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Real-Time Systems Symposium (RTSS)

作者： Ming Hu Zeke Xia Dengke Yan Zhihao Yue Jun Xia Yihao Huang Yang Liu Mingsong Chen School of Computer Science and Engineering Nanyang Technological University Singapore MoE Engineering Research Center of SW/HW Co-Design Tech. and App. East China Normal University China MOE International Joint Lab of Trustworthy Software East China Normal University China

As a promising distributed machine learning paradigm that enables collaborative training without compromising data privacy, Federated Learning (FL) has been increasingly used in large-scale A IoT (Artificial Intelligence of Things) system design. However, due to the lack of efficient management of straggling devices, existing FL methods greatly suffer from the problems of long response time (e.g., training and communication latency) and low inference accuracy. Things become even worse when taking various uncertain factors (e.g., network delays, performance variances caused by process variation) existing in AIoT scenarios into account. To address this issue, this paper proposes a novel asynchronous FL framework named GitFL, whose implementation is inspired by the famous version control system Git. Unlike traditional FL, the cloud server of GitFL maintains a master model (i.e., the global model) together with a set of branch models indicating the trained local models committed by selected devices, where the master model is updated based on both all the pushed branch models and their version information, and only the branch models after the pull operation are dispatched to devices. By using our proposed Reinforcement Learning (RL)-based device selection mechanism, a pulled branch model with an older version will be more likely to be dispatched to a faster and less frequently selected device for the next round of local training. In this way, GitFL enables both effective controls of model staleness and adaptive load balance of versioned models among straggling devices, thus avoiding performance deterioration while ensuring real-time performance. Comprehensive experimental results on well-known models and datasets show that, compared with state-of-the-art asynchronous and synchronous FL methods, GitFL can achieve up to 2.64X training acceleration and 7.88 % inference accuracy improvements in various uncertain scenarios.

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Analyzing multi-agent systems with probabilistic model checking approach 12

Analyzing multi-agent systems with probabilistic model check...

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International Conference on Software engineering (ICSE)

作者： Songzheng Song Jianye Hao Yang Liu Jun Sun Ho-Fung Leung Jin Song Dong Graduate School for Integrative Sciences and Engineering National University of Singapore Singapore Department of Computer Science and Engineering Chinese University of Hong Kong China Temasek Lab National University of Singapore Singapore ISTD Singapore University of Technology and Design Singapore School of Computing National University of Singapore Singapore

ISBN: (纸本)9781467310673

Multi-agent systems, which are composed of autonomous agents, have been successfully employed as a modeling paradigm in many scenarios. However, it is challenging to guarantee the correctness of their behaviors due to the complex nature of the autonomous agents, especially when they have stochastic characteristics. In this work, we propose to apply probabilistic model checking to analyze multi-agent systems. A modeling language called PMA is defined to specify such kind of systems, and LTL property and logic of knowledge combined with probabilistic requirements are supported to analyze system behaviors. Initial evaluation indicates the effectiveness of our current progress; meanwhile some challenges and possible solutions are discussed as our ongoing work.

关键词： Probabilistic logic Analytical models Educational institutions Cognition Semantics Games Multiagent systems

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A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Novel design and Enhanced Euler-Bernoulli Model with Cross-Section Optimization

A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Nove...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Emanuele Nicotra Chi Cong Nguyen James Davies Phuoc Thien Phan Trung Thien Hoang Bibhu Sharma Adrienne Ji Kefan Zhu Trung Dung Ngo Van Anh Ho Hung Manh La Nigel H. Lovell Thanh Nho Do Graduate School of Biomedical Engineering Faculty of Engineering UNSW Sydney Kensington Campus Australia More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Laboratory School of Materials Science Japan Advanced Institute of Science and Technology Kawaguchi Saitama Japan Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

关键词： Performance evaluation Navigation Metals Aneurysm Bending Mathematical models Rigidity

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Second-order Attention Guided Convolutional Activations for Visual Recognition

Second-order Attention Guided Convolutional Activations for ...

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International Conference on Pattern Recognition

作者： Shannan Chen Qian Wang Qiule Sun Bin Liu Jianxin Zhang Qiang Zhang Key Lab of Advanced Design and Intelligent Computing (Ministry of Education) Dalian University Dalian China School of Information and Communication Engineering Dalian University of Technology Dalian China International School of Information Science and Engineering (DUT-RUISE) Dalian University of Technology Dalian China School of Computer Science and Technology Dalian University of Technology Dalian China

Recently, modeling deep convolutional activations by the global second-order pooling has shown great advance on visual recognition tasks. However, most of the existing deep second-order statistical models mainly compute second-order statistics of activations of the last convolutional layer as image representations, and they seldom introduce second-order statistics into earlier layers to better fit network topology, thus limiting the representational ability to a certain extent. Motivated by the flexibility of attention blocks that are commonly plugged into intermediate layers of deep convolutional networks (ConvNets), this work makes an attempt to combine deep second-order statistics with attention mechanisms in ConvNets, and further proposes a novel Second-order Attention Guided Network (SoAG-Net) for visual recognition. More specifically, SoAG-Net involves several SoAG modules seemingly inserted into intermediate layers of the network, in which SoAG collects second-order statistics of convolutional activations by polynomial kernel approximation to predict channel-wise attention maps utilized for guiding the learning of convolutional activations through tensor scaling along channel dimension. SoAG improves the nonlinearity of ConvNets and enables ConvNets to fit more complicated distribution of convolutional activations. Experiment results on three commonly used datasets illuminate that SoAG-Net outperforms its counterparts and achieves competitive performance with state-of-the-art models under the same backbone.

