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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yongxue Shan Zhaoqian Zhong Chao Che Bo Jin Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

ISBN: (纸本)9781665429825

With the development of information technology, online health care service platforms have collected a large amount of patient comment information. Through fine-grained sentiment classification of this information, we can provide references for patients to seek medical treatment and help doctors understand their work. Therefore, we proposed a model that integrated pre-trained emotional information and semantic information at the word and character level for aspect-level sentiment classification. Specifically, we employed adversarial learning for training sentiment word embeddings and the two-layer bidirectional long short-term memory network to extract the sentiment embedding of the entire sentence in a specific aspect. We also combined the pre-trained sentiment feature vector with the structured semantic information by linear weighting and the multi-head self-attention mechanism, enabling the model to pay more attention to the information most relevant to a given aspect category. We performed experiments on the Chinese patient comments data set constructed by our research team and the proposed model outperformed the state-of-the-art methods, which proved the effectiveness of the proposed model for aspect-level sentiment classification of Chinese patient comments.

关键词： Training Biological system modeling Conferences Semantics Bit error rate Medical treatment Feature extraction

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PiCO+: Contrastive label Disambiguation for Robust Partial label Learning

arXiv

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arXiv 2022年

作者： Wang, Haobo Xiao, Ruixuan Li, Yixuan Feng, Lei Niu, Gang Chen, Gang Zhao, Junbo Key Lab of Intelligent Computing Based Big Data of Zhejiang Province Zhejiang University Hangzhou310027 China Department of Computer Sciences University of WisconsinMadison MadisonWI53706 United States The College of Computer Science Chongqing University Chongqing400044 China RIKEN Center for Advanced Intelligence Project Tokyo Japan

Partial label learning (PLL) is an important problem that allows each training example to be labeled with a coarse candidate set, which well suits many real-world data annotation scenarios with label ambiguity. Despite the promise, the performance of PLL often lags behind the supervised counterpart. In this work, we bridge the gap by addressing two key research challenges in PLL-representation learning and label disambiguation-in one coherent framework. Specifically, our proposed framework PiCO consists of a contrastive learning module along with a novel class prototype-based label disambiguation algorithm. PiCO produces closely aligned representations for examples from the same classes and facilitates label disambiguation. Theoretically, we show that these two components are mutually beneficial, and can be rigorously justified from an expectation-maximization (EM) algorithm perspective. Moreover, we study a challenging yet practical noisy partial label learning setup, where the ground-truth may not be included in the candidate set. To remedy this problem, we present an extension PiCO+ that performs distance-based clean sample selection and learns robust classifiers by a semi-supervised contrastive learning algorithm. Extensive experiments demonstrate that our proposed methods significantly outperform the current state-of-the-art approaches in standard and noisy PLL tasks and even achieve comparable results to fully supervised learning. Copyright © 2022, The Authors. All rights reserved.

关键词： Learning algorithms

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Modelling and analysis of haemoglobin catalytic reaction kinetic system

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Mathematical & Computer Modelling of Dynamical Systems 2020年第4期26卷 306-321页

作者： Liu, Yanhong Lv, Hui Wang, Bin Yang, Deyun Zhang, Qiang Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Dalian University Dalian China Department of Information Science and Technology Taishan University Taian China

In order to study whether haemoglobin (Hb) can replace peroxidase and has good catalytic properties. The key to exploring the characteristics of Hb peroxidase is to establish a suitable kinetic model, which is studied in this paper. First, according to the Hb catalytic reaction, a nonlinear system is established and improved. It is proved that the established system is in line with the practical significance. The stability of the original system is judged by analysing the stability of the simplified system. Then, considering the effect of time delay on Hb catalytic reaction, a nonlinear time-delay catalytic reaction system is obtained. For convenient application, the system is linearized using Taylor's formula, and the dynamic characteristics of Hopf bifurcation are analysed. The response diagrams of three system are plotted by setting perturbation parameters, and their variations are observed to analyse the differences among them. The results show that the nonlinear time-delay system can better describe the characteristics of the catalytic reaction. [ABSTRACT FROM AUTHOR]

关键词： central manifold theorem haemoglobin Hopf bifurcation Nonlinear system model time delay

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End-to-end speech emotion recognition based on one-dimensional convolutional neural network 2019

End-to-end speech emotion recognition based on one-dimension...

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3rd International Conference on Innovation in Artificial Intelligence, ICIAI 2019

作者： Gao, Mengna Zhang, Qiang Dong, Jing Yang, Deyun Zhou, Dongsheng Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China School of Information Science and Technology Taishan University Tai'an Shandong Province271000 China Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian116024 China

ISBN: (纸本)9781450361286

Real-time speech emotion recognition has always been a problem. To this end, we proposed an end-to-end speech emotion recognition model based on one-dimensional convolutional neural network, which contains only three convolution layers, two pooling layers and one full-connected layer. Through Adam optimization algorithm and back propagation mechanism, more discriminative features can be extracted continuously. Our model is quite simple in structure and easy to quickly complete the emotional classification task. Compared with traditional methods, there is no need to carry out the complex process of manually extracting features, and the model can automatically learn the emotional features from raw speech signals. In the emotional recognition experiments with EMODB, CASIA, IEMOCAP, and CHEAVD four speech databases, relatively high recognition rates were obtained. Experiments show that the proposed algorithm is of great benefit to the implementation of real-time speech emotion recognition. © 2019 ACM

关键词： Emotion Recognition

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Hybrid malware detection approach with feedback-directed machine learning

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Science China(Information Sciences) 2020年第3期63卷 240-242页

作者： Zhetao LI Wenlin LI Fuyuan LIN Yi SUN Min YANG Yuan ZHANG Zhibo WANG Hunan Provincial Key Lab of Internet of Things and Information Security Xiangtan University The College of Information Engineering Xiangtan University Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Computer Science Fudan University Shanghai Institute of Intelligent Electronics & Systems Shanghai Institute for Advanced Communication and Data Science School of Cyber Science and Engineering Wuhan University

Dear editor,In recent years, the number of mobile malware has increased at an alarming rate. According to a report from G DATA [1], there are approximately 9000 new Android malware instances each *** malicious applica... 详细信息

关键词： Hybrid malware detection approach with feedback-directed machine learning

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Supervised Deep Second-Order Covariance Hashing for Image Retrieval

Supervised Deep Second-Order Covariance Hashing for Image Re...

