Reviewerly/exHarmony huggingface dataset:
An Overview of the Transition Process of Specialized Subjects Curriculum in Vocational High Schools

Objectives The study outlined the change process focusing on the curriculum organization, operation system, and organizational characteristics of how Korea's vocational high school specialized subjects curriculum has changed from the first curriculum to the 2022 revised curriculum.&#x0D; Methods In order to achieve the purpose of this study, literature research was conducted by collecting the original text of the high school curriculum mounted in the National Curriculum Information Center. The change process was analyzed by studying the criteria for organizing and operating vocational high school curriculum, organizing specialized subjects, and time allocation criteria presented in the curriculum documents for each period.&#x0D; Results First, It was confirmed that vocational high schools have consistently presented standards for organizing and operating curricula centered on specialized subjects in consideration of changes in the industrial structure and the vocational world. Second, it was confirmed that the specialized curriculum system has been reorganized in consideration of industrial demand and manpower training types, vocational high school students' employment capabilities, and career development. Third, It has been confirmed that the efficiency of time planning has been improved by setting the time allocation standard for specialized subjects and presenting the completion time in consideration of the actual conditions of community and vocational high schools.&#x0D; Conclusions First, in order to implement an optimized student-tailored curriculum in consideration of career changes of vocational high school students, it is necessary to further flex the curriculum organization and operation standards in terms of education for lifelong job preparation. Second, it is necessary to systematically restructure specialized subjects by comprehensively considering the types of manpower training, characteristics of vocational high schools, students' career and development levels, and learning hierarchy of specialized subjects according to job changes in the new industrial technology and professional world. As the curriculum completion criteria are converted from units to credits through the operation of the vocational high school credit system, it is necessary to closely review the time distribution criteria and completion time of specialized subjects.
SENIOR CITIZENS AND ICT USE: NEW HORIZONS

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 7891-7895ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.2048Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
PERCEPTIONS OF THE EDUCATIONAL POTENTIAL OF ROBOTICS AND CODING IN INCLUSIVE SETTINGS

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 6923-6930ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1819Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
WHATSAPP AS A TOOL FOR COLLECTING VOICE NOTED ORAL JOURNALS FROM PARTICIPANTS IN A SHARED HOME READING INTERVENTION

WHATSAPP AS A TOOL FOR COLLECTING VOICE NOTED ORAL JOURNALS FROM PARTICIPANTS IN A SHARED HOME READING INTERVENTION
DEVELOPMENT OF A PEDAGOGICAL DIAGNOSTIC INSTRUMENT "PROFILER OF ARTISTIC POTENTIAL"

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 5055-5060ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1327Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
LFLD-CLbased NET: A Curriculum-Learning-Based Deep Learning Network with Leap-Forward-Learning-Decay for Ship Detection

Ship detection in the maritime domain awareness field has seen a significant shift towards deep-learning-based techniques as the mainstream approach. However, most existing deep-learning-based ship detection models adopt a random sampling strategy for training data, neglecting the complexity differences among samples and the learning progress of the model, which hinders training efficiency, robustness, and generalization ability. To address this issue, we propose a ship detection model called the Leap-Forward-Learning-Decay and Curriculum Learning-based Network (LFLD-CLbased NET). This model incorporates innovative strategies as Leap-Forward-Learning-Decay and curriculum learning to enhance its ship detection capabilities. The LFLD-CLbased NET is composed of ResNet as the feature extraction unit, combined with a difficulty generator and a difficulty scheduler. The difficulty generator in LFLD-CLbased NET effectively expands data samples based on real ocean scenarios, and the difficulty scheduler constructs corresponding curriculum training data, enabling the model to be trained in an orderly manner from easy to difficult. The Leap-Forward-Learning-Decay strategy, which allows for flexible adjustment of the learning rate during curriculum training, is proposed for enhancing training efficiency. Our experimental findings demonstrate that our model achieved a detection accuracy of 86.635%, approximately 10% higher than other deep-learning-based ship detection models. In addition, we conducted extensive supplementary experiments to evaluate the effectiveness of the learning rate adjustment strategy and curriculum training in ship detection tasks. Furthermore, we conducted exploratory experiments on different modules to compare performance differences under varying parameter configurations.
EMPLOYING SOCIAL MEDIA TO IMPROVE UNIVERSITY FIELD TRIPS EXPERIENCE