关键词： Visualization Tensors Limiting Network topology Computational modeling Image representation Pattern recognition

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Rapid non-contact visual measurement method for key dimensions of revolving workpieces

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International Journal of Metrology and Quality engineering 2021年第1期12卷

作者： Gao, Peipei Liu, Feng Sun, Xizhi Wang, Fang Li, Jiajun Nankai University College of Computer Science Tianjin300071 China Tianjin University State Key Lab Precision Measuring Technology and Instruments Tianjin300072 China Brunel University London College of Engineering Design and Physical Sciences UxbridgeUB8 3PH United Kingdom

Aimed at the rapid non-contact measurement problem of a revolving workpiece's radial and axial dimensions, a fast and high-precision visual inspection method has been presented in this paper. For the workpiece with large axial size, the proposed method established the measurement transformation chain using the object-image and object-object transformations, thus realizing the rapid axial dimensional measurements. For the workpiece with large radial size, this method determined the measurement transformation model based on two-dimensional target and measurement correspondence relationship, and further achieved rapid radial dimensional measurements. The experimental results have shown that the method is effective and can be applied to in situ dimensional measurement of revolving workpieces on high quality production lines. © 2021 P. Gao et al., Published by EDP sciences.

关键词： computer vision

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A User-Friendly Framework for Generating Model-Preferred Prompts in Text-to-Image Synthesis

arXiv

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arXiv 2024年

作者： Hei, Nailei Guo, Qianyu Wang, Zihao Wang, Yan Wang, Haofen Zhang, Wenqiang Shanghai Engineering Research Center of AI & Robotics Academy for Engineering & Technology Fudan University China Engineering Research Center of Robotics Ministry of Education Academy for Engineering & Technology Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China Tongji University China College of Design and Innovation Tongji University China

Well-designed prompts have demonstrated the potential to guide text-to-image models in generating amazing images. Although existing prompt engineering methods can provide high-level guidance, it is challenging for novice users to achieve the desired results by manually entering prompts due to a discrepancy between novice-user-input prompts and the model-preferred prompts. To bridge the distribution gap between user input behavior and model training datasets, we first construct a novel Coarse-Fine Granularity Prompts dataset (CFP) and propose a novel User-Friendly Fine-Grained Text Generation framework (UF-FGTG) for automated prompt optimization. For CFP, we construct a novel dataset for text-to-image tasks that combines coarse and fine-grained prompts to facilitate the development of automated prompt generation methods. For UF-FGTG, we propose a novel framework that automatically translates user-input prompts into model-preferred prompts. Specifically, we propose a prompt refiner that continually rewrites prompts to empower users to select results that align with their unique needs. Meanwhile, we integrate image-related loss functions from the text-to-image model into the training process of text generation to generate model-preferred prompts. Additionally, we propose an adaptive feature extraction module to ensure diversity in the generated results. Experiments demonstrate that our approach is capable of generating more visually appealing and diverse images than previous state-of-the-art methods, achieving an average improvement of 5% across six quality and aesthetic metrics. Data and code are available at https://***/Naylenv/UF-FGTG. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Generalized Scaling Law for Exciton Binding Energy in Two-Dimensional Materials

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Physical Review Applied 2020年第6期13卷 064062-064062页

作者： S. Ahmad M. Zubair O. Jalil M. Q. Mehmood U. Younis X. Liu K. W. Ang L. K. Ang Electrical Engineering Department Information Technology University (ITU) of the Punjab Lahore 54000 Pakistan NanoTech Lab Electrical Engineering Department Information Technology University (ITU) of the Punjab Lahore 54000 Pakistan College of Materials Science and Engineering Shenzhen Key Laboratory of Microscale Optical Information Technology Chinese Engineering and Research Institute of Microelectronics Shenzhen University 3688 Nanhai Avenue Shenzhen 518060 People’s Republic of China Department of Electrical and Computer Engineering National University of Singapore 4 Engineering Drive 3 Singapore 117583 Singapore Science and Math Cluster Singapore University of Technology and Design (SUTD) 8 Somapah Road Singapore 487372 Singapore

