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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Hu, Hanxu Chen, Zhipeng School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Nanjing University State Key Laboratory for Novel Software Technology Nanjing China University of Edinburgh Edinburgh United Kingdom Beijing China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In this paper, we propose a simple but effective method for dealing with the challenging fine-grained cross-modal retrieval task where it aims to enable flexible retrieval among subor-dinate categories across different modalities. Specifically, in order to enhance information interaction in different modalities for fine-grained objects, a channel mix method is developed and performed upon the channels of deep activations across dif-ferent modalities. After that, a 1 x 1 convolution is employed to aggregate the mixed channels into a unified feature vector. Moreover, equipped with a novel fine-grained cross-modal cen-ter loss, our method can further improve the intra-class separa-bility as well as inter-class compactness for multi-modalities. Experiments are conducted on the fine-grained cross-modal benchmark dataset and show our superiority over competing methods. Meanwhile, ablation studies also demonstrate the effectiveness of our proposals. © 2022 IEEE.

关键词： computer vision

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Adaptive Plasticity Improvement for Continual Learning

Adaptive Plasticity Improvement for Continual Learning

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yan-Shuo Liang Wu-Jun Li Department of Computer Science and Technology National Key Laboratory for Novel Software Technology Nanjing University P. R. China

Many works have tried to solve the catastrophic forgetting (CF) problem in continual learning (lifelong learning). However, pursuing non-forgetting on old tasks may damage the model's plasticity for new tasks. Although some methods have been proposed to achieve stability-plasticity trade-off, no methods have considered evaluating a model's plasticity and improving plasticity adaptively for a new task. In this work, we propose a new method, called adaptive plasticity improvement (API), for continual learning. Besides the ability to overcome CF on old tasks, API also tries to evaluate the model's plasticity and then adaptively improve the model's plasticity for learning a new task if necessary. Experiments on several real datasets show that API can outperform other state-of-the-art baselines in terms of both accuracy and memory usage.

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Automating App Review Classification based on Extended Semantic 9

Automating App Review Classification based on Extended Seman...

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9th International Conference on Dependable Systems and Their Applications, DSA 2022

作者： Zhou, Wan Wang, Yong Qu, Yang Li, Li School of Computer and Information Anhui Polytechnic University Anhui Wuhu China Nanjing University State Key Laboratory for Novel Software Technology Jinagsu Nanjing China

ISBN: (数字)9781665488778

ISBN: (纸本)9781665488778

Automatic classification of app reviews can help developers quickly read reviews to identify and fix software bugs or add new software functions to meet user requirements. Text mining technologies have been widely used in reviews classification in recent years. However, the accuracy of app reviews classification is limited because of the generally short length of reviews and limited information, and classification models are prone to overfitting due to the diversity and unstructured characteristics of app reviews. In this paper, we propose an automatic classification approach for app reviews based on extended semantic. Specifically, we first reduce noisy data in app reviews by preprocessing, and annotate app reviews using frame semantics and splice the annotation results with reviews to extend the semantic information and text length of reviews. Then, to reduce the probability of overfitting, we integrate the pre-trained models to learn the semantic information of extended app reviews and classify reviews. We evaluate the effectiveness of proposed approach in multiple popular apps, and the experimental results show that it outperforms the state-of-art baselines. © 2022 IEEE.

关键词： Application programs

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A electronic voting protocol based on blockchain and homomorphic signcryption

A electronic voting protocol based on blockchain and homomor...

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作者： Qu, Wenlei Wu, Lei Wang, Wei Liu, Zhaoman Wang, Hao School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China Shandong Provincial Key Laboratory for Software Engineering Jinan China

Compared with traditional voting methods, electronic voting can effectively avoid the phenomenon of fraud for personal gains in various links, it is faster and more accurate in the tallying stage. However, many electronic voting systems have many problems such as inability to verify ballots, easy to be forged, and low computing efficiency. We propose an electronic voting protocol based on homomorphic signcryption and blockchain. The protocol makes the voting process public through blockchain and replaces the traditional trusted third party with the smart contract. It uses the homomorphic encryption algorithm and the homomorphic signcryption algorithm to encrypt and sign the ballot and uses their aggregation properties to perform homomorphic tally on the encrypted votes. This not only reduces the excessive burden on the voters but also improves the voting efficiency. At the same time, it can satisfy the security of electronic voting, and the amount of calculation is small, so it is more convenient and flexible to use in large-scale voting. © 2020 John Wiley & Sons, Ltd.

