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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Karuna Yampray Department of Electronic Engineering International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan College of Creative Design and Entertainment Technology Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A great number of deep learning-based models have been recently proposed for automatic piano classification. In this paper, we describe our contribution to the challenge of automatic piano classification when the performer performs at the concert or stage. Among these models in deep learning, we use init-1D-WaveNet and init-2D-MLNet for comparison the accuracy in the piano beginning level of the Christmas song (Jingle bells). Our experimental results show that the assessment using the init-2D-MLNet still achieve high accuracy of 87.5%.

关键词： Deep learning Accuracy Instruments Collaboration Consumer electronics

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Process Mining for Improvement of IT Service in Automobile Industry 19

Process Mining for Improvement of IT Service in Automobile I...

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19th International Conference on ICT and Knowledge engineering, ICT and KE 2021

作者： Phasom, Phurint Chum-Im, Nattawut Kungcharoen, Kwanchai Premchaiswadi, Nucharee Premchaiswadi, Wichian Siam University School of Information Technology Bangkok Thailand Siam University Department of Computer Engineering Bangkok Thailand Dhurakij Pundit University College of Creative Design and Entertainment Technology Bangkok Thailand

ISBN: (纸本)9781665432122

This research presents the use process mining of the IT service process of an automotive company to identify deviations in their IT services processes, increasing productivity and quality of IT service. The research uses information from the BPI Conference 2013 held in Beijing, China, on August 26, 2013. The paper goes through the log to analyze the entire service process using various process mining techniques in the following area, Push-to-Front Analysis, Ping-Pong Behavior, Analysis of wait user, Process Conformity, and performance. As a result, the research discovers the status of the service performance and the deviation of the process. Moreover, we present our valuable insights into the actual process of the company. © 2021 IEEE.

关键词： Automotive industry

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Research on Efficient Software Defect Prediction Using Deep Learning Approaches 2

Research on Efficient Software Defect Prediction Using Deep ...

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2nd International Conference on Technological Advancements in Computational Sciences, ICTACS 2022

作者： Razauddin Madhuri G, Sindhu Oberoi, Ashish Vats, Aman Sivasangari, A. Siwach, Kuldeep Gulf Colleges Department of Computer and Information Sciences Saudi Arabia Faculty of Engineering & Technology Department of CSE Karnataka Bengaluru India Rimt University Department of CSE Gobindgarh Mandi India Manav Rachna International Institute of Research and Studies Faculty of Media Studies and Humanities Department of Journalism and Mass Communication Haryana Faridabad India Sathyabama Institute of Science and Technology Department of Ut Tamilnadu Chennai India School of Media and Entertainment SoME Gd Goenka University Gurgaon India

ISBN: (数字)9781665476577

ISBN: (纸本)9781665476577

Software Defect prediction results provide a list of source code artifacts that are prone to defects. Quality assurance teams can effectively devote more energy and allocate limited resources to defect-prone source code verification software products. A module that identifies defect prediction methods for frequent defects before the start of the testing phase. Measurement-based defect-prone modules improve software quality and reduce costs, leading to effective resource allocation. The previous method doesn't analyze the defect pattern, and it has less performance during software development. This work introduces a deep learning-based Pattern-based Modified Hidden Markova Fault Tree (PMHMFT) framework to extract the hidden fault analysis during cross-project validation. The proposed Modified Hidden Markova Fault Tree algorithm constructs the defect fault tree to analyze the cross-project code defect. To prevent defect based on software metrics software prediction model are used. Hidden Markova Fault tree-based classification categorize component as defective and non-defective. Using a Levy flight, optimize the method to search the fault classes efficiently compared to another method. The Markova Fault Tree model construct fault tree based given data;it is easy to identify the fault in software platform. The proposed PMHMFT to implement evaluate the performance using k-fold validation. Thus, the proposed work on software defect prediction achieves higher accuracy in true classification and prediction with less error rate. The software defects are predicted, and these predicted defects are optimized by using Levy flight optimization. Our proposed PMHMFT technique is very useful technique for predicting software defect and gives the better prediction rates in effective manner. © 2022 IEEE.

关键词： Defects

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Occlusion resistant face detection and recognition system 8

Occlusion resistant face detection and recognition system

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8th International Conference on Orange technology, ICOT 2020

作者： Tsai, An-Chao Ou, Yang-Yen Wu, Wei-Ching Wang, Jhing-Fa Tajen University Department of Computer Science and Entertainment Technology Taiwan National Cheng Kung University Department of Electrical Engineering Taiwan

ISBN: (纸本)9781665418522

The application of face recognition has become more and more popular with the development of deep learning algorithms and the calculation of hardware chips for accelerating neural networks. In face recognition, the recognition accuracy is easily affected by light, distance and occlusion. However, the occlusion is the most difficult issue to deal with. This work presents a convolutional neural network which was trained to improve the accuracy of face detection with the ability to capture facial features. The proposed method overcomes the situation when the face accompany with occlusion. All of the face regions and facial landmark are calculated via the face detection network for the inputted image. The face is then aligned by facial landmark and input into the face recognition network for identification. The experimental results of accuracy could reach 96.15% and 88.46% with the occlusion ration 25% and 50%, respectively. The proposed system successfully improves the face recognition accuracy while the face is occluded. © 2020 IEEE.

