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22nd IEEE International Conference on Trust, Security and Privacy in computing and Communications, TrustCom 2023

作者： Al Sibahee, Mustafa A. Luo, Chengwen Zhang, Jin Huang, Yijing Abduljabbar, Zaid Ameen Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Iraq University College Computer Technology Engineering Department Basrah61004 Iraq University of Basrah College of Education for Pure Sciences Department of Computer Science Basrah61004 Iraq Huazhong University of Science and Technology Shenzhen Institute Shenzhen518000 China

ISBN: (纸本)9798350381993

Document retrieval plays an essential role in many real-world applications especially when the data storage is outsourced. Due to the great advantages offered by cloud computing, clients tend to outsource their personal data to remote servers maintained by external service providers. This raises serious privacy concerns about outsourced data because such providers are usually considered untrusted entities. The majority of previous schemes of document similarity search share the same limitation: they focus mainly on static collections. Dynamic searchable schemes (DSE) allow adding or removing documents at the expense of more leakage than static schemes. To thwart certain attacks, DSE schemes should support forward privacy property, which ensures that newly added documents cannot be related to previously issued search queries. We design and implement dynamic secure similarity search schemes with forward privacy for textual documents utilizing simhash method for hamming similarity. Our scheme provides an efficient search time and a sufficient level of privacy. To show the practicality of our proposed scheme, we performed excremental results with large document collections. © 2023 IEEE.

关键词： Cryptography

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Auto-Attention Mechanism for Multi-View Deep Embedding Clustering

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SSRN 2023年

作者： Diallo, Bassoma Hu, Jie Li, Tianrui Khan, Ghufran Ahmad Liang, Xinyan Wang, Hongjun School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China Department of Computer Science and Application KL University Vaddeswaram522302 India

In several fields, deep learning has achieved tremendous success. Multi-view learning is a workable method for handling data from several sources. For clustering multi-view data, deep learning and multi-view learning are excellent options. However, a persistent challenge is the need for the current deep learning approach to independently drive divergent neural networks for different perspectives while working with data from multiple sources. The current methods have some drawbacks. The first is that a variety of viewpoints are used to calculate neural network statistics. Consequently, as the number of views rises, it results in a considerable calculation. The latter is that it tries vainly to unite various viewpoints at the training. Incorporating a triple fusion technique, this research suggests an innovative multi-view deep embedding clustering (MDEC) model. The suggested model may jointly acquire the specific knowledge in each view as well as the portion information of the collective views. The main goal of the MDEC is to lower the errors made when learning the features of each view and correlating data from many views. To address the optimization problem, MDEC model advises a suitable iterative updating approach. In testing modern deep learning and non-deep learning algorithms, the experimental study on small and large-scale multi-view data shows encouraging results for the MDEC model. In multi-view clustering, this work demonstrates the benefit of the deep learning-based approach over the non-ones. However, future work will address a variety of issues related to MDEC. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Pointsurface: Discriminative Point Cloud Surface Feature Extraction for 3d Face Recognition

SSRN

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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Application and Research of Deep Learning in Wheel Tread Defect Detection 18th

Application and Research of Deep Learning in Wheel Tread Def...

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18th International Forum of Digital Multimedia Communication, IFTC 2021

作者： Wang, Jiaxin Li, Hongtao Liang, Zheng Huang, Xiaoguang Wang, Juan State Grid Information and Telecommunication Group Co. LTD. Beijing China China Construction Second Engineering Bureau Ltd. East China Company Shanghai China China Shandong Foreign Trade Vocational College Qingdao China Institute of Cloud Computing and Big Data China Academy of Information and Communications Technology Beijing China

