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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Filus, Katarzyna Sobczak, Lukasz Domanska, Joanna Domanski, Adam Cupek, Rafal Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Gliwice Poland

ISBN: (纸本)9781665480451

Automated Ground Vehicles (AGVs) use deep-learning-based vision systems to perceive the surrounding environment and extract relevant information about it. Although deep learning models offer high capabilities, they require large amounts of data to be properly trained and tested. Testing is especially important when off-the-shelf models are used by the AGVs - to examine whether they can meet the demands of complex environments such as the production halls of automated factories. One area of such perception algorithms is object recognition. To test such systems, we propose a solution based on ArUco fiducial markers used for automatic labeling of objects. Our solution can be used to test deep learning systems in real time directly on a robot. Our solution requires minimal interference with the environment and additional infrastructure - the desired objects only need to be marked with a marker printed on a home printer. Therefore, the presented testing procedure can be used for testing of AGVs in real-life environments during a real ride from an actual robot perspective. Data gathered during the online testing can be used for the offline comparison of the accuracy of different deep learning models. Although we focus on the online and offline testing in our study, we also incorporated a marker masking procedure. Therefore, the resulting datasets may also be used for training. © 2022 IEEE.

关键词： Object recognition

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Passive on-chip isolators based on the thin-film lithium niobate platform

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Chinese Physics B 2025年第3期34卷 408-413页

作者： Jiacheng Liu Gongyu Xia Qilin Hong Pingyu Zhu Kai-Kai Zhang Keyu Xia Ping Xu Shiqiao Qin Zhihong Zhu College of Advanced Interdisciplinary Studies&Hunan Provincial Key Laboratory of Novel Nano Optoelectronic Information Materials and Devices National University of Defense TechnologyChangsha 410073China Institute for Quantum Information and State Key Laboratory of High-Performance Computing College of Computer Science and TechnologyNational University of Defense TechnologyChangsha 410073China College of Engineering and Applied Sciences National Laboratory of Solid State Microstructuresand Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210023China

Optical isolators,the photonic analogs of electronic diodes,are essential for ensuring the unidirectional flow of light in optical systems,thereby mitigating the destabilizing effects of back ***-film lithium niobate(TFLN),hailed as“the silicon of photonics,”has emerged as a pivotal material in the realm of chip-scale nonlinear optics,propelling the demand for compact optical *** report a breakthrough in the development of a fully passive,integrated optical isolator on the TFLN platform,leveraging the Kerr effect to achieve an impressive 10.3 dB of isolation with a minimal insertion loss of 1.87 *** theoretical simulations have demonstrated that our design,when applied to a microring resonator with a Q factor of 5×10^(6),can achieve 20 dB of isolation with an input power of merely 8 *** advancement underscores the immense potential of lithium niobate-based Kerr-effect isolators in propelling the integration and application of high-performance on-chip lasers,heralding a new era in integrated photonics.

关键词： thin-film lithium niobate Kerr effect optical isolator

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SPOC: A Scale for Potential Operation Consequences of UI Interactions

SPOC: A Scale for Potential Operation Consequences of UI Int...

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Artificial Intelligence x Multimedia (AIxMM), Conference on

作者： Florian Eggenkemper Teresa Rehers Jana Swerew Robert Mertens Centre for Cognitive Science & Institute of Psychology Technical University of Darmstadt Germany Dept. of Computer Science and Engineering HSW University of Applied Sciences Germany BHH University of Applied Sciences Hamburg Germany

ISBN: (数字)9798350390971

ISBN: (纸本)9798350390988

Many approaches rely on a semantic understanding of screen contents to perform further actions on this information. This is relevant for test automation, robotic process automation, or context based assistive tasks (digital agents). Most of these approaches just analyze basic information about user interface elements but fall short in predicting the consequences of single user interface actions. Possible consequences of UI actions include (permanent) data loss, effects on the reputation of people (e.g. posting on social media), or other irreversible consequences. This makes it necessary to identify and analyze the potential consequences of operation on user interfaces to prevent automatic systems from making possibly fatal mistakes with a high impact on the human user. This paper presents a Scale for Potential Operation Consequences (SPOC) and demonstrates an automatic rating system based on Large Language Models (LLMs), that can be performed locally or in cloud implementations. The LLM-based ratings were tested against a human control group based on a questionnaire (n=35), and proved to perform as well as their human counterparts, making SPOC a robust and automation-friendly rating system for user interface element actions.

