Reviewerly/exHarmony huggingface dataset:
EMBEDDING CULTURE AND LOCAL HERITAGE ELEMENTS IN LEARNING EXPERIENCES FOR THE DEVELOPMENT OF DIGITAL COMPETENCIES IN THE SENIOR POPULATION

EMBEDDING CULTURE AND LOCAL HERITAGE ELEMENTS IN LEARNING EXPERIENCES FOR THE DEVELOPMENT OF DIGITAL COMPETENCIES IN THE SENIOR POPULATION
EDUCATION FOR SUSTAINABLE DEVELOPMENT

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 5070-5077ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1330Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
USING INNOVATIVE ARTISTIC RESOURCES FOR LEARNING ABSTRACT CHEMISTRY CONCEPTS

Appears in: EDULEARN23 Proceedings Publication year: 2023Page: 6449 (abstract only)ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1711Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
IDENTIFICATION AND SYSTEMATISATION OF REGULAR ERRORS IN RESEARCH PROTOCOLS SUBMITTED FOR ANALYSIS BY AN ETHICS COMMITTEE: THE EXPERIENCE OF ESCOLA SUPERIOR DE SAÚDE – INSTITUTO POLITÉCNICO DO PORTO

IDENTIFICATION AND SYSTEMATISATION OF REGULAR ERRORS IN RESEARCH PROTOCOLS SUBMITTED FOR ANALYSIS BY AN ETHICS COMMITTEE: THE EXPERIENCE OF ESCOLA SUPERIOR DE SAÚDE – INSTITUTO POLITÉCNICO DO PORTO
Between the May 18 Daedong Spirit and the Discourse of Peaceful Unification

This paper embodies the May 18 Daedong spirit in light of the Korean traditional ideology, the Daedong and Sogang social theories, applies it to the thoughts and actions of citizens at the time to identify the cause of peaceful unification of the two Koreas, and presents a post-ideological methodology. As a research method, we critically consider previous studies related to the May 18 Daedong spirit, and consider the theory of Daedong and Sogang. In addition, the researcher examines the national and democratic values of the May 18 Daedong spirit and introduces the case of social integration of the May 18 Daedong spirit to emphasize national subjectivity as a point of contact between the May 18 Daedong spirit and peaceful unification. Finally, in the conclusion, the necessity and methodology of utilizing the Daedong spirit in efforts for peaceful unification are presented. The ideological discussion of the Daedong spirit can provide a theoretical opportunity to sublimate into a discourse of peaceful unification.
LIGHTWEIGHT CRYPTOGRAPHY METHOD IN THE INTERNET OF THINGS USING ELLIPTIC CURVE AND CROW SEARCH ALGORITHM

The Internet of Things (IoT) as an important technology consists of a heterogeneous and huge number of devices that generates an enormous amount of data in various applications. However, generating and transmitting huge amount of data in the IoT makes it crucial to implement a secure and safe data transmission scheme. Cryptography methods can secure the confidentiality, data integrity, access control, and authentication. Due to constrained resources in IoT devices, providing classical cryptography schemes isn’t efficient for IoT applications, so a lightweight cryptography scheme is one of the most important solutions to overcome security challenges in IoT. In this paper, a new security scheme called ECCCSASHA256 based on Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm (SHA-256) using Crow Search Algorithm (CSA) has been proposed for secure data transmission in IoT devices. The ECCCSASHA256 model uses the CSA for generating a private key to encode the elliptic curve. Furthermore, the proposed scheme uses SHA-256 model for hashing the incoming encoded data using ECC. The simulation results indicate that the average throughput of the proposed model was about 8.22% and 8.97% higher in encryption and 8.72% and 9.81% higher in decryption compared to 3DES&amp;ECC&amp;SHA-256 and RC4&amp;ECC&amp;SHA-256, respectively.
The Effect of Pre-early Childhood Teacher's Artificial Intelligence Utilization Awareness and Science Teaching Attitude on Acceptance Intention of Artificial Intelligence Utilization Education

The purpose of this study was to determine whether pre-early childhood teachers’ science teaching attitude mediated the relationship in which their artificial intelligence utilization awareness affected the acceptance intention of artificial intelligence utilization education. An online survey targeting 134 pre-early childhood teachers was conducted. SPSS 27.0 and Process Macro 3.5 were used to conduct descriptive statistical analysis, correlation, and Process Macro model 4. The results of the study showed that the science teaching attitude of pre-early childhood teachers mediated the relationship between their artificial intelligence utilization awareness and the acceptance intention of artificial intelligence utilization education. Positive perception of AI utilization in early childhood education and science teaching attitude were important for pre-early childhood teachers’ active education acceptance of AI utilization. The study results suggest that providing an opportunity to use AI in the curriculum of pre-early childhood teachers’ science education is necessary to improve the awareness of AI utilization and attitudes toward science teaching.
Perlindungan Hukum Terhadap Konsumen Atas Peredaran Produk Kosmetik Ilegal Yang Mengandung Bahan Berbahaya (Studi Badan Pengawas Obat dan Makanan Medan)

