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Unknown Type Streaming Feature Selection via Maximal Information Coefficient 22

Unknown Type Streaming Feature Selection via Maximal Informa...

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22nd IEEE international conference on data Mining (ICDM)

作者： Zhou, Peng Zhang, Yunyun Yan, Yuanting Zhao, Shu Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ China Hefei Peoples R China

ISBN: (纸本)9798350346091

Feature selection aims to select an optimal minimal feature subset from the original datasets and has become an indispensable preprocessing component before data mining and machine learning, especially in the era of big data. Most feature selection methods implicitly assume that we can know the feature type (categorical, numerical, or mixed) before learning, then design corresponding measurements to calculate the correlation between features. However, in practical applications, features may be generated dynamically and arrive one by one over time, which we call streaming features. Most existing streaming feature selection methods assume that all dynamically generated features are the same type or assume we can know the feature type for each new arriving feature on the fly, but this is unreasonable and unrealistic. Therefore, this paper firstly studies a practical issue of Unknown Type Streaming Feature Selection and proposes a new method to handle it, named UT-SFS. Extensive experimental results indicate the effectiveness of our new method. UT-SFS is nonparametric and does not need to know the feature type before learning, which aligns with practical application needs.

关键词： feature selection streaming feature unknown feature type maximal information coefficient

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Multi-Attribute Forecast of the Price in the Iberian Electricity Market 22nd

Multi-Attribute Forecast of the Price in the Iberian Electri...

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22nd international conference on intelligent data engineering and automated learning

作者： Peres, Goncalo Tallon-Ballesteros, Antonio J. Cavique, Luis Univ Aberta Lisbon Portugal Univ Huelva Huelva Spain

ISBN: (纸本)9783030916077;9783030916084

Electricity has been acquiring a more significant presence in our lives, and it is estimated that the future will be increasingly electric. Nowadays, we have access to enormous amounts of data that do not have much-added value if they cannot support decision-making or plan systems in advance and correctly. Forecasts are vital tools to support decision-making. We believe it is possible to resort to open data available on the Internet to make electricity price forecasts that - decision-makers can use in the sector. In this work, we study the multi-attribute hourly forecast of the electricity price in MIBEL (Iberian electricity market) for the 24 h of the following day, using open data. The realization of the multi-attribute predictions fell on the TIM ('Tangent Information Modeler') tool with AutoML ('Auto Machine learning') capabilities. The TOPSIS ('technique for order of preference by similarity to ideal solution') decision support technique was used to analyze the results.

关键词： Economic prediction Forecasting Iberian electricity market (MIBEL) Auto machine learning Multi-attribute decision

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32nd international conference on Flexible Automation and intelligent Manufacturing, FAIM 2023

32nd International Conference on Flexible Automation and Int...

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32nd international conference on Flexible Automation and intelligent Manufacturing, FAIM 2023

ISBN: (纸本)9783031382406

The proceedings contain 239 papers. The special focus in this conference is on . The topics include: A Review of Value Stream Mapping (VSM) for Viable Business Process Management Among Agro-Allied Companies During the 4IR;outsourcing Optimization in Footwear Industry: The Case of a Portuguese Company;UWB-Based Indoor Navigation in a Flexible Manufacturing System Using a Custom Quadrotor UAV;real-Time Defect and Object Detection in Assembly Line: A Case for In-Line Quality Inspection;Using Computer Vision to Improve SME Performance;Cyber-Physical Systems Based Smart Manufacturing of Disinfectants: A Need, and Solution Driven by COVID-19 Pandemic;CPPS-3D: A Methodology to Support Cyber Physical Production Systems Design, Development and Deployment;reinforcement learning-Based Model for Optimization of Cloud Manufacturing-Based Multi Objective Resource Scheduling: A Review;an Overview of Explainable Artificial Intelligence in the Industry 4.0 Context;Analyzing the Effects of Different 3D-Model Acquisition Methods for Synthetic AI Training data Generation and the Domain Gap;improving Regulations for automated Design Checking Through Decision Analysis Good Practices: A Conceptual Application to the Construction Sector;environment for the Design and Automation of New Cable-Driven Parallel Robot Architectures;preliminary Design of an Automatic Palletizing System During the Pre-sales Stage;a New Equipment for Automatic Calibration of the Semmes-Weinstein Monofilament;measuring the Moment-Curvature Relationship of a Steerable Catheter Using a Load Cell and Stereovision System;study of the Best Operational Parameters in Laser Marking of Plastic Parts;Laser Marking on White-Coloured Polyoxymethylene (POM) Polymer Substrate: Challenges and Perspectives;influence of Laser Beam Intensity Distribution on Keyhole Geometry and Process Stability Using Green Laser Radiation;advanced Characterization Techniques of Multi-material Machining Tool Coatings.

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Reusable Virtual Coach for Smoking Cessation and Physical Activity Coaching 22

Reusable Virtual Coach for Smoking Cessation and Physical Ac...

