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Generalizing to Unseen Speakers: Multimodal Emotion Recognition in Conversations With Speaker Generalization

IEEE Transactions on Affective Computing 2025年

作者： Tu, Geng Jing, Ran Liang, Bin Yu, Yue Yang, Min Qin, Bing Xu, Ruifeng School of Computer Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Chinese University of Hong Kong Hong Kong Hong Kong Peng Cheng Laboratory Shenzhen518066 China National University of Defense Technology College of Computer Changsha410073 China Chinese Academy of Sciences SIAT Shenzhen518055 China Harbin Institute of Technology Department of Computer Science Harbin150006 China

Multimodal Emotion Recognition in Conversations (MERC) aims to identify the emotion expressed in each utterance within conversational videos. Current efforts are directed toward modeling speaker-sensitive context dependencies and multimodal fusion. However, they still struggle to handle utterances from unseen speakers, hampering the model's generalizability. To tackle this challenge, we propose a Speaker Generalization Framework for MERC. Specifically, we build a prototype graph to learn Speaker-based Utterance Representations (SUR), leveraging prototypes as the bridge between seen and unseen speakers. Speaker-aware Contrastive Learning (CL) is then applied to refine SUR, pulling utterances (or prototypes) from the same speaker together while pushing those from different speakers apart. Further, we introduce a prototypical graph CL to generalize SUR to unseen speakers, ensuring that the same speakers exhibit similar graph structures, while dissimilar ones differ. To further enhance model generalization, we introduce Uncertainty-based Generalization for Speakers, randomly sampling SUR statistics from the estimated Gaussian distribution and probabilistically replacing the original SUR. Experimental findings on two datasets highlight that our framework substantially improves the generalization of various MERC models, surpassing state-of-the-art methods. © 2010-2012 IEEE.

关键词： Gaussian distribution

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Quantum programming languages: A tentative study

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science in China(Series F) 2008年第6期51卷 623-637页

作者： XU JiaFu SONG FangMin State Key Laboratory for Novel Software Technology Department of Computer Science and TechnologyNanjing University Nanjing 210093 China

Starting with some simple representative quantum programming languages, this paper lays stress on quantum computation, language paradigm, program structure, input/output, exception facility, and especially the recent results of the quantum computation group at Nanjing University, namely the functional quantum programming language NDQFP. All primitive functions and combining forms in NDQFP are given in the appendix.

关键词： quantum programming languages language paradigm imperative programming language declarative programming language lexical analyzer syntactic analyzer assembler interpreter

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A sufficient and necessary condition for the convergence of Böhm-like trees

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Journal of Computational Information Systems 2012年第16期8卷 6851-6862页

作者： Hu, Haixing Wu, Nan Song, Fangmin State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing 210093 China

In this paper, we introduce the definitions of Böhm-like trees and Böhm trees, and give a sufficient and necessary condition for the convergence of Böhm-like trees. It is shown that a Böhm-like tree is a Böhm tree of a lambda term if and only if it is recursively enumerable and has finite many free variables, but the convergence of a Böhm-like tree is undecidable. © 2012 Binary Information Press.

关键词： Calculations

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Towards High-Confidence Homogeneous Features: Partial Neighbourhood Ratio Based Difference Image for SAR Change Detection

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IEEE Geoscience and Remote Sensing Letters 2025年

作者： Cui, Bin Peng, Yao Jia, Huarong Guo, Shanchuan Du, Peijun Nanjing University School of Geography and Ocean Science Nanjing210023 China Nanjing University of Posts and Telecommunications School of Internet of Things Nanjing210023 China Nanjing university of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Beijing Institute of Control and Electronic Technology Beijing100048 China

The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image change detection. As a crucial step in unsupervised change detection, existing difference map generation methods primarily utilise neighbourhood information to counteract the interference caused by speckle noise. However, pixels within the neighbourhood can themselves be affected by heterogeneous pixels and noise. Therefore, this paper proposes a difference map generation method, partial neighbourhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighbourhood for difference calculation. Specifically, under the assumption that the local neighbourhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies inter-neighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The change detection results, obtained by applying both manual trial-and-error and dual-domain network on three SAR datasets, have validated the effectiveness of the proposed algorithm. © 2004-2012 IEEE.

关键词： Change detection

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A Redactable Blockchain-Based Anonymous Announcement Scheme for VANETs

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IEEE Transactions on Intelligent Transportation Systems 2025年

作者： Yang, Yuxiang Chen, Yuling Liu, Zhiquan Meng, Yan Sang, Haiwei Guizhou University State Key Laboratory of Public Big Data Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Jinan University College of Cyber Security Guangzhou510632 China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai2022240 China Guizhou Education University School of Mathematics and Big Data Guiyang550018 China