关键词： Face recognition

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Arbitrary axis-aligned and multi-scale drug recognition system 8

Arbitrary axis-aligned and multi-scale drug recognition syst...

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8th International Conference on Orange technology, ICOT 2020

作者： Chen, Che-Wen Tsai, An-Chao Chang, Wei-Yen Huang, Hsuan-Fu Wang, Jhing-Fa National Cheng Kung University Department of Electrical Engineering Tainan Taiwan Tajen University Department of Computer Science and Entertainment Technology Pingtung Taiwan

ISBN: (纸本)9781665418522

Medication safety has been a important research in many decays. If there is a mistake in the process of packaging medicines, it may cause great harm to patients. This research is aimed at the development of automatic drug image verification functions. Our system is composed of two stages. The first stage is arbitrary axis-aligned object detector, which is mainly based on deep residual network and feature pyramid network(FPN). The detector predicts the rotation bounding boxes for drugs by multi-scale feature maps generated by FPN. Then, the rotation bounding boxes are axially aligned, and the drug image is cropped according to axis-aligned bounding box. The second stage is feature matcher, which is based on feature embedding network. The embedding feature extracted by feature embedding network is combined with the geometric feature obtained by arbitrary axis-aligned object detector to determine whether the drug in image is belonging to expected category that user specify. Our system can detect multiscale rotating drugs and can identify drugs at any position, angle, and placement surface. And due to the addition of geometric features, it can effectively identify drugs with similar appearance but different sizes. Finally, according to the experimental results, a high accuracy rate of 0.047% FAR can be obtained. © 2020 IEEE.

关键词： Object detection

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NTIRE 2023 Quality Assessment of Video Enhancement Challenge

NTIRE 2023 Quality Assessment of Video Enhancement Challenge

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Liu, Xiaohong Min, Xiongkuo Sun, Wei Zhang, Yulun Zhang, Kai Timofte, Radu Zhai, Guangtao Gao, Yixuan Cao, Yuqin Kou, Tengchuan Dong, Yunlong Jia, Ziheng Li, Yilin Wu, Wei Hu, Shuming Deng, Sibin Xiao, Pengxiang Chen, Ying Li, Kai Zhao, Kai Yuan, Kun Sun, Ming Cong, Heng Wang, Hao Fu, Lingzhi Zhang, Yusheng Zhang, Rongyu Shi, Hang Xu, Qihang Xiao, Longan Ma, Zhiliang Agarla, Mirko Celona, Luigi Rota, Claudio Schettini, Raimondo Huang, Zhiwei Li, Ya'nan Wang, Xiaotao Lei, Lei Liu, Hongye Hong, Wei Chuang, Ironhead Lin, Allen Guan, Drake Chen, Iris Lou, Kae Huang, Willy Tasi, Yachun Kao, Yvonne Fan, Haotian Kong, Fangyuan Zhou, Shiqi Liu, Hao Lai, Yu Chen, Shanshan Wang, Wenqi Wu, Haoning Chen, Chaofeng Zhu, Chunzheng Guo, Zekun Zhao, Shiling Yin, Haibing Wang, Hongkui Meftah, Hanene Brachemi Fezza, Sid Ahmed Hamidouche, Wassim Déforges, Olivier Shi, Tengfei Mansouri, Azadeh Motamednia, Hossein Bakhtiari, Amir Hossein Aznaveh, Ahmad Mahmoudi Shanghai Jiao Tong University China Eth Zürich Switzerland University of Würzburg Germany Department of Tao Technology Alibaba Group China Kuaishou Technology China Interactive Entertainment Group of Netease Inc Guangzhou China Transsion China Hefei University of Technology China Department of Informatics Systems and Communication University of Milano - Bicocca Italy Xiaomi Inc. China China Ji Liang University China FreeTech Advanced Research Center KKCompany Taiwan ByteDance China Mgtv China Fuzhou University China Shopee Information Technology Co. Ltd. S-Lab Nanyang Technological University Singapore Hunan University China Hangzhou Dianzi University China Insa Rennes Cnrs Ietr - Umr 6164 Rennes France National Higher School of Telecommunications and Ict Oran Algeria Buaa Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyberspace Research Institute Shahid Beheshti University Tehran Iran

