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IEEE International Forum on Research and Technologies for Society and Industry Leveraging a better tomorrow (RTSI)

作者： Leo Ramos Edmundo Casas Eduardo Bendek Cristian Romero Francklin Rivas-Echeverría Dunetchka Cerpa Pablo Hernández Gonzalo Orellana José Luis Ibarra Carlos Rosas Albrecht Natalia Cuevas Juan Carlos Gallardo Kauel Inc. Menlo Park CA USA Computer Vision Center Universitat Autò noma de Barcelona Bellaterra Spain Faculty of Engineering University of Deusto Bilbao Spain Jet Propulsion Laboratory NASA Pasadena CA USA

ISBN: (数字)9798350362138

ISBN: (纸本)9798350362145

In this work, a platform for the detection, monitoring, and alerting of wildfires is developed, with the aim of demonstrating that the integration of artificial intelligence and other current technologies allows for the development of robust solutions to address various challenges. For this purpose, different versions of YOLOv8 were trained for the detection of fire and smoke. The result of this yielded the best model, the large version of YOLOv8, with metrics exceeding 80% precision and mean Average Precision. This model is integrated into a platform developed to keep a record of all generated alerts and to take actions on them. The platform features functionalities to record the location, date, time, and risk level of each alert. Additionally, it allows for viewing these alerts on a map and includes visualization elements to track the status of all alerts.

关键词： Measurement Industries Visualization Technological innovation computer vision Wildfires Artificial intelligence Monitoring

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Efficient displacement convex optimization with particle gradient descent

arXiv

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arXiv 2023年

作者： Daneshmand, Hadi Lee, Jason D. Jin, Chi Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science Princeton Universit United States

Particle gradient descent, which uses particles to represent a probability measure and performs gradient descent on particles in parallel, is widely used to optimize functions of probability measures. This paper considers particle gradient descent with a finite number of particles and establishes its theoretical guarantees to optimize functions that are displacement convex in measures. Concretely, for Lipschitz displacement convex functions defined on probability over Rd, we prove that O(1/ϵ2) particles and O(d/ϵ4) computations are sufficient to find the ϵ-optimal solutions. We further provide improved complexity bounds for optimizing smooth displacement convex functions. We demonstrate the application of our results for function approximation with specific neural architectures with two-dimensional inputs. © 2023, CC BY-NC-ND.

关键词： Convex optimization

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An ab initio framework for understanding and controlling quantum fluctuations in complex light-matter systems

An ab initio framework for understanding and controlling qua...

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CLEO: Fundamental Science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Rivera, Nicholas Uddin, Shiekh Zia Seyler, Devin Salamin, Yannick Sloan, Jamison Roques-Carmes, Charles Xu, Shutao Sander, Michelle Soljacic, Marin Department of Physics Harvard University CambridgeMA02138 United States Department of Physics Massachusetts Institute of Technology CambridgeMA02139 United States Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States E.L. Ginzton Laboratory Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering BU Photonics Center Boston University BostonMA02215 United States Division of Materials Science and Engineering Boston University BrooklineMA02446 United States Department of Biomedical Engineering Boston University BostonMA02215 United States

We develop a new general theory of quantum noise in photonics. As an example, we demonstrate strong quantum correlations and squeezing in supercontinuum generation. Our results enable overcoming quantum noise limits i... 详细信息

ISBN: (纸本)9781957171395

关键词： Supercontinuum generation

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Demo: Reconfigurable Distributed Antennas and Reflecting Surface (RDARS)-aided Integrated Sensing and Communication System

Demo: Reconfigurable Distributed Antennas and Reflecting Sur...

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IEEE International Conference on Communications in China (ICCC)

作者： Jintao Wang Chengwang Ji Jiajia Guo Shaodan Ma Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC) University of Macau China

Integrated sensing and communication (ISAC) system has been envisioned as a promising technology to be applied in future applications requiring both communication and high-accuracy sensing. Different from most research focusing on theoretical analysis and optimization in the area of ISAC, we implement a reconfigurable distributed antennas and reflecting surfaces (RDARS)-aided ISAC system prototype to achieve the dual-functionalities with the communication signal. A RDARS, composed of programmable elements capable of switching between reflection mode and connected mode, is introduced to assist in uplink signal transmission and sensing. The developed RDARS-aided ISAC prototype achieves reliable user localization without compromising the communication rate, showcasing its potential for future 6G systems.

