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2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Jin, Hailong Du, Wei Gu, Jiawei Zhang, Tianhao Shi, Xiaohu College of Computer Science and Technology of Jilin University Changchun China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

ISBN: (纸本)9781665401265

Protein secondary structure prediction is still a challenging task in bioinformatics, especially for 8-state (Q8) classification. To address this problem, we have proposed a deep learning based model by integrating graph convolutional network(GCN) and bidirectional long short-term memory (Bi-LSTM) network in this paper. In the model, GCN is utilized to synthesize the information of amino acids and their interactions, while Bi-LSTM has strong ability to capture the long-range dependencies of amino acids. For sequence representation, a new protein embedding derived by ProtTrans is used instead of the traditional amino acid one-hot encoding, together with evolutionary features of PSSM and HHM profiles. Amino acid contact potential derived from SPOTContact-Helical is used to construct amino acid graph. To verify the effectiveness of our proposed model, it is applied to several benchmark datasets, and obtained 78.05%, 76.81% 72.84%, 74.46% and 76.04% Q8 accuracy on CASP10, CASP11, CASP12, CB513 and TS115 datasets, respectively. Compared with 8 state-of-the-art competitions, our model obtained the best performance in most of datasets. © 2021 IEEE.

关键词： Amino acids

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An Enhanced Approach for Few-Shot Segmentation Via Smooth Downsampling Mask and Label Smoothing Loss

SSRN

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SSRN 2023年

作者： Jin, Hailong Li, Huiying College of Computer Science and Technology Jilin Province Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Changchun130012 China

Few-shot semantic segmentation aims to segment new categories with only a small number of annotated images. Previous methods mainly focused on exploiting the pixel-level correlation between the support image and the query image, combined with attention-based methods, resulting in significant advancements. In this paper, we introduce a new perspective to enhance few-shot segmentation. We identify that utilizing the bilinear interpolation method to downsample the mask leads to the loss of fine-grained information from the target features. To address this issue, we propose a Smooth Downsampling Mask (SDM) method. The SDM method is designed to retain more effective target semantic features by employing a cascaded downsampling approach with a smooth kernel for mask processing. Additionally, we propose a label smoothing loss to further enhance the performance, which provides direct guidance for low-resolution feature map optimization. Notably, our proposed method involves no additional learnable parameters and introduces only a small amount of extra computation. To validate the effectiveness of our approach, we conduct extensive experiments on three public benchmarks, PASCAL-$5^i$, COCO-$20^i$, and FSS-$1000$, and achieve considerable improvements. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Nutrition feature basis of food classification in traditional chinese medicine 7

Nutrition feature basis of food classification in traditiona...

引用

7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Zhao, Fucheng Han, Xiaosong Zhang, Yikun Niu, Peiyu Cheng, Jun Yi, Wan Jilin University Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Changchun130012 China

ISBN: (纸本)9781665442527

In TCM (Traditional Chinese Medicine) theory, the cold-and-hot property of food is considered as an important information to guide people's daily diet and keep them healthy. But the classification of this property mostly relies on experience from medical books, which lacks corresponding molecular chemistry basis and makes differentiation very difficult for ordinary people. To solve above question, in this paper, we collected 500 kinds of food and their nutrients, used ANOVA algorithm for feature selection to find out the nutrient feature that have a greater correlation with the cold and hot properties of food, and then used LightGBM algorithm to classify the processed data set. Finally, we constructed a reliable and accurate classification model and found the related nutritional elements are Tyrosine, Arginine, Thiamine, Vitamin B6 and Selenium. © 2021 IEEE.

关键词： Machine learning

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Time and Time-Frequency Features Integrated CNN Model for Heart Sound Signals Detection

Time and Time-Frequency Features Integrated CNN Model for He...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Ren, Zhimin Qiao, Yuheng Yuan, Yuping Zhou, You Liang, Yanchun Shi, Xiaohu College of Computer Science and Technology of Jilin University Changchun130012 China Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun130012 China Zhuhai College of Science and Technology School of Computer Science Zhuhai519041 China

