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arXiv 2024年

作者： Muluneh, Mequanent Argaw Peng, Yan-Tsung Degife, Worku Abebe Tadesse, Nigussie Abate Demeku, Aknachew Mebreku Su, Li Institute of Information Science Academia Sinica Taipei Taiwan Department of Information System University of Gondar Gondar Ethiopia Department of Computer Science National Chengchi University Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Taiwan Department of Information Management National Taiwan University of Science and Technology Taipei Taiwan Department of Material Science and Engineering National Taiwan University of Science and Technology Taipei Taiwan

Computational music research plays a critical role in advancing music production, distribution, and understanding across various musical styles in the world. Despite the immense cultural and religious significance, the Ethiopian Orthodox Tewahedo Church (EOTC) chants are relatively underrepresented in computational music research. This paper contributes to this field by introducing a new dataset specifically tailored for the analysis of EOTC chants, also known as Yaredawi Zema. This work provides a comprehensive overview of a 10-hour dataset, 369 instances, creation, and curation process, including rigorous quality assurance measures. Our dataset has a detailed word-level temporal boundary and reading tone annotation along with the corresponding chanting mode label of audios. Moreover, we have also identified the chanting options associated with multiple chanting notations in the manuscript by annotating them accordingly. Our goal in making this dataset available to the public1 is to encourage more research and study of EOTC chants, including lyrics transcription, lyric-to-audio alignment, and music generation tasks. Such research work will advance knowledge and efforts to preserve this distinctive liturgical music, a priceless cultural artifact for the Ethiopian people. © 2024, CC BY.

关键词： Music

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Diffusion-Based Augmentation for Lung Cancer Risk Prediction from Temporal CT Images 13

Diffusion-Based Augmentation for Lung Cancer Risk Prediction...

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Joint 13th International Conference on Soft computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Meng, Hsing-Chen Lin, Yi-Hui Huang, Chun-Rong Shih, Nai-Wen Wu, Shawn Lin, Yen-Yu National Yang Ming Chiao Tung University Graduate Degree Program of Ai Hsinchu Taiwan Taichung Veterans General Hospital Dept. of Radiation Oncology Taichung Taiwan National Yang Ming Chiao Tung University Dept. of Computer Science Hsinchu Taiwan Imaging Sy Research Institute Dept. of Diagnostic NY United States

ISBN: (纸本)9798350373332

Cancer risk prediction based on CT images of a patient taken at multiple time points in the past has become an emerging research topic. While deep learning methods yield promising results, they require a large amount of temporally sequenced data for reliable performance. Acquiring such time-series data is expensive and time-consuming. Hence, data aug-mentation based on a small, annotated, and temporally sequenced medical image dataset is essential to cancer risk prediction. This paper introduces a novel diffusion-based method for augmenting temporal medical images with an application to lung cancer risk prediction. Specifically, we develop our approach based on a conditional diffusion model owing to its high flexibility of condition design. We generate target images of a patient for training by overlaying tumors recently detected and masked by eroded segmentation maps onto CT images taken earlier to simulate tumor growth. Specifically, our conditional diffusion model is trained by using CT images taken in the first or second year, along with tumor segmentation maps in the third year, as well as the generated target images. Three distinct generation strategies are designed by using different combinations of CT images and eroded segmentation maps as conditions to synthesize lung CT images, hence diversifying and enriching the training set. Experimental results demonstrate that learning using 25 % of training data together with our augmented data achieves comparable performance to that using 100% of training data. © 2024 IEEE.

关键词： Lung cancer

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Utilization of Decentralized Autonomous Organization (DAO) for Virtual Property Transaction Governance in Metaverse: A Conceptual Framework

Utilization of Decentralized Autonomous Organization (DAO) f...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Gede Indra Raditya Martha Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Departement Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Digital Communication and Hotel & Tourism Institute Technology Bandung Bandung Indonesia Faculty of Computer Science University of Indonesia Jakarta Indonesia

Research related to digital assets & ownership is currently rising along with the escalation utilization of Blockchain through various use cases and its derevatives, for example in facilitating Digital Assets in the form of virtual property (virtual houses / buildings and virtual land) in the Metaverse. These digital assets often have utilities, features, and even limited copies of quantities so that these digital objects have more value for specific users on a platform and even general people who are interested in and value them. To be able to preserve the value of digital assets in the Metaverse such as virtual property previously mentioned especially for transactions, a set of regulations/governance, as well as sustainable development in the future in the Metaverse is really needed so that the use of Decentralized Autonomous Organization (DAO) in the metaverse is one way to maintain the sustainability of the ecosystem & market and its potential of virtual property. After conducting research for DAO development for virtual property transactions, starting with determining goals & purposes, breaking down the DAO focus object (virtual property), clustering relevant attributes, matching socio-economic impacts, and finally designing smart contracts & governance consensus, we obtained 4 main layers namely Consensus Governance, Smart Contract, Virtual Property & Socio-Economic.

