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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhu, Hengde Kong, Xiangyu Xie, Weicheng Huang, Xin He, Xilin Liu, Lu Shen, Linlin Zhang, Wei Gunes, Hatice Song, Siyang School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom Affect AI Anhui China Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Shenzhen China School of Electronic Science and Engineering (School of Microelectronics) South China Normal University Foshan China School of Software Technology Zhejiang University Hangzhou China Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)157735897X

In dyadic human-human interactions, individuals may express multiple different facial reactions in response to the same/similar behaviours expressed by their conversational partners depending on their personalised behaviour patterns. As a result, frequently-employed reconstruction loss-based strategies lead the training of previous automatic facial reaction generation (FRG) models to not only suffer from the 'one-to-many mapping' problem, but also fail to comprehensively consider the quality of the generated facial reactions. Besides, none of them considered such personalised behaviour patterns in generating facial reactions. In this paper, we propose the first adversarial FRG model training strategy which jointly learns appropriateness and realism discriminators to provide comprehensive task-specific supervision for training the target facial reaction generators, and reformulates the 'one-to-many (facial reactions) mapping' training problem as a 'one-to-one (distribution) mapping' training task, i.e., the FRG model is trained to output a distribution representing multiple appropriate/plausible facial reaction from each input human behaviour. In addition, our approach also serves as the first offline FRG approach that considers personalised behaviour patterns in generating of target individuals' facial reactions. Experiments show that our PerReactor not only largely outperformed all existing offline solutions for generating appropriate, diverse and realistic facial reactions, but also is the first offline approach that can effectively generate personalised appropriate facial reactions. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Shape-aware contrastive deep supervision for esophageal tumor segmentation from CT scans

Shape-aware contrastive deep supervision for esophageal tumo...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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Separate and Integrate Different Level Reasoning for Event Causality Identification

Separate and Integrate Different Level Reasoning for Event C...

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International Joint Conference on Neural Networks (IJCNN)

作者： Yuchang Deng Wenti Huang Tingxuan Chen Jun Long School of Software Xinjiang University Urumqi China School of Computer Science and Engineering Hunan University of Science and Technology Changsha China School of Computer Science and Engineering Central South University Changsha China Big Data Institute Central South University Changsha China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Document-level event causality identification (DECI) aims to discern the causal relationship between two event mentions within a document, which is a more challenging task compared to sentence-level event causality identification. However, the conventional DECI model applies the same method to all pairs of events without considering the distinctions and connections between intra-sentence and inter-sentence event pairs. This paper introduces a novel Separate and Integrate Different Level reasoning model (SIDL) to extract and fuse text features of events. Specifically, we separately encode intra-sentence and inter-sentence event pairs and leverage attention from pre-trained language models to identify the relevant context for aiding in the determination of causality. Additionally, we propose a causal reasoning strategy that leverages intra-sentence causal relationships to assist in the inference of inter-sentence causal relationships. We combine different features of event pairs by discriminating the characters and effects of intra-sentence and inter-sentence reasoning. Experimental results show that our approach achieves superior performance compared to previous models in two benchmarks EventStoryLine and CausalTimeBank.

关键词： Knowledge engineering Correlation Fuses Neural networks Cause effect analysis Benchmark testing Feature extraction

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Deep Unfolding 3D Non-Local Transformer Network for Hyperspectral Snapshot Compressive Imaging

Deep Unfolding 3D Non-Local Transformer Network for Hyperspe...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zheng Zhou Zongxin Liu Yongyong Chen Bingzhi Chen Biqing Zeng Yicong Zhou School of Electronics and Communication Engineering Guangzhou University Guangzhou China School of Software South China Normal University Foshan China School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Department of Computer and Information Science University of Macau Macau China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Hyperspectral compressive imaging has shown remarkable advancements through the adoption of deep unfolding frameworks, which integrate the proximal mapping prior into the data fidelity term to formulate the reconstruction problem. However, existing technologies still face challenges in effectively capturing spatial-spectral features during the iterative deep prior learning stage, leading to unsatisfactory performance degradation. To address this issue, we propose a deep unfolding 3D non-local transformer (3DNLT) network for hyperspectral compressive imaging. A learnable half-quadratic splitting (HQS) algorithm is utilized to iteratively update the linear projection. Furthermore, a 3D non-local attention ushaped transformer is presented as the deep proximal mapping prior module to obtain the spatial-spectral long-range dependency features, leading to enhance the network’s ability to capture fine-grained hyperspectral and spatial details. Experimental results on both synthetic and real hyperspectral image reconstruction have demonstrated the superior performance of the 3DNLT network compared to state-of-the-art methods.

关键词： Degradation Three-dimensional displays Image coding Imaging Transformers Robustness Iterative methods

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Haotian Chi Qi Ma Yuxuan Zhang Chenglong Fu Yuwei Wang Haijun Geng Xiaojiang Du School of Automation and Software Engineering Shanxi University Taiyuan China Department of Software and Information Systems University of North Carolina at Charlotte Charlotte NC USA Department of Electical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon.