ISBN: (纸本)9798350302493

This paper reports on the NTIRE 2023 Quality Assessment of Video Enhancement Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2023. This challenge is to address a major challenge in the field of video processing, namely, video quality assessment (VQA) for enhanced videos. The challenge uses the VQA Dataset for Perceptual Video Enhancement (VDPVE), which has a total of 1211 enhanced videos, including 600 videos with color, brightness, and contrast enhancements, 310 videos with deblurring, and 301 deshaked videos. The challenge has a total of 167 registered participants. 61 participating teams submitted their prediction results during the development phase, with a total of 3168 submissions. A total of 176 submissions were submitted by 37 participating teams during the final testing phase. Finally, 19 participating teams submitted their models and fact sheets, and detailed the methods they used. Some methods have achieved better results than baseline methods, and the winning methods have demonstrated superior prediction performance. © 2023 IEEE.

关键词： Image enhancement

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Taiwanese Speech Recognition Based on Hybrid Deep Neural Network Architecture

Taiwanese Speech Recognition Based on Hybrid Deep Neural Net...

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32nd Conference on Computational Linguistics and Speech Processing, ROCLING 2020

作者： Yeh, Yu-Fu Su, Bo-Hao Ou, Yang-Yen Wang, Jhing-Fa Tsai, An-Chao Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Computer Science and Entertainment Technology Tajen University Pingtung Taiwan

ISBN: (纸本)9789869576932

In this research, we developed the Taiwanese speech recognition system which used the Kaldi toolkit to implement. The Taiwanese corpus was collected by Taiwan Taiwanese National Reading Competition and Classmate Recording, and a total of about 11 hours of audio files were collected. Because the training data is small dataset, two audio augmentation methods are used to increase the training data, so that the acoustic model can be more robust and more effective training. One method is speed perturbation, which speeds up the original data by 1.1 times and slows it down by 0.9 times. Another method is to use multi-condition training data to simulate reverberation of the original speech and add background noise. The background noise includes music, speech, and noise. The acoustic model is trained for different hybrid deep neural network architectures which can use the advantages of each neural network by hybrid different neural networks, including TDNN, CNN-TDNN and CNN-LSTM-TDNN. In the experimental results, the CER in the domain of language modeling reaches 3.95%, and the CER of online decoding test is 3.06%. Compared with other researches on Taiwanese speech recognition of similar dataset size, the recognition results are better than other studies. © ROCLING *** rights reserved.

关键词： Deep neural networks

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Efficient Deep Models for Real-Time 4K Image Super-Resolution. NTIRE 2023 Benchmark and Report

Efficient Deep Models for Real-Time 4K Image Super-Resolutio...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Conde, Marcos V. Zamfir, Eduard Timofte, Radu Motilla, Daniel Liu, Cen Zhang, Zexin Peng, Yunbo Lin, Yue Guo, Jiaming Zou, Xueyi Chen, Yuyi Liu, Yi Hao, Jia Yan, Youliang Zhang, Yuanfan Li, Gen Sun, Lei Kong, Lingshun Bai, Haoran Pan, Jinshan Dong, Jiangxin Tang, Jinhui Ayazoglu, Mustafa Bilecen, Bahri Batuhan Li, Mingxi Zhang, Yuhang Fan, Xianjun Sheng, Yankai Sun, Long Liu, Zibin Gou, Weiran Li, Shaoqing Yi, Ziyao Xiang, Yan Kong, Dehui Xu, Ke Gankhuyag, Ganzorig Yoon, Kihwan Zhang, Jin Yu, Gaocheng Zhang, Feng Wang, Hongbin Zhou, Zhou Chao, Jiahao Gao, Hongfan Gong, Jiali Yang, Zhengfeng Zeng, Zhenbing Chen, Chengpeng Guo, Zichao Park, Anjin Liu, Yuqing Jia, Qi Yu, Hongyuan Yin, Xuanwu Zuo, Kunlong Zhang, Dongyang Fu, Ting Cheng, Zhengxue Zhu, Shiai Zhou, Dajiang Yu, Weichen Ge, Lin Dong, Jiahua Zou, Yajun Wu, Zhuoyuan Han, Binnan Zhang, Xiaolin Zhang, Heng Shao, Ben Zheng, Shaolong Yin, Daheng Chen, Baijun Liu, Mengyang Nistor, Marian-Sergiu Chen, Yi-Chung Huang, Zhi-Kai Chiang, Yuan-Chun Chen, Wei-Ting Yang, Hao-Hsiang Chang, Hua-En Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Vo, Tu Yan, Qingsen Zhu, Yun Su, Jinqiu Zhang, Yanning Zhang, Cheng Luo, Jiaying Cho, Youngsun Lee, Nakyung Computer Vision Lab CAIDAS IFI University of Würzburg Germany Sony Interactive Entertainment CA United States Huawei Technologies Co. Ltd. China NetEase Games AI Lab Nanjing University of Science and Technology China Tencent China Attrsense Korea Republic of Sanechips Co Ltd Ant Group China East China Normal University China Shopee Dalian University of Technology Xiaomi Inc. China China Zhejiang Dahua Technology Co. Ltd. China Multimedia Department Xiaomi Inc. China Korea Photonic Technology Institute Korea Republic of School of Computer Science and Engineering Southeast University China University Al. I. Cuza Iasi Romania Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Northwestern Polytechnical University China KC Machine Learning Lab CJ OliveNetworks AI Research

