Reviewerly/exHarmony huggingface dataset:
THE FUNDAMENTALITY OF THE LAWS OF INNOVATION PROCESSES IN THE EDUCATIONAL ECO ENVIRONMENT: THE ASPECT OF TEACHING PHYSICS ON THE BASIS OF STEM

In the article, as a result of the research, the main directions of innovations in the educational activity of higher education institutions in the context of the development of innovations, in particular STEM education, are identified and analyzed. It has been established that the development of innovativeness affects the modernization of higher education, in particular technical in the context of STEM education. It has been found that the development and implementation of STEM education as a component of innovativeness affects the modernization of physics teaching methods in technical higher education institutions, and physics in particular. This modernization requires taking into account the general trends in the development of psychological and pedagogical aspects of higher education in the context of globalization and European integration processes. The main regularities and conditions of functioning of innovative educational processes in technical institutions of higher education are considered. The concepts of interdisciplinarity and levels of integration of scientific knowledge are analyzed and highlighted: intradisciplinary, interdisciplinary, supradisciplinary, transdisciplinary. The result of the innovation process is the transformation of new types and ways of human life into socio-cultural norms and models that ensure their institutional design, integration and consolidation in the culture of society. New knowledge that arises as direct experience within the framework of research work is removed from the sphere of the cognitive process and transformed into an innovative process in new systems of technological activity. STEM innovations are one of the main socio-cultural prerequisites for the development of social practice, its enrichment with new cognitive, technological forms of human experience, which are subject to reproduction in the process of their mastery by students of higher education of the new generation. It was determined that the achievement of the professional goal for the subject of training is ensured by integrated scientific knowledge of physics and professional disciplines, which is a prerequisite for mastering methods of solving industrial problems, where the difference between educational and professional activities from practical and cognitive activities is considered, taking into account the concept of STEM education.
THE IMPORTANCE OF THE SCIENTOMETRIC SYSTEM “OPEN UKRAINIAN CITATION INDEX” (OUCI) FOR THE DEVELOPMENT OF UKRAINIAN SCIENCE

The article presents an analysis of the importance of the Open Ukrainian Citation Index (OUCI) for the development of Ukrainian science. It describes the role of OUCI in enhancing the visibility of Ukrainian scientific publications and their integration into the international scientific space. The emphasis is placed on the significance of OUCI in collecting and analyzing publication data, contributing to the improvement of scientific productivity in Ukraine. The article also considers the development prospects of the OUCI system, particularly its potential in accounting for citations from various scientific-metric databases. It highlights how OUCI can facilitate the creation of new scientific journals and publications that meet international standards. The key challenges and problems faced by the Ukrainian scientific community are discussed, and how OUCI can help resolve them. The necessity of integrating Ukrainian scientific research into the global scientific context and the importance of enhancing the international competitiveness of Ukrainian scientific publications are debated. Significant attention is given to the quality of scientific research and ensuring its compliance with international standards. The article also describes how OUCI can contribute to the formation of a more transparent and objective system for assessing scientific achievements. The authors point out the role of OUCI in increasing the visibility of scientific results, which is particularly important for young scientists and researchers who wish to make a name for themselves on the international stage. It is emphasized that OUCI not only plays a key role in enhancing the international profile of Ukrainian science but also contributes to the formation of a fairer and more transparent system for evaluating scientific research. The authors express hope that OUCI will become a strong foundation for the development of an innovative and competitive scientific environment in Ukraine.
STEM IN ACTION: VIDEO COURSE “ARDUINO IN TINKERCAD

In the context of an integrated approach to education, this paper emphasizes the importance of utilizing video resources as a powerful tool for STEM education. The authors present their experience in creating a series of video lessons aimed at disseminating knowledge on Arduino programming using the TinkerCAD platform. The use of this platform allows individuals to learn programming even when access to physical Arduino boards is limited, which is especially relevant in the face of destructions in Ukraine’s educational infrastructure and mass migration of educational participants. The need for multimedia Ukrainian content on YouTube is stressed as a means to popularize knowledge about methods and techniques of Arduino programming in TinkerCAD among a broad audience, including students of different age groups and educators interested in electronics and STEM education. The content of the video lessons is described, highlighting their significant feature — the hyperlink of TinkerCAD projects. This interactivity enables teachers and students to actively improve their skills and projects while simultaneously watching video lessons, simplifying the learning process. An analysis of the author’s YouTube channel demonstrates substantial interest in this content, both from students of general secondary education institutions of Ukraine and viewers of arious age groups from different countries. This popularity attests to the relevance and importance of the “Arduino in TinkerCAD” video course as a whole. In conclusion, this article will be a valuable instructional resource for educators seeking to implement innovative teaching methods for STEM subjects using Arduino, especially in the face of modern challenges.
User centered methodologies in design for social improvement: The case study of Prishtina

