Reviewerly/exHarmony huggingface dataset:
Speed Control of DC Motor based on H<sub>∞</sub> Mixed-sensitivity Algorithm

In order to restrain the uncertainty of system parameters and the influence of external disturbances on the speed of DC motor, this paper adopts the $H_{\infty}$ mixed-sensitivity algorithm to design a speed controller based on the model of the controlled motor. First, the Pseudo Random Binary Sequence (PRBS) is input to the motor and the corresponding speed output sequence is measured. The Least Squares Method (LSM) is used to conduct system identification; Then, design a weighting function based on the established system performance indicators, and use MATLAB to synthesize the $H_{\infty}$ controller; Finally, this article adopts the direct form conversion method to implement the controller, and successfully controls the speed of the DC motor through the STM32F407ZGT6 microcontroller. The experimental results show that this algorithm can achieve good speed tracking performance and anti-disturbances ability for DC motors.
Fast Motion Planning of Autonomous Navigation Reconnaissance UAV Based on Range Field

In order to solve the problems of slow convergence speed and low algorithm efficiency in path planning of UAV when using particle swarm optimization algorithm to face the unknown environment map, a method of particle initialization using artificial potential field method is proposed, Convert the no fly zone and target information covered by interference sources or obstacles in the known environment map into potential field values and display them on the grid map, and selects the grid with smaller potential field values during initialization. In the iterative optimization process, the smoothness of the solved trajectory, the obstacle avoidance of unknown obstacles and the distance from the target point are used as fitness values to find path points, and finally the B-spline curve is used to optimize the trajectory. The simulation results show that compared with PSO, the algorithm can save about 30% operation time under the same convergence effect.
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR SENTIMENT ANALYSIS OF TEXTS IN AZERBAIJANI AND ENGLISH

The prediction of sentiment of the text within different business spheres has been one of the challenging problems for a variety of linguistics. In this paper, the sentiment analysis of the texts is carried out using different machine learning (ML) techniques. Various feature extraction techniques and supervised learning algorithms are employed on the movie review dataset sourced from the Internet Movie Database and translated into Azerbaijani. Specifically, the techniques utilized encompass Support Vector Machine, Logistic Regression, Decision Trees, Random Forest, AdaBoost, XGBoost, and Naïve Bayes. The proposed models depict the importance of language corpus that Azerbaijani language lacks by comparing the results obtained from both Azerbaijani and English versions of the dataset.
Monitoring of Public Opinion on Typhoon Disaster Using Improved Clustering Model Based on Single-Pass Approach

Ambiguities in information and difficulties in distinguishing truth from fiction during natural disasters produce negative emotions and create problems in emergency rescue work. In this study, we focused on two aspects. First, we propose a method that improves upon the existing streaming data clustering method based on twin networks, which is a single-pass topic clustering method based on the Siamese-bidirectional gated recurring units (BiGRUs)-attention technique. Second, a bidirectional encoder representation from transformers (BERT)-BiGRU-conditional random field (CRF) sentiment analysis model based on the idea of sequence tagging was designed. Combining this method with the proposed topic clustering method, we propose a new disaster management method that analyzes the public opinion before and after a disaster. We conducted experiments that showed that the single-pass topic clustering model based on Siamese-BiGRU-attention outperformed other clustering methods in terms of clustering performance. Simultaneously, the BERT-BiGRU-CRF model was employed to statistically analyze data on daily public opinion monitoring. The statistics of the clustering results before and after disasters occur and the emotion distribution based on each category were obtained. Overall, the proposed method can help rescue workers and governmental officials understand the sentiments of the public more clearly and provide the necessary response measures more effectively during disasters.
Augmented Learning Environments as Assistive Technology for Kids with Learning Disabilities

