Reviewerly/exHarmony huggingface dataset:
Analysis of Teachers' and Students' Technology Application Behaviors in a Smart Paper-Pen Environment

The analysis of technology application behavior contributes to the discovery of the connection between classroom teaching and technology applications, further promoting the deep integration of technology and education. This paper combines the support role of smart paper-pen technology in classroom teaching with existing analytical frameworks to construct a framework for analyzing the technology application behavior of teachers and students. Using lag sequential analysis, this study examines nine lesson videos of smart classroom teaching in junior high school, aiming to explore the application of technology in classroom teaching and the behavioral patterns of teachers and students. The research findings indicate that smart paper-pen technology can enhance efficient classroom interactions, but this process is often teacher-driven, with limited student autonomy. Only a few classrooms see teachers utilizing technology for instructional assessment and classroom management. Overall, both teachers and students tend to apply technology at a superficial level, highlighting the need for further exploration of effective ways to leverage technology in education.
Analyzing Factors Influencing Primary School Teachers' Acceptance Willingness of Artificial Intelligence Technology

The integration of artificial intelligence (AI) technology with educational practices is a general trend. In this context, Using AI technology is an essential preparation for educators to adapt to the evolving landscape of pedagogical reform. The willingness of teachers to accept AI technology directly influences their actual utilization of it. Therefore, this study constructs a model for assessing the AI technology acceptance willingness among primary school teachers in China's western regions, based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The research sample encompasses 536 primary school teachers from seven provinces in western China. Using SPSS 26.0 software, this study conducted confirmatory factor analysis and correlation analysis of survey data, followed by structural equation modeling analysis using Mplus 8 software. The data analysis reveals that the primary factors influencing the willingness of primary school teachers in western China to adopt AI technology are Performance Expectancy and Social Influence, with educational backgrounds exerting a positive moderating effect within this relationship. The study offers insights to enhance AI technology acceptance among western China's primary school teachers, fostering innovation in education.
A Study on the Information Technology Application Ability of Teachers in Border Schools of the Guangxi Zhuang Autonomous Region

In the digital era, the level of information technology application ability of primary and secondary school teachers directly affects the quality of basic education, especially the urban-rural gap brought about by economic differences. Through a questionnaire survey of 2544 teachers from primary and secondary schools and kindergartens in 8 counties and cities in the border areas of Guangxi, it was found that most teachers recognize the promotional role of information technology in teaching and actively use information technology in teaching; teachers can skillfully use various multimedia teaching equipment and software; education resource platforms are the main channels for teachers in border schools of Guangxi to obtain digital education resources, and resources on education resource platforms are widely used in teaching. The study believes that teachers in border areas of Guangxi recognize the application of information technology in teaching, with an overall good level, but there are still weaknesses in resource utilization, troubleshooting, etc., and targeted strengthening of offline training is needed.
Augmented Reality System Based on Real-Time Object 6D Pose Estimation

Augmented Reality (AR) refers to the perfect integration of virtual information with the real environment through computer technology, enhancing people’s understanding of the real environment. Due to its excellent interactivity and realistic visual effects, it has received extensive attention from various fields. The effectiveness of augmented reality largely depends on whether virtual information can be accurately rendered in the real scene, with the most critical aspect being the 6D pose estimation of the target object in the real scene. Deep learning-based pose estimation methods have become indispensable technology in AR systems due to their high accuracy and wide applicability. Among existing pose estimation algorithms, YOLO6D has the advantages of good stability and fast speed. However, for AR applications that require interaction with the human eye, due to its complex network structure and large computational complexity, it still faces challenges in real-time performance and deployment on mobile devices. Therefore, this paper aims to achieve a mobile AR system based on real-time pose estimation, using the YOLO-6D pose estimation network as the foundation, and conducting research from the aspects of model compression and system design. By using model compression algorithms include channel pruning and knowledge distillation (KD), the computational and parameter complexity of the pose estimation model is reduced, and the network inference speed is improved. At the same time, the AR tasks are decomposed in the system design to alleviate the performance pressure during deployment on mobile devices, thus building a high-quality, low-latency mobile AR system.
Research on Detection Algorithm Based on YOLO and Lightweight Network EfficientNet

