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Smart World Congress (SWC), IEEE

作者： Zach Anthis Alexandros Basiakoulis Kyriakos Efstathiou Marianna Efstathiou Department of Communication Culture and Media UCL Centre for Digital Innovation (powered by Amazon Web Services) University College London London United Kingdom Department of Mechanical Engineering Laboratory for Machine Tools and Manufacturing Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical Engineering Computer Engineering and Informatics Digital Heritage Research Lab Cyprus University of Technology Limassol Cyprus

ISBN: (数字)9798331520861

ISBN: (纸本)9798331520878

This paper presents ongoing work on kanON, an AI-driven EdTech tool designed to foster interactive learning and support digital Cultural Heritage (DCH) preservation. kanON facilitates the digital reconstruction of the infamous Pythagorean monochord, by allowing users to seamlessly manipulate parameters such as origin (location and epoch), materials, and shape/dimensions. Through the integration of Machine Learning (ML) algorithms, the tool predicts missing archaeological data and generates real-time 3D renderings based on user input, accompanied by corresponding confidence metrics. A mixed-methods (pilot) study was conducted in classroom environments to evaluate its educational impact via quantitative Likert scale assessments and qualitative content analysis of educator feedback. Preliminary results demonstrate high levels of student engagement and reflect positive educator responses, highlighting its potential to enhance the in-class accessibility and understanding of complex historical concepts, as well as to enable the preservation and exploration of cultural artifacts through digital means. Future work will focus on refining kanON and expanding its applicability within both educational and DCH contexts, by introducing the capability of outputting a fully operational digital twin, that integrates mixed reality functionalities. The idea is to enable users to later engage with their (personalized) model dynamically (e.g., readjusting string length and tension), thus allowing them to produce different soundscapes in real-time. This advancement aims to deepen the pedagogical impact by merging tactile interaction with visual and auditory feedback, further enhancing the educational experience via immersive, experiential learning.

关键词： Visualization Uncertainty Three-dimensional displays Machine learning Educational technology Real-time systems User experience digital twins Cultural differences Usability

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Integrating HL7 FHIR into Clinical Decision Support Systems: A Real-World Application with Pepper Humanoid Robot in Hospital During Doctor Visits 9

Integrating HL7 FHIR into Clinical Decision Support Systems:...

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9th International Conference on Mathematics and computers in Sciences and Industry, MCSI 2024

作者： Safran, Valentino Horvat, Samo Ilijevec, Bojan Roj, Igor Rober Flis, Vojko Mlakar, Izidor University of Maribor Faculty of Electrical Engineering and Computer Science Laboratory of Digital Signal Processing Maribor Slovenia University Medical Centre Maribor Department of Thoracic Surgery Maribor Slovenia University Medical Centre Maribor Clinical Department of Abdominal and General Surgery Maribor Slovenia University Medical Centre Maribor Department of Nurses in Clinic for Surgery Maribor Slovenia University Medical Centre Maribor Clinical Department of Cardiac Surgery Maribor Slovenia

ISBN: (纸本)9798350377224

This study explores the integration of HL7 Fast Healthcare Interoperability Resources (FHIR) into a Clinical Decision Support System (CDSS) utilizing a Pepper robot to enhance doctor visits in a hospital setting. We evaluate a real-world implementation where the Pepper robot acts as an interface for data collection from patients, clinical monitors, and hospital information systems. The CDSS leverages FHIR to seamlessly integrate this data and present it visually, aiming to improve clinical decision-making processes for doctors. Our research investigates the potential of this FHIR-based CDSS with a robot interface to optimize healthcare delivery and potentially improve patient outcomes. © 2024 IEEE.

关键词： Anthropomorphic robots

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On Radically Expanding the Landscape of Potential Applications for Automated-Proof Methods

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SN computer Science 2021年第4期2卷 1-9页

作者： Uhlmann, Jeffrey Wang, Jie Department of Electrical Engineering and Computer Science University of Missouri 201 Naka Hall Columbia United States Laboratory for Analysis and Architecture of Systems French National Centre for Scientific Research Toulouse France