Assistive Technology and Medical Devices Research Center (A-MED) is being aware of the importance of helping students who are struggling in reading and writing. Therefore, the research center has developed the HadThai application with AR to help those students improve their Thai reading and writing. This research is not only useful for students, but also for teachers and parents. The development of literacy competency using the HadThai application with AR for elementary students with reading and writing problems aims to study about reading and writing ability of primary students with reading and writing problems after teaching with the HadThai with AR application, to compare reading and writing ability of primary students with reading and writing problems before and after teaching with the HadThai with AR application, and to study satisfaction of primary students with reading and writing problems after teaching with the HadThai and AR application. The sample in this study consisted of grade 1 and 3 students identified with reading and writing difficulties from a kindergarten school in Saraburi Province, using a purposive sampling method. Hence, the results revealed that (i) the competency level in reading and writing of grade 1 students with reading and writing problems was very good with .05 statistically significant after teaching, (ii) the competency level in reading and writing of grade 3 students with reading and writing problems was very good with .05 statistically significant after teaching, (iii) the competency level in writing of grade 3 students identified with reading and writing problems was very good with .05 statistically significant after teaching, (iv) higher competency level in word reading and writing of grade 1 and 3 students with reading and writing problems after teaching, and (v) grade 1 and 3 students with reading and writing problems has highest degree of satisfaction toward the teaching using the application.
An Augmented Reality Application to Better Understand 3D Astronomy Concepts in the 7th Grade

Understanding Astronomy involves the perception of 3D phenomena, most often too small or too vast to be easily interpreted or visualized. This research aims to develop and evaluate an Augmented Reality application prototype for learning 3D Astronomy concepts and phenomena in middle school. Relating the technology impact with the educational experience, this work evaluates the usability, in a real classroom context, of a developed prototype. The results revealed a high degree of acceptance and satisfaction among the students.
Affective Lexicon for Intelligent Tutoring Systems

Affective Tutoring Systems (ATS) is a next-generation Intelligent Tutoring System (ITS) that can detect learners' affective states and provide affective interventions to encourage learners and improve their motivation to learn using artificial intelligence techniques. However, little research has been done on creating an educational affective lexicon in English that those systems can use for providing affective intervention. There needs to be more evidence as to which phrases learners may like to receive from an ATS. This study investigates popular congratulating and encouraging phrases to build a comprehensive affective educational lexicon in English for ATS. Firstly, we examined and collected phrases by reviewing existing learning support systems from the literature that provide affective feedback to learners for constructing an affective lexicon. Secondly, 84 students from various qualifications and backgrounds evaluated the collected phrases by choosing the most popular ones in the congratulating and encouraging dimensions. The lexicon currently consists of 43 encouraging and 32 congratulating phrases categorised by popularity. The findings from this study will augment the ATS to provide better affective tutoring support for students by choosing more constructive and popular affective phrases.
Intelligent Tutoring Systems Examined in Social Experiments—Is the Magic Gone? A Meta-Analysis

Various meta-analysis studies were conducted to investigate the effects of Intelligent Tutoring Systems (ITSs). However, no study, to the best of our knowledge, examined ITSs in social experiments in real and natural learning contexts (not in lab experiment or specific research experiment) to reveal their true effects. To cover this gap, this study conducts a meta-analysis of ITSs examined in social experiments, where 14 studies ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N=3496$</tex> ) were finally included and analyzed. The obtained results revealed that ITSs have an overall small positive effect ( <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$g=0.36$</tex> ). They further revealed that ITSs have a better positive effect on knowledge (medium effect) compared to skills (small effect), as a learning outcome. The results also revealed that ITSs have a better positive effect (huge) when used for language knowledge learning compared to other domain subjects. Finally, ITSs have better positive effect (large) when used in primary education and for less than one year. The findings of this study can contribute to the literature by explaining how the effect of ITSs might vary when examined in real educational contexts, i.e., in the “field”.
Unique Mobile Applications for Place-Based Collaborative Learning: The DICE Model

DICE: Discover, Imagine, and ChangE is a pedagogical approach for mobile learning that grew out of a study focusing on the unique affordances of mobile technologies. DICE supports the designing of place-based learning activities with the assistance of mobile applications. As such, the model has been examined within the context of geography and cultural heritage, prior to and during the pandemic. The paper outlines DICE's main characteristics through data gathered from case studies in which young as well as adult learners took part. Through participating in these place-based, collaborative, and mobile-technology enhanced activities, learners uncover the invisible in familiar places and become more aware of their surroundings, while the place becomes a powerful object to learn with.
Construction of Electric Circuits Course Knowledge Graph