Deep learning based object detection algorithms have been applied in various fields of life. As a representative of one-stage detection algorithms, YOLO series algorithms are highly favored for their fast speed.To resolve the existing issues with complex network architecture and weak real-time performance. YOLOv3 and YOLOv4 are improved by adding the lightweight network EfficientNet, using deep separable convolution rather than regular convolution, reducing a significant number of network parameters, using transfer learning to speed up the model’s training speed, and speeding up the model’s convergence. Finally, the accuracy, number of parameters, and detection rate of the network structure before as well as after improvement are compared to ensure that the model is successful. According to the experimental findings, the number of parameters for E-YOLOv3 and E-YOLOv4 are only 1/9 and 1/7 of what they were before the enhancement, and the detection rate has increased by 4 and 3 frames/second, respectively.
Construction and Application of Multi-modal Knowledge Graph for Collection of Cultural Relics

In recent years, computer technology has advanced significantly and more and more museums have begun to prioritize digital protection of cultural relics, achieving fruitful results. However, the digital resources of these relics are often stored in document or relational database forms, and displayed on the museum website using basic division methods. Moreover, each cultural relic collection holds its own unique knowledge points and there must be rich associations between them due to certain commonalities, which cannot be fully applied by existing preservation techniques. Multi-modal Knowledge Graphs (MMKG) have undergone years of development and have achieved significant results and widespread applications. Although China has abundant multi-modal data on cultural relics, there is limited exploration of MMKG in the field of cultural relics. Therefore, this paper proposes to construct a MMKG for museum relics, RelicMMKG, to mine the information contained in the multimodal digital resources of museum relics. This includes using the BERT-BiLSTM-CRF model to identify multiple types of entities in the relic text introduction and using image descriptors to extract and calculate the similarity of visual features in the relic image data. Furthermore, based on the RelicMMKG and structured text and image data, a multifunctional application system is implemented to better explore the correlation between cultural relics, enabling efficient excavation and application of cultural relics data in the museum.
A Multi-objective Assignment Algorithm for UAVs in Multiple Task with Different Workloads

UAVs have been widely used in military and civilian fields. The research on UAV task assignment mainly involves trajectory planning, task assignment, and specific applications for various scenarios. Moreover, most of the previous studies are for single UAV. Studies for swarm of UAVs are relatively few. In this paper, we focus on the multi-objective assignment algorithm of UAVs for multiple tasks which have homogeneous application scenarios with different locations, same priorities and different workloads. First, a coding method that can simultaneously reflect path planning and task allocation is designed, Based on this, a multiple task assignment algorithm for UAVs swarm is designed, In order to balance the tasks completion time, the workload of each task and the total transfer distance, a multi-objective assignment model is further constructed, and the complete multi-objective UAVs task assignment algorithm is proposed. The task assignment scheme obtained by the algorithm can make the UAVs complete all tasks in a balanced time and resource consumption. The experimental results show that our proposed method can determine the UAVs task assignment plan effectively and efficiently.
Publications of the Shevchenko Scientific Society in the evaluation of european scientific journals: the communication aspect

The article examines reviews dedicated to the works of the Shevchenko Scientific Society published in scientific journals in Vienna, Berlin, Budapest, Prague, Warsaw, Lviv, and others. The collected empirical material is analyzed in terms of the issues surrounding the history of communication among scholars within the broader European intellectual space from the late 19th to the early 20th century. To address the research question, the article formulates the concept of scientific communication, characterizes general features of the development of scientific communication in Europe as a whole, and outlines the peculiarities of historical socio-political circumstances against which the formation of Ukrainian scientific culture took place. The historical circumstances of the initiation of scientific activities by the Shevchenko Scientific Society are depicted, including the reasons and prerequisites for the Society’s foundation, its functioning at the initial stages, and the names and orientations of the main publications and journals under the auspices of the Society. A brief description of the journals containing reviews were included: «Kievskaia Staryna», «Etnograficheskoe Obozrenie», «Lud», «Kwartalnik historyczny», «Wisła», «Biblioteka Warszawska», «Národopisný Sborník Českoslovanský», «Századok», «Zeitschrift des Vereins für Volkskunde», «Archiv für slavische Philologie», «Zeitschrift für österreichische Volkskunde» etc have been given. A number of facts from the biographies of some authors have been revealed, among them prominent European historians, ethnologists, philologists, literary experts, linguists, and lawyers. The trends in the assessment were analyzed, the features of rhetoric were revealed, the attitude of the authors of the researched publications to the publications of the National Academy of Sciences was revealed, and their thematic spectrum has been determined. Keywords: Shevchenko Scientific Society, scientific commu­nication, scientific journals, review.
Strengthening teacher's pedagogical competences for the implementation of the content of the course of music culture in distance teaching

