Reviewerly/exHarmony huggingface dataset:
Network Analysis of Facial Behaviours for Non-Contact Driver Distraction Detection

Distraction detection plays an important role in driver safety. We introduced a novel framework to identify the dynamic pattern of facial responses to distracted driving. For this purpose, facial expression signals were extracted from the driver’s video. Due to the cause-and-effect movement of different facial expressions time series, the connectivity analysis was used to extract correlation and causality features. The connectivity was quantified using functional and effective connectivity methods among each pair of facial expression signal. In order to improve the detection of distraction, the connectivity analysis between the time series extracted from facial expressions, has been combined with perinasal perspiration index (PP) extracted from the perinasal area of the face thermal images. Validation was performed using subject independent recognition manner, and the results demonstrated that the proposed method significantly improved distraction detection. The proposed system achieved accuracy of 60.96% in classification between distracted from normal driving. Moreover, the obtained graphs of facial expression flow are effectively visualized by different schemes to show the relationships. Results show that the difference between the sources and sinks relationships was a useful feature to distinguish between normal driving and distraction.
Better bubbles, massive mammals

Optimized bubble blowing The humble soap bubble can be the highlight of a child’s bath time or a poppable prop for performance art. Researchers at the University of Paris-Saclay and Slash Bubbles have developed an optimized recipe for giant bubbles that could benefit scientists, artists, and children alike. Typically, water and some kind of dish soap are necessary to make giant soap bubbles. Recipes vary in terms of the concentration of soap and additives, such as polymers. The study, which was published on arXiv and has not been peer-reviewed, examined two main factors in a recipe for a quality bubble: longevity and ease of blowing (2022, DOI: 10.48550/arXiv.2209.04435 ). Marina Pasquet , the study’s lead author, says artists inspired the study. “We wanted to see why artists used the concentrations they did,” Pasquet tells Newscripts. “You have as many recipes as there are bubble artists.” Adding glycerol and a long,
An overview of Cuckoo Optimization Algorithm based Image Processing

The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.
The Central Bank Digital Currency – Lessons learned from Bahamas

The Fourth Industrial Revolution has impacted positively on the financial system. Digitalisation in various aspects is one of the greatest advances in finance. Thanks to transformation, financial requirements are proceeding rapidly, safely, and conveniently. Nations aim at cashless society in tandem with financial inclusion as well as enhancing the opportunity for growing up microfinance and peer-to-peer payments. Besides existing currencies, there is an essential demand for a stable and secure currency which performs clearing and settlement payments. Central Bank Digital Currency (CBDC) has an appropriate nature to satisfy the requirements for an up-to-date and optimal currency issued by the Central Bank. In Vietnam, recently, the process of digitization has taken place dynamically; moreover, the attention to CBDC has also increased. In line with several monetary benefits, CBDC also assists SBV to consolidate its role in monetary activities. Therefore, conducting CBDC studies is significant. The journey from research to circulation of CBDC requires thoughtful consideration and lessons learned from a pioneer such as the Bahamas which is the only country still exchanging CBDC. Therefore, the purpose of this article is to study the experience from the Bahamas, an important premise for Vietnam, to prepare and modify efficiently before CBDC officially circulates in Vietnam.
Effects of Pre-questioning on the Students’ Reading Comprehension

This study was conducted to investigate the effect of Pre-questioning on the Students’ Reading Comprehension of the fifth semester students of Informatic Study Program Faculty of Computer Engineering Cokroaminoto Palopo University in academic year 2022/2023. The forms of questions were in multiple choice and essay, multiple choice used for reading comprehension tests and essay used for Pre-questioning tests. There were 40 students of the fifth semester students of Informatic Study Program Faculty of Computer Engineering Cokroaminoto Palopo University in academic year 2022/2023 as the sample on this study. The researcher used pre experimental method in this study. the sample was taken by using random sampling technique. There are two kinds of instrument used in this research, namely test and questionnaire. The result of the research shows that the reading comprehension ability of the fifth semester students of Informatics Study Program Faculty of Computer Engineering Cokroaminoto Palopo University was very effective. It was proven by the mean score in post-test was 7.55 and the T-test value 43.375 which was higher than the T-table value 2.315. And the students were interested in learning reading comprehension using pre-questioning technique.&#x0D;
Virtual reality and the problem of illusion

