Reviewerly/exHarmony huggingface dataset:
Analysis and Implementation of an Automatic Marking System for Single-choice Question Based on OpenCV

Aiming at a common choice question answer card style, with the help of OpenCV open source library, a variety of image processing technologies were used to to realize the function of an automatic marking system for single-choice question.
Psychological Features of Development of the Value Component of Students’ Personal Identity

The purpose of the article is to investigate the features of development of the value component of personal identity in student age empirically. Methods of the research. For the research there were used the following theoretical methods: analysis, synthesis, systematization, generalization of theoretical and empirical data on the research problem; empirical ones like testing by methods: “Methodology for personal identity research” (Колесніченко, 2020); “Methodology for diagnosis the level of reflexivity development” (Колесніченко, 2020); “Portrait of Values” (Семків, 2013); “Self-Assessment Questionnaire Test” (Сенчина, 2019); methods of mathematical statistics: descriptive statistics, correlation analysis. Results of the research. It has been established that the students under study have the identity status “Moratorium”, which is characterized by an identity crisis. The study of the value orientations has shown that the values of hedonism, kindness, and achievement are priorities for today’s student youth. In the course of the study of self-attitude, such components as self-respect, autosympathy and self-interest have been analyzed. It has been found that the studied students have an average level of self-respect. Self-sympathy of the subjects has an average level: in general, students are characterized by a favorable attitude towards themselves. Indicators of students’ self-interest are at a level higher than the average, which indicates the presence of a stable cognitive attitude to one’s own “Self”, interest in one’s own thoughts and feelings. It has been established that the average level of development of reflexivity prevails among student youth: students reflect on their current activities, are inclined to introspection in certain life situations. Conclusions. The conducted correlation analysis has confirmed the presence of significant positive relationships between the selected components of the value component of identity and the general status of identity. This indicates that value orientations, self-attitude and reflexivity can act as meaningful components of the value component of students’ personal identity, which has confirmed the proposed assumption.
HAWK-Net: Hierarchical Attention Weighted Top-K Network for High-resolution Image Classification

To handle high-resolution images on finite computational resources, many researches have been conducted on hierarchical networks to load features in only the most meaningful local regions. However, it is difficult to determine the correct number and location of patch regions at the appropriate scale in these methods. Then, incorrectly selected regions at different scales interfere with feature extraction and information integration. To solve this issue, we propose a hierarchical attention weighted network (HAWK-Net), which consists of a backbone network with differentiable Top-K selection and spatially gated blocks. The Top-K selected patches are identified from multiple image scaled features and extracted from an original high-resolution image. Then, patch features are aggregated via a novel gate mechanism under the uncertainty of the predicted information. Not only can multi-scale information uncertainty be modeled, but it also controls the gradient to the feature network coming from patch images with low confidence in the region proposal network in feedback during training. Our model is a simple yet efficient network structure that can learn from multiple scales and patches and is capable of end-to-end training. Based on benchmarks of multiple high-resolution images, our model achieves even higher performance with lower memory usage and reduced computation time.
Geothermal exploration at Kijiyama KJ-5 hot geothermal borehole in Northeastern Japan using DTS and DAS

We conducted geophysical explorations in the Kijiyama geothermal field to determine subsurface structures using DTS and DAS measurements in a hot geothermal borehole. The temperature reached 288ºC at the borehole depth of 2 km. We determined the 3D Vp structures that satisfied the geological models estimated by borehole geology. At 1 km depth, Vp increased from 4 km/s to approx. 5.5 km/s corresponding to the lithological boundary. We migrated the reflection arrivals obtained by the DAS offset VSP records. Intense reflections were located at depths of 1 km, 2–2.5 km, and 4 km. Given the geothermal gradient down to 2km depth, the deeper reflectors might suggest the presence of a supercritical water reservoir.
Automated processing development at a CO<sub>2</sub> site: Phase detection, location, magnitude

A network of passive, persistent, permanent compact volumetric phased arrays (i.e., SADAR® arrays, Zhang et al., 2023a; Nyffenegger et al., 2023, 2022) features multiple technical advantages for monitoring seismic activity at production, disposal injection, and CO2 sequestration sites compared to traditional surface networks or downhole deployments. A limited demonstration network was installed at the Newell County facility CO2 storage test site in southern Alberta, Canada, constructed and operated by the Containment and Monitoring Institute (Macquet et al., 2022). To date, 1.5 years of a analystreviewed bulletin has been constructed (Hutchenson et al., 2023). This bulletin provides the starting point for evaluating an automated process to report events in realtime, from phase detection, association of the various phases, location of the events, and determination of magnitudes. A comparison of the two bulletins illustrates the success of the automated system.
Reactive current profile of grid-forming wind power plant under symmetrical faults

