Reviewerly/exHarmony huggingface dataset:
English Reading Program Evaluation and Development at a Regional University in Korea

The current study aims to evaluate and improve a university English reading program in order to enhance students’ English reading skills. A total of 312 students and 16 professors in the program participated in the survey covering seven areas: students’ goals, the program’s strengths, student weaknesses, teaching, materials, assessment, and suggestions for improvement. Findings showed that students expressed various goals for taking reading courses. However, both students and professors agreed that the only notable strength of the program was ‘giving an opportunity to read in English.’ Students perceived that their great weaknesses in reading were lack of vocabulary and grammar, whereas professors thought their motivation to read was quite low. The two most common activities were text translation and grammar explanation. However, students preferred to learn practical expressions and English culture through reading contents. Both students and professors pointed out that current textbooks were boring and difficult. Regarding assessment, students preferred to include attendance, participation, attitude, and assignments, in addition to midterm and finals. Regarding suggestions for improvement, professors suggested teaching based on students’ proficiency levels and regular checking of instructional equipment. Based on these findings, a curriculum was created for the English reading program.
MultiVR: Digital Twin and Virtual Reality Based System for Multi-people Remote Control Unmanned Aerial Vehicles

The remote control of Unmanned Aerial Vehicles (UAVs) in emergency rescues, large-scale search, e-commerce and other fields is still a hot topic. The majority of current remote human-computer interaction techniques, such as joy-sticks, monitoring interfaces, or gesture control, lack flexibility, have weak immersion, and have poor interaction. The study proposes the development of a novel system for controlling multiple UAVs using virtual reality (VR). A virtual system and a physical system make up the control system. A VR application uses a head-mounted display (HMD) to show the user a digital twin environment of a drone and a faraway location. The operators pilot the quadcopters with a set of virtual reality handles. Control data is translated directly from VR to the real UAV in realtime. The experimental results showed a stable and convenient method of manipulation by the VR scene. With the support of the proposed system, ultra-remote control is possible, and immersive maneuvering makes controlling multiple drones easier.
Noise-Resistant Crowd Equalisation for Optical Communication Systems Based on Machine Learning

We propose a solution to noisy neural networks employed in future optical communication systems. The proposed approach includes breaking down large networks into smaller ones and forming ”crowds” using these elementary networks.
Topical Extractive Summarization

In this paper we propose a graph-based biased LexRank approach combined with topic modeling to create a topic-based extractive summarization model. Topical summarization task is used to obtain a customized summary for a particular reader. We achieve so by including information about topics of interest into an extractive summarization model. Topical information is derived via aspect embedding vectors from the ABAE model and used as a topic input for the biased LexRank model that runs on the heterogeneous graph of topics and sentences. Inclusion of the topical structure into a summary increases the targeting of the summary and makes the overall summarization model benefit from it. We conduct experiments on a novel dataset for extractive summarization quality evaluation constructed by ourselves from Wikipedia articles.
Data-Driven Decompositions

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Indonesian Automatic Speech Recognition with XLSR-53

This study focuses on the development of Indonesian Automatic Speech Recognition (ASR) using the XLSR-53 pre-trained model, the XLSR stands for cross-lingual speech representations. The use of this XLSR-53 pre-trained model is to significantly reduce the amount of training data in non-English languages required to achieve a competitive Word Error Rate (WER). The total amount of data used in this study is 24 hours, 18 minutes, and 1 second: (1) TITML-IDN 14 hours and 31 minutes; (2) Magic Data 3 hours and 33 minutes; and (3) Common Voice 6 hours, 14 minutes, and 1 second. With a WER of 20%, the model built in this study can compete with similar models using the Common Voice dataset split test. WER can be decreased by around 8% using a language model, resulted in WER from 20% to 12%. Thus, the results of this study have succeeded in perfecting previous research in contributing to the creation of a better Indonesian ASR with a smaller amount of data.
Anna Thorvaldsdottir - Anna Thorvaldsdottir, Aerial. LA Percussion Quartet, Icelandic Symphony Orchestra, CAPUT, Nordic Affect, Duo Harpverk, Franzson, Volkov, Dorsteinsdottir. Sono Luminus, SLE-70025.