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22nd ACM international conference on intelligent Virtual Agents (IVA)

作者： Baccinelli, Walter van der Burg, Sven Richardson, Robin Smits, Djura Geng, Cunliang Ridder, Lars Scheltinga, Bouke Albers, Nele Brinkman, Willem-Paul Meijer, Eline Reenalda, Jasper Netherlands eSci Ctr Amsterdam Netherlands Univ Twente Biomed Signals & Syst Enschede Netherlands Delft Univ Technol Dept Intelligent Syst Delft Netherlands Leiden Univ Med Ctr Publ Hlth & Primary Care Leiden Netherlands Roessingh Res & Dev Enschede Netherlands

ISBN: (纸本)9781450392488

Smoking tobacco and physical inactivity are key preventable behavioural risk factors of cardiovascular disease (CVD). Computerised coaching systems can help individuals to modify risky behaviours, thereby preventing CVD. However, most reported eHealth or computerized coaching systems are hard to reuse in slightly different settings. To provide an open-source, reusable computer coaching system, we developed Perfect Fit. The reusability is manifested by building around the open-source text- and voice-based contextual assistant framework Rasa. Rasa provides a simple, standard interface to many popular messaging and voice channels, and custom connectors are easily implemented. A set of algorithms have been developed and connected to Rasa to drive and personalize the conversation flow and the coaching process. Such algorithms make use of data stored in a devoted database. Furthermore, Perfect Fit adheres to best practices and standards in software engineering. The modular design of Perfect Fit will allow researchers to connect the virtual coach to any messaging or voice channel with only modest modification. Perfect Fit is available under open-source license in GitHub and is currently in prototype-phase. Concluding, Perfect Fit will deliver a virtual coach that can easily be adapted and reused in different settings. The coach helps individuals to achieve and maintain abstinence from smoking and sufficient physical activity (PA).

关键词： Contextual assistant conversational agent eHealth open-source Rasa software reusability virtual coach

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learning from Multiple Independent Advisors in Multi-agent Reinforcement learning 22

Learning from Multiple Independent Advisors in Multi-agent R...

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22nd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2023

作者： Subramanian, Sriram Ganapathi Taylor, Matthew E. Larson, Kate Crowley, Mark Vector Institute Toronto Canada University of Waterloo Waterloo Canada University of Alberta Edmonton Canada Alberta Machine Intelligence Institute Edmonton Canada

Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. learning from external demonstrators is a possible solution that mitigates this problem. However, most prior approaches in this area assume the presence of a single demonstrator. Leveraging multiple knowledge sources (i.e., advisors) with expertise in distinct aspects of the environment could substantially speed up learning in complex environments. This paper considers the problem of simultaneously learning from multiple independent advisors in multi-agent reinforcement learning. The approach leverages a two-level Q-learning architecture, and extends this framework from single-agent to multi-agent settings. We provide principled algorithms that incorporate a set of advisors by both evaluating the advisors at each state and subsequently using the advisors to guide action selection. We also provide theoretical convergence and sample complexity guarantees. Experimentally, we validate our approach in three different test-beds and show that our algorithms give better performances than baselines, can effectively integrate the combined expertise of different advisors, and learn to ignore bad advice. © 2023 international Foundation for Autonomous Agents and Multiagent Systems (***). All rights reserved.

关键词： Reinforcement learning

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EMPOWERING RURAL And REMOTE HEALTH PROFESSIONALS TRAINING: A COST-EFFECTIVE SKIN SUTURING SIMULATOR FOR MOBILE learning IN CLINICAL SKILLS ACQUISITION 22

EMPOWERING RURAL AND REMOTE HEALTH PROFESSIONALS TRAINING: A...

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22nd international conference on e-Society 2024, ES 2024 and 20th international conference on Mobile learning 2024, ML 2024

作者： Mosaad, Rebecca Micallef, Julia Olatinwo, Aliyat Brock, Gordon Dubrowski, Adam Ontario Tech University ON Canada Society for Rural Physicians of Canada QC Canada

Introduction: This study aimed to develop a cost-effective suturing and knot typing simulator that aligns with the expectations of experts, addressing the need for affordable yet high-quality medical training tools. The focus was on assessing the efficacy of a silicone skin suture task trainer, created through a 3D printed mold, for use in mobile learning, specifically in rural and remote contexts. Methods: Rural and remote trainees participating in a skills acquisition workshop, engaged in a 90-minute suturing simulation station. They received minimal feedback from physician educators to simulate independent practice. After the practice, they completed a survey assessing the acceptability and feasibility of the simulator for the intended training purpose, as well as providing feedback for future improvements. Results: Results from quantitative data revealed the simulator's potential to develop competence (4.2 out of 5) and confidence (4.1 out of 5). Participants expressed a readiness to practice suturing independently using the simulator (4.3 out of 5). Notably, the realism of the simulator was identified as an area for improvement in terms of anatomical correctness (3.6 out of 5) and accuracy (3.4 out of 5), while durability scored high (4 out of 5). Participants found the simulator easy to use (4.4 out of 5) and well-suited for developing cognitive (4.4 out of 5) and psychomotor skills (4.2 out of 5) related to suturing and knot typing. Several improvements were noted, especially in the areas of anatomical representativeness, material selection, and interactions between the simulator and clinical tools. Conclusions: This paper outlines the acceptability and feasibility of the simulator, designed to complement an online learning management system for hands-on clinical skill learning within the mobile learning paradigm. Despite high self-efficacy and educational value scores, concerns about realism suggest a need for a hybrid design approach that balances costs and

关键词： Additive Manufacturing Clinical Education Mobile learning Rural and Remote Education Simulation-Based Education Suturing Techniques

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Adaptive Robot Visual Tracking With Camera and Dynamic Parameter Convergence 22

Adaptive Robot Visual Tracking With Camera and Dynamic Param...

