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

作者： Rafi Mahmud Zain Sajarwo Anggai Tukiyat Ahmad Musyafa A. A. Waskita Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (数字)9798331542313

ISBN: (纸本)9798331542320

This paper analyzes presidential speeches from the United States, delivered by the current president, Joe Biden, from his inauguration until April 3, 2024, using BERT-based topic modeling techniques. Since the role and actions of the United States affect global affairs, inferring latent topics from these speeches are important for making informed decisions and also useful to researchers. We provide experiments combining different embedding methods and clustering algorithms for dimensionality reduction. Our experiment results demonstrated that using DistilBERT embeddings followed by HDBSCAN clustering and UMAP dimensionality reduction consistently produced meaningful and interpretable topics, and this paper introduces the UMASS and NPMI metrics for evaluating topic coherence, with the best values of these metrics among the other schemes investigated. Some of the dominant themes conveyed in the American president’s state of the union speech that were successfully revealed in this study include international cooperation, alongside significant attention to environmental, community, health, and economic issues.

关键词： Measurement Dimensionality reduction Economics Digital control Visualization Government Coherence Medical services Security informatics

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Lung Nodule Analysis in CT Images: Deep Learning for Segmentation and Measurement 24

Lung Nodule Analysis in CT Images: Deep Learning for Segment...

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8th International Conference on Medical and Health informatics, ICMHI 2024

作者： Li, Ai-Hsien Adams Aruperes, Cristian Daniel Lai, Yen-Jun Chien, Ting-Ying Chiu, Yen-Ling Chan, Chien-Lung Division of Cardiology Far Eastern Memorial Hospital New Taipei TaiwanGraduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan TaiwanInnovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan TaiwanGraduate Institute of Medicine Yuan Ze University Taoyuan Taiwan TaiwanDepartment of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Department of Information Management Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400716874

In the realm of research, the global health challenge posed by lung cancer remains pronounced, contributing substantially to annual cancer-related fatalities. The critical imperative lies in the early identification of pulmonary nodules, frequently indicative of impending lung cancer, to enhance patient outcomes and diminish mortality rates. Computed Tomography (CT) imaging stands out as a pivotal diagnostic instrument for the timely detection of these nodules. The swift proliferation of medical imaging data has underscored the pressing necessity for precise and efficient methodologies dedicated to nodule segmentation and measurement. These approaches are crucial in assisting radiologists in their diagnostic and clinical decision-making endeavors. In this study, we introduced a thorough method for analyzing lung nodules, leveraging dataset from Far Eastern Memorial Hospital (FEMH) comprising original CT images and manually annotated ground truth masks obtained with the assistance of radiologists at FEMH. This dataset is utilized for the segmentation of nodules. We employed advanced deep learning models, specifically the U-Net architecture, identified as the optimal model through our training process. We made substantial progress in nodule segmentation, attaining an Intersection over Union (IoU) score of 0.824 and a Dice Coefficient of 0.903 for the FEMH dataset. Furthermore, our performance improved when utilizing the merged dataset comprising FEMH and Luna16, yielding an IoU score of 0.862 and a Dice Coefficient of 0.926. Luna16 has been extensively utilized in numerous studies related to nodule detection and segmentation. In the next phase of the study, the best-performing model from our segmentation phase was utilized to predict nodule masks on the merged dataset. Subsequently, we measured the size of each predicted nodule by comparing it with the size ground truth mask in millimeters. In detail, this study achieved the Pearson Correlation Coefficient (PCC) at 0.

关键词： Computerized tomography

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Experimentation of Integrating Roadmapping and Sci-Fi Prototyping Methods

Experimentation of Integrating Roadmapping and Sci-Fi Protot...

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2024 Portland International Conference on Management of Engineering and Technology, PICMET 2024

作者： Nishinaka, Miwa Shirahada, Kunio Kishita, Yusuke Miyamoto, Dohjin Osawa, Hirotaka Kiyokawa, Sachiko Takeda, Hideaki The Graduate School of Management Kagawa University Kagawa Takamatsu Japan Ishikawa Nomi Japan The University of Tokyo Department of Precision Engineering Tokyo Japan Communication in Science and Technology Education and Research Program Hokkaido University Sapporo Japan Keio University Faculty of Science and Technology Kanagawa Yokohama Japan Graduate School of Education The University of Tokyo Tokyo Japan National Institute of Informatics Tokyo Japan