Grid-forming (GFM) controlled wind power plants (WPPs) has emerged as a promising solution to facilitate the stable integration of renewable energy into power grids. GFM-WPPs operate as voltage sources, capable of responding promptly to voltage disturbances. However, it remains uncertain whether their reactive current profiles (RCPs) conform to the present grid codes. This paper analyses the RCP of an aggregated GFM-WPP system. It offers transmission system operators a reference for grid codes compliance. It is found that the reactive current is naturally provided in proportion to voltage sag depth when inverter current is not limited, adhering to grid codes. However, when the current is limited during faults, the RCP is mainly affected by the active power rather than reactive power setpoints. By using the conventional fault ride-through setpoints of active power, the GFM-RCP can cover the grid code requirements. The profiles are validated by the electromagnetic transient simulations.
UNIVERSITIES AS A FACTOR OF SOCIAL DEVELOPMENT OF THE CITY

In a contemporary world increasingly grappling with urbanization challenges, the role of universities as hubs for scientific, educational, and cultural activities is intensifying. This article explores how higher education institutions can make a significant social contribution to urban infrastructure development. Particular emphasis is placed on partnerships between universities, local governance, and business sectors, collaboratively implementing projects with extensive societal impact. Principles of sustainable development are examined as a key component of such collaborations. The paper underscores the need for multi-level interaction between universities and different tiers of governance, ranging from local to European levels. The significance of active university involvement in societal matters and the coordination of actions across multiple governance levels to effectively address contemporary challenges are highlighted. Modern universities are extending their role as drivers of regional development beyond traditional academic functions. Through partnerships with business, local authorities, and the community, they can aptly respond to regional challenges and foster an innovative culture. Universities can play a pivotal role in addressing urban issues, including environmental, transportation, and social challenges. Their active participation in local politics and economics can contribute to the formation of more sustainable and «smart» cities. Universities are evolving into active social participants, interacting on both local and global scales. They become centers of culture and political influence while safeguarding academic freedom and critical thinking in an era of disinformation. It is anticipated that the physical and virtual spaces of universities will complement each other, enhancing adaptability and innovation in educational and social contexts. Strategic planning and engagement with diverse social groups are key for a university to become an effective agent of positive societal change.
UD-GCN: Uncertainty-Based Semi-supervised Deep GCN for Imbalanced Node Classification

Fraud detection, especially graph-based approaches, has received increasing attention in recent years because graph structures can fully represent the information about the relationships between data, which facilitates fraudster detection. However, many real-world applications have encountered difficulties in classifying in unbalanced data, which is common in areas such as financial fraud. We argue that the key to this difficulty is not only the data itself, i.e., the unbalanced number of samples from different classes, but equally stems from the fading of minority class node features by majority class neighbors due to the graph neural network neighborhood aggregation mechanism. We propose a semi-supervised deep graph convolutional classification model with unbalanced graph nodes that aims to address this challenge by exploiting the impact of the under-sampled adaptive coordination of the classifier's own features on model performance, which is integrated in a deep graph convolutional network that contains multiple layers of the same simplified graph network architecture and a nonlinear function that can be recursively optimized. Extensive experiments show that our approach still yields robust performance even in highly imbalanced graphs with sparse labels.
Two-stage Gaussian Process Regression (2sGPR) for the generation of surrogate values in meteorological measurements

Dealing with corrupted or missing data points is a common challenge in time-dependent data, such as wind speed and temperature measurements. Our study introduces a two-stage Gaussian Process Regression (2sGPR) approach that addresses this problem by first filling gaps in the corrupted time series with an initial value and then applying GPR to the distribution of multivariate inputs. Different initial fill methods were evaluated by a hyper parameter optimisation to find the best model. The results indicate that the 2sGPR approach effectively deals with both numerical and categorical input features. Compared to using persistence (i.e., carrying forward the last available value) for missing data, the 2sGPR approach reduces the error to the original data by up to 19%. The study also observed that the root mean square error of the model increases linearly with the size of the data gap. The proposed 2sGPR approach offers a valuable tool for substituting missing time-dependent data in real-time applications. By generating substitute values for incomplete data, the approach has the potential to improve the accuracy of machine learning algorithms in various fields, including finance, healthcare, and transportation, where complete time-series data is crucial.
Cryptography Background

