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Robust Self-Expression Learning with Adaptive Noise Perception

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SSRN

SSRN 2023年

作者： Wang, Yangbo Zhou, Jie Lu, Jianglin Wan, Jun Gao, Can Lin, Qingshui College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China SMILE Lab Department of ECE College of Engineering Northeastern University Boston02115 United States School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan430073 China Basic Teaching Department Liaoning Technical University Huludao125105 China

Self-expression learning methods often obtain a coefficient matrix to measure the similarity between pairs of samples. However, directly using the raw data to represent each sample under the self-expression framework may not be ideal, as noise points are inevitably involved in the process of representing clean samples. To address this issue, this work proposes a novel self-expression model called robust Self-Expression learning with adaptive Noise Perception (SENP). SENP decomposes each sample into a clean part and a noisy part, and samples with large self-expression losses can be recognized as the noise points. A reliable coefficient matrix can then be learned by using only the clean points to reconstruct the clean part of each sample. By simultaneously detecting the noisy part of each sample and noise points, and adaptively mitigating their negative impacts, the representative ability of the generated coefficient matrix is improved. Moreover, inspired by the solution of non-negative matrix factorization (NMF), an effective algorithm is formed to optimize SENP. Extensive experiments on well-known benchmark datasets demonstrate the superiority of SENP compared to several state-of-the-art methods. © 2023, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

Innovative Urban Planning for Harnessing Blockchain and Edge Artificial intelligence for Smart City Solutions

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Innovative Urban Planning for Harnessing Blockchain and Edge...

Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： K. E. Purushothaman N. Ragavendran S. P. Ramesh V. Ganesh Karthikeyan G. Uma Maheswari R. Saravanakumar Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering School of Computing Science & Engineering Galgotias University Greater Noida Uttar Pradesh India School of Computing (SoC) SASTRA Deemed to be University Thanjavur Tamil Nadu India RMK College of Engineering and Technology Chennai Tamil Nadu India Iconix Software Solution Tirunelveli Tamil Nadu India

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

This research explores the potential of blockchain and edge AI to revolutionize smart city planning and management. By incorporating these emerging technologies, cities can address current urban challenges, including traffic congestion, energy management, and data security. This research study highlights the benefits of blockchain, such as simplified processes, reduced costs, enhanced security, and transparency. Additionally, this study reviews the applications of edge AI in smart transportation, focusing on vehicle detection, counting, and identification algorithms. The potential of blockchain technology in smart energy trading, vehicle transfer, and encrypted communication is also discussed. Overall, this research study provides valuable insights for researchers to explore new application areas for blockchain and edge AI in the context of smart cities.

关键词： Smart cities Vehicle detection Scalability Urban planning Edge AI Blockchains Smart transportation Energy management Sustainable development Traffic congestion

Acupuncture State Detection at Zusanli (ST-36) Based on Scalp EEG and Transformer

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IEEE Journal of Biomedical and Health Informatics 2025年第6期29卷 4023-4034页

作者： Wenhao Rao Meiyan Xu Haochen Wang Weicheng Hua Jiayang Guo Yongheng Zhang Haibin Zhu Ziqiu Zhou Jiawei Xiong Jianbin Zhang Yijie Pan Peipei Gu Duo Chen School of Artificial Intelligence and Information Technology Nanjing University of Chinese Medicine Nanjing China Department of Computer Science and the Institute of Human Factors Engineering Minnan Normal University Zhangzhou China National Institute for Data Science in Health and Medicine Xiamen University Xiamen China China Mobile Group Jiangsu Company Ltd Sihong Branch Suqian China Second Affiliated Hospital of Nanjing University of Chinese Medicine Nanjing University of Chinese Medicine Nanjing China Department of Computer Science and Technology Tsinghua University Beijing China Eastern Institute for Advanced Study Eastern Institute of Technology Ningbo China College of Software Engineering Zhengzhou University of Light Industry Zhengzhou China

In clinical acupuncture practice, needle twirling (NT) and needle retention (NR) are strategically combined to achieve different therapeutic effects, highlighting the importance of distinguishing between different acupuncture states. Scalp EEG has been proven significantly relevant to brain activity and acupuncture stimulation. In this work, we designed an acupuncture paradigm to collect scalp EEG to study the differences in EEG changes during different acupuncture states. Since deep learning (DL) has been increasingly used in EEG analysis, we propose the Acupuncture Transformer Detector (ATD), a model based on Convolutional Neural Networks (CNN) and Transformer technology. ATD encapsulates the local and global features of EEG under the acupuncture states of Zusanli acupoint (ST-36) in an end-to-end classification framework. The experiment results from 28 healthy participants show that the proposed model can efficiently classify the EEG in different states, with an accuracy of $85.47\pm 0.73\%$ . In this study, time-frequency analysis revealed that power changes were mainly confined to the delta frequency band under different acupuncture states. Brain topography revealed that ST-36 was activated primarily on the left frontal and parieto-occipital areas. This method provides new ideas for automatic recognition of acupuncture status from the perspective of DL, offering new solutions for standardizing acupuncture procedures.

