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检索条件"机构=Inst. for Systems and Computer Engineering"

共 163 条记录，以下是1-10 订阅

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MoFLeuR: Motion-based Federated Learning Gesture Recognition 4

MoFLeuR: Motion-based Federated Learning Gesture Recognition

引用

4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Jamal Seyedmohammadi, S. Sheikholeslami, S. Mohammad Abouei, Jamshid Mohammadi, Arash Plataniotis, Konstantinos N. Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada Yazd University Dept. Of Electrical Engineering Yazd Iran

ISBN: (纸本)9798350315790

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: a) the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and b) clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency. © 2024 IEEE.

关键词： Internet of things

来源：评论

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

引用

2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Miyazaki, Takeru Araki, Shunsuke Uehara, Satoshi Kyushu Inst. of Information Sciences Faculity of Management and Information Sciences Fukuoka Japan Kyushu Inst. of Technology Faculty of Computer Science and Systems Engineering Fukuoka Japan The Univ. of Kitakyushu Faculty of Environmental Engineering Fukuoka Japan

ISBN: (纸本)9798350324174

In this paper, we propose a new pseudorandom bit sequence generator, which is based on a method digging out walls. Although each of these sequences lacks enough randomness, synthesized sequences of them can pass all of the NIST statistical test suite. We also describe a conjectural relation between the number of types on the square mazes and their sizes. © 2023 IEEE.

关键词： Digging Out the Walls Maze Generator Pseudorandom Bit Sequence

来源：评论

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FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction 4

FedDist-POIRec: Federated Distillation for Point-Of-Interest...

引用

4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Rezaei, Esmatollah Seyedmohammadi, S. Jamal Abouei, Jamshid Plataniotis, Konstantinos N. Yazd University Dept. Of Electrical Engineering Yazd Iran Concordia University Inst. Of Information Systems Engineering Montreal Canada University Of Toronto Edward S. Rogers Sr. Department Of Electrical And Computer Engineering Toronto Canada

ISBN: (纸本)9798350315790

Next Point-of-Interest (POI) recommendation plays a vital role in human mobility prediction within Location-based Social Networks (LSBN), assisting individuals to decide on their next destination. However, traditional centralized systems rely on large datasets of previous POI check-ins, which raises privacy and computation concerns. In this regard, Federated Learning (FL) has emerged as a promising solution, enabling distributed training of machine learning models while preserving user privacy. In this paper, we propose a novel FL-based POI recommendation system, FedDist-POIRec, that utilizes Federated Distillation (FD) in a semi-synchronous framework to promote privacy, recommendation efficiency, and cope with resource heterogeneity by considering heterogeneous model architectures among users. Additionally, to alleviate the sparsity of user checkin lists, we employ a innovative knowledge aggregation technique, where the knowledge of each user is aggregated with that of users who share similar preferences. Simulation results demonstrate that FedDist-POIRec outperforms the baseline methods in terms of recommendation efficiency and communication cost, while keeping the computation cost at an acceptable level. © 2024 IEEE.

关键词： Distillation

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FedStar Caching: Decision Center Assisted Federated Cooperative Edge Caching 4

FedStar Caching: Decision Center Assisted Federated Cooperat...

引用

4th IEEE International Conference on Human-Machine systems, ICHMS 2024

作者： Gharib, Sirvan Kesargheh, Mohammad Mansour Seyedmohammadi, S. Jamal Hajiakhondi, Zohreh Abouei, Jamshid Plataniotis, Konstantinos N. Yazd University Dept. Of Electrical Engineering Yazd Iran Concordia University Inst. Of Information Systems Engineering Montreal Canada Concordia University Dept. Of Electrical Engineering Montreal Canada University Of Toronto Dept. Of Electrical And Computer Engineering Toronto Canada

