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8th International Conference on Identification, Information and Knowledge in the Internet of Things, IIKI 2019

作者： Feng, Cong Rao, Yuan Nazir, Ambreen Wu, Lianwei He, Long Research Institute of Xi’an Jiaotong University Shenzhen China Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi’an Jiao Tong University Xi’an China Shaanxi Joint Key Laboratory for Artifact Intelligence Xi’an Jiao Tong University Xi’an China

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/)

关键词： Extraction

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Attention in Attention: Modeling Context Correlation for Efficient Video Classification

arXiv

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arXiv 2022年

作者： Hao, Yanbin Wang, Shuo Cao, Pei Gao, Xinjian Xu, Tong Wu, Jinmeng He, Xiangnan The CCCD Key Lab of Ministry of Culture and Tourism School of Data Science School of Information Science and Technology University of Science and Technology of China Anhui 230026 China The Wuhan Research Institute of Posts and Telecommunications Hubei Wuhan430205 China The School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology Anhui 230009 China The School of Data Science School of Computer Science and Technology University of Science and Technology of China Anhui 230026 China The Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Hubei Wuhan430070 China

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase ( Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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The minority matters: a diversity-promoting collaborative metric learning algorithm 22

The minority matters: a diversity-promoting collaborative me...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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A High Slope Efficiency Ring-cavity Single-frequency Fiber Laser Based on Yb: YAG Crystal-derived Silica Fiber

A High Slope Efficiency Ring-cavity Single-frequency Fiber L...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Ying Wan Jianxiang Wen Kai Zou Chen Jiang Fufei Pang Tingyun Wang Key Lab of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication School of Communication and Information Engineering Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai China

A high-concentration Yb-doped Y AG crystal-derived silica fiber (YCDSF) is fabricated using the melt-in-tube method. Based on this fiber, a ring-cavity single-frequency fiber laser (SFFL) has been accomplished, and it... 详细信息

ISBN: (纸本)9781665410953

关键词： Optical fibers Silicon compounds Laser noise Power lasers Optical fiber communication Sensors Power generation

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Compensating Verdet constant of optical fibers based on faraday rotating mirror measurement system

Compensating Verdet constant of optical fibers based on fara...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Jiawei Zeng Jianxiang Wen Hao Shi Yan Wu Le Ye Yanhua Dong Yi Huang Tingyun Wang Key Lab of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai China

Established Verdet constant test system based on Faraday rotating mirror (FRM). It is found that FRM eliminates the influence of linear birefringence, indicating that the introduction of FRM can compensate for the Ver... 详细信息

ISBN: (纸本)9781665410953

关键词： Optical fibers Sensitivity Asia Faraday effect Optical variables measurement Optical fiber communication Mirrors

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Global Collaboration to Enhance Information Technology & Quantitative Management: Preface for ITQM 2023

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Procedia Computer Science 2023年 221卷 xv-xvii页

作者： Shi, Yong Filip, Florin G. He, Jing Li, Jianping Kou, Gang Tien, James Berg, Daniel Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China Romanian Academy Calea Victoriei 125 Sector 1 Bucharest010071 Romania College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States University of Oxford United Kingdom School of Business Chengdu University Sichuan 610106 China College of Engineering University of Miami Coral GablesFL33124 United States

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Fast extraction of word embedding from Q-contexts

arXiv

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arXiv 2021年

作者： Kong, Junsheng Li, Weizhao Liu, Zeyi Liao, Ben Qiu, Jiezhong Hsieh, Chang-Yu Cai, Yi Zhang, Shengyu School of Software Engineering South China University of Technology China University of Cambridge United Kingdom Tencent Quantum Lab China Tsinghua University China The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education

The notion of word embedding plays a fundamental role in natural language processing (NLP). However, pre-training word embedding for very large-scale vocabulary is computationally challenging for most existing methods. In this work, we show that with merely a small fraction of contexts (Q-contexts) which are typical in the whole corpus (and their mutual information with words), one can construct high-quality word embedding with negligible errors. Mutual information between contexts and words can be encoded canonically as a sampling state, thus, Q-contexts can be fast constructed. Furthermore, we present an efficient and effective WEQ method, which is capable of extracting word embedding directly from these typical contexts. In practical scenarios, our algorithm runs 11∼ 13 times faster than well-established methods. By comparing with well-known methods such as matrix factorization, word2vec, GloVe and fasttext, we demonstrate that our method achieves comparable performance on a variety of downstream NLP tasks, and in the meanwhile maintains run-time and resource advantages over all these baselines. © 2021, CC BY.

关键词： Embeddings

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Feature re-learning with data augmentation for video relevance prediction

arXiv

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arXiv 2020年

作者： Dong, Jianfeng Wang, Xun Zhang, Leimin Xu, Chaoxi Yang, Gang Li, Xirong College of Computer and Information Engineering Zhejiang Gongshang University Hangzhou310035 China Key Lab of Data Engineering and Knowledge Engineering Renmin University of China AI & Media Computing Lab School of Information Renmin University of China Beijing100872 China

Predicting the relevance between two given videos with respect to their visual content is a key component for content-based video recommendation and retrieval. Thanks to the increasing availability of pre-trained image and video convolutional neural network models, deep visual features are widely used for video content representation. However, as how two videos are relevant is task-dependent, such off-the-shelf features are not always optimal for all tasks. Moreover, due to varied concerns including copyright, privacy and security, one might have access to only pre-computed video features rather than original videos. We propose in this paper feature re-learning for improving video relevance prediction, with no need of revisiting the original video content. In particular, re-learning is realized by projecting a given deep feature into a new space by an affine transformation. We optimize the re-learning process by a novel negative-enhanced triplet ranking loss. In order to generate more training data, we propose a new data augmentation strategy which works directly on frame-level and video-level features. Extensive experiments in the context of the Hulu Content-based Video Relevance Prediction Challenge 2018 justify the effectiveness of the proposed method and its state-of-the-art performance for content-based video relevance prediction. Copyright © 2020, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Meta Mask Correction for Nuclei Segmentation in Histopathological Image

Meta Mask Correction for Nuclei Segmentation in Histopatholo...

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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Shi, Jiangbo Jia, Chang Gao, Zeyu Gong, Tieliang Wang, Chunbao Li, Chen Xi'An Jiao Tong University National Engineering Lab for Big Data Analytics Shaanxi Xi'an710049 China School of Computer Science and Technology Xi'An Jiao Tong University Shaanxi Xi'an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech.RandD Xi'An Jiao Tong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of xi'An Jiaotong University Shaanxi Xi'an710061 China

ISBN: (纸本)9781665401265

Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks using a small amount of clean meta-data. Then the corrected masks can be used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model in an end-to-end way. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. It even achieves comparable performance with the model training on supervised data in some noisy settings. © 2021 IEEE.

关键词： Deep learning

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High Magnetic Field Effect on Magneto-optical Properties of the Terbium-doped Silica Fiber 27

High Magnetic Field Effect on Magneto-optical Properties of ...

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27th International Conference on Optical Fiber Sensors, OFS 2020

作者： Ye, Le Wen, Jianxiang Dong, Yanhua Huang, Yi Pang, Fufei Chen, Zhenyi Wang, Tingyun Key Lab of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University 99 Shangda Road Shanghai200444 China

A terbium doped silica fiber is fabricated by MCVD, and treated by high magnetic field. Compare to the untreated fiber, the Verdet constant of the treated fiber is increased by 29%. © 2021 The Author(s).

ISBN: (纸本)9781557523075

关键词： Silica

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