOIA information model. The authors suggest the extended model for RDF presentation of SOIA objects.
Predicting IoT Distributed Ledger Fraud Transactions with a Lightweight GAN Network

Decision-making and consensus in traditional blockchain protocols is formulated as a repeated Bernoulli trial that solves a computationally-intense lottery puzzle, called Proof-of-Work (PoW) in Bitcoin. This approach has shown robustness through practice, but does not scale with increasing network size and generation of new transactions. Resource constrained Internet of Things (IoT) networks are incompatible with full computation of schemes like Bitcoin's PoW. Our effort proposes a first step towards an alternative consensus using machine learning-based decision-making with prediction of fraud transactions to alleviate need for intense computation. To improve base approval probabilities for fraud detection in an ideal security setting, Vector GAN (VecGAN) is proposed to augment blockchain data in classifier training, which combines error-driven learning with Bayesian estimation to alleviate calculations. This two-step approach with augmentation and classification on new transactions is proposed as a novel approach to blockchain decision-making. Experimental prediction accuracy using VecGAN improved up to 3% on simplistic classifiers compared to other state-of-the-art augmentation techniques. Resource consumption in a realistic blockchain setting was reduced while improving block throughput by 50% compared to PoW. Future work will explore Sybil-spam defensive measures for realistic protocol implementation with this approach.
The Concept Of Developing Unmanned Self-Propelled Vehicles

The article discusses conceptual designs for development of unmanned self-propelled wheeled vehicles to study space bodies such as the Mars and the Moon. All previously used designs were conceptually developed in the 1960s on the basis of the Russian space school and have shown them to advantage, although they had some serious drawbacks. At present, when the race to conquer space of the Moon and Mars is underway, where more and more new 'players' are included, the development of self-propelled propellers capable of not only to move, but also to perform very complex research functions for a long period of time seems to be a very difficult and urgent task. A group of scientists carrying out research on the developing of an unmanned self-propelled wheeled vehicle capable of transporting wounded miners from the mines or fighters from the battlefield have developed a fundamentally new conceptual design based on two-wheeled modules connected by a unique coupling device that provides high stability, passability and reliability. Further tests of the developed unmanned self-propelled wheeled vehicle showed that this concept can be successfully used for development of space objects such as the Moon and Mars. Consultations with specialists of industrial organizations of Udmurtia proved the correctness of the conclusions and confirmed the technical possibilities of implementing this project at their production facilities. Considering that the industrial enterprises of the Udmurt Republic have all the necessary competencies in the development of spacecraft control systems, the production of unique electric motors and materials capable of operating in extreme space conditions gives the right to consider the proposed concept as a very promising development that has no analogues in the world and provides technological superiority in the process of exploration of the Moon and Mars.
Efficient Model based on Deep Learning for the Classification of Dementia

Alzheimer's disease to severe dementia are all forms of dementia, which are neurodegenerative disorders that affect brain memory. The most prevalent type of dementia that impairs thinking and memory is Alzheimer's disease. Network width, depth, and resolution are scaled uniformly by Efficient Net using a set of compound coefficients. This study uses callback functions to diagnose four types of dementia in order to slow down the learning process and increase accuracy. Additionally, contrast CNN and the Inception V3 model with the EfficientNet deep learning model. Using EfficienNetB2, classification accuracy is approximately 99.3%.
A Survey on Different Deep Learning Model for Human Activity Recognition based on Application

The field of human activity recognition (HAR) seeks to identify and classify an individual's unique movements or activities. However, recognizing human activity from video is a challenging task that requires careful attention to individuals, their behaviors, and relevant body parts. Multimodal activity recognition systems are necessary for many applications, including video surveillance systems, human-computer interfaces, and robots that analyze human behavior. This study provides a comprehensive analysis of recent breakthroughs in human activity classification, including different approaches, methodologies, applications, and limitations. Additionally, the study identifies several challenges that require further investigation and improvements. The specifications for an ideal human activity recognition dataset are also discussed, along with a thorough examination of the publicly available human activity classification datasets.
Ensemble Learning for fraud detection in Online Payment System

The imbalanced problem in fraud detection systems refers to the unequal distribution of fraud cases and non-fraud cases in the information that is used to train machine learning models. This can make it difficult to accurately detect fraudulent activity. As a general rule, instances of fraud occur much less frequently than instances of other types of occurrences, which results in a dataset which is very unbalanced. This imbalance can present challenges for machine learning algorithms, as they may become biased towards the majority class (that is, non-fraud cases) and fail to accurately detect fraud. In situations like these, machine learning models may have a high accuracy overall, but a low recall for the minority class (i.e., fraud cases), which means that many instances of fraud will be misclassified as instances of something else and will not be found. In this study, Synthetic Minority Sampling Technique (SMOTE) is used for balancing the data set and the following machine learning algorithms such as decision trees, Enhanced logistic regression, Naive Bayes are used to classify the dataset.Majority Voting mechanism is used to ensemble the DT,NB, ELR methods and analyze the performance of the model. The performance of the Ensemble of various Machine Learning algorithms was superior to that of the other algorithms in terms of accuracy (98.62%), F1 score (95.21%), precision (98.02%), and recall (96.75%).
THE EFFECT OF USING SHORT STORIES THROUGH WATTPAD ON READING COMPREHENSION AT THE TENTH SCIENCE OF MAN 1 METRO

