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23rd International Conference on Cyberworlds, CW 2024

作者： Takano, Nana Nishiguchi, Satoshi Toyoura, Masahiro Osaka Institute of Technology Graduate School of Information Science and Technology Osaka Japan Osaka Institute of Technology Faculty of Information Science and Technology Osaka Japan University of Yamanashi Department of Computer Science and Engineering Yamanashi Japan

ISBN: (纸本)9798331527174

The annotation of medical images for the purpose of segmentation demands a high level of expertise and experience, and the generation of suitable datasets represents a significant challenge. In this study, we propose a method that facilitates annotation on a whole slide image (WSI) without requiring detailed specification of the lesion area. The objective is to enable annotation without the need for specialised knowledge. In particular, the WSI is divided into smaller regions for annotation purposes, with the proportion of lesions in each region being compared with reference images of similar regions, for which the proportion of lesions is known. The proportion of lesions in the reference small region image deemed to be most analogous is then designated as the annotation. The outcomes of an annotation experiment on multiple subjects based on this approach indicated that as the level of experience of the annotator increased, the variability in the accuracy rate diminished. This suggests that the annotators had acquired knowledge and skills through the process of annotation. © 2024 IEEE.

关键词： Medical imaging

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Shape-Centric Augmentation with Strong Pre- Training for Training from Rendered Images

Shape-Centric Augmentation with Strong Pre- Training for Tra...

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2024 IEEE Region 10 Conference, TENCON 2024

作者： Inoue, Takeru Ohkoba, Masakazu Gakuta, Kouji Yamada, Etsuji Kariya, Aoi Kinosada, Masakazu Kitaide, Yujiro Miyamoto, Ryusuke Graduate School of Science and Technology Meiji University Department of Computer Science Japan Shinsei Printing Co. Ltd. Japan Digital Printing & Solutions Co. Ltd. Japan School of Science and Technology Meiji University Department of Computer Science Japan

ISBN: (纸本)9798350350821

Acquiring purpose-specific data is crucial for applying deep learning to applications. However, creating such data can be labor-intensive, necessitating the development of datasets at minimal cost. Consequently, there has been a recent trend towards utilizing computer-generated imagery (CGI) to generate purpose-specific datasets. Nonetheless, models trained on CGI data often exhibit suboptimal performance on real images due to domain gap issues. Thus, bridging this gap between synthetic and real data domains is crucial. We are developing a real parts classification application using 3D CAD models as training data. This study discusses our strategies for addressing the domain gap challenge in this application. To mitigate this gap, we explore three primary approaches: selecting architectures with a strong shape bias, leveraging large-scale pre-training to enhance generalization performance, and enhancing the shape bias of classifiers by manipulating color characteristics in the training data. We present evidence demonstrating the effectiveness of these meth-ods in bridging the domain gap between CG I training data and real-world images in our part classification application: the top-l accuracy became 98.34 % at the best case. © 2024 IEEE.

关键词： Image classification

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Hoax Detection in Social Media: A Literature Review 4

Hoax Detection in Social Media: A Literature Review

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4th International Conference on Artificial Intelligence and Data sciences, AiDAS 2023

作者： Saputra, Carsten Sebastian Wijaya, Norbert Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11530 Indonesia

ISBN: (纸本)9798350318432

For both print and digital media, the accuracy of the information has long been a problem that has impacted society and the business world. Information travels so quickly on social networks and is amplified that it can be skewed, false, or misleading. Inaccurate or incorrect information has great potential to affect millions of real-world users directly. Therefore, finding or getting accurate, trusted, and precise information becomes a problem. People start to spread fake news, which can cause certain events, such as mental disturbance, riots, and unnecessary anxiety. To prevent these problems, we will solve/research them with CountVectorizer and TF-IDF, which is what this study is all about (Discusses the detection of hoax news using CountVectorizer and TF-IDF). It was found that using these methods makes it possible to detect which words are most common in fake news and real news. © 2023 IEEE.

关键词： Digital storage

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Evaluation of Deep Convolutional Neural Network-Based Tattoo Detection 7

Evaluation of Deep Convolutional Neural Network-Based Tattoo...

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7th International Congress on Ambient Intelligence, Software Engineering, and e-Health and Mobile Health, AmITIC 2024

作者： Jiménez-Delgado, E. Quesada-López, C. Méndez-Porras, A. Lara-Petitdemange, A. Alfaro-Velasco, J. Graduate Program in Computer Science and Informatics University of Costa Rica Costa Rica Technological Institute of Costa Rica Computer Engineering Department Costa Rica School of Computer Science and Informatics University of Costa Rica Costa Rica

