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diverse Gaussian Sampling for Human Motion Prediction 16th

Diverse Gaussian Sampling for Human Motion Prediction

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16th international conference on Social Robotics, ICSR + InnoBiz 2024

作者： Luo, Jiefu Wang, Jiansheng Liu, Zhenfei Cheng, Jun Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong Department of Pediatric Orthopedics Shenzhen Children’s Hospital Guangdong Shenzhen518000 China

ISBN: (纸本)9789819611508

In many industrial applications, it is necessary to obtain accurate human motion prediction. the prediction of human movement based on a 3d skeleton, which forecasts future postures using the human skeletal structure, significantly aids in enabling machines to understand human behavior and react more intelligently. Additionally, by predicting human actions, potential dangers can be perceived, especially in safety-related issues, necessitating a broader range of predictions. In this paper, considering the greater diversity generated by Variational Autoencoders (VAEs), we employ VAEs for prediction. In contrast to other studies, our primary focus is on obtaining more diverse samples. Extensive experiments demonstrate that our proposed model performs well on the Human 3.6M [35] and HumanEva-I [36] datasets. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Prediction models

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Role-Playing in Education: An Experiential Learning Framework for Collaborative Co-design 16

Role-Playing in Education: An Experiential Learning Framewor...

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16th international conference on Society and Information Technologies, ICSIT 2025

作者： Leon, Cristo Lipuma, James Pathikonda, Sirimuvva Llaca Reyes, Rafael Arturo Jordan Hu College of Science and Liberal Arts New Jersey Institute of Technology NewarkNJ07102-1982 United States Department of Humanities and Social Sciences New Jersey Institute of Technology NewarkNJ07102-1982 United States Renewable Energy Program New Jersey Institute of Technology NewarkNJ07102-1982 United States Facultad de Psicología Universidad Veracruzana Veracruz de Ignacio de la Llave Xalapa91000 Mexico

ISBN: (纸本)9781950492831

this paper presented the Components of Role-playing in Experiential Learning (CREL) Framework. this framework aids educators and researchers in planning, implementing, and evaluating Experiential Learning scenarios to promote learner attainment of the needed competencies for 21st-century skills. Unlike traditional models, CREL leverages role-playing as a tool for simulation and simulacra, enabling participants to engage in "as if" scenarios through suspension of disbelief. this approach allows learners to navigate complex, dynamic, immersive challenges, bridging theoretical knowledge with practical application. the article began with an introduction to role-playing as a method for Experiential Learning. this helped situate role-playing within the broader context of meta-research and meta-education. Next, the article explored the limitations of current educational practices and the critical need for frameworks that bridge these gaps through innovative, experiential approaches. the paper examined the need to address the gap presented by the lack of a coherent understanding of role-playing and, more broadly, Experiential Learning. the article then presented the CREL framework in detail. Finally, it closed with a discussion of the meta-level outcomes, implications, and overall application of the CREL for education and research. © 2025 international conference on Society and Information Technologies. All Rights Reserved.

关键词： Contrastive Learning

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Research on Intelligent detection technology of Wireless Communication Signals Based on Yolov10 Model 16

Research on Intelligent Detection Technology of Wireless Com...

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16th international conference on Signal Processing Systems, ICSPS 2024

作者： Zhang, Wantong Li, Jia Chu, Zhaohui Luo, Bing Li, Gui Zhang, Yulu Ran, Yuzhou Li, Jian School of Information and Communication Engineering Yibin Institute University of Electronic Science and Technology of China Chengdu611731 China The 10th Research Institute of China Electronics Technology Group Corporation Chengdu610036 China Department of IoT Technology Research China Mobile Research Institute Beijing100053 China National Defense Science and Technology Innovation Institute Academy of Military Sciences Beijing100071 China

ISBN: (纸本)9781510689251

To address the challenge of efficient communication signal detection in complex electromagnetic environments, this paper proposes an intelligent signal detection method based on the Yolov10-ghost model, incorporating deep learning techniques. Building upon the Yolov10 architecture, the method introduces the C3Ghost module and replaces conventional Conv layers with GhostConv layers to enhance feature extraction and fusion while reducing model complexity. Additionally, the loss function is optimized to WIoU-v2, addressing class imbalance and improving suitability for object detection tasks. Experimental results show that the proposed method can efficiently detect continuous and burst signals in communication data, achieving an average detection accuracy (mAP) of 98.4%. the model parameters are reduced to 1.9M, with an inference time of 80.8ms per frame, demonstrating a strong balance between detection accuracy and real-time performance. Furthermore, the model's successful deployment on embedded platforms highlights its significant engineering application value. © 2025 SPIE.

