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25th International Computer Symposium on New Trends in Computer Technologies and Applications, ICS 2022

作者： Weng, Hao-Yu Tsai, Min-Yi Wang, Wei-Jen Huang, Cheng-Ta Department of Computer Science and Information Engineering National Central University Taoyuan City320 Taiwan Department of Information Management Yuan Ze University Taoyuan City320 Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan City320 Taiwan

ISBN: (纸本)9789811995811

Information technology and the Internet have progressed rapidly in people’s lives, the privacy of information has become an important issue due to the accessibility of data. Therefore, to enhance information security and privacy protection, this paper proposes a new high-capacity method for data hiding. The proposed method is based on 3D magic cube with double layers embedding. In the field of data hiding, the reference matrix-based methods attract many researchers because of its high-capacity. Recently, as the needs of the high payloads of the stego-image, the multi-layers-based technique is applied to provide larger embedding capacity. However, the image after data embedding will cause serious distortions, especially on multi-layers embedding, hence, keeping the image at a good visual quality and achieving high-payloads are the main challenges for data hiding. In this scheme, we take advantage of 3D magic cube and 3D reference space with double layers embedding for our data hiding technique to improve the embedding payloads. The experimental results show that our method not only provides good embedding capacity (4.00 bpp) but also keeps the image at acceptable quality (In terms of PSNR > 30). © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Embeddings

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Early-Life Risks of Central Precocious Puberty

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Endocrine Practice 2025年第6期31卷 758-765页

作者： Yin, Wei-Jou Hsu, Jhih-Wei Chen, Chun-Chang Su, Emily Chia-Yu Wang, San-Yuan Chen, Yan-Jen Chen, Yang-Ching College of Medicine Taipei Medical University Taipei Taiwan Department of Family Medicine School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Master Program in Clinical Genomics and Proteomics Department of Pharmaceutical Sciences Taipei Medical University Taipei Taiwan Department of Family Medicine Wan Fang Hospital Taipei Medical University Taipei Taiwan Nutrition Research Center Taipei Medical University Hospital Taipei Taiwan

Objectives: To investigate the factors in early life that may contribute to central precocious puberty (CPP). Methods: The study utilized data from the Taiwan Puberty Longitudinal Study, including 2241 children under pubertal assessment and a questionnaire of risk factors. We analyzed associations using the Fitting Generalized Linear Models in R (R Core Team, 2023), with R studio (Posit, 2023) version 4.3.1. Results: Among the 2241 children examined, 745 had CPP. Overall, higher gestational weight gain (GWG) increased the risk of CPP (odds ratio [OR]: 1.03, 95% CI: 1.01-1.05);while higher GWG served as a protective factor in females (OR: 0.97, 95% CI: 0.95-0.99). Maternal gestational diabetes mellitus (GDM) also increased the CPP risk, particularly in males (OR: 2.66, 95% CI: 1.00-7.25). Longer exclusive breastfeeding was linked to lower CPP risk overall (OR: 0.96, 95% CI: 0.93-1.00) but was not significant when analyzed by gender. Conclusions: Higher maternal GWG was associated with an increased risk of CPP overall, while GWG became negatively associated with CPP, only in females. Maternal GDM was linked to a higher risk of CPP, particularly in males. The duration of exclusive breastfeeding was inversely correlated with CPP risk, but this effect was not significant when analyzed by gender. © 2025 AACE

关键词： central precocious puberty early-life risk factors pediatrics

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Exploring Tourist Feedback on Riau Attractions Through Indonesian Language YouTube Opinion Using Naïve Bayes Algorithm

Exploring Tourist Feedback on Riau Attractions Through Indon...

