Reviewerly/exHarmony huggingface dataset:
Impact of Video Length Reduction due to Missing Landmarks on Sign Language Recognition Model

Sign Language Processing (SLP) has become an increasingly challenging field, particularly in the areas of sign language recognition (SLR), translation, and production. One of the primary challenges in SLP is pose estimation, which can be impacted by missing landmarks due to occlusions or limitations in the model’s performance. In this study, we propose a method for evaluating the impact of missing landmarks on the performance of an SLR transformer-based model for the Isolated Sign Language Recognition (ISLR) task. We train and test the Spoter model on two subsets of Peruvian Sign Language datasets, and evaluate its performance using top-1 and top-5 validation accuracy. The study finds that removing frames with missing landmarks did not significantly impact accuracy in most of the cases, which suggests that additional preprocessing steps may not be necessary to deal with missing landmarks in this particular task. These findings contribute to the ongoing research in SLP and highlight potential avenues for improving SLP tasks.
Supplemental Material for How Statistical Correlations Influence Discourse-Level Processing: Clause Type as a Cue for Discourse Relations

a URL, doi, or other permanent path for accessing the specified information in a public, openaccess repository, and the specified information must be provided in a format that is time-stamped, immutable, and permanent.Qualifying public, open-access repositories are committed to preserving data, materials, and/or registered analysis plans and keeping them publicly accessible via the web in perpetuity.Examples include the Open Science Framework (OSF) and the various Dataverse networks.Hundreds of other qualifying
Hardware Security for IoT in the Quantum Era: Survey and Challenges

The Internet of Things (IoT) has become a reality: Healthcare, smart cities, intelligent manufacturing, e-agriculture, real-time traffic controls, environment monitoring, camera security systems, etc. are developing services that rely on an IoT infrastructure.Thus, ensuring the security of devices during operation and information exchange becomes a fundamental requirement inherent in providing safe and reliable IoT services.NIST requires hardware implementations that are protected against SCAs for the lightweight cryptography standardization process.These attacks are powerful and non-invasive and rely on observing the physical properties of IoT hardware devices to obtain secret information.In this paper, we present a survey of research on hardware security for the IoT.In addition, the challenges of IoT in the quantum era with the first results of the NIST standardization process for post-quantum cryptography are discussed.
META-ANALYSIS OF MIND MAPPING IN VOCABULARY LEARNING OF THE PAST DECADE

The type of research used is meta-analysis research aimed at determining the level of effectiveness or quality of mind mapping in vocabulary learning, including at elementary, middle, high school, and college levels. This quality was examined using several previous research results that collected data with inclusion and exclusion conditions from the Google Scholar, DOAJ, and Scopus databases. The search results found up to 51 dates that met the requirements with the number of students (N), F-counts, t-counts, and r-counts. The results of data analysis obtained by the simulation software JASP showed that the impact on learning media vocabulary based on mind mapping is 73% significant, which included the High category. The moderator variable is the lowest level of education, the high impact of using mind mapping, namely the college level, with an estimate of the effect of 0.569, Based on the number of participants, the application of mind mapping has more influence on participants totaling more than 30 students with an estimate of 0.928 (strong category), while the lowest effect is seen from the number of participants who collected more than 91 students with an estimate of 0.529, meaning that this mind mapping learning model is most appropriate to be applied in high school rows with more than 30 participants in order to maximize student learning outcomes.&#x0D;
HATE SPEECH ON SOCIAL MEDIA: A CASE STUDY OF BLASPHEMY IN INDONESIAN CONTEXT

Many scholars have conducted research on hate speech, spanning from hate speech delivery tactics to the negative consequences they create, as well as the role of technology in the dissemination of hate speech on social media. However, research on hate speech categories and degrees is still relatively unexplored. As a result, the purpose of this research is to uncover the strategies and levels of hate speech on social media, primarily YouTube channels, in response to the Minister of Religion's comments about the sound of mosque loudspeakers that need to be adjusted in volume. This comment has generated both positive and negative reactions in Indonesian society. This research looks into netizen comments in the comments column on the YouTube channel that carries the statement. Purposive sampling was used to select 300 comments from among the 840 comments in the comments column. For the purposes of this study, the sample was obtained in the form of comments containing hate speech. The data was then analyzed using content analysis, in which the data was categorized and categorized according to hate speech categories. According to the study's findings, there are three types of hate speech in netizen comments: early warning, dehumanization and demonization, and violence and incitement. Early warning is the most common type of hate speech, followed by violence and hostility, as well as dehumanization and demonization. Due to cultural influences and contrasts in rank and power between the commentator and the person who is the subject of the hate speech, hate speech delivered by Indonesian netizens tends to be dominated by disagreement, negative character, and action.
The development of students with disabilities’ experiences concerning distance learning in the Emirati Universities during the COVID-19 pandemic

