Reviewerly/exHarmony huggingface dataset:
Hybrid Cross-Reality Collaborative System

This work introduces a Mixed Reality (MR)-based hybrid cross-reality collaborative system that enables recording and playback of user actions in a large task space over time. An expert records their actions, such as virtual object placement, that others can view in Augmented Reality (AR) or Virtual Reality (VR) later to complete a task. VR offers a pre-scanned 3D workspace for spatial understanding, while AR gives real-scale information for manipulating real objects. Users can easily switch between AR and VR views, enhancing task performance and co-presence during asynchronous collaboration. The system is showcased in an object assembly scenario with parts retrieved from a storehouse. A pilot study showed that this cross-reality system improved task completion speed with lower workload.
Recent Advances in Motion Planning and Control of Autonomous Vehicles

An autonomous vehicle operates without human intervention, marking advancements in navigating structured urban roads and unstructured environments [...]
Effect of Merger and Acquisition of Deposit Money Banks on Nigeria’s Economic Growth: A Pre and Post Analysis

The study examined the effect of Mergers and Acquisitions (M&amp;As) of Deposit Money Banks (DMBs) on Nigeria’s economic growth. Pre-M&amp;As period lasted from 1990–2004, while the Post-M&amp;As spanned from 2005–2019. Data was collected from the Central Bank of Nigeria (CBN) Statistical Bulletin and tested with Ordinary Least Square Regression Analysis. The results indicated that in the Pre-M&amp;As era, bank’s capital base, credit granted to the private sector and bank spread positively, enhanced economic growth howbeit and bank's gross credit adversely affected economic growth. Findings also revealed that Post-M&amp;As era contradicted the Pre-M&amp;As effect on DMBs, with all the bank indicators showing negative connection with economic growth, except credit granted to the private sector. These findings led to the conclusion that M&amp;As had little impact on the country's economic growth during the period under consideration. As a result, the study recommendedthat the CBN issue a special directive to the DMBs to extend more credit to the private sector as a means of accelerating economic growth.
Distributed Power High Dynamic Group Control Technology and Grid Connection Demonstration

Abstract Currently, high penetration distributed power generation group control technology makes it difficult to effectively regulate complex power grids. Therefore, on the ground of the division of distributed photovoltaic power generation clusters, this study proposes a dual-layer control model for distribution networks that integrates the cuckoo particle swarm algorithm and verifies its effectiveness. The experiment showcases that the standard deviations of the research algorithm on the four benchmark functions under the 50 dimension are 2.95e-02, 6.73e+02, 6.48e-04, and 6.45e-06, respectively, most of which are lower than the comparison algorithm. On the basis of connecting to the photovoltaic system, the voltage amplitude fluctuation of the double-layer control model designed through research using control algorithms is relatively small, basically maintaining around 1.05. In addition, after implementing control in practical applications, the actual line loss of the distribution network decreased from the maximum of 15.3% to 8.9%, meeting the line loss requirements of the actual distribution network, indicating the effectiveness of the double-layer control model. Meanwhile, after applying the double-layer control model, the voltage fluctuation of node 42 significantly decreased, and the numerical value remained at 1.05 p.u., achieving a good voltage condition effect. The voltage fluctuation of grid connection in distributed power generation has been significantly improved when applied to the actual grid connection. Overall, the proposed dual-layer control model for the distribution network is effective and practical, which can effectively promote the construction of practical grid connection demonstration projects.
Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributes

The reason behind the unfair outcomes of AI is often rooted in biased datasets. Therefore, this work presents a framework for addressing fairness by debiasing datasets containing a (non-)binary protected attribute. The framework proposes a combinatorial optimization problem where heuristics such as genetic algorithms can be used to solve for the stated fairness objectives. The framework addresses this by finding a data subset that minimizes a certain discrimination measure. Depending on a user-defined setting, the framework enables different use cases, such as data removal, the addition of synthetic data, or exclusive use of synthetic data. The exclusive use of synthetic data in particular enhances the framework's ability to preserve privacy while optimizing for fairness. In a comprehensive evaluation, we demonstrate that under our framework, genetic algorithms can effectively yield fairer datasets compared to the original data. In contrast to prior work, the framework exhibits a high degree of flexibility as it is metric- and task-agnostic, can be applied to both binary or non-binary protected attributes, and demonstrates efficient runtime.
Organizing Extracurricular Activities Associated with the Goal of Comprehensive Development for Students at VNU National Defense and Security Training Center