User Centered Design, also known as UCD, allows designing solutions that are able to satisfy the requirements expressed by the users, intended as people that have to do with the product in every stage of its life. This specific approach can be also useful in the design of services and processes that need to consider the user experience as a fundamental stage of the project. UCD was therefore considered a proper method to be applied to public administrations’ work, to provide inclusive and effective services and facilities to citizens, in order to improve their wellbeing. The city of Prishtina, the new capital of Kosova , was considered an interesting example of a city with unsolved issues that could get a great advantage applying a User Centered approach to the design of public services, because of its social and multicultural identity that makes UCD suitable to answer to users’ needs. In this chapter the author proposes some topics on which it would be necessary to design and apply new innovative development strategies.
Conceptual foundations of innovative development of universities in modern conditions

the article presents the results of a study of the conceptual foundations of innovative development of universities in modern conditions. The dynamics of the value of the capacity of the global market for higher education services is analyzed. The principles of ensuring the innovative development of the university are formulated. The conceptual foundations of innovative development of the university are presented schematically.
AI Chatbot for Tourist Recommendations: A Case Study in Vietnam

Abstract Living standards are rising due to a more developed society, and recreation, particularly tourism, is becoming more critical. Expanding the tourist industry is one of the most significant concerns in economic growth. Tourism revenue has helped increase residents’ income, leading to socio-economic development. In recent years, emerging Vietnamese tourism spots like Hon Son, Sapa, Hue, Phu Quoc in Vietnam, and others have consistently drawn travellers to visit and experience through social networking platforms. Tourism potential is tremendous, but foreign visitors’ information about tourist destinations still needs to be improved. This work proposes an approach to integrating machine learning algorithms into an information system to consult tourism traveling. Machine learning algorithms can classify question topics, predict user intent, and predict conversation scenarios to give appropriate responses. Our method is evaluated on the dataset, including 7319 samples on 11 topics collected from the TWCS dataset, using three algorithms: Bag of Words, BERT, and RoBERTa. BERT achieved the highest performance among the surveyed algorithms with 90 % in accuracy and 90.1 % in F1-Score. From the trained model, the team built a mobile application on Android to deploy the chatbot application with the Flutter framework based on Dart, an object-oriented programming language developed by Google using the concept of containers. The system’s functionality serves two primary user groups: administrators and application users. Administrators can utilize the application’s primary functions to manage content set up, and train a chatbot. Users can access information about locations, read location articles, check hotel prices, and use chatbots to find answers to their location-related questions. Administrators can also train the chatbot model to expand its knowledge.
Generic Graphical User Interface for CBIR Framework

Abstract Content-based image retrieval system (CBIR) is a well-known and widely used system for image retrieval. Most of the current CBIR systems are either command-based or specific to applications. However, due to the availability of a good computing facility, a graphical way of retrieving images may prove to be very useful for both industrial and research purposes. This paper proposes a generic and user-friendly graphical user interface (GUI) for CBIR framework. With the proposed GUI, any user with or without knowledge of CBIR can operate and retrieve images of their choice among a huge number of images. The GUI gives a vast range of facilities for selecting options. The proposed GUI is implemented and verified on a well-known image database.
A Rigid Image Registration by Combined Local Features and Genetic Algorithms

Abstract Image registration is an essential pre-processing step required for many image processing applications such as medical imaging and computer vision. The aim is to geometrically align two or more images of the same scene by establishing a mapping that relies on each point from one image to its corresponding point of another image. Scale invariant feature transform (SIFT) and speeded up robust features (SURF) are well-liked local features descriptors that have been extensively utilised for feature-based image registration due to their inherent properties such as invariance, changes in illumination, and noise. Moreover, the task of registration can be viewed as an optimization problem that can be solved by applying genetic algorithms (GAs). This paper presents an efficient feature image registration method based on combined local features and GAs. Firstly, the procedure consists of extracting the local features from the images by combining SIFT and SURF algorithms and matching them to refine the feature set data. Therefore, an adaptive GA based on fitness sharing and elitism techniques is employed to find the optimal rigid transformation parameters that best align the feature points by minimizing a distance metric. The suggested method is applied for registering medical images and the obtained results are significant compared to other feature-based approaches with reasonable computation time.
Source Code Features and their Dependencies: An Aggregative Statistical Analysis on Open-Source Java Software Systems