关键词： Training Event detection Transfer learning Smart homes Acoustics Internet of Things Security Monitoring Anomaly detection Microphones

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Contextual Graph Reconstruction and Emotional Variation Learning for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Rao, Yujing Cao, Min Ye, Mang Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan430072 China Soochow University School of Computer Science and Technology Suzhou215006 China

Conversational Emotion Recognition (CER) significantly benefits from the integration of multiple modalities. However, real-world scenarios are often plagued by hardware malfunctions and network failures that lead to missing modalities and incomplete emotional representations. Existing methods primarily focus on modeling inter-modal relationships within isolated utterances to generate missing data, so they do not adequately capture conversational context and dynamic emotional evolution. In conversations, emotional expressions are inherently context-dependent and dynamic. Insufficient modeling of these properties can substantially degrade performance. To address these challenges, we propose Contextual Graph Reconstruction and Emotional Variation Learning (CGR-EVL). Our approach flexibly constructs an utterance graph with diverse coverage based on speaker activity and modality availability, thereby capturing more contextual information. To enhance the handling of missing modalities, we further integrate temporal relations through a relational graph convolutional network to reconstruct missing features and introduce a speaker emotion-aware constraint to ensure emotional coherence. Additionally, we propose the concept of emotional entropy to quantify variation patterns and develop a novel loss function that aligns predicted and actual variations. Experiments on the IEMOCAP and MELD datasets show that CGR-EVL outperforms state-of-the-art methods, particularly under conditions of incomplete modalities. © 2010-2012 IEEE.

关键词： Graph neural networks

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MCAN: An Efficient Multi-Task Network for Facial Expression Analysis

MCAN: An Efficient Multi-Task Network for Facial Expression ...

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International Conference on computer Supported Cooperative Work in Design

作者： Haitao Wang Rui Wang Peng Zou Qingjian Ni Xiao Sun College of Software Engineering Southeast University Nanjing China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the development of artificial intelligence, artificial intelligence technology is widely used in robots. For example, emotional computing robots need to be able to complete the function of facial expression analysis. The multi-task deep learning model MCAN proposed in this paper is designed to complete facial expression analysis for robots which can predict discrete expressions and dimensional measures. The class center loss function proposed in this paper can increase the inter-class distance while reducing the intra-class distance. In addition, multi-head attention network was improved to focus on different areas of the input image. Finally, feature pyramid network allows information exchange and fusion between feature maps at different levels. Experiments have proven that MCAN achieves excellent results on both the AffectNet dataset and the AFEW-VA dataset, and also speeds up inference.

关键词： Measurement Deep learning Federated learning Computational modeling Predictive models Multitasking Feature extraction

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Brain-Controlled computer Tasks for Paralyzed Persons: Framework Overview

Brain-Controlled Computer Tasks for Paralyzed Persons: Frame...

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2023 International Conference on Industry Sciences and computer Science Innovation, iSCSi 2023

作者： Imran, Amna Chaudhary, Haider Tariq, Maimoona Ali, Zahra Ahad, Abdul Naqvi, Hammad Coelho, Paulo Jorge Pires, Ivan Miguel Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Shaanxi Xian710072 China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. Based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor. © 2024 The Authors. Published by ELSEVIER B.V.

关键词： Brain computer interface

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An efficient ring signcryption scheme for wireless sensor networks

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Wireless Networks 2025年

作者： Jin, Chunhua Cui, Yusheng Qin, Wenyu Chen, Zhiwei Chen, Guanhua Shan, Jinsong Faculty of Computer & Software Engineering Huaiyin Institute of Technology Huai’an233003 China

In the context of big data and cloud computing, data security is facing unprecedented challenges. In order to ensure data security, this article proposes an efficient ring signature encryption scheme. This scheme allows one member of the ring to use their own private key and the public keys of other members to sign the entire ring. The receiver only knows that the signature comes from a certain member of the ring, but does not know who that member is, thus achieving anonymity. We have also demonstrated that this scheme satisfies confidentiality, authentication, non repudiation, and integrity. Our proposed scheme can provide security proof in the random oracle model. Performance analysis shows that the proposed scheme not only achieves great optimization in calculation cost and communication consumption but also makes great progress in total energy consumption. In addition, we illustrate the practical applicability of our proposed scheme by providing a realistic use case scenario. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wireless sensor networks

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RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention

RGB-D Grasp Detection via Depth Guided Learning with Cross-m...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Ran Qin Haoxiang Ma Boyang Gao Di Huang State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing China Geometry Robotics and the School of Computer Science and Technology Harbin Institute of Technology Harbin China

Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities. However, depth maps are generally of a relatively lower quality with much stronger noise compared to RGB images, making it challenging to acquire grasp depth and fuse multi-modal clues. To address the two issues, this paper proposes a novel learning based approach to RGB-D grasp detection, namely Depth Guided Cross-modal Attention Network (DGCAN). To better leverage the geometry information recorded in the depth channel, a complete 6-dimensional rectangle representation is adopted with the grasp depth dedicatedly considered in addition to those defined in the common 5-dimensional one. The prediction of the extra grasp depth substantially strengthens feature learning, thereby leading to more accurate results. Moreover, to reduce the negative impact caused by the discrepancy of data quality in two modalities, a Local Cross-modal Attention (LCA) module is designed, where the depth features are refined according to cross-modal relations and concatenated to the RGB ones for more sufficient fusion. Extensive simulation and physical evaluations are conducted and the experimental results highlight the superiority of the proposed approach.

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