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2024 16th International Conference on Graphics and Image Processing, ICGIP 2024

作者： Hong, Fanghua Wang, Jinhu Lu, Andong Wang, Qunjing School of Electronic and Information Engineering Anhui University Hefei230601 China School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (数字)9781510688780

ISBN: (纸本)9781510688773

RGBT tracking aims to take full advantage of the complementary advantages of RGB and thermal infrared (TIR) modalities to achieve robust tracking in complex scenes. However, current approaches face limitations when dealing with the quality-imbalanced problem. In this paper, we introduce a novel augmentative fusion learning framework that aims to maximize the potential of existing fusion modules in modality quality imbalanced scenarios. In particular, we design a modality stochastic degradation strategy to improve the robustness of the fusion module in modality quality imbalance scenarios. Meanwhile, to further enhance the fusion performance with the modality quality imbalance inputs, a self-supervised constraint is introduced to reconstruct the modality features before degradation by combining high-quality modality and degraded modality information. Finally, the effectiveness of the proposed method is verified by evaluating it on two standard RGBT datasets and two state-of-the-art algorithms. And the results indicate that our method achieved superior performance without adding any parameters or computational complexity. © 2025 SPIE.

关键词： RGB color model

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DMF: A Deep Multimodal Fusion-Based Network Traffic Classification Model

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computers, Materials & Continua 2025年第5期83卷 2267-2285页

作者： Xiangbin Wang Qingjun Yuan Weina Niu Qianwei Meng Yongjuan Wang Chunxiang Gu Henan Key Laboratory of Network Cryptography Technology Information Engineering UniversityZhengzhou450001China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China

With the rise of encrypted traffic,traditional network analysis methods have become less effective,leading to a shift towards deep learning-based *** these,multimodal learning-based classification methods have gained attention due to their ability to leverage diverse feature sets from encrypted traffic,improving classification ***,existing research predominantly relies on late fusion techniques,which hinder the full utilization of deep features within the *** address this limitation,we propose a novel multimodal encrypted traffic classification model that synchronizes modality fusion with multiscale feature ***,our approach performs real-time fusion of modalities at each stage of feature extraction,enhancing feature representation at each level and preserving inter-level correlations for more effective *** continuous fusion strategy improves the model’s ability to detect subtle variations in encrypted traffic,while boosting its robustness and adaptability to evolving network *** results on two real-world encrypted traffic datasets demonstrate that our method achieves a classification accuracy of 98.23% and 97.63%,outperforming existing multimodal learning-based methods.

关键词： Deep fusion intrusion detection multimodal learning network traffic classification

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Remote entangling gate between a quantum dot spin and a transmon qubit mediated by microwave photons

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Chinese Physics B 2024年第2期33卷 52-59页

作者：朱行宇朱乐天涂涛李传锋 Key Laboratory of Quantum Information Chinese Academy of SciencesUniversity of Science and Technology of ChinaHefei 230026China School of Mechanical and Electronic Engineering Suzhou UniversitySuzhou 234000China Hefei National Laboratory University of Science and Technology of ChinaChinese Academy of SciencesHefei 230088China

Spin qubits and superconducting qubits are promising candidates for realizing solid-state quantum information *** a hybrid architecture that combines the advantages of different qubits on the same chip is a highly desirable but challenging *** we propose a hybrid architecture that utilizes a high-impedance SQUID array resonator as a quantum bus,thereby coherently coupling different solid-state *** employ a resonant exchange spin qubit hosted in a triple quantum dot and a superconducting transmon *** this hybrid system is highly tunable,it can operate in a dispersive regime,where the interaction between the different qubits is mediated by virtual *** utilizing such interactions,entangling gate operations between different qubits can be realized in a short time of 30 ns with a fidelity of up to 96.5%under realistic parameter *** utilizing this interaction,remote entangled state between different qubits can be prepared and is robust to perturbations of various *** results pave the way for exploring efficient fault-tolerant quantum computation on hybrid quantum architecture platforms.