ISBN: (纸本)9781665468190

Automatic heart sound diagnosis plays an important role in the early detection of cardiovascular diseases. Phonocardiogram (PCG) signals are often used in this field f or its low cost and non-invasive advantages. In this paper, we design a new time and time-frequency features integrated CNN (TTFI-CNN) model for heart sound signals detection. In the model, a 1D CNN and a BiLSTM are combined into 1D CRNN module to extract temporal features from the original PCG signal, and a 2D CNN module is applied to capture high-level features from time-frequency domain MFCCs inputs. The outputs of the two modules are recalibrated by attention mechanism to selectively emphasize informative features and suppress less useful ones. To verify the proposed TTFI-CNN model, it is applied to two public datasets with different classification t asks (binary and multiclassification). The TTFI-CNN model has achieved 97.15% accuracy, 97.13% sensitivity, and 97.17% specificity on physionet/cinc database, and obtained 3.31 and 2.61 precision scores on the PASCAL database A and B, respectively. Compared with the previous state-of-the-art methods, the TTFI-CNN performs best on all the above metrics. https://***/XxxNnnSssWww/TTFI-CNN. © 2022 IEEE.

关键词： Signal detection

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2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model for WMH Segmentation from 3D MR Brain Image

2.5D ASF-UNet: Adjacent Slice Spatial Feature Fusion Model f...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lan Huang Yinglu Sun Ziqi Zhao Chunjie Guo Yan Wang Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun China Department of Radiology the First Hospital of Jilin University Changchun China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Segmenting brain white matter hyperintensities (WMH) from 3D Magnetic Resonance (MR) images is crucial for the diagnosis, treatment, and prognosis of Multiple Sclerosis (MS). Unlike common 2D images, this task is more challenging and time-consuming. Classical deep learning methods for 3D image segmentation face two main challenges: 1) Pure 3D networks have more parameters and are prone to overfitting. 2) When using 2D networks to segment slices of 3D images, the lack of 3D structural information results in suboptimal segmentation after reconstruction. To address these difficulties, we propose the 2.5D ASF-UNet, which employs the 2.5D workflow and uses adjacent slices as the input for 2D segmentation network ASF-UNet. In ASF-UNet, separate down-sampling paths are used for the adjacent slices, and the Local Spatial Attention Module (LSAM) is designed to more effectively integrate 3D spatial information into the 2D network. Additionally, the Conv_Spectral_Block (CSB) is designed to extract and integrate local and global features. It allows the model to capture global spatial structures while preserving detailed information. Experimental results on MICCAI MSSEG 2016 and Local MS datasets show that 2.5D ASF-UNet achieve better segmentation performance than other deep learning methods.

关键词： Deep learning Image segmentation Solid modeling Three-dimensional displays Multiple sclerosis Feature extraction Brain modeling White matter Prognostics and health management Overfitting

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A Survey on Performance Optimization of High-Level Synthesis Tools

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Journal of Computer Science & Technology 2020年第3期35卷 697-720页

作者： Lan Huang Da-Lin Li Kang-Ping Wang Teng Gao Adriano Tavares College of Computer Science and Technology Jilin UniversityChangchun 130012China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041China Algorithm Center University of MinhoGuimaraes 4800058Portugal

Field-programmable gate arrays(FPGAs)have recently evolved as a valuable component of the heterogeneous *** register transfer level(RTL)design flows demand the designers to be experienced in hardware,resulting in a possible failure of ***-level synthesis(HLS)permits designers to work at a higher level of abstraction through synthesizing high-level language programs to RTL *** provides a promising approach to solve these ***,the performance of HLS tools still has *** example,designers remain exposed to various aspects of hardware design,development cycles are still time consuming,and the quality of results(QoR)of HLS tools is far behind that of RTL *** this paper,we survey the literature published since 2014 focusing on the performance optimization of HLS *** with previous work,we extend the scope of the performance of HLS tools,and present a set of three-level evaluation criteria,covering from ease of use of the HLS tools to promotion on specific metrics of *** also propose performance evaluation equations for describing the relation between the performance optimization and the *** find that it needs more efforts on the ease of use for efficient HLS *** suggest that it is better to draw an analogy between the HLS development process and the embedded system design process,and to provide more elastic HLS methodology which integrates FPGAs virtual machines.