Anna Thorvaldsdottir - Anna Thorvaldsdottir, Aerial. LA Percussion Quartet, Icelandic Symphony Orchestra, CAPUT, Nordic Affect, Duo Harpverk, Franzson, Volkov, Dorsteinsdottir. Sono Luminus, SLE-70025. - Volume 77 Issue 303
Survey on Detection of Manipulated Multimedia in Digital Forensics Using Machine Learning

The manipulation of multimedia has increased all over the world. Different tools are used to alter the multimedia and it is difficult to detect genuine and fake media. People are facing problems to detect if the media is real or fake. Due to manipulated media, cybercrime has becomeincreasingly widespread. We believe that personal security and privacy should be carried out easily and intelligently in this digital environment where all fundamental tasks are completed without issue. When we looked into the numbers, we discovered that a sizable proportion of people experience harassment or other forms of abuseregularly. Based on a review of the existing system, we presented an application that would use the CNN (Convolutional Neural Networks) method to distinguish between real and fraudulent media in a single application. CNN performs better with picture and voice or audio inputs than earlier networks and other techniques. CNN hidden extract feature from the input using pixels value and computation based on edges and outline of the inputs using pixels value and computation based on edges and outline of the inputs. The growing use of convolutional neural networks (CNNs) has had a substantial effect on defenders.
Research on Educational Informatization from the Perspective of Pedagogy

China's basic education informatization began in the 1980s and has a development history of more than 20 years. Since entering this century, educational informatization has shown a trend of rapid development. Both research literature and total investment are growing at a high speed. At present, behind the prosperity, there are also insurmountable problems in the theory of educational informatization, such as the deep integration of information technology into education. Education is a conscious social activity with the direct goal of affecting people's physical and mental development. Pedagogy is to study how to make people independent, self-improvement and self-reliance. Therefore, based on the research on educational informatization from the perspective of pedagogy, this paper considers educational informatization from the dimension of information technology, which is the pedagogical perspective of educational informatization research.
USING CRYPTOCURRENCY IN AFGHANISTAN

The world is on the brink of rewriting business and monitoring history. It is very crucial to mention academically how Afghan crypto-monitory transactions are taking place. even though it is a soft threat to official government organizations via tax evasion, money laundering, and terrorism financing. The qualitative method with content analysis was applied because the data was in textual form. The data was analyzed through Atlis.ti 9. First of all, the interviews were coded, and then themes were created. The result showed that Bitcoin and Binance had the most users, and there were six types of cryptocurrencies in Afghanistan. Furthermore, the advantages and disadvantages were highlighted. Lastly, it was highly suggested that the Afghan government have specific laws for general protection and to gain the benefits of the new world of high technology.
Project REACH (Reading Empowerment Asserting Connection at Home): Input to an Enhanced Reading Comprehension Skills of Grade 9 Students in English

Reading comprehension is one of the most useful lifetime skills that go beyond the classroom setting. Students who have excellent reading comprehension can succeed inside and outside the school. Relative to this, the study aimed to find out the possible impact of Project REACH (Reading Empowerment Asserting Connection at Home) on the reading comprehension skills of Grade 9 students of Alaminos Integrated National High School, A.Y. 2021-2022. This study was descriptive correlational for it aimed to accurately and systematically describe a population, situation, or phenomenon and at the same time it investigated relationships between variables without the researcher controlling or manipulating any of them. It aspired to find out the significant difference between the mean pre-test and post-test scores of Grade 9 students in English and the significant relationship between the Project REACH and reading comprehension skills of Grade 9 students in English. Eighty percent (80%) or one hundred and sixty (160) of the two hundred (200) Grade 9 students of Alaminos Integrated National High School who have undergone the Project REACH pilot implementation since the school year 2020-2021 were the respondents and the study covered the Fourth Quarter period of the academic year 2021-2022. The researcher utilized a teacher-made pre-test and post-test based on the particular reading skills, as well as a researcher-made survey questionnaire to test the students’ perception of the Project REACH components. Statistical treatments used in the study were frequency count, percent distribution, mean, standard deviation, paired sample t-test, and Pearson Product-Moment Correlation Coefficient (Pearson r). The result showed that among all the reading skills, only Getting the Main Idea is associated with the Project Reach’s Assessment, r=.258, when tested at P&lt;.01. However, when tested at P&lt;.05, it was found that Drawing Conclusions has a very weak association with Project REACH’s Content, r=.164 while Noting Details is associated with Project REACH’s Procedure, r=.184., Thus, partially rejecting the null hypothesis. Since the study reveals that there is the partial relationship between the level of reading comprehension skills of Grade 9 students and the Project REACH components, the school may continue supporting and improving the implementation of Project REACH, as well as involving all the learners in this kind of reading program and to religiously follow its mantra, “no child will be left behind”. Keywords: reading, reading comprehension, reading skills, Project REACH, reading materials.
PS-BPC01-6: DEEP LEARNING IS USEFUL FOR THE PATHOLOGICAL ANALYSES OF HUMAN KIDNEY BIOPSY IMAGES