Abstract Source code constitutes the static and human-readable component of a software system. It comprises an array of artifacts and features that collectively execute a specific set of tasks. Coding behaviours and patterns are formulated through the orchestrated utilization of distinct features in a specified sequence, fostering inter-dependencies among these features. This study seeks to explore into the presence of specific coding behaviours and patterns within Java, which could potentially unveil the extent to which developers endeavour to leverage the facilities and services that exist in the programming language aggregatively. In pursuit of investigating behaviours and patterns, 436 open-source Java projects are selected, each having more than 150 Java files (Classes and Interfaces), in a semi-randomized manner. For every project, 39 features have been chosen, and the frequency of each individual feature has been independently assessed. By employing linear regression, the interrelationships among all features across the complete array of projects are scrutinized. This analysis intends to uncover the manifestation of distinct coding behaviours and patterns. Based on the selected features, preliminary findings suggest a notable collective incorporation of diverse coding behaviours among programmers, encompassing Encapsulation and Polymorphism. The findings also point to a distinct preference for using a specific commenting mechanism, JavaDoc, and the potential existence of Code-Clone and dead code. Overall, the results indicate a clear tendency among programmers to strongly adhere to the fundamental principles of Object -Oriented programming. However, certain less obvious attributes of object-oriented languages appear to receive relatively less attention from programmers.
Enhancing Code Search with Token-Level Information Flow Graphs Generated from Aligned Structural and Textual Features

Large-scale code search is a crucial task in software engineering, yet existing deep learning based models often embed Abstract Syntax Trees (ASTs) and code sequences separately, limiting their ability to learn the correlation between structural and textual features. To address this limitation, we propose a novel code search model that automatically generates Token-Level Information Flow Graphs (TL-IFGs) from aligned AST nodes and source code tokens. Our model includes an aligner that establishes a one-to-one correspondence between AST leaves and code tokens, which we make publicly available, along with a processed dataset to facilitate further research. The model automatically generates a TL-IFG for each code snippet from the aligned datas by predicting the information flow at the token-level, which ensures that structural and textual features are highly correlated during the embedding process. We also generate TL-IFGs for descriptions and embed them using a similar process. Experimental results demonstrate that our model outperforms state-of-the-art code search models, indicating the effectiveness of our approach. Furthermore, an ablation study shows that the generated TL-IFGs for both code and description positively impact model performance.
Data Driven Skill Assessment for Cybersecurity Exercises

Measuring an individual’s cybersecurity competence in cybersecurity training is a challenging and complex task. It involves a wide range of skill assessments ranging from reconnaissance to post-exploitation in order to achieve some objectives on a target. Traditionally, flags (correct answer submissions) were used to measure individuals’ competence, which provides a holistic view of their performance in accomplishing a particular cybersecurity task. In this work, we investigate a deep learning-based diffusion model for measuring individual competence based on observed skills during a cybersecurity exercise. Our method helped identify weaknesses in an individual’s skill set, so a targeted intervention can be suggested to overcome the weaknesses and raise overall cybersecurity competence.
Distance Constrained Robotic Swarm Shepherding Based on Two-Phase Ant Colony Optimisation

This paper investigates a swarm shepherding problem which aims to herd multiple sub-swarm of robot agents (sheep) in a large-scale cluttered environment to a specific goal area using multiple distance-constrained robots (sheepdogs) located at different depots. We propose to formulate this challenging problem as a Multi-depot, Distance-constrained Close-Open Mixed Vehicle Routing Problem (MDCOMVRP). We also design a Two-phase Ant Colony Optimisation to address it by decomposing MDCOMVRP into a Multi-depot Open Vehicle Routing Problem (MOVRP) and a split problem. In the first phase, the Max-Min Ant System algorithm is employed to find open routes for all robots by transforming the MOVRP into a standard Travelling Salesman Problem using the proposed transformation method. In the second phase, a Modified Split algorithm is presented to construct a set of close or open distance-constrained routes, which are further optimised by the 2-opt local search method to generate the optimised sequence for each sheepdog robot to collect/drive sheep sub-swarms. Experiments are conducted to demonstrate that the proposed algorithm can solve MDCOMVRP successfully and assist the robots to complete the swarm shepherding mission efficiently.
A Novel Graph-based Framework for Explainable Image Classification: Features That Matter

