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Hierarchical Residual Embedding Network for Real-Time De-focus Blur Detection 2

Hierarchical Residual Embedding Network for Real-Time De-foc...

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2020 2nd International Conference on computer, Communications and Mechatronics Engineering, CCME 2020

作者： Zhang, Chunlei Nie, Jing Zheng, Xiao Electronic Engineering College Naval University of Engineering Wuhan430033 China Teaching and Research Support Center Naval University of Engineering Wuhan430033 China School of Computer Science National University of Defense Technology Changsha410073 China

Defocus blur detection, as an important pre-processing step of image processing, has attracted more and more attention. Albeit great success has been made, there are still several challenges for accurate defocus blur detection, such as the interference of background clutter, sensitivity to scales, missing boundary details and large computational burden. For handling these issues, we present a deep neural network which hierarchically embeds residual learning blocks for defocus blur detection. Based on the feature pyramid structure, we extract deep features with varying scales via utilizing a backbone fully convolutional network and generate a coarse score map by using the last layer of feature maps. Then we design a hierarchical residual embedding module to fuse different levels of features in a layer-wise manner. By embedding different layer-wise features in the top-down pathway, coarse-level semantic information from the deep layers can be seamlessly propagated to shallow layers, while fine details in the shallow layers can be used to refine the boundary between out-of-focus and in-focus regions. For each layer, a side output is generated by using a residual learning block. For capturing multi-scale information, the multiple side outputs of different layers are fed into a designed fusion block for yielding the final blur map result. Experimental results on two commonly used datasets show that our proposed network can more accurately locate the defocus blur regions with sharpened details being well preserved when compared to other previous state-of-the-arts. In addition, our approach is fast as well and can run at a speed of more than 25 FPS when processing an image with size 427 x 640. © Published under licence by IOP Publishing Ltd.

关键词： Deep neural networks

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Hardware Data Prefetch for XiangShan Processor

Hardware Data Prefetch for XiangShan Processor

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International Conference on Integrated Circuits and Microsystems (ICICM)

作者： Yuhan Zhu Jiachun Zheng Shuyan Ding Lunbo Li College of Computer Science & Engineering Nanjing University of Science and Technology Nanjing China Technical Research & Development Department Beijing Institute of Open Source Chip Beijing China Electric and Electronic Teaching & Experiment Center School of Electronic and Optical Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9781665460583

XiangShan, a rising star of RISC-V-based processors proposed by Chinese Academy of science, is famous for its high performance and agile methodology. At present, the second-generation Yanqi Lake architecture of XiangShan has already reached the frequency of 2GHz at the 14nm process node, which ranks the first in the field of open-source RISC-V-based processors. One of the key approaches that contribute to its high performance is prefetch. A timely and accurate prefetcher can effectively eliminate cache misses and thus hide memory access latency. Aiming at the specific environment of XiangShan, out-of-order and parallel, a stride-based prefetcher for the L1DCache is achieved on the Yanqi Lake structure of XiangShan. Serious benchmarks from SPEC2006 were run on XiangShan, the result of which demonstrates that our prefetcher improves the performance of XiangShan by an average of 2.5% and up to 13.3%.

关键词： Out of order Micromechanical devices Integrated circuits Program processors Codes Prefetching Stars

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Uncomfortable Conversations Can Lead to Better Faculty Preparation and Engineering Pedagogy Centered on Humanity 53

Uncomfortable Conversations Can Lead to Better Faculty Prepa...

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： White, Lance Pedersen, Rachelle Stanley, Christine Hammond, Tracy Watson, Karan Multidisciplinary Engineering Texas A&m University College StationTX United States Teaching Learning & Culture Texas A&m University College StationTX United States School of Education Texas A&m University College StationTX United States Computer Science & Engineering Texas A&m University College StationTX United States Electrical Engineering Texas A&m University College StationTX United States

