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arXiv 2023年

作者： Ma, Jie Wang, Pinghui Wang, Zewei Kong, Dechen Hu, Min Han, Ting Liu, Jun The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The Ministry of Education of Key Laboratory for Intelligent Networks and Network Security School of Automation Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The China Mobile Research Institute China The Shannxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China

Question answering methods are well-known for leveraging data bias, such as the language prior in visual question answering and the position bias in machine reading comprehension (extractive question answering). Current debiasing methods often come at the cost of significant in-distribution performance to achieve favorable out-of-distribution generalization ability, while non-debiasing methods sacrifice a considerable amount of out-of-distribution performance in order to obtain high in-distribution performance. Therefore, it is challenging for them to deal with the complicated changing real-world situations. In this paper, we propose a simple yet effective novel loss function with adaptive loose optimization, which seeks to make the best of both worlds for question answering. Our main technical contribution is to reduce the loss adaptively according to the ratio between the previous and the current optimization state on mini-batch training data. This loose optimization can be used to prevent non-debiasing methods from overlearning data bias while enabling debiasing methods to maintain slight bias learning. Experiments on the visual question answering datasets, including VQA v2, VQA-CP v1, VQA-CP v2, GQA-OOD, and the extractive question answering dataset SQuAD demonstrate that our approach enables QA methods to obtain state-of-the-art in- and out-of-distribution performance in most cases. The source code has been released publicly in https://***/reml-group/ALO. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Decentralized and Lightweight Cross-Chain Transaction Scheme Based on Proxy Re-signature 23

Decentralized and Lightweight Cross-Chain Transaction Scheme...

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23rd IEEE International Conference on Trust, security and Privacy in Computing and Communications, TrustCom 2024

作者： Zou, Huiying Duan, Jia Liu, Xi Ren, Wei Li, Tao Zheng, Xianghan Choo, Kim-Kwang Raymond Hunan Engineering Research Center of Geographic Information Security and Application The Third Surveying and Mapping Institute of Hunan Province Hunan China China University of Geosciences School of Computer Science Wuhan China Sichuan University Ministry of Education Key Laboratory of Data Protection and Intelligent Management China Casm State Key Laboratory of Geo-Information Engineering Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of Mnr Beijing China Fuzhou University College of Computer and Big Data China Sanya College School of Information and Intelligent Engineering China University of Texas at San Antonio Department of Information Systems and Cyber Security San Antonio United States

With the widespread application of digital assets and the rapid development of blockchain technology, achieving secure and efficient transactions between different blockchain networks has become an urgent challenge. Existing cross-chain methods impose limitations, e.g., hash lock technology exhibits low scalability, side-chain technology is overly complex, and notary schemes pose centralization risks. In order to address these issues, we propose a cross-chain transaction solution based on proxy re-signature technology. To tackle the centralization concerns, we employ proxy re-signature technology to decentralize the authority of notaries among transaction participants, minimizing the potential for centralization at a low cost. We further present an extended scheme that can guarantee more requirements such as higher transaction amount and shorter transaction time. Furthermore, we analyze and compare the signature technologies used in the proposed solution and provide a systematic proof of our approach's security. © 2024 IEEE.

关键词： blockchain transaction cross-chain technology notary schemes proxy re-signature

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Not all diffusion model activations have been evaluated as discriminative features 24

Not all diffusion model activations have been evaluated as d...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Benyuan Meng Qianqian Xu Zitai Wang Xiaochun Cao Qingming Huang Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Peng Cheng Laboratory Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and Key Laboratory of Big Data Mining and Knowledge Management CAS

ISBN: (纸本)9798331314385

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is available at https://***/Darkbblue/generic-diffusion-feature.

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Sindiff: Spoken-to-Sign Language Generation Based Transformer Diffusion Model

SSRN

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SSRN 2023年

作者： Liang, Wuyan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing210023 China

Sign language generation (SLG) is meaningful in facilitating the communication between deaf and healthy people. Recently, transformer-based diffusion models have been achieved success in human motion generation. However, it is still under-explored how to generate long-term sign language with diffusion model. Here, we propose a transformer-based diffusion framework, SinDiff, that explore all the advantages of transformer (i.e., dynamic attention and global context) for spoken-driven SLG. To meet the goal, we develop a linear biased multi-head self-attention with periodic position encoding to generalize longer-term sign language, and a linear biased cross-modal multi-head attention function to align the spoken and sign language modalities. Besides, a transformer-based spoken encoder is introduced to solve the limitations of sign data and learn the global spoken semantics. By experimental results on our testbed, we show that the word accuracy of the SinDiff for the back-translated spoken translation with ground truth spoken reach 21.02%, which has a 2.8% improvement over the 18.22% accuracy of the typical SLG method (GEN-OBT) on PHOENIX14T test set. And the generative visualization results further verify the feasibility of the proposed method. © 2023, The Authors. All rights reserved.

