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The 2nd International Conference on Distributed Sensing and intelligent systems (ICDSIS 2021)

作者： J. Jin Y. Fan Y. Liu J. Zhang F. Zou Z. Lyu School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei Anhui 230601 China Department of Medical Devices Zhejiang Pharmaceutical College Ningbo China 76203 China

With the increasing popularity of unmanned aerial vehicles (UAVs) and the continuous development of the commercial airline industry, anomalous and illegal flights occur frequently, which imposes a great threat on the aviation. Accurate prediction of UAV trajectories is a great demand to ensure the safety of airspace. Existing popular ground target trajectory prediction methods cannot be directly applied to the trajectory prediction of UAVs, since the trajectories of flying targets are significantly different from the trajectories of ground targets. The flight trajectory consists of a time series of radar data, and the attention mechanism can improve the processing performance of the time-series data. However, the traditional attention mechanism only considers the attention of each data point. In this paper, we propose a self-attention mechanism based on temporal residuals, which not only considers attention of track points, but also implements attention distribution of track data on the time domain to better represent the global dependence of the current prediction moment on all track points. On the basis of the time residual self-attention mechanism, we propose a track prediction model based on time Residual Self-Attention (TRSA). We conduct experiments on the flight data provided by global flight data services website VariFlight. Experimental results show that model TRSA can effectively improve the target trajectory performance in terms of root mean squared error (RMSE) and mean absolute error (MAE).

关键词： autonomous aerial vehicles deep learning (artificial intelligence) mean square error methods radar computing radar theory radar tracking time series

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EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review

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Computational Intelligence and Neuroscience 2022年第0期2022卷 6486570页

作者： Ahmad, Ijaz Wang, Xin Zhu, Mingxing Wang, Cheng Pi, Yao Khan, Javed Ali Khan, Siyab Samuel, Oluwarotimi Williams Chen, Shixiong Li, Guanglin CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Chinese Academy of Sciences Shenzhen China School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China School of Biomedical Engineering Sun Yat-Sen University Guangzhou China Department of Software Engineering University of Science and Technology Khyber Pakhtunkhwa Bannu Pakistan Institute of Computer Science and Information Technology The University of Agriculture Khyber Pakhtunkhwa Peshawar Pakistan

Epileptic seizure is one of the most chronic neurological diseases that instantaneously disrupts the lifestyle of affected individuals. Toward developing novel and efficient technology for epileptic seizure management, recent diagnostic approaches have focused on developing machine/deep learning model (ML/DL)-based electroencephalogram (EEG) methods. Importantly, EEG's noninvasiveness and ability to offer repeated patterns of epileptic-related electrophysiological information have motivated the development of varied ML/DL algorithms for epileptic seizure diagnosis in the recent years. However, EEG's low amplitude and nonstationary characteristics make it difficult for existing ML/DL models to achieve a consistent and satisfactory diagnosis outcome, especially in clinical settings, where environmental factors could hardly be avoided. Though several recent works have explored the use of EEG-based ML/DL methods and statistical feature for seizure diagnosis, it is unclear what the advantages and limitations of these works are, which might preclude the advancement of research and development in the field of epileptic seizure diagnosis and appropriate criteria for selecting ML/DL models and statistical feature extraction methods for EEG-based epileptic seizure diagnosis. Therefore, this paper attempts to bridge this research gap by conducting an extensive systematic review on the recent developments of EEG-based ML/DL technologies for epileptic seizure diagnosis. In the review, current development in seizure diagnosis, various statistical feature extraction methods, ML/DL models, their performances, limitations, and core challenges as applied in EEG-based epileptic seizure diagnosis were meticulously reviewed and compared. In addition, proper criteria for selecting appropriate and efficient feature extraction techniques and ML/DL models for epileptic seizure diagnosis were also discussed. Findings from this study will aid researchers in deciding the most efficient ML/DL m

关键词： Electroencephalography

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A bi-level formulation for label noise learning with spectral cluster discovery 29