Objective: The kidney plays a pivotal role in the maintenance of blood pressure. Non-neoplastic kidney biopsy images yield a great deal of diagnostic information, however, several studies have shown that there is room for improvement in pathological diagnostic reproducibility. Recently, deep learning has made major breakthroughs in various tasks. Therefore, we tried to analyze human kidney biopsy images using deep learning. Design and Method: We analyzed 5002 non-neoplastic kidney biopsy whole slide images (WSIs) obtained from 24 facilities in Japan using deep learning. Images of periodic acid-Schiff stained kidney sections were analyzed. Results: WSIs have billions of pixels, and deep neural networks (NNs) cannot accept such a huge image as an input. Therefore, we prepared 88 μm square 6,125,990 patch images (PIs) from the WSIs, and trained NNs with these PIs. We manually selected 259,682 typical PIs from the whole set of PIs, and annotated them into 56 classes. Using these annotated PIs, we trained EfficientNets B0-B7, whose network depth and width are carefully balanced for various input resolutions (224 to 600 pixels), and found that EfficientNetB4 (hereafter referred to CNN_Man) achieved the best F1 score. Using histological parameters obtained from renal biopsy reports made by nephrologists and pathologists at each facility, we confirmed the reliability of CNN_Man-based prediction. We also finely visualized histological features of WSIs based on CNN_Man prediction (Figure). In addition to these supervised histological analyses, we tried to extract disease features in a loosely supervised manner. In this loosely supervised learning, we tried to extract disease-related rather than histology-related glomerular features by training EfficientNetB4 using final diagnosis as true labels. It is noteworthy that the final-diagnosis-based training is not typical supervised learning because each disease class contains patch images with various histological features to varying degrees. We extracted glomerulus-related PIs by using CNN_Man, and then labeled these PIs into 20 final-diagnosis categories. EfficientNetB4 (hereafter CNN_Diag) was trained with the 20 class images in a 5-fold cross-validation manner. Using UMAP, we visualized the final layer parameters of CNN_Diag corresponding to each PI. This approach divided PIs from IgA nephropathy into many clusters, and each cluster reflected blood pressure, renal function, age, etc., even though they were not explicitly given as teacher labels. Conclusions: The analysis of kidney biopsy images using deep learning may be useful for the classification of renal diseases based on new perspectives.
The Application of Cloud-Computing Technology to Improve Patients' Medical History Access to Clinicians for Quality of Care in the Fourth Industrial Revolution

This study sought to investigate a framework for the application of a cloud-computing technology to improve patients’ medical history access to clinicians for quality of care in the fourth industrial revolution. The study applied literature review to investigate the application of a cloud-computing technology to improve patients’ medical history access. The study discovered that keeping and sharing patients’ medical history in addressing patients’ health problems has always been a nightmare without the presence of technology, such as cloud computing. Cloud computing may assist with the provision of enough storage space and adequate personal information security, and provide ease of access within an implausible time frame. The study therefore proposes a framework for the application of cloud-computing technology to improve patients’ medical history access to clinicians for quality of care in the fourth industrial revolution. It is hoped that the proposed framework will serve as a benchmark for the healthcare fraternity at large.
PS-P12-11: INFECTION CONTROL PRACTICES OF HEALTHCARE WORKERS INFECTED WITH COVID-19 IN TONDO MEDICAL CENTER FROM MARCH 2020 TO MAY 2021

Background: COVID-19 has put the lives of our healthcare workers at the forefront of the battle against pandemic. Understanding how this pandemic directly affects them is vital in ensuring safe and continuous provision of service to the public. Reviewing their infection control practices is imperative in protecting their health and the patients they serve. This study aimed to determine the infection control practices of healthcare workers infected with COVID-19 in Tondo Medical Center from March 2020 to May 2021. Methodology: This is descriptive retrospective study involving healthcare workers of Tondo Medical Center infected with COVID-19 from March 2020 to May 2021. Demographic data, exposure history, clinical manifestations and infection control practices data were surveyed, encoded and tabulated. Categorical variables were presented as frequencies and percentages. Results: A total of 425 healthcare workers developed COVID-19 from March 2020 to April 2021. Their mean age was 36.4 years; more female (57.4%) and single (56.0%) staff were affected. Most cases (41.4%) came from or low/lowest risk areas. Three clustering of cases were identified. More than half of the respondents (54.4%) had unknown exposure; housemates/relatives (21.0%) were the most common source of known exposure. Three out of 10 respondents were asymptomatic; majority had mild symptoms (62.2%), the most common of which was the loss of taste/smell (26.5%). Adherence to wearing of mask was highest in the community (99.5%) than in the workplace (93.4%) and least at home (78.3%). Majority practiced social distancing in the community (97.9%) and in the workplace (95.5%). Almost half (58.7%) practiced 1-meter social distancing. Respondents practiced frequent handwashing both at home (92.2%) and in the workplace (91.9%). Only 1–2 out of 10 washed their hands for 40 seconds or more. Majority employed hand hygiene with alcohol-based rub for less than 20 seconds in the workplace (71.6%) and at home (69.5%). Majority (98.3%) performed hand hygiene after coughing or sneezing and used tissue (44.4%). Most of the respondents decontaminated their work area before and after work (66.6%). Alcohol was most used in the workplace (70.7%). More than half (59.6%) ate in the pantry, mostly alone (65.5%). Most cleaned their cellphones daily (88.4%). Majority brought alcohol with them (97.2%) and almost half (47.5%) wore a mask for 4–8 hours. Conclusion: Some gaps have been identified in the knowledge and practices of infection control of healthcare workers infected with COVID-19 at Tondo Medical Center; hence reinforcement programs including refresher trainings and policy enhancement are warranted.
Partially Parallel Low-Complexity Chase Decoding of Reed-Solomon Codes

