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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Abnormality Detection in Chest X-Ray Images Using Uncertainty Prediction Autoencoders 23rd

Abnormality Detection in Chest X-Ray Images Using Uncertaint...

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23rd International Conference on Medical Image computing and computer-Assisted Intervention, MICCAI 2020

作者： Mao, Yifan Xue, Fei-Fei Wang, Ruixuan Zhang, Jianguo Zheng, Wei-Shi Liu, Hongmei School of Data and Computer Science Sun Yat-sen University Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing MOE Guangzhou China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Pazhou Lab Guangzhou China Guangdong Province Key Laboratory of Information Security Technology Guangzhou China

ISBN: (纸本)9783030597245

Chest radiography is widely used in annual medical screening to check whether lungs are healthy or not. Therefore it would be desirable to develop an intelligent system to help clinicians automatically detect potential abnormalities in chest X-ray images. Here with only healthy X-ray images, we propose a new abnormality detection approach based on an autoencoder which outputs not only the reconstructed normal version of the input image but also a pixel-wise uncertainty prediction. Higher uncertainty often appears at normal region boundaries with relatively larger reconstruction errors, but not at potential abnormal regions in the lung area. Therefore the normalized reconstruction error by the uncertainty provides a natural measurement for abnormality detection in images. Experiments on two chest X-ray datasets show the state-of-the-art performance by the proposed approach. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

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第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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Clairvoyance: Intelligent route planning for electric buses based on urban big data

arXiv

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arXiv 2021年

作者： Lu, Xiangyong Ota, Kaoru Dong, Mianxiong Yu, Chen Jin, Hai Graduate School of Information Sciences Tohoku University Sendai Japan Serviecs Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Information and Electronic Engineering Muroran Institute of Technology Hokkaido Muroran Japan

Nowadays many cities around the world have introduced electric buses to optimize urban traffic and reduce the local carbon emissions. In order to cut the carbon emissions and maximize the utility of electric buses, it is important to choose suitable routes for them. Traditionally, route selection is on the basis of dedicated surveys, which are costly in time and labor. In this paper, we mainly focus attention on planning electric bus routes intelligently, depending on the unique needs of each region throughout the city. We propose Clairvoyance, a route planning system that leverages a deep neural network and a multilayer perceptron to predict the future people’s trips and the future transportation carbon emission in the whole city, respectively. Given the future information of people’s trips and transportation carbon emission, we utilize a greedy mechanism to recommend bus routes for electric buses that will depart in an ideal state. Furthermore, representative features of the two neural networks are extracted from the heterogeneous urban datasets. We evaluate our approach through extensive experiments on the real-world data sources in Zhuhai, China. The results show that our designed neural network-based algorithms are consistently superior to the typical baselines. Additionally, the recommended routes for electric buses are helpful in reducing the peak value of carbon emissions and making full use of electric buses in the city. Copyright © 2021, The Authors. All rights reserved.

关键词： Multilayer neural networks

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

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arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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Novel Distributed Informatics Platform to Support Machine Learning Discovery of Metabolic Biomarkers in Hypoxia Predisposition

Novel Distributed Informatics Platform to Support Machine Le...

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16th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2023

作者： Stell, Anthony Chauhan, Vedant Amador, Sandra Beuschlein, Felix Favier, Judith Gil, David Greenwood, Philip de Krijger, Ronald Kroiss, Matthias Ortuno, Samanta Patocs, Attila Walch, Axel School of Computing and Information Systems University of Melbourne Melbourne Australia Zurich Switzerland Universite Paris Cite PARCC INSERM Equipe Labellisee par la Ligue contre le Cancer Paris France Department of Computer Science Technology and Computation University of Alicante Alicante Spain Dept. of Pathology University Medical Center Utrecht and Princess Maxima Center for Pediatric Oncology Utrecht Netherlands University Hospital Munich Ludwig-Maximilians-Universitat Munchen Germany ELKH Hereditary Cancer Research Group Department of Laboratory Medicine Semmelweis University and Department of Molecular Genetics National Institute of Oncology Budapest Hungary Research Unit Analytical Pathology Helmholtz Zentmm Munchen Oberschleissheim Germany Department of Computer Science and Systems University of Murcia Murcia Spain Health and Biomedical Research Institute of Alicante Alicante Spain

To realise the scientific and clinical benefits of machine learning (ML) in a multi-centre research collaboration, a common issue is tire need to bring high-volnme data, complex analytical algorithms, and large-scale processing power, all together into one place. This paper describes tire detailed architecture of a novel platform that combines these features, in tire context of a proposed new clinical/bioinformatics project, Hypox-PD. Hypox-PD uses ML methods to identify new metabolic bionrarkers, through the analysis of high-volume data including mass spectrometry and imaging morphology of biobank tissue. The platform features three components: a content delivery network (CDN);a standardised orchestration application;and lrigh-specification processing power and storage. The central innovation of this platform is a distributed application that simultaneously manages the workflow between these components, provides a virtual mapping of the domain data dictionary, and presents the project data/metadata in a FAIR-compliant external interface. This paper presents the detailed design specifications of this platform, as well as initial test results in establishing the benchmark challenge of current direct transfer times without any specialised support. An initial costing of CDN usage is also presented, which indicates that significant performance improvement may be achievable at a reasonable cost to research budgets. © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Budget control

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Precision Rehabilitation for Patients Post-Stroke based on Electronic Health Records and Machine Learning

arXiv

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arXiv 2024年

作者： Gao, Fengyi Zhang, Xingyu Sivarajkumar, Sonish Denny, Parker E. Aldhahwani, Bayan M. Visweswaran, Shyam Shi, Ryan Hogan, William Bove, Allyn Wang, Yanshan Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Communication Science and Disorders University of Pittsburgh PittsburghPA United States Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Physical Therapy University of Pittsburgh PittsburghPA United States Department of Medical Rehabilitation Sciences Umm Al-Qura University Makkah Saudi Arabia Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Department of Computer Science University of Pittsburgh Medical Center PittsburghPA United States Data Science Institute Medical College of Wisconsin MilwaukeeWI United States Hillman Cancer Center University of Pittsburgh Medical Center PittsburghPA United States