关键词： Photons

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Unguided Machine Learning-Based Computation Offloading for Near-Memory Processing 38

Unguided Machine Learning-Based Computation Offloading for N...

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38th international conference on VLSI design, VLSId 2025

作者： Maity, Satanu Ghose, Manojit Kumar, Avinash Chakraborty, Anol Chakraborty, Ankit Indian Institute of Information Technology Department of Computer Science and Engineering Assam Guwahati India Jorhat Engineering College Department of Computer Science and Engineering Assam India

ISBN: (纸本)9798331522445

Recent data-intensive applications encounter memory wall bottlenecks in the traditional processor-centric computing architecture due to the need for frequent and extensive off-chip data movement. the emerging 3d-enabled near-memory processing (NMP) is a potential solution where computation can be offloaded and performed near memory, eliminating the need for expansive off-chip data transfer. Recently, some crucial works in this context have been proposed that use a supervised machine learning approach that requires a well-labeled dataset, which necessitates creating a time-consuming simulation process. Furthermore, their major focus was on profiling and offloading the entire application to the NMP side, causing a sub-optimal performance because of the low processing power on the NMP cores. this paper presents an unguided machine-learning-based approach (UCO) for computation offloading in NMP architecture that offloads only the memory-intensive regions of an application. the approach extracts several application performance characteristics and creates two clusters using them - one for the CPU side and another for the NMP side. the proposed approach's effectiveness is demonstrated through a series of comprehensive experiments with standard simulators and dataintensive benchmark applications. the results indicate that UCO improves performance (1.31x and 1.10x), saves energy (16% and 12%), and reduces off-chip data transfer (78% and 73%) compared to conventional and state-of-the-art approaches. © 2025 IEEE.

关键词： Unsupervised learning

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Performance Analysis of mdx II: A Next-Generation Cloud Platform for Cross-disciplinary data Science Research 15

Performance Analysis of mdx II: A Next-Generation Cloud Plat...

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15th international conference on Cloud Computing and Services Science, CLOSER 2025

作者： Takahashi, Keichi Hayami, Tomonori Mukaizono, Yu Teramae, Yuki date, Susumu D3 Center University of Osaka Osaka Japan

ISBN: (纸本)9789897587474

mdx II is an Infrastructure-as-a-Service (IaaS) cloud platform designed to accelerate data science research and foster cross-disciplinary collaborations among universities and research institutions in Japan. Unlike traditional high-performance computing systems, mdx II leverages OpenStack to provide customizable and isolated computing environments consisting of virtual machines, virtual networks, and advanced storage. this paper presents a comprehensive performance evaluation of mdx II, including a comparison to Amazon web Services (AWS). We evaluated the performance of a 16-vCPU VM from multiple aspects including floating-point computing performance, memory throughput, network throughput, file system and object storage performance, and real-world application performance. Compared to an AWS 16-vCPU instance, the results indicated that mdx II outperforms AWS in many aspects and demonstrated that mdx II holds significant promise for high-performance data analytics (HPdA) workloads. We also evaluated the virtualization overhead using a 224-vCPU VM occupying an entire host. the results suggested that the virtualization overhead is minimal for compute-intensive benchmarks, while memory-intensive benchmarks experienced larger overheads. these findings are expected to help users of mdx II to obtain high performance for their data science workloads and offer insights to the designers of future data-centric cloud platforms. Copyright © 2025 by SCITEPRESS - Science and technology Publications, Lda.

