Mu Li

Harbin Institute of Technology

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Dongdong Zhang

Microsoft Research Asia (China)

David Zhang

University College London

Shujie Liu

Microsoft (United States)

Ming Zhou

eBay (Ireland)

Ming Zhou

Xiangya Hospital Central South University

Wangmeng Zuo

Harbin Institute of Technology

Feiyun Sun

Harbin Institute of Technology

Jinxing Li

Harbin Institute of Technology

Wentao Shang

Shandong Jianzhu University

Lei Cui

Fuzhou University

Cooperative Institutions

Harbin Institute of Technology

Chinese Academy of Sciences

Shanghai Jiao Tong University

University of Chinese Academy of Sciences

Amazon (United States)

University of Hong Kong

University of Science and Technology of China

Tsinghua University

Zhejiang University

Beihang University

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Design and implementation of Wavelet Transform using Adaptive Genetic Algorithms and Analog filters

Mu Li Yi He Yang liang Ou Jun Nian Wang

This paper presents a low power and low-voltage analog switched-current (SI) filters implementation of wavelet transform (WT) for real-time requirements in signal processing.First, an adaptive genetic algorithm (AGA) is used to calculate the transfer function of the filters, whose impulse response is the required wavelet base.This approach improves the approximation performance than the previous traditional approaches and allows for the circuit implementation of any other wavelet base.Next, the approximation wavelet function is implemented using SI filters based on the cascade structure with SI differentiators as main building blocks.The Gaussian wavelet is selected as an example to illustrate the design procedure.Simulations demonstrate that the proposed method implements WT is an excellent way.

Differentiator

Stationary wavelet transform

Second-generation wavelet transform

Cascade algorithm

10.2991/icacsei.2013.30

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Citations (0)

Robust Video Hashing via Multilinear Subspace Projections

IEEE Transactions on Image Processing (2012)

Mu Li Vishal Monga

The goal of video hashing is to design hash functions that summarize videos by short fingerprints or hashes. While traditional applications of video hashing lie in database searches and content authentication, the emergence of websites such as YouTube and DailyMotion poses a challenging problem of anti-piracy video search. That is, hashes or fingerprints of an original video (provided to YouTube by the content owner) must be matched against those uploaded to YouTube by users to identify instances of "illegal" or undesirable uploads. Because the uploaded videos invariably differ from the original in their digital representation (owing to incidental or malicious distortions), robust video hashes are desired. We model videos as order-3 tensors and use multilinear subspace projections, such as a reduced rank parallel factor analysis (PARAFAC) to construct video hashes. We observe that, unlike most standard descriptors of video content, tensor-based subspace projections can offer excellent robustness while effectively capturing the spatio-temporal essence of the video for discriminability. We introduce randomization in the hash function by dividing the video into (secret key based) pseudo-randomly selected overlapping sub-cubes to prevent against intentional guessing and forgery. Detection theoretic analysis of the proposed hash-based video identification is presented, where we derive analytical approximations for error probabilities. Remarkably, these theoretic error estimates closely mimic empirically observed error probability for our hash algorithm. Furthermore, experimental receiver operating characteristic (ROC) curves reveal that the proposed tensor-based video hash exhibits enhanced robustness against both spatial and temporal video distortions over state-of-the-art video hashing techniques.

Feature hashing

Robustness

Dynamic perfect hashing

Universal hashing

K-independent hashing

10.1109/tip.2012.2206036

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Citations (49)

A Joint Rule Selection Model for Hierarchical Phrase-Based Translation

Meeting of the Association for Computational Linguistics (2010)

Lei Cui Dongdong Zhang Mu Li Ming Zhou Tiejun Zhao

In hierarchical phrase-based SMT systems, statistical models are integrated to guide the hierarchical rule selection for better translation performance. Previous work mainly focused on the selection of either the source side of a hierarchical rule or the target side of a hierarchical rule rather than considering both of them simultaneously. This paper presents a joint model to predict the selection of hierarchical rules. The proposed model is estimated based on four sub-models where the rich context knowledge from both source and target sides is leveraged. Our method can be easily incorporated into the practical SMT systems with the log-linear model framework. The experimental results show that our method can yield significant improvements in performance.

