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IEEE Workshop on Multimedia Signal Processing

作者： Ching-Yu Chiu Wen-Yi Hsiao Yin-Cheng Yeh Yi-Hsuan Yang Alvin Wen-Yu Su Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan National Cheng Kung University Taiwan

ISBN: (数字)9781728193205

ISBN: (纸本)9781728193236

Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data.

关键词： Training Source separation Training data Production Data models Multiple signal classification Music information retrieval

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DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection

DeepGuard: Deep Generative User-behavior Analytics for Ranso...

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IEEE International Conference on Intelligence and Security Informatics, ISI

作者： Gaddisa Olani Ganfure Chun-Feng Wu Yuan-Hao Chang Wei-Kuan Shih Social Networks and Human Centered Computing Program Taiwan International Graduate Program Institute of Information Science Nangang Taipei Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu Taiwan National Tsing Hua University Hsinchu City Taiwan

ISBN: (数字)9781728188003

ISBN: (纸本)9781728188010

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

关键词： Training Training data Organizations Tools Ransomware Security Informatics

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Highly reflective silver mirror enhanced by several dielectric films prepared under the low substrate temperature

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Heliyon 2024年第15期10卷 e35507页

作者： Wu, Hsing-Yu Chen, Hong-Wei Huang, Shao-Rong Hsiao, Li-Jen Cheng, Ching-Ling Yu, Guo-Yu Sun, Yung-Shin Hsu, Jin-Cherng Taiwan Space Agency Taiwan National Central University Center for Astronomical Physics and Engineering Department of Optics and Photonics Taoyuan City 320317 Taiwan National Yang Ming Chiao Tung University Institute of Space Systems Engineering Hsinchu City 300025 Taiwan Fu Jen Catholic University Department of Physics New Taipei City 242062 Taiwan Diopter Precision Taiwan Co. Ltd. Taoyuan City 324 Taiwan National United University Department of Electrical Engineering Miao-Li 36063 Taiwan Department of Engineering and Technology School of Computing and Engineering University of Huddersfield Queensgate Huddersfield HD1 3DH United Kingdom Fu Jen Catholic University Graduate Institute of Applied Science and Engineering New Taipei City 242062 Taiwan

Optical paths in telescopes frequently incorporate silver mirrors for high sensitivity. Unfortunately, silver mirrors without protective coatings are susceptible to sulfurization and oxidation, compromising their quality. Even with protective layers, insufficient adhesion between the coating and the silver film can lead to peeling, exposing the silver to external environments and affecting its quality. This study aimed to identify dielectric materials with superior adhesion to silver, rendering them ideal choices for silver coating applications. By electron gun evaporation, different dielectric layers were deposited on the top and bottom of the silver film under a substrate temperature below 150 °C. These coatings were composed of materials with desired refractive indices, including aluminum oxide (Al₂O₃), aluminum-doped silicon, magnesium fluoride (MgF₂), and other dielectrics. Following the deposition, a tape adhesion test was conducted to evaluate the bond strength of the samples. X-ray photoelectron spectroscopy (XPS) analysis was carried out to investigate the interaction between silver and its neighboring layers. The results revealed that Al₂O₃ and MgF₂ exhibited exceptional adhesion to silver. Moreover, these multilayer coatings can effectively enhance the reflectance of silver in the visible (VIS) wavelength ranges. © 2024 The Authors

关键词： Enhanced silver coating Silver adhesion Silver mirror Space optics XPS

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Estimation of Current Distribution inside MEA in Single Cell Type PEFC by Particle Filter Using Circumference Magnetic Field

Estimation of Current Distribution inside MEA in Single Cell...

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Fuel Cell Seminar and Energy Exposition 2019

作者： Taguchi, Tomoyuki Gotoh, Yuji Izumi, Masaaki Nara, Takaaki Department of Materials Science and Production Engineering Oita University 700 Dannoharu Oita870-1192 Japan Department of Innovative Engineering Oita University 700 Dannoharu Oita870-1192 Japan Department of Mechanical Systems Engineering University of Kitakyushu 1-1 Hibikino Wakamatsu-ku Kitakyushu Fukuoka808-0135 Japan Department of Information Physics and Computing Graduate School of Information Science and Technology University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan

ISBN: (纸本)9781607688884

The fuel cell which is mainly used as a power supply in electric vehicles and house is polymer electrolyte fuel cell (PEFC). It is a fuel cell using the Membrane Electrode Assembly (MEA) as the electrolyte. In order to develop a more efficient fuel cell, it is necessary to measure the current distribution inside the MEA. When the power generation current in the fuel cell is generated, the static magnetic field around the fuel cell is distributed. Therefore, there is a possibility that the generated current distribution inside the MEA can be obtained using the distribution of the static magnetic field around the fuel cell. In this paper, the distribution of the static magnetic field around the PEFC is measured, and the generated current distribution inside the MEA is estimated by particle filter using Ampere's law. © 2020 ECS-The Electrochemical Society.