ISBN: (纸本)9798350315790

With the explosion of data, Wireless Edge Caching (WEC) has become a promising approach for locally accessing cached contents. Due to the limited storage capacity of local caching devices and the varying preferences of humans for content, it is necessary to predict popular contents. In this article, we will address two distinct objectives: 1) Popularity prediction of the content that should be cached at the edge to effectively utilize edge device memory. 2) Edge devices collaboration for efficient content delivery. Accordingly, we propose an FL-based Star Cooperative Caching (FedStar Caching), utilizing a Star network topology for realizing an efficient cooperation among Femto-cell Access Points (FAPs) using a Decision Center (DC) to enhance power efficiency and delay performance. Considering human sensitivity to privacy, collecting users' data on a central server is not desirable, therefore, we leverage Federated Learning (FL) to alleviate this challenge. Simulation results demonstrate that the proposed method outperforms alternatives in terms of cache efficiency, as well as delay and power consumption. © 2024 IEEE.

关键词： Stars

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Fluid and kinetic modeling of runaway electron generation from tritium beta decay and Compton scattering 50

Fluid and kinetic modeling of runaway electron generation fr...

引用

50th EPS Conference on Plasma Physics, EPS 2024

作者： Ekmark, I. Hoppe, M. Fülöp, T. Tinguely, R.A. Sweeney, R. Jansson, P. Antonsson, L. Vallhagen, O. Pusztai, I. Department of Physics Chalmers University of Technology Göteborg Sweden Department of Electrical Engineering KTH Royal Inst. Technology Stockholm Sweden Plasma Science and Fusion Center Massachusetts Inst. Technology CambridgeMA United States Commonwealth Fusion Systems DevensMA United States Department of Computer Science and Engineering Chalmers Univ. Technology Göteborg Sweden

ISBN: (纸本)9798331305239

We derived kinetic sources of energetic electrons generated by tritium beta decay and Compton scattering and compared them to the corresponding fluid RE generation rates [2]. In the SPARC example considered, using kinetic (as opposed to fluid) sources yielded 25 % higher RE current. The reason for this is that energetic electrons generated by the kinetic sources with p c can run away since collisions for these electrons are kinetically resolved. © 2024 50th EPS Conference on Plasma Physics, EPS 2024. All rights reserved.

关键词： Compton scattering

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Towards 3D-Denser Ultrasound Image Simulation from 2D CT-Scan for Ultrasound-Guided Percutaneous Nephrolithotomy Training

Towards 3D-Denser Ultrasound Image Simulation from 2D CT-Sca...

引用

2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Selladurai, Sathiyamoorthy Sainsbury, Ben Watterson, James Hibbert, Rebecca Anila Satheesh, B. Thittai, Arun K. Rossa, Carlos Carleton University Department of Systems and Computer Engineering OttawaON Canada Marion Surgical SeguinON Canada University of Ottawa Faculty of Medicine Division of Urology OttawaON Canada Mayo Clinic Department of Radiology RochesterMN United States Indian Inst. of Technology Madras Chennai India

ISBN: (纸本)9781665410205

Virtual reality (VR) simulation can improve the outcomes of percutaneous nephrolithotomy (PNCL) - a surgery to extract kidney stones using ultrasound (US) or fluoroscopy image guidance. These simulators almost exclusively employ fluoroscopy, and no commercial VR simulator is available for US-guided PNCL (usPCNL). In this paper, we proposed the first step towards developing an usPCNL simulator that integrates a volumetric US model of the patient's anatomy derived from parallel 2D computed tomography (CT) scans. A critical challenge in US image generation from CT scans is that the limited spatial resolution of CT slices may lead to inaccuracies in the simulated US images. The proposed algorithm interpolates successive CT images to create an augmented dataset with increased spatial resolution. Each CT slice is then converted into a US image based on principles of linear acoustics and spatial impulse response. These images are then combined to form two different volumetric US images, one derived from the original sparse CT scans, and one created with the augmented data. From these volumetric US images, new images can be formed along arbitrary imaging planes not captured in the original CT data. The obtained simulated images are compared with their corresponding real US images acquired experimentally, and further evaluated quantitatively using normalized root mean square error (NRMSE) and dice similarity coefficient (DSC). The results reveal an NRMSE of 0.235pm 0.051 and a DSC of 0.9139pm 0.062, showcasing a close resemblance between simulated and actual ultrasound images. Additionally, we show that denser CT scan data leads to a 25% improvement in image quality based on peak signal-to-noise ratio compared to the original dataset. This initial work is laying the foundation for the development of the usPCNL simulator, which could potentially have significant benefits for training and enhancing skills in this medical procedure. © 2024 IEEE.

