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Text-guided Reconstruction Network for Sentiment Analysis with Uncertain Missing Modalities

IEEE Transactions on affective computing 2025年

作者： Shi, Piao Hu, Min Nakagawa, Satoshi Zheng, Xiangming Shi, Xuefeng Ren, Fuji Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine National Smart Eldercare International Science and Technology Cooperation Base School of Computer Science and Information Engineering Anhui Hefei230601 China Bozhou University School of Electronic and Information Engineering Bozhou236800 China University of Tokyo Graduate School of Information Science and Technology Tokyo113-8656 Japan University of Electronic Science and Technology of China College of Computer Science and Engineering Chengdu611731 China University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

Multimodal Sentiment Analysis (MSA) is an attractive research that aims to integrate sentiment expressed in textual, visual, and acoustic signals. There are two main problems in the existing methods: 1) the dominant role of the text is underutilization in unaligned multimodal data, and 2) the modality under uncertain missing feature is not sufficiently explored. This paper proposes a Text-guided Reconstruction Network (TgRN) for MSA with uncertain missing modalities in non-aligned sequences. The TgRN network includes three primary modules: Text-guided Extraction Module (TEM), Reconstruction Module (RM) and Text-guided Fusion Module (TFM). First, the TEM consists of the text-guided cross attention units and self-attention units to capture inter-modal features and intra-modal features, respectively. Second, leveraging enhanced attention units and a three-way squeeze-and-excitation block, the RM is designed to learn semantic information from incomplete data and reconstruct missing modality features. Third, the TFM utilizes a progressive modality-mixing adaptation gate to explore the dynamic correlations between nonverbal and verbal modalities, effectively addressing the modality gap issue. Finally, under the supervision of sentiment prediction loss and reconstruction loss, the TgRN effectively processes both uncertain missing-modality conditions and ideal complete modality conditions. Extensive experiments on CMU-MOSI and CH-SIMS demonstrate that our proposed method outperforms state-of-the-art approaches. © 2010-2012 IEEE.

关键词： Semantics

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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near Field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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Clustering Interval and Triangular Granular Data: Modeling, Execution, and Assessment

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IEEE Transactions on Neural Networks and Learning Systems 2024年第6期36卷 10000-10014页

作者： Yiming Tang Wenbin Wu Witold Pedrycz Jianwei Gao Xianghui Hu Zhaohong Deng Rui Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada School of Computer and Information Hefei University of Technology Hefei China Faculty of Automatic Control Electronics and Computer Science Silesian University of Tecghnology Gliwice Poland Research Center of Performance and Productivity Analysis Istinye University Istanbul TÃrkiye School of Computer Science and Engineering Southeast University Nanjing China School of Artificial Intelligence and Computer Jiangnan University Wuxi Jiangsu China

In current granular clustering algorithms, numeric representatives were selected by users or an ordinary strategy, which seemed simple; meanwhile, weight settings for granular data could not adequately express their structural characteristics. Aiming at these problems, in this study, a new scheme called a granular weighted kernel fuzzy clustering (GWKFC) algorithm is put forward. We propose the representative selection and granularity generation (RSGG) algorithm enlightened by the density peak clustering (DPC) algorithm. We build interval and triangular granular data on the strength of numeric representatives obtained by RSGG under the principle of justifiable granularity (PJG), in which we establish some combinations of functions and boundary constraints and prove their properties. Furthermore, we present a novel distance formula via the kernel function for granular data and design new weights to affect the coverage and specificity of granular data. In addition, based upon these factors, we come up with the GWKFC algorithm of granular clustering, and its performance with different granularity is assessed. To sum up, a macro framework involving granular modeling, granular clustering, and assessment has been set up. Lastly, the GWKFC algorithm and ten other granular clustering algorithms are compared by experiments on some artificial and UCI datasets together with datasets with large data or those of high dimensionality. It is found that the GWKFC algorithm can provide better granular clustering results by contrast with other algorithms. The originality is embodied as follows. First, we improve the previous density radius and present the RSGG algorithm to acquire numeric representatives. Second, we propose a new strategy to determine granular data boundaries and further obtain novel weights enlightened by the idea of volume. Lastly, we employ the kernel function to calculate the distance between granular data, which has a stronger spatial division ability than the pr