In this paper, we examine the potential of optimization-based computer-assisted proof methods to be applied much more widely than commonly recognized by engineers and computer scientists. More specifically, we contend that there are vast opportunities to derive valuable mathematical results and properties that may be narrow in scope, such as in highly specialized engineering control applications, that are presently overlooked, because they have characteristics atypical of those that are conventionally studied in the areas of pure and applied mathematics. As a concrete example, we demonstrate use of sum-of-squares (SOS) optimization for certifying polynomial nonnegativity as a part of a proposed dimension-pinning strategy to prove that the inverse of the relative gain array (RGA) of a d-dimensional positive-definite matrix is doubly stochastic for d≤ 4. However, it is not specifically this result and solution method that are of principal interest in this paper but rather how they illustrate the relevance of optimization-based proof techniques to engineering system design more broadly. We believe that our paper is the first to explicitly emphasize the fundamental distinction between methods that can be applied to prove results/properties over a fixed number of dimensions versus those that hold generally. The latter class of problems is the conventional domain of mathematicians, but the former is what we propose to be a fertile and largely unrecognized class of problems that are amenable to automated-proof technologies, e.g., as we demonstrate using our novel dimension-pinning approach. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Automated proofs Automated software methods computer-aided system design computer-assisted proofs Dimension pinning Inverse relative gain array IRGA Relative gain array RGA SOS proofs Sum-of-squares optimization

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A methodological framework for estimating ambient PM_(2.5)particulate matter concentrations in the UK

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Journal of Environmental Sciences 2025年第4期150卷 676-691页

作者： David Galán-Madruga Parya Broomandi Alfrendo Satyanaga Ali Jahanbakhshi Mehdi Bagheri Aram Fathian Rasoul Sarvestan J.Cárdenas-Escudero J.O.Cáceres Prashant Kumar Jong Ryeol Kim Department of Atmospheric Pollution National Centre for Environment HealthHealth Institute Carlos III.Ctra.Majadahonda a Pozuelo km 2.2Madrid 28220Spain Department of Civil and Environmental Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan Department of Electrical and Computer Engineering School of Engineering and Digital SciencesNazarbayev UniversityKabanbay Batyr Ave.53Astana 010000Kazakhstan School of Architecture Building and Civil EngineeringLoughborough UniversityLeicestershire LE113TUUnited Kingdom Neotectonics and Natural Hazards Institute RWTH Aachen UniversityAachen 52056Germany UNESCO Chair on Coastal Geo-Hazard Analysis Research Institute for Earth SciencesTehran 13185-1494Iran Water SedimentHazardsand Earth-surface Dynamics(waterSHED)LabDepartment of GeoscienceUniversity of CalgaryCalgary Alberta T2N 1N4Canada Department of Climatology Faculty of Geography and Environmental SciencesHakim Sabzevari UniversitySabzevar ***Iran Laser Chemistry Research Group Department of Analytical ChemistryFaculty of ChemistryComplutense University of MadridPlaza de Ciencias 1Madrid 28040Spain Analytical Chemistry Department FCNETUniversity of PanamaUniversity CityUniversity MailPanama 4Panama City 3366Panama Global Centre for Clean Air Research(GCARE) School of SustainabilityCivil and Environmental EngineeringFaculty of Engineering and Physical SciencesUniversity of SurreyGuildford GU27XHUnited Kingdom Institute for Sustainability University of SurreyGuildford GU27XHUnited Kingdom

Scientific evidence sustains PM_(2.5)particles’inhalation may generate harmful impacts on human beings’health;therefore,theirmonitoring in ambient air is of paramount relevance in terms of public *** to the limited number of fixed stations within the air qualitymonitoring networks,development ofmethodological frameworks tomodel ambient air PM_(2.5)particles is primordial to providing additional information on PM_(2.5)exposure and its *** this sense,this work aims to offer a global easily-applicable tool to estimate ambient air PM_(2.5)as a function of meteorological conditions using a multivariate *** PM_(2.5)data measured by 84 fixed monitoring stations and meteorological data from ERA5(ECMWF Reanalysis v5)reanalysis daily based data between 2000 and 2021 across the United Kingdom were attended to develop the suggested *** from January 2017 to December 2020 were employed to build amathematical expression that related the dependent variable(PM_(2.5))to predictor ones(sea-level pressure,planetary boundary layer height,temperature,precipitation,wind direction and speed),while 2021 data tested the *** indicators evidenced a good performance of model(maximum values of RMSE,MAE and MAPE:1.80μg/m^(3),3.24μg/m^(3),and 20.63%,respectively),compiling the current legislation’s requirements for modelling ambient air PM_(2.5)concentrations.A retrospective analysis of meteorological features allowed estimating ambient air PM_(2.5)concentrations from 2000 to *** highest PM_(2.5)concentrations relapsed in theMid-and Southlands,while Northlands sustained the lowest concentrations.