Currently, there are many illegal cosmetics circulating in the market, whether they do not have a distribution permit or contain hazardous materials, including local and imported cosmetics. Therefore, it is necessary to have legal protection for consumers so that their rights are still fulfilled for the losses they experience. Cosmetic products are supervised by the Food and Drug Supervisory Agency or BPOM which is in charge of supervising the distribution of cosmetics as well as medicines and food in Indonesia. The research objectives to be achieved in this study are to find out how the law provides protection to consumers against the occurrence of illegal cosmetic circulation, to find out how the legal rules for the circulation of illegal cosmetics in Indonesia are, and to find out how legal remedies can be taken by the public for the circulation of illegal cosmetic products. . The research methods used are: Library Research and Field Research. Types of Normative Juridical Research. The conclusion obtained is that legal protection for consumers can be done by applying the principle of Strict Liability, namely mistakes are not a determining factor, but there are exceptions that allow business actors to be released from responsibility due to compelling circumstances. With the principle of absolute responsibility, it is hoped that business actors will prioritize the interests or rights of consumers, so that consumers do not experience losses for their actions or for cosmetic products produced or sold. The legal arrangements for the distribution of cosmetics in Indonesia include Law Number 8 of 1999 concerning Consumer Protection, Law Number 36 of 2009 concerning Health, BPOM Regulation Number 2 of 2020 concerning Supervision of Production and Distribution of Cosmetics, etc. Legal remedies that can be taken by the community are through the Court (Litigation) and BPSK (Non-Litigation).
Discussion on the Application of Cloud Computing Scene as a Service Model

In the context of the digital economy, Diversified application scenes have created a huge space for the cloud computing market.In response to the diversity and complexity of cloud computing industry operation and maintenance scenes, this paper proposes scene as a service model based on diversified scenes for the integration and coordination of horizontal and vertical cloud computing services.The framework model of scene cloud scheme and the realization process of ScaaS model are discussed, so as to promote the implementation and operation of scene cloud solution.
The LALR Parser Implementation using Grammar Rules

Syntactic parsing deals with the syntactic structure of a sentence. It refers to the grammatical arrangement of words in a complete sentence. A syntactic analysis of English words will be presented using bottom-up parsing in which the LALR parser defines the best syntax analysis. A compiler is constructed that generates tokens of the identifiers that are characters, alphabets, etc. using a symbol table, checks for the syntax of sentences by dividing them into phrases, generating errors, debugging them, and giving us the correct sentence.
Multimodal Music Teaching Mode Based on Human-computer Interaction Technology

Music teaching is an activity completed by both "teaching" and "learning".Therefore, whether the two sides of the teaching can achieve close communication and cooperation has become a very important factor in determining the quality and efficiency of music course teaching.The multi-modal teaching mode integrates multiple modes, fully mobilizes students' various senses, attracts students' attention, makes students more relaxed and easy to grasp and understand the knowledge they have learned, and achieves better teaching effects.In the multimodal music teaching class, teachers make full use of ppt courseware to play audio and video, display pictures and tables, use artistic words, font colors and underlines, and use rich body language to enable students to learn language and music knowledge in vivid teaching activities.Human-Computer Interaction is a key technology in the field of virtual reality.Its main purpose is to improve the interaction between the user and the computer from the user's point of view, thereby increasing the immersion and authenticity of the system.In human-computer interaction, human behavior recognition is a very critical technology, which helps human beings to achieve a natural and harmonious state of human-computer interaction.This paper studied the multimodal music teaching model based on Kinect somatosensory technology.It was found that most of the indicators of the experimental class using the multimodal music teaching model are better than the control class, and the average scores of the experimental class are all more than 85.5.It showed that the use of multimodal teaching mode to teach can enhance students' classroom participation, help students understand and memorize knowledge, and stimulate students' interest in learning.
An Effective Counterfeit Medicine Authentication System Using Blockchain and IoT