The paper touches upon the problem of differentiating between virtual reality and original reality. The article makes a contribution to the modern ontology, as it discusses the problem of the status of virtual reality, which is formed by modern technical and mathematical means using cognitive technologies. The work is based on the concepts of E.Husserl and H.Putnam, as well as theories of psychophysiology and cognitive sciences. Arguments are provided for the following opposing statements: that it is possible to differentiate between them and that it is not. To specify the distinctive features of virtual reality, the latter is described as a system of perceived existing things and in comparison to other forms of such systems: subjective and objective, individual and social, natural and cultural, material and ideal, original and fictional, abstract and transcendental, evident and illusionary, true and false, erroneous and imitative, deceitful and mistaken, performative and simulating, conditional and unconditional ones. This part of the article qualifies virtual reality as illusion created via imitation. It is further revealed that the key distinctive feature of original reality, which is defined in the modern phenomenology as the maximal level of evidence, is applicable to virtual reality as well. The major argument in favor of the provable difference between virtual and original reality, which the contemporary analytic philosophy offers, can be confuted by means of a thought experiment based on the doubling of virtual reality. The final part of the article discusses psychological (experiment-based cognitive) arguments in favor of differentiating between virtual and original reality successfully: effect of presence and immersion. Both effects are typical of the reality types under comparison, as well as of experiencing these realities. The paper concludes that neither philosophy nor cognitive sciences can offer a compelling argument to differentiate between original reality and virtual reality.
An Analysis of Code-Mixing Used by the Lecturer at Universitas Cokroaminoto Palopo

This research aims to find out the types of code-mixing and student’s perception on code-mixing used by the lecturer in English class. This research used both Qualitative and Quantitative methods. The subject in this research are the lecturer and the first year of science students at Cokroaminoto Palopo University. The instrument of this research are observation sheet, recorder, and questionnaire. The result of this research shows that (1) There are three types of code-mixing found in the lecturer’s utterances during the English teaching-learning process. They are Intra sentential, intra lexical, and involve a change of pronunciation and the results showed that the most frequent is intra sentential. (2) Regarding students’ perception of code-mixing, the researcher found that most students agreed to the use of code-mixing in the English teaching-learning process in the first year of science student at Cokroaminoto Palopo University because code-mixing can make their communication with their lecturer more effective.&#x0D;
Development of Comic-Based Biology Learning Materials on the Topic of Motion Systems in Humans

This research aims to develop comic-based learning media that is appropriate, practical and effective to use. This development research used the ADDIE development model, consisting of five stages, namely, Analysis, Design, Development, Implementation, and Evaluation. The data analysis technique used is a combination of qualitative and quantitative. Based on the results of the validation of material experts, the assessment achieved an average of very decent with a percentage of 92.8% in the subject matter expert section 1 while the material expert 2 received a score of 100%. Based on the results of media expert validation, the average rating achieved was very feasible with a percentage of 96.4%. Based on the results of the questionnaire sheet, it gets a practicality value of 90.00% in the very practical category. Moreover, the N-Gain result is 0.60 in the medium category. It can be concluded that comic media is effectively used in the learning process with moderate effectiveness and is considered very practical. Keywords: learning materials, comics, biology learning. Abstrak: Penelitian ini bertujuan untuk mengembangkan media pembelajaran berbasis komik yang layak, praktis dan efektif untuk digunakan. Penelitian pengembangan ini menggunakan model pengembangan ADDIE, terdiri dari lima tahapan yaitu, Analysis, Design, Development, Implementation, dan Evaluation. Teknik analisis data yang digunakan adalah kombinasi dari kualitatif dan kuantitatif. Berdasarkan hasil validasi ahli materi penilaian yang dicapai rata-rata sangat layak dengan presentase 92,8% pada bagian ahli materi 1 sedangkan pada ahli materi 2 mendapatkan nilai 100%. Berdasarkan hasil validasi ahli media, penilaian yang dicapai rata-rata sangat layak dengan persentase 96,4% . Berdasarkan hasil dari lembar qusioner mendapatkan nilai kepraktisan sebesar 90,00% dengan kategori sangat praktis. Selanjutnya, hasil N-Gain sebesar 0,60 dengan kategori sedang. Dapat disimpulkan bahwa media komik efektif digunakan dalam proses pembelajaran dengan kategori keefektifan sedang dan dinilai sangat praktis. Kata kunci: bahan ajar, komik, pembelajaran biologi. DOI: http://dx.doi.org/10.23960/jpmipa/v24i1.pp119-127
Peningkatan Kemampuan Membaca Pemahaman Siswa Menggunakan Metode SQ3R (Survey, Question, Read, Recite, Riview)