ISBN: (纸本)9789811922657

The surface problems of wheels include peeling, cracks and scratches, etc. In the process of automatic detection, it is of practical significance to acquire real-time wheel tread images through image collectors on rails and make quick and effective analysis. This paper studies the application of deep learning in wheel tread defect detection, and classifies and identifies common wheel tread defect images such as cracks, peeling, scratches and pits. Firstly, through data set enhancement, the wheel tread defect gallery is created, which contains 35,000 pictures, including nine kinds of defect pictures and one kind of normal pictures, with an average of 3,500 pictures in each category, and the resolution of each image is processed into 224 × 224. Then, the initial results of wheel tread detection by six classic models are compared, including VGG16, VGG19, ResNet18, ResNet34, ResNet50 and ResNet101. In the experiment process, the training efficiency is improved through transfer learning. The results show that the ResNet50 model has higher performance and better effect in identifying wheel tread defects. The loss function combining the Center loss and Softmax loss is adopted, and Adam is adopted as the optimization algorithm. The experimental results show that, compared with traditional machine vision recognition method and the original ResNet50 network, the method adopted in this paper has achieved good results in recognition accuracy and real-time performance. © 2022, Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_data$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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MorphTE: injecting morphology in tensorized embeddings 22

MorphTE: injecting morphology in tensorized embeddings

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Guobing Gan Peng Zhang Sunzhu Li Xiuqing Lu Benyou Wang College of Intelligence and Computing Tianjin University Tianjin China School of Data Science The Chinese University of Hong Kong Shenzhen China and Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9781713871088

In the era of deep learning, word embeddings are essential when dealing with text tasks. However, storing and accessing these embeddings requires a large amount of space. This is not conducive to the deployment of these models on resource-limited devices. Combining the powerful compression capability of tensor products, we propose a word embedding compression method with morphological augmentation, Morphologically-enhanced Tensorized Embeddings (MorphTE). A word consists of one or more morphemes, the smallest units that bear meaning or have a grammatical function. MorphTE represents a word embedding as an entangled form of its morpheme vectors via the tensor product, which injects prior semantic and grammatical knowledge into the learning of embeddings. Furthermore, the dimensionality of the morpheme vector and the number of morphemes are much smaller than those of words, which greatly reduces the parameters of the word embeddings. We conduct experiments on tasks such as machine translation and question answering. Experimental results on four translation datasets of different languages show that MorphTE can compress word embedding parameters by about 20 times without performance loss and significantly outperforms related embedding compression methods.

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EPL: Empirical Prototype Learning for Deep Face Recognition

arXiv

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arXiv 2024年

作者： Fan, Weijia Wen, Jiajun Jia, Xi Shen, Linlin Zhou, Jiancan Li, Qiufu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Computer Vision Institute Shenzhen University Shenzhen518060 China School of Computer Science University of Birmingham Birmingham United Kingdom Aqara Lumi United Technology Coopration Shenzhen China

Prototype learning is widely used in face recognition, which takes the row vectors of coefficient matrix in the last linear layer of the feature extraction model as the prototypes for each class. When the prototypes are updated using the facial sample feature gradients in the model training, they are prone to being pulled away from the class center by the hard samples, resulting in decreased overall model performance. In this paper, we explicitly define prototypes as the expectations of sample features in each class and design the empirical prototypes using the existing samples in the dataset. We then devise a strategy to adaptively update these empirical prototypes during the model training based on the similarity between the sample features and the empirical prototypes. Furthermore, we propose an empirical prototype learning (EPL) method, which utilizes an adaptive margin parameter with respect to sample features. EPL assigns larger margins to the normal samples and smaller margins to the hard samples, allowing the learned empirical prototypes to better reflect the class center dominated by the normal samples and finally pull the hard samples towards the empirical prototypes through the learning. The extensive experiments on MFR, IJB-C, LFW, CFP-FP, AgeDB, and MegaFace demonstrate the effectiveness of EPL. Our code is available at https://***/WakingHours-GitHub/EPL. © 2024, CC BY.

