Reviewerly/exHarmony huggingface dataset:
Individual Investors’ Awareness and Participation in the Dar es Salaam Stock Exchange, Tanzania

Participation of individual investors in Dar es Salaam stock markets stands at 1%, which is very low compared to Kenya, which is currently at 4%. Thus, the study evaluated the level of awareness of individuals that explained their low participation in the Dar es Salaam stock exchange. Primary data were collected from 200 participants and 200 non-participants and analysed using descriptive statistics and binary logistic regression. Findings revealed that non-participant individuals had a low level of awareness regarding DSE and its activities compared to individual participants. Regarding voting and decision-making by shareholders, only 13% of participants were aware of this right. Financial awareness was also found out to be low among individuals. Binary logistic results indicated that awareness of DSE, awareness creation seminars, and access to media were likely to influence individuals' participation decisions. Thus, the study concluded that the level of awareness among individuals was deficient and affected their participation in the stock market. Therefore, the intensification of public awareness through seminars by DSE is essential. Also, DSE should increase the use of mobile trading platform and internet technologies to disseminate awareness information on investment opportunities for participation decision.
"MOBILE LEARNING AND SIMULATION FOR THE DEVELOPMENT OF HANDS-ON CLINICAL SKILLS"

In relation to simulation-based health professions education (SBHPE), mobile learning offers new ways of developing clinical skills and is becoming a key component of how we teach and learn across the spectrum of learners. Learners typically
"A TECHNOLOGY ACCEPTANCE MODEL ANALYSIS OF METAVERSE TECHNOLOGY IN EDUCATION AND BEHAVIOURAL INTENTION TO USE IT AMONG UNIVERSITY STUDENTS. A COMPARATIVE ANALYSIS OF STUDENTS’ BEHAVIOUR IN THE NETHERLANDS AND GREECE"

The present study proposes a framework for university students' metaverse technologies in education acceptance andintention to use. The study is based on the Technology Acceptance Model (TAM). Data used are coming from twouniversities and are
SOCIO-PEDAGOGICAL NEED OF INTERCULTURAL COMMUNICATION IN THE DEVELOPMENT OF ACCULTURAL SKILLS IN STUDENTS

In this article, the issues related to explaining and teaching the processes of acculturation to students and the theoretical and methodological bases of forming an acculturation worldview in them are researched. In this regard, foreign and national researches have been scientifically studied. Based on this goal, this article aims to study the issues of inculcating the sincerity of cultural relations in the educational process and forming acculturative skills in students. Based on this goal, it is appropriate to analyze the essence of the concept of acculturation, its definition in dictionaries, and to research the need to create socio-pedagogical mechanisms for the formation of acculturative skills.
INTEGRATION OF MEDIA EDUCATION IN THE EDUCATIONAL PROCESS

The state of development of modern technologies depends primarily on the intellectual potential of society, that is, on the development of the education sector. Issues of the content and quality of education are considered as a priority. In developed and developing countries of the world, special attention is paid to informatization of education. Ways of developing education and increasing its effectiveness are considered, special attention is paid to the introduction of new information technologies in education.
THE ORETICAL BASICS OF SOCIAL COMPETENCE DEVELOPMENT IN FUTURE SOCIOLOGISTS

Modern requirements for the educational process give us the opportunity to accept the organization and formation of professional social competence as a pedagogical process. It is aimed at providing students with high-quality professional competences with the development of significant and motivational spheres and interpersonal and intercultural social competences of professional activity. For this reason, the essence and content of the concept of social competence are studied sociologically and pedagogically in the article. It is also explained that the development of social competence in future specialists is an urgent issue of today, and the analysis of social processes of society is explained using theoretical and methodological approaches.
Complementary Data Regularization for Nonlinear Matrix-Valued Image Denoising

In this study, we present some findings from applying a complementary data regularization approach to a nonlinear image denoising model. The proposed method uses optimizing non-quadratic energy functionals with normalized data terms. This study aims to develop an image denoising technique that combines well-known robust diffusivity coefficients such as total variation regularization with normalized data term known as complementary data regularization. We apply our modified mathematical approach to the matrix-valued images, a challenging computational task. The primary purpose of the complementary regularization idea of data terms is to create a compatible connection and a reliable balance between diffusion and data terms. The standard finite difference scheme has been considered as a discretization method for the numerical solution of the partial differential equation obtained from the variational optimization of the modified energy function. The performance of the proposed model is demonstrated through numerous numerical experiments in terms of image quality analysis and computational time. Comparison of the results with some well-known classical methods such as Perona–Malik, total variation, and non-local mean methods in the literature is the heart of this work. Keywords: image denoising, isotropic diffusion, complementary regularization, variational optimization. https://doi.org/10.55463/issn.1674-2974.50.10.8
Vision of the third mission in Saint Petersburg State University and the universities of the Republic of Korea

