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7th International Conference on Computational Science and Technology, ICCST 2020

作者： Mohamad, N.S. Sulaiman, J. Saudi, A. Zainal, N.F.A. Mathematics with Economics Program Universiti Malaysia Sabah Kota Kinabalu Sabah Malaysia Faculty of Computing and Informatics Universiti Malaysia Sabah Kota Kinabalu Sabah88400 Malaysia

ISBN: (纸本)9789813340688

We evaluate the first-order approximation solution piecewise by first-order polynomial collocation with Quadrature scheme on second-type Fredholm integral equations. This discretization derived the formulation to solve the first order piecewise approximation equation in which the linear system was built. The SOR method was described as a linear solver in which its formulation was constructed and applied in this study. In order to obtain the approximation solutions, the combination of SOR iterative method with the first-order piecewise polynomial by collocation with quadrature scheme has shown that performance of SOR method is superior than Jacobi method in terms of number of iterations and time of completion. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Fredholm integral equations

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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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Evaluation of Imprecise Subtractors into Test Zone Search for VVC Encoding

Evaluation of Imprecise Subtractors into Test Zone Search fo...

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Symposium on Integrated Circuits and Systems Design (SBCCI)

作者： Rafael Ferreira Luciano Agostini Cláudio M. Diniz Bruno Zatt ViTech Group Graduate Program in Computing Federal University of Pelotas (UFPel) Pelotas Brazil PGMICRO Institute of Informatics Federal University of Rio Grande do Sul (UFRGS) Porto Alegre Brazil

The error-tolerant behavior of some video encoding algorithms has motivated researchers to exploit approximate computing in the seek for energy-efficient coding systems. If approximate adders have been widely evaluated, approximate subtractors did not receive much attention. Subtractors, however, are extensively used during the encoding process and optimizing them represents an opportunity for further improving energy efficiency. This study explores the adoption of approximate subtractors in Versatile Video Coding (VVC) encoders, with a specific focus on the distortion metric used in the Test Zone Search (TZS) algorithm for inter-picture prediction. The detailed analysis reveals the impact of imprecise subtractors on each stage of TZS, presenting unprecedented data about the number of calls and energy consumption per stage. The results show that the use of approximate subtractors leads to changes in the number of calls for each TZS stage, induces a minimal impact in terms of coding efficiency, and enables nearly 6% reduction in energy consumption. Therefore, this work provides substantial contributions to the understanding and to the optimization of video coding processes, energy-constrained scenarios.

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Tsung-Wei Lin Sofia Ormazabal Arriagada Che Lin Graduate Institute of Communication Engineering National Taiwan University (NTU) Taiwan International Graduate Program AIoT NTU Research Center for Information Technology Innovation Academia Sinica Department of Electrical Engineering NTU Center for Advanced Computing and Imaging in Biomedicine NTU Smart Medicine and Health Informatics Program NTU

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples.

关键词： Lungs Genomics Medical services Colorectal cancer Breast Multitasking Oncology Graph neural networks Prognostics and health management Cancer

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Three-Dimensional Wound Reconstruction using Point Descriptors: A Comparative Study

Three-Dimensional Wound Reconstruction using Point Descripto...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： José R. A. Souto Fellipe M. C. Barbosa Bruno M. Carvalho Graduate Program in Systems and Computing Federal University of Rio Grande do Norte Natal Brazil Department of Informatics and Applied Mathematics Federal University of Rio Grande do Norte Natal Brazil

Precise measurements of chronic wound areas are very important for measuring the efficacy of different treatments, since it provides a clear picture of the chosen treatment evolution. The most used techniques still in use for measuring the wound areas involve manual measuring, paper scales and/or acetate tracing, with each health professional possibly measuring the wounds in slightly different ways, specially when dealing with the wound's depth. Thus, it is common to observe inconsistent wound area estimations by different health workers on the same wounds. Moreover, the measuring process requires direct contact with the wound, increasing the contamination and infection risks, besides possibly causing discomfort for the patient. An alternative that has already been proved successful is the use of Structure from Motion (SfM) to recover three-dimensional representations of a wound's surface and estimate that area. In this work, we analyze the effectiveness of several widely used point descriptors (BRIEF, DRINK, FREAK, ORB, SIFT and SURF) in the SfM wound reconstruction task when applied to real images of realistic synthetic wounds made of latex placed on different areas of the body. The results showed that SIFT, SURF and DRINK produced better area estimates. Since the execution time is a factor, we selected DRINK as the standard descriptor for our system, since it is approximately 9.3 and 4.9 times faster than SIFT and SURF, respectively.

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Acceptance Analysis of IWB Systems in Education using UTAUT2 Model: The Moderating Role of Perceived Technological Innovations

Acceptance Analysis of IWB Systems in Education using UTAUT2...

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IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS)

作者： Noelyn M. De Jesus Eltimar T. Castro Cory Antonio Buyan College of Informatics & Computing Sciences Batangas State University Batangas Phlippines Graduate Program Department Technological Institute of the Philippines Manila Philippines Business Technology Ballard Power Systems Burnaby Canada

Interactive Whiteboard (IWB) Systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members’ intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members’ behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members’ usage behavior when using IWB systems in teaching.