This paper proposes the partially parallel low-complexity Chase (PPLCC) decoding for Reed-Solomon (RS) codes. With the formulated test-vectors, the Kötter’s interpolation based Chase decoding events are processed in a partially parallel manner, maintaining both low decoding complexity and latency. The decoding will be terminated once a codeword candidate that satisfies the maximum-likelihood (ML) criterion is found. Furthermore, a skipping rule is introduced to reduce the decoding complexity by assessing the Hamming distance between an estimated codeword and the test-vector. Simulation results show that the proposed PPLCC decoding achieves an improved tradeoff between decoding complexity and latency over several benchmark decoding schemes.
Development of wet preservation learning media based on mobile application integrated augmented reality on invertebrate material

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Herunata Herunata, Elok Lailatus Sarifah, Erti Hamimi; Development of wet preservation learning media based on mobile application integrated augmented reality on invertebrate material. AIP Conf. Proc. 10 January 2023; 2569 (1): 070013. https://doi.org/10.1063/5.0112180 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Smart Wheelchair-An Effective Transport for Handicapped and Aged Citizens

A wheelchair is a chair fitted with wheels. A survey says that around 132 million people use wheelchair around the world. But majority of them are dependent on others for their movements, especially people with some disorders. This dependent nature had hindered them from succeeding. To overcome this problem, they can use smart wheelchair, which is auto movable based on head tilt movements. It collects information from the patient through in built sensors and enhances the seating position. It is also designed with obstacle and fall detection system which reduces the chance of collision during their journey. This makes a physically challenged or dependent person as physically independent person. This wheelchair can also be used by aged people who lack motor skills. In this paper, we can review the art of smart wheelchair and the features of it.
A network approach to value creation from intangible assets

The research concerns the meta-network (multimodal) approach to an IT sector organisation where employees, knowledge, resources (intangible assets), tasks, and value creation play a key role and are dependent on each other. At the centre of considerations is the look at value creation from intangible assets understood, inter alia, as legally protected software. A meta-network approach was used to identify and measure influential network nodes using out-degree and in-degree centralities and visualisation of two-mode networks. Each network is two-mode, which illustrates the multimodal approach to human behaviour in an organisation understood as interaction with resources, tasks performed, or value creation. Influential nodes were identified and the network was correlated using the quadratic assignment procedure (QAP). Value creation in the network perspective is a consequence of interactions and interdependencies occurring between the knowledge network, the resource network, and the task network. The concept of this research is a valuable and innovative approach for managers who, in the network approach to the analysis of value creation and intangible assets, will see a comprehensive vein to employee behaviour in organisational conditions based on relationships and interactions.
An Analysis of the Satisfaction of NCS-Based Development Curriculum Reflecting the Demand of Employees in the Domestic Textile and Fashion Industry

Background/Objectives: Using past research and a survey, this study evaluated employee satisfaction to understand the practical application and appropriateness of the National Competency Standards (NCS) curriculum and propose an effective education plan to improve the curriculum content.&#x0D; Methods: A curriculum satisfaction survey was conducted From February to October 2022 on 338 workers, after which the data were evaluated using frequency analyses and descriptive statistics.&#x0D; Results: It was found that most education participants were engaged in the textile and clothing industries and others were associated with fiber analysis or textile-related industries. Workers involved in design, trade, sales, customer counseling, process management, quality control, and R&amp;D had participated in the training for which knowledge of clothing materials and manufacturing processes was needed. Satisfaction with the four curricula was generally high, with the newly added subjects showing a high satisfaction rate of 4.5 points or more. Therefore, identifying textile fashion industry company requirements in NCS curricula could be of practical assistance to both companies and employees.&#x0D; Conclusion/Implications: This study provides information on the education needs of workers and highlights to need to promote technological development links. Linking curricula with industry job systems based on NCS could improve practical job skills, strengthen employee and management expertise, and help better develop the domestic fashion industry.