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Poster Abstract: Representation learning from Multimodal Sensor data with Maximally Correlated Autoencoders 21

Poster Abstract: Representation Learning from Multimodal Sen...

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21st ACM/IEEE international conference on Information Processing in Sensor Networks (IPSN)

作者： Ma, Fei Gu, Weixi Ni, Shiguang Zhang, Lin Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China China Acad Ind Internet Dept Intelligent Ind Res Beijing Peoples R China Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen Peoples R China

ISBN: (纸本)9781665496247

With the development of sensing technology, multiple sensors are widely used in Internet of Things (IoT) devices. A key challenge is to learn feature representations from multimodal sensor data to combine the information of different sensors. Although progress has been made by previous works, the correlation between different sensors is still not well exploited, which may limit the performance of representation learning. To address this problem, we propose a deep learning approach to learn representations from multimodal sensor data with maximally correlated autoencoders (MCA). It can efficiently capture high dependence between different modalities at different feature levels. The learned representations are further used for the recognition task. Experimental results on the real-world RGB-D dataset demonstrate the high effectiveness of MCA.

关键词： Representation learning Performance evaluation Deep learning Correlation Multimodal sensors Information processing Semisupervised learning

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stacked Ensemble of Convolutional Neural Networks for Follicles Detection on Scalp Images 21st

Stacked Ensemble of Convolutional Neural Networks for Follic...

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21st international conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Ruta, Dymitr Cen, Ling Ruta, Andrzej Vu, Quang Hieu Khalifa Univ EBTIC Abu Dhabi U Arab Emirates PLACEMAKE IO London England Zalora Data Sci Grp Singapore Singapore

ISBN: (纸本)9783031234798;9783031234804

An average person's head is covered with up to 100000 hairs growing out of follicular openings on the scalp's skin. automated hair therapy requires precise detection and localization of follicles on the scalp and still poses a significant challenge for the computer vision and pattern recognition systems. We have proposed an automated vision system for follicles detection based on the classification of digitized microscopic scalp images using an ensemble of convolutional neural networks (CNN). A pool of adapted state-of-the-art CNNs have been transfer-trained on over 700k microscopic skin image regions of 120x120 pixels and their outputs further fed to the final stacked ensemble learning layer to capture a wider context of the connected neighboring regions of the original FullHD scalp images. A high validated f1 score (0.7) of detecting regions with follicles beats the industry's benchmark and brings this technology a step closer towards automated hair treatment as well as other emerging applications such as personal identification based on follicular scalp map.

关键词： Pattern recognition Convolutional neural networks stacked ensemble learning

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Designing Incentive Mechanisms for Fair Participation in Federated learning 25

Designing Incentive Mechanisms for Fair Participation in Fed...

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25th IEEE international conferences on High Performance Computing and Communications, 9th international conference on data Science and Systems, 21st IEEE international conference on Smart City and 9th IEEE international conference on Dependability in Sensor, Cloud and Big data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Xu, Han Nanda, Priyadarsi Liang, Jie University of Technology Sydney Faculty of Engineering and It Sydney Australia

ISBN: (纸本)9798350330014

Federated learning offers a collaborative method for training machine learning models using distributed data sources. Given its reliance on diverse contributions from multiple participants, equitable representation and rewards are paramount to its success. Upholding fairness is crucial to encourage active participation in this collaborative process. This paper presents a comprehensive analysis of existing mechanisms designed to incentivise fair engagement in federated learning, structured around a taxonomy aligned with the evolving dynamics of the federated learning sources. The study offers insights into cultivating environments that prioritise fairness and broad participation while suggesting avenues for future research. © 2023 IEEE.

关键词： learning systems

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Reinforcement of Collaborative Work in the Metaverse Through Playful Activities 21

Reinforcement of Collaborative Work in the Metaverse Through...

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21st IEEE/ACIS international conference on Software engineering Research, Management and Applications, SERA 2023

作者： Guillen-Yparrea, Nicia Hernandez-Rodriguez, Felipe Tecnologico de Monterrey Engineering and Sciences School Monterrey Mexico Tecnologico de Monterrey Engineering and Sciences School Saltillo Mexico

ISBN: (纸本)9798350345889

This article explores the potential of the metaverse as a tool to reinforce collaborative work and improve student participation. A Rally was conducted as a playful activity in the metaverse, and data were collected through a validated survey to assess the impact on student motivation, participation, and collaboration. The main idea was to take advantage of the features of the metaverse to create an interactive and motivating learning environment that fosters collaboration and the exchange of ideas among students. To achieve this, interdisciplinary teams were formed that allowed students to interact and work together in problem solving and knowledge construction. The results of the research show that the use of playful activities in the metaverse improves student participation and motivation, as well as their ability to collaborate and communicate effectively. In conclusion, the proposal presented in this article offers an innovative and effective alternative for the reinforcement of collaborative work in the context of online education and collaboration. © 2023 IEEE.