关键词： Monte Carlo methods

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Haptic Feedback System of the Additional Hand Position for Multiple Task Situations using a Wearable Robot Arm

Haptic Feedback System of the Additional Hand Position for M...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Yukiko Iwasaki Kozo Ando Hiroyasu Iwata Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Green Computing Systems Research Organization Tokyo Japan

ISBN: (数字)9781728150734

ISBN: (纸本)9781728150741

Wearable robot arm systems have been proposed that enable various tasks that humans can not do with their only two natural arms. However, many of these do not have a somatosensory feedback system so that users have to confirm the robot's state by looking every time. This active confirmation can often be a `switching cost' of task and reduce the multitasking efficiency performed by natural arms and additional robotic arm. In this paper, we focused on the proprioception among the somatosensation, and propose a “Position haptic belt” which is a new biofeedback system for a wearable robot arm. The system represents the three-dimensional position of a robot hand by the vibrator's placement and vibration frequency. The prototype we developed is able to identify the robot hand position with an accuracy of about 10 cm, and it is used to conduct a case study to evaluate the usability in multiple task situation. The result suggested that the effectiveness of the somatosensory feedback information of robot hand position, even for the wearable robot arm which can be easily confirmed its state on user's own eyes.

关键词：

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Mixing-specific data augmentation techniques for improved blind violin/piano source separation

arXiv

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arXiv 2020年

作者： Chiu, Ching-Yu Hsiao, Wen-Yi Yeh, Yin-Cheng Yang, Yi-Hsuan Su, Alvin Wen-Yu Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan Yating Music Team Taiwan AI Labs Taiwan Research Center for IT Innovation Academia Sinica Taiwan Dept. CSIE National Cheng Kung University Taiwan

关键词： Mixing

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Correction: A meta-classification-based approach for outlier identification in GNSS networks

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GPS Solutions 2025年第2期29卷 1-1页

作者： Suraci, Stefano Sampaio de Oliveira, Leonardo Castro Klein, Ivandro Goldschmidt, Ronaldo Ribeiro Rofatto, Vinicius Francisco Defense Engineering Department Military Institute of Engineering Leonardo Castro de Oliveira Rio de Janeiro Brazil Defense Engineering Program Military Institute of Engineering Rio de Janeiro Brazil Graduate Program in Geodetic Sciences Federal University of Paraná Curitiba Brazil Dept. of Civil Construction Federal Institute of Santa Catarina Florianopolis Brazil Systems and Computing Program Military Institute of Engineering Rio de Janeiro Brazil Institute of Geography Federal University of Uberlândia Monte Carmelo Brazil

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NTIRE 2024 Quality Assessment of AI-Generated Content Challenge

arXiv

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arXiv 2024年

作者： Liu, Xiaohong Min, Xiongkuo Zhai, Guangtao Li, Chunyi Kou, Tengchuan Sun, Wei Wu, Haoning Gao, Yixuan Cao, Yuqin Zhang, Zicheng Wu, Xiele Timofte, Radu Gankhuyag, Ganzorig Yoon, Kihwan Xu, Yifang Fan, Haotian Kong, Fangyuan Zhao, Shiling Dong, Weifeng Yin, Haibing Zhu, Li Wang, Zhiling Huang, Bingchen Peng, Fei Fu, Huiyuan Ming, Anlong Wang, Chuanming Ma, Huadong He, Shuai Dou, Zifei Chen, Shu Yu, Zihao Guan, Fengbin Motamednia, Hossein Hosseini-Benvidi, S. Farhad Mahmoudi-Aznaveh, Ahmad Mansouri, Azadeh Saha, Avinab Mishra, Sandeep Gupta, Shashank Sureddi, Rajesh Saha, Oindrila Celona, Luigi Bianco, Simone Napoletano, Paolo Schettini, Raimondo Yang, Junfeng Fu, Jing Zhang, Wei Cao, Wenzhi Liu, Limei Peng, Han Yuan, Weijun Li, Zhan Cheng, Yihang Deng, Yifan Zhang, Huacong Xie, Haiyi Wang, Chengwei Chen, Baoying Zeng, Jishen Yang, Jianquan Wang, Weigang Fang, Xi Lv, Xiaoxin Yan, Jun Li, Haohui Qu, Bowen Li, Yao Luo, Shuqing Wang, Shunzhou Gao, Wei Lu, Zihao Conde, Marcos V. Timofte, Radu Lu, Yiting Li, Xin Wang, Xinrui Guan, Fengbin Chen, Zhibo Liao, Ruling Ye, Yan Wang, Qiulin Li, Bing Zhou, Zhaokun Geng, Miao Chen, Rui Tao, Xin Liang, Xiaoyu Sun, Shangkun Fang, Xi Wang, Weigang Yan, Jun Ma, Xingyuan Li, Jiaze Yang, Mengduo Xu, Haoran Zhou, Jie Zhu, Shiding Yu, Bohan Zhi, Tianwu Zhang, Yabin Li, Yaohui Li, Yang Xu, Jingwen Liu, Jianzhao Liao, Yiting Li, Junlin Chen, Baoying Zeng, Jishen Xie, Haiyi Wang, Chengwei Yang, Jianquan Chen, Pengfei Xu, Xinrui Shen, Jiabin Duan, Zhichao Liu, Jiahe Yan, Qi Qu, Youran Zeng, Xiaohui Wang, Lele Liao, Renjie Shanghai Jiao Tong University China Nanyang Technological University Singapore ETH Zürich Switzerland University of Würzburg Germany Korea Electronics Technology Institute Korea Republic of ByteDance China Hangzhou Dianzi University China Sankuai China Beijing University of Posts and Telecommunications China Beijing Xiaomi Mobile Software Co. Ltd. China University of Science and Technology of China China High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran University of Texas Austin United States University of Massachusetts Amher United States Department of Informatics Systems and Communication Xiangjiang Laboratory Hunan University of Technology and Business China Jinan University China Sun Yat-sen University China Alibaba Group China Cisco Systems Inc. United States DP Technology Ltd. Shopee Pte. Ltd Tongji University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China University of Würzburg Germany Kuaishou Technology China Peking University China Zhejiang University China Bytedance Inc China Xidian University China University of British Columbia Canada University of Toronto Canada