At present, there are more and more researches on knowledge graph in various fields. This paper focuses on the knowledge graph in the field of education. According to the general process of knowledge graph construction, this paper constructed electric circuits course knowledge graph. Firstly, collect original text data from two sources: print books and publicly available online resources. Then, the construction of the domain ontology of the electric circuits course is presented, which defines entities, attributes, and relations in triples. Next, the knowledge extraction part is introduced, and a comparison experiment of named entity recognition on unstructured data is conducted, comparing the recognition performance of different entity types and different models. The results show that the BERT-Bi-LSTM-CRF model has the best effect, with a precision of 91.85%. In addition, relation extraction is performed to transform unstructured data into structured triples, which are stored in MongoDB and Neo4j databases. Finally, the construction of the electric circuits course knowledge graph is completed.
LSTM Cocktail to Generate Merged Strategies for Sequencing

Learning material is designed based on didactical concepts and methods. To create an item sequence, strategies are applied by rule-based descriptions. Merging them requires their combination, which is complex to apply. In this paper, strategy combination is applied using LSTMs. Samples are generated to train different LSTM models for specific strategies. Those models are then merged by averaging their weights. The approach gives teachers the controllability over the selection of an appropriate trade-off between different strategies, without the need to create complex rules by hand.
MetaClassroom: A WebXR-Based Hybrid Virtual Reality Classroom

The primary objective of this study is to design and implement a virtual reality classroom that incorporates the OMO (Online Merge Offline) concept. Utilizing WebXR technology, a virtual reality classroom environment is created, enabling teachers and students to interact within this immersive space via various devices, including mobile phones, tablets, laptops, and VR devices. Currently, the system has established two virtual reality scenes in both classroom and discussion room settings to provide students and teachers with a highly realistic virtual reality classroom experience. It is worth mentioning that these virtual reality scenes have been tested by university students and instructors and received positive feedback.
Development and Testing of a Real Time Virtual Reality Body Avatar Creation and Body Appreciation Experience for Young Adults

Body image has a profound impact on overall wellbeing and quality of life. In this paper, we presented a pilot study of a virtual reality program of a real time 3D body avatar creation and appreciation experience. We present a method that allows participants to alter an avatar with different body shapes, skin tones and hair in real time until they believe it looks like them, then paint parts of the avatar that they appreciate for various reasons. Through a pre and post survey, we have found the average learning outcome measures for the VR group showed a more promising trend increase in testing scores compared to the control workshop group. However, the body appreciation scale measured in both the VR experience and the workshop group showed a very limited improvement after one session and requires further research in the area. Majority of the participants feel positive about the VR experience. The real time 3D avatar alternation method presented has the potential to be used for player embodiment, body appreciation and body image education to improve body image.
An Adaptive, Structure-Aware Intelligent Tutoring System for Learning Management Systems

Intelligent Tutoring Systems (ITS) can be used to provide personalized assistance in Learning Management Systems (LMS). The main drawbacks to this end are that they are usually self-contained or system-dependent, and that integrations often lack in a representation of the considered learning content structure (i.e., are not structure-aware). The former prevents the reuse of devised didactic concept implementations. The latter can cause the user to get lost during the guidance process. Both are challenging to overcome because of the heterogeneous, LMS-specific approaches to structuring learning content. The aim of this thesis is to investigate frame conditions for reusing structure-aware ITS functionalities across LMS and to illustrate the gained insights in an elaborated ITS. Therefore, a system architecture is proposed that can retrieve the required learning process data from an LMS by employing a thin adaptation layer and transform this data into a generic data structure. This structure is used for providing assistance and for generating and integrating the learning content structure representation. The focus of this thesis is on the system-independent implementation of structure-aware intelligent assistance scenarios. The approach will be evaluated in terms of applicability and effectiveness considering different state-of-the-art LMS.
Peer-Review to Support Teaching Text Analysis to Digital Humanists Using R

Teaching programming for non-programmers is a difficult task for teachers. This manuscript describes an intervention designed to help students from Digital Humanities while learning R for text analysis. Intervention based on a peer review reported a good performance regarding supporting the students while learning R, providing them with adequate space to exchange ideas and good practices.
The Design and Analysis of a Storytelling Chatbot with Natural Language Processing Techniques for Enhancing EFL Reading

This study explored the applications of natural language processing techniques on the analysis of storybook to design the storytelling chatbots. Two natural language processing techniques, co-reference and dependency parsing techniques, were applied to process the text of 157 stories books as a means to implement in the invite-narration, positive recognition, follow-up strategies when the chatbot retells the story with the students. The results of the study found that 19 (42%) students demonstrated full narratives which can re-tell the overall story in the storybook, while 22 (49%) students could only retell a simplified story. Affordances and limitations of the use of the two techniques in developing chatbots for reading are discussed.
Authoring Tools for Teaching in VR — an Evaluation Study