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Multiversion Low-Power Hardware Accelerator for the AV1 Interpolation Filters

Multiversion Low-Power Hardware Accelerator for the AV1 Inte...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Daiane Freitas Mateus Grellert Cláudio M. Diniz Guilherme Correa Video Technology Research Group (ViTech) Graduate Program in Computing (PPGC) Federal University of Pelotas (UFPel) Embedded Computing Lab (ECL) Federal University of Santa Catarina (UFSC) PGMICRO Institute of Informatics (INF) Federal University of Rio Grande do Sul (UFRGS)

One of the new tools included in the AV1 video codec is the adaptive filtering scheme used in the sample interpolation process. This scheme includes three different filter families called Regular, Sharp and Smooth, offering high flexibility for motion estimation (ME) and motion compensation (MC). However, the high number of interpolation filters also leads to greater complexity and energy consumption, since the generation of samples at sub-pixel position is a costly process. This paper proposes a low-power and high-throughput hardware accelerator focused on the AV1 interpolation filters called Multiversion Interpolation Processor (MVIP). The accelerator includes the three AV1 interpolation filter families, with versions that employ operand isolation for power reduction in unused filters. The accelerator also includes a precise MVIP assuming the MC scenario, besides two approximate versions to reduce the cost on the ME scenario. The proposed design is able to process 8K video at 50fps in MC and 2,656.14 Msamples/sec in ME, with a power dissipation of 41.30mW.

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A BCLC Staging System for Hepatocellular Carcinoma using Swin Transformer and CT Imaging 46

A BCLC Staging System for Hepatocellular Carcinoma using Swi...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Chang, Shun-Cheng Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan Division of Gastroenterology and Hepatology Department of Internal Medicine National Taiwan University Hospital Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350371499

The Barcelona Clinic Liver Cancer (BCLC) staging system plays a crucial role in clinical planning, offering valuable insights for effectively managing hepatocellular carcinoma. Accurate prediction of BCLC stages can significantly ease the workload on radiologists. However, few datasets are explicitly designed for discerning BCLC stages. Despite the common practice of appending BCLC labels to clinical data within datasets, the inherent imbalance in BCLC distribution is further amplified by the diverse purposes for which datasets are curated. In this study, we aim to develop a BCLC staging system using the advanced Swin Transformer model. Additionally, we explore the integration of two datasets, each originally intended for separate objectives, highlighting the critical challenge of preserving class distribution in practical study designs. This exploration is pivotal for ensuring the applicability of our developed staging system in the designed clinical settings. Our resulting BCLC staging system demonstrates an accuracy of 55.81% (±7.8%), contributing to advancing medical image-based research for predicting BCLC stages. © 2024 IEEE.

关键词： Distribution transformers

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ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular Carcinoma Using 3D CT with Multiple Instance Learning

ReViT: A Hybrid Approach for BCLC Staging of Hepatocellular ...

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2024 IEEE-EMBS International Conference on Biomedical and Health informatics, BHI 2024

作者： Chang, Shun-Cheng Yu, Hsin-Pei Hsieh, Yi-Hsien Wang, Pochuang Wang, Weichung Su, Tung-Hung Kao, Jia-Horng Lin, Che Taipei10617 Taiwan Department of Computer Science and Information Engineering Ntu Taipei10617 Taiwan Institute of Applied Mathematical Sciences Ntu Taipei10617 Taiwan National Taiwan University Hospital Division of Gastroenterology and Hepatology Department of Internal Medicine Taipei10048 Taiwan Department of Electrical Engineering Ntu Taipei10617 Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taipei10617 Taiwan Smart Medicine and Health Informatics Program Ntu Taipei10617 Taiwan

ISBN: (纸本)9798350351552

Deep learning has revolutionized medical imaging, offering advanced methods for accurate diagnosis and treatment planning. The BCLC staging system is crucial for staging Hepatocellular Carcinoma (HCC), a high-mortality cancer. An automated BCLC staging system could significantly enhance diagnosis and treatment planning efficiency. However, we found that BCLC staging, which is directly related to the size and number of liver tumors, aligns well with the principles of the Multiple Instance Learning (MIL) framework. To effectively achieve this, we proposed a new preprocessing technique called Masked Cropping and Padding(MCP), which addresses the variability in liver volumes and ensures consistent input sizes. This technique preserves the structural integrity of the liver, facilitating more effective learning. Furthermore, we introduced Re ViT, a novel hybrid model that integrates the local feature extraction capabilities of Convolutional Neural Networks (CNNs) with the global context modeling of Vision Transformers (ViTs). Re ViT leverages the strengths of both architectures within the MIL framework, enabling a robust and accurate approach for BCLC staging. We will further explore the trade-off between performance and interpretability by employing TopK Pooling strategies, as our model focuses on the most informative instances within each bag. © 2024 IEEE.

关键词： Convolutional neural networks

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Goal-oriented inference of environment from redundant observations

arXiv

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arXiv 2023年

作者： Takahashi, Kazuki Fukai, Tomoki Sakai, Yutaka Takekawa, Takashi Informatics Program Graduate School of Engineering Kogakuin University of Technology and Engineering Japan Neural Coding and Brain Computing Unit Okinawa Institute of Science and Technology Japan Brain Science Institute Tamagawa University Japan

The agent learns to organize decision behavior to achieve a behavioral goal, such as reward maximization, and reinforcement learning is often used for this optimization. Learning an optimal behavioral strategy is difficult under the uncertainty that events necessary for learning are only partially observable, called as Partially Observable Markov Decision Process (POMDP). However, the real-world environment also gives many events irrelevant to reward delivery and an optimal behavioral strategy. The conventional methods in POMDP, which attempt to infer transition rules among the entire observations, including irrelevant states, are ineffective in such an environment. Supposing Redundantly Observable Markov Decision Process (ROMDP), here we propose a method for goal-oriented reinforcement learning to efficiently learn state transition rules among reward-related"core states" from redundant observations. Starting with a small number of initial core states, our model gradually adds new core states to the transition diagram until it achieves an optimal behavioral strategy consistent with the Bellman equation. We demonstrate that the resultant inference model outperforms the conventional method for POMDP. We emphasize that our model only containing the core states has high explainability. Furthermore, the proposed method suits online learning as it suppresses memory consumption and improves learning speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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