Organizing extracurricular activities during the time of studying Defense and Security Education at the Training Center shows that the student's awareness of the importance of these activities is not high enough and they have not become an essential need for the students. The construction of extracurricular activities in each course has not been truly diverse, and the conditions of facilities, materials, and space for extracurricular activities have not met the requirements and the number of students. Therefore, when studying at the Training Center, students do not have many extracurricular activities to improve and develop their skills to meet the requirements of comprehensive development. Based on survey results and actual research, it is proposed that necessary measures be taken to improve the effectiveness of organizing extracurricular activities for students through the Defense and Security Education course, helping students have the opportunity to develop physical fitness, communication skills, and survival skills in new conditions towards the goal of comprehensive development contributing to training high-quality human resources in the future.
Developing Topics of Experiential Activities in Defense and Security Teaching in Order to Develop Learners' Ability to Learn about Nature and Society

This article aims to propose a set of topics for experiential activities in defense and security teaching that can enhance learners' abilities to learn about nature and society. The article begins by highlighting the importance of defense and security education in shaping learners' understanding of the role of military and security forces in protecting the nation and society. The article then presents five topics for experiential activities, which include visiting security and military agencies, practical experience in military training, practicing emergency response skills, learning self-defense techniques, and engaging in social activities related to security. The article argues that these activities can not only help learners gain a better understanding of defense and security but also provide them with opportunities to develop essential skills such as decision-making, problem-solving, and collaboration. Furthermore, the article suggests that these activities can instill a sense of civic responsibility and a willingness to contribute to the community's safety and well-being. Overall, the article proposes that incorporating experiential activities into defense and security teaching can enrich learners' learning experiences and foster their abilities to learn about nature and society. By providing hands-on experiences and opportunities for practical application, defense and security education can prepare learners to be responsible and engaged citizens who understand the importance of national defense and community safety.
Team Work Practices and Employee Performance in Local Government Authorities in Tanzania

This paper examined the effects of team work practices on employee performance of local government authorities in Tanzania. The specific objectives were to; determine the influence of shared vision, employee respect, employee ethics, collective decision, team level trust, intrapersonal skills and team cohesiveness on employee performance in LGAs. The study used multiple regression analysis to test the set hypotheses and construct the model. The study was based on cross-sectional survey design in which the quantitative approach was used to collect and analyse data from 368 employees. Research data was collected using a structured survey questionnaire as a research instrument where both descriptive and inferential statistics were used to arrive at conclusions. The study findings showed that shared vision, intrapersonal skills, trust and cohesiveness had the statistically positive and significant relationship between teamwork practice and employee performance. Further, findings revealed that shared decision, respect and workplace ethics were statistically insignificant. The study concluded that LGAs should create an environment that assists shared decision, respect and workplace ethics since this in turn will increase performance of employees. The study recommends team work practices be considered as part of organisations strategy to improve employee performance.
Development and Application of a Novel Engineering-Based Data Literacy Education Program for Elementary Students

Objectives In this study, a data literacy education program based on Nobel Engineering for elementary school students was developed and applied. This program consists of a total of 7 sessions, which are 22 hours long. Using a data literacy assessment tool for elementary students, the effectiveness of the proposed program was confirmed.&#x0D; Methods Two classes of sixth grades in a Chungcheongbuk-do elementary school were chosen, and they were split into two groups: an experimental group (19 students) and a control group (18 students). These two groups received experimental treatment each, and using an independent sample T-test, their differences were assessed. In addition, a paired-sample T-test was conducted to compare before and after experimental treatment for each of the two groups.&#x0D; Results As a result of the independent sample T-test for the experimental group and the control group, there were statistically significant differences in overall data literacy and in the areas of ‘data collection and understanding, data analysis and evaluation, and data-based communication’ among the four sub-areas. As a result of the paired sample t-test of the experimental group, statistically significant improvements were confirmed in both overall data literacy and sub-areas before and after the experimental treatment.&#x0D; Conclusions The conclusions and implications of the study were presented as follows: (1) The need for a specific approach considering the sub-areas of data literacy when designing educational programs; (2) The cultivation of the ‘communication competency' area due to the characteristics of novel engineering-based classes and benefits as a method of presenting situations to students; (3) The necessity of providing teaching and learning strategies to support learners in classes that utilize the various computing environments; and (4) The need for additional research on data recognition for students and the development of educational programs based on this study.
Innovative Tool for Rural Schools - Virtual Classroom

