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IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Zixin Wu Xiaoyun Zhou Yi Zhu Chenyu Wang Guosheng Hao School of Computer Science and Technology Jiangsu Normal University Xuzhou Jiangsu China Jiangsu Engineering Technology Research Center of ICT in Education Xuzhou Jiangsu China Key Laboratory of Safety-Critical Software (Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information Technology Nanjing Jiangsu China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Privacy protection is one of the main approaches to improve the security and reliability of personalized course recommendation systems. However, the current privacy protection methods for personalized course recommendation mainly focus on data protection, but ignore the potential privacy leakage that may be caused by the system behavior. In addition, privacy protection is not fully considered in the initial phases of system development and design. To address this issue, this paper presents a methodology for detecting privacy leakage in personalized course recommendation systems using the process algebra Pi-Calculus. Firstly, the basic semantics and evolution rules of Pi-Calculuss as well as the definition of mutual simulation techniques are introduced. Then the personalized course recommendation system is mapped to a process algebra Pi-Calculus, a formal description and analysis of the interaction behavior of the components of the personalized course recommendation system is given, and finally a framework for privacy leakage detection is introduced and applied to a case study to detect whether there is privacy leakage behavior in the recommendation system by the inter-simulation technique. Experiments demonstrate that the method can effectively detect potential privacy leakage problems in the early stages of system development.

关键词： Privacy Algebra Semantics Data protection Software quality Systems modeling Data transfer Software reliability Protection Recommender systems

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Leveraging Knowledge Distillation for Improved Event Extraction in QA Models

Leveraging Knowledge Distillation for Improved Event Extract...

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Frontiers technology of information and computer (ICFTIC), IEEE International Conference on

作者： Peiyao Zhou Zhikui Hu Kangli Zi Dawei Zhang School of Computer Science Jiangsu University of Science and Technology Zhenjiang China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Event extraction is an important task in natural language processing, and it is widely utilized in intelligence domains such as business and military for information extraction. Recently, many works have successfully transformed document-level event extraction into Question-answering (QA) tasks with remarkable results. This approach embeds event argument information within the questions, introducing prior knowledge into the process. Jin et al. highlighted the importance of high-quality QA pairs for practical QA tasks, emphasizing that constructing these pairs remains a key challenge[1]. In this study, we propose a Prompt-based question generation method to automatically generate questions containing event arguments, which converts event extraction into a QA task. We introduce an event ontology-based information retrieval module to enhance answer accuracy and select the most relevant document segments as input text. Additionally, we employ knowledge distillation to build the QA model, transferring knowledge from a large pre-trained model to a more compact and efficient one, improving the student model's performance. Experiments show that our model performs strongly in mainstream benchmarks, with 4.8 improvements on WikiEvents.

关键词： Accuracy Benchmark testing information retrieval Question generation Question answering (information retrieval) Data mining Business

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An Efficient Image Encryption Scheme Based on Variable Row-Columns Scrambling and Selective Block Diffusion 14

An Efficient Image Encryption Scheme Based on Variable Row-C...

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14th International Conference on Digital Image processing, ICDIP 2022

作者： Huang, Weiye Lyu, Xin Zhang, Chao Li, Xin Su, Yifei Zeng, Tao Wang, Xinyuan Xu, Zhennan Huang, Yucong College of Computer and Information Hohai University Jiangsu Province Nanjing211100 China Key Laboratory of Water Big Data Technology of Ministry of Water Resources Hohai University Jiangsu Province Nanjing211100 China Information Center Ministry of Water Resources Beijing100053 China

ISBN: (纸本)9781510657564

With the continuous development of multimedia technology and shooting hardware, more and more high-quality images appear in life and production. At present, there are many image encryption methods to ensure image security, but most of them cannot meet the real-time requirements of large-size image encryption, which causes great obstacles to the application and popularization of image encryption. In this paper, an efficient image encryption method based on variable row-columns scrambling and dynamic threshold selective block diffusion is designed. The pixels are operated in batches row by column and block, and the length of chaotic sequence required is reduced without reducing the security, thus reducing the time consuming of the encryption system. In addition, the modified five-point sampling is adopted to select chaotic sequence, which improves the utilization rate of chaotic sequence and further improves the encryption efficiency. Experimental results show that the proposed method has a higher encryption efficiency than the existing methods with similar security, and has strong practicability. © 2022 SPIE.