This research is a classroom action research conducted at SDN 02 Pinceppute which aims to improve students' reading comprehension skills in Indonesian lessons with reading text material using the SQ3R method (survey, guestion, read, recite, and review) in class V SDN 02 Pinceppute. The research subjects were fifth grade students of SDN 02 Pinceppute with a total of 18 students in the odd semester of the 2019/2020 Academic Year. Data collection techniques with test techniques, non-test techniques, and field notes are carried out through direct observation in the learning process using student activity observation sheets, teacher activity observation sheets and documentation. From the data collected, it was categorized into a very poor category to a very good category and the Maximum Completeness Criteria (KKM) used at SDN 02 Pinceppute. The results showed that there was an increase in the reading comprehension skills of fifth graders at SDN Pinceputte through the application of the SQ3R method. This is evidenced by an increase in the average student learning outcomes in the first cycle of 64.4 and the second cycle of 73.1. In the first cycle, it was found that of the 18 students who took the first cycle test, there were 7 students who achieved the KKM score and 11 students had not achieved the KKM score. While in the second cycle there was an increase which showed as many as 14 students had reached the KKM score and 4 students had not reached the specified KKM, which was 70..&#x0D;
Using emerging technology to draw learning across the curriculum

To drive the wider adoption of STEM in schools, researchers have promoted the benefits of teaching STEM subjects integrated across the curriculum. This integration can support more authentic learning where learning is framed in real-world application or driven through problem/project-based learning. The integration of digital technologies (DT), where the learning moves away from consumption to creation, provides for further application of learning where the development of artefacts can situated within other subjects. This integration, however, raises new challenges for effective teaching and learning, and while new technologies and approaches can support this practice, this is still evolving. In this study we explore how one high school has integrated the creation of digital artefacts situated, in the DT class, with learning in the Māori Performing Arts class. The study explores how mixed reality (MR) combined with design thinking approaches, provide new opportunities to integrate learning and support engagement in STEM. Drawing on a participatory action research methodology, this article explores the experiences and perceptions of three teachers as they adopt MR to engage and teach students drawing on critical DT skills.
Students’ Interest In Learning English At The Third Semester Students Of Health Faculty Of Mega Buana University

Students’ Interest in Learning English at the Third Semester Student of Health Faculty of Mega Buana University. How the students’ interest in learning English at the third semester students of Health Faculty of Mega Buana University? The objective of the research was found out the students’ interest in learning English at the third semester students of Health Faculty of Mega Buana University. The researcher applied purposive sampling technique in getting data because the sample data sources need special consideration. Special consideration here by taking the students that have good mean score in the class especially in English score. Based on the purposive sampling technique, the researcher’s subjects are 8 from 73 students in the classroom in Public Health Study Program Health Faculty. Based on the result the most important is their interest in learning English. They gave the positive response. For example their interest in learning English is very good, very interesting, very big and very strong. This situation can be seen from the observation and the result of the interview. They had big interest and big motivation when learn in the class they are always present. They said that English is very important especially in their study program&#x0D;
What Can Big Data Protection Learn from Trade Secret Protection?

Journal Article What Can Big Data Protection Learn from Trade Secret Protection? Get access Ge Jiang Ge Jiang Associate Professor, Tsinghua University, Beijing, China email: jiangge@mail.tsinghua.edu.cn Search for other works by this author on: Oxford Academic Google Scholar GRUR International, Volume 72, Issue 6, June 2023, Pages 525–526, https://doi.org/10.1093/grurint/ikad047 Published: 26 April 2023
AI Based Pilot System for Visually Impaired People

Significantly analysing visual defectiveness people represents more of the population around the world roughly speaking more than 10 million. Their incorporation in the public is an essential point. In order to give Audible environment for the blind people this project mainly concentrates on the field of Assistive devices for Visually Impairment Persons. To make their daily life comfortable like to travel from one place to other place many supporting systems is developed and being developed. The basic idea for our proposed method is to design Auto-Assistance system for visually impaired person. Model is skilled in a way that if input is given it starts generating the Sentences about the Image. Experiments show that it is often generating exact descriptions for image. New dataset has been collected in real time which consists of 300 images as of now with different categories like Vehicles, Animals and indoor objects. We have applied our algorithm to several images gathered in real time that is our own data set and found that the conversion is completely successful. The system is light weight and socially useful.
A Scoping Review on the Use of Machine Learning on Longitudinal Electronic Health Records for Early Detection and Prevention of Diseases. (Preprint)