This chapter provides a comprehensive overview of cryptographic principles and historical evolution. Beginning with ancient methods, it progresses through classical and modern cryptography, addressing symmetric and asymmetric cryptographic algorithms. The discussion extends to cryptographic protocols, standards, and cryptographic primitives like hash functions and digital signatures. The chapter analyzes vulnerabilities and attacks, fostering an understanding of challenges in this domain.
Selecting an Applicable Cybersecurity Assessment Framework: Qualitative Metrics-Based Multiple-Factor Analysis

Recently, a survey of cybersecurity assessment methods focused on general characteristics was conducted. Among its major findings, it revealed the methods' adoption issues. This paper presents a follow-up to the study. It provides an in-depth analysis of the methods' adoption-related properties based on qualitative metrics. As a result, the proposals which demonstrate a higher adoption potential were identified. The methods are good candidates for first-order improvements that would lead to obtaining solutions that would ultimately meet a broader application. The evaluations were performed by a single analyst, based on descriptions and individual observations. The major contribution of the study is related to providing a new view on method characteristics in reference to a systematic set of qualitative metrics and showing a path to selecting the method most suitable to a given context in terms of applicability and usability.
Nature-inspired Algorithms

Nature-inspired algorithms represent problem-solving methodologies derived from natural processes, mirroring behaviours observed in the natural world. These algorithms, designed to emulate adaptation seen in animals, plants, and human problem-solving, exhibit efficacy across diverse domains, including optimization, machine learning, and computer vision. This chapter defines nature-inspired algorithms, explores their origins, characteristics, and operational principles, and examines their deployment in problem-solving scenarios. A taxonomy that offers a structured classification elucidating variations and relationships among nature-inspired algorithms will also be discussed for a better understanding of the given topic.
Deep learning-based idiomatic expression recognition for the Amharic language

Idiomatic expressions are built into all languages and are common in ordinary conversation. Idioms are difficult to understand because they cannot be deduced directly from the source word. Previous studies reported that idiomatic expression affects many Natural language processing tasks in the Amharic language. However, most natural language processing models used with the Amharic language, such as machine translation, semantic analysis, sentiment analysis, information retrieval, question answering, and next-word prediction, do not consider idiomatic expressions. As a result, in this paper, we proposed a convolutional neural network (CNN) with a FastText embedding model for detecting idioms in an Amharic text. We collected 1700 idiomatic and 1600 non-idiomatic expressions from Amharic books to test the proposed model’s performance. The proposed model is then evaluated using this dataset. We employed an 80 by 10,10 splitting ratio to train, validate, and test the proposed idiomatic recognition model. The proposed model’s learning accuracy across the training dataset is 98%, and the model achieves 80% accuracy on the testing dataset. We compared the proposed model to machine learning models like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest classifiers. According to the experimental results, the proposed model produces promising results.
Intelligent Waste Bot Using Internet of Things and Deep Learning for a Smart City

The buildup of solid garbage in metropolitan areas is causing environmental contamination and may be harmful to human health if not adequately controlled. A sophisticated/intelligent waste management system is required to handle various wastes. Separating garbage into its component parts is a crucial stage in waste management and is generally done manually by hand- picking. We propose a bot-based waste management system with nodes consisting of mechanical robotic arms to pick up waste material. Deep learning is a kind of machine learning that harvests information by layering data representation and feature extraction. The robotic arm has been designed to move in the same way as a human arm does. This is accomplished by using an Arduino Nano microcontroller with servo motor control which is programmed in the Java language. Convolution neural network, a deep, feed- forward artificial neural network, was used to accurately assess the image. This technique is offered as a part of a solid waste sorter. The combination of robotic waste management with a deep learning system allows trash to be classified and disposed off automatically. As a result, trash segregation with deep learning includes image acquisition from a camera, object recognition, prediction, and categorization into biodegradable and non-biodegradable categories.
Elis Regina, digital necromancy, and negative presumption: a study of bill n° 3592/23 in dialogue with the European Union