关键词： hybrid quantum architectures circuit quantum electrodynamics entangling gate

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High-Dimensional Multi-Label Data Stream Classification With Concept Drifting Detection

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IEEE Transactions on Knowledge and Data engineering 2023年第8期35卷 8085-8099页

作者： Li, Peipei Zhang, Haixiang Hu, Xuegang Wu, Xindong Anhui Province Key Laboratory of Industry Safety and Emergency Technology Hefei230601 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Second People's Hospital of Hefei Anhui Hefei230011 China Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education of China School of Computer Science and Information Engineering Anhui Hefei230002 China

Multi-label data streams such as Web texts and images have been popular on the Web. These data present the characteristics of multiple label, high dimensionality, high volume, high velocity and especial concept drift etc. Thus, multi-label data stream classification is a very challenging and significant task especially in the handling of high-dimensional data with concept drifts. However, this challenge has received little attention from the research community. Therefore, we propose the max-relevance and min-redundancy based algorithm adaptation approach for the efficient and effective classification on multi-label data streams with high-dimensional attributes and concept drifts. In order to reduce the impact from the high-dimensional data with noisy attributes, we first refine the minimal-redundancy-maximal-relevance criterion based on mutual information to select qualified features in multi-label data streams. Second, we propose the data distribution based concept drifting detection approach to distinguish concept drifts hidden in data streams. Finally, we build an incremental ensemble classification model for efficiently classifying multi-label data streams. Extensive studies show that our approach can get optimal subsets of features while maintaining a good performance in the multi-label classification, as compared to several state-of-the-art multi-label feature selection algorithms using two efficient multi-label classification methods as base classifiers. Meanwhile, our approach is superior to three well-known multi-label data stream classification approaches in the effectiveness and efficiency. © 1989-2012 IEEE.

关键词： Classification (of information)

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Histological Image Diagnosis of Breast Cancer Based on Multi-Attention Convolution Neural Network

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Journal of Shanghai Jiaotong university(science) 2025年第1期30卷 91-106页

作者： XU Wangwang XU Liangfeng LIU Ninghui LU Na Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei230601China School of Computer and Information Hefei University of TechnologyHefei230601China First Affiliated Hospital of Anhui Medical University Hefei230022China

Breast cancer is a serious and high morbidity disease in women,and it is the main cause of cancer death in ***,getting tested and diagnosed early can reduce the risk of *** present,there are clinical examinations,imaging screening and biopsies,among which histopathological examination is the gold ***,the process is complicated and time-consuming,and misdiagnosis may *** paper puts forward a classification framework based on deep learning,introducing multi-attention mechanism,selecting kernel convolution instead of ordinary convolution,and using different weights and combinations to pay attention to the accuracy index and growth rate of the *** addition,we also compared the learning rate *** function can fine-tune the learning rate to achieve good performance,using label softening to reduce the loss error caused by model error recognition in the label,and assigning different category weights in the loss function to balance the positive and negative *** used the BreakHis data set to automatically classify histological images into benign and malignant,four categories and eight *** results showed that the accuracy of binary classifications ranged from 98.23%to 98.83%,and that of multiple classifications ranged from 97.89%to 98.11%.

关键词： breast cancer deep learning attentional mechanism classification diagnosis

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ViGT: proposal-free video grounding with a learnable token in the transformer

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science China(information sciences) 2023年第10期66卷 196-212页

作者： Kun LI Dan GUO Meng WANG School of Computer Science and Information Engineering Hefei University of Technology Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Intelligent Interconnected Systems Laboratory of Anhui Province Institute of Artificial Intelligence Hefei Comprehensive National Science Center