Blockchain serves as a trust layer for data exchange in Vehicular Ad-hoc Networks (VANETs) due to its immutability and transparency. However, it can also be abused to spread false and inaccurate information. Additionally, the explosive growth of data in VANETs and the limited storage capacity of edge nodes make it challenging to maintain the entire blockchain. To address these challenges, we propose an anonymous vehicle announcement scheme based on redactable blockchain. Specifically, we propose a novel ephemeral trapdoor revocable chameleon hash scheme (ETRCH) that enables decentralized management and enforced revocation of redaction privileges. ETRCH deploys multiple regulators, each capable of creating multiple ephemeral trapdoors. These regulators enable k-out-of-n edge nodes associated with the same ephemeral trapdoor to collaboratively rewrite blockchain data, thereby addressing storage limitations. In addition, k-out-of-n regulators can cooperate to update the ephemeral trapdoor and revoke the redaction privileges of any malicious regulator discovered to be abusing their privileges. To facilitate the threshold authentication of announcement messages, we construct a redactable threshold ring signature scheme (RTRS) based on ETRCH, enabling anonymous signing of announcements in VANETs. Additionally, if a regulator detects a false or misleading message, the content can be promptly rewritten. Finally, we conducted rigorous security analysis and comprehensive experiments to evaluate the performance of the proposed scheme. The results demonstrate that compared to VANETs systems based on immutable blockchains, our scheme is both secure and efficient. © 2000-2011 IEEE.

关键词： Vehicular ad hoc networks

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Meta-Learning with Task-Adaptive Selection

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Wan, Quan Wang, Maofa Shan, Weifeng Wang, Bin Zhang, Lu Leng, Zhixiong Yan, Bingchen Xu, Yanlin Chen, Huiling Beijing Information Science and Technology University School of Applied Science Beijing100192 China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guilin541004 China Institute of Disaster Prevention School of Emergency Management Langfang065201 China China Geological Survey Command Center of Natural Resources Comprehensive Survey Beijing100055 China Southern University of Science and Technology Department of Earth and Space Sciences Shenzhen518055 China Wenzhou University Department of Computer Science and Artificial Intelligence Wenzhou325035 China

The gradient-based meta-learning algorithm gains meta-learning parameters from a pool of tasks. Starting from the obtained meta-learning parameters, it can achieve better results through fast fine-tuning with only a few gradient descent updates. The two-layer meta-learning approach that shares initialization parameters has achieved good results in solving few-shot learning domain. However, in the training of multiple similar tasks in the inner layer, the difficulty and benefits of the tasks have been consistently overlooked, resulting in conflicts between tasks and ultimately compromising the model to unexpected positions. Therefore, this paper proposes a task-adaptive selection metalearning algorithm called TSML. Specifically, we construct a task selection trainer to assess the difficulty of tasks and calculate their future benefits. Designing more optimal training strategies for each task based on difficulty and benefit, altering the current compromise in multi-task settings, and balancing the impact of tasks on meta-learning parameters. Additionally, the outer meta-parameter updating method for traditional meta-learning has been adjusted, enabling the meta-parameters to attain a better position. By doing so, we can rapidly improve the generalization and convergence of the meta-learning parameters on unknown tasks. Experimental results indicate a 2.1% improvement over the base model in the 4-conv setting, with a more pronounced effect as the neural network is progressively complexified, reaching a 4.1% improvement in resnet12. © 1991-2012 IEEE.

关键词： Zero-shot learning

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Towards Fair Federated Learning via Unbiased Feature Aggregation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： He, Zeqing Wang, Zhibo Dong, Xiaowei Sun, Peng Ren, Ju Ren, Kui Zhejiang University State Key Laboratory of Blockchain and Data Security China Institute of Blockchain and Data Security Hangzhou310027 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Zhongguancun Laboratory Beijing100094 China

Federated learning (FL) is a distributed machine learning framework that enables multiple clients to collaboratively train models without raw data exchange. Prior studies on FL mainly focus on optimizing learning performance, enhancing privacy preservation, and improving attack resilience. However, little work studies how to mitigate the unfairness of federated trained models while unfair models would make discriminatory decisions toward certain groups or populations (e.g., favoring males over females), leading to serious ethical concerns. Thus, it is crucial to mitigate model unfairness in FL, yet challenging as this requires centralized access to each data point's fairness-sensitive information (e.g., race, gender), which is prohibited in FL. In this work, we propose a novel fair FL framework FedUFA, where the server can aggregate clients' learned knowledge in an unbiased manner, to obtain fair and high-usability federated trained models. Specifically, to unearth the bias in clients' local data and account for potentially heterogeneous local models, we propose a knowledge distillation-based FL scheme, where clients' knowledge of learned features on a public dataset is amalgamated to the server for aggregation. We train an unbiased feature mapper at the server to remove fairness-sensitive latent features and extract fair representations from clients' submitted raw features. In particular, we design an adversarial training method to train the mapper, which involves a predictor aiming to maximize the prediction accuracy on the FL task and a discriminator intending to help identify fairness-sensitive features. Extensive experiments on real-world datasets demonstrate the effectiveness of FedUFA. © 2025 IEEE. All rights reserved,