关键词： Intelligent automation Social networking (online) Large language models Semantics User interfaces

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Real space characterization of nonlinear hall effect in confined directions

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npj Computational Materials 2024年第1期10卷 2829-2835页

作者： Sheng Luo Chuang-Han Hsu Guoqing Chang Arun Bansil Hsin Lin Gengchiau Liang Department of Electrical and Computer Engineering National University of SingaporeSingapore 117576Singapore Institute of Physics Academia SinicaTaipei 11529Taipei Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingaporeSingapore Department of Physics Northeastern UniversityBostonMA 02115USA Industry Academia Innovation School National Yang-Ming Chiao Tung UniversityHsinchu City 300093Taipei

The nonlinear Hall effect(NLHE)is a phenomenon which could produce a transverse Hall voltage in a time-reversal-invariant ***,we report the real space characterizations of NLHE evaluated through quantum transport in TaIrTe4 nanoribbon without the explicit Berry curvature dipole(BCD)*** first characterize the NLHE in both transverse confined directions in global-level *** impact of quantum confinement in NLHE is evaluated by adjusting the width of ***,the probing area is trimmed to the atomic scale to evaluate the local texture,where we discover its patterns differ among the probed neighboring atomic *** analysis of charge distribution reveals the connections between NLHE’s local patterns and its non-centrosymmetric nature,rendering nearly an order of Hall voltage enhancement through probe *** work paves the way to expand the range of NLHE study and unveil its physics in more versatile material systems.

关键词： directions nonlinear confined

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical Systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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Navigating the Future of Healthcare: AI-Powered Solutions, Personalized Treatment Plans, and Emerging Trends in 2023 1

Navigating the Future of Healthcare: AI-Powered Solutions, P...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Arshi, Oroos Chaudhary, Aryan Singh, Rashmi School of Computer Science University of Petroleum and Energy Studies Department of Cyber Security and Forensics Dehradun India Bio Tech Sphere Research India Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida India

ISBN: (纸本)9798350330915

Artificial intelligence (AI) technology is advancing rapidly, changing how patients are diagnosed and treated and how the healthcare system functions. This paper investigates the rapidly developing area of artificial intelligence applications in healthcare, emphasizing personalized medicine and treatment plans. This research aims to examine artificial intelligence's (AI) substantial impact on addressing the crises facing the healthcare industry. A background on these concerns is provided contextually in the introduction. The body of literature is thoroughly reviewed, offering an understanding of the current knowledge on the topic and laying the groundwork for future discussions. The paper discusses the healthcare industry's challenges and the need for innovative solutions, focusing on the role of AI technology in treatment planning and personalized medicine, highlighting the intricacies of AI-driven decision-making and personalized patient guidance. To provide insights into the evolving landscape and its implications for healthcare professionals and patients, the research also looks at the more significant effects of AI on the advancements in healthcare today. One way to look forward is to look at the top AI trends predicted to impact healthcare in 2023. Summarising the key findings of the research, the conclusion emphasizes how AI may change how healthcare is delivered, enhance patient outcomes, and accelerate the sector's ongoing transformation. © 2023 IEEE.

关键词： AI innovations & technologies artificial intelligence machine learning personalized medicine

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Visual‐Inertial SLAM for Unstructured Outdoor Environments: Benchmarking the Benefits and Computational Costs of Loop Closing

arXiv

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arXiv 2024年

作者： Schmidt, Fabian Blessing, Constantin Enzweiler, Markus Valada, Abhinav Institute for Intelligent Systems Esslingen University of Applied Sciences Esslingen Germany Department of Computer Science University of Freiburg Freiburg Germany

Simultaneous Localization and Mapping (SLAM) is essential for mobile robotics, enabling autonomous navigation in dynamic, unstructured outdoor environments without relying on external positioning systems. These environments pose significant challenges due to variable lighting, weather conditions, and complex terrain. Visual‐Inertial SLAM has emerged as a promising solution for robust localization under such conditions. This paper benchmarks several open‐source Visual‐Inertial SLAM systems, including traditional methods (ORB‐SLAM3, VINS‐Fusion, OpenVINS, Kimera, and SVO Pro) and learning‐based approaches (HFNet‐SLAM, AirSLAM), to evaluate their performance in unstructured natural outdoor settings. We focus on the impact of loop closing on localization accuracy and computational demands, providing a comprehensive analysis of these systems’ effectiveness in real‐world environments and especially their application to embedded systems in outdoor robotics. Our contributions further include an assessment of varying frame rates on localization accuracy and computational load. The findings highlight the importance of loop closing in improving localization accuracy while managing computational resources efficiently, offering valuable insights for optimizing Visual‐Inertial SLAM systems for practical outdoor applications in mobile robotics. The dataset and the benchmark code are available under https://***/iis-esslingen/vi-slam_lc_benchmark. Copyright © 2024, The Authors. All rights reserved.

关键词： SLAM robotics

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An Introduction to Gossip Protocol Based Learning in Peer-to-Peer Federated Learning 3

An Introduction to Gossip Protocol Based Learning in Peer-to...

