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TechRxiv 2021年

作者： Akouaydi, Hanen Hamdi, Yahia Boubaker, Houcine Zaied, Mourad Cheikh, Faouzi Alaya Alimi, Adel M. BP 1173 Sfax3038 Tunisia B.P. 6072 Gabes Tunisia The Norwegian Colour and Visual Computing Laboratory Department of Computer Science Norwegian University of Science and Technology POBox 191 2802 Norway Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg South Africa

Contributions: An innovative e-learning project is presented in this paper, which is a mobile workbook that teaches handwriting at school. This mobile application proposes a new qualitative and quantitative analysis process of online cursive handwriting. It gives a real-time feedback, detects mistakes and helps teachers evaluate children’s writing skills. The main aim of this notebook is to aid kids learn how to write correctly. We analyze handwriting according to major criteria like shape, kinematics of the trace, position respect to the reference lines, stroke order and direction. Background: Beta-elliptic model with dissimilarity distance (DD) and similarity detection (SD), and SVM with Cartesian Fourier Descriptor Model (FDcarM) are used to analyze the handwriting quality. Our work apprehends dynamic and visual representations of the acquired traces, describes the overall geometry of a trace as a function of the undulations of its curvature function and selects efficient features adapted to various handwriting styles. This work demonstrates that beta-elliptic model is not only a model for segmentation and recognition but also a tool to evaluate handwriting. Intended outcomes: A masterclass environment that motivates childrents to perform better on in application evaluations. The mobile workbook should be positively received by learners and teachers. Application design: Our application provides interfaces for both learners and experts which are flexible with the particularity of each child and makes experienced kids engaged with challenging tasks. Findings: For the validation of our proposed system,we collected a database of 400 Tunisian children from pre-schools and primary schools. Results demonstrate the efficiency and robustness of our application an the great majority of expertts expressed a positive attitude toward it. © 2021, CC BY.

关键词： Function evaluation

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Wildland Fire Detection and Monitoring using a Drone-collected RGB/IR Image Dataset

Wildland Fire Detection and Monitoring using a Drone-collect...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Xiwen Chen Bryce Hopkins Hao Wang Leo O’Neill Fatemeh Afghah Abolfazl Razi Peter Fulé Janice Coen Eric Rowell Adam Watts School of Computing Clemson University Clemson SC Department of Electrical and Computer Engineering Clemson University Clemson SC School of Forestry Northern Arizona University Flagstaff AZ National Center for Atmospheric Research Boulder Colorado Desert Research Institute Reno NV US Forest Service Pacific Wildland Fire Science Lab Seattle WA

Drone-based Unmanned Aerial Systems (UAS) provide an efficient means for early detection and monitoring of remote wildland fires due to their rapid deployment, low flight altitudes, high 3D maneuverability, and ever-expanding sensor capabilities. Recent sensor advancements have made side-by-side RGB/IR sensing feasible for UASs. The aggregation of optical and thermal images enables robust environmental observation, as the thermal feed provides information that would otherwise be obscured in a purely RGB setup, effectively "seeing through" thick smoke and tree occlusion. In this work, we present Fire detection and modeling: Aerial Multi-spectral image dataset (FLAME 2) [1], the first ever labeled collection of UAS-collected side-by-side RGB/IR aerial imagery of prescribed burns. Using FLAME 2, we then present two image-processing methodologies with Multi-modal Learning on our new dataset: (1) Deep Learning (DL)-based benchmarks for detecting fire and smoke frames with Transfer Learning and Feature Fusion. (2) an exemplary image-processing system cascaded in the DL-based classifier to perform fire localization. We show these two techniques achieve reasonable gains than either single-domain video inputs or training models from scratch in the fire detection task.

关键词： Training Location awareness Three-dimensional displays Transfer learning Fires Real-time systems Pattern recognition

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electrical frequency discrimination by fungi Pleurotus ostreatus

arXiv

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arXiv 2022年

作者： Przyczyna, Dawid Szacilowski, Konrad Chiolerio, Alessandro Adamatzky, Andrew Academic Centre for Materials and Nanotechnology AGH University of Science and Technology Krakow Poland Istituto Italiano di Tecnologia Center for Converging Technologies Soft Bioinspired Robotics Via Morego 30 Genova16165 Italy Unconventional Computing Lab UWE Bristol United Kingdom Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece

We stimulate mycelian networks of oyster fungi Pleurotus ostreatus with low frequency sinusoidal electrical signals. We demonstrate that the fungal networks can discriminate between frequencies in a fuzzy or threshold based manner. Details about the mixing of frequencies by the mycelium networks are provided. The results advance the novel field of fungal electronics and pave ground for the design of living, fully recyclable, electron devices. Copyright © 2022, The Authors. All rights reserved.

