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Generating empathetic responses through emotion tracking and constraint guidance

Frontiers of Computer Science 2024年第2期18卷 243-245页

作者： Jing LI Donghong HAN Zhishuai GUO Baiyou QIAO Gang WU School of Computer Science and Engineering Northeastern UniversityShenyang 110000China Key Laboratory of Intelligent Computing in Medical Image(Ministry of Education) Northeastern UniversityShenyang 110000China Neusoft Corporation Shenyang 110000China

1 Introduction Empathy is an essential human trait,which reflects the ability of understanding and reflecting on the thoughts and feelings of *** empathetic dialogue system can improve user's experience and establishlong-termhuman-machine *** speaker's emotions and predict response's emotions are necessary steps in empathetic dialogue generation.

关键词： interaction. constraint steps

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Dynamic Prediction of Intraoperative Hypotension Based on Hemodynamic Monitoring Data With a Transformer-Based Deep Learning Model

Dynamic Prediction of Intraoperative Hypotension Based on He...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Kai Ren, Mucheng Xu, Jun Zeng, Xian Nanjing University of Information Science and Technology Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing China

ISBN: (纸本)9798350386226

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National University Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes. © 2024 IEEE.

关键词： Blood pressure

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An Adaptive Speech Noise Reduction Method Based on Noise Classification 13

An Adaptive Speech Noise Reduction Method Based on Noise Cla...

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13th IEEE International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2023

作者： Niu, Ben Wei, Yangjie College of Computer Science and Engineering Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

ISBN: (纸本)9798350315196

To improve the accuracy and robustness of existing speech denoising methods, this study proposes an adaptive speech noise reduction method based on noise classification. First, to adapt to different time-varying characteristics of various noises in complex environments, a new acoustic feature matrix is constructed by combining the LogFbank features and the perceptual linear prediction coefficients, and a noise classifier based on the support vector machine is designed to classify the experimental noise. Then, to address the problem of 'music noise' in traditional speech denoising methods, the voice activity detection threshold is adaptively updated according to the judgment result of background noise types, and the optimal parameters for the improved minima controlled recursive averaging noise estimation algorithm are obtained to accurately estimate current noise and reduce it. Finally, the performance of the proposed method is evaluated using a serial of experiments on different situations, and the experimental results prove the effectiveness and feasibility of our proposed method in this study. © 2023 IEEE.

关键词： Support vector machines

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The impact of parsing accuracy on syntax-based SMT

The impact of parsing accuracy on syntax-based SMT

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International Conference on Natural Language Processing and Knowledge Engineering

作者： Zhang, Hao Wang, Huizhen Xiao, Tong Zhu, Jingbo Key Laboratory of Medical Image Computing (Northeastern University) Ministry of Education China

ISBN: (纸本)9781424468966

In statistical machine translation (SMT), syntax-based models generally rely on the syntactic information provided by syntactic parsers in source language, target language or both of them. However, whether or how parsers impact the performance of syntax-based systems is still an open issue in the MT field. In this paper, we make an attempt to explore answers to this issue, and empirically investigate the impact of parsing accuracy on MT performance in a state-of-the-art syntax-based system. Our study shows that syntax-based system is not very sensitive to the parsing accuracy of parsers used in building MT systems. © 2010 IEEE.

关键词： Machine translation

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A uniform solution to the independent set problem through tissue P systems with cell separation

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Frontiers of Computer Science 2012年第4期6卷 477-488页

作者： Xingyi ZHANG Xiangxiang ZENG Bin LUO Zheng ZHANG Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityHefei 230039China Department of Computer Science Xiamen UniversityXiamen 361005China Key Laboratory of Image Processing and Intelligent Control Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhan 430074China

Membrane computing is an emergent branch of natural computing, which is inspired by the structure and the functioning of living cells, as well as the organization of cells in tissues, organs, and other higher order structures. Tissue P systems are a class of the most investigated computing mod- els in the framework of membrane computing, especially in the aspect of efficiency. To generate an exponential resource in a polynomial time, cell separation is incorporated into such systems, thus obtaining so called tissue P systems with cell separation. In this work, we exploit the computational efficiency of this model and construct a uniform family of such tissue P systems for solving the independent set problem, a well-known NP-complete problem, by which an efficient so- lution can be obtained in polynomial time.

