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Automatic Life Event Tree Generation for Older Adults 1

24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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IEEE International Conference on Robotics and Biomimetics

作者： Xin Xu Guoyu Zuo Jiangeng Li Gao Huang Faculty of Information Technology Beijing University of Technology Beijing China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing China Faculty of Information Beijing University of Technology Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China

ISBN: (纸本)9781665481106

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches.

关键词： Training Learning systems Representation learning Filtering Biomimetics Buildings Reinforcement learning

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IRS-Enhanced Anti-Jamming Precoding Against DISCO Physical Layer Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Cai, Yi Zhang, Yunjing Lee Swindlehurst, A. Han, Zhu Electronic Information School Soochow University Suzhou China State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston United States Center for Pervasive Communications and Computing University of California Irvine United States

Illegitimate intelligent reflective surfaces (IRSs) can pose significant physical layer security risks on multi-user multiple-input single-output (MU-MISO) systems. Recently, a DISCO approach has been proposed an illegitimate IRS with random and time-varying reflection coefficients, referred to as a "disco" IRS (DIRS). Such DIRS can attack MU-MISO systems without relying on either jamming power or channel state information (CSI), and classical anti-jamming techniques are ineffective for the DIRS-based fully-passive jammers (DIRS-based FPJs). In this paper, we propose an IRS-enhanced anti-jamming precoder against DIRS-based FPJs that requires only statistical rather than instantaneous CSI of the DIRS-jammed channels. Specifically, a legitimate IRS is introduced to reduce the strength of the DIRS-based jamming relative to the transmit signals at a legitimate user (LU). In addition, the active beamforming at the legitimate access point (AP) is designed to maximize the signal-to-jamming-plus-noise ratios (SJNRs). Numerical results are presented to evaluate the effectiveness of the proposed IRS-enhanced anti-jamming precoder against DIRS-based FPJs. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel state information

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Pronounced advance on typhoon track forecast with global convectionpermitting model

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Science Bulletin 2025年第9期70卷 1424-1428页

作者： Jun Gu Chun Zhao Gudongze Li Jiawang Feng Mingyue Xu Qiuyan Du Zihan Xia Yubin Li Guanghua Chen Xiaoyu Hao Junshi Chen Hong An Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of ChinaHefei 230026China Laoshan Laboratory Qingdao 266237China CAS Center for Excellence in Comparative Planetology University of Science and Technology of ChinaHefei 230026China School of Atmospheric Physics Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China School of Computer Science and Technology University of Science and Technology of ChinaHefei 230026China

Typhoons pose significant threats to coastal and inland regions,with their impacts exacerbated by climate change and population growth[1].Recent studies have shown increased frequency of powerful storms,slower translation speeds,northward shifts in the Western North Pacific(WNP)basin,and a near tripling of global exposure to typhoons since 1970[1-4].These changes present new challenges for forecasters and researchers,particularly in predicting impacts on inland and high-latitude populations lacking prior exposure or resilience to typhoon effects.

关键词： translation speed global convection permitting model population growth recent track forecast typhoon population growth storm frequency climate change

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Fundus Image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Long-term cybersecurity applications enabled by quantum networks

arXiv

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arXiv 2023年

作者： Peters, Nicholas A. Alshowkan, Muneer Chapman, Joseph C. Pooser, Raphael C. Rao, Nageswara S.V. Newell, Raymond T. Quantum Information Science Section Oak Ridge National Laboratory Oak RidgeTN37831 United States Advanced Computing Methods for Engineered Systems Section Oak Ridge National Laboratory Oak RidgeTN37831 United States MPA-Quantum Los Alamos National Laboratory Los AlamosNM87545 United States

If continental-scale quantum networks are realized, they will provide the resources needed to fulfill the potential for dramatic advances in cybersecurity through quantum-enabled cryptography applications. We describe recent progress and where the US is headed as well as argue that we go one step further and jointly develop quantum and conventional cryptography methods for joint deployments along the quantum backbone infrastructure. Copyright © 2023, The Authors. All rights reserved.