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2020国际计算机前沿大会

作者： Qian Wang Yue Wu Jianxin Zhang Hengbo Zhang Chao Che Lin Shan Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University School of Computer Science and Engineering Dalian Minzu University School of Economic and Management Dalian University of Science and Technology

Recently, deep hashing methods play a pivotal role in image retrieval tasks by combining advanced convolutional neural networks(CNNs) with efficient hashing. Meanwhile, second-order representations of deep convolutional activations have been established to effectively improve network performance in various computer vision applications. In this work, to obtain more compact hash codes, we propose a supervised deep second-order covariance hashing(SDSo CH) method by combining deep hashing with second-order statistic model. SDSo CH utilizes a powerful covariance pooling to model the second-order statistics of convolutional features, which is naturally integrated into the existing point-wise hashing network in an end-to-end manner. The embedded covariance pooling operation well captures the interaction of convolutional features and produces global feature representations with more discriminant capability, leading to the more informative hash codes. Extensive experiments conducted on two benchmarks demonstrate that the proposed SDSoCH outperforms its first-order counterparts and achieves superior retrieval performance.

关键词： Deephashing Point-wisemanner Covariancepooling Imageretrieval

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LightXML: Transformer with dynamic negative sampling for high-performance extreme multi-label text classification

arXiv

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arXiv 2021年

作者： Jiang, Ting Wang, Deqing Sun, Leilei Yang, Huayi Zhao, Zhengyang Zhuang, Fuzhen SKLSDE and BDBC Lab Beihang University Beijing China Key Lab of Intelligent Information Processing of CAS Institute of Computing Technology CAS Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Academy for Multidisciplinary Studies Capital Normal University Beijing China

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML. Our code is available at http://***/kongds/LightXML. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Second-order Attention Guided Convolutional Activations for Visual Recognition

Second-order Attention Guided Convolutional Activations for ...

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International Conference on Pattern Recognition

作者： Shannan Chen Qian Wang Qiule Sun Bin Liu Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

关键词： Visualization Tensors Limiting Network topology Computational modeling Image representation Pattern recognition

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DCET-Net: Dual-Stream Convolution Expanded Transformer for Breast Cancer Histopathological Image Classification

DCET-Net: Dual-Stream Convolution Expanded Transformer for B...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ying Zou Shannan Chen Qiule Sun Bin Liu Jianxin Zhang Key Lab of Advanced Design and Intelligent Computing Ministry of Education Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Engineering Dalian Minzu University Dalian China

ISBN: (纸本)9781665429825

Researches on breast cancer histopathological image classification have achieved a great breakthrough using deep backbones of Convolutional Neural Networks (CNNs) in recent years. However, due to the inductive bias of locality, CNNs are unable to effectively extract the global feature information of breast cancer histopathological images, limiting the improvement of the classification results. To overcome this shortcoming, this paper reasonably introduces an extra backbone stream of a pure transformer, which consists of a self-attention mechanism to capture global receptive fields of histopathological images, thereby compensating the locality characteristic of CNNs backbone. Based on two backbone streams of CNN and transformer, a dual-stream network called DCET-Net is proposed, which considers local features and global ones simultaneously, and progressively combines them from these two streams to form the final representations for classification. DCET-Net is extensively evaluated on the representative BreakHis histopathological image dataset, and experimental results demonstrate that it is highly competitive with the state-of-the-art CNN methods in breast cancer histopathological image classification task.

关键词： Limiting Biological system modeling Streaming media Transformers Feature extraction Breast cancer Convolutional neural networks

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A method of terminology alignment in Historical Books based on the correspondence between Pivot language and position

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Journal of Physics: Conference Series 2021年第1期1883卷

作者： Xingchen Xie Xiaoting Wu Xiaopeng Wei Chao Che Key Laboratory of Advanced Design and Intelligent Computing Dalian University Dalian Liaoning 116622 China

In view of the most difficult problem of terminology translation in the translation of historical books, this paper extracts term translation pairs from bilingual parallel corpus as a reference to improve the quality and efficiency of the translation of historical books. However, the small size of the corpus of historical books leads to poor word alignment, which brings a large number of errors to term alignment. At the same time, the special grammar of ancient Chinese will also affect term alignment. In order to solve the above two problems, this paper proposes a method of term alignment of historical books based on pivot language and position correspondence. This method introduces modern Chinese as the pivot language, makes use of the position correspondence between modern Chinese and English terms in sentences, and uses the sliding window method to generate candidate term pairs, which avoids the influence of word alignment errors on term alignment. In addition, if we first align to modern Chinese, the ancient Chinese terminology will complete the omitted characters in modern Chinese and replace the false characters, so as to avoid the influence of the special usage in ancient Chinese on the term alignment. The experimental results in Historical Records show that the accuracy of this method is slightly better than that of the method based on word alignment, which verifies the effectiveness of this method without word alignment.

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