关键词： Acupuncture Electroencephalography Transformers Convolutional neural networks Needles Brain modeling Medical diagnostic imaging Training Temperature measurement Feature extraction

ToFM: Topic-specific Facet Mining by Facet Propagation within Clusters 12

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ToFM: Topic-specific Facet Mining by Facet Propagation withi...

12th IEEE International Conference on big Knowledge, ICBK 2021

作者： Li, Hongxuan Wei, Bifan Liu, Jun Guo, Zhaotong Qi, Jingchao Wu, Bei Liu, Yong Shi, Yuanyuan Xi'An Jiaotong University School of Computer Science and Technology Xi'an China Xi'An Jiaotong University National Engineering Lab For Big Data Analytics Xi'an China School of Continuous Education Xi'An Jiaotong University Xi'an China Servyou Software Group Co. Ltd. China

ISBN: (纸本)9781665438582

Mining the facets of topics is an essential task for information retrieval, information extraction and knowledge base construction. For the topics in courses, there are three challenges: different topics have different facet, the labels of facets rarely appear in the topic description text and not all topics have enough textural information to mine facets. In this paper we propose a weakly-supervised algorithm for topic-specific facet mining (ToFM for short) based on our finding that similar topics in a cluster have similar facet sets. For example, topics Binary Search Tree, Suffix Tree and AVL tree in Tree cluster have example, insertion, deletion, traversal and other similar facets. ToFM first splits topics in a domain into several topic clusters based on the topic description text. Then ToFM extracts initial facet sets for all topics from the corresponding Wikipedia article pages. Finally, ToFM performs a normalized facet propagation within each topic cluster to acquire final facet sets of every topic. We evaluate the performance of ToFM on six real-world datasets and experimental results show that ToFM achieves better performance than the existing facet mining algorithms. © 2021 IEEE.

关键词： Information retrieval

Towards Efficient Resume Understanding: A Multi-Granularity Multi-Modal Pre-Training Approach

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arXiv 2024年

作者： Jiang, Feihu Qin, Chuan Zhang, Jingshuai Yao, Kaichun Chen, Xi Shen, Dazhong Zhu, Chen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Career Science Lab BOSS Zhipin PBC School of Finance Tsinghua University China Institute of Software Chinese Academy of Sciences China Shanghai Artificial Intelligence Laboratory China The Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Guangzhou HKUST Fok Ying Tung Research Institute China

In the contemporary era of widespread online recruitment, resume understanding has been widely acknowledged as a fundamental and crucial task, which aims to extract structured information from resume documents automatically. Compared to the traditional rule-based approaches, the utilization of recently proposed pre-trained document understanding models can greatly enhance the effectiveness of resume understanding. The present approaches have, however, disregarded the hierarchical relations within the structured information presented in resumes, and have difficulty parsing resumes in an efficient manner. To this end, in this paper, we propose a novel model, namely ERU, to achieve efficient resume understanding. Specifically, we first introduce a layout-aware multi-modal fusion transformer for encoding the segments in the resume with integrated textual, visual, and layout information. Then, we design three self-supervised tasks to pre-train this module via a large number of unlabeled resumes. Next, we fine-tune the model with a multi-granularity sequence labeling task to extract structured information from resumes. Finally, extensive experiments on a real-world dataset clearly demonstrate the effectiveness of ERU. © 2024, CC BY.

