Reviewerly/exHarmony huggingface dataset:
Semantic Organ Segmentation using Regression Solutions (Loss Functions) based on Organ Distance Map

Convolutional neural networks have been widely proposed/developed to conduct efficient and accurate organ segmentation, wherein a variety of loss functions have been considered to achieve optimal organ detection from the input data. Despite extraordinary performance of neural networks, noticeable errors located at the boundary/proximity of the target organs are observed as well as outliers in the form of island of voxels or voids/holes within the solid/uniform part of the target. To address these issues, we set out to investigate an alternative semantic segmentation solution based on the distance map using the regression solutions. In the proposed framework the distance maps obtained from the target masks are directly considered as the target within the training of the model, wherein regression networks and loss functions are used for the development of the model. Since the distance maps are continuous-valued data (as opposed to binary masks) any regression solutions could be employed to achieve the segmentation of the target organ/lesion. To implement the regression-based segmentation framework, a residual neural network was trained using the conventional segmentation framework and a hybrid Jac-card/cross-entropy loss function and the distance map framework using an L2 loss function. These models were tested for kidney segmentation using the decathlon dataset. The regression-based approach outperformed the conventional deep learning-based segmentation method with statistically significant differences (p-values <0.05). In addition to the overall superior performance of the regression-based approach, this method led to no outliers in the form of islands of voxels or holes inside the target structure. However, the conventional segmentation approach resulted in 5 outliers out of 30 subjects in the test dataset. Moreover, owing to the fact that the segmentation problem is converted to a regression task, we employed an L2 loss function since this function would perform better if the training dataset were not noisy compared to the L1 loss function. We intentionally added some noise and wrong samples to the training dataset and repeated the training. In this case, the L1 loss function led to better results than the L2 loss function. This would be regarded as an advantage of the proposed framework that the regression loss function with specific properties could be employed for the segmentation tasks. Overall, this study demonstrated the superior performance of the proposed regression-based solution over the conventional deep learning segmentation approach.
Augmented Reality Visualization of Radiation Imaging Data

We present recent developments towards improved radiation image visualization using the augmented reality (AR) smart glasses HoloLens2. Radiation imaging systems, such as the handheld dual particle imager developed at the University of Michigan, are capable of determining directional information from detected double scatter events for both fast neutrons and gamma rays. To date, the resulting gamma ray or neutron images are mostly displayed on computer screens, showing the resulting estimated ’hot spot’ in angular space. We propose a more intuitive and user friendly visualization of the radiation image using an AR device such as the HoloLens2. The AR device is capable of mapping it’s surroundings, thus allowing for the radiation hot spot to be displayed to the user directly as an overlaid projection on top of their visual experience of the surrounding space. We present the overall project, the dual particle imaging system, and the data pipeline from detector pulses to AR visualization.
Contrastive Learning versus Deformable Data Augmentation in Semantic Organ Segmentation

Extraction of the underlying patterns/features from the input data is essential for the development of an accurate and robust segmentation model. In this regard, self- or contrastive learning techniques are employed to enhance the performance of deep learning models particularly when the size of the training dataset is suboptimal. This work set out to assess the impact of contrastive learning on the performance of the semantic segmentation model. Moreover, a deformable augmentation approach is introduced to be compared with the contrastive-learning approach. The comparison of the contrastive learning and the proposed deformable data-augmentation technique was conducted on two datasets concerning hippocampus and kidney segmentation from the MR and CT images (Decathlon dataset), respectively. The accuracy of the segmentation for the two datasets was investigated with and without applying contrastive learning and the proposed deformable data-augmentation technique. The deformable data-augmentation technique resulted in Dice indices of 0.920±0.022 for kidney segmentation and 0.898±0.027 for hippocampus segmentation. The contrastive-learning technique exhibited superior performance to the initial model in both datasets with statistically significant differences (p-value <0.03). However, the proposed deformable data-augmentation approach outperformed contrastive learning due to the increased dataset within training. The contrastive learning and the original model exhibited Dice indices of 0.871±0.039 and 0.868±0.042 for kidney segmentation, and 0.872±0.045 and 0.865±0.048 for hippocampus segmentation. The proposed deformable data-augmentation technique dramatically improved the performance of the segmentation model, and a combination of the contrastive-learning and data augmentation would lead to a robust segmentation model with no outliers.
Topology-based Cost Function: a Novel Approach for Organ Delineation in Medical Images with Deep Learning Methods

