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21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020

作者： Ge, Meng Xu, Chenglin Wang, Longbiao Chng, Eng Siong Dang, Jianwu Li, Haizhou Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Japan Advanced Institute of Science and Technology Ishikawa Japan Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany

ISBN: (纸本)9781713820697

Speaker extraction aims to extract the target speech signal from a multi-talker environment given a target speaker's reference speech. We recently proposed a time-domain solution, SpEx, that avoids the phase estimation in frequency-domain approaches. Unfortunately, SpEx is not fully a time-domain solution since it performs time-domain speech encoding for speaker extraction, while taking frequency-domain speaker embedding as the reference. The size of the analysis window for time-domain and the size for frequency-domain input are also different. Such mismatch has an adverse effect on the system performance. To eliminate such mismatch, we propose a complete time-domain speaker extraction solution, that is called SpEx+. Specifically, we tie the weights of two identical speech encoder networks, one for the encoder-extractor-decoder pipeline, another as part of the speaker encoder. Experiments show that the SpEx+ achieves 0.8dB and 2.1dB SDR improvement over the state-of-the-art SpEx baseline, under different and same gender conditions on WSJ0-2mix-extr database respectively. Copyright © 2020 ISCA

关键词： Extraction

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Unsupervised partial point set registration via joint shape completion and registration

arXiv

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arXiv 2020年

作者： Li, Xiang Wang, Lingjing Fang, Yi NYU Multimedia and Visual Computing Lab NYU Tandon Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering NYU Tandon United States Department of Electrical and Computer Engineering NYU Abu Dhabi United Arab Emirates

We propose a self-supervised method for partial point set registration. While recent proposed learning-based methods have achieved impressive registration performance on the full shape observations, these methods mostly suffer from performance degradation when dealing with partial shapes. To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process. To achieve this, we design a latent code for each pair of shapes, which can be regarded as a geometric encoding of the target shape. By doing so, our model does need an explicit feature embedding network to learn the feature encodings. More importantly, both our shape completion network and the point set registration network take the shared latent codes as input, which are optimized along with the parameters of two decoder networks in the training process. Therefore, the point set registration process can thus benefit from the joint optimization process of latent codes, which are enforced to represent the information of full shape instead of partial ones. In the inference stage, we fix the network parameter and optimize the latent codes to get the optimal shape completion and registration results. Our proposed method is pure unsupervised and does not need any ground truth supervision. Experiments on the ModelNet40 dataset demonstrate the effectiveness of our model for partial point set registration. Copyright © 2020, The Authors. All rights reserved.

关键词： Encoding (symbols)

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Metaverse Services: The Way of Services Towards the Future

Metaverse Services: The Way of Services Towards the Future

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Xu, Xiaofei Sheng, Quan Z. Benatallah, Boualem Chen, Zhong Gazda, Robert Saddik, Abdulmotableb Ei Singh, Munindar P. Faculty of Computing Harbin Institute of Technology China Macquarie University School of Computing NSW2109 Australia Dublin City University School of Computing Ireland Peking University School of Computer Science China Wireless Networking Lab Interdigital United States University of Ottawa School of Electrical Engineering and Computer Science Canada North Carolina State University Department of Computer Science United States

ISBN: (纸本)9798350304855

With the emergence of new generation of digital technologies, e.g., artificial intelligence, blockchain, cloud computing, big data, edge computing, 5G/6G, VR/AR/MR, and the Internet of Things, an exciting era of metaverse is coming. Interacted and linked with the physical world, metaverse offers a platform of a new social ecosystem, dealing with digital twins and empowering virtual-reality symbiosis. In metaverse, social activities and business processes are performed based on the sequences of workflow or service processes. Bridging both the virtual space and the real world, such metaverse services are more complicated and present many new challenges and research topics. In this paper, the concept and characteristics of metaverse services are presented, the key technologies and typical use cases are reviewed, and the future challenges and opportunities of metaverse services are also discussed. © 2023 IEEE.

