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

作者： Maulana, Fairuz Iqbal Adi, Puput Dani Prasetyo Lestari, Dian Ariyanto, Sandy Vikki Purnomo, Agung Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia National Research and Innovation Agency Telecommunication Research Center Jakarta11480 Indonesia Universitas Airlangga Faculty of Medicine Surabaya60132 Indonesia University of Madura Informatics Engineering Study Program Pamekasan Indonesia Bina Nusantara University BINUS Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia

ISBN: (纸本)9798350313956

Using the Scopus database, this study aims to investigate the use of artificial intelligence for cancer detection in the last ten years from 2013 to 2022. The researchers used bibliometric analysis combined with VosViewer and Rstudio software quantification method for literature analysis. The results of the flushed articles show data such as publication year, journal, country, keywords, and authors, to the highest number of citations. Common keywords used by researchers are artificial intelligence, medical, and human. Researchers limited the findings through keywords such as in the last ten years, documents are journals, and English only so that 1868 articles were obtained. The results found that Harvard Medical School affiliation had the highest number of articles, with 101 articles, by subject area, Medicine had a proportion of 41.9% (n=1320). This bibliometric study will be useful for other researchers to examine the development of research on Artificial Intelligence for Cancer Detection in the last ten years. © 2023 IEEE.

关键词： Diseases

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Improving the Transferability of Clinical Note Section Classification Models with BERT and Large Language Model Ensembles 5

Improving the Transferability of Clinical Note Section Class...

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5th Workshop on Clinical Natural Language Processing, ClinicalNLP 2023. held at ACL 2023

作者： Zhou, Weipeng Afshar, Majid Gao, Yanjun Dligach, Dmitriy Miller, Timothy A. Department of Biomedical Informatics and Medical Education School of Medicine University of Washington United States Department of Medicine School of Medicine and Public Health University of Wisconsin United States Department of Computer Science Loyola University ChicagoIL United States Computational Health Informatics Program Boston Children's Hospital Harvard Medical School United States

ISBN: (纸本)9781959429883

Text in electronic health records is organized into sections, and classifying those sections into section categories is useful for downstream tasks. In this work, we attempt to improve the transferability of section classification models by combining the dataset-specific knowledge in supervised learning models with the world knowledge inside large language models (LLMs). Surprisingly, we find that zero-shot LLMs out-perform supervised BERT-based models applied to out-of-domain data. We also find that their strengths are synergistic, so that a simple ensemble technique leads to additional performance gains. © 2023 Association for Computational Linguistics.

关键词： Large dataset

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Classification of Rupiah Banknote for the Visually Impaired using Convolutional Neural Network with Custom Architecture and RMSProp Optimizer

Classification of Rupiah Banknote for the Visually Impaired ...

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International Conference on ICT For Smart Society (ICISS)

作者： Dede Kurniadi Murni Lestari Rahmi Asri Mulyani Muhammad Rikza Nashrulloh Yosep Septiana Indri Tri Julianto Department of Computer Science Institut Teknologi Garut Garut Indonesia Informatics Study Program Institut Teknologi Garut Garut Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Visual impairment is a condition that affects the sense of sight. In Indonesia, around 1.5% of the 4 million people are blind. In their daily activities, the visually impaired rely on their sense of touch to recognize the value of money by feeling the double lines on the right and left sides of the rupiah banknote. However, if the money is in bad condition, they are unable to recognize it, so they have to involve the help of others with the risk of fraud. Research is underway to develop a paper currency detection tool to assist the visually impaired in everyday transactions and reduce the risk of fraud. The research in this paper focuses on creating a classification model for detecting Indonesian rupiah banknotes. The method approach used in this research involves using a custom architecture Convolutional Neural Network model that combines elements from the AlexNet and VGGNet architectures, along with the RMSProp Optimizer. Before training the model, the private dataset that has been collected is augmented to increase the dataset and then split data. Next, the model is trained and tested using a private dataset of 800 images of rupiah banknotes for 2016, 2020, and 2022 emissions years, divided into eight classes of banknotes with denominations of IDR 1000, IDR 2000, IDR 5000, IDR 10,000, IDR 20,000, IDR 50,000, IDR 75,000, and IDR 100,000. Finally, the model's performance is evaluated using loss function, accuracy, precision, recall, and AUC-ROC evaluation metrics. The model evaluation results show a value loss function of 1.28, an accuracy of 95.31 %, a precision of 95.24%, a recall of 93.75%, and an AUC-ROC value of 0.99. This evaluation result indicates that the model is highly effective for use in the rupiah currency nominal detection system for the visually impaired.