Medical images segmentation plays a crucial role in diagnosis and treatment planning. Manual segmentation is a tedious and time-consuming task for experts and prone to human errors; thus, many efforts have been made to automate this process and facilitate the hardships of manual segmentation. Among these methods, deep learning approaches showed tremendous progress in the accuracy of medical image segmentation and became a dominant method. Despite these achievements, deep learning methods suffer from a defect in comprehensively learning the shape/topology of the target object. This defect cause islands of voxels, topological inconsistency, and unrealistic shapes in the final segmentation. To address this defect, an initial guess as prior knowledge for deep neural networks was proposed, and others refine the final segmentation mask to eliminate islands of voxels and fill undesired holes. In spite of these efforts, the main problem of learning topological features in neural networks is still unsolved. In this study, we proposed a novel cost function that encourages the neural network to capture the underlying shape morphology of the segmentation target in 3D medical images. The key idea of our proposed method is to constitute a shape space by vectorizing the 3D segmentation masks and extracting eigenvectors of this space through exploiting Principal Component Analysis (PCA) method. With a weighted sum of extracted eigenvectors, reconstruction of the primary segmentation mask is available. Therefore, the reconstruction weights can discriminate between realistic and unrealistic shape topology in the final output. We realized that the kurtoses of reconstruction weights for realistic shapes are higher than those of unrealistic ones. Accordingly, by penalizing the network to generate higher kurtoses of reconstruction weights for predicted segmentation, more realistic/valid shapes are generated, and networks learn to extract topology-based features. We employed an MRI hippocampus segmentation dataset and a 3D fully convolutional segmentation network to validate this approach. The network is once trained with a conventional cost function (e.g., Binary Cross-Entropy (BCE)) and once with the topology-based cost function (ours). The quantitative evaluation for the test set showed an improvement from 0.81 to 0.86 in the Dice score. Furthermore, the defects such as islands of voxels and topological inconsistency vanished when the shape/morphology-based loss function was used.
Syntactic Choices in Indonesian Research Articles from the Disciplines of History Education and Law: What do Authors Prefer?

Syntactic studies within both spoken and written texts have been widely conducted. However, studies on this area within research articles are still receiving minor attention from academic scholars. Based on this limited attention, the present study intends to investigate syntactic choices within research articles from the disciplines of History Education and Law in Indonesian journals that receive the Science and Technology Index (SINTA 2). The reason for choosing these selected disciplines is that they have not been investigated to date. For the analysis, a syntactic framework from earlier studies was employed to identify whether research article authors employ simple, compound, complex, and compound-complex sentences. The results revealed that most authors from these two data sets tend to use simple sentences in all sections of research articles. The reason for this finding is discussed in the Finding and Discussion section of this paper.&#x0D; Keywords: Research articles, syntactic choices, the History Education and Law disciplines&#x0D; &#x0D; &#x0D;
Study on recognition and classification of English accents using deep learning algorithms

Abstract The recognition and classification of English accents have high practical value in areas such as security management and information retrieval. This study introduced two English accent features, filter bank (FBank) and Mel-frequency cepstral coefficient (MFCC), based on deep learning techniques. It then combined convolutional neural network (CNN), gated recurrent unit, and an attention mechanism to design a 1D CNN-BiGRU-Attention model for English accent recognition and classification. Experimental tests were conducted on the VoxForge dataset. The results showed that compared to MFCC, FBank performed better in English accent recognition and classification, and 70FBank achieved the highest F 1 value. Among the recurrent neural network, long short-term memory, and other models, the BiGRU model had the best performance. The average F 1 value of the 1D CNN-BiGRU-attention model was the highest, reaching 85.52%, and all the F 1 values were above 80% for different accents, indicating that the addition of the attention mechanism effectively improved the model’s recognition and classification effectiveness. The results prove the reliability of the method proposed in this article for English accent recognition and classification, making it suitable for practical application and promotion.
The Role of Technology in Epidemic Management of Covid-19