The video grounding(VG) task aims to locate the queried action or event in an untrimmed video based on rich linguistic descriptions. Existing proposal-free methods are trapped in the complex interaction between video and query, overemphasizing cross-modal feature fusion and feature correlation for VG. In this paper, we propose a novel boundary regression paradigm that performs regression token learning in a transformer. Particularly, we present a simple but effective proposal-free framework, namely video grounding transformer(ViGT), which predicts the temporal boundary using a learnable regression token rather than multi-modal or cross-modal features. In ViGT, the benefits of a learnable token are manifested as follows.(1) The token is unrelated to the video or the query and avoids data bias toward the original video and query.(2) The token simultaneously performs global context aggregation from video and query ***, we employed a sharing feature encoder to project both video and query into a joint feature space before performing cross-modal co-attention(i.e., video-to-query attention and query-to-video attention) to highlight discriminative features in each modality. Furthermore, we concatenated a learnable regression token [REG] with the video and query features as the input of a vision-language transformer. Finally, we utilized the token [REG] to predict the target moment and visual features to constrain the foreground and background probabilities at each timestamp. The proposed ViGT performed well on three public datasets:ANet-Captions, TACoS, and YouCookⅡ. Extensive ablation studies and qualitative analysis further validated the interpretability of ViGT.

关键词： video grounding temporal sentence grounding boundary regression token learning proposal-free

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Drug-Target Interactions Prediction Based on Signed Heterogeneous Graph Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 231-244页

作者： Ming CHEN Yajian JIANG Xiujuan LEI Yi PAN Chunyan JI Wei JIANG College of Information Science and Engineering Hunan Normal University School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Computer Science Department BNU-HKBU United International College

Drug-target interactions(DTIs) prediction plays an important role in the process of drug *** computational methods treat it as a binary prediction problem, determining whether there are connections between drugs and targets while ignoring relational types information. Considering the positive or negative effects of DTIs will facilitate the study on comprehensive mechanisms of multiple drugs on a common target, in this work, we model DTIs on signed heterogeneous networks, through categorizing interaction patterns of DTIs and additionally extracting interactions within drug pairs and target protein pairs. We propose signed heterogeneous graph neural networks(SHGNNs), further put forward an end-to-end framework for signed DTIs prediction, called SHGNN-DTI,which not only adapts to signed bipartite networks, but also could naturally incorporate auxiliary information from drug-drug interactions(DDIs) and protein-protein interactions(PPIs). For the framework, we solve the message passing and aggregation problem on signed DTI networks, and consider different training modes on the whole networks consisting of DTIs, DDIs and PPIs. Experiments are conducted on two datasets extracted from Drug Bank and related databases, under different settings of initial inputs, embedding dimensions and training modes. The prediction results show excellent performance in terms of metric indicators, and the feasibility is further verified by the case study with two drugs on breast cancer.

关键词： Drug-target interactions Signed heterogeneous network Link sign prediction Graph neural networks

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3D Palmprint MCI Synthesis for 2D-3D Heterogeneous Palmprint Recognition 18th

3D Palmprint MCI Synthesis for 2D-3D Heterogeneous Palmprin...

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18th Chinese Conference on Biometric Recognition, CCBR 2024

作者： Su, Le Fei, Lunke Zhao, Shuping Li, Shuyi Wei, Jia School of Computer Science and Technology Guangdong University of Technology Guangzhou China Faculty of Information Technology Beijing University of Technology Beijing China School of Computer and Information Hefei University of Technology Hefei China

ISBN: (纸本)9789819610679

Cross-2D-to-3D (2D-3D) heterogeneous palmprint recognition aims to matching a 2D palmprint probe with the 3D palmprint galleries, which shows great potential for biometric applications due to rich information of 3D images and the low-cost of 2D image acquisition. However, the large structural discrepancy between 2D and 3D palmprint images makes it hard to directly conduct matching between 2D-3D heterogeneous palmprint images. In this paper, we propose a palmprint image generative adversarial network (PIGAN) to convert 2D palmprint images into 3D domain for 2D-3D heterogeneous palmprint recognition. We first calculate the mean curvature images (MCIs) to present the 3D palm surface measurements of 3D palmprint images. Then, we employ an image-to-image generation backbone to transform 2D palmprint images into 3D MCI representations. To make the fake MCIs realistic, we impose both adversarial and identity-aware learning losses to protect the discriminative information of MCIs, and further introduce a visual-recovery loss to restore the visual-specific texture characteristics of palmprints. By this way, high-quality 3D palmprint MCI can be synthesized with high similarity as the real ones at both feature and visual levels, such that the pixel-level gap between 2D and 3D palmprint images can be effectively reduced for 2D-3D heterogeneous palmprint recognition. Extensive experimental results on the widely-used PolyU 2D-3D palmprint database clearly show the effectiveness of the proposed PIGAN in improving the performance of 2D-3D heterogeneous palmprint recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Palmprint recognition