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3rd IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Naik, Dishita Grace, Paul Naik, Nitin Jenkins, Paul Mishra, Durgesh Prajapat, Shaligram Birmingham City University United Kingdom School of Computer Science and Digital Technologies Aston University United Kingdom Cardiff School of Technologies Cardiff Metropolitan University United Kingdom School of Computer Science & Information Technology Symbiosis University of Applied Sciences India International Institute of Professional Studies Devi Ahilya University India

ISBN: (纸本)9798350343274

Machine learning (ML) has been progressively implemented in a distributed manner to harness the data abundance produced on billions of end user devices. Federated learning (FL) is a type of distributed machine learning that provides robust data privacy by training ML models locally on each participating node without directly exchanging raw data with others. The local ML model updates from all nodes are aggregated in order to obtain a global model. There are different ways to aggregate local model updates for obtaining a global model, such as centralized and decentralized/peer-to-peer. Centralized FL (CFL) requires a server to collect and aggregate all model updates for producing a global model, whereas decentralized FL (DFL)/peer-to-peer FL (P2PFL) requires coordination among all the nodes to communicate and aggregate all models updates and produce a global model. In CFL, the server can pose a bottleneck problem as it is a single point of failure, though, this issue can be resolved using DFL/P2PFL. However, the design of the decentralized/peer-to-peer architecture is complex and challenging, and may incur significant communication overhead due to a large number of nodes involved in the learning process. Gossip protocols are one of the most effective ways to communicate in DFL/P2PFL and optimises its performance, therefore, this decentralized learning is also known as gossip protocol-based learning. Considering the importance of gossip protocol in DFL/P2PFL, this paper will analyse gossip protocol, working of gossip protocol, types of gossip protocol, benefits and limitations of gossip protocol, usages of gossip protocol in DFL/P2PFL and its two main types structured P2PFL and unstructured P2PFL. © 2023 IEEE.

关键词： Aggregates

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Web of Things Semantic Interoperability in Smart Buildings 26

Web of Things Semantic Interoperability in Smart Buildings

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26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2022

作者： Laadhar, Amir Dongo, Junior Enevoldsen, Søren Revaz, Frédéric Gabioud, Dominique Pedersen, Torben Bach Meyer, Martin Nielsen, Brian Thomsen, Christian Department of Computer Science Aalborg University Aalborg Denmark Institute of Sustainable Energy School of Engineering University of Applied Sciences Western Switzerland Valais Sion Switzerland

Buildings are the largest energy consumers in Europe and are responsible for approximately 40% of EU energy consumption and 36% of the greenhouse gas emissions in Europe. Two-thirds of the building consumption is for residential buildings. To achieve energy efficiency, buildings are being integrated with IoT devices through the use of smart IoT services. For instance, a smart space heating service reduces energy consumption by dynamically heating apartments based on indoor and outdoor temperatures. The W3C recommends the use of the Web of Things (WoT) standard to enable IoT interoperability on the Web. However, in the context of a smart building, the ability to search and discover building metadata and IoT devices available in the WoT ecosystems remains a challenge due to the limitation of the current WoT Discovery, which only includes a directory containing only IoT devices metadata without including building metadata. Integrating the IoT device's metadata with building metadata in the same directory can provide better discovery capabilities to the IoT services providers. In this paper, we integrate building metadata into the W3C WoT Discovery through the construction of a Building Description JSON-LD file. This Building Description is integrated into the W3C WoT Discovery and based on the domOS Common Ontology (dCO) to achieve semantic interoperability in smart residential buildings for the WoT IoT ecosystem within the Horizon 2020 domOS project. This integration results in a Thing and Building Description Directory. dCO integrates the SAREF core ontology with the Thing Description ontology, devices, and building metadata. We have implemented and validated the WoT discovery on top of a WoT Thing and Building Description Directory. The WoT Discovery implementation is also made available for the WoT community. © 2022 The Authors. Published by Elsevier B.V.

关键词： Internet of things

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A Study of Different Machine Learning Approaches for Relation Discovery Task

A Study of Different Machine Learning Approaches for Relatio...

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2025 International Conference on Energy, Power, Environment, Control and Computing, ICEPECC 2025

作者： AL-Aswadi, Fatima N. Za’al Alma’aitah, Wafa Chan, Huah Yong Gan, Keng Hoon Iqbal, Zahid Al-Dhamari, Ahlam Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia Faculty of Computer Science and Engineering Hodeidah University P.O. Box 3114 Hodeidah Yemen Department of Intelligence Systems Faculty of Artificial Intelligence Al-Balqa Applied University Salt19117 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Gelugor11800 Malaysia Department of Computer Science Air University Kharian Pakistan Department of Computer Science University of Gujrat Gujrat Pakistan Department of Electrical and Computer Engineering Curtin University Malaysia Sarawak Miri98009 Malaysia

ISBN: (纸本)9781837243235

Relation extraction and classification, collectively referred to as relation discovery, are fundamental tasks in the ontology construction process. In recent years, numerous studies have dedicated their efforts to devising hybrid approaches, supporting inclusive innovation in computational linguistics and ontology engineering. These approaches merge traditional methods with Machine Learning (ML) or Deep Learning (DL) techniques to tackle relation discovery tasks. This research examines the applicability and efficacy of four distinct models— Support Vector Machine (SVM), Naive Bayes (NB), k-Nearest Neighbors (KNN), and Convolutional Neural Network (CNN)—in relation extraction and classification. Two different datasets, ACL RD-TEC 2.0 and ACLRelAcS, are utilized, alongside two distinct text representation methodologies, BOW and TF-IDF-BOW. An in-depth analysis of the results, including the advantages and limitations of each model, is presented in the results and discussion sections. © The Institution of Engineering & Technology 2025.

关键词： Deep learning

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