This study investigates the developments in the experiences of students with disabilities during the COVID-19 pandemic, focusing on distance learning at three Emirati universities. It explores the students' experiences during the first semester of distance learning and analyses how these experiences evolved one year into the pandemic. The research adopts a qualitative approach, using semi-structured interviews to collect data from 21 participants, including 12 students with disabilities and 9 disability support staff. The study's findings highlight that during the initial months of online learning, the students encountered various technical challenges, such as difficulties with technology access, Wi-Fi availability, and lack of training in using assistive technologies. However, as the pandemic progressed, the students faced different challenges in the second year of online learning. These challenges included navigating online classes and exams, communication issues with instructors and peers, assignment difficulties, and feelings of social isolation. The results indicate significant developments in the students' experiences in the second year of distance learning compared to the first year.
Reinforcement Learning: Advancements, Limitations, and Real-world Applications

This paper aims to review the advancements, limitations, and real-world applications of RL. Additionally, it will explore the future of RL and the challenges that must be addressed to enhance its widespread applicability. By addressing these challenges, RL can be further harnessed to tackle complex real-world problems.
The Influence of Oral Communication on the Learning Engagement of Students

The study entitled “The Influence of Oral Communication to the Learning Engagement of the Students” aims to determine the significant influence of oral communication on the learning engagement of students in English. The researchers conducted a survey using a descriptive-correlational research design. The data were gathered from a sample size of 197 respondents using the validated modified questionnaire. The researchers interpreted the data using statistical tools such as Mean, Pearson Product-Moment Correlation and Simple Linear Regression. According to the findings, the level of oral communication of the students is good. The result indicated that oral communication significantly influenced the student's learning engagement. Furthermore, it showed that oral communication for the students should be interactive to boost their learning engagement. The researchers emphasized the need for future researchers to conduct other researches using learning engagement topics not included in the current study.
The credibility of the digital media: Teacher perceptions and practice

The current digital literacy skill that teachers have to master is the ability to determine the credibility of the digital media for education used to support distance learning. The teachers were asked to sort several digital media and differentiate which digital media provided many benefits and best facilitate learning. This research was comparative quantitative research. The participants were 108 teachers directed to assess the credibility of digital media to compare between high and low credibility. The purpose of this study was to understand how teachers determine the credibility of the digital media whether they decided based on star reviews or the number of the features provided by the digital media that were used to facilitate learning. The results shown that when using the digital media with high star reviews, the digital media with more features gained more credibility, on the other hand, the digital media with fewer features were not able to compensate for the low star reviews. The ultimate goal proposed is to change the attitude and behavior of the teacher. When the teacher knew the information and chose the digital media to use, the teacher finds a change in their attitude. In general, digital media use in learning tends to be seen from the star reviews given by digital media users, which can trigger credibility assessment.
Digital Literacy Skills and System Quality as Predictors of Learning Management Systems Use of Postgraduate Students in Ibadan Nigeria

&lt;p class="Default"&gt;&lt;span lang="EN-US"&gt;The use of learning management systems (LMS) was observed to be weak among students in Nigeria. Therefore, digital literacy skills and system quality were used as predictors to predict LMS use among postgraduate students in universities in Ibadan, Nigeria. A survey design of the correlational type was adopted. At the same time, the population of the study comprised all the 4,750 postgraduate students of two universities in Ibadan, Nigeria, and 328 postgraduate students were drawn using a multi-stage sampling technique. The instrument adopted for data collection was a questionnaire which had three sections. The questionnaire return rate was 316 copies, representing a 96.3% retrieval rate. Pearson’s Product Moment Correlation Coefficient and multiple regression analysis were conducted to test the null hypotheses at a 0.05 significance level. A significant relationship between digital literacy skills and the use of learning management (r =.319; p &amp;lt; 0.05) was found, while a significant relationship between system quality and the use of learning management systems (r =.501; p &amp;lt; 0.05) was equally established. There is a significant relationship between digital literacy skills and LMS system quality (r =.529; p &amp;lt; 0.05). Furthermore, a joint influence of digital literacy skills and system quality on the use of learning management systems of postgraduate students in universities in Ibadan, Nigeria (F =53.594; R=0.505; p &amp;lt; 0.05) was established. It was recommended that university management and lecturers should ensure the system quality of the LMS type to be adopted must support the convenience of use and have fewer technicalities in its use.&lt;/span&gt;&lt;/p&gt;
Code Summarization