关键词： membrane computing tissue P system cell sep- aration independent set problem

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Single Cell Clustering Based on Multi-view Generation 2nd

Single Cell Clustering Based on Multi-view Generation

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wang, Yueyue Wu, Zheyu Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9789819619061

The rapid development of single-cell RNA sequencing technology has opened new horizons for biomedical research. As the initial step in single-cell data analysis, clustering forms the foundation for downstream analyses. However, due to the inherent high dimensionality, noise, and sparsity of single-cell data, most current clustering methods exhibit poor stability. To address these limitations, we propose a novel clustering method based on multi-view generation (scCMVG) coupled with noise perturbation. Specifically, scCMVG introduces disturbances at both gene level and cellular level to disrupt the original associations between genes and cells. It then employs autoencoder and graph convolutional network to reconstruct the disturbed data and generate new feature representations from it. Through this strategy, the model's noise resistance can be enhanced, increasing the ability to capture diverse cellular states and gene expression patterns. Experimental results demonstrate scCMVG superior clustering performance across multiple single-cell RNA sequencing datasets, outperforming existing methods. Consequently, scCMVG emerges as a valuable tool for clustering applications. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gene expression

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Designs of 3D Mesh and Torus Optical Network-on-Chips:Topology,Optical Router and Routing Module

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China Communications 2017年第5期14卷 17-29页

作者： Lei Guo Weigang Hou Pengxing Guo Key Laboratory of Medical Image Computing of Northeastern University Ministry of EducationShenyang 110819China School of Computer Science and Engineering Northeastern UniversityShenyang 110819China

As a nanometer-level interconnection,the Optical Network-on-Chip(ONoC)was proposed since it was typically characterized by low latency,high bandwidth and power efficiency. Compared with a 2-Dimensional(2D)design,the 3D integration has the higher packing density and the shorter wire length. Therefore,the 3D ONoC will have the great potential in the future. In this paper,we first discuss the existing ONoC researches,and then design mesh and torus ONoCs from the perspectives of topology,router,and routing module,with the help of 3D integration. A simulation platform is established by using OPNET to compare the performance of 2D and 3D ONoCs in terms of average delay and packet loss rate. The performance comparison between 3D mesh and 3D torus ONoCs is also conducted. The simulation results demonstrate that 3D integration has the advantage of reducing average delay and packet loss rate,and 3D torus ONoC has the better performance compared with 3D mesh solution. Finally,we summarize some future challenges with possible solutions,including microcosmic routing inside optical routers and highly-efficient traffic grooming.

关键词： Optical Network-on-Chip topology and optical router routing module

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Adaptive Wavelet Filters as Practical Texture Feature Amplifiers for Parkinson’s Disease Screening in OCT

arXiv

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arXiv 2025年

作者： Zhang, Xiaoqing Shi, Hanfeng Li, Xiangyu Ye, Haili Xu, Tao Li, Na Hu, Yan Lv, Fan Chen, Jiangfan Liu, Jiang Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Ophthalmology Optometry and Vision Science Wenzhou Medical University Wenzhou China Centre for Computational Science and Mathematical Modelling Coventry University Coventry United Kingdom School of Computer Science University of Nottingham Ningbo China Ningbo315100 China School of Ophthalmology and Optometry Wenzhou Medical University Wenzhou325035 China Department of Electronic and Information Engineering Changchun University Changchun130022 China