The aim of this research is to find out the effect of using short stories through wattpad on reading comprehension achievement at the tenth grade science of Man 1 Metro. Wattpad is an application-type social media platform for people who want to read or write stories. This research used quasi experimental design. The subject of this research is the tenth grade science students at Man 1 Metro year 2023. The result data analysis indicates that value t obtained is higher than the value of t table (2.990&gt;2.042). And the significant 0.004 is lower than 0.05. The conclusion is teaching English using short stories through wattpad is effective to increase students’ reading comprehension.
A Novel Blockchain-Reinforcement Learning Framework for Securing Wireless Sensor Networks with Energy Efficiency

This paper introduces a novel approach to Reinforcement Learning (RL), focusing on the development and implementation of a Q-learning based algorithm. Reinforcement Learning, a critical branch of machine learning, enables agents to make decisions by interacting with their environment and learning from the consequences of their actions. Our study emphasizes the Q-learning model, a popular, model-free, off-policy algorithm that offers a robust framework for agents to learn optimal strategies in diverse settings. By iteratively updating the action-value function (Q-function) based on observed rewards and future reward estimations, our algorithm aims to achieve efficient learning and decision-making. This work contributes to the field by providing a detailed algorithmic structure, complete with mathematical formulations, that facilitates a deeper understanding of the Q-learning process and its practical applications in various domains.
Review Paper on Enhanced Image Captioning with Deep Learning: Encoder-Decoder and Attention Mechanism

Image captioning involves the generation of textual descriptions that describe the content within an image. This process finds extensive utility in diverse applications, including the analysis of large, unlabelled image datasets, uncovering concealed patterns to facilitate machine learning applications, guiding self-driving vehicles, and developing software solutions to aid visually impaired individuals. The implementation of image captioning relies heavily on deep learning models, a technological frontier that has simplified the task of generating captions for images. This paper focuses on the utilisation of encoder-decoder model with attention mechanism for image captioning. In classic image captioning model, the words usually describe only a part of the image, however with attention mechanism special attention is given to the low level and high level features of the image. Object detection using attention mechanism has shown to have increased the CIDEr score by 15%. With the use of stable dataset of MSCOCO through keras datasets, it is possible to score more on caption generation and accurate description of image.
Compressed Sensing-Based Practical and Efficient Privacy-Preserving Federated Learning

Federated learning (FL) is a popular distributed learning framework that is proposed to address privacy concerns in traditional machine learning. However, recent research has highlighted an issue where model or gradient updates can be exploited to infer sensitive information from the training data, resulting in severe privacy leakage. Existing defenses against gradient leakage attacks often suffer from high computation overhead or compromised model performance. In addition, most defense methods lack sufficient protection for labels. To overcome the above shortcomings, we develop a compressed sensing (CS)-based practical and efficient privacy-preserving FL scheme. In order to provide simultaneous protection for both original data and labels, we propose a CS-based gradient perturbation method, which eliminates the information in the gradient that is commonly exploited by attackers to extract labels, and increases the discrepancy between the perturbed and original gradients. Meanwhile, double aggregation is adopted together to ensure individual gradients are not easily disclosed by attackers. We also design a novel gradient reconstruction method that adaptively estimates the true gradient sparsity used for decompressing, thereby improving the model performance in practical scenarios. Furthermore, our CS-based gradient compression reduces communication overhead and requires low computation overhead as it only involves fast matrix multiplication. Extensive experiment results demonstrate the strong privacy protection effects of our proposed scheme compared to other approaches across various settings, with advantages in terms of communication overhead, computation overhead, and model accuracy.
UI/UX design of the PDBI digital art gallery web application using design thinking method

Documentation of digital artwork is essential for designers, serving as a copyrightable asset that can be included in a student's portfolio. User interface design is chosen as the gateway for developing the PDBI Digital Art Gallery Application. It is anticipated that a well-designed user interface will be part of the feasibility analysis for further development into a comprehensive and beneficial application. The research method employed in this study is design thinking, involving the construction of a user interface and usability testing for the PDBI digital art gallery application using a qualitative approach with a case study research type. Data collection techniques include observation, competitor analysis, and questionnaire distribution to obtain user experience feedback on the application. The introduction of the PDBI Digital Art Gallery application is expected to enable PDBI students to easily archive, showcase, sell, and create digital works, empowering student digital products as intended.
Design of Learning Management System for Air Navigation Engineering