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 4722-4729ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1253Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
FAST TRACK FOR UNIVERSITY - SME COOPERATION

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 6870-6874ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1806Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
STRATEGIES OF THE MEXICAN STATE FOR THE INCLUSION OF INDIGENOUS PEOPLES IN A DIGITAL SOCIETY

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 7842-7848ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.2039Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
High-Dimensional Feature Selection Based on Improved Binary Ant Colony Optimization Combined with Hybrid Rice Optimization Algorithm

In the realm of high-dimensional data analysis, numerous fields stand to benefit from its applications, including the biological and medical sectors that are crucial for computer-aided disease diagnosis and prediction systems. However, the presence of a significant number of redundant or irrelevant features can adversely affect system accuracy and real-time diagnosis efficiency. To mitigate this issue, this paper proposes two innovative wrapper feature selection (FS) methods that integrate the ant colony optimization (ACO) algorithm and hybrid rice optimization (HRO). HRO is a recently developed metaheuristic that mimics the breeding process of the three-line hybrid rice, which is yet to be thoroughly explored in the context of solving high-dimensional FS problems. In the first hybridization, ACO is embedded as an evolutionary operator within HRO and updated alternately with it. In the second form of hybridization, two subpopulations evolve independently, sharing the local search results to assist individual updating. In the initial stage preceding hybridization, a problem-oriented heuristic factor assignment strategy based on the importance of the knee point feature is introduced to enhance the global search capability of ACO in identifying the smallest and most representative features. The performance of the proposed algorithms is evaluated on fourteen high-dimensional biomedical datasets and compared with other recently advanced FS methods. Experimental results suggest that the proposed methods are efficient and computationally robust, exhibiting superior performance compared to the other algorithms involved in this study.
Research on the Construction of Student Physical Health Diagnosis and Sports Prescription Network Service Platform

College physical education teaching is all undertaken by various college physical education teaching departments. According to China’s education guidance outline, every college student needs to participate in physical education courses, but the number of physical education teachers in various colleges is generally small. Even if only grade one and grade two students offer physical education courses, the number is already quite large. In addition, there are huge individual differences among students, and the weekly physical education curriculum can no longer meet the needs of students to obtain more fitness knowledge and cultivate the awareness of lifelong sports. A large number of studies have shown that scientific and reasonable physical exercise can promote human health and improve the quality of life, and it is very important to formulate reasonable exercise prescriptions according to personal physical conditions to improve physical health and avoid physical injury. Therefore, this paper studies the construction of student physical health diagnosis and sports prescription network service platform. As a professional sports medicine service platform, the research team of this project has studied it for more than ten years. The results of data analysis show that there are many problems in the current physical health diagnosis and prescription network platform, and the number of patients is too small; The diagnosis result is not reliable enough; There is no common language between doctors and nurses; The diagnostic results cannot be used for prescription drugs or equipment.
A Decentralized Mail Using Blockchain

Currently, email services have become a global standard for official communication and conducting business negotiations. Traditional email systems rely on centralized servers to provide their services. The attacking central server sends confidential messages and personal information to the attacker. To that end, this white paper uses blockchain technology which uses Proof-of-Work and Proof-of-Stack consensus mechanisms to create a decentralized email system with trust, accountability, and scalability.
An approach to Instance segmentation of Quadcopters for Suspicious Surveillance

The development of an aerial surveillance system could be very effective in meeting surveillance system challenges as well as for upholding the safety and security of the people. A cost-effective and efficient solution is Quadcopters, which are used for aerial surveillance are outfitted with cutting-edge image processing technology that captures detailed photographs of every object beneath. A quadcopter-based solution is proposed to monitor desired locations for any unusual activity of quadcopters, such as the desired surveillance. In this paper, an approach to Instance segmentation on the different Detectron based Deep learning models for generating an alert for security personnel after detecting any unusual activity is proposed. The Deep learning models relies on detecting the anomalies in received image or video streams captured. In the initial feature extraction step, a well-known Faster RCNN algorithm is modified for fast learning with feature reduction. The ability of five different types of CNNs to identify objects of interest in surveillance images was tested. The fastest ResNet-50 based Faster RCNN with the highest average precision, performed admirably in threat detection and average precision with IOU and accuracy of the system across all categories.
Speech to Text App Customized for Police Functioning in Different Languages