The current pharmaceutical sector is dealing with several issues. The pharmaceutical sector nowadays is dominated by counterfeit medications. Health research funding groups estimate that between 10 and 30 percent of the pharmaceuticals in the market are counterfeited. Globally, this fatal issue is creating several serious health risks. WHO estimates that 30% of all medications worldwide are fake. In practically every region of the world, this issue is becoming worse by the second. As drugs travel across several scattered networks, it is challenging to identify fakes. By integrating Blockchain technology into the network, the pharmaceutical supply chain may be made safer. Every area of the network keeps track of the medications through a record, making it possible to identify any introduction of fake medications. System transparency and dependability are improved with Blockchain security. By employing Blockchain technology, the initiative seeks to guarantee medicine quality, transaction security, and data protection. By eliminating information about expired and fake medications from the system, this effort increases system dependability and helps to make the Blockchain a stable and widely used technology. To determine whether or not counterfeit medications have been distributed, IOT technology is also employed.
Web App For Predicting Fake Job Posts Using Ensemble Classifiers

Taking advantage of the internet, cheating people online has become common these days, there are numerous types of scams done online, employment scam is one of them, where fake job postings are published online to attract people who are unemployed and looking for work. By becoming victims of these fake job postings, many people lose their personal information and money. This needs to be dealt with intelligence and caution. In this paper, we have proposed a comparative study using different ensemble classifiers which work effectively on this problem and help to predict whether a particular job is genuine or fake. We have used five different ensemble classifiers: Random Forest, LightGBM, XGboost, Catboost, and Extra tree. EMSCAD(Employment Scam Aegean Dataset) which contains 17880 job posts is used to train the models. We have created a web application to make it further easy for users to take advantage of this facility. XGBoost classifier has achieved 96.8% accuracy. which is highest among other classifiers also it was observed that the Light GBM classifier is faster than the other classifiers.
Language Agnostic Program Conformance Analysis

Conformance analysis is a crucial part of software development that is used for the verification of software systems. Most of the prominent techniques for testing are based on dynamic analysis. Although dynamic analysis handles run-time integration, it does not address the quality of the code and hence the maintainability of the software. Static analysis, which is the analysis of code without execution, can be used to mitigate these issues. Using this technique, the adherence of code to a coding standard can be ascertained. The use of a specific standard improves readability and code maintainability. Traditional methods used for this purpose have been language-specific, and support for user-specified guidelines has been poor.This paper presents a tool that checks the conformance of the source code with user-curated guidelines. A simple, intuitive, and concise manner to specify guidelines is introduced. These guidelines are language-agnostic, thus allowing a uniform style to express them for multiple languages. A library of predefined guidelines has been provided to facilitate rapid development, and methods to add new guidelines are also expounded. Additionally, these guidelines can be used to verify if certain programming constructs have been used. Thus, it brings the codebase closer to the requirement specification document, allowing the programmer to effortlessly implement the code without worrying about its conformance. Lastly, an universal coding metric and benchmarking tool named LAPCA Score is provided which quantifies the extent of guideline violations that can be used to measure the degree of code compliance.
Quality Evaluation Model of Automatic Machine Translation based on Deep Learning Algorithm

With the continuous development of computer technology, machine translation methods have also experienced a long research process. In recent years, the research of artificial neural network has brought new solutions to machine translation. The application of sequence sequence model has made a qualitative leap in the performance of machine translation. The training of neural machine translation model depends on large-scale bilingual parallel data, which contains sufficient knowledge for machine learning. The training process is the process of data representation and knowledge extraction. How to use data enhancement methods to make model learning easier and knowledge extraction more sufficient is an important research topic. Automatic machine translation quality evaluation model based on deep learning algorithm is a method to evaluate the quality of automatic machine translation. This paper introduces the basic idea of qemt and some examples. We also give an example to illustrate how to use it to evaluate the quality of automatic machine translation. Qemt (quality evaluation model) is a method to evaluate the quality of automatic machine translation by comparing human translations generated by different natural language processing models such as part of speech markers or semantic similarity measures.
Zomato Review Analysis Using Machine Learning

Restaurant ratings and reviews have a noteworthy influence on shaping the public’s perception of a restaurant and influencing the dining decisions of individuals. With the rise of online platforms and review websites, it is now easier for customers to share their experiences and opinions about restaurants. Potential diners can easily access information about the quality of food, service, atmosphere, and value for money offered by a restaurant. Many customers visit a restaurant based on reviews given by the customer or other app users. India has a rich culinary heritage and offers a diverse range of cuisines that cater to different tastes and preferences. The restaurant industry in India is growing rapidly, and new restaurants are popping up all the time, offering customers an ever-increasing variety of dining options. Starting a new restaurant can be challenging, especially in a highly competitive market like India, where there are already many established restaurants. Major challenges that persist in the industry comprise elevated real estate expenses, escalating food prices, insufficient skilled labour, and customer acquisition. The system aims to perform sentimental analysis and exploratory data analysis on Zomato reviews. Sentimental analysis is performed using the SVM approach to determine the accuracy of the sentiment model. The model would deliver the top three cuisines in a location based on sentiment analysis, which would help new restaurants make decisions. The system shows factors affecting restaurant businesses by doing data analysis on various parameters of the dataset. The SVM model has adequate accuracy.
Wavelet Attention Network for Few-shot learning

