Reviewerly/exHarmony huggingface dataset:
Professional Training of Future Specialists in the Field of Culture and Arts on the Basis of Axiological Approach

The article examines the professional development of future specialists in the field of culture and arts on the basis of axiological approach. The author identifies the essence of the basic concepts of research, focuses on the formation of values in the process of artistic and cultural-educational activities, interpretation of intonation and image content of a musical work, possible approaches to the concept of choreographic composition or staging. It is argued that the axiological orientation of education contributes to the involvement of artistic values and gaining experience in transmitting these values, development of intellectual and creative abilities and abilities in a particular art, which characterize the professional development and self-improvement of future culture and arts.
Scientific Approaches to the Development of the Communicative Experience of the Future Artist of an Instrumental Ensemble

In the view of modern trends in education, the level of competitiveness and the degree of general and professional competencies development becomes a determining indicator for future specialists. Therefore, our study reviews the analysis and characteristics of scientific approaches, as well as defines and analyses the essence of scientific approaches that will ensure an effective process of developing the communicative experience of a future artist of an instrumental ensemble. To implement the set objective, we used theoretical and empirical research methods (analysis of academic literature, synthesis, systematization, generalization). The analysis of scientific approaches made it possible to single out those approaches that are the most effective for developing the communicative experience of a future artist of an instrumental ensemble, specifically: systemic, axiological, activity and competence-based approaches. In the process of applying the systemic approach, we carry out interdisciplinary integration and establish connections between all components of the educational process. The axiological approach is used to develop a system of general human and professional values in students that determine their attitude to the world, to their activities, to themselves as persons and professionals. The implementation of the activity approach in the educational process brings to the fore the active cognitive activity of students, its practical component, which is supposed to ensure their development, motivation to study and enrichment of experience. By the competence-based approach, scholars mean the focus of the educational process on the formation and development of general and special (professional) competencies of a person, and, for our study specifically, the competence-based approach will contribute to the active research and self-study activities of ensemble artists, their intellectual and cultural development. The implementation of each of these approaches in the educational process guides us towards the development and formation of students’ skills, which ensure the dialogue between the teacher and the student and the interaction between the team members.
Color image ROI encryption algorithm based on a novel 4D hyperchaotic system

Abstract The significance of safeguarding the security of image information has escalated significantly, owing to the exponential proliferation of digital images containing sensitive information being disseminated on the Internet. In this paper, we first propose a novel 4D hyperchaotic system and design a new image encryption algorithm in conjunction with the hyperchaotic system. The algorithm uses a split random swap permutation method to permute the image and combines the S-box to diffuse the image. To improve the diffusivity of this encryption algorithm, a cross-random diffusion method is designed to diffuse the image again. Then, we propose a region of interest (ROI) encryption scheme for images. This scheme can automatically identify irregular privacy targets in images and encrypt them. To ensure the security of the region of interest location information during transmission, the scheme compresses the location information of the privacy target using a run-length encoding technique and then embeds the compressed data into the ciphertext image using reversible steganography based on histogram shift. The experimental results and security analysis unequivocally demonstrate that the image encryption algorithm proposed in this paper exhibits robust resistance against a wide array of attacks, thereby ensuring a high level of security. Additionally, the devised image ROI encryption scheme effectively safeguards diverse privacy targets.
User Interfaces in Virtual Reality Environments

This article presents a view of the user interfaces development based on the XR platform with an orientation towards possible use in education. First, the types of user interfaces are described here in connection with the classification of virtual reality types regarding triggered actions, user perception and interaction with real objects. Subsequently, user interfaces and their usability are examined, and mainly the support of user interfaces at the hardware level. In the end, the application areas of the XR platform are outlined, including the necessary user interfaces in order to make it easier, simpler, faster and more transparent for students and pupils to communicate with computing technology as well as with their surrounding environment during the educational process.
Enhancing Interactive Learning in Engineering Education: Exploring the Metaverse's Virtual Horizons