关键词： Internet of things

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A Clinician-Friendly Platform for Ophthalmic Image Analysis Without Technical Barriers

arXiv

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arXiv 2025年

作者： Wang, Meng Lin, Tian Hou, Qingshan Lin, Aidi Wang, Jingcheng Peng, Qingsheng Nguyen, Truong X. Fang, Danqi Zou, Ke Xu, Ting Xue, Cancan Quek, Ten Cheer Yu, Qinkai Liu, Minxin Zhou, Hui Xiao, Zixuan He, Guiqin Liang, Huiyu Shi, Tingkun Chen, Man Liu, Linna Peng, Yuanyuan Wang, Lianyu Hu, Qiuming Chen, Junhong Zhang, Zhenhua Chen, Cheng Zhao, Yitian Liu, Dianbo Wu, Jianhua Chen, Xinjian Zhang, Changqing Nguyen, Triet Thanh Meng, Yanda Zheng, Yalin Tham, Yih Chung Cheung, Carol Y. Fu, Huazhu Chen, Haoyu Cheng, Ching-Yu Centre for Innovation and Precision Eye Health Yong Loo Lin School of Medicine National University of Singapore Singapore119228 Singapore Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Singapore119228 Singapore Joint Shantou International Eye Center Shantou University The Chinese University of Hong Kong Guangdong Shantou515041 China Shantou University Medical College Guangdong Shantou515041 China School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Big Vision Medical Technology Ltd. Suzhou215011 China Singapore Eye Research Institute Singapore National Eye Centre Singapore169856 Singapore Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong 999077 Hong Kong Binh Dinh Eye Hospital Viet Nam Department of Computer Science University of Exeter ExeterEX4 4RN United Kingdom University of Science and Technology Hospital Guangdong Shenzhen518000 China Shenzhen Qianhai Shekou Free Trade Zone Hospital Guangdong Shenzhen518000 China Department of Ophthalmology Meizhou People’s Hospital Meizhou Academy of Medical Sciences Guangdong Meizhou514000 China Department of Ophthalmology Yeungnam University College of Medicine Daegu42417 Korea Republic of Aier Eye Hospital of Wuhan University Hubei Wuhan430000 China School of Biomedical Engineering Anhui Medical University Anhui Hefei230032 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing211100 China Guangxi Jingliang Eye Hospital Guangxi Nanning530021 China Puning People’s Hospital Guangdong Jieyang522000 China Shandong Qingdao266042 China Department of Electrical and Electronic Engineering the University of Hong Kong Hong Kong Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Zhejiang Ningbo315201 China School of Electronics and Information Engineering Soochow University

Artificial intelligence (AI) shows remarkable potential in medical imaging diagnostics, but current models typically require retraining when deployed across different clinical centers, limiting their widespread adoption. We introduce GlobeReady, a clinician-friendly AI platform that enables ocular disease diagnosis without retraining/fine-tuning or technical expertise. GlobeReady achieves high accuracy across imaging modalities: 93.9–98.5% for an 11-category fundus photo dataset and 87.2–92.7% for a 15-category OCT dataset. Through training-free local feature augmentation, it addresses domain shifts across centers and populations, reaching an average accuracy of 88.9% across five centers in China, 86.3% in Vietnam, and 90.2% in the UK. The built-in confidence-quantifiable diagnostic approach further boosted accuracy to 94.9–99.4% (fundus) and 88.2–96.2% (OCT), while identifying out-of-distribution cases at 86.3% (49 CFP categories) and 90.6% (13 OCT categories). Clinicians from multiple countries rated GlobeReady highly (average 4.6 out of 5) for its usability and clinical relevance. These results demonstrate GlobeReady’s robust, scalable diagnostic capability and potential to support ophthalmic care without technical barriers. © 2025, CC0.

关键词： Ophthalmology

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Marginalized denoising autoencoder via graph regularization for domain adaptation

Marginalized denoising autoencoder via graph regularization ...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MoE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Domain adaptation, which aims to learn domain-invariant features for sentiment classification, has received increasing attention. The underlying rationality of domain adaptation is that the involved domains share some common latent factors. Recently neural network based on Stacked Denoising Auto-Encoders (SDA) and its marginalized version (mSDA) have shown promising results on learning domain-invariant features. To explicitly preserve the intrinsic structure of data, this paper proposes a marginalized Denoising Autoencoders via graph Regularization (GmSDA) in which the autoencoder based framework can learn more robust features with the help of newly incorporated graph regularization. The learned representations are fed into the sentiment classifiers and experiments show that the GmSDA can effectively improve the classification accuracy when comparing with some state-of-the-art models on the cropped Amazon benchmark data set. © Springer-Verlag 2013.