关键词： Quantum cryptography

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Opportunities for Retrieval and Tool Augmented Large Language Models in Scientific Facilities

arXiv

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arXiv 2023年

作者： Prince, Michael H. Chan, Henry Vriza, Aikaterini Zhou, Tao Sastry, Varuni K. Dearing, Matthew T. Harder, Ross J. Vasudevan, Rama K. Cherukara, Mathew J. Advanced Photon Source Argonne National Laboratory IL United States Center for Nanoscale Materials Argonne National Laboratory IL United States Argonne Leadership Computing Facility Argonne National Laboratory IL United States Business and Information Systems Argonne National Laboratory IL United States Center for Nanophase Materials Oak Ridge National Laboratory Oak RidgeTN United States

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output. © 2023, CC BY.

关键词： Microelectronics

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Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model 21

Establishing an Interactive Virtual Library for Medical Manu...

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2024 IEEE/ACS 21st International Conference on Computer systems and Applications, AICCSA 2024

作者： Hamdi, Hassen Zarrouk, Rim Mahmoudi, Ramzi Benameur, Narjes University of Sfax Multimedia InfoRmation Systems and Advanced Computing Laboratory MIRACL Tunisia FSG University of Gafsa University of Monastir Laboratoire Technologie Imagerie Médicale LTIM-LR12ES06 FMM Monastir5019 Tunisia Université Paris-Est Laboratoire d'Informatique Gaspard-Monge Unité Mixte CNRS-UMLV-ESIEE UMR8049 ESIEE Paris Cité Descartes Noisy Le Grand93162 France Laboratoire Technologie Imagerie Médicale-LTIM-LR12ES06 Tunisia University of Tunis ElManar Higher Institute of Medical Technologies of Tunis Laboratory of Biophysics and Medical Technologies Tunisia

ISBN: (纸本)9798331518240

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's 'medicine and health' subject area, which belongs to the 'natural sciences and mathematics' class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service). © 2024 IEEE.

关键词： Cloud platforms

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Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

arXiv

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arXiv 2023年

作者： Chen, Jinbiao Zhang, Zizhen Cao, Zhiguang Wu, Yaoxin Ma, Yining Ye, Te Wang, Jiahai School of Computer Science and Engineering Sun Yat-sen University China School of Computing and Information Systems Singapore Management University Singapore Department of Industrial Engineering & Innovation Sciences Eindhoven University of Technology Netherlands Department of Industrial Systems Engineering & Management National University of Singapore Singapore Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education Sun Yat-sen University China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Mixture Gaussian Distribution-Based Collaborative Reinforcement Learning for 3D UAV Localization Optimization Against Jamming Attacks

Mixture Gaussian Distribution-Based Collaborative Reinforcem...

引用

IEEE Conference on Global Communications (GLOBECOM)

作者： Yujiao Zhu Mingzhe Chen Sihua Wang Yuchen Liu Gaolei Li Changchuan Yin Tony Q.S. Quek Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, the optimization of unmanned aerial vehicle (UAV) localization under jamming attacks is studied. In the considered network, a base station (BS) collaborates with an active UAV to localize a target UAV. During this positioning process, a jamming UAV transmits discontinuous signals to passive UAVs to interfere the distance information measurement. To localize the target UAV under jamming attacks, the BS jointly use two localization methods: 1) generative adversarial network (GAN)-based positioning method and 2) time difference of arrival (TDOA)-based positioning method. Since GAN-based positioning method cannot defense in a strong jamming signal while TDOA-based positioning method may consume more energy and sacrifice localization accuracy, the BS must select an appropriate positioning method (GAN-based or TDOA-based methods) and four distance measurement information of passive UAVs to estimate the position of the target UAV. This problem is formulated as an optimization problem whose goal is to minimize the positioning error between the estimated and the ground truth positions of the target UAV while considering jamming attacks and the trajectory of passive UAVs. To solve this problem, we propose a mixture Gaussian distribution model-based collaborative reinforcement learning (RL) method which enables the active UAV to determine its transmit power and trajectory, and enables the BS to select the most appropriate subsets of distance measurement information and the optimal positioning method according to the movement of passive UAVs and the unknown jamming attack pattern of the jamming UAV. Simulation results show the proposed method can reduce the positioning error of the target UAV by up to 36.5% compared to the method that does not consider the GAN-based positioning method.

关键词： Location awareness Simulation Collaboration Reinforcement learning Gaussian distribution Autonomous aerial vehicles Distance measurement Trajectory Jamming Optimization

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