Covid-19 pandemic has accelerated the adoption of virtual education, which has prompted the exploration of educational models that enhance interaction and learning in virtual environments. This study aims to analyze the potential of the metaverse in engineering education. A comprehensive review of the existing literature on the use of metaverse-based technologies for engineering education was conducted. The metaverse provides engineering students with more realistic simulation scenarios. This platform also facilitates interaction among students, allowing for collaboration and shared learning experiences even when they are physically distant. It is concluded that, in metaverse education, it presents both advantages and challenges regarding its use. To ensure a positive impact of this technology in education, it is imperative to regulate its use and complement it with solid theoretical training.
Cost-Sensitive LapSVM for Question Classification

Community-based Question Answering (CQA) services, such as Baidu Zhidao, have attracted increasing attention over recent years, where the users can voluntarily post the questions and obtain the answers by the other users from the community. However, a large number of labeled data is needed in the process of training the model. Semi-supervised learning that can exploit the abundant unlabeled samples can solve the problem of manually labeled data. In this paper, a semi-supervised question classification method based on Cost-LapSVM is proposed. Traditional LapSVM is a natural out-of-sample extension, which can classify data that becomes available after the training process, without having to retrain the classifier or resort to various heuristics. In many real-world applications, different misclassifications often have different costs. The Cos-LapSVM algorithm is mainly used to solve cost sensitive learning problems.It could utilize the unlabeled samples for semi-supervised learning through the manifold regularization term. During the experiments, the method effectively utilizes a large number of unlabeled question samples and a few of labeled question samples to improve the question classification accuracy.
Enhancing Brain Tumour Aid Diagnosis with Augmented Reality rendering-based 6 DoF Object Pose Estimation

Accurately estimating the position and orientation of objects in augmented reality (AR) is a crucial task known as 6 Degrees of Freedom (6 DoF) object pose estimation. This capability enables machines to understand their surroundings in three dimensions, enabling them to perform various tasks like object recognition, tracking, interaction, and manipulation. In this paper, we propose an enhanced approach for estimating the pose of objects in AR using 6 DoF, specifically focusing on augmenting and visualising brain tumours in 3D to assist with diagnosis. Recently, there has been significant progress in developing generalisable 6 DoF object pose estimators that demonstrate competitive performance. These estimators can accurately determine the pose of an unseen object without requiring specific training or fine-tuning for each new object being tested. We tackle the challenge of estimating 6 DoF poses using only a single RGB image by leveraging a state-of-the-art object pose estimator called Gen6D. By utilising RGB data images and applying Gen6D to estimate the 6 DoF pose of a phantom head texture-less object, we can achieve augmented visualisations of brain tumours. The adoption of deep learning techniques for 6 DoF pose estimation holds immense potential and practical value in the field of medicine, particularly for medical practice and training purposes.
Automated Domain Modeling with Large Language Models: A Comparative Study

Domain modeling is an essential part of software engineering and serves as a way to represent and understand the concepts and relationships in a problem domain. Typically, software engineers interpret the problem description written in natural language and manually translate it into a domain model. Domain modeling can be time-consuming and highly depends on the expertise of software engineers. Recently, Large Language Models (LLMs) have exhibited remarkable ability in language understanding, generation, and reasoning. In this paper, we conduct a comprehensive, comparative study of using LLMs for fully automated domain modeling. We assess two powerful LLMs, GPT3.5 and GPT4, employing various prompt engineering techniques on a data set containing ten diverse domain modeling examples with reference solutions created by modeling experts. Our findings reveal that while LLMs demonstrate impressive domain understanding capabilities, they are still impractical for full automation, with the top-performing LLM achieving F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> scores of 0.76 for class generation, 0.61 for attribute generation, and 0.34 for relationship generation. Moreover, the F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score is characterized by higher precision and lower recall; thus, domain elements retrieved by LLMs are often reliable, but there are many missing elements. Furthermore, modeling best practices are rarely followed in auto-generated domain models. Our data set and evaluation provide a valuable baseline for future research in automated LLM-based domain modeling.
Testing the variational quantum eigensolver on the four-site Heisenberg model

We perform an optimization study of quantum computing methods specifically suited for the investigation of spin models. Our method is in particular applied to the Heisenberg model defined on a small cluster, in the case where a term describing the coupling of the spins to an external magnetic field is also included. The problem is studied referring to a square geometry by means of a quantum computer using the Variational Quantum Eigensolver (VQE). Differently from previous VQE applications, we exploit the Bayesian optimization, often used in machine learning, as a minimization procedure. Moreover, a post-processing error mitigation is implemented to improve the accuracy of the results.
Hyper-Relational Knowledge Graph Embedding Based on Type Constraints