关键词： computerized tomography

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A Study on a New Pseudorandom Number Generator with Deterministic Maze Construction

A Study on a New Pseudorandom Number Generator with Determin...

引用

IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Takeru Miyazaki Shunsuke Araki Satoshi Uehara Faculity of Management and Information Sciences Kyushu Inst. of Information Sciences Fukuoka Japan Faculty of Computer Science and Systems Engineering Kyushu Inst. of Technology Fukuoka Japan Faculty of Environmental Engineering The Univ. of Kitakyushu Fukuoka Japan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In this paper, we propose a new pseudorandom number generator (PRNG), which uses a new deterministic maze construction for our maze-based PRNG [1]. We evaluate some relations between a size and a number of shapes for the mazes generated by our maze construction. Then, we also estimate a number of shapes and the maximum duplicate number of mazes by the size of the maze. By using the asymptotic equations of these estimated values, we can derive the minimum required maze size for the required number of sequences in which most or all sequences are different.

关键词： Shape Generators Consumer electronics

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MoFLeuR: Motion-based Federated Learning Gesture Recognition

MoFLeuR: Motion-based Federated Learning Gesture Recognition

引用

IEEE International Conference on Human-Machine systems

作者： S. Jamal Seyedmohammadi S. Mohammad Sheikholeslami Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Inst. of Information Systems Engineering Concordia University Montreal Canada Dept. of Electrical and computer Engineering University of Toronto Toronto Canada Dept. of Electrical Engineering Yazd University Yazd Iran

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

Most of the recent works on Human Gesture Recognition (HGR) using motion data rely on gathering a dataset, that faces two major challenges: $a)$ the datasets are originally stored on the smart devices at the end-users, and gathering them in one place is not feasible due to communication limitations, and $b)$ clients are reluctant to share their private data with a central server due to privacy concerns. In this paper, we address these issues and propose a privacy-preserving framework based on Federated Learning (FL) for HGR using motion data. Furthermore, we consider data heterogeneity which have destructive effects on the performance of the global model. Accordingly, we propose a communication and computation-efficient client selection method, mitigating the impact of data heterogeneity in the FL process. In the proposed framework, clients are not requested to share sensitive information about their local datasets with the edge server. Simulation results show that the proposed MoFLeuR algorithm improves the performance of the global model in the presence of different degrees of data heterogeneity, and it outperforms the baseline algorithms in terms of different metrics, namely accuracy, convergence speed, and communication and computation efficiency.

关键词： Performance evaluation Federated learning Inertial sensors Computational modeling Simulation Human-machine systems Gesture recognition

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How to Construct Pseudorandom Bit Sequences from Mazes by a Method Digging Out Walls

How to Construct Pseudorandom Bit Sequences from Mazes by a ...

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

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FedDist-POIRec: Federated Distillation for Point-Of-Interest Recommendation in Human Mobility Prediction

FedDist-POIRec: Federated Distillation for Point-Of-Interest...

引用

IEEE International Conference on Human-Machine systems

作者： Esmatollah Rezaei S. Jamal Seyedmohammadi Jamshid Abouei Konstantinos N. Plataniotis Dept. of Electrical Engineering Yazd University Yazd Iran Inst. of Information Systems Engineering Concordia University Montreal Canada Edward S. Rogers Sr. Department of Electrical and Computer Engineering University of Toronto Toronto Canada

ISBN: (数字)9798350315790

ISBN: (纸本)9798350315806

关键词： Training Privacy Data privacy Costs Social networking (online) Federated learning Simulation

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