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Low-altitude Friendly-Jamming for Satellite-Maritime Communications via Generative AI-enabled Deep Reinforcement Learning

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arXiv 2025年

作者： Huang, Jiawei Wang, Aimin Sun, Geng Li, Jiahui Wang, Jiacheng Niyato, Dusit Leung, Victor C.M. College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computer Science and Technology Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518115 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

Low Earth Orbit (LEO) satellites can be used to assist maritime wireless communications for data transmission across wide-ranging areas. However, extensive coverage of LEO satellites, combined with openness of channels, can cause the communication process to suffer from security risks. This paper presents a low-altitude friendly-jamming LEO satellite-maritime communication system enabled by a unmanned aerial vehicle (UAV) to ensure data security at the physical layer. Since such a system requires trade-off policies that balance the secrecy rate and energy consumption of the UAV to meet evolving scenario demands, we formulate a secure satellite-maritime communication multi-objective optimization problem (SSMCMOP). In order to solve the dynamic and long-term optimization problem, we reformulate it into a Markov decision process. We then propose a transformer-enhanced soft actor critic (TransSAC) algorithm, which is a generative artificial intelligence-enable deep reinforcement learning approach to solve the reformulated problem, so that capturing global dependencies and diversely exploring weights. Simulation results demonstrate that the TransSAC outperforms various baselines, and achieves an optimal secrecy rate while effectively minimizing the energy consumption of the UAV. Moreover, the results find more suitable constraint values for the system. Copyright © 2025, The Authors. All rights reserved.

关键词： Multiobjective optimization

Test-Time Domain Adaptation by Learning Domain-Aware Batch Normalization

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Plataniotis, Konstantinos N. Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Edward S Rogers Sr. ECE Department University of Toronto TorontoM5S3G8 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

Test-time domain adaptation aims to adapt the model trained on source domains to unseen target domains using a few unlabeled images. Emerging research has shown that the label and domain information is separately embedded in the weight matrix and batch normalization (BN) layer. Previous works normally update the whole network naively without explicitly decoupling the knowledge between label and domain. As a result, it leads to knowledge interference and defective distribution adaptation. In this work, we propose to reduce such learning interference and elevate the domain knowledge learning by only manipulating the BN layer. However, the normalization step in BN is intrinsically unstable when the statistics are re-estimated from a few samples. We find that ambiguities can be greatly reduced when only updating the two affine parameters in BN while keeping the source domain statistics. To further enhance the domain knowledge extraction from unlabeled data, we construct an auxiliary branch with label-independent self-supervised learning (SSL) to provide supervision. Moreover, we propose a bi-level optimization based on meta-learning to enforce the alignment of two learning objectives of auxiliary and main branches. The goal is to use the auxiliary branch to adapt the domain and benefit main task for subsequent inference. Our method keeps the same computational cost at inference as the auxiliary branch can be thoroughly discarded after adaptation. Extensive experiments show that our method outperforms the prior works on five WILDS real-world domain shift datasets. Our method can also be integrated with methods with label-dependent optimization to further push the performance boundary. Our code is available at https://***/ynanwu/MABN. © 2023, CC BY.

关键词： Time domain analysis

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

Leveraging Image Captions for Selective Whole Slide Image Annotation

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arXiv 2024年

作者： Qiu, Jingna Aubreville, Marc Wilm, Frauke Öttl, Mathias Utz, Jonas Schlereth, Maja Breininger, Katharina Department Artificial Intelligence in Biomedical Engineering FAU Erlangen-Nürnberg Erlangen Germany Technische Hochschule Ingolstadt Ingolstadt Germany Flensburg University of Applied Sciences Flensburg Germany Pattern Recognition Lab Department of Computer Science FAU Erlangen-Nürnberg Erlangen Germany Center for AI and Data Science Universität Würzburg Würzburg Germany

Acquiring annotations for whole slide images (WSIs)-based deep learning tasks, such as creating tissue segmentation masks or detecting mitotic figures, is a laborious process due to the extensive image size and the significant manual work involved in the annotation. This paper focuses on identifying and annotating specific image regions that optimize model training, given a limited annotation budget. While random sampling helps capture data variance by collecting annotation regions throughout the WSI, insufficient data curation may result in an inadequate representation of minority classes. Recent studies proposed diversity sampling to select a set of regions that maximally represent unique characteristics of the WSIs. This is done by pretraining on unlabeled data through self-supervised learning and then clustering all regions in the latent space. However, establishing the optimal number of clusters can be difficult and not all clusters are task-relevant. This paper presents prototype sampling, a new method for annotation region selection. It discovers regions exhibiting typical characteristics of each task-specific class. The process entails recognizing class prototypes from extensive histopathology image-caption databases and detecting unlabeled image regions that resemble these prototypes. Our results show that prototype sampling is more effective than random and diversity sampling in identifying annotation regions with valuable training information, resulting in improved model performance in semantic segmentation and mitotic figure detection tasks. Code is available at https://***/DeepMicroscopy/Prototype-sampling. Copyright © 2024, The Authors. All rights reserved.

关键词： Image annotation