<sec> <title>BACKGROUND</title> Electronic Health Records (EHRs) contain information on many risk- and preventive factors and early signs, which can be used for prevention of diseases. However, the accumulating volume and variety of EHRs over time, makes it impossible for clinicians to analyse these large-scale EHRs during a medical visit. Machine learning can assist in early diagnostic tasks because it is able to handle multidimensional data and to incorporate interactions. Although machine learning have become well developed, studies mainly focus on methodologies and techniques but lack a healthcare focus. </sec> <sec> <title>OBJECTIVE</title> The objective of this scoping review is to provide an overview of the extent of evidence in relation to early detection for timely prevention of diseases, by using machine learning on longitudinal electronic health records. By reviewing machine learning attempts for a variety of diseases, generated knowledge and clinical benefits are investigated. </sec> <sec> <title>METHODS</title> A literature search was performed in collaboration with a medical information specialist in following databases: PubMed, Embase.com, Clarivate Analytics/Web of Science Core Collection and the Institute of Electrical and Electronics Engineers [IEEE] Xplore Digital Library and DBLP. Studies were eligible when longitudinal EHRs were used, aiming for the early detection of diseases by machine learning, in a prevention context. Studies with a technical focus or using imaging or hospital admission data were beyond the scope of this review. Study screening, selection and data extraction was performed independently by two researchers. </sec> <sec> <title>RESULTS</title> Twenty studies were included in this review and detected a variety of diseases. By developing and comparing machine learning models, knowledge about diagnostic performance, timing of detection, important predictors and clinical usefulness was created. Applying these machine learning models in practice, patients might benefit from a personalised healthcare, preliminary offside screening and ultimately prevention, with practical benefits like workload reduction and policy insights. EHR variables can successfully be used in a time-including neural network or LSTM technique. </sec> <sec> <title>CONCLUSIONS</title> While machine learning models based on textual EHRs are still in the developmental stage, diagnostic insights can assist clinicians in an accurate and earlier detection of diseases. With the addition of personal responsible factors, a personalised healthcare and therefore targeted prevention methods can be pursued. If machine learning models become mature enough and applied at higher level, the preventive healthcare may be improved by preliminary screenings, a reduced workload and policy insights. </sec> <sec> <title>CLINICALTRIAL</title> project osf.io/8g7ep </sec>
Voting System using Blockchain Ethereum (dApp)

The use of E-Voting systems has become popular in these recent years due to the ability of Blockchain environment to provide a more efficient and convenient voting process. Earlier, the security and integrity of e-voting systems has been very concerning, as they are vulnerable to cyber-attacks and manipulation. On the contrary, Blockchain technology provides a decentralized and distributed platform that can ensure the integrity and immutability of data. This research paper proposes an e-voting system based on blockchain technology. The proposed system aims to provide a secure, transparent, and tamper-proof voting process. The system utilizes smart contracts, which automates the voting processes and ensures the accuracy of the results. The system also provides transparency and accuracy, allowing voters to verify their vote and ensuring that the results are accurate and trustworthy. The proposed system will be tested and evaluated to determine its effectiveness and feasibility. The evaluation will focus on the security, scalability, and usability of the system. The security evaluation will test the system's ability to prevent attacks and ensure the confidentiality of the votes. The scalability evaluation will test the system's ability to handle a large number of voters and transactions. The usability evaluation will test the ease of use and accessibility of the system for all types of voters
Poster Abstract: Camera-Assisted Training of Non-Vision Sensors for Anomaly Detection

Cameras are becoming pervasive and used for image classification and object detection in various applications, including anomaly detection. However, cameras pose a privacy threat and require significant power resources. To address these issues, researchers have explored non-vision sensors, but pre-training them for anomaly detection is challenging because anomalies are difficult to define and vary significantly across indoor environments. Thus, we propose a new approach to training non-vision sensors using a tiny camera and a pre-trained MobileNetV2 model. Data from non-vision sensors are labelled based on the image classification from the tiny camera, and an anomaly detection model is trained using these labelled data. The Random Forest model is used as the final model, achieving an accuracy of 95.58%.