One of the important fields in Natural Language Processing (NLP) is Speech-to-text (ST). In this paper, we look at the various methods that can be employed to ensure your seamless conversion of speech to text, primarily focusing on its use for police functioning . We are going to focus on the CTC+Attention model, End-to-end Speech Translation (ST) models, a Speech Recognition model developed using Google Speech Recognition model, PCM A-law codecs and also Voice Recognition Module V3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[12]</sup> . We’ll also be observing different techniques and algorithms which can be applied to achieve the required functionalities.
Automation of Library Management System using Autonomous Robot

In this paper automation of library management system is achieved by designing an autonomous robot to automate the process of picking the required book and dropping them to the borrower table and also to replace the book back to the shelf and also a webpage is created to access the books in library. Manually searching a book in a library is a labour - intensive procedure and if the book is misplaced either purposefully or accidentally it requires extra time and effort to retrieve it. To address this issue, an attempt is currently being made to automate libraries so that books can be quickly discovered and easily picked up and positioned using a robotic arm equipped with RFID equipment, Zigbee, Colour Sensor and DC Motors. The user will have to enter the book details either to borrow or return the book on the webpage and accordingly the robot will receive command and performs the task. A rack is designed and different coloured tags are placed in each row so the robot can find the desired row in the rack. The experimental results showed that the robot is capable to carry book weighing up to 500 grams.
An intrusion monitoring system design based on 3D face recognition

The embedded microprocessor, face recognition sensor, and body sensing radar sensor are used to design an intrusion monitoring system with a 2-layer architecture of “upper computer + lower computer”, which has the main functions of automatic identification of intrusion objects, illegal intrusion alarm, intrusion object identification and statistical analysis of intrusion behavior. The system uses a combination of “binocular stereo vision + structured light + active infrared fill light” technology to perform face recognition and prevent the intrusion of illegal objects. The test results show that the face recognition technology adopted in this paper can effectively improve the reliability of face recognition and reduce the false recognition rate, and the system is easy to deploy, has good environmental adaptiveness, and is flexible in use.
Banking System Incidents Analysis Using Knowledge Graph

Risk incidents in the banks' systems have caused significant social impacts and economic losses. This study proposes a risk incident knowledge modeling and analysis approach based on the knowledge graphs to realize the effective integration and continuous accumulation of incident knowledge. The authors are the first to analyze the advantages of knowledge graphs in risk incident knowledge integration for the bank's core system. Moreover, they study and compare the related field's state-of-the-art models (including CRF, BiLSTM, BiLSTM-CRF, BERT-BiLSTM-CRF). This paper proposes an improved Bert-BiLSTM-CRF model to perform entity recognition which replaces “individual word mask and training” with “full word mask and training” targeted to solve the problem of low accuracy in the extraction of incident text entities in the banking system. Experiments on 1000 banking system incident material show that the improved Bert-BiLSTM-CRF model outperforms the state-of-the-art models based on the comparison of recall (R), precision (P), and F1-measure, with a 2%-9% improvement in the F1-measure.
POS-Only Tagging Using RNN for Croatian Language

Part-of-speech (POS) tagging is one of the fundamental tasks in the field of natural language processing. This paper gives an overview of methods used for POS tagging and explores the research done so far, with a focus on POS tagging of the Croatian language. The latest techniques of POS tagging come from the field of deep learning. Commonly used deep learning models are various types of artificial neural networks, such as RNN, LSTM and BiLSTM models. In order to determine the efficiency of using different neural networks for POS tagging, RNN and LSTM models were implemented. The models were trained and evaluated on the hr500k dataset (reference corpus of Croatian texts). The results showed the overfitting problems of RNN models which is why the highest accuracy models achieved on the test dataset was 78.57%. The result indicated the need to also research MSD tagging of Croatian language given its morphologically complex structure.
The Guitar Fretboard Deep-Dive

Question and answers from Milton Mermikides's keynote speech, "Diamonds, Abaci, and Hexagrams: Exploring the Pitch Surface of the Guitar Fretboard."
IMPROVING SUSTAINABLE MOBILITY, SPATIAL-DATA AND CORE SKILLS THROUGH A SERVICE-LEARNING PROJECT

