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5th IEEE International Conference on Electronics technology, ICET 2022

作者： Guo, Rui Zheng, Ai Huang, Qi Sun, Huabo Chen, Lei Beijing Microelectronics Technology Institute Fpga Department Beijing China Smart Network Innovation Center China United Network Communications Limited Beijing China University of Electronic Science and Technology of China Sichuan Provincial Key Lab of Power System Wide-area Measurement and Control Chengdu China

ISBN: (纸本)9781665485081

With the extensive utilization of Intelligence Electronic Devices (IED), the modern smart substation is regard of a typical Internet of Things (IoT) system. The process-layer network in smart substations, which carries the measurements and control signals, needs to operate reliably in the face of unforeseen failures and external malicious attacks. In this paper: i) a mathematical framework for the process-layer network, attacks, and signals is modeled;ii) fundamental network performance limitations from system-theoretic and graph-theoretic perspectives are characterized;and iii) A DLL-based Router Scheme (DRS) is designed to ensure the network performance for the effective operation of IoT. Finally, we validate our design through a series of cases, and the results show that the designed DRS is effective to smooth the network performance. © 2022 IEEE.

关键词： Internet of things

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Locate Then Generate: Bridging Vision and Language with Bounding Box for Scene-Text VQA

arXiv

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arXiv 2023年

作者： Zhu, Yongxin Liu, Zhen Liang, Yukang Li, Xin Liu, Hao Bao, Changcun Xu, Linli School of Data Science University of Science and Technology of China China School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China Tencent YouTu Lab China

In this paper, we propose a novel multi-modal framework for Scene Text Visual Question Answering (STVQA), which requires models to read scene text in images for question answering. Apart from text or visual objects, which could exist independently, scene text naturally links text and visual modalities together by conveying linguistic semantics while being a visual object in an image simultaneously. Different to conventional STVQA models which take the linguistic semantics and visual semantics in scene text as two separate features, in this paper, we propose a paradigm of "Locate Then Generate" (LTG), which explicitly unifies this two semantics with the spatial bounding box as a bridge connecting them. Specifically, at first, LTG locates the region in an image that may contain the answer words with an answer location module (ALM) consisting of a region proposal network and a language refinement network, both of which can transform to each other with one-to-one mapping via the scene text bounding box. Next, given the answer words selected by ALM, LTG generates a readable answer sequence with an answer generation module (AGM) based on a pre-trained language model. As a benefit of the explicit alignment of the visual and linguistic semantics, even without any scene text based pre-training tasks, LTG can boost the absolute accuracy by +6.06% and +6.92% on the TextVQA dataset and the ST-VQA dataset respectively, compared with a non-pre-training baseline. We further demonstrate that LTG effectively unifies visual and text modalities through the spatial bounding box connection, which is underappreciated in previous methods. © 2023, CC BY.

关键词： Semantics

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Multi-video Moment Ranking with Multimodal Clue

arXiv

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arXiv 2023年

作者： Hou, Danyang Pang, Liang Lan, Yanyan Shen, Huawei Cheng, Xueqi Data Intelligence System Research Center Institute of Computing Technology CAS Beijing China CAS Key Lab of Network Data Science and Technology Institute of Computing Technology CAS Beijing China University of Chinese Academy of Sciences Beijing China Institute for AI Industry Research Tsinghua University Beijing China

Video corpus moment retrieval (VCMR) is the task of retrieving a relevant video moment from a large corpus of untrimmed videos via a natural language query. State-of-the-art work for VCMR is based on two-stage method. In this paper, we focus on improving two problems of two-stage method: (1) Moment prediction bias: The predicted moments for most queries come from the top retrieved videos, ignoring the possibility that the target moment is in the bottom retrieved videos, which is caused by the inconsistency of Shared Normalization during training and inference. (2) Latent key content: Different modalities of video have different key information for moment localization. To this end, we propose a two-stage model MultI-video raNking with mUlTimodal cluE (MINUTE). MINUTE uses Shared Normalization during both training and inference to rank candidate moments from multiple videos to solve moment predict bias, making it more efficient to predict target moment. In addition, Mutilmdaol Clue Mining (MCM) of MINUTE can discover key content of different modalities in video to localize moment more accurately. MINUTE outperforms the baselines on TVR and DiDeMo datasets, achieving a new state-of-the-art of VCMR. Our code will be available at GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Ma, Qinghe Zhang, Jian Qi, Lei Yu, Qian Shi, Yinghuan Gao, Yang Nanjing University State Key Laboratory for Novel Software Technology China Nanjing University National Institute of Healthcare Data Science China Southeast University School of Computer Science and Engineering Key Lab of Computer Network and Information Integration (Ministry of Education) China School of Data and Computer Science ShandongWomen’s University China Nanjing Drum Tower Hospital Jiangsu Nanjing China

Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However, the coexistence of limited annotation and domain shift is quite common, which motivates us to introduce a novel and challenging scenario: Mixed Domain Semi-supervised medical image Segmentation (MiDSS), where limited labeled data from a single domain and a large amount of unlabeled data from multiple domains. To tackle this issue, we propose the UST-RUN framework, which fully leverages intermediate domain information to facilitate knowledge transfer. We employ Unified Copy-paste (UCP) to construct intermediate domains, and propose a Symmetric GuiDance training strategy (SymGD) to supervise unlabeled data by merging pseudo-labels from intermediate samples. Subsequently, we introduce a Training Process aware Random Amplitude MixUp (TP-RAM) to progressively incorporate style-transition components into intermediate samples. To generate more diverse intermediate samples, we further select reliable samples with high-quality pseudolabels, which are then mixed with other unlabeled data. Additionally, we generate sophisticated intermediate samples with high-quality pseudo-labels for unreliable samples, ensuring effective knowledge transfer for them. Extensive experiments on four public datasets demonstrate the superiority of UST-RUN. Notably, UST-RUN achieves a 12.94% improvement in Dice score on the Prostate dataset. © 1982-2012 IEEE.

关键词： domain shift Intermediate samples Semi-supervised image segmentation uncertainty estimation

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Multi-label Text Classification for Judicial Texts via Dual Graph and label Feature Fusion

Multi-Label Text Classification for Judicial Texts via Dual ...

引用

Smart World Congress (SWC), IEEE

作者： Qiliang Gu Qin Lu Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

The legal judgement prediction (LJP) of judicial texts represents a multi-label text classification (MLTC) problem, which in turn involves three distinct tasks: the prediction of charges, legal articles, and terms of penalty. Nevertheless, extant multi-label text classification models tend to eschew the consideration of multiple correlations and semantic information between labels when predicting judicial texts, which may result in the loss of pertinent information. Furthermore, these models do not take full advantage of the local and overall information of the labels in the process of selecting appropriate labels for the text using the multi-head attention mechanism. To address these issues, we propose a new model, BGFLFF, which explores label correlation and semantics among the three tasks by employing Graph Convolutional network (GCN) and multi-head attention mechanisms. In particular, we propose a Bi-Graph Fusion GCN (BGF-GCN), which fuses the co-occurrence matrix of labels with the cosine similarity matrix, thereby fully exploiting the multiple correlations between labels. Furthermore, prior to embedding the tags, the interpretations of the labels are obtained via Google in order to enhance the semantic information between the labels. To further reinforce the interconnections between labels and text and more effectively comprehend the interrelationships between the three tasks, we propose a Multi-Head label Feature Fusion Attentional Mechanism (MH-LFFAtt), which assigns distinct weights to the pertinent label information in the text by fusing the local and overall features of the labels. The experimental results demonstrate that the F1 score exhibits an improvement of up to ${2. 2 8 \%}$ and a minimum of ${1. 2 9 \%}$ across the various tasks of the two datasets. This evidence substantiates the assertion that BGFLFF outperforms the existing baseline model.

关键词： Attention mechanisms Correlation Law Graph convolutional networks Fuses Text categorization Semantics Predictive models Multitasking Internet

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Image recognition and detection based on fast area convolutional neural network

Image recognition and detection based on fast area convoluti...

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2021 International Conference on Computer, Communication, Control, Automation and Robotics, CCCAR 2021

作者： Zhang, Hanwen Qin, Zhen Xie, Hua School of Automation Engineering UESTC Chengdu China Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China

Nowadays, image recognition and detection technologies based on traditional artificial neural networks and convolutional neural networks are slightly inadequate in terms of training and recognition time and accuracy, and are difficult to deploy on devices with limited hardware resources. Therefore, this article proposes a recognition and detection technology based on fast regional convolutional neural networks. We use RPN (Region Proposal network) instead of Selective Search method to rebuild the network, and add a new ROI pooling layer before the fully connected layer of CNN. Determine the category. The average detection accuracy on our data set can reach 83.8%, and the training time is only 0.34 hours. © Published under licence by IOP Publishing Ltd.

关键词： Image recognition

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MITF:Cross-modal Image-text Retrieval Method with Mapping Images to Text Features 23

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23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Lou, Xinyue Li, You Qi, Rui Chen, Yufeng Xu, Jinan School of Computer Science and Technology Beijing Jiaotong University Beijing100044 China Beijing Key Lab of Traffic Data Analysis and Mining Beijing100044 China

ISBN: (纸本)9780000000002

To address the cross-modal image-text retrieval challenge, it’s essential to close the semantic gap between vision and language. However, the prevailing dual-stream model which separates image encoder and text encoder during training complicates the problem. In this paper, we propose a mapping network to map information from different modalities (image and text) to a single modality (text), called MITF network, with the purpose of improving the fusion and alignment of cross-modal semantics. Given a frozen pre-trained model, Chinese-CLIP, we train the MITF network to convert the visual embedding into the corresponding pseudo language tokens. Additionally, we also introduce a mechanism that automatically learns prompt to enhance the model’s understanding of pseudo language tokens. To speed up the retrieval process, the Faiss index is employed. Experimental findings on three datasets show that our method achieves an absolute 3.7% MR gain and quadruples retrieval speed compared to the vanilla Chinese-CLIP model. Additionally, the image-text feature visualization results further show that our method improves the alignment between image features and text features. © 2024 China National Conference on Computational Linguistics.