ISBN: (纸本)9798350302493

This paper introduces a novel benchmark for efficient up-scaling as part of the NTIRE 2023 Real-Time Image Super-Resolution (RTSR) Challenge, which aimed to upscale images from 720p and 1080p resolution to native 4K (×2 and ×3 factors) in real-time on commercial GPUs. For this, we use a new test set containing diverse 4K images ranging from digital art to gaming and photography. We assessed the methods devised for 4K SR by measuring their runtime, parameters, and FLOPs, while ensuring a minimum PSNR fidelity over Bicubic interpolation. Out of the 170 participants, 25 teams contributed to this report, making it the most comprehensive benchmark to date and showcasing the latest advancements in real-time SR. © 2023 IEEE.

关键词： Program processors

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Asymmetric Transfer Hashing with Adaptive Bipartite Graph Learning

arXiv

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arXiv 2022年

作者： Lu, Jianglin Zhou, Jie Chen, Yudong Pedrycz, Witold Hung, Kwok-Wai The College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States The College of Computer Science and Software Engineering Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The School of Information Technology and Electrical Engineering University of Queensland BrisbaneQLD4072 Australia The Department of Electrical & Computer Engineering University of Alberta Edmonton Canada The Systems Research Institute Polish Academy of Sciences Warsaw Poland Tencent Music Entertainment Guangdong Shenzhen518060 China

Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, the known hashing methods assume that the query and retrieval samples lie in homogeneous feature space within the same domain. As a result, they cannot be directly applied to heterogeneous cross-domain retrieval. In this paper, we propose a Generalized Image Transfer Retrieval (GITR) problem, which encounters two crucial bottlenecks: 1) the query and retrieval samples may come from different domains, leading to an inevitable domain distribution gap;2) the features of the two domains may be heterogeneous or misaligned, bringing up an additional feature gap. To address the GITR problem, we propose an Asymmetric Transfer Hashing (ATH) framework with its unsupervised/semi-supervised/supervised realizations. Specifically, ATH characterizes the domain distribution gap by the discrepancy between two asymmetric hash functions, and minimizes the feature gap with the help of a novel adaptive bipartite graph constructed on cross-domain data. By jointly optimizing asymmetric hash functions and the bipartite graph, not only can knowledge transfer be achieved but information loss caused by feature alignment can also be avoided. Meanwhile, to alleviate negative transfer, the intrinsic geometrical structure of single-domain data is preserved by involving a domain affinity graph. Extensive experiments on both single-domain and cross-domain benchmarks under different GITR subtasks indicate the superiority of our ATH method in comparison with the state-of-the-art hashing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Hash functions

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RoleSeer: Understanding Informal Social Role Changes in MMORPGs via Visual Analytics

arXiv

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arXiv 2022年

作者： Xie, Laixin Wu, Ziming Xu, Peng Li, Wei Ma, Xiaojuan Li, Quan School of Information Science and Technology ShanghaiTech University Shanghai China Interactive Entertainment Group Tencent Inc. Shenzhen China User Experience Center NetEase Inc. Hangzhou China College of Creative Design Shenzhen Technology University Shenzhen China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Massively multiplayer online role-playing games create virtual communities that support heterogeneous "social roles" determined by gameplay interaction behaviors under a specific social context. For all social roles, formal roles are pre-defined, obvious, and explicitly ascribed to the people holding the roles, whereas informal roles are not well-defined and unspoken. Identifying the informal roles and understanding their subtle changes are critical to designing sociability mechanisms. However, it is nontrivial to understand the existence and evolution of such roles due to their loosely defined, interconvertible, and dynamic characteristics. We propose a visual analytics system, RoleSeer, to investigate informal roles from the perspectives of behavioral interactions and depict their dynamic interconversions and transitions. Two cases, experts' feedback, and a user study suggest that RoleSeer helps interpret the identified informal roles and explore the patterns behind role changes. We see our approach's potential in investigating informal roles in a broader range of social games. © 2022, CC BY.

关键词： Visualization

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