ISBN: (纸本)9798350336429

A group of engineering educators at Texas A&M University participating in a book club professional development experience. This book club has listened to and discussed several books and movies including the books 'Uncomfortable Conversations With a Black Man' by Emmanuel Acho, 'Pedagogy of the Oppressed' by Paulo Friere, 'Inclusive Conversations' by Mary-Frances Winters, and the movies 'Picture a Scientist' and 'Coded Bias.' Engineering in the academe is at a critical juncture considering the frequency and acceptance of difficult conversations related to society and social justice having a place in the discipline. Historically, Engineering has steered away from uncomfortable difficult conversations, but it is time to shed our old skin of a habitual depoliticization and emerge free from this restriction to develop our next generation of engineers. Not only are these types of discussions and conversations essential to ensure that the disci-pline continues to become more diverse, inclusive, and equitable, but these discussions better prepare engineers to consider societal factors when they ultimately are designing products, services, and tools for society as a whole. Several thousand engineers are graduating from undergraduate programs every year with little to no discussion of social justice in their programs This study will identify the impact participation in this book club has had in professionally developing faculty participants to engage in uncomfortable discussions and how they might have attempted to integrate societal issues into the courses that they teach at Texas A&M University. Interviews were conducted with members of this book club via Zoom and transcribed automatically. Those transcripts were then analyzed using thematic analysis to identify the major recurrent themes amongst participants. This work builds upon a previous publication by the authors that utilized a focus group design paired with a quantitative Likert scale survey to gauge perceptions of

关键词： Book Club Diversity Equity Faculty Development Inclusion Social Justice

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Equivariant diffusion for crystal structure prediction 24

Equivariant diffusion for crystal structure prediction

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Proceedings of the 41st International Conference on Machine Learning

作者： Peijia Lin Pin Chen Rui Jiao Qing Mo Jianhuan Cen Wenbing Huang Yang Liu Dan Huang Yutong Lu School of Computer Science and Engineering Sun Yat-sen University Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China and National Supercomputer Center in Guangzhou China Dept. of Comp. Sci. Tech. Institute for AI Tsinghua University Beijing China and Institute for AIR Tsinghua University Beijing China National Supercomputer Center in Guangzhou China Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CSP as a conditional generation task. However, ensuring permutation, rotation, and periodic translation equivariance during diffusion process remains incompletely addressed. In this work, we propose EquiCSP, a novel equivariant diffusion-based generative model. We not only address the overlooked issue of lattice permutation equivariance in existing models, but also develop a unique noising algorithm that rigorously maintains periodic translation equivariance throughout both training and inference processes. Our experiments indicate that EquiCSP significantly surpasses existing models in terms of generating accurate structures and demonstrates faster convergence during the training process. Code is available at https://***/EmperorJia/EquiCSP.

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A Method for Automatic English Oral Fluency Scoring

A Method for Automatic English Oral Fluency Scoring

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International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Su-Mei Li Zi-Xi Zhu Xiao-Ning Li Xin-Guang Li Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou Guangdong China Experimental teaching center Guangdong University of Foreign Studies Guangzhou Guangdong China School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou Guangdong China School of Computer Science and Technology South China Business College (SCBC) of Guangdong University of Foreign Studies Guangzhou Guangdong China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

In order to address the issue of incomplete feature extraction in traditional oral fluency scoring methods, a method based on feature fusion is proposed in the paper, which integrates traditional acoustic features and deep features of audio to complete the scoring of oral fluency. For the audio to be tested, IS10_paraling features are extracted as traditional acoustic features, and a fine-tuned wav2vec2.0 pre-trained model is used to extract deep features of the audio. These two types of features are concatenated to form fusion features, which are then input into a multilayer perceptron scoring model to obtain the oral fluency score. Experimental results on the speechocean762 dataset show that compared to scoring methods based on single types of audio features, the fusion feature-based method improves scoring performance. The multilayer perceptron scoring model based on fusion features achieves a Pearson correlation coefficient of 0.744 with human scoring, demonstrating the effectiveness of the proposed method.

关键词： Correlation coefficient Training Human-machine systems Data visualization Multilayer perceptrons Feature extraction Acoustics User experience Informatics Spectrogram

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A Novel Fuzzy C-means Clustering Algorithm Based on Local Density 11th

A Novel Fuzzy C-means Clustering Algorithm Based on Local De...

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11th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2020

作者： Liu, Jian-jun Fan, Jian-cong College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Provincial Key Laboratory for Information Technology of Wisdom Mining of Shandong Province Shandong University of Science and Technology Qingdao China Provincial Experimental Teaching Demonstration Center of Computer Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9783030469306

Fuzzy C-means (FCM) clustering algorithm is a fuzzy clustering algorithm based on objective function. FCM is the most perfect and widely used algorithm in the theory of fuzzy clustering. However, in the process of clustering, FCM algorithm needs to randomly select the initial cluster center. It is easy to generate problems such as multiple clustering iterations, low convergence speed and unstable clustering. In order to solve the above problems, a novel fuzzy C-means clustering algorithm based on local density is proposed in this paper. Firstly, we calculate the local density of all sample points. Then we select the sample points with the local maximum density as the initial cluster center at each iteration. Finally, the selected initial cluster center are combined with the traditional FCM clustering algorithm to achieve clustering. This method improved the selection of the initial cluster center. The comparative experiment shows that the improved FCM algorithm reduces the number of iterations and improves the convergence speed. © 2020, IFIP International Federation for Information Processing.