关键词： Semantics

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THC-DL: Three-Party Homomorphic Computing Delegation Learning for Secure Optimization

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IEEE Internet of Things Journal 2025年

作者： Yang, Ze Tian, Youliang Xiong, Jinbo Niu, Kun Li, Mengqian Zhang, Xinyu Ma, Jianfeng Guizhou University Guizhou Provincial Key Laboratory of Cryptography and Blockchain Technology College of Computer Science and Technology Guiyang550025 China Guizhou University College of Big Data and Information Engineering Guiyang550025 China Fujian Normal University Fujian Provincial Key Laboratory of Network Security and Cryptology College of Computer and Cyber Security Fuzhou350117 China Xidian University State Key Laboratory of Integrated Services Network Xi’an710071 China Xidian University State Key Laboratory of Integrated Services Network School of Cyber Engineering Xi’an710071 China

Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving machine learning (PPML) in delegation learning, yet faces critical challenges in functionality (non-linear activation support), practicality (ciphertext blow-up from iterative computations), and security (data leakage risks caused by public knowledge of the mathematical principles applied in model training). To tackle these challenges, we propose THC-DL, a three-party HE framework addressing these challenges holistically for the first time. We elaborately design the ciphertext secure comparison (DL-CSC) protocol to satisfy secure comparison with private inputs, enabling efficient non-linear operations with O(1) communication complexity that reduces runtime to 12.5% of the DGK (Dolev-Greensmith-Kent protocol, the well-known comparison protocol). Second, we construct a Truncation-Mapping (Tru-Map) scheme, a mechanism that transforms input data by truncating and mapping it into a domain that facilitates more efficient processing, and the addition of truncated mapped data to resolve ciphertext blow-up by adaptively scaling ciphertexts during iterative training, ensuring correctness. Third, we formalize data leakage risks in HE-based quadratic convex optimization (standard in ML) and apply THC-DL to construct a secure optimization scheme. Theoretical analysis confirms THC-DL’s resilience against input recovery attacks, even when adversaries exploit public model parameters. Experiments on a real-world platform validate DL-CSC’s efficiency and scalability while reducing the computational complexity and communication complexity from O(n) to O(1) where n denotes the length of the input bits. © 2014 IEEE.

关键词： Convex optimization

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From Relevance to Utility: Evidence Retrieval with Feedback for Fact Verification

arXiv

引用

arXiv 2023年

作者： Zhang, Hengran Zhang, Ruqing Guo, Jiafeng de Rijke, Maarten Fan, Yixing Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China University of Amsterdam Netherlands The University of Amsterdam Netherlands

Retrieval-enhanced methods have become a primary approach in fact verification (FV);it requires reasoning over multiple retrieved pieces of evidence to verify the integrity of a claim. To retrieve evidence, existing work often employs off-the-shelf retrieval models whose design is based on the probability ranking principle. We argue that, rather than relevance, for FV we need to focus on the utility that a claim verifier derives from the retrieved evidence. We introduce the feedback-based evidence retriever (FER) that optimizes the evidence retrieval process by incorporating feedback from the claim verifier. As a feedback signal we use the divergence in utility between how effectively the verifier utilizes the retrieved evidence and the ground-truth evidence to produce the final claim label. Empirical studies demonstrate the superiority of FER over prevailing baselines.1 Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Trajectory Privacy Protection with Pricing Awareness on Ride-on-Demand System 10

Trajectory Privacy Protection with Pricing Awareness on Ride...

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10th IEEE International Conference on Cyber Security and Cloud Computing and 9th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Jia, Sihui Long, Saiqin Zheng, Zhirun Deng, Qingyong Wang, Ping Tian, Shujuan Xiangtan University School of Computer Science and School of Cyberspace Science Hunan International Scientific and Technological Cooperation Base of Intelligent network Xiangtan China Jinan University School of Information Science and Technology Guangzhou China Xiangtan University School of Mathematics and Compulational Science Xiangtan China Guangxi Normal University Guangxi Key Lab of Multi-source Information Mining and Security Guilin China

ISBN: (纸本)9798350312461

With the widespread use of the Ride-on Demand (RoD) system, many privacy issues have been exposed, and there is growing concern about whether private information will be leaked. For this problem, our previous work addressed the issue of the user's initial and final location leakage and provided a strong utility guarantee in the RoD system. Further, the trajectory information is also important in the RoD system, it could contain a lot of private information about the user, such as health or identity, so it's important to publish a distorted and productive trajectory. For this purpose, in this paper, we provide our trajectory protection method with pricing awareness based on previous work;the method uses supply and demand density function to guide the division of a discrete spatial grid, then uses the Markov chain to generate distorted trajectories on the grid to ensure trajectory continuity, and makes corresponding defenses against several attacks, such as Bayesian. The experiment results on real-world datasets prove the validity and robustness of the method. © 2023 IEEE.

关键词： Costs

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