Phrase

Hierarchical database model

Context model

Source

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Citations (15)

Modular Blind Video Quality Assessment

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2024)

Wen Wen Mu Li Yabin Zhang Yiting Liao Junlin Li

10.1109/cvpr52733.2024.00267

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Emotion Classification by Jointly Learning to Lexiconize and Classify

Proceedings of the 17th international conference on Computational linguistics - (2020)

Deyu Zhou Shuangzhi Wu Qing Wang Jun Xie Zhaopeng Tu

Emotion lexicons have been shown effective for emotion classification (Baziotis et al., 2018). Previous studies handle emotion lexicon construction and emotion classification separately. In this paper, we propose an emotional network (EmNet) to jointly learn sentence emotions and construct emotion lexicons which are dynamically adapted to a given context. The dynamic emotion lexicons are useful for handling words with multiple emotions based on different context, which can effectively improve the classification accuracy. We validate the approach on two representative architectures – LSTM and BERT, demonstrating its superiority on identifying emotions in Tweets. Our model outperforms several approaches proposed in previous studies and achieves new state-of-the-art on the benchmark Twitter dataset.

10.18653/v1/2020.coling-main.288

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Citations (9)

Degradation of diatrizoate in water by Fe(II)-activated persulfate oxidation

Chemical Engineering Journal (2018)

Wentao Shang Zijun Dong Mu Li Xiulan Song Miao Zhang

Persulfate

Degradation

10.1016/j.cej.2018.12.139

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Citations (86)

Chunk-based Decoder for Neural Machine Translation

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2017)

Shonosuke Ishiwatari JingTao Yao Shujie Liu Mu Li Ming Zhou

Shonosuke Ishiwatari, Jingtao Yao, Shujie Liu, Mu Li, Ming Zhou, Naoki Yoshinaga, Masaru Kitsuregawa, Weijia Jia. Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2017.

Association (psychology)

10.18653/v1/p17-1174

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Citations (19)

Facile synthesis of micro-eggette patterned nanofiltration membrane with enhanced anti-fouling and rejection performance

Desalination (2023)

Wentao Shang Wenjie Liu Weiliang Wang Noman Khalid Khanzada Jiaxin Guo

Nanofiltration

Membrane Fouling

Biofouling

Phase inversion

Interfacial polymerization

Membrane Technology

Filtration (mathematics)

10.1016/j.desal.2023.116524

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Citations (12)

Twofold video hashing with automatic synchronization

2022 IEEE International Conference on Image Processing (ICIP) (2014)

Mu Li Vishal Monga

Video hashing finds a wide array of applications in content authentication, robust retrieval and anti-piracy search. While much of the existing research has focused on extracting robust and secure content descriptors, a significant open challenge still remains: Most existing video hashing methods are fallible to temporal desynchronization. That is, when the query video results by deleting or inserting some frames from the reference video, most existing methods assume the positions of the deleted (or inserted) frames are either perfectly known or reliably estimated. This assumption may be okay under typical transcoding and frame-rate changes but is highly inappropriate in adversarial scenarios such as anti-piracy video search. For example, an illegal uploader will try to bypass the `piracy check' mechanism of YouTube/Dailymotion etc by performing a cleverly designed non-uniform resampling of the video. We present a new solution based on dynamic time warping (DTW), which can implement automatic synchronization and can be used together with existing video hashing methods. The second contribution of this paper is to propose a new robust feature extraction method called flow hashing (FH), based on frame averaging and optical flow descriptors. Finally, a fusion mechanism called distance boosting is proposed to combine the information extracted by DTW and FH. Experiments on real video collections show that such a hash extraction and comparison enables unprecedented robustness under both spatial and temporal attacks.

Robustness

Dynamic Time Warping

Feature hashing

Optical Flow

10.1109/icip.2014.7026085

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Citations (2)

Scaling Distributed Machine Learning with the Parameter Server

Mu Li

Big data may contain big values, but also brings lots of challenges to the computing theory, architecture, framework, knowledge discovery algorithms, and domain specific tools and applications. Beyond the 4-V or 5-V characters of big datasets, the data processing shows the features like inexact, incremental, and inductive manner. This brings new research opportunities to research community across theory, systems, algorithms, and applications. Is there some new "theory" for the big data? How to handle the data computing algorithms in an operatable manner? This report shares some view on new challenges identified, and covers some of the application scenarios such as micro-blog data analysis and data processing in building next generation search engines.

10.1145/2640087.2644155

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Citations (917)