关键词： Face recognition

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CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing 39

CA-Edit: Causality-Aware Condition Adapter for High-Fidelity...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Xian, Xiaole He, Xilin Niu, Zenghao Zhang, Junliang Xie, Weicheng Song, Siyang Yu, Zitong Shen, Linlin Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China University of Exeter United Kingdom Great Bay University China

ISBN: (纸本)157735897X

For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional finetuning for different editing effects or tend to affect beyond the editing regions. Alternatively, inpainting methods can edit the target image region while preserving external areas. However, current inpainting methods still suffer from the generation misalignment with facial attributes description and the loss of facial skin details. To address these challenges, (i) a novel data utilization strategy is introduced to construct datasets consisting of attribute-text-image triples from a data-driven perspective, (ii) a Causality-Aware Condition Adapter is proposed to enhance the contextual causality modeling of specific details, which encodes the skin details from the original image while preventing conflicts between these cues and textual conditions. In addition, a Skin Transition Frequency Guidance technique is introduced for the local modeling of contextual causality via sampling guidance driven by low-frequency alignment. Extensive quantitative and qualitative experiments demonstrate the effectiveness of our method in boosting both fidelity and editability for localized attribute editing. Our codes will be made publicly available. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Security Analysis of DNP3 Protocol in SCADA System

Security Analysis of DNP3 Protocol in SCADA System

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IEEE International Conference on Control System, computing and Engineering (ICCSCE)

作者： Qais Saif Qassim Musab A. M. Ali Nooritawati Md Tahir Department of IT College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Oman College of Engineering Universiti Teknologi MARA (UiTM) Malaysia Institute for Big Data Analytics and Artificial Intelligence (IBDAAI) Universiti Teknologi MARA Malaysia

The safeguarding of Supervisory Control and data Acquisition (SCADA) systems and their cyber-security have been the subject of extensive research for many years, owing to the severe ramifications that can result from their breach or compromise. SCADA is a system that facilitates the monitoring and control of physical infrastructures in industrial processes from a centralized control station. The utilization of this technology is prevalent in crucial infrastructure domains, such as the electricity, oil, and gas industries, encompassing production and distribution. The compromise of data integrity in SCADA systems through cyber-attacks, specifically the unauthorized manipulation of sensors or control signals, has the potential to disrupt the operation of critical national infrastructure significantly. Therefore, this research is intended to investigate the limitations and drawbacks of one of the commonly used communication protocols in SCADA, namely; DNP3, investigate the cyber-attacks exploiting the weaknesses of this protocol, and put forward a recommendation to protect the SCADA networks and prevent devastating consequences.

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Text simplification for Malay corpus: A Review 6

Text simplification for Malay corpus: A Review

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6th International Conference on Computer and Information Sciences, ICCOINS 2021

作者： Omar, Salehah Bakar, Juhaida Abu Mohd Nadzir, Maslinda Harun, Nor Hazlyna Yusoff, Nooraini Politeknik Sultan Abdul Halim mu'Adzam Shah Department of Information Technology and Communication Jitra06000 Malaysia School of Computing Universiti Utara Malaysia Data Science Research Lab Sintok06010 Malaysia Universiti Utara Malaysia School of Computing Sintok06010 Malaysia Universiti Malaysia Kelantan Institute for Artificial Intelligence and Big Data Kelantan16100 Malaysia

ISBN: (纸本)9781728171517

Text Simplification (TS) is one of the directions for recent studies in NLP. The TS aims to rewrite the complicated text into a simpler sentence, which is easier to understand by human and machine. Several applications such as the development of the Simple Wikipedia corpus are derived from those studies. The studies involve a different technique in order to achieve the objective of TS in terms of structure and the meaning of human language. In this paper, we used narrative review as review methodology and highlighted the purpose of TS, TS-user, TS approaches and techniques, datasets, evaluation metrics as well as challenges in formalizing TS in Malay corpus. Promising results are expected for TS in Malay corpus. © 2021 IEEE.

关键词： Natural language processing systems

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