More and more universities are looking for solutions to integrate virtual reality (VR) into teaching and research. But most of the VR systems available for educational purposes are difficult to use without technical expertise, yet. This ability to operate easily, however, would make VR particularly valuable in the classroom. Therefore, this paper describes an exploratory study of university teachers' perceptions on authoring tools in an immersive virtual reality environments. The goal was to get an overview which of these tools, e.g. creation of 3D objects and annotations, teachers consider usable and useful. Overall, all 14 participants in the study were enthusiastic about the possibilities of content creation in VR. For the most part, they found the authoring tools easy to use and see their usefulness for teaching with respect to this first impression.
Development of an AR Food Education System to Support Elementary School Nutrition Education

Eating behaviors are developed gradually during childhood, so it is crucial to educate students about the nutrition of snacks and drinks sold in the market from an early age. This underscores the importance of school education in shaping and cultivating children's eating habits. Numerous studies have shown that taking students to supermarkets for nutrition education can improve their awareness of nutrition labels and buying habits. However, most nutritional education courses in Taiwan's elementary schools are conducted in traditional classrooms and do not effectively incorporate real-world learning situations. To improve the effectiveness of nutrition education in elementary schools, this study developed an AR food education system that combined game design, augmented reality, and image recognition technologies to support nutrition teaching activities. An experiment was conducted in an elementary school, inviting 27 6th grade students to participate and evaluate the effect of the proposed system on nutrition education. Results showed that the system attracted students' attention and provided a sense of satisfaction after learning. Moreover, the proposed approach enhanced students' learning performance in nutrition education.
WhatsApp Communities: Educational Use Cases

This paper explores the potential use cases of WhatsApp Communities (WACs) in the realm of education. We examine the unique features of WACs, such as the capacity to manage up to 50 groups and the Announcement Channel functionality, which enables sending tailored messages to all community members. Our discussion highlights how these capabilities can enhance communication and collaboration among students, teachers, and administrators, offering valuable opportunities for virtual education. Furthermore, we emphasize the potential of WACs in promoting learner literacy and citizenship, ultimately contributing to the ongoing evolution of our education systems for the future.
Healthy Pepper: Nutritional Education Through Social Robotics and Storytelling

Childhood overweight and obesity represent an alarming problem worldwide with strong consequences both at health and social levels. Learning based on storytelling is an effective approach to enhancing children's knowledge and correct behaviors, especially when it is conveyed by engaging novel technologies. Among these technologies, social robots have a special appeal for educational purposes. The paper presents a pilot project in which the social robot Pepper is used as an educational storyteller in the context of nutrition. To investigate the appropriateness of the approach, a preliminary study was performed to compare the effects of Pepper vs a human storyteller. Results are interesting in terms of engagement, user experience, trust, and learning of relevant concepts, suggesting that the proposed approach can be effective in this domain.
Design and Implementation of Campus Surveillance System Based on ZLMediaKit

The need for the campus safety arises with the increase of the number of colleges and campus facilities, posing significant challenges to traditional campus surveillance system. As a result, it is necessary to build a reliable, cross-platform and extensible surveillance system for campus monitoring. Therefore, this paper mainly proposes a web-based campus surveillance system based on ZLMediaKit, which is an open-source and high-performance streaming service framework. The system consists of a push/pull streaming server and a management front-end page. The streaming server supports numerous streaming media protocols and massive client connections, while the front-end page can access to back-end video streams and real-time display. Experimental results show that our system is stable and capable of handling multi-terminal streams with small consumption of resources.
Automatic Evaluation of Chinese Short-Answer Questions Based on Structure of Observed Learning Outcomes Taxonomy

This study aims to evaluate Chinese short-answer questions according to the Structure of Observed Learning Outcomes (SOLO) taxonomy, which divides students' responses into five levels, from prestructural to extended abstract. To automate the evaluation process, supervised classification was used. For this purpose, three deep-learning models were adopted: LSTM, C-LSTM, Nezha-BiLSTM. Results showed that the accuracy of LSTM was the lowest at 0.83, the accuracy of C-LSTM was 0.89, and the accuracy of Nezha-BiLSTM was the highest at 0.90. And the accuracy of each classification model is high in the prestructural and multistructural responses. The method proposed could help teachers perform scoring more efficiently.