关键词： Training Measurement Accuracy Visual impairment Data models Fraud Classification algorithms Convolutional neural networks Currencies Optimization

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer 24

Predicting Microsatellite Instability from Histology Images ...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Chen, Yun-Chi Chao, Tung Phandita, Wimaya Nitya Sun, Tzu-Yu Wang, Huai-Zhi Hsieh, Yi-Hsien Hsu, Le-Yin Lin, Che Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan National Taiwan University of Science and Technology Department of Computer Science and Information Engineering Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan NTU Data Science Degree Program Taipei Taiwan NTU Department of Electrical Engineering Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan NTU Smart Medicine and Health Informatics Program Taipei Taiwan

ISBN: (纸本)9798331518622

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients. © 2024 IEEE.

关键词： Diseases

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Testing Approach for IoT System (Case Study: Air Quality Monitoring System)

Testing Approach for IoT System (Case Study: Air Quality Mon...

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International Conference on Information Management and Technology (ICIMTech)

作者： Theresia Herlina Rochadiani Haryono Soeparno Ford Lumban Gaol Yulyani Arifin Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Informatics Department Pradita University Tangerang Indonesia

The Internet of Things (IoT) was evaluated as part of this study. The IoT system in this study is the air quality monitoring system. The system consists of three layers, three end devices with air quality monitoring sensors as the perception layer, the LoRaWAN network as the network layer, and the monitoring website as the presentation layer. Before being made available to the public, IoT hardware and software must undergo testing. For IoT systems, there are no established standards or quality criteria. This study's goal is to evaluate the methodology for an air quality monitoring system. Website speed is measured using Pingdom tools. The website received a performance grade of B. Pingdom offers advice for enhancing website performance. Sucuri is utilized as a security test tool to evaluate the website's security, and consequently, no malware was discovered. The latency and data dependability are monitored to assess the system's performance fully. The outcome demonstrates that all system components consistently and accurately receive data with a latency of less than 1 s.

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GPT Attack for Adversarial Privacy Policies

GPT Attack for Adversarial Privacy Policies

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International Conference on Big Data Computing and Communications (BIGCOM)

作者： Isaac Joy Jun Wu Jingrui He Informatics Program UIUC Champaign USA Department of Computer Science UIUC Champaign USA School of Information Sciences UIUC Champaign USA

ISBN: (数字)9798331509538

ISBN: (纸本)9798331509545

While Machine Learning (ML) is widely used in many industries, one sector that is just beginning to leverage this technology is the legal field. Efforts like the construction of the OPP-115 Privacy Policy corpus make it possible to train Natural Language Processing (NLP) and ML models for legal use, and tools such as Polisis, Claudette, etc., which utilize these models can now provide users with descriptive annotations of various types of contracts, including Privacy Policies. However, these tools remain vulnerable to adversarial attacks, specifically adversarial legal documents, which differ from typical text based adversarial attacks due to the necessity of preserving the continuity of a legal document. Therefore, focusing on the OPP-115 corpus and Polisis specifically, we propose a framework that consists of a Doc2Vec model and a variety of shallow and deep learning methods for classification, followed by adversarial attacks on Privacy Policy segments which utilize GPT 3.5, a state-of-the-art LLM, in order to create an adversarial Privacy Policy. We evaluate the effectiveness of our method by comparing attack results to those produced by current methods, including Bert Attack and Text Fooler. Our procedure reveals not just that an attack using GPT 3.5 is an effective means of producing an adversarial policy, but that it is surprisingly easy to prompt a widely used state-of-the-art LLM to perform such a task.

关键词： Deep learning Industries Privacy Law Annotations Focusing Benchmark testing Big Data Natural language processing Contracts

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Critical Factors of Immersive Learning in Higher Education: A Systematic Literature Review 10

Critical Factors of Immersive Learning in Higher Education: ...

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10th International Conference on ICT for Smart Society, ICISS 2023

作者： Sunardi Meyliana Ramadhan, Arief Rosmansyah, Yusep Bina Nusantara University BINUS Graduate Program Doctor of Computer Science Computer Science Department Jakarta Indonesia School of Information System Bina Nusantara University Information System Departement Jakarta Indonesia School of Computing Telkom University Bandung Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (纸本)9798350339543

Immersive learning has gained significant attention with the rising trend of spatial computing, particularly in the after-pandemic era. Numerous research has explored the potential of immersive learning in higher education, primarily on the educational sector. However, prior research has frequently focused too narrowly on the effects of technology and neglected to address the crucial element influencing successful immersive learning in higher education. This study seeks to pinpoint the crucial element contributing to the development of immersive learning experiences. The methodology uses a systematic literature review (SLR) from 2018 up to 2023 to investigate the critical factors of immersive Learning in Higher Education. From the 728 papers initially retrieved, 274 were considered potential candidates, and ultimately, 86 articles were selected based on their relevance to the research question. The results reveal that the critical factors include learning design, technology, immersion, engagement, interactivity, and usability. Academic interests will benefit from this SLR's consequences as institutions create models for designing suitable immersive learning, especially within the context of higher education. © 2023 IEEE.

