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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Sulistio, Bambang Heryadi, Yaya Lukas Suparta, Wayan Sonata, Ilvico Binus Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Universitas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group Jakarta Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta Indonesia

ISBN: (纸本)9798350327472

Capital market transactions provide an opportunity for investors to acquire ownership of company shares and capital gains, as well as dividends. However, alongside the benefits, there are risks of capital loss and liquidation, leading to stress and depression due to profit targets and decision-making errors. To mitigate the risk of decision-making errors in investment, data analysis is needed, including sentiment analysis, which influences stock prices. This study aims to develop a new deep learning model to classify Indonesian public opinion on JCI stocks, especially the Energy sector, obtained from the Twitter social media platform. The model will perform sentiment analysis and categorize opinions as negative, neutral, or positive. We created a dataset that was trained using Bidirectional Encoder Representations from Transformers (BERT) to summarize the analysis of public sentiment above so that it can assist investors in studying public sentiment as a reference for investing with a yield precision of 76%, Recall of 77%, and F1-score on 76%. © 2023 IEEE.

关键词： Tweets

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Generation of target speech with speaker individuality based on accent conversion for English pronunciation learning

Generation of target speech with speaker individuality based...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Rei Hamakawa Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper first provides an overview of the English pronunciation learning support tool. The tool aims to use "accent-modified speech that retains the learner’s voice quality" as the "target speech." We propose a new conversion model based on conventional methods for this accent conversion. Specifically, we improve the conventional LSTM-based DNN model for accent conversion by adopting a transformer-based model. Our experiments investigated the model’s ability to handle the unique katakana pronunciation characteristic of Japanese speakers. The results confirmed the effectiveness of the proposed conversion method, although challenges remain, such as the scarcity of Japanese speech data and the need to improve the accuracy of speaker identity retention.

关键词： Accuracy Synthesizers Training data Symbols Switches Information processing Phonetics Transformers Data models Speech processing

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Machine learning-based evaluation of cancer pain from functional brain images

Transactions of Japanese Society for Medical and Biological ...

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Transactions of Japanese Society for Medical and Biological Engineering 2021年第Proc期Annual59卷 780-782页

作者： Ogawa, Mayuko Ben, Hui Ono, Yumie Lin, Wenying Electrical Engineering Program Graduate School Science and Technology Meiji University Japan School of Science and Technology Meiji University Japan National Taiwan University Hospital Cancer Center Taiwan

Pain management is one of the important treatments of cancer patients. This study aims to develop a classifier of chronic cancer pain patients from their brain metabolic activity measured by FDG-PET. We compared FDG-PET brain images of 74 painful and 29 painless cancer patients to clarify the brain activity specific to chronic pain of cancer. Using the detected brain activity pattern, we further developed a classifier that determines the presence or absence of chronic pain from PET brain images by machine learning methods. The painful cancer patients showed significantly increased activity in the amygdala, hippocampus, and decreased activity in the cingulate gyrus and precuneus (p © 2021, Japan Soc. of Med. Electronics and Biol. Engineering. All rights reserved.

关键词： Diseases

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Global-Local Consistency for Cost-Effective Anomaly Localization with Point Annotations

Global-Local Consistency for Cost-Effective Anomaly Localiza...

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IIAI International Conference on Advanced Applied Informatics (IIAIAAI)

作者： Keiichi Nakanishi Ryoya Katafuchi Terumasa Tokunaga Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350377903

ISBN: (纸本)9798350377910

Creating pixel-level ground-truth (GT) masks is quite costly for deep learning-based image segmentation. Specialists in areas such as anomaly detection and medical diagnostics face difficulties in producing many GT masks due to limited resources. To reduce this burden, we propose a cost-effective image segmentation framework with point annotations (CoSPA) that performs image segmentation with only point annotations. Point annotations refer to the partial and sparse labeled coordinates in an image. The key idea is to ensure consistency between the predictions for the same coordinates from the two different networks of CoSPA. This new consistency enables the CoSPA to improve segmentation performance and extend to semi-supervised learning. For the MVTec AD dataset, we verified the cost-effectiveness of CoSPA through an anomaly detection task. We demonstrated that the point annotating costs were reduced by 80% compared to creating GT masks. Subsequently, the CoSPA realized by 87% of the mean Intersection over Union (mIoU) achieved using the fully supervised method, DeepLabV3+. Moreover, the mIoU of CoSPA using only 30% of all point annotations defeated that of the unsupervised method, PaDiM. This study offers a new direction for economic anomaly localization.