The efficacy of any machine learning model is largely contingent on the quality of the features used for training. Hence, the extraction of robust and discriminative features from raw data is a critical step. This task, however, presents significant challenges. Although modern deep learning models are very advanced, they are often criticized for their black-box nature. The predictions made by these models are not readily interpretable in terms of the features that influenced them. In our research, we propose a novel framework that innovatively combines the prowess of convolutional layers for feature extraction with robustness of Graph Neural Networks (GNNs) to model the relationships of neuron activations for better interpretability. The proposed architecture initially generates features to produce class-based neuron activations, these activations are then incorporated into graph structure. The GNN incorporates the relationship between neuron activations to produce final classifications. The proposed model provides explainability as in the predictions can be traced back to the specific neurons that contributed to them. The proposed model not only matches the accuracy of state-of-the-art models but also provides explainability through target class-specific feature importance.
Exploring synthesizing 2D mammograms from 3D digital breast tomosynthesis images

In this study, we propose a novel deep learning approach to synthesizing 2D mammograms from 3D digital breast tomosynthesis (DBT) images. The objective of our work is to eliminate the need for obtaining two separate mammography scans, 3D and 2D, by creating a method of projecting 3D DBT images to 2D. Our method is compared to the state-of-the-art proprietary Hologic C-VIEW software and two simple baselines. We identify several potential directions for improvement. We make our code and model weights available as open-source, thereby providing the first publicly accessible deep learning model for converting 3D DBT images to synthesized 2D mammograms.
Semantic Segmentation for Improved Cell Nuclei Analysis

Generalised cell nuclei segmentation on Hematoxylin and Eosin stained tissue allows for faster analysis of tissue slides by pathologists. Improving the quality of automated processes in turn allows for more accurate diagnosis and prognosis. Existing methods make use of synthetic or private datasets that do not allow for collaboration by other researchers. Evaluation metrics vary among papers, making model performance hard to gauge. To incite further research in the space, this study proposes evaluating existing preprocessing techniques and neural networks on the publicly available datasets: MoNuSeg, MoNuSAC, TNBC and CryoNuSeg. The U-Net architecture is compared as the baseline against DeepLabV3+, YOLOv8 and STDC. A qualitative analysis is performed on predictions to assess the shortcomings of existing methods. Evaluation metrics are discussed to reduce ambiguity when training, and more robust metrics are outlined. This study contributes multiple resources to assist further research utilising public data, including end-to-end dataset preparation, along with training and evaluation pipelines for the aforementioned datasets and models.
What EXACTLY are We Looking at?: Investigating for Discriminance in Ultra-Fine-Grained Visual Categorization Tasks

Comparing discriminative features at different levels of granularity is an inherent part of the human object recognition process and AI-based models mimic such behaviour with significant success in a variety of applications. Beyond fine-grained visual categorization (FGVC), ultra-fine-grained visual categorization (Ultra-FGVC) has attracted early interest despite overfitting issues, however, human interpretability issues still remain. This study explores and draws for the first time, qualitative insights into Ultra-FGVC images through saliency-based explanation methods that provide intuitive hints on where the models are looking at the images. We present empirical evidence on the accuracy of CNN models on three Ultra-FGVC datasets whereby the ResNet50 achieves the maximum performance with a top-1 accuracy of 50.3% on one of the datasets while the AlexNet achieves the least performance with a top-1 accuracy of 22.5% on the same dataset. The findings in this study are likely to spur further research in the domain towards standardizing the acceptance of AI tools for assisted object recognition at ultra-fine granularities.
FIN: A Deep Multi-task Model for Face Information Detection

In recent years, with the development of autonomous driving technology, designing a driver monitoring system that can run on vehicles has become increasingly important. The driving system analyzes various factors such as head pose, gaze direction, and eye state to determine the driver’s attentiveness and aid the system in evaluating the driver’s capability to control the vehicle. In this paper, we present a lightweight multi-task deep learning model that utilizes full-face images to detect head pose, gaze direction, and eye state simultaneously. We propose a task-based cross-dimensional attention module (TCAM) that selectively filters and enhances relevant features for each specific task. Additionally, to tackle the lack of diverse head pose angles in the eye state dataset, we introduce a data augmentation technique to generate eye state data under different head poses, enhancing the model’s robustness in detecting eye states under varying head poses. We demonstrate the results of our work on the 300WLP, AFLW2000, BIWI, Gaze360, and MRL datasets. Extensive experiments show that our method can obtain competitive results with lower parameter costs. Furthermore, we establish a baseline for eye state detection using full-face images as the input on the CEW datasets.