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22nd IEEE international conference on Industrial Informatics, IndIN 2024

作者： Pan, Yongping Zhang, Jingxuan Lai, Beixian Li, Zhiwen Li, Weibing School of Advanced Manufacturing Sun Yat-sen University Shenzhen518007 China School of Computer Science and Engineering Sun Yat-sen University Guangzhou510006 China

ISBN: (纸本)9798331527471

Robot visual servoing gives the possibility of exact pose control in unstructured environments, and homography-based visual servoing (HBVS) enables 3-D control over robot end-effectors using 2-D data from cameras. However, existing methods of visual servoing usually depend on the exact information of camera and robot models. This work put forward a dynamics-based HBVS control method to achieve 3-D visual tracking under both unknown camera extrinsic and robot dynamic parameters, where exact parameter estimation is performed online by using a composite learning technique. The proposed method achieves exponential stability along with the convergence of both camera extrinsic and dynamic parameters when a weak condition known as interval excitation is satisfied. Experiments conducted with a 7-degree-of-freedom robot manipulator known as Franka Emika Panda have provided evidence that the proposed method is effective concerning parameter estimation as well as 3-D robot tracking. © 2024 IEEE.

关键词： Visual servoing

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Contactless Hand Knuckle Modality for Identity Verification Using Siamese Network 22

Contactless Hand Knuckle Modality for Identity Verification ...

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22nd international conference on Cyberworlds, CW 2023

作者： Hammami, Siwar Jarraya, Islem Hamdani, Tarek M. Alimi, Adel M. Sfax Tunisia Sfax Tunisia Tunisia University of Johannesburg Dept. of Elec. and Electronic Engineering Science Faculty of Engineering and the Built Environment South Africa

ISBN: (纸本)9798350315653

People can be recognized by a lot of unique biometric features either physiological or behavioral. Therefore, a biometric security method for a smartphone is proposed in this paper for easier use and to improve recognition results by using a new knuckle modality: the major knuckle of the thumb finger modality instead of the major knuckle of the middle finger modality which is the most used in related works. In finger knuckle recognition domains, different CNN (Convolutional Neural Network) architectures have been applied, requiring a huge database. To deal with this problem, this paper presents a Siamese network for identity verification using contactless major knuckle of thumb finger modality. Actually, the Siamese network has been well used for one-shot learning that aims at learning and recognizing from little data. The FMK (Finger Major Knuckle) dataset was proposed and constructed for testing and evaluation. In fact, this study achieved good results with accuracy of 82% for the major knuckle of the left thumb finger and 79.63% for the major knuckle of the left middle finger. © 2023 IEEE.

关键词： Convolutional neural networks

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32nd international conference on Flexible Automation and intelligent Manufacturing, FAIM 2023

32nd International Conference on Flexible Automation and Int...

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32nd international conference on Flexible Automation and intelligent Manufacturing, FAIM 2023

ISBN: (纸本)9783031381645

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Research on Seismic Wave First Arrival Picking Based on Improved U-Net3+ 2

Research on Seismic Wave First Arrival Picking Based on Impr...

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2nd international conference on Robotics, Artificial Intelligence and intelligent Control, RAIIC 2023

作者： Jiang, Xuan Shen, Tong Tan, Mingjie Wang, Jianlin Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (纸本)9798350328004

Seismic P-wave first arrival picking is a fundamental and vital component of seismic monitoring, with its accuracy playing a critical role in subsequent data processing. In order to address the problems of low signal-to-noise ratio data pickup accuracy and poor robustness of the traditional seismic wave first arrivals, this paper proposes an improved U-Net3+ seismic P-wave first arrivals picking method. Compared with the traditional method, the model does not require manual setting of thresholds and pre-processing operations such as filtering of data, and only requires input of normalized three-component seismic waveform data to intelligently identify seismic P-wave first arrivals. To minimize information loss during seismic waveform extraction and enhance the utilization of effective feature information at the P-wave first arrival time, the residual unit is incorporated solely in the encoder and decoder part of U-Net3+. The model exhibits a lightweight structure and excels in feature extraction, enabling effective identification of low signal-to-noise ratio data. The results demonstrate hit rates of 81.4%, 86.5%, and 87.5% for data with signal-to-noise ratios below 5dB, 10dB, and 20dB, respectively. The average pickup error is 0.054s, outperforming mainstream deep learning methods like U-Net and U-Net3+. These findings present a novel approach for automated P-wave first arrival picking. © 2023 IEEE.

关键词： Pickups

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