关键词： Fungi

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Time Delay Estimation with Fixed-time SMC Scheme for Nonlinear Dynamics

Time Delay Estimation with Fixed-time SMC Scheme for Nonline...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Saim Ahmed Ahmad Taher Azar Arezki Fekik Haoping Wang Chakib Ben Njima College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems and Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria CNRS LS2N UMR 6004 Nantes Université École Centrale Nantes Nantes France School of Automation Nanjing University of Science and Technology China University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Time delay estimation using fixed-time terminal sliding mode control (TDEFxSMC) for unknown nonlinear systems with uncertainty and external disturbances is presented in this study. Firstly, the notion of fixed-time terminal sliding mode control (FxSMC) is outlined and thoroughly examined. Using the FxSMC scheme, nonsingular fixed-time control, non-chatter control inputs, and high performance tracking positions have been achieved. To estimate the unknown dynamics, proposed method incorporates a TDE scheme. The Lyapunov theorem is applied to find whether the proposed system can achieve its stability in a fixed-time. The effectiveness of the suggested method is evaluated and demonstrated by presenting the results of the proposed method implemented to the nonlinear robot dynamics.

关键词： Uncertainty Automation Delay effects Estimation 2-DOF Stability analysis Nonlinear dynamical systems

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Flip Learning: Weakly Supervised Erase to Segment Nodules in Breast Ultrasound

arXiv

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arXiv 2025年

作者： Huang, Yuhao Chang, Ao Dou, Haoran Tao, Xing Zhou, Xinrui Cao, Yan Huang, Ruobing Frangi, Alejandro F. Bao, Lingyun Yang, Xin Ni, Dong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen China Lab Shenzhen University Shenzhen China Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen China School of Computing University of Leeds Leeds United Kingdom Shenzhen RayShape Medical Technology Co. Ltd Shenzhen China Division of Informatics Imaging and Data Science School of Health Sciences University of Manchester Manchester United Kingdom Department of Computer Science School of Engineering University of Manchester Manchester United Kingdom Department of Electrical Engineering Department of Cardiovascular Sciences KU Leuven Belgium Alan Turing Institute London United Kingdom NIHR Manchester Biomedical Research Centre Manchester Academic Health Science Centre Manchester United Kingdom Department of Ultrasound Affiliated Hangzhou First People’s Hospital School of Medicine Westlake University China School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing China

Accurate segmentation of nodules in both 2D breast ultrasound (BUS) and 3D automated breast ultrasound (ABUS) is crucial for clinical diagnosis and treatment planning. Therefore, developing an automated system for nodule segmentation can enhance user independence and expedite clinical analysis. Unlike fully-supervised learning, weakly-supervised segmentation (WSS) can streamline the laborious and intricate annotation process. However, current WSS methods face challenges in achieving precise nodule segmentation, as many of them depend on inaccurate activation maps or inefficient pseudo-mask generation algorithms. In this study, we introduce a novel multi-agent reinforcement learning-based WSS framework called Flip Learning, which relies solely on 2D/3D boxes for accurate segmentation. Specifically, multiple agents are employed to erase the target from the box to facilitate classification tag flipping, with the erased region serving as the predicted segmentation mask. The key contributions of this research are as follows: 1) Adoption of a superpixel/supervoxel-based approach to encode the standardized environment, capturing boundary priors and expediting the learning process. 2) Introduction of three meticulously designed rewards, comprising a classification score reward and two intensity distribution rewards, to steer the agents’ erasing process precisely, thereby avoiding both under- and over-segmentation. 3) Implementation of a progressive curriculum learning strategy to enable agents to interact with the environment in a progressively challenging manner, thereby enhancing learning efficiency. Extensively validated on the large in-house BUS and ABUS datasets, our Flip Learning method outperforms state-of-the-art WSS methods and foundation models, and achieves comparable performance as fully-supervised learning algorithms. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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A Survey on Deep Learning for Skin Lesion Segmentation