关键词： Federated learning

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CoT-Drive: Efficient Motion Forecasting for Autonomous Driving with LLMs and Chain-of-Thought Prompting

arXiv

引用

arXiv 2025年

作者： Liao, Haicheng Kong, Hanlin Wang, Bonan Wang, Chengyue Ye, Wang He, Zhengbing Xu, Chengzhong Li, Zhenning State Key Laboratory of Internet of Things for Smart City University of Macau China Department of Computer and Information Science University of Macau China Senseable City Lab Massachusetts Institute of Technology CambridgeMA United States

Accurate motion forecasting is crucial for safe autonomous driving (AD). This study proposes CoT-Drive, a novel approach that enhances motion forecasting by leveraging large language models (LLMs) and a chain-of-thought (CoT) prompting method. We introduce a teacher-student knowledge distillation strategy to effectively transfer LLMs’ advanced scene understanding capabilities to lightweight language models (LMs), ensuring that CoT-Drive operates in real-time on edge devices while maintaining comprehensive scene understanding and generalization capabilities. By leveraging CoT prompting techniques for LLMs without additional training, CoT-Drive generates semantic annotations that significantly improve the understanding of complex traffic environments, thereby boosting the accuracy and robustness of predictions. Additionally, we present two new scene description datasets, Highway-Text and Urban-Text, designed for fine-tuning lightweight LMs to generate context-specific semantic annotations. Comprehensive evaluations of five real-world datasets demonstrate that CoT-Drive outperforms existing models, highlighting its effectiveness and efficiency in handling complex traffic scenarios. Overall, this study is the first to consider the practical application of LLMs in this field. It pioneers the training and use of a lightweight LLM surrogate for motion forecasting, setting a new benchmark and showcasing the potential of integrating LLMs into AD systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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Rethinking Polyp Segmentation from the Perspectives of Matching Views and Seeking Camouflage

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Multimedia Systems 2025年第3期31卷 1-17页

作者： Jiang, Zhengfang Chen, Haipeng Yang, Yongping Liu, Xianzhu Lyu, Yingda College of Software Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Jilin University Changchun130012 China Department of Colorectal Anal Surgery The Second Hospital of Jilin University Changchun130022 China National and Local Joint Engineering Research Center of Space Optoelectronics Technology Changchun University of Science and Technology Changchun130022 China College of Opto-Electronic Engineering Changchun University of Science and Technology Changchun130022 China Public Computer Education and Research Center Jilin University Changchun130012 China

Accurate segmentation of polyps from colonoscopy images is of great significance for the prevention of colorectal cancer. Despite notable progress, polyp segmentation remains a challenging task due to the diversity of polyp size and shape, as well as the camouflage property of polyps caused by their high similarity to surrounding tissues. To address these challenges, inspired by human perceptual experience in finding objects, we propose a multi-perspective collaborative network (MPCNet), innovatively identifying the potential polyps from the perspectives of matching views and seeking camouflage. Firstly, from the former perspective, a global–local aware module is proposed to provide various feature receptive fields to match suitable views for polyps of different sizes and shapes. Then, from the latter perspective, a texture enhancement module (TEM) and an interaction refinement module (IRM) are proposed to mine and complement clues essential for breaking the camouflage. Specifically, TEM mines texture-related clues and amplifies the subtle texture differences between polyps and their background, while IRM complements boundary clues and further optimizes feature representation through the interaction between boundaries and regions, thereby generating accurate segmentation results with complete regions and fine boundaries. Extensive experiments on five benchmark polyp datasets show that our MPCNet outperforms other state-of-the-art methods. In particular, we achieve a mean Dice of 81.7% and 82.4% on the most challenging datasets CVC-ColonDB and ETIS, respectively, which are significantly higher than competing methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Image segmentation

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An Intelligent Privacy Protection Scheme for Efficient Edge Computation Offloading in IoV

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Chinese Journal of Electronics 2024年第4期33卷 910-919页

作者： Liang YAO Xiaolong XU Wanchun DOU Muhammad Bilal School of Software Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies

As a pivotal enabler of intelligent transportation system(ITS), Internet of vehicles(Io V) has aroused extensive attention from academia and industry. The exponential growth of computation-intensive, latency-sensitive,and privacy-aware vehicular applications in Io V result in the transformation from cloud computing to edge computing,which enables tasks to be offloaded to edge nodes(ENs) closer to vehicles for efficient execution. In ITS environment,however, due to dynamic and stochastic computation offloading requests, it is challenging to efficiently orchestrate offloading decisions for application requirements. How to accomplish complex computation offloading of vehicles while ensuring data privacy remains challenging. In this paper, we propose an intelligent computation offloading with privacy protection scheme, named COPP. In particular, an Advanced Encryption Standard-based encryption method is utilized to implement privacy protection. Furthermore, an online offloading scheme is proposed to find optimal offloading policies. Finally, experimental results demonstrate that COPP significantly outperforms benchmark schemes in the performance of both delay and energy consumption.

关键词： Industries Privacy Energy consumption Transportation Computational efficiency Encryption Protection

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