The goal of few-shot learning is to recognize new objects using a few labeled samples. Metric-based methods can effectively achieve this goal by building an appropriate feature space, for which a convolutional neural network (CNN) often acts as the backbone network. Unfortunately, CNNs are usually prone to interference from noise, and the inherent noise may affect the effectiveness of the embedded feature. To suppress the impact of noise on the model's final performance, we enhance CNN by wavelet transformation, i.e., a wavelet attention network (WAN) is built for few-shot classification tasks. The key element of our WAN is the wavelet attention (WA) block, which decomposes the feature map into low-frequency and high-frequency components. The low-frequency component stores the basic object structural information of the signal, and the high-frequency component stores the detailed information and noise. Then, WAN suppresses the noise while retaining the basic information through a learnable frequency filter. We conduct experiments on two widely used few-shot benchmark datasets and the results show that WAN can perform competitively in few-shot classification tasks.
Moth: A Hardware Accelerator for Neural Radiance Field Inference on FPGA

Neural Radiance Field (NeRF) is a state-of-the-art algorithm in the field of novel view synthesis and has the potential to be used in AR/VR. However, the inference of NeRF is time-consuming. Motivated by resource-constraint scenarios on the edge and mixed reality devices, our essential idea is to bridge this gap while improving throughput and power consumption. This paper proposes a high-performance FPGA-based accelerator, with a fully-pipelined design tailored for the vanilla NeRF algorithm. We also design a mechanism to monitor the output of the rendering module to reduce operations. Experimental results show that our accelerator achieves 3.63× energy efficiency over implementation on GPU NVIDIA V100, and 1.31× speed up over state-of-the-art ASIC design if running under the same clock frequency as ASIC.
SpCNA: An FPGA-based Accelerator for Point Cloud Convolutional Neural Networks

3D convolutional neural networks (CNNs) are commonly used to process and analyze point clouds for object detection. However, the submanifold sparse convolutions and traditional sparse convolutions, which play important roles in 3D CNNs on point cloud, often encounter performance issues when accelerated by existing convolutional neural network accelerators due to the unstructured sparsity of sparse convolution. In this paper, we present SpCNA, an FPGA-based accelerator for sparse CNNs on 3D point clouds. To avoid computations that involve a large number of zeros in point cloud, it utilizes hash tables to map input-weight-output pairs. The computational performance of our design achieves 11.00 GFLOP/s. It can provide 1.2× and 1.98× energy efficiency improvement compared to Nvidia Xavier SoC and Nvidia 2080ti GPU, respectively.
Using an Efficient Detection Method to Prevent Personal Data Leakage for Web-Based Smart City Platforms

Many Internet of Things and information exchange technologies bring convenience, cost-efficiency, and sustainability to smart city solutions. These changes have improved our day-to-day quality of life, with impacts on: (a) lifestyle (e.g., automation and robotic reaction), (b) infrastructure (efficient energy consumption), and (c) data-driven management (data sensing, collection, and investigation). It is common to integrate Web-based interfaces and such solutions for developing platforms. When software and hardware components store, retrieve, and transfer such information, people may suffer from personal data leakage. This paper introduces a privacy information detection method, using a data weighting mechanism to save time and cost in finding personal information leaks over Web services. According to an initial evaluation, the proposed method can reduce time by 62.19% when processing 8,000 crawled files, and roll-back verification shows that it maintains 90.08% accuracy for finding marked content.
Student Satisfaction Classification Algorithm Using the Minority Synthetic Oversampling Technique

This study is based on the university students’ opinions on the social network Twitter, to learn the teaching performance in the context of virtual learning using sentiment analysis technique. However, to establishing the classification algorithm, an imbalance was evidenced in the amounts of opinions that qualify the teaching performance with the satisfied and dissatisfied class. Therefore, the objective of this investigation is to determine the improvement in the performance of the student satisfaction classification algorithm, based on the class balancing method from the application of the minority synthetic oversampling technique (SMOTE). From the methodological point of view, the research is a non-experimental design, applied type, and quantitative approach. The data was collected through the social network Twitter for fifteen weeks to a population defined by mechanical and electrical engineering students. After the application of the SMOTE data balancing technique, it was identified that the algorithm which presents the best performance is Logistic Regression. It was possible to identify that the impact of improvement of the algorithm turned out to be an average of 2.17% in the accuracy, 84.78% in precision, 42% in the Recall (Sensitivity) and 58.33% in the F1-score. Therefore, it is demonstrated that the algorithm classifies with high probability the opinions of the students.