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A Fitness-Based Migration Policy for Biased Random-Key Genetic Algorithms 26th

A Fitness-Based Migration Policy for Biased Random-Key Gene...

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26th International Conference on Applications of Evolutionary Computation, EvoApplications 2023, held as part of EvoStar 2023

作者： Boiani, Mateus Parpinelli, Rafael Stubs Dorn, Márcio Institute of Informatics Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil Graduate Program in Applied Computing Santa Catarina State University Santa Catarina Joinville Brazil Center for Biotechnology Federal University of Rio Grande do Sul Rio Grande do Sul Porto Alegre Brazil National Institute of Science and Technology - Forensic Science Rio Grande do Sul Porto Alegre Brazil

ISBN: (纸本)9783031302282

Population diversity management is crucial for the quality of solutions in Evolutionary Algorithms. Many techniques require assistance to handle diverse problem characteristics and may prematurely converge in local optima. Maintaining diversity enables the algorithm to search the space and produce better results effectively. Parallel models are a common approach to preserving diversity;however, design decisions affect optimization process characteristics. For example, the Island model’s migration policy affects convergence speed. This study proposes and evaluates a fitness-based migration policy for the Biased Random-Key Genetic Algorithm (BRKGA) and compares it to two traditional strategies. The results in continuous search spaces demonstrate that the proposed policy can enhance BRKGA optimization with appropriate parameters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Genetic algorithms

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APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System

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IEEE Access 2023年 11卷 74000-74020页

作者： Javed, Safdar Hussain Ahmad, Maaz Bin Asif, Muhammad Akram, Waseem Mahmood, Khalid Das, Ashok Kumar Shetty, Sachin College of Computing and Information Sciences Karachi75190 Pakistan Lahore Garrison University Department of Computer Science Lahore54810 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan International Institute of Information Technology at Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Simulation and Visualization Engineering Virginia Modeling Analysis and Simulation Center Department of Modeling SuffolkVA23435 United States Old Dominion University Center for Cybersecurity Education and Research SuffolkVA23435 United States

The objective of Advanced Persistent Threat (APT) attacks is to exploit Cyber-Physical Systems (CPSs) in combination with the Industrial Internet of Things (I-IoT) by using fast attack methods. Machine learning (ML) techniques have shown potential in identifying APT attacks in autonomous and malware detection systems. However, detecting hidden APT attacks in the I-IoT-enabled CPS domain and achieving real-time accuracy in detection present significant challenges for these techniques. To overcome these issues, a new approach is suggested that is based on the Graph Attention Network (GAN), a multi-dimensional algorithm that captures behavioral features along with the relevant information that other methods do not deliver. This approach utilizes masked self-attentional layers to address the limitations of prior Deep Learning (DL) methods that rely on convolutions. Two datasets, the DAPT2020 malware, and Edge I-IoT datasets are used to evaluate the approach, and it attains the highest detection accuracy of 96.97% and 95.97%, with prediction time of 20.56 seconds and 21.65 seconds, respectively. The GAN approach is compared to conventional ML algorithms, and simulation results demonstrate a significant performance improvement over these algorithms in the I-IoT-enabled CPS realm. © 2013 IEEE.

关键词： Real time systems

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AVTENet: A Human-Cognition-Inspired Audio-Visual Transformer-Based Ensemble Network for Video Deepfake Detection

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IEEE Transactions on Cognitive and Developmental Systems 2025年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min Taiwan International Graduate Program Academia Sinica Social Networks and Human-Centered Computing Program Taipei11529 Taiwan National Tsing Hua University Institute of Information Systems and Applications Hsinchu30013 Taiwan National Chengchi University Department of Computer Science Taipei11605 Taiwan National Tsing Hua University Department of Electrical Engineering Institute of Communications Engineering Hsinchu300044 Taiwan Academia Sinica Research Center for Information Technology Innovation Taipei11529 Taiwan

The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous studies on detecting artificial intelligence-generated fake videos only utilize visual modality or audio modality. While some methods exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multimodal datasets of deepfake videos involving acoustic and visual manipulations, and are mostly based on convolutional neural networks with low detection accuracy. Considering that human cognition instinctively integrates multisensory information including audio and visual cues to perceive and interpret content and the success of transformer in various fields, this study introduces the audio-visual transformer-based ensemble network (AVTENet). This innovative framework tackles the complexities of deepfake technology by integrating both acoustic and visual manipulations to enhance the accuracy of video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multimodal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that the proposed model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. We also compare AVTENet against humans in detecting video forgery. The results show that AVTENet significantly outperforms humans. © 2016 IEEE.