A bi-level formulation for label noise learning with spectra...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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Reconfigurable intelligent Surface Enhanced Wireless Localization: Phase Optimization for Malicious Interference Mitigation

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IEEE Transactions on Communications 2025年

作者： Zhang, Yi Xie, Yajing Wang, Lijia Liwang, Minghui Wang, Xianbin Xiamen University Ministry of Education of China Department of Information and Communication Engineering Key Laboratory of Multimedia Trusted Perception and Efficient Computing Xiamen China Tongji University Shanghai Research Institute for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai China Western University Department of Electrical and Computer Engineering ON Canada

Recently, unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) have merged as important enabling technologies for localization coverage extension and localization accuracy improvement under signal blockage and malicious interference conditions. However, most existing works assume known locations of jammers, which is generally impractical in real-world networks. To overcome this challenge, we propose a novel RIS-enhanced wireless localization framework against malicious interference with the support of either narrowband or orthogonal frequency division multiplexing (OFDM) pilot signals. A two-stage anti-jamming localization approach is developed to first estimate the unknown channel and signal information of the jammer and then localize the user’s position by eliminating the jamming signal. More importantly, we utilize the full potential of RIS to improve localization accuracy by optimizing the phase shift profile during the iterative process. Extensive simulation results demonstrate the commendable performance of our proposed framework, which can not only mitigate the jamming effect but also achieve better localization accuracy, offering a good reference for future heterogeneous and complex wireless networks. © 1972-2012 IEEE.

关键词： Frequency division multiplexing

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Structure-consistent Restoration Network for Cataract Fundus Image Enhancement

arXiv

引用

arXiv 2022年

作者： Li, Heng Liu, Haofeng Fu, Huazhu Shu, Hai Zhao, Yitian Luo, Xiaoling Hu, Yan Liu, Jiang Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China IHPC A*STAR Singapore Department of Biostatistics School of Global Public Health New York University New York United States Cixi Institute of Biomedical Engineering Chinese Academy of Sciences China Shenzhen People's Hospital Shenzhen China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China

Fundus photography is a routine examination in clinics to diagnose and monitor ocular diseases. However, for cataract patients, the fundus image always suffers quality degradation caused by the clouding lens. The degradation prevents reliable diagnosis by ophthalmologists or computer-aided systems. To improve the certainty in clinical diagnosis, restoration algorithms have been proposed to enhance the quality of fundus images. Unfortunately, challenges remain in the deployment of these algorithms, such as collecting sufficient training data and preserving retinal structures. In this paper, to circumvent the strict deployment requirement, a structure-consistent restoration network (SCR-Net) for cataract fundus images is developed from synthesized data that shares an identical structure. A cataract simulation model is firstly designed to collect synthesized cataract sets (SCS) formed by cataract fundus images sharing identical structures. Then high-frequency components (HFCs) are extracted from the SCS to constrain structure consistency such that the structure preservation in SCR-Net is enforced. The experiments demonstrate the effectiveness of SCR-Net in the comparison with state-of-the-art methods and the follow-up clinical applications. The code is available at https://***/liamheng/ArcNet-Medical-Image-Enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Natural Language Processing for Smart Healthcare

arXiv

引用

arXiv 2021年

作者： Zhou, Binggui Yang, Guanghua Shi, Zheng Ma, Shaodan The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The State Key Laboratory of Internet of Things for Smart City University of Macau 999078 China The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China

Smart healthcare has achieved significant progress in recent years. Emerging artificial intelligence (AI) technologies enable various smart applications across various healthcare scenarios. As an essential technology powered by AI, natural language processing (NLP) plays a key role in smart healthcare due to its capability of analysing and understanding human language. In this work, we review existing studies that concern NLP for smart healthcare from the perspectives of technique and application. We first elaborate on different NLP approaches and the NLP pipeline for smart healthcare from the technical point of view. Then, in the context of smart healthcare applications employing NLP techniques, we introduce representative smart healthcare scenarios, including clinical practice, hospital management, personal care, public health, and drug development. We further discuss two specific medical issues, i.e., the coronavirus disease 2019 (COVID-19) pandemic and mental health, in which NLP-driven smart healthcare plays an important role. Finally, we discuss the limitations of current works and identify the directions for future works. Copyright © 2021, The Authors. All rights reserved.