The research problem of this paper is the competencies of teachers for the implementation of distance teaching in Music Culture. We started with the special assumptions that the research will demonstrate that there is no significant difference in teachers' assessments of their competencies for the implementation of distance teaching in Music Culture, depending on the impact of their length of service in teaching and their knowledge of ICT and participation in professional development programs in the field of ICT. The research was conducted in the first half of the 2021/2022 school year on a sample of 170 teachers who were surveyed by an online questionnaire. Data collected by the research and processed by descriptive statistics showed that there is no significant difference in the competencies of teachers for the implementation of distance teaching in Music Culture depending on the impact of their length of service in teaching and their knowledge of information and communication technologies: 𝞆²(2) = .164; p = .921 and the participation in professional training programs in the field of information and communication technologies: 𝞆²(2) = 1.839; p = .399. Based on the research results, the paper proposes a program of professional development of teachers with the aim of increasing the level of their digital competence for the effective implementation of teaching Music Culture. The skills necessary to possess this competence are: the need for ICT in everyday work; ability to search and process information through ICT; ability to access forums and other ways of using ICT and information.
Pedagogical perspective of social learning in the metaverse: A case study of computer graphics programming

The potential of using the metaverse as a contemporary digital educational environment for social learning presents a current pedagogical challenge. Considering that social learning presents a powerful model for acquiring new knowledge and skills, it is valid to observe it as an effective complement topromoting learning and development in various contexts. Given the limited empirical evidence on social learning effects in the metaverse environment, this study aimed to examine the impact of students' activities in this digital environment on their perception of the learning platform and learning outcomes. The results indicate that the pedagogical component played a significant role in students' intention to use the metaverse, but that technology did not. The study also revealed that students who participated in online activities achieved significantly better results in specific areas of computer graphics programming. The research emphasizes the need for deepening the understanding of complex social learning and adapting pedagogy for learning in the metaverse, especially the characteristics that would support collective intelligence.
Type-Representation Enhanced Event Detection via Structure Encoder

Event detection, aiming to detect whether a sentence contains the type of events of interest, is one of the important tasks in information Extraction. There exist many methods for event detection, which usually treat event detection as a classification problem. These existing methods suffer from the imbalanced training data of different event types. To relieve this problem, this paper proposes a new Type-Representation-Enhanced Event Detection (TREED) method, which introduce hierarchical event type representations to enhance classification accuracy. Specifically, TREED first models the hierarchical relationship among event types in the dataset; Next, upon interactions between the representations of trigger words and event types, it obtains the new representations of trigger words integrating event type information. Finally, TREED classifies the events based on the obtained more accurate trigger word representations. Experimental results on a commonly used benchmark dataset, ACE-2005, demonstrate that the proposed model can improve both accuracy and recall of the event detection task.
MedPipe: End-to-End Joint Search of Data Augmentation and Neural Architecture for 3D Medical Image Classification

Data augmentation plays a crucial role in deep learning-based medical imaging analysis, but manually designing tailored data augmentation strategies for each dataset is impractical. Although automatic data augmentation (ADA) techniques have been explored, they often focus solely on data augmentation without considering the importance of neural architecture. Similarly, neural architecture search (NAS) methods mainly concentrate on optimizing the neural architecture while overlooking the impact of data augmentation. However, both data augmentation and neural architecture are interrelated and should be considered together. The joint optimization of data augmentation and neural architecture can lead to improved model performance by harnessing the complementary effects of customized data augmentation strategies and compatible neural architectures. Despite this, the seamless integration of data augmentation and neural architecture search remains under-explored. To address this research gap, we propose MedPipe, an approach that enables end-to-end joint search of data augmentation and neural architecture. We introduce a compact data augmentation search space and unify data augmentation and neural architecture into a cohesive network. This allows simultaneous exploration, optimizing their synergy for enhanced performance. Experimental evaluation on nine medical datasets highlights the necessity of the joint search for data augmentation and neural architecture, demonstrating the superior performance of our approach. Our work opens up possibilities for future applications in diverse medical domains.
A Framework for an Automated Assessment System