关键词： Location awareness Image segmentation Costs Annotations Supervised learning Semisupervised learning Medical diagnosis Informatics Anomaly detection Faces

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Generation of Photo Slideshow with Song based on Closeness between Concept of Lyrics and That of Images

Generation of Photo Slideshow with Song based on Closeness b...

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Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Mei Hashimoto Michiharu Niimi Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan Faculty of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka Japan

ISBN: (数字)9798350367331

ISBN: (纸本)9798350367348

This paper proposes a method that allows users to easily convert a large number of still images into movies by displaying photos in sync with memories or favorite songs, which offers a new media viewing method for users to look back at the vast number of photos in their photo folders. We assume here that users select a song and have images stored in local PC. One method of synchronization involves selecting images based on elapsed time of song. However, since lyrics convey meaning, it can be better to display the images that align with both lyrics’ meaning and images’ meaning matched. In order to do this, we need the space which shares the concept of both lyrics and images, and select colors as the concept. Displayed images are determined based on the distance between points that are mapped from words and images to color space. Specifically, the color concepts from words are extracted using the Color Image Scale, and that from images are determined as the most frequent colors by cluster analysis. Experimental results show that it is possible to easily create movies where images with colors that match the mood of the lyrics are displayed.

关键词： Image color analysis Mood Refining Asia Information processing Color Media Motion pictures Synchronization

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Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit graduated Optimization

arXiv

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arXiv 2023年

作者： Sato, Naoki Iiduka, Hideaki Computer Science Course Graduate School of Science and Technology Meiji University Japan Department of Computer Science Meiji University Japan

The graduated optimization approach is a method for finding global optimal solutions for nonconvex functions by using a function smoothing operation with stochastic noise. We show that stochastic noise in stochastic gradient descent (SGD) has the effect of smoothing the objective function, the degree of which is determined by the learning rate, batch size, and variance of the stochastic gradient. Using this finding, we propose and analyze a new graduated optimization algorithm that varies the degree of smoothing by varying the learning rate and batch size, and provide experimental results on image classification tasks with ResNets that support our theoretical findings. We further show that there is an interesting relationship between the degree of smoothing by SGD’s stochastic noise, the well-studied "sharpness" indicator, and the generalization performance of the model. © 2023, CC BY.

关键词： Optimization algorithms

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EUDRL: Explainable Uncertainty-Based Deep Reinforcement Learning for Portfolio Management 15

EUDRL: Explainable Uncertainty-Based Deep Reinforcement Lear...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Lina, Jia Banda, Shahab S. Paib, Hao-Ting Rawal, Bharat S. International Graduate School of Artificial Intelligence Yunlin County Douliu City Taiwan Bachelor Program of Big Data Applications National Pingtung University Pingtung Taiwan Department of Computer Science and Digital Technologies University GgramblingLA United States

ISBN: (纸本)9798350370249

Supervised deep learning (SDL) has shown remarkable success in various financial applications, such as stock prediction and fraud detection. However, SDL's reliance on class labels renders it unsuitable for portfolio management (PM) tasks, where such labels are often unavailable. To address this limitation, we propose a novel two-level architecture based on deep reinforcement learning (DRL) for PM, which does not require class labels. Our approach comprises several local agents that provide trading decisions and uncertainty assessments for individual stocks, and a global agent that makes portfolio management decisions based on the outputs of the local agents. Additionally, we incorporate the concept of explainable AI (XAI) into our framework using the SHAP (Shapley additive explanations) method, enhancing the transparency and interpretability of the global agent's decisions. Our experimental results demonstrate that the proposed architecture consistently yields profitable outcomes in the market. ©2024 IEEE.

