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IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Cecilia De Vicariis Ekaterina Ivanova Vittorio Sanguineti Etienne Burdet Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Imperial College of Science Technology and Medicine London UK School of Electronic Engineering and Computer Science Queen Mary University of London UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

We refer to joint action as any social interaction where two or more people coordinate their actions in space and time to bring about a change in the environment. We are constantly involved in joint actions in our daily life - lifting a piece of furniture, playing team sport, and during a rehabilitation session. Here, we aim at characterizing emergent coordination in pairs of participants mechanically coupled through a virtual elastic band while moving the hand through multiple via-points. We assessed the level of coordination in terms of temporal synchronization and trajectory similarity metrics. Overall, we found that participants changed their behaviour when mechani-cally coupled toward coordination, both in space and time, compared to their solo performances. However, coordination rapidly disappeared when the mechanical coupling was removed. These results provide insights into the understanding of the mecha-nisms underlying the development of joint coordination while haptically connected.

关键词： Measurement Couplings Robot kinematics Biomechatronics Robot sensing systems Trajectory Haptic interfaces Synchronization Sports

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Quantum Graph Optimization Algorithm

arXiv

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arXiv 2024年

作者： Huang, Yuhan Nugraha, Ferris Prima Jin, Siyuan Zhang, Yichi Zeng, Bei Shao, Qiming Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Department of Information Systems The Hong Kong University of Science and Technology Hong Kong Department of Physics The Hong Kong University of Science and Technology Hong Kong

Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants, present a promising way, potentially exceeding the capabilities of classical algorithms, for addressing QUBO tasks. However, the possibility of using messagepassing machines, inspired by classical graph neural networks, to enhance the power and performance of these quantum algorithms for QUBO tasks was not investigated. This study introduces a novel variational quantum graph optimization algorithm that integrates the message-passing mechanism, which demonstrates significant improvements in performance for solving QUBO problems in terms of resource efficiency and solution precision, compared to QAOA, its variants, and other quantum graph neural networks. Furthermore, in terms of scalability on QUBO tasks, our algorithm shows superior performance compared to QAOA, presenting a substantial advancement in the field of quantum approximate optimization. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

Multi-Object RANSAC: Efficient Plane Clustering Method in a ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Seunghyeon Lim Youngjae Yoo Jun Ki Lee Byoung-Tak Zhang Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea AIIS Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large- scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by postprocessing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

关键词： Point cloud compression Instance segmentation Clustering methods Robot vision systems Grasping Cameras Clutter

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Application of Artificial Intelligence in Malignant Tumors: A science Mapping Analysis

Application of Artificial Intelligence in Malignant Tumors: ...

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IEEE Control and System Graduate Research Colloquium (ICSGRC)

作者： Samuel-Soma M. Ajibade Anwar P.P. Abdul Majeed Muhammed Basheer Jasser Ghassan Saleh ALDharhani Ismail Ahmed Al-Qasem Al-Hadi Ganesh Kumar Department of Computing and Information Systems School of Engineering & Technology Sunway University Petaling Jaja Selangor Malaysia Department of Computer Engineering Istanbul Ticaret Universitesi Istanbul Turkiye Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur Malaysia Faculty of Information Science and Technology UKM University Bangi Selangor Malaysia

ISBN: (数字)9798350386554

ISBN: (纸本)9798350386561

A science mapping analysis was performed on Scopus papers spanning from 2003 to 2022 to investigate the utilization of artificial intelligence techniques in malignant tumors research. The analysis unveiled a progressive rise in publications, amounting to a total of 206, with articles accounting for 55.34% and conference papers making up 44.66%. Zuherman Rustam and Universitas Indonesia made substantial contributions. India's exceptional performance might be due to the availability of financial support from prestigious organizations such as Universitas Indonesia, the National Natural science Foundation of China, and Brazil's Conselho Nacional de Desenvolvimento Científico e Tecnológico. An investigation of social networks, specifically looking at collaborations between authors, revealed robust international cooperation, which improved the availability of resources and infrastructure. The hotspot research revealed the prominent terms to be genetic algorithms, diseases, and feature extraction. The cluster analysis identified three main areas of focus: Precision Health Analytics, Genomic Cancer Profiling, and Integrated AI Diagnosis. In summary, AIMTR research is actively tackling cancer and oncological concerns through the use of computational approaches, making substantial contributions to both societal and scientific progress.

关键词： Productivity Social networking (online) Genomics Organizations Market research Paper making Artificial intelligence Bioinformatics Cancer Genetic algorithms

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Disentangled Motion Modeling for Video Frame Interpolation

arXiv

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arXiv 2024年

作者： Lew, Jaihyun Choi, Jooyoung Shin, Chaehun Jung, Dahuin Yoon, Sungroh Interdisciplinary Program in AI Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of School of Computer Science and Engineering Soongsil University Korea Republic of AIIS ASRI and INMC Seoul National University Korea Republic of

Video Frame Interpolation (VFI) aims to synthesize intermediate frames between existing frames to enhance visual smoothness and quality. Beyond the conventional methods based on the reconstruction loss, recent works have employed generative models for improved perceptual quality. However, they require complex training and large computational costs for pixel space modeling. In this paper, we introduce disentangled Motion Modeling (MoMo), a diffusion-based approach for VFI that enhances visual quality by focusing on intermediate motion modeling. We propose a disentangled two-stage training process. In the initial stage, frame synthesis and flow models are trained to generate accurate frames and flows optimal for synthesis. In the subsequent stage, we introduce a motion diffusion model, which incorporates our novel U-Net architecture specifically designed for optical flow, to generate bi-directional flows between frames. By learning the simpler low-frequency representation of motions, MoMo achieves superior perceptual quality with reduced computational demands compared to the generative modeling methods on the pixel space. MoMo surpasses state-of-the-art methods in perceptual metrics across various benchmarks, demonstrating its efficacy and efficiency in VFI. © 2024, CC BY-NC-SA.