In the 21st century researchers redefine the role of universities in the life and development of society. Activities in this area have become so important that they are considered as a special mission along with the traditional educational and research missions. At the same time, no consensus has been formed yet on which areas of university activities should be in the centre of the third mission. Experts from Western universities put emphasis on the commercialization of the products of educational and especially research missions in the interests of the society. Our study examines the vision of the third mission at St. Petersburg State University, as well as at universities in the Republic of Korea. The material for the study make the speeches of the participants of the 9th Forum of Rectors of the leading Universities of Russia and the Republic of Korea as well as a wide range of information on this topic on the St. Petersburg University portal. The analysis showed the similarity of the approaches of St. Petersburg State University and Korean universities to interaction with society and industry. St. Petersburg State University meets the society’s expectations about education and development. Representatives of employers are involved in the development of specialized programs and evaluation of learning outcomes. Internships in research institutes and enterprises provide students with employment and future career growth. University pays a special attention to creating comfortable working conditions at the university, in which students and teachers are part of the local society. In addition, access to the university’s cultural and museum resources is open to all citizens.
UniMF: A Unified Multimodal Framework for Multimodal Sentiment Analysis in Missing Modalities and Unaligned Multimodal Sequences

In current multimodal sentiment analysis, aligned and complete multimodal sequences are often crucial. Obtaining complete multimodal data in the real world presents various challenges, and aligning multimodal sequences often requires a significant amount of effort. Unfortunately, most multimodal sentiment analysis methods fail when dealing with missing modalities or unaligned multimodal sequences. To tackle these two challenges simultaneously in a simple and lightweight manner, we present the Unified Multimodal Framework (UniMF). The primary components of UniMF comprise two distinct modules. The first module, Translation Module, translates missing modalities using information from existing modalities. The second module, Prediction Module, uses the attention mechanism to fuse the multimodal information and generate predictions. To enhance the translation performance of the Translation Module, we introduce the Multimodal Generation Mask (MGM) and utilize it to construct the Multimodal Generation Transformer (MGT). The MGT can generate the missing modality while focusing on information from existing modalities. Furthermore, we introduce the Multimodal Understanding Transformer (MUT) in the Prediction Module, which includes the Multimodal Understanding Mask (MUM) and a unique sequence, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiModalSequence</i> ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MMSeq</i> ), representing a unified multimodality. To assess the performance of UniMF, we perform experiments on four multimodal sentiment datasets, and UniMF attains competitive or state-of-the-art outcomes with fewer learnable parameters. Furthermore, the experimental outcomes signify that UniMF, supported by MGT and MUT - two transformers utilizing special attention mechanisms, can efficiently manage both generating task of missing modalities and understanding task of unaligned multimodal sequences. The implementation of this work is publicly accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/gw-zhong/UniMF</uri> .
Secured Dynamic Request Scheduling and Optimal CSP Selection for Analyzing Cloud Service Performance Using Intelligent Approaches

Due to its heterogeneity, cloud services providers’ (CSPs) rapid expansion presents several challenges, such as optimal service selection and privacy preservation. Multiple users using the cloud service at once increases the delay for service selection and request. Service interruptions result from centralized provisioning and insecurity. Existing work constraints include access control loss, service disruptions, security issues, trust management issues, and delays. Blockchain-based request scheduling and optimal CSP selection in edge-assisted clouds were presented in this research. Five phases—Data User (DS) authentication, sensitivity-aware request scheduling, policy verification, trust management, and optimal CSP selection—are proposed. In the first phase, DU authentication detects and eliminates authorized users. We suggested a chaotic map-based camellia encryption algorithm (CMCE) to boost security. The gateway schedules service requests using Johnson’s rule-based Stochastic Gradient Descent method, considering delay, throughput, and priority, in the second phase. This schedules the request into sensitive and non-sensitive services. Policy verification is done in the third phase utilizing Dynamic Policy-based Access control, which allows only sensitive requests. In phase four, we calculate the CSP trust value to boost security. Based on CSP behavior, we introduced the Multi Behavior Analysis-based Nomadic People Optimizer method. Every CSP’s trust value is modified based on user feedback over time. Finally, the best CSP is chosen for data user service, and suggested Dynamic and non-cooperative Game Theory is to choose the best CSP from a list. CloudSim is used to simulate and assess.
Transformer-Conformer Ensemble for Crash Prediction Using Connected Vehicle Trajectory Data