Binding energy calculation in two-dimensional (2D) materials is crucial in determining their electronic and optical properties pertaining to enhanced Coulomb interactions between charge carriers due to quantum confinement and reduced dielectric screening. Based on full solutions of the Schrödinger equation in a screened hydrogen model with a modified Coulomb potential (1/rβ−2), we present a generalized and analytical scaling law for the exciton binding energy, Eβ=E0(aβb+c)(μ/ϵ2), where β is a fractional-dimension parameter that accounts for the reduced dielectric screening. The model is able to provide accurate binding energies, benchmarked using the reported Bethe-Salpeter equation and experimental data, for 58 monolayer 2D and eight bulk materials, respectively, through β. For a given material, β is varied from β=3 for bulk three-dimensional materials to a value lying in the range 2.55–2.7 for 2D monolayer materials. With βmean=2.625, our model improves the average relative mean square error by a factor of 3 in comparison to existing models. The results can be used for Coulomb engineering of exciton binding energies in the optimal design of 2D materials.

关键词： Excitons Optoelectronics 2-dimensional systems Quantum wells Semiconductors Scaling methods

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Consistent Discretization of a Class of Predefined-Time Stable Systems

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IFAC-PapersOnLine 2020年第2期53卷 628-633页

作者： Esteban Jiménez-Rodríguez Rodrigo Aldana-López Juan D. Sánchez-Torres David Gómez-Gutiérrez Alexander G. Loukianov Relativity6 Inc. Computer Science & Artificial Intelligence Lab Jalisco 45040 México Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza 50009 España Research Laboratory on Optimal Design Devices and Advanced Materials -OPTIMA- Department of Mathematics and Physics ITESO Jalisco 45604 México Multi-agent autonomous systems lab Intel Labs Intel Tecnología de México Jalisco 45019 Mexico Tecnologico de Monterrey Escuela de Ingeniería y Ciencias Jalisco 45138 Mexico Department of Electrical Engineering Cinvestav-Guadalajara Jalisco 45019 México

As the main contribution, this document provides a consistent discretization of a class of fixed-time stable systems, namely predefined-time stable systems. In the unperturbed case, the proposed approach allows obtaining not only a consistent but exact discretization of the considered class of predefined-time stable systems, whereas in the perturbed case, the consistent discretization preserves the predefined-time stability property. All the results are validated through simulations and compared with the conventional explicit Euler scheme, highlighting the advantages of this proposal.

关键词： Predefined-time stability Discrete-time systems Digital implementation Stability of nonlinear systems Fixed-time stability

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Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

Queer in HCI: Strengthening the Community of LGBTQIA+ Resear...

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2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Devito, Michael Ann Lustig, Caitlin Simpson, Ellen Allison, Kimberley Chuanromanee, Tya Spiel, Katta Ko, Amy Rode, Jennifer Dym, Brianna Muller, Michael Klaus Scheuerman, Morgan Marie Walker, Ashley Brubaker, Jed Ahmed, Alex Department of Information Science University of Colorado United States Human Centered Design and Engineering University of Washington United States School of Information Studies Syracuse University United States Department of Psychology Macquarie University Australia University of Notre Dame United States Human-Computer Interaction Group Tu Wien Austria The Information School University of Washington United States Knowledge Lab University College London United Kingdom Department of Information Science University of Colorado Boulder United States Ai Interactions Ibm Research United States Information Science University of Colorado Boulder United States Pitch Lab Northwestern University United States University of Colorado Boulder United States Carnegie Mellon University United States

ISBN: (纸本)9781450380959

As Queer Human-computer Interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics. © 2021 Owner/Author.

关键词： Computation theory

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Fuzzy logic controller design for intelligent air-conditioning system

Fuzzy logic controller design for intelligent air-conditioni...

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IEEE International Conference on Control science and Systems engineering (CCSSE)

作者： Nizam Uddin Ahamed Zahari Bin Taha Ismail Bin Mohd Khairuddin M.F. Rabbi S.A.M. Matiur Rahaman Kenneth Sundaraj Mechatronic and System Design Lab Faculty of Manufacturing Engineering Universiti Malaysia Pekan Pahang Malaysia Faculty of Manufacturing Engineering Universiti Malaysia Pekan Pahang Malaysia College of Computer Science and Information System Najran University Saudi Arabia AI-Rehab Research Group Universiti Teknikal Malaysia Melaka Melaka Malaysia

ISBN: (纸本)9781467398732

In efficient air cooling systems may cause of wasting energy in a great amount specially in the urban area. Being the most popular cooling system, air-conditioners have been used in domestic usage as well as in industrial applications. However, the unintelligent nature of such cooling system gives rise to excess energy consumption which causes a huge problem in the current energy hungry world. In this paper, we present design of a fuzzy logic controller for the intelligent air-conditioning system. The performance of the controller is also simulated. The proposed controller has the adaptive nature to control fan and compressor speed which leads to reducing power consumption. Also, the system controls the operation mode to retain the healthy oxygen level and humid condition of the indoor environment.

关键词： Humidity Fuzzy logic Fans Energy consumption Power demand

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