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Simulating the Shastry-Sutherland Ising Model Using Quantum Annealing

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PRX Quantum 2020年第2期1卷 020320-020320页

作者： Paul Kairys Andrew D. King Isil Ozfidan Kelly Boothby Jack Raymond Arnab Banerjee Travis S. Humble Bredesen Center for Interdisciplinary Research and Graduate Education University of Tennessee Knoxville Tennessee USA Quantum Computing Institute Oak Ridge National Laboratory Oak Ridge Tennessee USA D-Wave Systems Inc. Burnaby British Columbia Canada Neutron Sciences Division Oak Ridge National Laboratory Oak Ridge Tennessee USA Department of Physics and Astronomy Purdue University West Lafayette Indiana USA

A core concept in condensed matter physics is geometric frustration that leads to emergent spin phases in magnetic materials. These distinct phases, which depart from the conventional ferromagnet or the antiferromagnet, require unique computational techniques to decipher. In this study, we use the canonical Ising Shastry-Sutherland lattice to demonstrate new techniques for solving frustrated Hamiltonians using a quantum annealer of programmable superconducting qubits. This Hamiltonian can be tuned to produce a variety of intriguing ground states ranging from short- and long-range orders and fractional order parameters. We show that a large-scale finite-field quantum annealing experiment is possible on 468 logical spins of this model embedded into the quantum hardware. We determine microscopic spin configurations using an iterative quantum annealing protocol and develop mean-field boundary conditions to attenuate finite-size effects and defects. We not only recover all phases of the Shastry-Sutherland Ising model—including the well-known fractional magnetization plateau in a longitudinal field—but also predict the spin behavior at the critical points with significant ground-state degeneracy and in the presence of defects. The results lead us to establish the connection to the diffuse neutron scattering experiments by calculation of the static structure factors.

关键词： Frustrated magnetism Quantum computation Quantum simulation Ising model

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Interpretation of variable consistency dominance-based rough set approach by minimization of asymmetric loss function 7th

Interpretation of variable consistency dominance-based rough...

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7th International Symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM 2019

作者： Kusunoki, Yoshifumi Blaszczyński, Jerzy Inuiguchi, Masahiro Slowiński, Roman Graduate School of Engineering Osaka University 2-1 Yamada-oka SuitaOsaka565-0871 Japan Graduate School of Engineering Science Osaka University 1-3 Machikaneyama ToyonakaOsaka560-8531 Japan Poznań University of Technology Institute of Computing Science Piotrowo 3a Poznań60-965 Poland Systems Research Institute Polish Academy of Sciences Warsaw01-447 Poland

ISBN: (纸本)9783030148140

In this paper, we give a statistical interpretation of the variable consistency dominance-based rough set approach (VC-DRSA), which is a version of DRSA useful for practical reasoning about ordinal data. This study is building a bridge between theories of rough sets and statistics, and it is developing, moreover, a new direction of study about VC-DRSA. We consider a classification problem for each pair of complementary upward and downward unions of decision classes, and define an empirical risk function with an asymmetric loss function consisting of hinge and 0–1 loss functions. Then, we prove that approximations of two decision classes by VC-DRSA correspond to the minimum of the empirical risk function. © Springer Nature Switzerland AG 2019.

关键词： Risk assessment

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