关键词： data Science

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Laser Engraving for 3d Surfaces: A Robotized Application Case Study 16th

Laser Engraving for 3D Surfaces: A Robotized Application Ca...

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16th APCA international conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Alvarez, Mariano Brancalião, Laiany Carneiro, Jorge Costa, Paulo Coelho, João Paulo Gonçalves, José Instituto Politécnico de Bragança Campus de Santa Apolónia Bragança5300-253 Portugal Instituto Politécnico de Bragança Campus de Santa Apolónia Bragança5300-253 Portugal Faculty of Engineering of the University of Porto Porto4200-465 Portugal University of Léon Campus de Vegazana s/n León24071 Spain GRESTEL – Produtos Cerâmicos S.A. Vagos Portugal INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal

ISBN: (纸本)9783031817236

One of the industry’s most common applications of lasers is engraving, which is generally performed on flat surfaces. However, there are many situations where the object to be engraved has an unevenly curved geometry. In those cases, the light power density will be different along the surface for a fixed head, leading to a poor engraving result. this work deals with this problem by designing a robotic application capable of detecting variations on the object surface and automatically creating a trajectory to engrave on it correctly. this was made possible through a robotic manipulator, a time-of-flight distance sensor, and a data processing algorithm over the measured data. Obtained results were acquired using a custom-made test rig and validated by delivering consistent engraving results on irregular surface shapes. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robotic assembly

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Tight Bounds on List-decodable and List-Recoverable Zero-Rate Codes 16

Tight Bounds on List-Decodable and List-Recoverable Zero-Rat...

引用

16th Innovations in theoretical Computer Science conference, ITCS 2025

作者： Resch, Nicolas Yuan, Chen Zhang, Yihan Informatics’ Institute University of Amsterdam Netherlands School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Institute of Science and Technology Austria Klosterneuburg Austria

ISBN: (纸本)9783959773614

In this work, we consider the list-decodability and list-recoverability of codes in the zero-rate regime. Briefly, a code C Ď rqsn is (p, , L)-list-recoverable if for all tuples of input lists (Y1, . . ., Yn) with each Yi Ď rqs and |Yi| ", the number of codewords c P C such that ci R Yi for at most pn choices of i P rns is less than L;list-decoding is the special case of "1. In recent work by Resch, Yuan and Zhang (ICALP 2023) the zero-rate threshold for list-recovery was determined for all parameters: that is, the work explicitly computes p*:"p*(q, , L) with the property that for all Ε ą 0 (a) there exist positive-rate (p* - Ε, , L)-list-recoverable codes, and (b) any (p* + Ε, , L)-list-recoverable code has rate 0. In fact, in the latter case the code has constant size, independent on n. However, the constant size in their work is quite large in 1/Ε, at least |C| ě (1Ε)O(qL). Our contribution in this work is to show that for all choices of q, and L with q ě 3, any (p* + Ε, , L)-list-recoverable code must have size Oq,,L(1/Ε), and furthermore this upper bound is complemented by a matching lower bound Ωq,,L(1/Ε). this greatly generalizes work by Alon, Bukh and Polyanskiy (IEEE Trans. Inf. theory 2018) which focused only on the case of binary alphabet (and thus necessarily only list-decoding). We remark that we can in fact recover the same result for q "2 and even L, as obtained by Alon, Bukh and Polyanskiy: we thus strictly generalize their work. Our main technical contribution is to (a) properly define a linear programming relaxation of the list-recovery condition over large alphabets;and (b) to demonstrate that a certain function defined on a q-ary probability simplex is maximized by the uniform distribution. this represents the core challenge in generalizing to larger q (as a binary simplex can be naturally identified with a one-dimensional interval). We can subsequently re-utilize certain Schur convexity and convexity properties established for a related function

关键词： decoding

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An Efficient Hybrid deep Neural Network for Intelligence Quotient detection from Brain Magnetic Resonance Imaging

An Efficient Hybrid Deep Neural Network for Intelligence Quo...