关键词： Fuzzy clustering

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Planning Under Uncertainties with Closed-Loop Sensitivity: Recent Results and Perspectives 16th

Planning Under Uncertainties with Closed-Loop Sensitivity: ...

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16th European Robotics Forum, ERF 2025

作者： Belvedere, Tommaso Afifi, Amr Wasiela, Simon Pupa, Andrea CNRS Université de Rennes Inria IRISA Campus de Beaulieu Rennes France Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands LAAS-CNRS Université de Toulouse CNRS UPS Toulouse France Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Modena Italy

ISBN: (纸本)9783031894701

This paper presents a comprehensive summary of recent advancements in motion planning under parametric uncertainties, focusing on the application of closed-loop state sensitivity. This concept provides a framework for quantifying how deviations in model parameters affect the behavior of a system in closed-loop, facilitating the generation of robust trajectories. Various methods have been proposed to improve the resilience of robotic systems to model inaccuracies. However, these approaches often face challenges such as computational complexity and limitations in real-time applications. This paper synthesizes key results from several recent works, highlighting the development of techniques that optimize trajectory robustness while reducing computational overhead. Additionally, we outline the practical applications of these methods, discussing their validation through simulations and experiments on robotic systems subject to non-negligible uncertainties in their models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Robots

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Model for Analysis of High-Frequency Modulated Random Signals 13

Model for Analysis of High-Frequency Modulated Random Signal...

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13th International Conference on Advanced computer Information Technologies, ACIT 2023

作者： Javorskyj, Ihor Yuzefovych, Roman Lychak, Oleh Semenov, Pavlo Sliepko, Roman Romanyshyn, Yulia Karpenko Physico-mechanical Institute of NAS of Ukraine Department of Methods and Facilities for Acquisition and Processing Diagnostic Signals Lviv Ukraine Bydgoszcz University of Science and Technology Institute of Telecommunication and Computer Sciences Bydgoszcz Poland Lviv Polytechnic National University Department of Applied Mathematics Lviv Ukraine Odesa National Maritime University Department of Hoisting and Transport Machines and Engineering of Port Technological Equipment Odesa Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Records Management and Information Activity Ivano-Frankivsk Ukraine

The use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency random processes is discussed. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform. © 2023 IEEE.

关键词： Random processes

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Validity and Fairness of an Automated Assessment of Creativity in Computational Music Remixing

Validity and Fairness of an Automated Assessment of Creativi...

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2023 Workshop on Automated Assessment and Guidance of Project Work, AAGPW 2023

作者： Rahimi, Seyedahmad Smith, Jason Brent Truesdell, Erin J.K. Vinay, Ashvala Boyer, Kristy Elizabeth Magerko, Brian Freeman, Jason McKlin, Tom School of Teaching & Learning College of Education University of Florida GainesvilleFL32611 United States Georgia Tech Center for Music Technology 840 McMillan Street NW AtlantaGA30308 United States Expressive Machinery Lab. Georgia Institute of Technology AtlantaGA30308 United States Computer & Information Science & Engineering University of Florida GainesvilleFL32611 United States The Findings Group DecaturGA30030 United States

Creativity is one of the most crucial skills for success in life in the 21st century. However, assessing creativity in an automated, objective way is challenging. In this study, we designed and validated an automated assessment (an unobtrusive, formative assessment) of creativity in EarSketch, a computational music remixing platform where students write Python or JavaScript code to create pieces of music. Specifically, using an existing dataset of EarSketch projects (n = 53), we investigated the validity and fairness of an automated assessment of creativity. Our findings show that the automated assessment of creativity has reasonable convergent validity (r = .50) and discriminant validity;and this assessment is fair (i.e., no significant differences in terms of gender, grade, or race were found). The results of this research have the potential to inform the design of innovative educational programs and interventions that foster creativity and innovation in STEM education. As we continue to explore new ways of assessing creativity, we can pave the way for a more creative and innovative society, where individuals are equipped with the skills they need to tackle future challenges. © 2023 Copyright for this paper by its authors.

关键词： Automation

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Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data ...

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2022 Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2022

作者： Ponzi, Valerio Russo, Samuele Wajda, Agata Brociek, Rafal Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome Via dei Marsi 78 Roma00185 Italy Institute of Energy and Fuel Processing Technology Zabrze41-803 Poland Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland Institute for Systems Analysis and Computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

关键词： Principal component analysis

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