关键词： Pixels

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jung Uk Kim Yong Man Ro Image and Video Systems Lab School of Electrical Engineering KAIST South Korea Department of Computer Science and Engineering Kyung Hee University South Korea

Although multispectral pedestrian detection studies have shown remarkable detection performances, they are still vulnerable to adversarial attacks. We see the similarity relations between object candidates were not maintained because of the adversarial attacks, resulting in performance degradation. In this paper, we introduce a new method that can preserve the similarity relation between candidates against adversarial attacks using multispectral knowledge. First, we propose Similarity Relation Generation (SRG) module to generate the optimal similarity relation between clean candidates by referring to the two modalities (color and thermal). Second, we propose Adversarial Similarity Relation Preserving (ASRP) module to guide the similarity relation between adversarial candidates to be similar to that of the clean candidates. By maintaining the relationship between candidates, our multispectral detector can distinguish between pedestrian/background classes even in adversarial attacks. Comprehensive experimental results show that our method conspicuously improves the adversarial robustness.

关键词： Degradation Detectors Color Signal processing Robustness Acoustics Speech processing

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Enhancing Ship Detection on Satellite Images with Modified DeepLabV3+

Enhancing Ship Detection on Satellite Images with Modified D...

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Artificial Intelligence and Emerging Technology (Global AI Summit), International Conference on

作者： Rohit Sharma Shubham Dahatonde Srimanta Kumar Acharjya Sourabh Singh Verma Vijaypal Singh Dhaka Ramesh Singh Rawat School Of Computing & Intelligent Systems Manipal University Jaipur Jaipur India Department of Computer Science and engineering C. V Raman Global University Bhubaneswar India Dept. of computer Science and Engg Graphic Era deemed to be University Deharadun Uttrakhand India

ISBN: (数字)9798350379716

ISBN: (纸本)9798350379723

This paper presents a advance approach for ship detection in satellite imagery utilizing a modified DeepLabV3+ architecture, specifically designed to overcome the challenges inherent in such data. The proposed model features an enhanced feature extraction process and a refined atrous spatial pyramid pooling (ASPP) module, which together improve the detection of ships across various sizes and shapes. Comprehensive experiments on publicly available satellite datasets reveal that the modified DeepLabV3+ significantly outperforms existing state-of-the-art methods, achieving an accuracy of 98%. These findings demonstrate the model's robust ability to identify and localize ships in complex maritime settings, offering promising potential for improved maritime situational awareness and operational efficiency.

关键词： Training Image segmentation Accuracy Land surface Feature extraction Data models Robustness Satellite images Marine vehicles Artificial intelligence

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AnDet: ML-Based Anomaly Detection of UEs in a Multi-cell B5G Mobile Network for Improved QoS

AnDet: ML-Based Anomaly Detection of UEs in a Multi-cell B5G...

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International Conference on Computing, Networking and Communications (ICNC)

作者： Amar Sinha Anmol Agrawal Sandip Roy Venkanna Uduthalapally Debanjan Das Rajarshi Mahapatra Sachin Shetty Department of Computer Science and Engineering IIIT Naya Raipur India Department of Electronics and Communication Engineering IIIT Naya Raipur India Center for Secure & Intelligent Critical Systems Old Dominion University Virginia USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

The rise of 5G technology has revolutionized wireless communication, ushering in a new era of unparalleled connectivity. Approaching Beyond 5G networks, Software Defined Networks have emerged as a promising paradigm to enhance flexibility and scalability in cellular networks. However, the challenge of 5G is to ensure the network's performance based on the different Quality of Service requirements for offering interactive services. Proactive detection of anomalies can significantly improve network performance. To address this issue, we present AnDet, a robust ML-based model specifically designed for anomaly detection in SDN-enabled B5G cellular networks. Next, we deploy the proposed AnDet for anomaly detection on SDN-enabled B5G cellular networks, enhancing overall network performance and reliability. Moreover, we incorporate Explainable AI approaches into the AnDet model to ensure transparency and interpretability. Finally, we capture different performance metrics for models and deployments to evaluate the proposed solution. The extensive simulation results show that AnDet effectively identifies anomalies in SDN-enabled cellular networks operating in B5G environments, achieving an impressive 97.2% accuracy in anomaly detection.

关键词： Cellular networks Wireless communication Measurement 5G mobile communication Simulation Scalability Quality of service

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Learner Action Patterns in the Problem-Solving Process Related to program Code Composition Based on Tracking System Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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On the Generalization of Machine-Learning-aided QoT Estimation in Optical Networks 32

On the Generalization of Machine-Learning-aided QoT Estimati...

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32nd Wireless and Optical Communications Conference, WOCC 2023

作者： Gao, Hanyu Zhang, Liang Zhang, Bin Chen, Xiaoliang Li, Zhaohui School of Electronics and Information Technology Sun Yat-sen University Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China George Mason University Department of Electrical and Computer Engineering FairfaxVA22030 United States

ISBN: (纸本)9798350337150

This paper presents a composable machine learning method for generalizing the quality-of-transmission (QoT) metric estimation in optical networks. The composable machine learning approach characterizes this metric for lightpaths of arbitrary lengths by compositions of launch, propagation and readout modules. Results verify the feasibility of the design and show its successful application in facilitating autonomous lightpath provisioning. © 2023 IEEE.

关键词： Fiber optic networks

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