关键词： audio-visual AVTENet Deepfake detection

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Defect Detection and Classification in Printed Circuit Boards using Convolutional Neural Networks

Defect Detection and Classification in Printed Circuit Board...

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International Conference on Edge computing and Applications (ICECAA)

作者： Yolanda D. Austria Arnel C. Fajardo Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauay an City Isabela Philippines

PCB defect detection and classification is vital in electronics manufacturing to ensure high-quality and safe products. Manual inspection is time-consuming, costly, and prone to errors. To address this, automated methods using deep learning models have gained preference. These models learn from diverse datasets of defect images or videos. In this study, a deep learning model was developed and trained using a dataset containing various defect classes, including breakout, copper, crack, cut, excessive conductor, missing conductor, mouse bite, pinhole, spur, and scratch. The model showed promising results in detecting defects across all classes.

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Defect Recognition on Single Layer Bare Printed Circuit Boards for Quality Control and Visual Inspection: A Low-Sample-Size Deep Transfer Learning Approach

Defect Recognition on Single Layer Bare Printed Circuit Boar...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 model performs exceptionally well, with mean Average Precision (mAP) of 98.3%, Precision of 98.6%, and Recall of 96.0%. Numerous datasets provide high detection rates across a range of fault types following thorough testing and inference. This extensive evaluation demonstrates the robustness of the model by reliably identifying Fissure, Mousebite, EC-Short, MC-Spur, Breakout, Pinhole, and Copper defects. The study emphasizes how the YOLOv8 design can identify flaws, providing a dependable and effective way to guarantee the dependability and caliber of printed circuit boards throughout the production process. The foundation for utilizing YOLOv8 in industrial quality control is established by this study, which holds significant potential for improving defect detection and quality control in the manufacturing industry.

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Learning Adaptation and Generalization from Human-Inspired Meta-Reinforcement Learning Using Bayesian Knowledge and Analysis

Learning Adaptation and Generalization from Human-Inspired M...

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Artificial Intelligence and Knowledge Engineering (AIKE)

作者： Joshua Ho Chien-Min Wang Chung-Ta King Yi-Hsin You Chi-Wei Feng Yen-Min Chen Bo-Yi Kuo Academia Sinica Institute of Information Science Taipei Taiwan Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan Center of Intelligent Healthcare National Taiwan University Hospital Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Department of Computer Science National Taiwan University Taipei Taiwan

Over the last decades, there has been growing interest in research in multiple and interdisciplinary fields of human-AI computing. In particular, approaches integrating the intersecting design with reinforcement learning (RL) have received more attention. However, the current research on RL may need to consider its enhancement from a human-inspired approach further. In the present work, we focus on enabling a meta-reinforcement learning (meta-RL) agent to achieve adaptation and generalization according to modeling Markov decision processes using Bayesian knowledge and analysis. By introducing a novel framework called human-inspired meta-RL (HMRL), we incorporate the agent performing resilient actions to leverage the dynamic dense reward based on the knowledge and prediction of a Bayesian analysis. The proposed framework can make the agent learn generalization and prevent the agent from failing catastrophically. The experimental results show that our approach helps the agent reduce computational costs with learning adaptation. Finally, we conclude and anticipate that integrating human-inspired meta-RL can enable learning more formulations relating to robustness and scalability, leading to promising directions and more complex AI goals in the future.

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Research on the Application and Analysis of Mechanical Equipment Intelligence Based on Knowledge Graph 2

Research on the Application and Analysis of Mechanical Equip...

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2nd International Conference on computer, Big Data and Artificial Intelligence, ICCBDAI 2021

作者： Qi, Rui Chen, Jing Yu, ZengWen Ke, WenJun Wang, Rui Graduate School of the Second Research Institute of China Aerospace Science and Industry Corporation Beijing China Beijing Institute of Computer Technology and Applications Beijing China Xidian University Shaanxi Xi'an China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

With the rapid evolution of manufacturing technology, mechanical equipment products are being replaced more frequently, and competition in the industry is becoming increasingly fierce. The collection and analysis of mechanical equipment technical intelligence is a key need for many enterprises. Using artificial intelligence technology to build an intelligence analysis system for all types of data is the inevitable trend of intelligence analysis. This paper proposes an intelligent processing and analysis framework of mechanical equipment intelligence. Based on the construction of knowledge graph, the knowledge embedding model is used to complete knowledge reasoning, predict development trends for mechanical equipment, and allow for more intelligent decision-making. A case study involving 3D printing shows the functions of the intelligence process and analysis framework. © 2022 institute of Physics Publishing. All rights reserved.

关键词： 3D printers

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