The article presents the application of a virtual classroom in a rural school in collaboration with other institutions. The Erasmus+ Strategic Partnerships project to develop a virtual classroom in a rural school aims to create a virtual collaborative space where rural schools can support each other and immediately fill their gaps in terms of teachers, content and activities. The idea involves not only the technological environment, but also the surrounding community, which is primarily made up of rural schools and other organisations active in the field of education and rural development.
Building Social Presence for Transnational Families Through Mixed Reality Shared Experiences

Sparked by the frustrations experienced in transnational family communication and inspired by an interest in exploring the potentials of a mixed reality (MR) future landscape, this study investigates the primary research question: how can we use mixed reality to build social presence for transnational family communication? This study reviews literature and contextual works from relevant fields, including presence and social presence, mixed reality, transnational relationships (inter-family and human-space relationships), and technology for social presence for transnational families. Utilizing the Research through Design methodology for iterative prototyping and paired user testing methods, this study explores the use of social MR platforms for transnational family communication. It describes four MR prototypes, paired user testing, and user responses from a live exhibition. This study contributes to theory at the overlapping fields of social presence, mixed reality research, transnational family relationship, and human-space relationship. The mixed reality prototypes, design frameworks, and evaluation criteria for designing mixed reality spaces to build social presence for transnational families also provide significance to design practice. Potential audiences are researchers in fields including mixed reality, communications, diaspora, and transnational studies as well as members of transnational families that may benefit from the research insights presented in this paper.
Automatic Difficulty Balance in Two-Player Games with Deep Reinforcement Learning

Regardless of the goal of a game, it should be a pleasant and fun experience for its players. For some games to be enjoyable, the level of difficulty must be carefully calibrated, otherwise, players will feel bored or frustrated. Multiplayer scenarios in particular, where one player’s satisfaction might not translate to the enjoyment of other players and poses extra challenges in balancing the difficulty. The performance of one player is relative to the opponent, versus single-player scenarios where we can fully control the environment. We propose an AI automation framework for difficulty balancing in two-player games, where balancing is seen as a Reinforcement Learning task. A Game Master (GM) agent learns how to use handicap game mechanics, signaled by a reward function that evaluates a weighted combination of aesthetic criteria that encourages dramatization and allows a player in the lead to go back and a player in the rear to catch up, creating the desired rubber banding effect that balances out skill gaps. The quality of the games with the trained GM embedded is examined by measuring the same aesthetic criteria on the resulting games, and by analyzing the resulting changes in the game.
Evaluasi Tempat Pemakaman Umum (TPU) di Kota Purwokerto, Kabupaten Banyumas

&#x0D; &#x0D; &#x0D; &#x0D; This researcher evaluated the spatial layout of the Public Burial Place (TPU) in Purwokerto City, Banyumas Regency. With a focus on spatial aspects, this study uses a Geographic Information System (GIS) approach to analyze the suitability of burial grounds. The research method includes mapping and spatial analysis in the Purwokerto City area, which consists of four sub-districts and 27 kelurahan. In the analysis of burial ground suitability, important variables such as soil type, including inseptisol, entisol, and ultisol, are explored in detail. The results revealed that most areas in Purwokerto City have the potential to be used as burial grounds. GIS approach and spatial analysis help in identifying the most suitable areas for TPU development. This research provides valuable insights for spatial planning and burial ground management in the face of population growth challenges and land limitations.&#x0D; &#x0D; &#x0D; &#x0D;
Modal Identification and Optimization Algorithm for Football Players Based on Preparation, Action, Reflection, and Evaluation Model

The iterative reconstruction of the football match scene is conducive to the change of the angle to improve interactivity and immersion. A method for estimating the 3D posture and body shape of football players was proposed as they are the most important elements in football match scenarios. A trained preparation, action, reflection, and evaluation (PARE) model was developed to generate 3D poses and body shapes in multi-view images of players. Manually annotated 2D joint points were used as optimization objectives for the model. The single multi-view optimization was conducted using the differentiability of the parametric Mannequin and its orthogonal projection to map the 3D posture and body shape to 2D joint points. The difference between them and the annotation was calculated using the backpropagation neural network to update the parameters. The process was repeated until the difference was minimized. To validate the proposed method, a dataset of the multiview of players was constructed from a football match. The experiment results demonstrated that the proposed method effectively estimated the 3D posture and body shape of football players. Compared with methods such as human mesh recovery (HMR), SMPL optimization in the loop (SPIN), and synthetic training for accurate 3D human pose and shape (STRAPS), the proposed PARE method improved the accuracy of 2D joint points by 9.2 to 37.5% in a single view and by 34.9 to 54.1% in multiple views.
"DAO IMPLEMENTATION CHALLENGES MODEL IN IRANIAN COMPANIES"