ISBN: (纸本)9781890843458

In 2023, we proposed a hypothetical knowledge management framework that incorporates science fiction prototyping (SFP) into the traditional roadmapping (RM) method to make RM more innovative and create a future beyond what we can currently imagine. Our main objective is to create a new organizational knowledge co-creation method for generating innovative ideas by incorporating humanistic factors utilizing SFP into RM. In the current article, we present the exploratory study we conducted to examine the hypothetical framework. Experimental workshops were held in November 2023 to compare the integrated method, in which SFP is applied to RM, with the original RM method. We followed a crossover design in the workshops and analyzed the results qualitatively and quantitatively following the perspectives proposed in the hypothetical framework. The qualitative analysis revealed successful integration regarding diverse and broader perspectives. The quantitative analysis utilizing Wilcoxon signed rank tests showed that the scores of the integrated method were significantly higher than those of RM for variables evaluating the effectiveness of the integration, while not for variables related to participants' feelings and satisfaction with the workshops, and sustainability. These findings indicate that the integration was successful in terms of its effectiveness as a method, while participants' subjective feelings toward the workshops were not different in both methods. As for sustainability, which requires a long-term perspective, no apparent differences were observed in these short-term workshops. © 2024 PICMET.

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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Privacy-Preserving Training of Monocular Depth Estimators via Self-Supervised Federated Learning

Privacy-Preserving Training of Monocular Depth Estimators vi...

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IEEE Conference on Vehicular Technology (VTC)

作者： Elton F. de S. Soares Carlos Alberto V. Campos Brazil Research Lab IBM Research Rio de Janeiro RJ Brazil Graduate Program in Informatics Federal University of the State of Rio de Janeiro Rio de Janeiro RJ Brazil

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Monocular depth estimation is gaining attention in computer vision for autonomous driving due to its cost-effectiveness and versatility. Recent works have used self-supervised learning to tackle this problem. However, they overlook crucial challenges for real-world deployment, such as data privacy, network consumption, computational cost, and unstable connectivity. Recent studies have shown the potential benefits of federated learning in addressing these challenges. Thus, we introduce FedSCDepth, a novel method that combines federated learning and deep self-supervision to efficiently learn monocular depth estimators with performance comparable to the state-of-the-art. Our evaluation using the KITTI dataset’s Eigen’s Split reveals that FedSCDepth achieves high efficacy, with a test loss below 0.13. It also demonstrates impressive efficiency, requiring only an average of 1.5k training steps and up to 0.415 GB of weight data transfer per autonomous vehicle in each round.

关键词： Training Data privacy Vehicular and wireless technologies Costs Federated learning Transportation Prototypes Self-supervised learning Autonomous vehicles Testing

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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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Towards Continual Federated Learning of Monocular Depth for Autonomous Vehicles

Towards Continual Federated Learning of Monocular Depth for ...

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IEEE Conference on Vehicular Technology (VTC)

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Recent investigations in computer vision for autonomous vehicles have focused on depth estimation from images, owing to its cost-efficiency and adaptability. Monocular depth estimation, using a single camera, is notable for its adaptability compared to binocular techniques that require two fixed cameras. Sophisticated methodologies employ self-supervised deep neural networks, while latest research proposes the use of federated learning to tackle crucial challenges for autonomous driving, such as data privacy, network usage, computation distribution, and connectivity robustness. Nevertheless, continual learning presents an additional challenge for both centralized and federated training, as updating models with novel datasets may induce the forgetting of previously acquired samples, thereby diminishing the model’s versatility. Meanwhile, recent research indicates that experience replay strategies can be used to alleviate forgetting in autonomous driving use cases. We introduce ERFedSCDepth, an innovative approach amalgamating experience replay, federated learning, and deep self-supervision to allow the training of monocular depth estimators with high effectiveness and efficiency. Assessment using KITTI and DDAD datasets shows the efficacy of our approach, achieving enhanced continual learning performance over state-of-the-art baseline at standard depth metrics.

关键词： Continuing education Training Data privacy Vehicular and wireless technologies Federated learning Computational modeling Transportation Cameras Autonomous vehicles Testing

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Determinant Factors Affecting the Information System Success of Community-Based Application to Fight Covid-19 Pandemic

Determinant Factors Affecting the Information System Success...

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2021 International Conference on Computer, Control, informatics and Its Applications - Learning Experience: Raising and Leveraging the Digital Technologies During the COVID-19 Pandemic, IC3INA

作者： Purwanto, Adnan Asih, Dybio Dompu Hot Nurjabar, Ilham Cahyani, Trisiwi Indra Hidayat, Deni Manik, Lindung Computer Science Graduate Program University of Nusa Mandiri Jakarta Indonesia Research Center for Informatics National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9781450385244

National governments around the world have made every effort to fight Covid-19. One of which is by building mobile applications that can be used to trace and monitor the citizens' health during this pandemic. These applications are expensive. It requires a significant amount of time and effort to develop and maintain the information systems. Therefore, there is a continuous struggle to identify the key factors influencing the information systems' adoption, utilization, and success. This study was performed to address the problem by identifying parameters that affect the information system success of "10 Rumah Aman (10 Safe Houses)", a community-based application in fighting the Covid-19 pandemic, using the DeLone and McLean (D&M) model. As a result, system quality positively affected the use and user satisfaction. Furthermore, service quality positively influenced use but not user satisfaction. Meanwhile, information quality had no significant effect either on use or satisfaction. Both use and user satisfaction had a significant effect on net benefits. © 2021 ACM.