关键词： Diffusion

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Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space

arXiv

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arXiv 2025年

作者： Liang, Yuheng Liu, Feng Liu, Mingzhou Yao, Yu Wang, Zheyu Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Intelligent Information Processing and Communication Technology China Nanjing University of Science and Technology ZiJin College School of Computer and Artificial Intelligence China

Continuous Emotion Recognition (CER) plays a crucial role in intelligent human-computer interaction, mental health monitoring, and autonomous driving. Emotion modeling based on the Valence-Arousal (VA) space enables a more nuanced representation of emotional states. However, existing methods still face challenges in handling long-term dependencies and capturing complex temporal dynamics. To address these issues, this paper proposes a novel emotion recognition model, Mamba-VA, which leverages the Mamba architecture to efficiently model sequential emotional variations in video frames. First, the model employs a Masked Autoencoder (MAE) to extract deep visual features from video frames, enhancing the robustness of temporal information. Then, a Temporal Convolutional Network (TCN) is utilized for temporal modeling to capture local temporal dependencies. Subsequently, Mamba is applied for long-sequence modeling, enabling the learning of global emotional trends. Finally, a fully connected (FC) layer performs regression analysis to predict continuous valence and arousal values. Experimental results on the Valence-Arousal (VA) Estimation task of the 8th competition on Affective Behavior Analysis in-the-wild (ABAW) demonstrate that the proposed model achieves valence and arousal scores of 0.5362 (0.5036) and 0.4310 (0.4119) on the validation (test) set, respectively, outperforming the baseline. The source code is available on GitHub: https://***/FreedomPuppy77/Charon. Copyright © 2025, The Authors. All rights reserved.

关键词： Regression analysis

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SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification

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IEEE Transactions on Multimedia 2025年

作者： Wang, Lei Zhan, Yibing Ma, Leilei Tao, Dapeng Ding, Liang Gong, Chen Nanjing University of Science and Technology Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Jiangsu Nanjing210094 China JD Explore Academy Beijing100000 China Anhui University School of Computer Science and Technology Anhui Heifei230601 China Yunnan University FIST LAB School of Information Science and Engineering Yunnan Kunming650091 China

Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the considerable discrepancies between single- and multi-label images, i.e., a multi-label image involves multiple co-occurred categories and fickle object scales. On the other hand, previous multi-label image classification (MLIC) methods tend to design elaborate models, bringing expensive computation. In this article, we introduce a simple but effective augmentation strategy for multi-label image classification, namely SpliceMix. The "splice"in our method is two-fold: 1) Each mixed image is a splice of several downsampled images in the form of a grid, where the semantics of images attending to mixing are blended without object deficiencies for alleviating co-occurred bias;2) We splice mixed images and the original mini-batch to form a new SpliceMixed mini-batch, which allows an image with different scales to contribute to training together. Furthermore, such splice in our SpliceMixed mini-batch enables interactions between mixed images and original regular images. We also provide a simple and non-parametric extension based on consistency learning (SpliceMix-CL) to show the potential of extending our SpliceMix. Extensive experiments on various tasks demonstrate that only using SpliceMix with a baseline model (e.g., ResNet) achieves better performance than state-of-the-art methods. Moreover, the generalizability of our SpliceMix is further validated by the improvements in current MLIC methods when married with our SpliceMix. The code is available at https://***/zuiran/SpliceMix. © 1999-2012 IEEE.