The worldwide repercussions of the Covid-19 pandemic have compelled nations to seek innovative solutions. Concurrently, the evolution of technology has unveiled a host of emerging solutions that hold the potential to revolutionize infectious disease management. This study offers a comprehensive overview of pandemic control, encompassing prevention, contact tracing, detection, and treatment. Particularly in the context of the Covid-19 era, the paper retrospectively evaluates technological applications in these domains. Synthesizing insights, it provides a nuanced understanding of technology's interplay with disease management. Drawing from the collective wisdom of the Covid-19 epoch, this study informs the development of effective measures intertwined with technological advancements. Furthermore, the paper forecasts and recommends paths for technology-driven infectious disease management, establishing a basis for informed decisions amid global challenges.
IMPLEMENTATION OF AN INTEGRATIVE APPROACH IN THE PROFESSIONAL TRAINING OF SPECIALISTS IN TECHNICAL SPECIALTIES

The urgency of the issue lies in the fact that the innovative development of society requires an appropriate reaction from the educational environment. Ensuring the demand for competent specialists with creative thinking is possible only by improving the methodological foundations of higher education. One of the promising areas is the implementation of an integrative approach. It is aimed at overcoming the isolated teaching of educational components and forming a unified system of knowledge in the minds of students. The conducted analysis of domestic and foreign publications proved the presence of persistent interest in the problem of integration in education, which additionally confirmed the relevance of the initiated research. It is justified that ensuring the quality of higher education is possible thanks to the perception of integrability as one of the key principles of modern didactics, but this requires the construction of educational systems with new properties and functions. Levels of implementation of integration in professional training (establishing intersubject connections, the combination of various forms of educational activities and ensuring interdisciplinary integration) and requirements for its effective implementation are highlighted. Our own experience of implementing an integrative approach, which was implemented in the process of training future specialists in the field of mechanical engineering (using the example of the integration of professional and labor protection disciplines), is given. The complications that arose at the same time (lack of coordination between the program’s educational components, uneven level of knowledge and skills of students, difficulties in the interaction between teachers of different disciplines, etc.) were identified, and ways of their elimination were proposed. Prospects for further study of the stated problem consist in the continuation of the initiated research in the context of finding optimal ways of forming and implementing interdisciplinary educational tasks, as well as updating the principles of scientific research work of students, taking into account the requirements of an integrative approach.
USE OF CHATGPT DURING THE CREATION OF DIGITAL MEASUREMENT COMPLEXES FOR PERFORMING EDUCATIONAL RESEARCH OF THE STEM CENTER OF THE JUNIOR ACADEMY OF SCIENCES OF UKRAIN

One of the key tasks for the effective development of STEM education is the introduction of modern teaching aids, which include digital measuring complexes, which are an integral part of a modern STEM center or STEM laboratory. The widespread use of digital measuring complexes is restrained by their high cost. A possible solution to the problem of wide availability of digital measuring complexes is their creation on the basis of Arduino platforms and sensors compatible with them. Arduino platforms require programming, the language of which can take quite a long time to learn. In this paper, an approach to the creation of digital measuring complexes on the Arduino platform, which involves the use of ChatGPT to create software code, is proposed and investigated. The proposed approach is studied on two examples. The study showed that the use of Arduino platforms in combination with the Excel Data Streamer data streaming tool makes it possible to create measuring complexes accessible to a wide range of teachers and students for performing laboratory work and educational research. Using ChatGPT to create program code greatly simplifies the task, eliminating the need for deep learning of a programming language at the initial stage. Along with this, ChatGPT-generated program codes contain comments to the commands, which facilitates a gradual and unobtrusive learning of the programming language, which in turn contributes to the development of such important competencies for STEM education as algorithmic thinking and digital literacy. Arduino platforms allow you not only to register data, but also to perform certain control operations. Using the Excel Data Streamer tool provides high computational capabilities. In general, the proposed approach is workable, quite flexible and allows to quickly create digital measuring complexes for various tasks.
METHODOLOGY OF BUILDING TRANSDISCIPLINARY KNOWLEDGE BASES