关键词： Clustering algorithms Data models Computational modeling Numerical models Kernel Phase change materials Granular computing Uncertainty Fuzzy sets Noise

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A Novel Spatial-Temporal Convolutional Neural Network for Remote Photoplethysmography 12

A Novel Spatial-Temporal Convolutional Neural Network for Re...

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12th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2019

作者： Hu, Min Guo, Dong Wang, Xiaohua Ge, Peng Chu, Qian School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China

ISBN: (纸本)9781728148526

Remote photoplethysmography (rPPG) using a simple consumer-level webcam has great potential for healthcare, human-computer interaction, affective computing, and many other fields. However, traditional methods based on the skin reflection model and signal processing achieved limited success. Thus, this paper presents a novel end-to-end spatial-temporal convolutional neural network for the rPPG recovering task: To preserve the details, the pooling layer is moved to the branch network, and the attention mechanism is used to enhance the backbone network performance. Moreover, the ConvLSTM was used to learn the relationship between frames. The manual features extraction and signal post-processing steps can be omitted through our end-to-end neural network. The experimental results conducted on PURE and COHFACE datasets show that the proposed method significantly improved the quality of the recovered rPPG signal. © 2019 IEEE.

关键词： Photoplethysmography

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Answer-Centric Local and Global Information Fusion for Conversational Question Generation

Answer-Centric Local and Global Information Fusion for Conve...

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IEEE International Conference on Big Knowledge (ICBK)

作者： Panpan Lei Xiao Sun Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei Anhui China

ISBN: (纸本)9781665438599

Conversational Question Generation (CQG) is a new concern in Question Generation (QG) study. Recently Seq2Seq neural network model has been widely used in the QG area. CQG model is also based on the Seq2Seq neural network model. We note a problem: the CQG model's input is not a single sentence, but a long text and conversation history. Seq2Seq model can't effectively process long input, the model will generate questions not related to the answer. To solve this problem, we propose an answer-centric local and global information fusion model. We extract the evidence sentence containing the answer in the passage and encode the evidence sentence and the passage information separately. On the one hand, we add answer-centered position tags in the passage to reinforce the attention of information related to the answer. On the other hand, we put the key sentence into the question type prediction model. By combining the answer position embedding to predict the question type, and then put the predicted question types in the key sentence to guide the generation of the question. Finally, we use a gate mechanism to merge key sentence information and passage information. The experimental results show that we have achieved better results.

关键词： Conferences Neural networks Predictive models Logic gates History Data mining

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An Automatic Method for Brain Tumors Segmentation Based on Deep Convolutional Neural Network

An Automatic Method for Brain Tumors Segmentation Based on D...

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IEEE International Conference on Medical Imaging Physics and Engineering

作者： Hui Lin Benjian Zhang Xin Guo Dong Guo Jia Jing Luo Huiyuan Zhao Yumeng Min Hu Fuji Ren School of Physics Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei China School of Computer and Information Hefei University of Technology Hefei China Graduate School of Advanced Technology and Science University of Tokushima Tokushima Japan

ISBN: (纸本)9781665426091

[Purpose] Accurate outline of tumor targets is critical to a high quality radiotherapy plan. Manual segmentation is of great workload and has a strong artificial subjectivity. Using deep learning method to assist automatically segmenting of tumor target is the penetration and application of artificial intelligence in medicine. [Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch. [Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) $90\%\pm 4\%$ . [Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.

关键词： Image segmentation Convolution Magnetic resonance imaging Transforms Predictive models Brain modeling Feature extraction

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Self-to-self transitions in open quantum systems:the origin and solutions

Self-to-self transitions in open quantum systems:the origin ...