关键词： Image classification

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Improved Res2Net Based on Multi-scale Attention Mechanism for Breast Cancer Image Classification 12th

Improved Res2Net Based on Multi-scale Attention Mechanism fo...

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12th International Conference on computer Engineering and Networks, CENet 2022

作者： Lei, Suqin Deng, Xing Shao, Haijian Xia, Wang Department of Computer Jiangsu University of Science and Technology Jiangsu Zhenjiang212003 China College of Automation Key Laboratory of Ministry of Education of Complex Engineering System Measurement and Control Southeast University Jiangsu Nanjing210009 China School of Information Science and Technology Nantong University Nantong Jiangsu226019 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

ISBN: (纸本)9789811969003

Breast cancer is one of the most common cancers among female diseases. Since the classification accuracy of pathological images is crucial to the diagnosis of breast cancer, in order to reduce the error of manual diagnosis and improve the automatic classification accuracy of pathological images, this paper proposes a high-accuracy breast cancer classification method based on multi-scale attention mechanism enhanced Res2Net. By constructing a multi-channel residual connection in the residual block, and introducing a convolutional attention mechanism to enhance the feature extraction ability of Res2Net, and using group convolution to replace part of the ordinary convolution. In addition, since the channel information interaction across groups cannot be realized in the group convolution, which reduces the feature extraction ability. In this paper, the channel shuffling operation is used to reorganize the feature maps after the group convolution to achieve the information interaction between different groups. The evaluation results on the BreaKHis dataset show that, the accuracy and precision of the method in this paper have reached 98.5% and 98.9% respectively, in binary classification of histopathological images compared with the benchmark method, and the advantages of recall rate and F1 value are also more obvious. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Compressive Sensing Optimization Algorithm for Indoor Visible Light 3D Positioning

Compressive Sensing Optimization Algorithm for Indoor Visibl...

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2023 International Conference on Engineering of Optical System and Precision Instruments, ICEOP 2023

作者： Cheng, Yue Shao, Jianhua Wu, Ruiqi School of Computer and Electronic Information School of Artificial Intelligence Nanjing Normal University Nanjing210023 China Jiangsu Key Laboratory on Opto-Electronic Technology Nanjing Normal University Nanjing210023 China School of Electronic Science and Engineering Nanjing University Nanjing210033 China

A compressive sensing optimization algorithm for indoor three-dimensional (3D) positioning based on visible light was proposed in this paper. This is the first work that applies compressive sensing to indoor 3D positioning. The solution of the fingerprint's weight was transformed into a compressive sensing reconstruction, the coordinates of the point to be positioned are obtained by reconstruction using the KNN algorithm and the grey wolf optimization (GWO) algorithm. The self-exploration GWO (SEGWO) algorithm was proposed by introducing a self-exploration mechanism into GWO to improve the convergence performance of the algorithm. The localization error of the proposed method in 3D space is 5.79cm, which is significantly lower than other similar algorithms. © Published under licence by IOP Publishing Ltd.

关键词： Compressed sensing

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Clothed Human Performance Capture with a Double-layer Neural Radiance Fields

Clothed Human Performance Capture with a Double-layer Neural...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition, CVPR 2023

作者： Wang, Kangkan Zhang, Guofeng Cong, Suxu Yang, Jian Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education China Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of CAD&CG Zhejiang University China

This paper addresses the challenge of capturing performance for the clothed humans from sparse-view or monocular videos. Previous methods capture the performance of full humans with a personalized template or recover the garments from a single frame with static human poses. However, it is inconvenient to extract cloth semantics and capture clothing motion with one-piece template, while single frame-based methods may suffer from instable tracking across videos. To address these problems, we propose a novel method for human performance capture by tracking clothing and human body motion separately with a double-layer neural radiance fields (NeRFs). Specifically, we propose a double-layer NeRFs for the body and garments, and track the densely deforming template of the clothing and body by jointly optimizing the deformation fields and the canonical double-layer NeRFs. In the optimization, we introduce a physics-aware cloth simulation network which can help generate physically plausible cloth dynamics and body-cloth interactions. Compared with existing methods, our method is fully differentiable and can capture both the body and clothing motion robustly from dynamic videos. Also, our method represents the clothing with an independent NeRFs, allowing us to model implicit fields of general clothes feasibly. The experimental evaluations validate its effectiveness on real multi-view or monocular videos. ©2023 IEEE.