The motivation for yearly assessment of student outcomes, as part of the continuous improvement process, to facilitate informed decisions for enhancing student success. A significant component of ABET accreditation is implementing this assessment, but it remains problematic due to faculty perceptions of externally-defined rules that require significant additional work, as well as the notion that it is not part of the faculty member's core activities. To address this, we worked to create a primarily automated system that minimizes additional workload for the faculty. Coupled with ongoing discussion emphasizing the importance of assessment of student outcomes, we arrived at MSA <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , the Michigan System for Automated Accreditation Assessment. Using commonly available technologies, our system improves upon prior work by increasing the level of automation and functionality while reducing faculty workload and preserving security of student information.
Graph Generation with Recurrent and Graph Neural Networks

Graph generation has applications as diverse as drug discovery, materials design, and code completion. In this paper, we propose a novel auto-regressive graph generation model, where graph generation is viewed as a decision process. The proposed model combines the power of graph neural networks (GNNs) with generative modeling techniques, and incorporates both graph topology and node features, allowing the generation of graphs with desired properties. Extensive experiments on molecule datasets demonstrate the effectiveness of our approach, achieving high validity, diversity, and similarity to the target molecules.
Complexity Coping by Methodical Agile and Modular Product Development - A Bibliometric Review

This bibliometric analysis identifies the extent to which the methodical development of modular product families (product side) and agile project development (process side) can be beneficial for coping with complex product development tasks. The accomplished co-occurrence analysis reveals an indirect relationship between the development of modular product families and agile product development. This indirect connection is because the different methods are incorporated in the field of Design Methodology. However, the analysis leaves a gap in the direct interaction of both subjects, a subsequently accomplished bibliographic coupling points out. A consecutive in-depth abstract and paper analysis reveals further research on their integration is necessary.
On the Influence of Time-Correlation in Initial Training Data for Model-Based Policy Search

Model-based policy search techniques are a way to quickly learn real-world robotics behaviors. Indeed, learning a model allows the robot to imagine interactions with its environment and to furthermore refine its behavior without actually interacting with it. Nevertheless, a good model is critical for such approaches to succeed, and initial model training steps can change drastically the performance of the policy search algorithm. In this paper, we propose to study the impact of various initial data gathering methods for bootstrapping model training in a model-based policy search algorithm. We compare five initialization methods, two being used in the state of the art and the last three one representing various degrees of time-correlation. We then show a link between the time-correlation of the initialization method and an environment metric that we call consistency, empirically demonstrating on three robotic systems that model prediction error can be predicted from environment consistency and initialization method action sequences time-correlation. We finally show the impact the initialization method has on a state of the art model-based policy search algorithm, demonstrating consistent results with the proposed metric and the initialization methods prediction errors, allowing for a better choice of initial training data beforehand.
Collision Avoidance and Trajectory Planning for Autonomous Mobile Robot: A Spatio-Temporal Deep Learning Approach

The field of autonomous mobile robots has been gaining significant attention in various industries and research domains. As the future of robotic process automation unfolds, there is an increasing demand for precise robot movement in terms of collision avoidance and trajectory planning. This paper presents a camera-based autonomous mobile robot system that addresses these requirements. The proposed system utilizes a deep learning variational autoencoder with a spatio-temporal model for image analysis processing. This approach enables the system to effectively analyze and understand the visual information. By leveraging deep learning techniques, the system can extract meaningful features and representations from the images, facilitating accurate perception and understanding of the robot's surroundings. This paper contributes to the advancement of autonomous mobile robot systems by proposing a deep learning techniques with reinforcement learning algorithms. The approach offers promising possibilities for enhancing the control and interaction capabilities of mobile robots in real-world scenarios.
Research on Secure Communication Model for Station Based on Multi-Factor Identity Authentication

With the advancement of communication technology, the diversity and number of terminal devices in the station communication network continue to grow, and the traditional single authentication method has been difficult to meet the needs of modern complex network environments, especially in critical areas such as smart grids. Unauthorized access and identity impersonation have gradually become the primary security problems faced by these domains. In response to this problem, this paper provides an in-depth discussion of the multi-factor identity authentication technique that fuses passwords, certificates, trustworthy hardware and other identity authentication factors, and constructs an identity authentication scheme based on decision-level fusion. To further enhance the accuracy and efficiency of identification, we have also incorporated deep learning techniques that enable the model to automatically learn and extract key features. The experimental results show that the station secure communication model based on multi-factor identity authentication can effectively improve the security of terminal devices in the station communication network while providing authentication accuracy.