关键词： Reinforcement

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Reinforcement learning-Based Skill Acquisition study for Linearly Heated Robots 21

Reinforcement Learning-Based Skill Acquisition Study for Lin...

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21st IEEE international conference on Mechatronics and Automation (IEEE ICMA)

作者： Zhang, Zhiliang Ikeura, Ryojun Hayakawa, Soichiro Wang, Zheng Mie Univ Dept Engn 1577 Kurimamachiya Cho Tsu Mie Japan

ISBN: (纸本)9798350388084;9798350388077

With the development of science and technology, the manufacturing industry is undergoing a quiet transformation, with artificial intelligence and intelligent robots gradually entering the smart manufacturing. Currently, the traditional shipboard manufacturing industry is undergoing a baptism of robotics and ushering in profound changes to meet the growing demand for productivity, quality and safety. However, traditional industrial robots are limited to preprogrammed movements and lack the ability to perform multi-skilled tasks. In particular, programming industrial robots for complex hull steel plate machining processes, especially multi-pass machining, is challenging. In order to allow the robot to learn skills like a human apprentice, it can learn the skills related to linear heating of ship plates from experienced workers and make autonomous judgments to complete the hull steel plate machining according to the requirements. This study focuses on enabling machines to acquire workers' skills and plan heating lines for ship hull steel plates. We achieve this through demonstrative learning and maximum gradient-based reinforcement learning. It can be seen through data simulation and experiments that the linear heating robot in this study achieves 83.26% accuracy in planning the heating line. This not only provides support for workers in the complex linear heating process, but also further improves workers' operational efficiency and economic benefits.

关键词： robot linear heating skill demonstration reinforcement learning

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Application of the S-PPED-C learning Model for Developing Embedded Teaching Innovations 27th

Application of the S-PPED-C Learning Model for Developing Em...

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27th international conference on Interactive Collaborative learning, ICL 2024

作者： Inchan, Surasak Akatimagool, Somsak Department of Teacher Training in Electrical Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (纸本)9783031856488

Nowadays, modern industries and smart technologies are focused on the application of intelligent technology. However, most employees still lack high ability in embedded systems. To solve this problem, the engineering education system must upskill and adapt the basic skills of embedded technology as much as possible. This research aims to apply the S-PPED-C learning model to develop embedded teaching innovations. The S-PPED-C learning model based on blended learning management was designed with 6 steps: surveying, preparing, practicing, evaluating, discussing, and concluding. The innovative learning package created includes a teacher’ manual, learning activity plan, laboratory-based learning media for embedded systems, and achievement tests. The research tool was implemented with a sample of 16 undergraduate students in the Department of Information and Communication engineering, the Faculty of engineering and Industrial Technology at Phetchaburi Rajabhat University, Thailand. The results of the research found that;1) the developed S-PPED-C learning model was effective with the E1/E2 efficiency value of 83.13/83.33 according to the standard criteria of 80/80, 2) the evaluated results of the learning achievement of the students were 81.26% according to the standard criteria, and 3) the comparison of learning achievement of student after studying was significantly higher than before studying at the.05 level. It can be seen that the developed S-PPED-C learning model can promote learners with learning skills in the 21st century and is consistent with professional standards in the Industry 4.0 era. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Principal Component Analysis Scheme Based on Homomorphic Encryption in a Distributed Environment 21st

Principal Component Analysis Scheme Based on Homomorphic Enc...

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21st international conference on Web Information Systems and Applications (WISA)

作者： Wang, Dong Ma, Ming Cheng Liu, Lingli Du, Xiongxiong Li, Xiaoruo Zhu, Bingnan Henan Univ Henan Int Joint Lab Intelligent Network Theory & Kaifeng 475000 Peoples R China Henan Univ Sch Software Kaifeng 475000 Peoples R China

ISBN: (纸本)9789819777068;9789819777075

Principal component analysis (PCA) is a widely used technique in the field of machine learning and one of the main dimensionality reduction methods. PCA can convert high-dimensional data into lower-dimensional representations, thereby helping to extract important information from data, and is an effective tool for data analysis and pattern recognition. In this paper, we propose a scheme for executing PCA on encrypted data based on homomorphic encryption in a distributed environment. In a distributed setting, computing nodes do not need to perform matrix operations such as matrix multiplication or addition, which reduces the computational burden on the nodes. To ensure the security of the data, we employ the CKKS homomorphic encryption scheme, which allows for approximate calculations on real numbers, meeting the requirements of machine learning. Additionally, we horizontally partition the data to facilitate its application in distributed computing. Furthermore, we optimize the detailed computation of HPCA (Homomorphic PCA), yielding promising results on various datasets.

关键词： Homomorphic Encryption Distributed Computing Principal Component Analysis (PCA) CKKS scheme Goldschmidt's Algorithm

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A 3D Camera Protocol for Object Pose Estimation from Point Cloud in Robot Operations 21

A 3D Camera Protocol for Object Pose Estimation from Point C...