arXiv

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arXiv 2022年

作者： Mirikharaji, Zahra Abhishek, Kumar Bissoto, Alceu Barata, Catarina Avila, Sandra Valle, Eduardo Celebi, M. Emre Hamarneh, Ghassan Medical Image Analysis Lab School of Computing Science Simon Fraser University BurnabyV5A 1S6 Canada Institute for Systems and Robotics Instituto Superior Técnico Avenida Rovisco Pais Lisbon1049-001 Portugal RECOD.ai Lab Institute of Computing University of Campinas Av. Albert Einstein 1251 Campinas13083-852 Brazil RECOD.ai Lab School of Electrical and Computing Engineering University of Campinas Av. Albert Einstein 400 Campinas13083-952 Brazil Department of Computer Science and Engineering University of Central Arkansas 201 Donaghey Ave. ConwayAR72035 United States

Skin cancer is a major public health problem that could benefit from computer-aided diagnosis to reduce the burden of this common disease. Skin lesion segmentation from images is an important step toward achieving this goal. However, the presence of natural and artificial artifacts (e.g., hair and air bubbles), intrinsic factors (e.g., lesion shape and contrast), and variations in image acquisition conditions make skin lesion segmentation a challenging task. Recently, various researchers have explored the applicability of deep learning models to skin lesion segmentation. In this survey, we cross-examine 177 research papers that deal with deep learning-based segmentation of skin lesions. We analyze these works along several dimensions, including input data (datasets, preprocessing, and synthetic data generation), model design (architecture, modules, and losses), and evaluation aspects (data annotation requirements and segmentation performance). We discuss these dimensions both from the viewpoint of select seminal works, and from a systematic viewpoint, examining how those choices have influenced current trends, and how their limitations should be addressed. To facilitate comparisons, we summarize all examined works in a comprehensive table as well as an interactive table available online. Copyright © 2022, The Authors. All rights reserved.

关键词： computer aided diagnosis

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Synthesis of Non-Integer Order Uncertain Thau-Luenberger Observer for Non-Integer Order Multiple Model with Unknown Inputs

Synthesis of Non-Integer Order Uncertain Thau-Luenberger Obs...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Abdelghani Djeddi Ahmad Taher Azar Arezki Fekik Nashwa Ahmad Kamal Chakib Ben Njima Faculty of Sciences and Technology Echahid Cheikh Larbi Tebessi University Tebessa Algeria College of Computer & Information Sciences Prince Sultan University Riyadh Saudi Arabia Automated Systems & Soft Computing Lab (ASSCL) Prince Sultan University Riyadh Saudi Arabia Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Department of Electrical Engineering University Akli Mohand Oulhadj-Bouria Rue Drissi Yahia Bouira Algeria École Centrale Nantes CNRS LS2N UMR 6004 Nantes Université Nantes France Faculty of Engineering Cairo University Giza Egypt University of Sousse Tunisia

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The present work introduces a novel method for state estimation of physical systems represented by an uncertain Non-Integer Order Multiple Model (NIO-MM) model with unknown inputs. The key concept underlying this approach involves formulating a non-integer order Thau-Luenberger Observer (NIO-TLO) designed specifically for the NIO-MM. This observer is developed to handle situations where both known and unknown inputs are subject to uncertainties, and the model itself incorporates bounded unknown inputs. The analysis of the convergence of the error of the state estimation is presented through the formulation of linear matrix inequalities (LMIs). To validate the proposed approach, an academic example is examined.

关键词： Couplings Interpolation Uncertainty Simulation Computational modeling Observers Vectors

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TGGLinesPlus: A robust topological graph-guided computer vision algorithm for line detection from images

arXiv

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arXiv 2024年

作者： Yang, Liping Driscol, Joshua Gong, Ming Slack, Katie Zhang, Wenbin Wang, Shujie Potts, Catherine G. GeoAIR Lab Department of Geography and Environmental Studies University of New Mexico AlbuquerqueNM87131 United States University of New Mexico AlbuquerqueNM87131 United States Department of Computer Science University of New Mexico AlbuquerqueNM87106 United States Woodwell Climate Research Center FalmouthMA02540 United States Department of Electrical and Computer Engineering University of Dayton DaytonOH45469 United States Knight Foundation School of Computing and Information Sciences Florida International University MiamiFL33199 United States Department of Geography The Pennsylvania State University University ParkPA16802 United States Earth and Environmental Systems Institute The Pennsylvania State University University ParkPA16802 United States D-Wave Quantum Inc. BurnabyBC Canada