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international conference on web Research (ICWR)

作者： Shayan Rostami Hossein Sadr Ali Ashraf Mojdeh Nazari Department of Computer Engineering Ayandegan Institute of Education Tonekabon Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Clinical Research Development Unit of Poursina Hospital Guilan University of Medical Sciences Rasht Iran Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Accurate IQ detection through non-invasive methods, such as brain MRI analysis, can play a critical role in the early diagnosis and prevention of neurological disorders. this study proposes a hybrid approach that leverages the feature extraction power of Convolutional Neural Networks (CNN) and the robust classification capabilities of the XGBoost algorithm to enhance the accuracy of IQ prediction models. CNNs effectively process MRI images by extracting complex visual features, while XGBoost optimizes classification performance through ensemble learning techniques. the integration of these methods results in a more precise and reliable IQ detection system. Furthermore, the model's architecture and data processing techniques offer potential for adaptation in web-based diagnostic platforms and large-scale, distributed medical research applications. this study presents an innovative approach by combining convolutional neural networks (VGG16 and ResNet-50) with the XGBoost algorithm to classify intelligence levels from brain MRI images. Unlike previous studies that used CNNs solely for feature extraction, this research employs XGBoost for final classification, achieving an 83 % accuracy in the VGG16+XGBoost model, showing a notable improvement over the 73 % reported in the most similar prior study. the use of 3d NIfTI data and sagittal slices for enhanced data analysis is also a key innovation of this research.

关键词： Adaptation models Visualization Technological innovation Accuracy three-dimensional displays Magnetic resonance imaging Feature extraction Brain modeling Classification algorithms Convolutional neural networks

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Metamaterials for low frequency tonal (tyre) noise mitigation in vehicles

Metamaterials for low frequency tonal (tyre) noise mitigatio...

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7. internationale ATZ-Fachtagung Fahrzeugakustik - 7th international ATZ conference on Vehicle Acoustics, 2023

作者： Pires, Felipe Alves Sangiuliano, Luca Reff, Björn Palandri, Jacopo Wolf-Monheim, Friedrich Pluymers, Bert deckers, Elke desmet, Wim Claeys, Claus Flanders Make@KU Leuven Leuven Belgium Division LMSD Department of Mechanical Engineering KU Leuven Heverlee Belgium Aachen Germany Department of Mechanical Engineering Technology KU Leuven Diepenbeek Belgium

ISBN: (纸本)9783658465544

Stringent environmental and noise standards in the automotive sector, as well as customer expectations, are pushing the limits of classic noise and vibration mitigation solutions. In the search of novel vibro-acoustic solutions, locally resonant metamaterials come to the fore as they promise to combine a light weight with excellent vibro-acoustic properties, at least in dedicated target frequency regions. In this publication, a metamaterial solution is applied to a vehicle to reduce the interior structure-borne noise due to the acoustic tyre resonances around 220 Hz. this metal 3d printed novel solution is applied on the rear wheel arches of a vehicle and serves as an alternative for dynamic damper solutions that are typically installed in line with the suspension on the vehicle body. Numerical and experimental analyses show how 3d printed resonant metamaterials can be a performant lightweight alternative to the common noise and vibration solutions used in the automotive sector. © der/die Autor(en), exklusiv lizenziert an Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2025.

关键词： Acoustic impedance

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A taskonomy of 3d web use 11

A taskonomy of 3D web use

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proceedings of the 16th international conference on 3d web technology

作者： Jankowski, Jacek Digital Enterprise Research Institute NUI Galway Ireland

ISBN: (纸本)9781450307741

this paper aims to shed some light on the fundamental tasks users engage in while browsing the 3d web. Firstly, we will provide an overview and discussion of several field studies that provide insights on how the current version of the web is used focusing on the tasks that users carry out. then we will present a comprehensive review of the state of the art of mouse-based 3d interaction techniques for desktop 3d Virtual Environments (dVEs). Finally, we will introduce a simple taxonomy - a "3d web Taskonomy" that was designed to assists designers and developers of interactive 3d web applications to better evaluate their options when choosing and implementing an interaction technique. © 2011 ACM.

关键词： Taxonomies

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