关键词： Information systems

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Unitig-centered pan-genome machine learning approach for predicting antibiotic resistance and discovering novel resistance genes in bacterial strains

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Computational and Structural Biotechnology Journal 2024年 23卷 1864-1876页

作者： Do, Duyen Thi Yang, Ming-Ren Vo, Tran Nam Son Le, Nguyen Quoc Khanh Wu, Yu-Wei Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Business Administration College of Management Lunghwa University of Science and Technology Taoyuan City Taiwan Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei Taiwan Clinical Big Data Research Center Taipei Medical University Hospital Taipei Taiwan TMU Research Center for Digestive Medicine Taipei Medical University Taipei Taiwan

In current genomic research, the widely used methods for predicting antimicrobial resistance (AMR) often rely on prior knowledge of known AMR genes or reference genomes. However, these methods have limitations, potentially resulting in imprecise predictions owing to incomplete coverage of AMR mechanisms and genetic variations. To overcome these limitations, we propose a pan-genome-based machine learning approach to advance our understanding of AMR gene repertoires and uncover possible feature sets for precise AMR classification. By building compacted de Brujin graphs (cDBGs) from thousands of genomes and collecting the presence/absence patterns of unique sequences (unitigs) for Pseudomonas aeruginosa, we determined that using machine learning models on unitig-centered pan-genomes showed significant promise for accurately predicting the antibiotic resistance or susceptibility of microbial strains. Applying a feature-selection-based machine learning algorithm led to satisfactory predictive performance for the training dataset (with an area under the receiver operating characteristic curve (AUC) of > 0.929) and an independent validation dataset (AUC, approximately 0.77). Furthermore, the selected unitigs revealed previously unidentified resistance genes, allowing for the expansion of the resistance gene repertoire to those that have not previously been described in the literature on antibiotic resistance. These results demonstrate that our proposed unitig-based pan-genome feature set was effective in constructing machine learning predictors that could accurately identify AMR pathogens. Gene sets extracted using this approach may offer valuable insights into expanding known AMR genes and forming new hypotheses to uncover the underlying mechanisms of bacterial AMR. © 2024 The Authors

关键词： Genes

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Backcasting Perspectives on Services of Future Quantum Internet

Backcasting Perspectives on Services of Future Quantum Inter...

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Quantum Software (QSW), IEEE International Conference on

作者： Shota Nagayama Toshihiko Sasaki Akihito Soeda Mercari R4D Mercari Inc Tokyo Japan Graduate School of Media and Governance Keio University Fujisawa Japan Department of Applied Physics Graduate School of Engineering The University of Tokyo Tokyo Japan Principles of Informatics Research Division National Institute of Informatics Tokyo Japan Informatics Program SOKENDAI (The Graduate University for Advanced Studies) Tokyo Japan Department of Physics Graduate School of Science The University of Tokyo Tokyo Japan

ISBN: (数字)9798350368475

ISBN: (纸本)9798350368482

A quantum internet is an emerging quantum information technology, possibly becoming a pinnacle of wide-area distributed system allowed under the laws of physics. The current expansion of quantum technologies would lead to innumerable types of quantum systems, composed of various kinds of quantum devices connected by a dedicated quantum network. However, the (inter)connectivity offered by a quantum internet should ideally be agnostic of the technologies used by each component system. Furthermore, concurrent progresses of the quantum technologies complicate the task of defining what a quantum internet is. In this paper, we adopt a backcasting approach to elucidate what a quantum internet should be. We begin by discussing what we believe to be the requirements of an ultimate distributed quantum system, from which we explain what role a quantum internet would play in achieving this ultimate system. We then lead to more practical requirements on a quantum internet, considering the known and expected constraints in designing a quantum internet. We also organize and categorize the relationship among known quantum network technologies and services relevant to a quantum internet. We hope this document to lay a common ground for all the key stakeholders of a quantum internet, including researchers, industrial enterprises, and policy makers and regulators.

关键词： Quantum system Protocols Quantum mechanics Transportation Logic gates Internet Quantum networks

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