Decentralized autonomous organizations (DAOs) are blockchain-based organizations fed by a peer-to-peer (P2P) networkof contributors. Their management is decentralized without top executive teams and built on automated rules encoded insmart
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Unsupervised Interpretable Basis Extraction for Concept – Based Visual Explanations

An important line of research attempts to explain CNN image classifier predictions and intermediate layer representations in terms of human understandable concepts. In this work, we expand on previous works in the literature that use annotated concept datasets to extract interpretable feature space directions and propose an unsupervised post-hoc method to extract a disentangling interpretable basis by looking for the rotation of the feature space that explains sparse one-hot thresholded transformed representations of pixel activations. We do experimentation with existing popular CNNs and demonstrate the effectiveness of our method in extracting an interpretable basis across network architectures and training datasets. We make extensions to the existing basis interpretability metrics found in the literature and show that, intermediate layer representations become more interpretable when transformed to the bases extracted with our method. Finally, using the basis interpretability metrics, we compare the bases extracted with our method with the bases derived with a supervised approach and find that, in one aspect, the proposed unsupervised approach has a strength that constitutes a limitation of the supervised one and give potential directions for future research.
EMOTIONAL INTELLIGENCE AS A FACTOR OF SUCCESSFUL ACTIVITIES IN THE SOCIAL SPHERE

The article examines the role of emotional intelligence in the social sphere and its influence on the success of professional and personal activities. The author examines the concept of emotional intelligence, its components and importance in interaction with the environment. Special attention is paid to the relationship of emotional intelligence with interpersonal relations, leadership and the ability to communicate effectively. The article also highlights the practical aspects of developing emotional intelligence, giving examples of specific strategies and training aimed at improving this aspect of personal development. It is noted that in modern society, where high emotional intelligence becomes a key element of successful interaction and achievement of goals, understanding and improving this aspect becomes a necessity for achieving a high level of professional and personal growth. The results of the analysis and understanding of the importance of emotional intelligence in the social sphere are presented, and indicate practical implications for personal development and achieving success in various spheres of life. The article reveals the main aspects of the influence of emotional intelligence on the formation of effective interpersonal relations, the development of leadership qualities and the resolution of conflicts in the social environment. The author investigates methods and techniques for the development of emotional intelligence and their application to improve the quality of professional interaction in social institutions. The advantages of integrating emotional intelligence into the practical activities of specialists in the field of social sciences and social work are noted. The results of the study indicate the importance of understanding and developing emotional intelligence for increasing the effectiveness of social interactions and achieving success in the social sphere.
A Graph Machine Learning Framework to Compute Zero Forcing Sets in Graphs

This paper studies the problem of computing zero-forcing sets (ZFS) in graphs and provides a machine-learning solution. Zero-forcing is a vertex coloring process to color the entire vertex set from a small subset of initially colored vertices constituting a ZFS. Such sets have several applications in network science and networked control systems. However, computing a minimum ZFS is an NP-hard problem, and popular heuristics encounter scalability issues. We investigate the greedy heuristic for this problem and propose a combination of the random selection and greedy algorithm called the random-greedy algorithm, which offers an efficient solution to the ZFS problem. Moreover, we enhance this approach by incorporating a data-driven solution based on graph convolutional networks (GCNs), leveraging a random selection process. Our machine-learning architecture, designed to imitate the greedy algorithm, achieves significant speed improvements, surpassing the computational efficiency of the greedy algorithm by several orders of magnitude. We perform thorough numerical evaluations to demonstrate that the proposed approach is considerably efficient, scalable to graphs about ten times larger than those used in training, and generalizable to several different families of synthetic and real-world graphs with comparable and sometimes better results in terms of the size of ZFS. We also curate a comprehensive database comprising synthetic and real-world graph datasets, including approximate and optimal ZFS solutions. This database serves as a benchmark for training machine-learning models and provides valuable resources for further research and evaluation in this problem domain. Our findings showcase the effectiveness of the proposed machine-learning solution and advance the state-of-the-art in solving the ZFS problem.