关键词： Smart contract

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Test Generation for Mutation Testing by Symbolic Execution 23

Test Generation for Mutation Testing by Symbolic Execution

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Su, Rui Zhang, Zhiyi Zhou, Yuqian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Army Engineering of PLA Nanjing China

ISBN: (纸本)9798350359398

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process. © 2023 IEEE.

关键词： software testing

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On the intrinsic structures of spiking neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2024年第1期25卷 9206-9279页

作者： Shao-Qun Zhang Jia-Yi Chen Jin-Hui Wu Gao Zhang Huan Xiong Bin Gu Zhi-Hua Zhou National Key Laboratory for Novel Software Technology and School of Intelligent Science and Technology Nanjing University China National Key Laboratory for Novel Software Technology and School of Artificial Intelligence Nanjing University China School of Mathematical Sciences Jiangsu Second Normal University China Department of Machine Learning Mohamed bin Zayed University of Artificial Intelligence UAE

Recent years have emerged a surge of interest in spiking neural networks (SNNs). The performance of SNNs hinges not only on searching apposite architectures and connection weights, similar to conventional artificial neural networks, but also on the meticulous configuration of their intrinsic structures. However, there has been a dearth of comprehensive studies examining the impact of intrinsic structures; thus developers often feel challenging to apply a standardized configuration of SNNs across diverse datasets or tasks. This work delves deep into the intrinsic structures of SNNs. Initially, we draw two key conclusions: (1) the membrane time hyper-parameter is intimately linked to the eigenvalues of the integration operation, dictating the functional topology of spiking dynamics; (2) various hyper-parameters of the firing-reset mechanism govern the overall firing capacity of an SNN, mitigating the injection ratio or sampling density of input data. These findings elucidate why the efficacy of SNNs hinges heavily on the configuration of intrinsic structures and lead to a recommendation that enhancing the adaptability of these structures contributes to improving the overall performance and applicability of *** by this recognition, we propose two feasible approaches to enhance SNN learning, involving developing self-connection architectures and stochastic spiking neurons to augment the adaptability of the integration operation and firing-reset mechanism, respectively. We theoretically prove that (1) both methods promote the expressive property for universal approximation, (2) the incorporation of self-connection architectures fosters ample solutions and structural stability for SNNs approximating adaptive dynamical systems, (3) the stochastic spiking neurons maintain generalization bounds with an exponential reduction in Rademacher complexity. Empirical experiments conducted on various real-world datasets affirm the effectiveness of our proposed methods.

关键词： spiking neural network intrinsic structures integration operation self-connection architecture firing-reset mechanism stochastic excitation rademacher complexity

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MDTeamGPT: A Self-Evolving LLM-based Multi-Agent Framework for Multi-Disciplinary Team Medical Consultation

arXiv

引用

arXiv 2025年

作者： Chen, Kai Li, Xinfeng Yang, Tianpei Wang, Hewei Dong, Wei Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University China Nanyang Technological University Singapore School of Computer Science Carnegie Mellon University United States

Large Language Models (LLMs) have made significant progress in various fields. However, challenges remain in Multi-Disciplinary Team (MDT) medical consultations. Current research enhances reasoning through role assignment, task decomposition, and accumulation of medical experience. Multi-role collaboration in MDT consultations often results in excessively long dialogue histories. This increases the model’s cognitive burden and degrades both efficiency and accuracy. Some methods only store treatment histories. They do not extract effective experience or reflect on errors. This limits knowledge generalization and system evolution. We propose a multi-agent MDT medical consultation framework based on LLMs to address these issues. Our framework uses consensus aggregation and a residual discussion structure for multi-round consultations. It also employs a Correct Answer Knowledge Base (CorrectKB) and a Chain-of-Thought Knowledge Base (ChainKB) to accumulate consultation experience. These mechanisms enable the framework to evolve and continually improve diagnosis rationality and accuracy. Experimental results on the MedQA and PubMedQA datasets demonstrate that our framework achieves accuracies of 90.1% and 83.9%, respectively, and that the constructed knowledge bases generalize effectively across test sets from both datasets. Our code is publicly available at: https://***/MDTeamGPT. © 2025, CC BY.