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2023 IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology, IAICT 2023

作者： Kurniawan, Rahmad Iskandar, Iwan Lestari, Fitra Harpizon, Habibi Al Rasyid Husti, Ilyas Faculty of Mathematics and Natural Sciences Universitas Riau Department of Computer Science Pekanbaru Indonesia Universitas Islam Negeri Sultan Syarif Kasim Riau Department of Informatics Engineering Pekanbaru Indonesia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi Malaysia Graduate Program Universitas Islam Negeri Sultan Syarif Kasim Riau Pekanbaru Indonesia

ISBN: (纸本)9798350313635

YouTube is a widely-used platform in Indonesia, with 93.8% of its users. As such, it presents a valuable opportunity for marketing tourist destinations, particularly in Riau province, which aims to become Indonesia's top Halal travel destination. Tourism is a vital contributor to the economic growth of regions, and each province in Indonesia competes to promote its tourist attractions to attract more visitors every year. However, the large volume of data can challenge the manual analysis of feedback from YouTube's features, such as likes, dislikes, and comments. A literature review suggests that the Naive Bayes algorithm, which uses machine learning, is helpful for sentiment analysis. Therefore, this study aims to analyze public sentiment toward tourist destinations in Riau province by analyzing YouTube comments using the Naïve Bayes algorithm. The study used 1680 opinions collected from 10 YouTube videos showcasing tourist destinations in Riau. The Naive Bayes algorithm classified 60% of the comments as positive, 32% as neutral, and 8% as negative. The experimental results indicated an accuracy and precision of 73%, a recall of 94%, and an F-1 Score of 82%. The study used the word frequency technique to reveal that Riau could become a popular halal tourist destination based on several frequently occurring words in the comments. © 2023 IEEE.

关键词： Sentiment analysis

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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International Conference on Computer, Control, informatics and Its Applications (IC3INA)

作者： Muchamad Galih Anggara Ahmad Hindasyah Julfa Muhammad Amda A. A. Waskita Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10 −3 , StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10 −5 , StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists.

关键词： Fungi Training Digital control Adaptation models Accuracy Microscopy Transformers informatics Tuning Image classification

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Longitudinal Mammogram Exam-based Breast Cancer Diagnosis Models: Vulnerability to Adversarial Attacks

arXiv

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arXiv 2024年

作者： Zhou, Zhengbo Hao, Degan Arefan, Dooman Zuley, Margarita Sumkin, Jules Wu, Shandong Intelligent Systems Program University of Pittsburgh PittsburghPA United States Department of Radiology University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics Department of Bioengineering University of Pittsburgh PittsburghPA United States

In breast cancer detection and diagnosis, the longitudinal analysis of mammogram images is crucial. Contemporary models excel in detecting temporal imaging feature changes, thus enhancing the learning process over sequential imaging exams. Yet, the resilience of these longitudinal models against adversarial attacks remains underexplored. In this study, we proposed a novel attack method that capitalizes on the feature-level relationship between two sequential mammogram exams of a longitudinal model, guided by both cross-entropy loss and distance metric learning, to achieve significant attack efficacy, as implemented using attack transferring in a black-box attacking manner. We performed experiments on a cohort of 590 breast cancer patients (each has two sequential mammogram exams) in a case-control setting. Results showed that our proposed method surpassed several state-of-the-art adversarial attacks in fooling the diagnosis models to give opposite outputs. Our method remained effective even if the model was trained with the common defending method of adversarial training. © 2024, CC BY.

关键词： Lung cancer

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Analysis on vocational high school teacher competency gaps: Implication for VHS teacher training needs 2

Analysis on vocational high school teacher competency gaps: ...

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2nd International Conference on Vocational Education and Technology, IConVET 2019

作者： Wahyuni, D.S. Agustini, K. Sindu, I.G.P. Sugihartini, N. Informatics Engineering Education Study Program Universitas Pendidikan Ganesha81116 Indonesia

This study proposed a novel approach that integrates qualitative and quantitative methods to identify the competency gaps of VHS teachers in Vocational High Schools (VHSs). It comprised four research steps, namely (1) determining the ideal teacher competencies through Fuzzy Delphi technique, (2) arranging the competency priorities by using the analytic hierarchy process, (3) conducting VHS teacher performance evaluation by using a 360-degree rater, and (4) the competency gaps analysis using the IPA matrix. The sample members involved in this study were 7 experts from the industry, academia, and government, and 50 VHS teachers from several regencies in Bali Province, Indonesia. This study suggested that the gaps analysis between the existing VHS teacher competencies and the ideal ones include (1) the pedagogical competency - the low level of factual knowledge mastery in vocational knowledge and skills;(2) mastery in the application of contents;(3) mastery of content knowledge related to vocational teaching subjects;(4) networking and collaboration between the relevant industry and government;(5) continuous professional development;and (6) entrepreneurship. These findings imply that these needs of competencies should be the focus of teacher training programs for VHS teachers. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Personnel training

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Implementation of Face Patterns and Smile Recognition for Intelligent Class Attendance Systems 6

Implementation of Face Patterns and Smile Recognition for In...