The goal of code summarizing, an emerging topic, is to automatically create human-readable summaries of source code.Understanding and maintaining huge codebases for developers becomes more difficult as software systems become more complicated.By utilizing natural language processing (NLP) and machine learning techniques, code summarization aims to close this gap by automatically producing clear and detailed descriptions of code parts, such as functions or complete programs.Advanced deep learning models may provide summaries that are correct and relevant to the context by examining the syntax and semantics of the code, identifying significant components, and learning from enormous datasets of code-summary pairings.These descriptions help engineers collaborate and understand the code, which can improve output and software quality.The usefulness and efficiency of code summarization approaches are currently being improved, however there are still issues like handling ambiguous code structures and supporting multiple programming languages.
ENSEMBLE STACKING DALAM ANALISA SENTIMEN REAKSI VETERAN MILITER AS TERHADAP PENGAMBILALIHAN AFGHANISTAN OLEH TALIBAN

Abstrak— Sentiment analysis can be used to glean information about user opinions and identify social or political trends. There have been many studies on sentiment analysis using machine learning or lexicon-based methods that have been quite impressive. However, machine learning models often have difficulty generalizing to new data due to various reasons, such as overfitting and limited training data. These models are also prone to bias and variance, which negatively affect the accuracy of their predictions. This study discusses the application of the ensemble stacking method in sentiment analysis with the topic of the takeover of Afghanistan by the Taliban. By monitoring social media, the author uses a dataset in the form of comments on YouTube news channels related to the topic raised. Several studies have shown how the ensemble stacking method predicts better than the single model. The research was carried out by creating a sentiment classification model with logistic regression machine learning algorithms, SVM, KNN, and CART then the ensemble stacking classifier formed by the base learner of the four algorithms. As a result, for a single classifier, the highest average accuracy is the logistic regression algorithm of 74.6 percent. The four algorithms are compiled and predicted by logistic regression, and the stacking ensemble classifier that is applied produces better accuracy than the stand-alone classifier, which is 75.3 percent
Assessing the Current State of National Biodiversity Data Integration: An Initiative by the Taiwan Biodiversity Information Alliance (TBIA)

Biodiversity data that adheres to the FAIR principles (Findable, Accessible, Interoperable, and Reusable) is essential for any research and application, particularly in understanding biodiversity, valuing ecosystem services, and guiding environmental monitoring and conservation. However, most biodiversity-related projects are carried out independently, resulting in the dispersion of data across various databases from different organisations. Data users often need to search, collect, and integrate data from multiple sources, which is time consuming and labour intensive. Furthermore, the lack of unified data standards and taxonomy backbones among different databases poses additional challenges for data analysis and application. To address these issues and maximize the value of existing data, we launched a collaborative bottom-up initiative aimed at establishing a national biodiversity data portal in Taiwan. The collaboration among eight units, including research institutes, governmental organisations, and museums, has led to the formation of the Taiwan Biodiversity Information Alliance (TBIA), along with the launching of a 5-year (2021–2025) Taiwan Biodiversity Information Action Campaign. Furthermore, the alliance has developed important documentation, such as the Taiwan Biodiversity Data Standard and the Principles of Opening and Sharing Sensitive Biodiversity Data. These documents have played a crucial role in facilitating data integration across the various units involved by implementing a convergence mechanism that adheres to the Darwin Core data standards (Darwin Core Task Group 2009) and follows a unified taxonomy backbone aligned with the Taiwan Catalogue of Life. This mechanism simplifies and expedites the information-sharing process, enabling users to access relatively comprehensive national biodiversity data. This presentation provides an introduction to TBIA and its development, illustrates recent achievements of TBIA, and provides a brief overview of the Taiwan’s biodiversity data status after integration, accomplished through the efforts of TBIA. The data integration effort has resulted in a cumulative count of 19 million records, while simultaneously enriching the portal with diverse data types owing to the distinctive profession and strength of each participating unit. For instance, integration has resulted in the incorporation of significantly more specimen records of marine and wetland species. Our future task involves promoting data use, establishing new connections with potential databases and publishing records from the integration effort in international data repositories (e.g., Global Biodiversity Information Facility), enhancing the accessibility of the data worldwide.
Implementing Face Detector using Viola-Jones Method

This paper presents implementing a face detection algorithm based on the Viola-Jones method.The Viola-Jones method is a well-known and efficient face detection algorithm that uses Haar-like features, Adaboost, integral images, and the cascade of classifiers.The implementation in this paper was done in MATLAB and was tested using the MIT + MCU database.The results show that the detector achieves a detection rate of 60%, which is lower than the 90% detection rate of the original Viola-Jones method.However, the detector achieves a better false positive rate rejection.The design choices made in this implementation affect the trade-off between the system's accuracy and speed.
Minimalist DCT-based Depthwise Separable Convolutional Neural Network Approach for Tangut Script