Line detection is a classic and essential problem in image processing, computer vision and machine intelligence. Line detection has many important applications, including image vectorization (e.g., document recognition and art design), indoor mapping, and important societal challenges (e.g., sea ice fracture line extraction from satellite imagery). Many line detection algorithms and methods have been developed, but robust and intuitive methods are still lacking. In this paper, we proposed and implemented a topological graph-guided algorithm, named TGGLinesPlus, for line detection. Our experiments on images from a wide range of domains have demonstrated the flexibility of our TGGLinesPlus algorithm. We benchmarked our algorithm with five classic and state-of-the-art line detection methods and evaluated the benchmark results qualitatively and quantitatively, the results demonstrate the robustness of TGGLinesPlus. Copyright © 2024, The Authors. All rights reserved.

关键词： Sea ice

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DPA-2:a large atomic model as a multitask learner

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npj Computational Materials 2024年第1期10卷 185-199页

作者： Duo Zhang Xinzijian Liu Xiangyu Zhang Chengqian Zhang Chun Cai Hangrui Bi Yiming Du Xuejian Qin Anyang Peng Jiameng Huang Bowen Li Yifan Shan Jinzhe Zeng Yuzhi Zhang Siyuan Liu Yifan Li Junhan Chang Xinyan Wang Shuo Zhou Jianchuan Liu Xiaoshan Luo Zhenyu Wang Wanrun Jiang Jing Wu Yudi Yang Jiyuan Yang Manyi Yang Fu-Qiang Gong Linshuang Zhang Mengchao Shi Fu-Zhi Dai Darrin M.York Shi Liu Tong Zhu Zhicheng Zhong Jian Lv Jun Cheng Weile Jia Mohan Chen Guolin Ke Weinan E Linfeng Zhang Han Wang AI for Science Institute BeijingP.R.China DP Technology BeijingP.R.China Academy for Advanced Interdisciplinary Studies Peking UniversityBeijingP.R.China State Key Lab of Processors Institute of Computing TechnologyChinese Academy of SciencesBeijingP.R.China University of Chinese Academy of Sciences BeijingP.R.China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingP.R.China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingboP.R.China CAS Key Laboratory of Magnetic Materials and Devices and Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of SciencesNingboP.R.China School of Electronics Engineering and Computer Science Peking UniversityBeijingP.R.China Shanghai Engineering Research Center of Molecular Therapeutics&New Drug Development School of Chemistry and Molecular EngineeringEast China Normal UniversityShanghaiP.R.China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine and Department of Chemistry and Chemical BiologyRutgers UniversityPiscatawayNJUSA Department of Chemistry Princeton UniversityPrincetonNJUSA College of Chemistry and Molecular Engineering Peking UniversityBeijingP.R.China Yuanpei College Peking UniversityBeijingP.R.China School of Electrical Engineering and Electronic Information Xihua UniversityChengduP.R.China State Key Laboratory of Superhard Materials College of PhysicsJilin UniversityChangchunP.R.China Key Laboratory of Material Simulation Methods&Software of Ministry of Education College of PhysicsJilin UniversityChangchunP.R.China International Center of Future Science Jilin UniversityChangchunP.R.China Key Laboratory for Quantum Materialsof Zhejiang Province Department of PhysicsSchool of ScienceWestlake UniversityHangzhouP.R.China Atomistic Simulations Italian Institute of TechnologyGenovaItaly State Key Laboratory of Physical Chemistry of Solid Surface iChEMCollege of Chemistry and Chemical EngineeringXiame

The rapid advancements in artificial intelligence(AI)are catalyzing transformative changes in atomic modeling,simulation,and ***-driven potential energy models havedemonstrated the capability to conduct large-scale,long-duration simulations with the accuracy of ab initio electronic structure ***,the model generation process remains a bottleneck for large-scale *** propose a shift towards a model-centric ecosystem,wherein a large atomic model(LAM),pretrained across multiple disciplines,can be efficiently fine-tuned and distilled for various downstream tasks,thereby establishing a new framework for molecular *** this study,we introduce the DPA-2 architecture as a prototype for ***-trained on a diverse array of chemical and materials systemsusing a multi-task approach,DPA-2demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning *** approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research.

关键词： DPA establishing thereby

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