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6th International Conference on Computer and informatics Engineering, IC2IE 2023

作者： Miftakhurrokhmat Suparyanto, Teddy Elwirehardja, Gregorius Natanael Karimah, Shofiyati Nur Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Amikom Yogyakarta Faculty of Computer Science Department of Informatics Yogyakarta Indonesia Bina Nusantara University Binus Graduate Program - Master of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

Students 'attendance in class is one important success parameter in face-to-face learning processes. Conventional attendance systems, such as paper-based attendance sheets or identity card systems, require a long time in the manual recapitulation process. Without additional verifications, even computer vision-based methods are prone to fraudulent practices by the students instead of gaining their excitement and attention in a class. To stimulate students' attention in a class, this work designs an intelligent class attendance system, in which facial pattern and smile recognition are implemented by using the latter as an additional task-based verification to reduce the risks of fake attendance. For the face recognition module, this pilot study used FaceNet as a feature extractor combined with SVM for classification, whereas the Haar cascade algorithm is used for recognizing smiles. This face recognition pipeline was implemented as a service installed on minicomputers or Internet of Things (IoT) devices in each classroom and connected to an IP camera. Every recorded attendance will be sent as a notification to a mobile application for students that requires their active participation to confirm it with a smiling self-photo. The proposed pipeline obtained 92.86% accuracy on the test data, and 66.67% accuracy when evaluated in a real-life simulation setting through the implemented system. The lower accuracy in the simulation indicated that further improvements are indispensable, especially since the model obtained 28.57% False Negative Rate. Future studies will need to acquire more data and experiment with more efficient methods of attendance verification. © 2023 IEEE.

关键词： class attendance convolutional neural network deep learning face recognition smile

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Online Retail Pattern Quality Improvement: From Frequent Sequential Pattern to High-Utility Sequential Pattern

Online Retail Pattern Quality Improvement: From Frequent Seq...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Ridowati Gunawan Informatics Study Program Sanata Dharma University Yogyakarta Indonesia

ISBN: (纸本)9781665401524

There has been a change in people's shopping behavior, especially during the Covid-19 pandemic, from what traditionally requires direct face-to-face meetings between sellers and buyers, to virtual face-to-face through various shopping media. All activities carried out by customers, click streams performed, items purchased, the number of items including the price will be recorded in a log. Activity records in the log are very useful to be able to find out the pattern of activity sequences from customers, especially the order of items purchased by customers. However, the management certainly needs more knowledge, not just the order of goods that are often purchased by customers. Does the order of items purchased also provide maximum profit? There have been many methods to get frequent sequential patterns from customer activities, but getting a pattern that chooses more quality by adding utility value needs to be considered. In this research, the method used to obtain frequent sequential patterns is using PrefixSpan (Prefix-projected Sequential PAtterN) and the method used to obtain a high-utility sequential pattern is the USpan (Utility Sequential PAtterN) method. USpan is applied to the BMS (Blue Martini DataSet) dataset, which is the dataset used in KDD (Knowledge Discovery in Databases) CUP 2000 which consists of clickstream data from an e-commerce. The experimental results show that the frequent sequential pattern will always appear in the high-utility sequential pattern but not vice versa. It is certain that a high-utility pattern must be sequential, but a sequential pattern is not necessarily a high-utility sequential. From the results of the high-utility sequential pattern, it can be used as input to provide recommendations to customers to carry out the shopping process on items that can provide greater profits. The conclusion of the research conducted is that the high-utility sequential pattern mining can produce a higher quality pattern than just getting a fr

关键词： Seminars COVID-19 Pandemics Databases Media Knowledge discovery Electronic commerce

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Comparing Classification Algorithms to Analyze Twitter Sentiment on Public Opinion on Fuel Oil 24

Comparing Classification Algorithms to Analyze Twitter Senti...