关键词： evaluation criterion field-programmable gate array(FPGA) high-level synthesis(HLS) performance optimization quality of results(QoR)

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Shortcut Learning in In-Context Learning: A Survey

arXiv

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arXiv 2024年

作者： Song, Rui Li, Yingji Shi, Lida Giunchiglia, Fausto Xu, Hao College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Information Engineering and Computer Science University of Trento Italy

Shortcut learning refers to the phenomenon where models employ simple, non-robust decision rules in practical tasks, which hinders their generalization and robustness. With the rapid development of Large Language Models (LLMs) in recent years, an increasing number of studies have shown the impact of shortcut learning on LLMs. This paper provides a novel perspective to review relevant research on shortcut learning in In-Context Learning (ICL). It conducts a detailed exploration of the types of shortcuts in ICL tasks, their causes, available benchmarks, and strategies for mitigating shortcuts. Based on the corresponding observations, we summarize the unresolved issues in existing research and attempts to outline the future research landscape of shortcut learning. © 2024, CC BY.

关键词： Contrastive Learning

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Crossed-Time Delay Neural Network for Speaker Recognition 1

引用

27th International Conference on MultiMedia Modeling, MMM 2021

作者： Chen, Liang Liang, Yanchun Shi, Xiaoshu Zhou, You Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China

ISBN: (数字)9783030678326

ISBN: (纸本)9783030678319

Time Delay Neural Network (TDNN) is a well-performing structure for deep neural network-based speaker recognition systems. In this paper we introduce a novel structure, named Crossed-Time Delay Neural Network (CTDNN) to enhance the performance of current TDNN for speaker recognition. Inspired by the multi-filters setting of convolution layers from convolution neural networks, we set multiple time delay units with different context size at the bottom layer and construct a multilayer parallel network. The proposed CTDNN gives significant improvements over original TDNN on both speaker verification and identification tasks. It outperforms in VoxCeleb1 dataset in verification experiment with a 2.6% absolute Equal Error Rate improvement. In few shots condition, CTDNN reaches 90.4% identification accuracy, which doubles the identification accuracy of original TDNN. We also compare the proposed CTDNN with another new variant of TDNN, Factorized-TDNN, which shows that our model has a 36% absolute identification accuracy improvement under few shots condition. Moreover, the proposed CTDNN can handle training with a larger batch more efficiently and hence, utilize calculation resources economically. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Deep Feature-Based Text Clustering and Its Explanation

Deep Feature-Based Text Clustering and Its Explanation

引用

International Conference on Data engineering

作者： Renchu Guan Hao Zhang Yanchun Liang Fausto Giunchiglia Lan Huang Xiaoyue Feng Key Laboratory for Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China DISI University of Trento Trento Italy

Text clustering is a critical step in text data analysis and has been extensively studied by the text mining community. Most existing text clustering algorithms are based on the bag-of-words model, which faces the high-dimensional and sparsity problems and ignores text structural and sequence information. Deep learning-based models such as convolutional neural networks and recurrent neural networks regard texts as sequences but lack supervised signals and explainable results. In this paper, we propose a deep feature-based text clustering (DFTC) framework that incorporates pretrained text encoders into text clustering tasks. This model, which is based on sequence representations, breaks the dependency on supervision. The experimental results show that our model outperforms classic text clustering algorithms on almost all the considered datasets. In addition, the explanation of the clustering results is significant for understanding the principles of the deep learning approach. Our proposed clustering framework includes an explanation module that can help users understand the meaning and quality of the clustering results. Our code is available at https://***/KEAML-JLU/DeepTextClustering.

关键词：

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Community discovery algorithm and its technical improvement based on link structure–taking web community algorithm as an example

Community discovery algorithm and its technical improvement ...

引用

International Conference on Applications and Techniques in Cyber Intelligence, ATCI 2020

作者： Gao, Rui Yang, Wenzhe Shi, Xiaohu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130012 China

ISBN: (纸本)9783030539795

Link analysis is an important way to discover potential web communities. This paper analyzes the characteristics of the Web link structure, and studies the traditional maximum flow algorithm and the maximum flow algorithm based on HITS algorithm for edge capacity allocation. The traditional maximum flow problem, but there are still shortcomings. Based on the analysis of existing link similarity definitions, this paper proposes a new definition of link similarity and topic dissimilarity to better describe the relationship between linked pages, and to measure based on link similarity and topic dissimilarity. The similarity of the pages gives a more reasonable and efficient maximum flow-side capacity allocation scheme. The community discovery of 6 topics shows that the maximum flow algorithm proposed in this paper using the link similarity and topic dissimilarity model to allocate edge capacity can better solve the problems of existing algorithms and significantly improve the quality of the Web community. This paper proposes new methods and ideas for page similarity measurement, and provides new strategies for improving the Web community discovery algorithm based on link analysis. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Computer programming

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