关键词： Diagnosis

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Loss decoupling for task-agnostic continual learning 23

Loss decoupling for task-agnostic continual learning

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yan-Shuo Liang Wu-Jun Li National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University P. R. China

Continual learning requires the model to learn multiple tasks in a sequential order. To perform continual learning, the model must possess the abilities to maintain performance on old tasks (stability) and adapt itself to learn new tasks (plasticity). Task-agnostic problem in continual learning is a challenging problem, in which task identities are not available in the inference stage and hence the model must learn to distinguish all the classes in all the tasks. In task-agnostic problem, the model needs to learn two new objectives for learning a new task, including distinguishing new classes from old classes and distinguishing between different new classes. For task-agnostic problem, replay-based methods are commonly used. These methods update the model with both saved old samples and new samples for continual learning. Most existing replay-based methods mix the two objectives in task-agnostic problem together, inhibiting the models from achieving a good trade-off between stability and plasticity. In this paper, we propose a simple yet effective method, called loss decoupling (LODE), for task-agnostic continual learning. LODE separates the two objectives for the new task by decoupling the loss of the new task. As a result, LODE can assign different weights for different objectives, which provides a way to obtain a better trade-off between stability and plasticity than those methods with coupled loss. Experiments show that LODE can outperform existing state-of-the-art replay-based methods on multiple continual learning datasets.

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Stitching, Fine-tuning, Re-training: A SAM-enabled Framework for Semi-supervised 3D Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Shumeng Qi, Lei Yu, Qian Huo, Jing Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China School of Computer Science and Engineering China Shandong Women's University School of Data and Computer Science China

Segment Anything Model (SAM) fine-tuning has shown remarkable performance in medical image segmentation in a fully supervised manner, but requires precise annotations. To reduce the annotation cost and maintain satisfactory performance, in this work, we leverage the capabilities of SAM for establishing semi-supervised medical image segmentation models. Rethinking the requirements of effectiveness, efficiency, and compatibility, we propose a three-stage framework, i.e., Stitching, Fine-tuning, and Re-training (SFR). The current fine-tuning approaches mostly involve 2D slice-wise fine-tuning that disregards the contextual information between adjacent slices. Our stitching strategy mitigates the mismatch between natural and 3D medical images. The stitched images are then used for fine-tuning SAM, providing robust initialization of pseudo-labels. Afterwards, we train a 3D semi-supervised segmentation model while maintaining the same parameter size as the conventional segmenter such as V-Net. Our SFR framework is plug-and-play, and easily compatible with various popular semi-supervised methods. We also develop an extended framework SFR+ with selective fine-tuning and re-training through confidence estimation. Extensive experiments validate that our SFR and SFR+ achieve significant improvements in both moderate annotation and scarce annotation across five datasets. In particular, SFR framework improves the Dice score of Mean Teacher from 29.68% to 74.40% with only one labeled data of LA dataset. The code is available at https://***/ShumengLI/SFR. © 1982-2012 IEEE.

关键词： Image segmentation

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Contrastive Noise-Guided Invertible Network for Image Steganography

引用

IEEE Transactions on Consumer Electronics 2024年

作者： Zhang, Le Lu, Yao Lu, Guangming Harbin Institute of Technology at Shenzhen Department of Computer Science and Technology Shenzhen518057 China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Harbin Institute of Technology at Shenzhen Department of Computer Science and Technology Shenzhen518057 China

Image steganography aims to produce stego images through hiding secret images in the cover images to achieve covert communication. To simultaneously improve the invisibility and revealing quality of covert communication, this paper proposes a Contrastive Noise-Guided Invertible Network (CNGI-Net). Specifically, the Noised-Guided Invertible (NGI) mechanism is first proposed to actively and gradually generate adaptive noise in the forward hiding process using the interaction mechanism. The generated adaptive noise effectively guides and adjusts the cover-secret fusion process, as well as blurs the secret information in the stego images, which can significantly improve the transmission security. Besides, benefiting from the reversible property of NGI, high-quality revealed secret images can also be progressively decoupled from the stego images along the backward flow of NGI. To further ensure the invisibility and security of communication, Contrastive Hiding Learning (CHL) is proposed to improve the cover-stego similarity using the contrastive information. Extensive experiments demonstrate that the proposed CNGINet significantly promotes concealment security of transmission process and the quality of revealing for covert communication. Especially, the quality of stego and revealed secret images on 1, 2, 3 image hiding has been respectively promoted by 1.53, 0.43, 0.38 dB and 4.26, 3.45, 4.98 dB on ImageNet dataset, compared to SOTA steganography methods. © 1975-2011 IEEE.

关键词： contrastive hiding learning (CHL) contrastive noise-guided invertible network (CNGI-Net) covert communication Image steganography noisedguided invertible (NGI)

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