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21st international Joint conference on Computer Science and Software engineering (JCSSE)

作者： Charngtong, Chiwin Dheeravongkit, Arbtip Vonzbunvona, Sunachai King Mongkuts Univ Technol Thonburi Bangkok Inst Field Robot Bangkok Thailand

ISBN: (纸本)9798350381771;9798350381764

The utilization of industrial robots and cameras in industrial manufacturing has become widespread. However, the high cost of industrial 3D cameras, in particular, poses a significant challenge in the selection process. The advancements in RGB-D camera technology, which is significantly involved in the development of robotics and more affordable than industrial 3D cameras, are noteworthy. In this research, the Intel Realsense L515 RGB-D camera and the NVIDIA Jetson Xavier NX single board computer were selected for the implementation of an object pose estimation application. The object segmentation algorithm, YOLOv7, was proposed for object detection, which enables the calculation of the X, Y, and Z position of the object. Subsequently, a master and object segmentation point cloud was generated, and a point cloud preprocessing and registration methodology was proposed to determine the R-x, R-y, and R-z angles of the object, utilizing the Open3D library. In addition, an industrial robot interfacing via TCP/IP and serial communication to enable the transformation of the object pose into the robot pose for subsequent transmission is proposed. A web-based application was developed using the Django framework to facilitate RGB-D camera monitoring and parameter setting. The experiments were conducted using a three-way tube with a diameter of 25.4 mm. as the object, resulting in the RMS error in X, Y, Z, R-x, R-y, and R-z are 5.3, 4.3, 4.3 mm., 2.2, 1.4, 2.5 degrees respectively. The maximum error in X, Y, Z, R-x, R-y, and R-z are 15.7, 13.6, 6.5 mm., 4.9, 3.5, 7 degrees respectively.

关键词： RGB-D Camera Object Pose Estimation Deep learning Point cloud 3D Computer Vision

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Ethics by Design for intelligent and Sustainable Adaptive Systems 1

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21st international conference of the Italian-Association-for-Artificial-Intelligence (AIxIA)

作者： Squadrone, Luca Croce, Danilo Basili, Roberto Univ Roma Tor Vergata Dept Enterprise Engn Via Politecn 1 I-00133 Rome Italy

ISBN: (数字)9783031271816

ISBN: (纸本)9783031271809;9783031271816

AI systems are increasingly dependent on the data and information sources they are developed with. In particular, learning machines are highly exposed to undesirable problems due to biased and incomplete coverage of training data. The autonomy exhibited by machines trained on low-quality data raises an ethical concern, as it may infringe on social rules and security constraints. In this paper, we extensively experiment with a learning framework, called Ethics by Design, which aims to ensure a supervised learning policy that can pursue both the satisfaction of ethical constraints and the optimization of task (i.e., business) accuracy. The results obtained on tasks and datasets confirm the positive impact of the method in ensuring ethical compliance. This paves the way for a large set of industrial applications, whose ethical dimension is critical to increasing the trustworthiness with respect to this technology.

关键词： Ethical issues of AI Ethics by design in machine learning Bias in deep learning Empirical evaluation of ethical AI systems

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Koopman-Based data-Driven Techniques for Adaptive Cruise Control System Identification 27

Koopman-Based Data-Driven Techniques for Adaptive Cruise Con...

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27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Meng, Yiming Li, Hangyu Ornik, Melkior Li, Xiaopeng University of Wisconsin-Madison Department of Civil and Environmental Engineering MadisonWI53706 United States University of Illinois Urbana-Champaign Coordinated Science Laboratory UrbanaIL61801 United States University of Illinois Urbana-Champaign Department of Aerospace Engineering UrbanaIL61801 United States

ISBN: (纸本)9798331505929

Accurately identifying the intrinsic model of Adaptive Cruise Control has the potential to enhance the prediction of automated car-following behavior, helping vehicles' decision-making and contributing to safer and more efficient traffic flows. Moreover, white box models offer an analytical base for evaluating the impact of automated driving functions on macroscopic traffic dynamics, consequently aiding the management of the whole intelligent transportation system. Many existing system identification techniques have been applied to automated vehicles. However, most of these studies focus on identifying parameters for models of a fixed prototype. Their reliance on accurate estimation of state time derivatives prevents their real applications, challenged by low sampling rates, noisy measurements, and limited observation periods. In contrast, the Koopman operator learning framework presents a promising improvement that can identify the nonlinear evolutionary properties of continuous-time systems. In this study, we apply Koopman-based methods to data driven Adaptive Cruise Control model identification. Additionally, as the challenge remains in establishing a practical relationship between identification accuracy and sampling rate, we numerically compared the performance of three Koopman-based learning frameworks, finite-difference, Koopman-logarithm, and a newly devised resolvent-type method, with that of a commonly used offline simulation-based batch optimization approach. We introduce a novel modification to the resolvent-type method, and the experimental results demonstrate its state of the art performance, particularly in identifying the potential existence of parametric noise at lower sampling rates. © 2024 IEEE.

关键词： Continuous time systems

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