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A Local Differential Privacy Trajectory Protection Method Based on Temporal and Spatial Restrictions for Staying Detection

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Tsinghua science and technology 2024年第2期29卷 617-633页

作者： Weiqi Zhang Zhenzhen Xie Akshita Maradapu Vera Venkata Sai Qasim Zia Zaobo He Guisheng Yin College of Computer Science and Technology Harbin Engineering UniversityHarbin 150001China School of Computer Science and Technology Shandong UniversityQingdao 266237China Department of Computer Science Georgia State UniversityAtlantaGA 30303USA College of Information Science and Technology Jinan UniversityGuangzhou 510632China

The widespread availability of GPS has opened up a whole new market that provides a plethora of location-based ***-based social networks have become very popular as they provide end users like us with several such services utilizing GPS through our ***,when users utilize these services,they inevitably expose personal information such as their ID and sensitive location to the *** to untrustworthy servers and malicious attackers with colossal background knowledge,users'personal information is at risk on these ***,many privacy-preserving solutions for protecting trajectories have significantly decreased utility after *** have come up with a new trajectory privacy protection solution that contraposes the area of interest for ***,Staying Points Detection Method based on Temporal-Spatial Restrictions(SPDM-TSR)is an interest area mining method based on temporal-spatial restrictions,which can clearly distinguish between staying and moving ***,our privacy protection mechanism focuses on the user's areas of interest rather than the entire ***,our proposed mechanism does not rely on third-party service providers and the attackers'background knowledge *** test our models on real datasets,and the results indicate that our proposed algorithm can provide a high standard privacy guarantee as well as data availability.

关键词： local-differential privacy stay points detection trajectory data areas of interest temporal-spatial clustering

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Delay-Constrained Multicast Throughput Maximization in MEC Networks for High-Speed Railways 19th

Delay-Constrained Multicast Throughput Maximization in MEC ...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Xu, Junyi Wei, Zhenchun Yuan, Xiaohui Lyu, Zengwei Feng, Lin Han, Jianghong School of Computer Science and Information Technology Hefei University of Technology Hefei China Department of Computer Science and Engineering University of North Texus DentonTX United States

ISBN: (纸本)9783031545306

Multi-access Edge Computing presents a compelling solution for delivering seamless connectivity to computing services. In this study, we aim to optimize multicast throughput to ensure high-quality experiences for passengers engaged in inter-train interactions within dedicated MEC networks designed for high-speed railways. Considering the unique challenges associated with high-speed railways, we model multicast routing paths as group Steiner trees. Subsequently, we devise a rapid tree construction method by converting the root search into the Generalized Assignment Problem (GAP). This innovative approach skillfully balances accuracy and computational efficiency. We demonstrate that this problem can be effectively reduced to the bounded knapsack problem with setups. In addition, we recognize the presence of precedence constraints between tasks and their respective outcomes. Consequently, we introduce a new variant of the knapsack problem, which we refer to as the Precedence-constrained Bounded Knapsack Problem with Setups. Our approach, termed the GAP- and knapsack-based Group Steiner Tree (GKGST), offers a relative performance guarantee of 1/2. We evaluate the GKGST algorithm against three baseline algorithms, which are adapted and extended from existing literature. Simulation results indicate that our proposed algorithm exhibits considerable potential for enhanced performance. © ICST Institute for computer sciences, Social Informatics and Telecommunications engineering 2024.

关键词： Computational efficiency

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