The n-ary facts widely exist but receive less attention in research community. In this paper, A hyper-relational knowledge graph embedding based on type constraints is proposed for n-ary facts. In the model, each hyper-relational fact is represented as a primary triple and a set of key-value pairs. Meanwhile, the previous knowledge of entity types is integrated to learn more accurate semantics. The model captures not only the main structure information encoded by triples in the knowledge graph, but also the information encoded by triples composed of entity types and their associated key-value pairs. A convolutional neural network is used to learn the feature vectors of hyper-relational facts and their type facts, and the prediction score is obtained by combining the two feature vectors through minimum operation and fully connected projection. The experimental results show that the proposed method outperforms the traditional knowledge graph embedding model for link prediction and the MRR index is improved by 25.3% compared to the existing hyper-relation baseline model.
Engineering, the Profession in Trouble: Lack of Programme Development Standards That Support the AI Chatbot? A System View

As the world embraces the transformative potential of artificial intelligence (AI), large language models (LLM), and conversational agents also known as chatbots, the engineering profession stands at a crucial juncture. This paper critically examines the current state of engineering education and its readiness to incorporate AI chatbots effectively. Focusing on the lack of standardized programme development that supports AI chatbots, this paper sheds light on the implications of this gap for engineering education. Standardization is defined as the degree to which educational programmes meet common national and international quality standards. By synthesizing existing literature, this study investigates the challenges, opportunities, and strategies required to bridge this divide and ensure that engineering programmes are adequately equipped to produce graduates who can harness the power of AI chatbots. We found that strong and continuing trends are emerging in the use of AI and chatbots in engineering education and industry. We further noted that current standards from accreditation bodies need to respond to enable AI and chatbot incorporation from curriculum to pedagogical levels of engineering education. Industry-academia partnerships are vital in managing the integration of AI into engineering education.
Programmatic access to open statistical data for population studies: The SDMX standard

BACKGROUNDThe public sector publishes vast amounts of open data and metadata.APIs (application programming interfaces) are transforming the way data are collected, documented, and disseminated.The transformation is slow, however, due to differences in communication protocol, data definition, and data format.The development is of particular relevance to demography, being a data-intensive science.It paves the way to the automation of data acquisition and the integration of data acquisition and data analysis.Together with the parallel development of literate programming, which allows the integration of text and computer code in a single document, programmatic access to data makes workflows transparent, verifiable, and easy to replicate by others.The Statistical Data and Metadata Exchange (SDMX) standard, which has emerged as a popular option for data and metadata exchange, makes finding and retrieving data and metadata easy and swift.Query strings form URLs with a standardised syntax. OBJECTIVEThe aim of this paper is to describe the SDMX standard and demonstrate its benefits to our profession by retrieving demographic data and the associated metadata from online databases disseminated by a variety of data providers.The software environment used is R.
FLight: A lightweight federated learning framework in edge and fog computing

Abstract The number of Internet of Things (IoT) applications, especially latency‐sensitive ones, have been significantly increased. So, cloud computing, as one of the main enablers of the IoT that offers centralized services, cannot solely satisfy the requirements of IoT applications. Edge/fog computing, as a distributed computing paradigm, processes, and stores IoT data at the edge of the network, offering low latency, reduced network traffic, and higher bandwidth. The edge/fog resources are often less powerful compared to cloud, and IoT data is dispersed among many geo‐distributed servers. Hence, Federated Learning (FL), which is a machine learning approach that enables multiple distributed servers to collaborate on building models without exchanging the raw data, is well‐suited to edge/fog computing environments, where data privacy is of paramount importance. Besides, to manage different FL tasks on edge/fog computing environments, a lightweight resource management framework is required to manage different incoming FL tasks while does not incur significant overhead on the system. Accordingly, in this article, we propose a lightweight FL framework, called FLight, to be deployed on a diverse range of devices, ranging from resource‐limited edge/fog devices to powerful cloud servers. FLight is implemented based on the FogBus2 framework, which is a containerized distributed resource management framework. Moreover, FLight integrates both synchronous and asynchronous models of FL. Besides, we propose a lightweight heuristic‐based worker selection algorithm to select a suitable set of available workers to participate in the training step to obtain higher training time efficiency. The obtained results demonstrate the efficiency of the FLight. The worker selection technique reduces the training time of reaching 80% accuracy by 34% compared to sequential training, while asynchronous one helps to improve synchronous FL training time by 64%.
BYOA and Security: Examining Perspective-Taking and Self-Determination