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 2517-2524ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0730Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
Optimal Cryptographic Approach Based on Secure Post-Quantum Cryptographic Algorithms and Classical Algorithms

Quantum computing is an incipient and exhilarating field of future computing. Classical computers nowadays are susceptible to security threats and attacks. Security is a major concern in communication and data exchange. The existing encryption technologies that use a shared key are thought to be the most dependable means of secure key generation and distribution. The most popular cryptographic algorithm, RSA Algorithm, has been cracked by a quantum computer utilizing Shor's algorithm. Quantum teleportation is an impeccable way by which information and cryptographic keys can be exchanged securely by building potent quantum circuits. This can obviate information from sniffed or interpreted as quantum computing works predicated on the principle of skepticism. This chapter aims at developing an unbreakable secure cryptographic quantum network utilizing quantum circuits for seamless data exchange in virtual networks.
Abstract animation, emergent audiovisual motion and micro-expression: A case study of analogue music tracking with Robert Schumann’s Forest Scenes in AudioVisualizer

Abstract animation inthe form of “visual music” facilitates both discovery and priming of musicalmotion that synthesises diverse acoustic parameters. In this article, twoscenes of AudioVisualizer, an open-source Chrome extension, are appliedto the nine musical poems of Robert Schumann’s Forest Scenes, with thegoal to establish a basic framework of expressive cross-modal qualities that inaudiovisual synchrony become apparent through visual abstraction and theemergence of defined dynamic Gestalts. The animations that build thisarticle’s core exemplify hands-on how particular ways of real-time analoguemusic tracking convert score structure and acoustic information into continuousdynamic images. The interplay between basic principles of information captureand concrete simulation in the processing of music provides one crucial entrypoint to fundamental questions as to how music generates meaning andnon-acoustic signification. Additionally, the considerations in this articlemay motivate the creation of new stimuli in empirical music research as well asstimulate new approaches to the teaching of music.
Physical-aware Cross-modal Adversarial Network for Wearable Sensor-based Human Action Recognition

Wearable sensor-based Human Action Recognition (HAR) has made significant strides in recent times. However, the accuracy performance of wearable sensor-based HAR is currently still lagging behind that of visual modalities-based systems, such as RGB video and depth data. Although diverse input modalities can provide complementary cues and improve the accuracy performance of HAR, wearable devices can only capture limited kinds of non-visual time series input, such as accelerometers and gyroscopes. This limitation hinders the deployment of multimodal simultaneously using visual and non-visual modality data in parallel on current wearable devices. To address this issue, we propose a novel Physical-aware Cross-modal Adversarial (PCA) framework that utilizes only time-series accelerometer data from four inertial sensors for the wearable sensor-based HAR problem. Specifically, we propose an effective IMU2SKELETON network to produce corresponding synthetic skeleton joints from accelerometer data. Subsequently, we imposed additional constraints on the synthetic skeleton data from a physical perspective, as accelerometer data can be regarded as the second derivative of the skeleton sequence coordinates. After that, the original accelerometer as well as the constrained skeleton sequence were fused together to make the final classification. In this way, when individuals wear wearable devices, the devices can not only capture accelerometer data, but can also generate synthetic skeleton sequences for real-time wearable sensor-based HAR applications that need to be conducted anytime and anywhere. To demonstrate the effectiveness of our proposed PCA framework, we conduct extensive experiments on Berkeley-MHAD, UTD-MHAD, and MMAct datasets. The results confirm that the proposed PCA approach has competitive performance compared to the previous methods on the mono sensor-based HAR classification problem.
Composability of Cloud Accelerators in Virtual World Simulations

Immersive and interactive experiences offered by virtual world simulations (VWS) are becoming increasingly indispensable in today's digital era, particularly in domains such as gaming, engineering, and education. Cloud computing provides a compelling solution for VWS, with its scalability, costeffectiveness, and accessibility, combined with efficient processing capabilities of hardware accelerators such as FPGAs and GPUs. This paper presents APEIRON, an in-progress distributed library built on the Ray framework, that aims to facilitate the effective composition of hardware accelerators on public clouds and emerging disaggregated platforms. Additionally, this work introduces a novel composability metric, analyzing the intricate relationship between resource utilization and workload performance. This study focuses on theoretical composability analyses, laying groundwork for optimized utilization of hardware resources to enhance efficiency, thereby potentially reducing operational costs.