Crash prediction is one of the important elements of real time traffic management strategies. Previous studies have demonstrated the use of infrastructure-based detector data and UAV video to predict a crash in the near future. The main limitation of such data is limited coverage. In this work, we have used connected vehicle trajectory data that can have wide coverage as well as provide insight into the trajectory that might lead to a crash. The trajectory data was provided by Wejo which collects data from the manufacturer and was spaced at 3 seconds. GPS locations and their associated time series features such as speed, acceleration and yaw rate were used to feed into an ensembled Transformer and Conformer model. A voting classifier was used to obtain the output of the final model which achieved a recall of 76% and the false alarm rate of 30%. This study showed how connected vehicle trajectory data can aid in getting insight into crashes. While most previous studies focus on using aggregated data to estimate crashes, the proposed work shows that trajectory data mining can also provide competitive results.
Pose-Guided Attention Learning for Cloth-Changing Person Re-Identification

The change in appearance is a great challenge for cloth-changing person re-identification. Existing methods tackle this challenge by learning the shape features of the human body, however, these features are easily affected by the human pose or camera perspective. Thus, the ability to learn and extract the invariant features of person in varying conditions is crucial to overcome the above challenge. To address the issue of invariant feature extraction for cloth-changing person re-identification, a Pose-Guided Attention Learning (PGAL) framework is proposed in this paper. First, we introduce the human pose estimation network to remove the background effects and align the fine-grained key points features of human body. Then, to fully exploit the available appearance information, we develop a Feature Enhancement Module (FEM) that improves the feature representation of non-key point regions of human body through the Multi-Head Self Attention. Finally, in order to adaptively learn the invariant features of the person, we construct an Attention Learning Module (ALM) to achieve automatic selection of multi-granularity features by utilizing three different loss functions. Comparing with current popular methods on four cloth-changing person Re-ID datasets, the experimental results show the superiority of our method.
On Factorization of N-Qubit Pure States and Complete Entanglement Analysis of 3-Qubit Pure States Containing Exactly Two Terms and Three Terms

A multi-qubit pure quantum state is called separable when it can be factored as the tensor product of 1-qubit pure quantum states. Factorizing a general multi-qubit pure quantum state into the tensor product of its factors (pure states containing a smaller number of qubits) can be a challenging task, especially for highly entangled states. A new criterion based on the proportionality of the rows of certain associated matrices for the existence of certain factorization and a factorization algorithm that follows from this criterion for systematically extracting all the factors is developed in this paper. 3-qubit pure states play a crucial role in quantum computing and quantum information processing. For various applications, the well-known 3-qubit GHZ state which contains two nonzero terms, and the 3-qubit W state which contains three nonzero terms, have been studied extensively. Using the new factorization algorithm developed here we perform a complete analysis vis-à-vis entanglement of 3-qubit states that contain exactly two nonzero terms and exactly three nonzero terms.
Nodal Importance-based Hierarchical Dynamic Clustering Scheme for Target Tracking in WSNs (November 2023)

Clustering for target tracking over wireless sensor networks (WSNs) is an important method to achieve a trade-off between network lifetime and tracking accuracy. Due to the stochastic and non-uniform distribution of nodes within the network, the untimely onset of coverage holes is exhibited in prevailing clustering schemes, thereby diminishing the overall network coverage performance. To address this issue, we propose the Nodal Importance-based Hierarchical Dynamic Clustering (NIHDC) scheme. Firstly, we introduce the concept of nodal importance rank to quantify the impact of regional states on individual nodes, which is correlated with the regional node density and energy levels. Secondly, by jointly considering energy factor and nodal importance rank, we propose an importance-ascending hierarchical scheduling algorithm to strike a balance between network lifetime and tracking accuracy, in which the Binary African Vulture Optimization Algorithm (BAVOA) is utilized to search for the optimal cluster. Simulation results demonstrate that, compared to existing protocols, NIHDC achieves at least 17.7% improvement in network lifetime while maintaining comparable tracking accuracy, and delays the emergence of coverage holes.
Offline Data-Driven Optimization at Scale: A Cooperative Coevolutionary Approach