ISBN: (纸本)9798350364538

In this study, two deep learning models for automatic tattoo detection were analyzed;a modified Convolutional Neural Network (CNN) and pre-trained ResNet-50 model. In order to achieve this, ResNet-50 uses transfer learning with fine-tuning. The purpose of this study was to evaluate the accuracy, precision, recall, F1-score, and computational efficiency of the system being considered. To augment the dataset included 1000 photos that were equally divided between those showing tattoos and those that did not show tattoos. A k-fold cross-validation approach was employed in training and testing the models. Although custom CNNs are effective, utilizing pre-trained ones like ResNet-50 can offer even better outcomes. Specifically, ResNet-50 attained a higher accuracy (0.86 compared to 0.79), precision (0.85 versus 0.78), recall (0.91 against 0.86), and F1-score (0.91 vis-a-vis 0.86) as compared to custom CNNs. In selecting these models for examination, two main motivations were considered. The first motivation is to see whether transfer learning with a pre-trained ResNet-50 model does well when compared with a customized CNN designed specifically for tattoo detection. Secondly,the intent of this study is to know what advantages can be derived from each approach and their demerits too. Furthermore, it seeks to determine if transfer learning can provide an alternative in contrast to the common CNN techniques with regards to precision and computational efficiency. In this research, two models will be evaluated in order to answer the question of what is better for tattoo detection: transfer learning or designing custom architectures. © 2024 IEEE.

关键词： Convolution

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Crowd Simulation Using Velocity Field Map and LSTM Neural Network 3

Crowd Simulation Using Velocity Field Map and LSTM Neural Ne...

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3rd International Conference on Intelligent Cybernetics technology and Applications, ICICyTA 2023

作者： Peng, Yuanyuan Oshita, Masaki Kyushu Institute of Technology Graduate School of Computer Science and Systems Engineering Iizuka Fukuoka Japan

ISBN: (纸本)9798350394559

In this paper, we propose a novel method that uses a velocity field map and long short-term memory (LSTM) neural network to control agents to avoid collisions with nearby agents by considering their previous trajectories. Our method uses a series of continuous velocity field maps that represent the surrounding conditions of the controlling agent. A velocity field map comprises 2-channel images containing the positions and velocities of the controlling and nearby agents. We employed an LSTM neural network to predict the current velocity field map from the velocity field maps of the previous frames to determine the desired velocity of the controlling agent. The agent is controlled using a social force method based on the desired velocity. The novelty of our method lies in the use of the velocity field map and LSTM neural network. By using the velocity field map, the information of the controlling agent and its surrounding salutation is efficiently represented. In addition, by using a series of continuous velocity field maps and LSTM, the agent is efficiently controlled considering the previous frames. We present experimental results and discuss the advantages of the proposed approach. © 2023 IEEE.

关键词： Long short-term memory

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Enhancing EV Charging Demand Forecasting for Highway Rest Area Stations: Integrating Day Type, Traffic Volume, and Weather Conditions

Enhancing EV Charging Demand Forecasting for Highway Rest Ar...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Lee, Yeaeun Kim, Byeongchang Kim, Dongju Hwang, Euiseok Choi, Jaehyuk Kim, Hyunsup Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju Korea Republic of Gwangju Institute of Science and Technology Ai Graduate School Gwangju Korea Republic of Hyundai Motors Company Korea Republic of

ISBN: (纸本)9798350362480

With global EV sales projected to reach 3.5 million units by 2023 and public charging stations increasing by 40%, effective management and optimization of charging infrastructure have become critical. While current research primarily focuses on models that address either users or charging stations, these approaches often overlook external factors such as weather and traffic conditions, which significantly impact driving patterns and energy consumption. The integration of these external variables into forecasting models is thus critical for enhancing prediction accuracy and infrastructure optimization. In this study, we conducted an in-depth analysis of EV charging patterns at highway rest areas. This study employed deep learning models, GRU, and LSTM architectures, trained using various data combinations. The experiments revealed that the inclusion of traffic data notably improves forecasting precision. In particular, the LSTM model demonstrated a 10.5% reduction in mean absolute percentage error(MAPE), decreasing the standard deviation from 4.95 to 3.87 when external factors were included. © 2024 IEEE.

关键词： deep learning demand forecasting Electric vehicle charging station external variables

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A Proposal for Estimating Court Boundaries and Displaying Player Positions and Trajectories in Broadcast Handball Videos

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2023年第12期143卷 1123-1129页

作者： Masuda, Takeshi Sawano, Hiroaki Graduate School of Business Administration and Computer Science Aichi Institute of Technology 2-49-2 Jiyugaoka Chikusa-ku Aichi Nagoya464-0807 Japan

This study proposes a method for estimating court boundaries and displaying player positions and trajectories for broadcast handball videos. The videos typically cover only about one-third of the court, and the camera angle changes from left to right to follow the players. Initially, an object detection algorithm YOLOv5 is used to detect a goal object in the video, and the camera direction is determined from its position. Court boundaries are estimated in two ways: one using a bounding box around the goal object, and the other with a red area marked on the court. Once the court boundaries are estimated, DeepSort, an object tracking model, is employed to track the players, and their positions and movements are displayed on a top view of the court. The proposed methods were compared by conducting experiments on videos from different venues, and the results indicate that the estimation rates of the two methods were 80.7% and 85.3%, respectively. © 2023 The Institute of Electrical Engineers of Japan.