Parkinson’s disease (PD) is a prevalent neurodegenerative disorder globally. The eye’s retina is an extension of the brain and has great potential in PD screening. Recent studies have suggested that texture features extracted from retinal layers can be adopted as biomarkers for PD diagnosis under optical coherence tomography (OCT) images. Frequency domain learning techniques can enhance the feature representations of deep neural networks (DNNs) by decomposing frequency components involving rich texture features. Additionally, previous works have not exploited texture features for automated PD screening in OCT. Motivated by the above analysis, we propose a novel Adaptive Wavelet Filter (AWF) that serves as the Practical Texture Feature Amplifier to fully leverage the merits of texture features to boost the PD screening performance of DNNs with the aid of frequency domain learning. Specifically, AWF first enhances texture feature representation diversities via channel mixer, then emphasizes informative texture feature representations with the well-designed adaptive wavelet filtering token mixer. By combining the AWFs with the DNN stem, AWFNet is constructed for automated PD screening. Additionally, we introduce a novel Balanced Confidence (BC) Loss by mining the potential of sample-wise predicted probabilities of all classes and class frequency prior, to further boost the PD screening performance and trustworthiness of AWFNet. The extensive experiments manifest the superiority of our AWFNet and BC over state-of-the-art methods in terms of PD screening performance and trustworthiness. © 2025, CC BY-NC-SA.

关键词： Neurodegenerative diseases

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Feature Selection for Microarray Data via Community Detection Fusing Multiple Gene Relation Networks Information

Feature Selection for Microarray Data via Community Detectio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhang, Shoujia Li, Wei Xie, Weidong Wang, Linjie Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China

ISBN: (纸本)9781665468190

In recent decades, the rapid development of gene sequencing and computer technology has increased the growth of high-dimensional microarray data. Some machine learning methods have been successfully applied to it to help classify cancer. In most cases, high dimensionality and the small sample size of microarray data restricted the performance of cancer classification. This problem usually issolved bysome feature selection methods. However, most of them neglect the exploitation of relations among genes. This paper proposes a novel feature selection method by fusing multiple gene relation network information based on community detection (MGRCD). The proposed method divides all genes into different communities. Then, the genes most associated with cancer classification are selected from each community. The proposed method satisfies both maximum relevances gene with cancer and minimum redundancy among genes for the selected optimal feature subset. The experiment results show that the proposed gene selection method can effectively improve classification performance. © 2022 IEEE.

关键词： Genes

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Dual-Granularity Contrastive Learning for Session-Based Recommendation 9th

Dual-Granularity Contrastive Learning for Session-Based Rec...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wang, Zihan Wu, Gang Wang, Haotong School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China

ISBN: (纸本)9783031466601

The data encountered by Session-based Recommendation System(SBRS) is typically highly sparse, which also serves as one of the bottlenecks limiting the accuracy of recommendations. So Contrastive Learning(CL) is applied in SBRS owing to its capability of improving embedding learning under the condition of sparse data. However, existing CL strategies are limited in their ability to enforce finer-grained (e.g., factor-level) comparisons and, as a result, are unable to capture subtle differences between instances. More than that, these strategies usually use item or segment dropout as a means of data augmentation which may result in sparser data and thus ineffective self-supervised signals. By addressing the two aforementioned limitations, we introduce a novel dual-granularity CL framework. Specifically, two extra augmentation views with different granularities are constructed and the embeddings learned by them are compared with those learned from original view to complete the CL tasks. At factor-level, we employ Disentangled Representation Learning to obtain finer-grained data, with which we can explore connections of items on latent factor independently and generate factor-level embeddings. At item-level, the star graph is deployed as the augmentation method. By setting an additional satellite node, non-adjacent nodes can establish additional connections through satellite nodes instead of reducing the connections of the original graph, so data sparsity can be avoided. Compare the learned embeddings of these two views with the learned embeddings of the original view to achieve CL at two granularities. Finally, the item-level and factor-level embeddings obtained are referenced to generate personalized recommendations for the user. The proposed model is validated through extensive experiments on two benchmark datasets, showcasing superior performance compared to existing methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Benchmarking

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