Data-driven evolutionary algorithms (DDEAs) have received increasing attention during the past decade, but most existing studies are dedicated to solving relatively small-scale problems. For large-scale optimization problems (LSOPs), special efforts must be made to both the surrogate and the evolutionary components to overcome the “curse of dimensionality”, which remains a challenge in this research area. To address this research limitation, we propose a novel cooperative coevolution-based DDEA (CC-DDEA). First, a hierarchical surrogate-joint learning model is designed to provide fitness approximations at both global and subdivided spaces, thus being able to guide the evolutionary population searching at different granularities. Then, optimization is conducted on both the global level and local sub-space level in the manner of cooperative coevolution. In the local-level search, we introduce a gradient-based operator to accelerate the convergence efficiency of sub-spaces, owing to the differentiable property of our surrogate model. Additionally, the entire framework is used in conjunction with a progressive and dynamic space division strategy, enabling local parallel-to-global unified search and facilitating the final convergence. Experiments on up to 1000-dimensional problems and the comparisons with state-of-the-art DDEAs validate the powerfulness of the proposed algorithm.
Segmentation and Classification of Individual Clouds in Images Captured with Horizon-Aimed Cameras for Nowcasting of Solar Irradiance Absorption

One important aspect of solar energy generation especially in inter-tropical sites is the local variability of clouds. Satellite images do not have temporal resolution enough to nowcast its impacts on solar plants, this monitoring is made by local cameras. However, cloud detection and monitoring are not trivial due to cloud shape dynamics, the camera is a linear and self-adjusting device, with fish-eye lenses generating a flat image that distorts images near the horizon. The present work focuses on cloud identification to predict its effects on solar plants that are distinct for every site’s climatology and geography. We used RASPBERY-PI-based cameras pointed at the horizon to allow observation of clouds’ vertical distribution, not possible with a unique fish-eye lens. A large number of cloud image identification analyses led the researchers to use deep learning methods such as U-net, HRnet, and Detectron. We use transfer learning with weights trained over the “2012 ILSVRC ImageNet” data set and architecture configurations like Resnet, Efficient, and Detectron2. While cloud identification proved a difficult task, we achieved the best results by using Jaccard Coefficient as a validation metric, with the best model being a U-net with Resnet18 using 486 × 648 resolution. This model had an average IoU of 0.6, indicating a satisfactory performance in cloud segmentation. We also observed that the data imbalance affected the overall performance of all models, with the tree class creating a favorable bias. The HRNet model, which works with different resolutions, showed promising results with a more refined segmentation at the pixel level, but it was not necessary to detect the most predominant clouds in the sky. We are currently working on balancing the dataset and mapping out data augmentation transformations for our next experiments. Our ultimate goal is to use such models to predict cloud motion and forecast the impact it will have on solar power generation. The present work has contributed to a better understanding of what techniques work best for cloud identification and paves the way for future studies on the development of a better overall cloud classification model.
GEOMAGNETIC RESEARCHES IN THE EASTERN PART OF NORTH MACEDONI

Republic of North Macedonia, according to current knowledge, has deposits of gold that were exploited in the past. Existence of contemporary geophysical methods, especially high precision magnetometers, gives relatively easy and very efficient possibility for defining the area of alluvial terraces, not only on the existing rivers but, also, in the old riverbeds. To define the most prospective areas for detailed research, bearing in mind that the gold and accompanying heavy precious metals are non-magnetic, which are of exploitation interest, almost always, paragenetic, magnetite is present with them. Magnetite as a mineral have the highest magnetic level, according to that is easy de-tectable. Areas with registered anomalies should be investigated with this method and core drilling in order to define depth of the ore.
Thematic Analysis of the Metaverse within Society 5.0 in Education

Boasting numerous capabilities, the Metaverse holds great potential for transforming education within the context of Society 5.0. This thematic analysis aims to provide a comprehensive overview of the Metaverse’s role within the context of Society 5.0, with a specific focus on its integration into the field of education. Society 5.0, characterized by the merging of physical and cyberspace, aligns seamlessly with the principles of the Metaverse, enabling students to access virtual classrooms and educational content using XR technologies, irrespective of time and location. Within this digital ecosystem, education stands to benefit significantly, as it can transcend physical constraints and provide immersive, personalized, and interactive learning experiences. The study outlines vital objectives, emphasizing the need to discuss the implementation of educational policies in the Metaverse and address potential negative implications for students. Through a comprehensive review of existing literature, the research establishes a conceptual framework based on the Six Pillars, outlining essential principles for creating practical educational applications in the Metaverse. While recognizing the transformative potential of the Metaverse in education, the study also acknowledges its negative implications, such as privacy concerns and ethical issues. This research provides valuable insights for policymakers, educators, and stakeholders seeking to navigate the evolving landscape of education in the digital age. Keywords: Metaverse, education, Society 5.0. https://doi.org/10.55463/issn.1674-2974.50.10.16