Objective In this study, we utilized statistical analysis and machine learning methods to examine whether rehabilitation exercises can improve patients post-stroke functional abilities, as well as forecast the improvement in functional abilities. Our dataset is patients’ rehabilitation exercises and demographic information recorded in the unstructured electronic health records (EHRs) data and free-text rehabilitation procedure notes. Through this study, our ultimate goal is to pinpoint the specific rehabilitation exercises that can effectively aid post-stroke patients in improving their functional outcomes in basic mobility (BM) and applied cognitive (AC) domains. data sources We collected data for 265 stroke patients from the University of Pittsburgh Medical Center, accessed through the Rehabilitation datamart With Informatics iNfrastructure for Research (ReDWINE). Methods We employed a pre-existing natural language processing (NLP) algorithm to extract data on rehabilitation exercises and developed a rule-based NLP algorithm to extract Activity Measure for Post-Acute Care (AM-PAC) scores, covering basic mobility (BM) and applied cognitive (AC) domains, from procedure notes. AM-PAC scores were collected at the initial rehabilitation visit and followed up at one and two months—key recovery periods. Changes in AM-PAC scores were classified based on the minimal clinically important difference (MCID), and significance was assessed using Friedman and Wilcoxon tests. To identify impactful exercises, we used Chi-square tests, Fisher's exact tests, and logistic regression for odds ratios. Additionally, we developed five machine learning models—logistic regression (LR), Adaboost (ADB), support vector machine (SVM), gradient boosting (GB), and random forest (RF)—to predict outcomes in functional ability. Results Statistical analyses revealed significant associations between functional improvements and specific exercises. In the AC domain, the BALANCE exercise showed substant

关键词： Statistical tests

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The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm

arXiv

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arXiv 2022年

作者： Bao, Shilong Xu, Qianqian Yang, Zhiyong He, Yuan Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China Peng Cheng Laboratory China

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a diversity control regularization term to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML. © 2022, CC BY-NC-SA.

关键词： Embeddings

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Wireless Hallucination in Generative AI-enabled Communications: Concepts, Issues, and Solutions

arXiv

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arXiv 2025年

作者： Wang, Xudong Wang, Jiacheng Feng, Lei Niyato, Dusit Zhang, Ruichen Kang, Jiawen Xiong, Zehui Du, Hongyang Mao, Shiwen State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology Guangzhou510006 China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering University of Hong Kong Pok Fu Lam Hong Kong Department of Electrical and Computer Engineering Auburn University Auburn United States

Generative AI (GenAI) is driving the intelligence of wireless communications. Due to data limitations, random generation, and dynamic environments, GenAI may generate channel information or optimization strategies that violate physical laws or deviate from actual real-world requirements. We refer to this phenomenon as wireless hallucination, which results in invalid channel information, spectrum wastage, and low communication reliability but remains underexplored. To address this gap, this article provides a comprehensive concept of wireless hallucinations in GenAI-driven communications, focusing on hallucination mitigation. Specifically, we first introduce the fundamental, analyze its causes based on the GenAI workflow, and propose mitigation solutions at the data, model, and post-generation levels. Then, we systematically examines representative hallucination scenarios in GenAI-enabled communications and their corresponding solutions. Finally, we propose a novel integrated mitigation solution for GenAI-based channel estimation. At the data level, we establish a channel estimation hallucination dataset and employ generative adversarial networks (GANs)-based data augmentation. Additionally, we incorporate attention mechanisms and large language models (LLMs) to enhance both training and inference performance. Experimental results demonstrate that the proposed hybrid solutions reduce the normalized mean square error (NMSE) by 0.19, effectively reducing wireless hallucinations. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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Open-World Group Retrieval with Ambiguity Removal: A Benchmark

Open-World Group Retrieval with Ambiguity Removal: A Benchma...

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International Conference on Pattern Recognition

作者： Ling Mei Jianhuang Lai Zhanxiang Feng Xiaohua Xie School of Electronics and Information Technology Sun Yat-sen University China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China School of Data and Computer Science Sun Yat-Sen University China

ISBN: (纸本)9781728188089;9781728188096

Group retrieval has attracted plenty of attention in artificial intelligence, traditional group retrieval researches assume that members in a group are unique and do not change under different cameras. However, the assumption may not be met for practical situations such as open-world and group-ambiguity scenarios. This paper tackles an important yet non-studied problem: re-identifying changing groups of people under the open-world and group-ambiguity scenarios in different camera fields. The open-world scenario considers that there are probably non-target people for the probe set appear in the searching gallery, while the group-ambiguity scenario means the group members may change. The open-world and group-ambiguity issue is very challenging for the existing methods because the changing of group members results in dramatic visual variations. Nevertheless, as far as we know, the existing literature lacks benchmarks which target on coping with this issue. In this paper, we propose a new group retrieval dataset named OWGA-Campus to consider these challenges. Moreover, we propose a person-to-group similarity matching based ambiguity removal (P2GSM-AR) method to solve these problems and realize the intention of group retrieval. Experimental results on OWGA-Campus dataset demonstrate the effectiveness and robustness of the proposed P2GSM-AR approach in improving the performance of the state-of-the-art feature extraction methods of person re-id towards the open-world and ambiguous group retrieval task.

关键词： Visualization Benchmark testing Cameras Feature extraction Robustness Pattern recognition Probes

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