关键词： Classification (of information)

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Structure preserving low-rank representation for semi-supervised face recognition

Structure preserving low-rank representation for semi-superv...

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20th International Conference on Neural Information Processing, ICONIP 2013

作者： Peng, Yong Wang, Suhang Wang, Shen Lu, Bao-Liang Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong Unviersity Shanghai 200240 China Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI 48109 United States MOE-Microsoft Key Lab. for Intelligent Computing and Intelligent Systems Shanghai Jiao Tong Unviersity Shanghai 200240 China

ISBN: (纸本)9783642420412

Constructing an informative and discriminative graph plays an important role in the graph based semi-supervised learning methods. Among these graph construction methods, low-rank representation based graph, which calculates the edge weights of both labeled and unlabeled samples as the low-rank representation (LRR) coefficients, has shown excellent performance in semi-supervised learning. In this paper, we additionally impose twofold constraints (local affinity and distant repulsion) on the LRR graph. The improved model, termed structure preserving LRR (SPLRR), can preserve the local geometrical structure but without distorting the distant repulsion property. Experiments are taken on three widely used face data sets to investigate the performance of SPLRR and the results show that it is superior to some state-of-the-art semi-supervised graphs. © Springer-Verlag 2013.

关键词： Face recognition

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用于糖尿病培训的大型语言模型:一项前瞻性研究（英文）

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Science Bulletin 2025年第6期 934-942页

作者：李灏萱江泽铧管洲榆包玉倩刘月星胡婷婷李佳佳刘茹涵吴量程棣纪宏伟王勇王亚星 Carol Y.Cheung 郑颖丰王继红李震吴卫兵 Cynthia Ciwei Lim Yong Mong Bee Hong Chang Tan Elif I.Ekinci David C.Klonoff Justin B.Echouffo-Tcheugui Nestoras Mathioudakis Leonor Corsino Rafael Simó Charumathi Sabanayagam Gavin Siew Wei Tan 程景煜黄天荫李华婷蔡淳毛丽娟 Lee-Ling Lim 覃宇宗盛斌贾伟平 Shanghai University of Sport School of Clinical Medicine Beijing Tsinghua Changgung Hospital Tsinghua Medicine Tsinghua University Beijing Visual Science and Translational Eye Research Institute(BERI) Tsinghua Medicine Tsinghua University Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes MoE Key Lab of Artificial Intelligence Artificial Intelligence Institute Shanghai Jiao Tong University Department of Cardiology the Affiliated Hospital of Qingdao University CEMS NCMIS HCMS MADIS Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematics University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Beijing Institute of Ophthalmology Beijing Tongren Hospital Capital Medical University Beijing Ophthalmology and Visual Sciences Key Laboratory Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual ScienceGuangdong Provincial Clinical Research Center for Ocular Diseases Department of Renal Medicine Singapore General Hospital Sing Health-Duke Academic Medical Centre Department of Endocrinology Singapore General Hospital Department of Endocrinology Austin Health Department of Medicine The University of Melbourne(Austin Health) Australian Centre for Accelerating Diabetes Innovations(ACADI) The Univ