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第三十九届中国控制会议

作者： Fang Gao Yaoxiong Wang Qing Gao Dewen Cao Feng Shuang College of Electrical Engineering Guangxi University Institute of Intelligent Machines Chinese Academy of Sciences School of Automation Science and Electrical Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University University Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University

The information of quantum pathways can be extracted in the framework of the Hamiltonianencoding and Observable-decoding *** closed quantum systems,only off-diagonal elements of the Hamiltonian in the Hilbert space is required to be encoded to obtain the desired *** open quantum systems,environment-related terms will appear in the diagonal elements of the Hamiltonian in the Liouville *** diagonal encodings have to be performed to differentiate different pathways,which will lead to self-to-self transitions and inconsistency of pathway amplitudes with Dyson *** this work,a well-designed transformation is proposed to avoid the counterintuitive transitions and the inconsistency,with or without control *** simulations show that the method are consistent with Dyson expansion.

关键词： Self-to-self transitions in open quantum systems:the origin and solutions origin

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A Novel Spatial-Temporal Convolutional Neural Network for Remote Photoplethysmography

A Novel Spatial-Temporal Convolutional Neural Network for Re...

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics

作者： Min Hu Dong Guo Xiaohua Wang Peng Ge Qian Chu School of Computer and Information of Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei China

关键词：

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Facial Expression Recognition Based on Contextual Generative Adversarial Network

Facial Expression Recognition Based on Contextual Generative...

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IEEE International Conference on Cloud computing and Intelligence Systems (CCIS)

作者： Qian Chu Min Hu Xiaohua Wang Yu Gu Tian Chen Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei University of Technology Hefei Anhui China

ISBN: (数字)9781728138633

ISBN: (纸本)9781728138640

In recent years, deep neural networks have been widely concerned by researchers in facial expression recognition. However, insufficient facial training data of the public available database is a major challenge in deep learning, which will lead to an obvious decrease in the effectiveness of learning result; many data augmentation techniques have thus been widely used to enrich the training dataset. In this paper, we introduce a contextual loss function to construct a Contextual Generation Adversarial Network with one generator and one discriminator. The proposed method can map the neutral expression to six basic expressions to expand the database. The experimental results on CK + and KDEF databases show that the proposed method can effectively improve the ability to extract facial features and the ability to generate higher quality images. The data augmentation used the proposed method improves the recognition rate of facial expressions on KDEF and CK + datasets.

关键词： XML

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Suppression and facilitation of motion perception in humans: A reply to Schallmo & Murray (2018)

arXiv

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arXiv 2019年

作者： Tzvetanov, Tzvetomir Anhui Province Key Laboratory of Affective Computing Advanced Intelligent Machine School of Computer Science and Information Engineering Hefei University of Technology Hefei China

In a recent publication (Tzvetanov (2018), bioRxiv 465807), I made an extensive analysis with computational modelling and psychophysics of the simple experimental design of Dr. *** (Tadin, Lappin, Gilroy and Blake (2003), Nature, 424:312-315) about motion perception changes in humans due to size and contrast of the stimulus. This publication sparked from strong claims made in Schallmo et al. (2018) (eLife, 7:e30334) about two important points: (1) "divisive normalization", not inhibitory and excitatory mechanisms, creates the observed psychophysical results and (2) drug-enhanced inhibition showed perceptual outcomes that hint to "weaker suppression" (i.e. inhibition) not stronger "suppression". Schallmo & Murray (2018, bioRxiv, 495291) presented concerns about my extensive publication, specifically about the parts where I directly analysed some of their methods, results and claims. Here, I show that their concerns do not provide clear answers to my specific points and further do not mention other major critiques of data interpretation and modelling of this experimental design. Therefore, I maintain all my claims that were elaborated in details in my first publication (Tzvetanov, 2018, bioRxiv 465807): the specific ones that analyse the results of their and other studies, but also the more broad modelling that is applicable to any study using the simple experimental design of Dr. ***. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

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