The main goal of this study is to better understand the challenges of building transdisciplinary knowledge bases and their formalization in digital education. The article substantiates the need to develop mechanisms for building transdisciplinary knowledge bases to ensure the educational and research activities of student youth. The essence of the concept of transdisciplinarity and its importance in the educational process, scientific research for the construction of digital knowledge bases are analyzed. The transdisciplinary nature of ontologies in scientific research is determined. The methodology of transdisciplinarity of the knowledge base in terms of the development of network systems for managing information sources accompanying the process of supporting the educational and research activities of students is described. The e‑scenario is defined as a means of forming a transdisciplinary knowledge base of educational and research activities of students. The principles of the ontological approach to designing a transdisciplinary knowledge base are presented and the procedure for its formation in the format of operationally structured e‑scenarios is described. The general ontological graph-structure of the e‑scenario of the transdisciplinary knowledge base is provided. It analyzes the conditions that enable transdisciplinary knowledge production and shows how transdisciplinary institutionalization can be better understood using an ontological approach. It can be argued that the ontological approach in the models of transdisciplinary knowledge production proved to be useful for a deeper understanding of the processes of institutionalization of transdisciplinarity. Prospective conclusions of the practical application of the given methodology and the described principles of building transdisciplinary knowledge bases to ensure the educational and research activities of student youth have been made.
DEVELOPMENT OF EMOTIONAL AND VOCATIONAL QUALITIES OF STUDENT YOUTH AS AN INDICATOR OF ITS COMMUNICATIVE COMPETENCE

The article examines communicative competence as an integrated quality of students of secondary education institutions. The definition of the concept of “communicative competence” is presented, in which attention is focused on the importance of the emotional and volitional sphere of the student, which determines his ability to successfully carry out communicative interaction in society. It is noted that the question of the formation of emotional and volitional qualities of schoolchildren remains relevant even today and requires constant attention and comprehensive research. Attention is focused on the theoretical understanding of the emotional qualities of the student’s character, which is related to the strength of feelings that accompany his activity aimed at satisfying his own needs, and depend on the intensity and nature of the corresponding experiences, as well as on the strong-willed character traits that determine energy, with how a young person strives to achieve his goals in communication and manages his mental processes and states. In order to diagnose the formation of emotional and volitional qualities of students, the test method “Attitude to criticism” was proposed and implemented, taking into account that the formation of communicative competence of students of secondary education institutions, including the development of their emotional and volitional qualities, is possible under the condition of communicative orientation of educational disciplines, the use of didactic games, interactive non-game learning methods, organization of the educational process based on the subject-subject communicative interaction of the teacher and pupil, promotion of participation of school pupils in extracurricular work, in particular participation in school self-governance activities, involvement in research work, participation in scientific circles, readers’ conferences, discussion clubs, round tables, debates, creative studios, etc. The results of testing and an oral survey of young people are described, qualitative indicators of positive changes regarding the levels of formation of their emotional and volitional sphere are analyzed.
INVESTIGATION OF THE INFLUENCE OF TEACHING THE FUNDAMENTALS OF THE INVENTIVE TECHNOLOGY “EURONICS” ON THE DEVELOPMENT OF THE CREATIVE POTENTIAL OF HIGH SCHOOL PUPILS

There is a problem of a gradual decrease in creative activity of a person: from 18–19% creatively active among first graders to 1% among high school students. And then the decrease occurs among students of higher educational institutions. Sociological studies were conducted in Ukraine, according to which one in seven engineers is an inventor under the age of 30, one in five from 31 to 40, one in four from 41 to 50, and one in two after 50. It is clear that in our time of scientific and technological progress and competitive struggle, this is unacceptable. As Shiryaeva’s research shows, thanks to learning the theory of invention, the creative potential of 56% of eighth-graders has increased. However, neither Shiryaeva nor other specialists in teaching students the basics of invention theory provided methods for developing the creative potential of high school students to create pioneering inventions. This second problem was overcome thanks to the creation of the “Evronika” technology and gaining experience in its use when preparing the team for participation in the Third All-Ukrainian Competition of Young Inventors. Analysis of the team’s solution to the problem of extinguishing fires in high-rise buildings allowed us to identify the main methodological creative tools needed by high school students to create pioneering inventions. With their use, a method of teaching students to create such inventions was developed. Approbation of the methodology confirmed the possibility of teaching students to create pioneering inventions, as evidenced by the patents they received in Ukraine and awards for winning international competitions among adult inventors. However, the monitoring of the development of their creative abilities by conducting control works at the beginning and at the end of training showed that the students did not have an awareness of the need to create pioneering inventions and they did not yet have the corresponding desire. Therefore, the task of further research is to identify the ways of forming such aspirations in high school students, as well as to develop a suitable teaching method with improvement of existing and development of new methodological tools of the theory of invention and methods of their application in the learning process.