关键词： Multilayer neural networks

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Classification of Multisource Remote Sensing Data Using Slice Mamba

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： He, Yan Tu, Bing Jiang, Puzhao Liu, Bo Li, Jun Plaza, Antonio Institute of Optics and Electronics Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Faculty of Computer Science Wuhan430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica Cáceres10003 Spain

The multisource remote sensing (RS) data have yielded promising results in target detection and classification tasks. However, most existing methods primarily focus on the spatial features inherent in spectral information, while the continuous spectral characteristics are generally neglected. This oversight leads to insufficient extraction of spectral information, thereby limiting detection performance. Recently, the Mamba architecture, based on state space models (SSMs), integrates the advantages of long-range sequence modeling and linear computational efficiency, demonstrating significant potential in low-dimensional scenarios. Inspired by this, we propose Slice Mamba for multisource RS data fusion classification. Specifically, we design two scanning methods: lateral slice scanning (LatSS) and longitudinal slice scanning (LonSS), which construct sequences from lateral and longitudinal perspectives to facilitate information interaction between pixels. In conjunction with the Mamba architecture, we develop the lateral slice Mamba block (LatSMB) and the longitudinal slice Mamba block (LonSMB) to capture continuous spatial-spectral features. Based on this, we establish the slice feature extraction (SFE) module for extracting spatial-spectral feature information and design the cross-information fusion (CIF) module to form a complementary structure for effectively modeling spatial-spectral features, thereby achieving the fusion and classification of multisource heterogeneous features. Experimental results on three benchmark datasets demonstrate that Slice Mamba outperforms existing advanced methods in fusion classification performance and exhibits greater robustness when applied to multispectral datasets. © 2025 IEEE.

关键词： information fusion

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Performer: A High-Performance Global-Local Model-Augmented with Dual Network Interaction Mechanism

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IEEE Transactions on Cognitive and Developmental Systems 2024年

作者： Tan, Dayu Hao, Rui Hua, Linfeng Xu, Qi Su, Yansen Zheng, Chunhou Zhong, Weimin Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Institutes of Physical Science and Information Technology Hefei230601 China East China University of Science and Technology Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education Shanghai200237 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China

In deep learning, Convolutional Neural Networks (CNNs) focus on local information through convolutional kernels, while transformers attend to global information using self-attention mechanisms. The union of these distinct approaches enables a more comprehensive extraction of image features. However, the feature map dimensions of CNN and Transformer differ, leading to dimension mismatch issues when combining these architectures. Additionally, the parameter size of the hybrid model integrating both architectures remains large, making it difficult to train. To further augmenting the interpretation of complex image patterns, we present Performer, a dual-network architecture that seamlessly combines CNNs and transformers, resulting in a novel and efficient representation learning model. In the Performer model, we innovate by devising a unique interaction methodology for CNN and transformer architectures to enhance the image feature extraction capabilities mutually. To counteract issue of dimensionality mismatch, we also introduce a refined transformer block, a advancement over the transformer block of ViT. To validate the effectiveness of Performer, we conduct extensive experiments on both classification and segmentation tasks. Performer achieve an accuracy of 83.37% on the ImageNet-200 dataset. For semantic segmentation, Performer excels on the CamVid and Hippocampus datasets. On CamVid, our model achieves a mean Intersection over Union (mIoU) of 63.27% and Pixel Accuracy of 92.11%, demonstrating superior performance in capturing fine details and handling complex scenes effectively. The code is available at https://***/hlfthh/ Performer. © 2016 IEEE.

关键词： Semantic Segmentation

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