关键词： Semantics

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Pptcdr: Personalized Preference Transfer Cross-Domain Recommendation Based on Heterogeneous Graph Neural Networks

SSRN

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SSRN 2023年

作者： Zhu, Fan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing210023 China School of Computer Science Nanjing University of Posts and Telecommunications No. 9 Wenyuan Road Nanjing210023 China

Cross-domain recommendation methods aim to solve the problems of data sparsity and cold start by leveraging rich information from the source domain to enhance the recommendation accuracy of the target domain. Many methods ignore auxiliary information, which is often structured. Therefore, based on the above issue, we propose a method called PPTCDR (Personalized Preference Transfer Cross-Domain Recommendation) based on heterogeneous graph neural networks. PPTCDR introduces auxiliary information to enrich the information of the source and target domains, alleviates the problem of inaccurate embedding caused by data sparsity, and performs feature embedding of users and items separately in the source and target domains. In addition, for user preference transfer, in order to achieve personalization, a personalized bridging function is provided for each user. PPTCDR first constructs heterogeneous graphs separately in the source and target domains, and learns the latent embeddings of users and items using heterogeneous graph neural networks. Then, a meta-network is used to generate a personalized bridging function for each user. Through the personalized bridging function, the user's embedding in the source domain can be transformed to the target domain, and the transformed user embedding is used as the initial embedding of cold start users in the target domain. In this paper, we set up three cross-domain tasks on the Amazon dataset to verify and compare the performance of the experiment. PPTCDR achieves MAE and RMSE performances of 0.67 and 0.94 respectively, which are respectively improved by 23% and 18% compared to the state-of-the-art works EMCDR and PTUPCDR. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Online Discriminative Semantic-Preserving Hashing for Large-Scale Cross-Modal Retrieval 18th

Online Discriminative Semantic-Preserving Hashing for Large-...

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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Yi, Jinhan He, Yi Liu, Xin Department of Computer Science and Technology Huaqiao University Xiamen361021 China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen China Fujian Key Laboratory of Big Data Intelligence and Security Xiamen China

ISBN: (纸本)9783030891879

Cross-modal hashing has drawn increasing attentions for efficient retrieval across different modalities, and existing methods primarily learn the hash functions in a batch based mode, i.e., offline methods. Nevertheless, the multimedia data often comes in a streaming fashion, which makes the batch based learning methods uncompetitive for large-scale streaming data due to the large memory consumption and calculation. To address this problem, we present an Online Discriminative Semantic-Preserving Hashing (ODSPH) method for large-scale cross-modal retrieval, featuring on fast training speed, low memory consumption and high retrieval accuracy. Within the proposed ODSPH framework, we utilize the newly coming data points to learn the hash codes and update hash functions in a stream manner. When new data comes, the corresponding hash codes are obtained by regressing the class label of the training examples. For hash function, we update it with the accumulated information from each round. Besides, we design a novel momentum updating method to adaptively update the hash function and reduce quantization loss, which can produce discriminative hash codes for high retrieval precision. Extensive experiments on three benchmark datasets show that the proposed ODSPH method improves the retrieval performance over the state-of-the-arts. © 2021, Springer Nature Switzerland AG.

关键词： Semantics

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Quantum adversarial metric learning model based on triplet loss function

arXiv

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arXiv 2023年

作者： Hou, Yan-Yan Li, Jian Chen, Xiu-Bo Ye, Chong-Qiang School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China College of Information Science and Engineering ZaoZhuang University ZaoZhuang Shandong277160 China School of Cyberspace Security Security Beijing University of Posts Telecommunications Beijing100876 China Information Security Center State Key Laboratory of Networking and Switching Technology Beijing University of Post and Telecommunications Beijing100876 China GuiZhou University Guizhou Provincial Key Laboratory of Public Big Data Guizhou Guiyang 550025 China

Metric learning plays an essential role in image analysis and classification, and it has attracted more and more attention. In this paper, we propose a quantum adversarial metric learning (QAML) model based on the triplet loss function, where samples are embedded into the high-dimensional Hilbert space and the optimal metric is obtained by minimizing the triplet loss function. The QAML model employs entanglement and interference to build superposition states for triplet samples so that only one parameterized quantum circuit is needed to calculate sample distances, which reduces the demand for quantum resources. Considering the QAML model is fragile to adversarial attacks, an adversarial sample generation strategy is designed based on the quantum gradient ascent method, effectively improving the robustness against the functional adversarial attack. Simulation results show that the QAML model can effectively distinguish samples of MNIST and Iris datasets and has higher ∊-robustness accuracy over the general quantum metric learning. The QAML model is a fundamental research problem of machine learning. As a subroutine of classification and clustering tasks, the QAML model opens an avenue for exploring quantum advantages in machine learning. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Darkfight: A Detection Method of Violent Behavior in Dark Place for intelligent Monitoring System