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China Automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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ExpertDRL: Request Dispatching and Instance Configuration for Serverless Edge Inference with Foundation Models

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IEEE Transactions on Mobile Computing 2025年

作者： Zeng, Yue Zhou, Junlong Ye, Baoliu Qu, Zhihao Guo, Song Gong, Tianjian Li, Pan Nanjing University of Science and Technology Department of Computer Science and Engineering Jiangsu Nanjing210094 China Nanjing University State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing210023 China Hohai University Key Laboratory of Water Big Data Technology of Ministry of Water Resources Nanjing211100 China Hong Kong University of Science and Technology Hong Kong Southwest University College of Electronic and Information Engineering Chongqing400715 China

The prevalence of the pre-training & fine-tuning paradigm enables machine learning models to quickly adapt to various downstream tasks by fine-tuning pre-trained foundation models (FMs), greatly facilitating various IoT applications that rely on model inference in dynamic edge serverless environments. Efficiently dispatching inference requests and configuring instances to batch inference requests can significantly enhance resource efficiency. However, existing serverless inference solutions are tailored for traditional models, make coarse-grained request dispatching and instance configuration decisions, fail to exploit the shared model backbone characteristics of the FM and capture delayed rewards in dynamic environments, and ignore communication latency between edge sites, resulting in high costs and constraint violations. In this paper, we leverage our insight that fine-grained batch inference requests can effectively exploit the shared model backbone feature of FM to save monetary costs. We propose an algorithm that incorporates deep reinforcement learning (DRL) and expert intervention for fine-grained request dispatching and instance configuration, where the DRL component outputs fractional solutions as guidance, while the expert intervention module integrates our insights—batching reduces monetary costs at the expense of increased inference latency, whereas higher configurations shorten inference latency. This module rounds fractional solutions and adjusts instance configurations to search for optimal solutions while satisfying constraints, with theoretical guarantees rigorously proved. Finally, we conducted our experiments on an OpenFaas-based platform and simulator, and extensive trace-driven evaluation results show that ExpertDRL can save costs by up to 85.14% and improve request acceptance ratio by up to 26.93%, compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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Retain, Blend, and Exchange: A Quality-aware Spatial-Stereo Fusion Approach for Event Stream Recognition

arXiv

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arXiv 2024年

作者： Chen, Lan Li, Dong Wang, Xiao Shao, Pengpeng Zhang, Wei Wang, Yaowei Tian, Yonghong Tang, Jin The School of Electronic and Information Engineering Anhui University Hefei230601 China The School of Computer Science and Technology Anhui University Hefei230601 China The Department of Automation Tsinghua University Beijing China Peng Cheng Laboratory Shenzhen China Shenzhen China Peng Cheng Laboratory China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China School of Electronic and Computer Engineering Shenzhen Graduate School Peking University China

Existing event stream-based pattern recognition models usually represent the event stream as the point cloud, voxel, image, etc., and design various deep neural networks to learn their features. Although considerable results can be achieved in simple cases, however, the model performance may be limited by monotonous modality expressions, sub-optimal fusion, and readout mechanisms. In this paper, we propose a novel dual-stream framework for event stream-based pattern recognition via differentiated fusion, termed EFV++. It models two common event representations simultaneously, i.e., event images and event voxels. The spatial and three-dimensional stereo information can be learned separately by utilizing Transformer and Graph Neural Network (GNN). We believe the features of each representation still contain both efficient and redundant features and a suboptimal solution may be obtained if we directly fuse them without differentiation. Thus, we divide each feature into three levels and retain high-quality features, blend medium-quality features, and exchange low-quality features. The enhanced dual features will be fed into the fusion Transformer together with bottleneck features. In addition, we introduce a novel hybrid interaction readout mechanism to enhance the diversity of features as final representations. Extensive experiments demonstrate that our proposed framework achieves state-of-the-art performance on multiple widely used event stream-based classification datasets. Specifically, we achieve new state-of-the-art performance on the Bullying10k dataset, i.e., 90.51%, which exceeds the second place by +2.21%. The source code of this paper has been released on https://***/Event-AHU/EFV_event_classification/tree/ EFVpp. Copyright © 2024, The Authors. All rights reserved.