关键词： Object detection

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Real-Time Road Damage Detection and Infrastructure Evaluation Leveraging Unmanned Aerial Vehicles and Tiny Machine Learning

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IEEE Internet of Things Journal 2024年第12期11卷 21347-21358页

作者： Khan, Muhammad Waseem Obaidat, Mohammad S. Mahmood, Khalid Batool, Dania Badar, Hafiz Muhammad Sanaullah Aamir, Muhammad Gao, Wu Comsats University Islamabad Sahiwal Campus Department Of Computer Science Sahiwal57000 Pakistan Superior University Department Of Computer Science Lahore54000 Pakistan The University Of Jordan King Abdullah Ii School Of Information Technology Amman11942 Jordan University Of Science And Technology Beijing School Of Communication And Computing Engineering Beijing100083 China Amity University Amity School Of Engineering And Technology Uttar Pradesh Noida201301 India National Yunlin University Of Science And Technology Graduate School Of Intelligent Data Science Douliu64002 Taiwan University Of Palermo Department Of Engineering Palermo90133 Italy Emerson University Department Of Emerging Computing Technologies Multan60000 Pakistan Northwestern Polytechnical University Center For Bio-Inspired System-on-Chip School Of Computer Science Xi'an710060 China

Road damage detection (RDD) through computer vision and deep learning techniques can ensure the safety of vehicles and humans on the roads. Integrating unmanned aerial vehicles (UAVs) in RDD and infrastructure evaluation (IE) has also emerged as a key enabler, contributing significantly to data acquisition and real-time monitoring of road damages, such as potholes, cracks, and surface anomalies, facilitating proactive maintenance and improved road conditions. These UAVs are low-powered and resource-constrained devices that work autonomously to perform pattern detection and decision making leveraging tiny machine learning (Tiny ML) algorithms. These Tiny ML algorithms are designed to run on edge devices, IoT devices, UAVs, etc. In this study, the RDD2022 data set collected using UAVs and dashboard cameras of vehicles was utilized to train pure and mixed models that exhibit class instance imbalance in certain classes which is addressed by implementing data augmentation as a regularization technique. State-of-the-art two-stage detectors: Faster R-CNN ResNet101 and one-stage detectors: SSD MobileNet V1 FPN, YOLOv5, and Efficientdet D1 are employed. The results indicate that the two-stage detector achieved an impressive mean average precision (mAP) of 88.49% overall and 96.62% for focused classes. Notably, the state-of-the-art Efficientdet D1 approach achieved a competitive mAP of 86.47% overall and 95.12% for focused classes, with significantly lower computational cost. These findings highlight the potential of advanced object detection techniques, particularly Efficientdet D1, to enhance the accuracy and efficiency of RDD systems, thereby improving passenger safety and overall performance. © 2014 IEEE.

关键词： Object detection

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Server Cloud Based Security Surveillance Systems: a Design science Research 11

Server Cloud Based Security Surveillance Systems: a Design S...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Nurenie, Alexander Ramadhan, Arief Abdurachman, Edi Trisetyarso, Agung Zarlis, Muhammad Bina Nusantara University Binus Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350305968

In this research, the author addresses the prevalent issues faced by users of cloud services, especially those using Peer-to-Peer (P2P) technology, such as connection losses, security concerns, and poor video quality. The study does not delve into the complex problems at the data center level but instead proposes a novel Design science Research approach. This new method, tested with five clients in varied locations, utilizes port forwarding to establish a direct server-client connection, bypassing traditional P2P frameworks. The results show that this approach significantly improves quality over the P2P system. Feedback from clients before and after the experiment indicates a positive response to this new method. This research offers insights into overcoming P2P-related issues in cloud services and contributes to the development of cloud-based server science, presenting a cost-effective, independent alternative to data center-dependent P2P services, with plans for ongoing research in this area. © 2023 IEEE.

关键词： Cost effectiveness

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ANALYSIS OF ROCKING COLUMN BEHAVIOR CONSIDERING STRESS DISTRIBUTION OF COLUMN USING FIBER ELEMENTS

Journal of Structural and Construction Engineering

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Journal of Structural and Construction Engineering 2022年第802期87卷 1201-1212页

作者： Hu, Rong Muramoto, Makoto Graduate School of Science and Technology Doctoral Program of Architecture Kyoto Institute of Technology Japan Faculty of Design and Architecture Kyoto Institute of Technology Japan

In this paper, a numerical model considering the embedment reaction of column foot by the beam-column finite element method was proposed for evaluating the restoring force characteristics of the rocking column in traditional timber structures. Wood compression tests in contact with stones were conducted to investigate the characteristics of the embedment reaction and initial deformation generated at the end of the wood, and these characteristics were considered in the column foot numerical model. The validity and applicability of the numerical model were verified by analyzing several static loading tests of the rocking column. © 2022 Architectural Institute of Japan. All rights reserved.

关键词： Finite element method

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