With its vast potential, information technology has revolutionized education through various innovations. The quality of the teaching and learning process has significantly improved with the aid of information technology. Face-to-face learning is no longer the sole option as the presence of an LMS eases the process of teaching and learning. In the course of developing an Air Navigation Engineering Learning Management System using the ADDIE method (Analysis, Design, Development, Implementation, and Evaluation), the design was implemented on WordPress due to its flexibility in integrating plugins. This LMS website for Air Navigation Engineering prioritizes material, material delivery, quizzes, assessments, and certificates. The website's data validation yielded an overall feasibility rate of 96.6%, as confirmed by objective validators. Multiple respondents attested to an overall usability rate of 96.2%. Efficient material delivery is facilitated through the use of instructional videos. Additionally, the website offers a payment system for accessing necessary materials.
E-Apron Movement Control (AMC) Course Module Based Web Using 4D RND Model

This development research direct to determine the development process, feasibility, and responses of cadets and lecturers to electronic module-based learning media in the Apron Movement Control (AMC) course in the Air Transportation Management Diploma 3 Study Program and to produce teaching material products in the form of emodules made using the Canva Application and distributed through the LMS or Learning Management System (LMS) of the Surabaya Aviation Polytechnic. The developed learning media can be used to increase the abilities and interests of cadets in the Apron Movement Control (AMC) Course. The research and development technique used is the RnD or Research and Development method with the Four-D model. This research went through four stages of development, namely Define, Design, Development and Disseminate. The subject matter expert in this study was the Apron Movement Control (AMC) Course Lecturer, while the media expert in this study was the research advisor. In addition to being tested by media experts and material experts, the authors conducted trials through development subjects, namely Diploma 3 Air Transportation Management cadets at both Level 2 and Level 3. As an instrument for data collection in the validation of cadets and lecturers, this the study used interviews and questionnaires which would later be tested using a Likert scale. Based on the analysis of the data collected by the researcher, the validity of the material contained in the Electronic Apron Movement Control Module is 62.4%, 73.5% validity for the media and 73% for students' validity which showed that usability measurement of this E-Module “Worthy”. So that conclusion that can be drawn in this research that E-Apron Movement Control Module Based Web can be used to users.
APPROACHES OF BUILDING A REAL-WORLD OBJECT DETECTOR DATA SOURCE

Object detection is a crucial task in computer vision and AI applications, enabling machines to identify and locate objects within images or video frames. The accuracy and performance of an object detector heavily rely on the quality and diversity of the training data. Several structured approaches of building an object detector data source have been build, drawing inspiration from Apple's Create ML documentation on the topic. Additionally, real-world applications available on both the App Store and Google Play that leverage object detection technology are showcased.
Toward Sensor Measurement Reliability in Blockchains

In this work, a secure architecture to send data from an Internet of Things (IoT) device to a blockchain-based supply chain is presented. As is well known, blockchains can process critical information with high security, but the authenticity and accuracy of the stored and processed information depend primarily on the reliability of the information sources. When this information requires acquisition from uncontrolled environments, as is the normal situation in the real world, it may be, intentionally or unintentionally, erroneous. The entities that provide this external information, called Oracles, are critical to guarantee the quality and veracity of the information generated by them, thus affecting the subsequent blockchain-based applications. In the case of IoT devices, there are no effective single solutions in the literature for achieving a secure implementation of an Oracle that is capable of sending data generated by a sensor to a blockchain. In order to fill this gap, in this paper, we present a holistic solution that enables blockchains to verify a set of security requirements in order to accept information from an IoT Oracle. The proposed solution uses Hardware Security Modules (HSMs) to address the security requirements of integrity and device trustworthiness, as well as a novel Public Key Infrastructure (PKI) based on a blockchain for authenticity, traceability, and data freshness. The solution is then implemented on Ethereum and evaluated regarding the fulfillment of the security requirements and time response. The final design has some flexibility limitations that will be approached in future work.
A Machine Learning-Based Framework for Detecting Malicious HTTPS Traffic

Malicious traffic detection plays an essential role for Network Operators to prevent attackers from manipulating the network systems. In the past, many Network Intrusion Detection Systems (e.g., Snort, etc.) were designed to inspect the packets using pre-defined rules in order to identify the malicious traffic. Despite achieving good performance in the context of non-encrypted traffic, these systems are ineffective nowadays due to encrypted traffic (e.g., HTTPS, QUIC, etc.) and complex network behaviors of compromised computers. Therefore, many studies focus on malicious traffic detection mechanisms using Machine Learning (ML), which analyzes flow-based features using ML algorithms to detect the traffic generated by malware. There are two main kinds of features for malicious traffic detection containing protocol-agnostic features and TLS/SSL features. Using all these features can result in high time complexity and performance degradation, so it cannot meet the real-time requirement of the Intrusion Detection Systems. Therefore, in this paper, we take into account different kinds of flow-based features and implement a feature selection to select an appropriate feature set to improve the detection accuracy and execution time for malicious traffic detection. Besides, the proposed framework is evaluated using various datasets: CTU-13, MCFP, and CIC-AndMal201. The experimental results show that the framework can achieve an accuracy of 99 percent in considered scenarios.