关键词： Artificial intelligence

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Suicide ideation detection based on documents dimensionality expansion

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computers in Biology and Medicine 2025年第Pt B期192卷 110266-110266页

作者： Esmi, Nima Shahbahrami, Asadollah Gaydadjiev, Georgi de Jonge, Peter Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Intelligent Systems Research Center Khazar University Baku Azerbaijan Department of Computer Engineering University of Guilan Rasht Iran Computer Engineering Laboratory Delft University of Technology Delft Netherlands Faculty of Behavioural and Social Sciences University of Groningen Groningen Netherlands

Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cultural differences, personal information and mixed emotions. Conventional deep learning models often struggle to capture patterns in informal text, as they miss long-range dependencies, explain words and phrases literally, and have difficulty processing non-standard inputs like emojis. To address these limitations, we expand data dimensionality, transforming and fusing textual data and signs from a 1D to a 2D space. This enables the use of pre-trained 2D CNN models, such as AlexNet, Restnet-50, and VGG-16 removing the need to design and train new models from scratch. We apply this approach to a dataset of social media posts to classify informal documents as either related to suicide or non-suicide content. Our results demonstrate high classification accuracy, exceeding 99%. In addition, our 2D visual data representation conceals individual private information and helps explainability. © 2025 The Authors

关键词： Convolutional neural networks

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Automatic Context Pattern Generation for Entity Set Expansion

arXiv

引用

arXiv 2022年

作者： Li, Yinghui Huang, Shulin Zhang, Xinwei Zhou, Qingyu Li, Yangning Liu, Ruiyang Cao, Yunbo Zheng, Hai-Tao Shen, Ying Shenzhen International Graduate School Tsinghua University Guangdong Shenzhen518055 China Peng Cheng Laboratory Guangdong Shenzhen518066 China The School of Intelligent Systems Engineering Sun Yat-Sen University Guangdong Guangzhou510275 China The Tencent Cloud Xiaowei Beijing100190 China The Department of Computer Science and Technology Tsinghua University Beijing100190 China

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various Natural Language Processing (NLP) and Information Retrieval (IR) downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing corpus-based ESE methods have achieved great progress, they still rely on corpora with high-quality entity information annotated, because most of them need to obtain the context patterns through the position of the entity in a sentence. Therefore, the quality of the given corpora and their entity annotation has become the bottleneck that limits the performance of such methods. To overcome this dilemma and make the ESE models free from the dependence on entity annotation, our work aims to explore a new ESE paradigm, namely corpus-independent ESE. Specifically, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments are available at https://***/geekjuruo/GAPA. Copyright © 2022, The Authors. All rights reserved.

关键词： Information retrieval

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Performance and energy-aware bi-objective tasks scheduling for cloud data centers

arXiv

引用

arXiv 2021年

作者： Materwala, Huned Ismail, Leila Intelligent Distributed Computing and Systems Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Al Ain Abu Dhabi15551 United Arab Emirates National Water and Energy Center United Arab Emirates University Al Ain Abu Dhabi United Arab Emirates

Cloud computing enables remote execution of users' tasks. The pervasive adoption of cloud computing in smart cities' services and applications requires timely execution of tasks adhering to Quality of Services (QoS). However, the increasing use of computing servers exacerbates the issues of high energy consumption, operating costs, and environmental pollution. Maximizing the performance and minimizing the energy in a cloud data center is challenging. In this paper, we propose a performance and energy optimization bi-objective algorithm to tradeoff the contradicting performance and energy objectives. An evolutionary algorithm-based multi-objective optimization is for the first time proposed using system performance counters. The performance of the proposed model is evaluated using a realistic cloud dataset in a cloud computing environment. Our experimental results achieve higher performance and lower energy consumption compared to a state-of-the-art algorithm. © 2021, CC BY.

关键词： Energy efficiency

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