关键词： E-learning

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YNote: A Novel Music Notation for Fine-Tuning LLMs in Music Generation

arXiv

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arXiv 2025年

作者： Lu, Shao-Chien Yeh, Chen-Chen Cho, Hui-Lin Hsu, Chun-Chieh Hsu, Tsai-Ling Wu, Cheng-Han Shih, Timothy K. Lin, Yu-Cheng Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan International Bachelor Program in Informatics Yuan Ze University Taoyuan Taiwan Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The field of music generation using Large Language Models (LLMs) is evolving rapidly, yet existing music notation systems, such as MIDI, ABC Notation, and MusicXML, remain too complex for effective fine-tuning of LLMs. These formats are difficult for both machines and humans to interpret due to their variability and intricate structure. To address these challenges, we introduce YNote, a simplified music notation system that uses only four characters to represent a note and its pitch. YNote’s fixed format ensures consistency, making it easy to read and more suitable for fine-tuning LLMs. In our experiments, we fine-tuned GPT-2 (124M) on a YNote-encoded dataset and achieved BLEU and ROUGE scores of 0.883 and 0.766, respectively. With just two notes as prompts, the model was able to generate coherent and stylistically relevant music. We believe YNote offers a practical alternative to existing music notations for machine learning applications and has the potential to significantly enhance the quality of music generation using LLMs. Copyright © 2025, The Authors. All rights reserved.

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Evaluation of an agent-based reinforcement learning framework for model-based testing

Evaluation of an agent-based reinforcement learning framewor...

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Inteligencia Ambiental, Ingeniería de Software y Salud Electrónica y Móvil (AmITIC), V Congreso Internacional en

作者： Jose Ramírez-Méndez Christian Quesada-López Graduate Program in Computing and Informatics University of Costa Rica San Pedro Costa Rica Department of Computer Science and Informatics University of Costa Rica San Pedro Costa Rica

ISBN: (数字)9798350364538

ISBN: (纸本)9798350364545

This study evaluates an agent-based reinforcement learning framework for model-based testing (MBT). The framework’s performance was assessed on three key metrics: effectiveness and efficiency in achieving model coverage objectives, the quantity and uniqueness of generated test cases, and code coverage. The results show improved metrics for the framework compared to traditional testers in model coverage evaluation and test case metrics. Specifically, the framework achieved higher effectiveness and efficiency, generating a higher average number of test cases with a substantial proportion of unique cases, indicating more diverse and thorough testing. Additionally, the framework, along with random and greedy testers, achieved over 70% coverage across branch, method, and line code metrics. The framework showed slightly higher values in method and line coverage compared to other testers. The evaluation highlights the use of agent-based reinforcement learning to support model-based testing in planning test case generation, exploring models using multiple strategies, and learning guided by testing metrics. Future work will focus on further refining the framework learning model, including error coverage evaluation, and testing its applicability across different software systems.

关键词： Measurement Software testing Codes Calculators Refining Reinforcement learning Software systems Planning Testing

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ConvNeXt Model for Breast Cancer Image Classification

ConvNeXt Model for Breast Cancer Image Classification

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Devin Setiawan Andrea Stevens Karnyoto Indo Intan Bens Pardamean Department of Electrical Engineering and Computer Science University of Kansas Lawrence United States Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Informatics Engineering Universitas Dipa Makassar Makassar Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331539603

ISBN: (纸本)9798331539610

Breast cancer is an occurrence of cancer that attacks breast tissue and is the most common cancer among women worldwide, affecting one in eight women. In this modern world, breast cancer image classification simplifies the process of analyzing, providing objective and accurate results. By leveraging machine learning algorithms and computer vision techniques, we developed breast cancer detection. The dataset is histopathology dataset from BreakHis and UNHAS Hospital. We chose the ConvNeXt-Tiny model then modified the classifier head as the proposed method. Before the dataset is processed by the model, we augment the images by applying random horizontal and vertical flips, adjustments to brightness, contrast, saturation, and hue using color jitter. The augmentation process simulates real-world variance and enhances the model's ability to generalize to unseen data. Our proposed model gained better performance (accuracy, F1-Score) results compared two other techniques to VGG16 and SVM. According to our experiments, the F1-Score for the ConvNeXt-Tiny model with classifier head modification is higher at 0.9516, than the gain for VGG16 at 0.9292, and the gain for the SVM at 0.83.

关键词： Support vector machines Accuracy Head Computational modeling Transfer learning Breast cancer Magnetic heads Data models Reliability Image classification

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