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24th International Seminar on Intelligent Technology and Its Applications, ISITIA 2023

作者： Widarma, Gita Novita, Rice Mustakim Rozanda, Nesdi Evrilyan Universitas Islam Negeri Sultan Syarif Kasim Riau Graduate Program Information Systems Department Pekanbaru Indonesia Postgraduate Programs Universitas Negeri Padang Information Systems Pekanbaru Indonesia Postgraduate Programs Institut Pertanian Bogor Master of Informatics Engineering Pekanbaru Indonesia Postgraduate Programs Universiti Teknologi Malaysia Master of Information Systems Management Pekanbaru Indonesia

ISBN: (纸本)9798350313956

Twitter is currently used as one of the applications that is often used to convey opinions or unrest by the public such as the current issue that is currently rife, namely regarding the increase in fuel oil prices (BBM) in Indonesia. With so much data scattered in a tweet that only consists of a few fragments of words, there must be valuable information or meaning that Twitter users want to convey. Text mining is a very appropriate method to do the analysis because in text mining there is a method that focuses on an opinion which will be concluded into positive and negative opinions. There are several algorithms that are most often used in data mining, namely K-Nearest Neighbor, Decision Tree, and Naive Bayes. The three algorithms used in this study were also compared with the use of feature selection Particle Swarm Optimization (PSO) which is well known for improving the performance of algorithms. There are 2165 negative opinion data and 2835 positive sentiment data. The data will be tried based on distinctive sums of data namely 1000 data, 3000 data, and 5000 data. The classic K-Nearest Neighbor algorithm obtained an accuracy result of 75.60% and made it the best when tested with 1000 data. The use of Particle Swarm Optimization (PSO) is proven to improve accuracy as when tested using 3000 data with an accuracy value of 78.21% on the K-Nearest Neighbor algorithm. In the mean time, in testing 5000 Data, the classic K-Nearest Neighbor once more gotten the most elevated exactness with a esteem of 74.02%. © 2023 IEEE.

关键词： Decision trees

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Borneo Smart Forest Information System for Management of Dipterocarp Plants in Kalimantan Rainforest

Borneo Smart Forest Information System for Management of Dip...

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Information Technology International Seminar (ITIS)

作者： Masna Wati Novianti Puspitasari Ummul Hairah Joan Angelina Widians Anindita Septiarini Ade Fiqri Tjikoa Dept. of Informatics Faculty of Engineering Universitas Mulawarman Samarinda Indonesia Dept. of Informatics Universitas Mulawarman Doctoral Program Dept. of Computer Science and Electronics Universitas Gadjah Mada Yogyakarta Indonesia

ISBN: (纸本)9798350398205

Kalimantan’s tropical rainforest is home to indigenous plants of the Dipterocarpaceae family (tribe), which is renowned for having the most endemic species. Dipterocarp is a family of pantropical plants, many of which are used in the wood industry. This study aims to provide information on the potential of Dipterocarp in Kalimantan forests. Education and conservation efforts for Dipterocarp trees, a prominent component of the forest ecosystem, are crucial in light of the significant harm done to Kalimantan’s rainforests. The authors develop the Borneo Smart Forest information system. This system digitally organizes data on the diversity of Dipterocarp in the Kalimantan rainforest based on Web and QR-Code technology. Borneo Smart Forest (BSF) system for monitoring plant inventory and plant markers can be read by smartphones using QR codes. It gives visitors a comprehensive overview of the plants. A QR code is a printed, two-dimensional barcode with limited space for data storage. This technology allows the plant to show earlier administrator-made additions to the inventory information system. The QR code will direct the user to the BSF website, which has data about the plants. The development of this system is one of the efforts to support the achievement of Dipterocarp conservation.

关键词： Seminars Memory Forestry QR codes Vegetation Wood industry Information technology

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