The purpose of this paper is to analyze Brazilian Bill of Rights n° 3592/23, especially the context that motivated it, namely, the recreation of the image of the singer Elis Regina via Artificial Intelligence for use in a car commercial, and its potential legal consequences, if approved. In addition, it also proposes to analyze legally the legal instrument built by this project and by the discussions that circulate around it, which can be called "presumption of refusal to use the image after death", due to the current regulations on digital inheritance and use of Artificial Intelligence technologies. Finally, it proposes a dialogue between this Brazilian legal movement and the current regulatory landscape on the subject in the European Union, focusing on the rules for using Artificial Intelligence, Copyright and Image. For this research, a case study was conducted based on the institutional responses to the commercial that used the image of Elis Regina, with support from hypothetical-deductive analytical methods. Then, a comparative approach was carried out, with bibliographic review and theoretical study. The paper is divided into three parts: first, analyzing the phenomenon of "digital necromancy"; following with an analysis of the model case; and finally conducting a comparative theoretical study, to conclude that this Brazilian case offers an important opportunity for dialogue between Brazil and the European Union, possible due to common regulatory bases on the subject, and useful by proposing a new model for legally thinking about the dilemma of using an image after the death of individuals.
RSFnet: A Relation Semantic Fusion-Based Entity Relation Extraction Method

In existing methods for entity-relation extraction, both entity-driven and relation-driven approaches commonly suffer from insufficient interaction between entities and relationships. Specifically, there is a lack of utilization of the semantic information inherent in relationships. This paper proposed a relation semantic fusion-based entity relation extraction method (RSFnet). Firstly, all possible subjects are extracted from the sentence, and a mapping mechanism is used to obtain corresponding potential relations. At the same time, we treated relations as prior knowledge and used attention mechanisms to obtain sentence representations with relation semantics. The subject information is used as prior features, and the subject features are obtained through a bi-directional long-short term memory (BiLSTM) network. The updated sentence representations and enhanced subject features are further utilized for object and relation extraction, ultimately outputting triplets. The performance of the proposed model was validated through experimental results on three datasets. Additionally, this paper adopts convolutional encoding, resulting in better inference performance than methods based on Bidirectional Encoder Representations from Transformers (BERT), indicating that our model can improve triplet extraction performance while maintaining inference speed.
Digital Forensic Evidence Analysis In Revealing Defamation On Social Media (Twitter) Using The Static Forensics Method

This research addresses the persistent challenge of defamation, notably prevalent on the Twitter platform, where the discovery of digital evidence is hampered by robust privacy protections. The study aims to investigate and identify digital evidence in defamation cases on Twitter, focusing on optimizing the evidence discovery process. Employing static forensics to prevent data alterations during acquisition from devices associated with defamation, the research successfully uncovered various digital evidence, including text from deleted comments, usernames, emails, and deleted image files linked to defamation. Out of the initial 28 reported data instances, 22 pieces of evidence were identified, resulting in an impressive 79% accuracy rate. The investigative procedures align with the chain of custody, ensuring the reliability of the collected evidence. This study not only contributes valuable insights into digital evidence discovery in online defamation cases but also highlights the efficacy of static forensics as a method. These findings provide a foundation for robust digital forensic practices, crucial for addressing challenges posed by online defamation on social media platforms.
Machine Learning Data Markets: Evaluating the Impact of Data Exchange on the Agent Learning Performance

In recent years, the increasing availability of distributed data has led to a growing interest in transfer learning across multiple nodes. However, local data may not be adequate to learn sufficiently accurate models, and the problem of learning from multiple distributed sources remains a challenge. To address this issue, Machine Learning Data Markets (MLDM) have been proposed as a potential solution. In MLDM, autonomous agents exchange relevant data in a cooperative relationship to improve their models. Previous research has shown that data exchange can lead to better models, but this has only been demonstrated with only two agents. In this paper, we present an extended evaluation of a simple version of the MLDM framework in a collaborative scenario. Our experiments show that data exchange has the potential to improve learning performance, even in a simple version of MLDM. The findings conclude that there exists a direct correlation between the number of agents and the gained performance, while an inverse correlation was observed between the performance and the data batch sizes. The results of this study provide important insights into the effectiveness of MLDM and how it can be used to improve learning performance in distributed systems. By increasing the number of agents, a more efficient system can be achieved, while larger data batch sizes can decrease the global performance of the system. These observations highlight the importance of considering both the number of agents and the data batch sizes when designing distributed learning systems using the MLDM framework.
PEMBERDAYAAN BIMBINGAN BELAJAR MELALUI SOSIALISASI MEDIA BELAJAR SMART SPINNER

The purpose and activity is as a means to introduce a learning media for English subjects at the Master English tutoring place. The activity is in the nature of socialization and mentoring. The participants in the activity consisted of 5 people with details of student heads and two mentors from tutoring. The results of these activities are in the form of understanding students and mentors can operationalize learning media in the form of smart spinners for teaching and learning activities in English subjects. The survey results show that the media provided is quite good, effective, and interesting to use in teaching and learning English in class.