关键词： Deep reinforcement learning

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Theoretical Analysis on Localization Error Bound for Wireless System Using Leaky Coaxial Cable

Theoretical Analysis on Localization Error Bound for Wireles...

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International Conference on Advanced computer Theory and Engineering, ICACTE

作者： Ji Ziying Junjie Zhu Yafei Hou Satoshi Denno Graduate School of Environmental Life Natural Science and Technology Okayama University Japan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

Leaky CoaXial (LCX) cable is a line-shaped cable used as antenna for wireless system. Due to its stable and uniform radiation characteristics with changeable shape, LCX cable is largely used for wireless coverage over many linear-cell environments such as shopping mall, tunnel, underground mall or walkway etc. These scenarios usually require location-aware services for many applications. We have investigated many localization methods for LCX system. However, its theoretical localization error bound is still unknown, In this paper, using the LCX channel model which is developed based on the Geometrically-Based Single-Bounce (GBSB) model, we give a brief Fisher Information analysis for LCX-based localization and the impact of different LCX configurations on the position error bound (PEB) is evaluated through the Cramér-Rao lower bound.

关键词： Location awareness Wireless communication Lower bound Analytical models Coaxial cables Shape Channel models Indoor positioning systems Antennas Information analysis

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Strengthening IT Governance in the Crypto Marketplace: Leveraging Penetration Testing and Standards Alignment

Strengthening IT Governance in the Crypto Marketplace: Lever...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Yulianto, Semi Krisnanik, Erly Hartawan, Muhammad Syarif Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia University of Pembangunan Nasional Veteran Information System Department Jakarta12450 Indonesia University of Krisnadwipayana Faculty of Technology Jakarta13077 Indonesia Asia e University Ict Department Malaysia

ISBN: (纸本)9798350307306

This research explores the vital role of penetration testing in cybersecurity, specifically its alignment with ISO 27001:2022, COBIT 2019, and NIST CSF standards in the context of crypto asset exchange management. These sectors grapple with rapidly evolving threat landscapes, necessitating robust cybersecurity strategies. However, the integration of penetration testing with these standards remains underexamined. Through a comprehensive approach, including literature reviews, surveys, case studies, and interviews, this study investigates the incorporation of penetration testing into an organization's cybersecurity. It unveils the current state of penetration testing practices, interprets ISO 27001:2022, COBIT 2019, and NIST CSF perspectives, and pinpoints integration challenges and opportunities. This study offers practical insights and best practices for integrating penetration testing into cybersecurity frameworks by emphasizing alignment. It underscores the importance of aligning penetration testing with information security standards in the crypto asset exchange management sector, enhancing security, proactively identifying vulnerabilities, and effectively mitigating risks in the context of growing crypto asset importance. This research provides invaluable guidance for safeguarding digital assets in this evolving domain. © 2023 IEEE.

关键词： Cybersecurity

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Sharing Food with FoodLifeSavr Smartphone App 2

Sharing Food with FoodLifeSavr Smartphone App

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2nd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Andres, Arfandi Tanjaya, Edwin Setiawan Ghaniyardi Sandhi, Joshua Christian Warnars, Harco Leslie Hendric Spits Bina Nusantara University Information Systems Department School of Information Systems Jakarta11480 Indonesia Bina Nusantara University Computer Science Department Binus Graduate Program - Computer Science Jakarta11480 Indonesia

ISBN: (纸本)9781665400527

In this era, people often overeat, make parties or events, where there are excessive availability of food. As well as shops or restaurants that sell food, has ready-to-eat foods that only lasts one day and then treated as expired food. Often this precious food is thrown in the trash, and meanwhile, many destitute people cannot eat enough per day. Therefore, this research paper aims to mobilize people who overeat and donate their excess food that can still be consumed to those in need. This smartphone application is equipped with activities such as registering for new donors who want to join the mobile application and logging in for donors who are already registered in the mobile application. Donors can monitor couriers taking photos of recipients with food donated from donors. In addition, donors can select the food category for donation and input amount, track the donated food that the courier has picked up, and send it to the recipient. This mobile application is also equipped with donors to share experiences, ideas, and views based on topics created by staff and available threads. © 2022 IEEE.

关键词： Mobile computing

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