Bring your own applications (BYOA) in organizational networks introduces security risks. We examine whether individuals who BYOA at work are motivated to use multi-factor authentication (MFA) to protect the organization’s security, a practice we describe as bring your own security (BYOS). The behaviors are examined through dual theoretical lenses (a) intrinsic motivation, capturing the personal satisfaction related to the use, and (b) perspective-taking, capturing the user’s ability to consider information security from the organization’s perspective. We analyzed survey data of 154 samples using partial least squares structural equation modeling (PLS-SEM). Our results demonstrate that while the intrinsic motivation path is instrumental in influencing MFA use, perspective-taking offers significant explanatory power to information security behaviors. This article extends the current knowledge of the impact of perspective-taking on voluntary security behavior, which makes a novel and significant contribution to cybersecurity research and practice.
Identifying dietary consumption patterns from survey data: a Bayesian nonparametric latent class model

Dietary assessments provide the snapshots of population-based dietary habits. Questions remain about how generalisable those snapshots are in national survey data, where certain subgroups are sampled disproportionately. We propose a Bayesian overfitted latent class model to derive dietary patterns, accounting for survey design and sampling variability. Compared to standard approaches, our model showed improved identifiability of the true population pattern and prevalence in simulation. We focus application of this model to identify the intake patterns of adults living at or below the 130% poverty income level. Five dietary patterns were identified and characterised by reproducible code/data made available to encourage further research.
Designing access control security protocol for Industry 4.0 using Blockchain‐as‐a‐Service

Abstract Industry 4.0 is a revolution of the operations in the industrial manufacturing for increased productivity, trade and commerce. It is heavily reliant on the automation of the processes and equipment along with complex interconnectivity and insightful analysis using machine learning. The interconnectivity of the manufacturing devices from various industrial sites brings with it several security issues related to communication. This article focuses on solving the security issue of access control between such devices and enable seamless secure communication for the proper functioning of the industry. An access control scheme has been proposed that achieves the necessary security features of anonymity, traceability, and forward secrecy. It is also shown that the proposed scheme takes less communication and computational costs, and is strongly resilient against various attacks such as impersonation attack, replay attack, and denial‐of‐service attack as compared to other relevant schemes.
Multilingual Symptom Prediction by Simultaneous Learning Using BERT

Natural language processing (NLP) techniques have become increasingly significant in the medical domain. However, the amount of relevant medical text data remains limited. In this work, we propose a BERT-based multilingual simultaneous learning (MSL) model for reducing the problem of scarce data. We evaluate the benefit of MSL on the NTCIR-13 MedWeb multi- label symptoms classification task. The results indicate that the MSL model performs slightly better than Single-Task Learning (STL) models. Additionally, it shows that the similarity between languages has an impact on the performance of the MSL model.
Using student personas when developing digital mathematics learning resources to improve teacher training

In this study, we discuss how the use of student personas can support pre-service teachers developing digital mathematics learning resources and improve mathematics teacher training programs. The use of modern technologies in schools requires that pre-service teachers prepare for new challenges, including the development of digital learning resources. To be able to develop such resources during teacher training, pre-service mathematics teachers without direct contact with students need insights into particular characteristics of mathematics students. We argue that personas, a tool and approach adopted from user experience research, can provide such insights. For this purpose, we interviewed four pre-service mathematics teachers who were developing digital learning resources in the span of a semester. We analyzed the data using grounded qualitative content analysis approaches and identified the following key aspects of personas usage for mathematics teacher training: (A) personas can act as an information tool, (B) personas can act as a planning and feedback tool, (C) the use of personas can trigger reflection processes for pre-service teachers, and (D) sustained work with personas may increase its impact. Our results indicate that personas can offer pre-service teachers support when developing digital learning resources and contribute to their professional development.