The Tangut script, a lesser-explored dead script comprising numerous characters, has received limited attention in deep learning research, particularly in the field of optical character recognition (OCR). Existing OCR studies primarily focus on widely-used characters like Chinese characters and employ deep convolutional neural networks (CNNs) or combinations with recurrent neural networks (RNNs) to enhance accuracy in character recognition. In contrast, this study takes a counterintuitive approach to develop an OCR model specifically for the Tangut script. We utilize shorter layers with slimmer filters using a depthwise separable convolutional neural network (DSCNN) architecture. Furthermore, we preprocess the dataset using a frequency-based transformation, namely the Discrete Cosine Transform (DCT). The results demonstrate successful training of the model, showcasing faster convergence and higher accuracy compared to traditional deep neural networks commonly used in OCR applications.
'Language Corpora annotation and processing' Niladri Sekhar Dash (2021)

Language Corpora annotation and processingNiladri Sekhar Dash (2021)Singapore: Springer Nature. Pp. 272ISBN: 9789811629624 (pbk)ISBN: 9789811629594 (hbk)ISBN: 9789811629600 (eBook)
Peningkatan Hasil Belajar Siswa Melalui Penggunaan Media Pembelajaran Berbasis Website Di Sekolah Dasar

This study aims to determine the use of Web-based learning media in improving student learning outcomes in class IV at SDN No. 178491 Pohan Door, Toba Samosir Regency. Pre-research results show that teachers rarely use media or materials that can motivate students. This contradicts the 2013 Curriculum, which emphasizes using electronic media to prevent boredom during learning. The research was conducted for three months, from November 2022 to January 2023, with 16 fourth-grade students as research subjects. This study uses classroom action research carried out in two cycles. Qualitative data were analyzed using qualitative descriptive analysis by comparing cycles I and II. In contrast, quantitative data on student learning outcomes were analyzed using descriptive comparative analysis by comparing initial test scores, cycles I and II. The results showed that using Web-based learning media significantly increased the learning outcomes of fourth -grade students at SDN No. 178491 Pohan Door, Toba Samosir Regency. The average student learning outcomes increased from 62.50 at the beginning of the study to 73.13 at the end of cycle I and increased again to 85.63 at the end of cycle II. The initial completeness level of 12.50% increased to 50% at the end of cycle I and again to 100% at the end of cycle II. The results of the observer's assessment showed that student activity and teacher performance also increased from cycle I to cycle II. In conclusion, the application of Web-based learning media can positively impact student learning outcomes, increasing the level of completeness, student activity, and teacher performance. Therefore, it is suggested that teachers use Web-based learning media in the learning process to motivate and improve student learning outcomes.
Perencanaan Desain Renovasi Masjid Ahyani Thoriqul Jannah Tangerang Banten

&lt;p align="center"&gt;Abstract &lt;/p&gt;&lt;p&gt;This community service activity was carried out in the Tangerang sub-district. This Abdimas is a partnership program with cooperation partners Ahyani Thoriqul Jannah Masjid Prosperity Council (DKM) Mosque Tangerang Banten. The Ahyani Thoriqul Jannah Mosque, Tangerang, Banten is currently an old building which is feared to endanger the safety of the congregation/community who are carrying out worship. In addition, the capacity of the mosque is no longer sufficient to accommodate the Friday prayer congregation. Therefore, DKM Ahyani Thoriqul Jannah Tangerang Banten intends to completely renovate the mosque building with the principles of the congregation, by the congregation and for the congregation. DKM Ahyani Thoriqul Jannah Tangerang Banten faces a problem where there are no congregation members who are experts in their field. The team of community service lecturers at Indraprasta University PGRI offered DKM Ahyani Thoriqul Jannah Tangerang Banten a mutually beneficial partnership to carry out the renovation of the Ahyani Thoriqul Jannah mosque. DKM Ahyani Thoriqul Jannah resolved licensing issues, residents/congregants objected to mosque renovations and so on. While the team of community service lecturers at Indraprasta PGRI University prepared experts who would carry out tasks including pre-planning including reviewing the location of the mosque, land area, land form and soil structure, planning in the form of mosque architectural design and budget plans and participating in supervising the implementation of development from a technical point of view. Keywords: mosque design, mosque, &lt;/p&gt;&lt;p align="left"&gt; &lt;/p&gt;&lt;p align="left"&gt; &lt;/p&gt;
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 12 • Number of submissions sent for review : 11 • Number of submissions accepted : 10 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 83.3 • Average number of reviews per paper : 1 • Total number of reviewers involved : 10 • Contact person for queries : Name : Wisanu Pecharapa Email : wisanu.pe@kmitl.ac.th Affiliation : King Mongkut’s Institute of Technology Ladkrabang - College of Materials Innovation and Technology