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models(LLMs)provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: Chat GPT-3.5, Chat GPT-4.0, Google Bard, Lla MA-7B, Lla MA2-7B, Baidu ERNIE Bot, Ali Tongyi Qianwen, Med GPT, Huatuo GPT, and Chinese Lla MA2-7B on diabetes-related queries, based on the Chinese National Certificate Examination for Primary Diabetes Care in China(NCE-CPDC) and the English Specialty Certificate Examination in Endocrinology and Diabetes of Membership of the Royal College of Physicians of the United Kingdom. Second, we assessed the training of primary care physicians(PCPs) without and with the assistance of Chat GPT-4.0 in the NCE-CPDC examination to ascertain the reliability of LLMs as medical assistants. We found that Chat GPT-4.0 outperformed other LLMs in the English examination, achieving a passing accuracy of 62.50%, which was significantly higher than that of Google Bard, Lla MA-7B, and Lla MA2-7B. For the NCE-CPFC examination, Chat GPT-4.0, Ali Tongyi Qianwen, Baidu ERNIE Bot, Google Bard, Med GPT, and Chat GPT-3.5 successfully passed, whereas Lla MA2-7B, Huatuo GPT,Chinese LLa MA2-7B, and Lla MA-7B failed. Chat GPT-4.0(84.82%) surpassed all PCPs and assisted most PCPs in the NCE-CPDC examination(improving by 1%–6.13%). In summary, LLMs demonstrated outstanding competence for diabetes-related questions in both the Chinese and English language, and hold great potential to assist future diabetes training for physicians globally.

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A GPU-based accelerator for chinese word segmentation

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Gu, Xiwu Li, Ruixuan Wen, Kunmei Peng, Bei Xiao, Weijun Intelligent and Distributed Computing Lab College of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China Department of Electrical and Computer Engineering University of Minnesota twin cities 200 Union Street SE Minneapolis MN 55455 United States

ISBN: (纸本)9783642292521

The task of Chinese word segmentation is to split sequence of Chinese characters into tokens so that the Chinese information can be more easily retrieved by web search engine. Due to the dramatic increase in the amount of Chinese literature in recent years, it becomes a big challenge for web search engines to analyze massive Chinese information in time. In this paper, we investigate a new approach to high-performance Chinese information processing. We propose a CPU-GPU collaboration model for Chinese word segmentation. In our novel model, a dictionary-based word segmentation approach is proposed to fit GPU architecture. Three basic word segmentation algorithms are applied to evaluate the performance of this model. In addition, we present several optimization strategies to fully exploit the potential computing power of GPU. Our experimental results show that our model can achieve significant performance speedups up to 3-fold compared with the implementations on CPU. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Graphics processing unit

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TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay 9

TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Karan, Mladen Glavaš, Goran Šnajder, Jan Bašić, Bojana Dalbelo Vulić, Ivan Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence Information Retrieval Group Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9781941643402

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic similarity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic overlap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 participating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. © 2015 Association for Computational Linguistics

关键词： Semantics

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Discovering High Utility Episodes in Sequences

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 473-486页

作者： Gan, Wensheng Lin, Jerry Chun-Wei Chao, Han-Chieh Yu, Philip S. Jinan University College of Cyber Security Guangzhou510632 China Pazhou Lab Guangzhou510330 China Western Norway University of Applied Sciences Department of Computing Mathematics and Physics Bergen5063 Norway National Dong Hwa University Department of Electrical Engineering Hualien974 Taiwan University of Illinois at Chicago Department of Computer Science ChicagoIL60607 United States

Sequence data are more commonly seen than other types of data (e.g., transaction data) in real-world applications. For the mining task from sequence data, several problems have been formulated, such as sequential pattern mining, episode mining, and sequential rule mining. As one of the fundamental problems, episode mining has often been studied. The common wisdom is that discovering frequent episodes is not useful enough. In this article, we propose an efficient utility mining approach, namely, UMEpi: utility mining of high-utility episodes from complex event sequences. We propose the concept of remaining utility of episodes and achieve a tighter upper bound, namely, episode-weighted utilization (EWU), which will provide better pruning. Thus, the optimized EWU-based pruning strategies can achieve better improvements in mining efficiency. The search space of UMEpi w.r.t. a prefix-based lexicographic sequence tree is spanned and determined recursively for mining high-utility episodes, by prefix-spanning in a depth-first way. Finally, extensive experiments on four real-life datasets demonstrate that UMEpi can discover the complete high-utility episodes from complex event sequences. Furthermore, the improved variants of UMEpi significantly outperform the baseline in terms of execution time, memory consumption, and scalability. © 2020 IEEE.

关键词： Artificial intelligence

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