关键词： Classification (of information)

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Privacy Computing with Right to Be Forgotten in Trusted Execution Environment

Privacy Computing with Right to Be Forgotten in Trusted Exec...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Hui Liu Hongzhi Luo Shaofeng Li Tian Dong Guoxing Chen Yan Meng Haojin Zhu Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen China

Sharing private data is at risk of potential data breaches, including the violation of the “right to be forgot-ten” principle, undermining people's willingness to share their data. A common solution is to involve the Trusted Execution Environment (TEE), which allows the data provider to verify the computation process without trusting others. However, previous works have either encountered incomplete computations or lacked scalability. In this paper, we propose TEE RASE, a secure data-sharing framework that addresses these issues. TEE RASE protects every phase of the data lifecycle and enables individuals to share personal data with a predefined privacy budget. In particular, TEE RASE applies comprehensive privacy budgeting mechanisms to efficiently manage privacy budgets and employs an asynchronized execution approach that decouples budget consumption from data computation. TEE RASE records the predefined privacy budgets, verifies privacy consumption requests, updates the remaining budgets, and deletes data that have exhausted their budgets by preventing any attempts to access them. We implement a prototype of TEE RASE and evaluate its effectiveness with a realistic case study on Genome-Wide Association Study.

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Learning Perceptual Hallucination for Multi-Robot Navigation in Narrow Hallways

Learning Perceptual Hallucination for Multi-Robot Navigation...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jin-Soo Park Xuesu Xiao Garrett Warnell Harel Yedidsion Peter Stone Department of Electrical and Computer Engineering The University of Texas at Austin United States Department of Computer Science George Mason University United States Everyday Robots United States Department of Computer Science The University of Texas at Austin United States Army Research Laboratory United States Sony AI Japan

While current systems for autonomous robot navigation can produce safe and efficient motion plans in static environments, they usually generate suboptimal behaviors when multiple robots must navigate together in confined spaces. For example, when two robots meet each other in a narrow hallway, they may either turn around to find an alternative route or collide with each other. This paper presents a new approach to navigation that allows two robots to pass each other in a narrow hallway without colliding, stopping, or waiting. Our approach, Perceptual Hallucination for Hallway Passing (PHHP), learns to synthetically generate virtual obstacles (i.e., perceptual hallucination) to facilitate passing in narrow hallways by multiple robots that utilize otherwise standard autonomous navigation systems. Our experiments on physical robots in a variety of hallways show improved performance compared to multiple baselines.

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Integrating I4.0 Knowledge Graphs with Large Language Models Beyond SPARQL Endpoints 1

Integrating I4.0 Knowledge Graphs with Large Language Models...

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Joint of the 1st Software Lifecycle Management for Knowledge Graphs Workshop and the 3rd International Workshop on Semantic Industrial Information Modelling, SOFLIM2KG-SEMIIM 2024

作者： Wahid, Abdul Yahya, Muhammad Zaman, Farooq Zhou, Baifan Breslin, John G. Intizar, Muhammad Ali Kharlamov, Evgeny Data Science Institute University of Galway IDA Business Park Lower Dangan GalwayH91 AEX4 Ireland Valeo Vision Systems Tuam Galways Ireland Information Technology University Lahore Pakistan Department of Computer Science Oslo Metropolitan University Oslo Norway School of Electronic Engineering Dublin City University Dublin Ireland Bosch Center for AI Renningen Germany

Industry 4.0 (I4.0) knowledge graphs are a common way to represent industrial information models. Conventional SPARQL querying systems require the users to be familiar with the data schema and SPARQL syntax. However, this is often very difficult for many users in industrial production, who have mostly an engineering background, instead of a semantic web. Recent developments in large language models (LLMs) make it possible for non-semantic experts to use natural language to query knowledge graphs (KG). In this work, we present a framework and preliminary results of integrating Industry 4.0 KGs with LLMs to improve how data is represented, reasoned, and processed in manufacturing contexts, facilitating user interaction with KGs and contributing to operational efficiency. Our technique enhances Language Models (LLMs) by utilising the semantic complexity and interdependence of Knowledge Graphs (KGs). This allows us to incorporate domain-specific knowledge. We used the FAISS library and LLaMA2 to optimise the storage and retrieval of vectors, which improved the system’s performance and scalability. This integration allows for advanced fault detection, proactive maintenance, and process optimisation, resulting in decreased periods of inactivity and improved productivity. We introduce the framework’s architecture, implementation strategy, and possible advantages while also discussing the difficulties associated with data integration and scalability. The results of our study show that the integration of KG-LLM surpasses traditional approaches in terms of operational efficiency, as evidenced by enhanced fault detection, proactive maintenance, and process optimisation, thereby opening up possibilities for the advancement of more intelligent and resilient production systems. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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