SSRN

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SSRN 2022年

作者： Wang, Jun Xu, Xiaolong Nanjing University of Posts & Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China Nanjing University of Posts & Telecommunications School of Computer Science Nanjing210023 China

With the continuous development of deep learning, the use of deep learning methods for intelligent violent behavior recognition has become an active research field in computer vision. However, there are relatively few studies on violent behavior recognition in dark light environment. In this paper, we propose the DarkFight network model for violent behavior recognition in dark light environment. In the proposed DarkFight model, Resnet50 is used as the basic network model to extract features. In order to obtain multi-scale spatiotemporal features, DarkFight adds a multi-scale attention module PSA to the network. The DarkFight model uses Zero-DCE for low-light image enhancement. In order not to lose the spatiotemporal characteristics of the low-light environment, DarkFight uses the initial dark-light data and the enhanced data as the input to the two paths of the model. In addition, this paper researches the replacement of the traditional temporal global average pooling layer of the network with the BERT module to better utilize the temporal information when fusing the two path features. We construct a violent behavior dataset VDID (Violence Detection in Dark) in a dark-light environment, and conduct experimental verification on this dataset. The verification results show that the recognition precision of the DarkFight model increases by 2.51%, the accuracy increases by 3.4%, and the F1-score improves by 0.032. © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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SLBERT: An Improved FLAT-BERT for Named Entity Recognition

SSRN

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SSRN 2022年

作者： Fei, Yuefan Xu, Xiaolong Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

Named Entity Recognition (NER) aims to identify named entities mentioned in unstructured text and classify them into predefined named entity classes. Although models using word fusion have achieved relatively high performance, the recognition of ambiguous entities in text remains challenging, and the performance of the models depends on a sufficient amount of manually labeled data;exploring models that fuse more feature dimensions and schemes that fuse existing datasets is of farreaching interest. In this work, we propose an improved scheme of FLAT-BERT, SLBERT, that can be used in the field of named entity *** scheme consists of two phases that more fully exploit the global semantics of the text. In the first stage, the model is trained based on the current dataset, another dataset is predicted for cross-dataset fusion, and the correctness of label annotation is improved by entity confidence modeling. In the second stage, the global semantics of the text is extracted using Sentence BERT and embedded into the feature matrix encoded using Lattice BERT for subsequent Token classification. We have conducted experiments on several datasets, and the experimental results demonstrate the effectiveness of the proposed scheme in this paper. In terms of F1 values, compared with FLAT-BERT, the improvement is 0.15%, 0.34%, 0.61%, and 0.58% on datasets OntoNotes, MSRA, Resume, and Weibo, respectively. © 2022, The Authors. All rights reserved.

关键词： Semantics

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PrintListener: Uncovering the Vulnerability of Fingerprint Authentication via the Finger Friction Sound

arXiv

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arXiv 2024年

作者： Zhou, Man Su, Shuao Wang, Qian Li, Qi Zhou, Yuting Ma, Xiaojing Li, Zhengxiong School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Wuhan University China Institute for Network Sciences and Cyberspace Tsinghua University China Department of Computer Science and Engineering University of Colorado Denver United States Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China

Fingerprint authentication has been extensively employed in contemporary identity verification systems owing to its rapidity and cost-effectiveness. Due to its widespread use, fingerprint leakage may cause sensitive information theft, enormous economic and personnel losses, and even a potential compromise of national security. As a fingerprint that can coincidentally match a specific proportion of the overall fingerprint population, MasterPrint rings the alarm bells for the security of fingerprint authentication. In this paper, we propose a new side-channel attack on the minutiae-based Automatic Fingerprint Identification System (AFIS), called PrintListener, which leverages users’ fingertip swiping actions on the screen to extract fingerprint pattern features (the first-level features) and synthesizes a stronger targeted PatternMasterPrint with potential second-level features. The attack scenario of PrintListener is extensive and covert. It only needs to record users’ fingertip friction sound and can be launched by leveraging a large number of social media platforms. Extensive experimental results in real-world scenarios show that Printlistener can significantly improve the attack potency of MasterPrint. © 2024, CC0.

关键词： Friction

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