关键词： Air quality PM_(2.5)particles Meteorological variables Prediction model Long-term trend

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Topic Tracking Algorithm Based on Topic Structure Characteristics 22

Topic Tracking Algorithm Based on Topic Structure Characteri...

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6th High Performance Computing and Cluster Technologies Conference, HPCCT 2022

作者： Li, Shengdong Lv, Xueqiang Shi, Juntao Zhang, Haichuan Wu, Jianwei Wang, Yuan Department of Computer Engineering Langfang Yanjing Vocational Technical College Langfang065200 China Beijing Key Laboratory of Internet Culture and Digital Dissemination Research Beijing Information Science and Technology University Beijing100101 China Department of Mechanical and Electrical Engineering Langfang Yanjing Vocational Technical College Langfang065200 China

ISBN: (纸本)9781450396646

Topic tracking task is used for public opinion monitoring, and its key technology is text classification algorithm. However, existing text classification algorithms need large-scale train corpus during training, while topic tracking task only provides a small amount of train corpus, resulting in that it has poor performance. We analyze story description contents in train corpus, and find that the report description contents of the same topic have the topic structure characteristics of high similarity. We use topic information to represent highly similar topic structure characteristics to make up for the lack of train corpus in text classification algorithm, and fuse topic structure characteristics and text classification algorithm to make the topic tracking algorithm consider topic structure characteristics, and propose Topic Tracking Algorithm based on Topic Structure characteristics (TTATS). To verify its performance, we carry out quantitative and qualitative experiments. The experimental results of multiple dimensions show that it has preferable topic tracking performance. © 2022 ACM.

关键词： Classification (of information)

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LanCOPE: Language-Guided Category-Level Object Pose Estimation from a Single RGB Image

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IEEE Robotics and Automation Letters 2025年第7期10卷 7555-7562页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Dai, Zhenqi Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha 410082 China Central South University School of Architecture and Art Changsha 410082 China The University of Western Australia Department of Computer Science and Software Engineering 6009 WA Australia

Monocular RGB-based category-level object pose estimation is more practical and cost-effective for robotics. However, existing methods do not fully exploit the rich semantic and contextual information in multimodal data (e.g. language) that provides additional object attributes to guide the model in extracting category features more reliably. We propose a language-guided category-level object pose estimation method (LanCOPE), taking a single RGB image as input. Our method uses DINOv2 to recover depth from a single RGB image and converts it into point cloud to perceive the object's geometry. We then introduce language descriptions for the RGB image, estimated point cloud and overall scene to better guide the point cloud encoder and image encoder in learning category features. We develop a cross-modal differential perception feature fusion network to fuse multimodal features. This network employs a differential perception module to eliminate redundant information across different modalities, highlighting signifcant semantic differences and similarities. Furthermore, it uses a cross-attention mechanism to fuse the semantic information of the language and vision features, improving the overall perception. Finally, we design a denoising network based on the skip fusion transformer to recover the object pose accurately. Extensive experiments on REAL275 and Wild6D datasets show that LanCOPE achieves state-of-the-art performance. Our code is available at LanCOPE. © 2016 IEEE.

关键词： category-level object pose estimation languag RGB image

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Enhancing Privacy in Federated Learning through Local Training

arXiv

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arXiv 2024年

作者： Bastianello, Nicola Liu, Changxin Johansson, Karl H. School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai200237 China

In this paper we propose the federated learning algorithm Fed-PLT to overcome the challenges of (i) expensive communications and (ii) privacy preservation. We address (i) by allowing for both partial participation and local training, which significantly reduce the number of communication rounds between the central coordinator and computing agents. The algorithm matches the state of the art in the sense that the use of local training demonstrably does not impact accuracy. Additionally, agents have the flexibility to choose from various local training solvers, such as (stochastic) gradient descent and accelerated gradient descent. Further, we investigate how employing local training can enhance privacy, addressing point (ii). In particular, we derive differential privacy bounds and highlight their dependence on the number of local training epochs. We assess the effectiveness of the proposed algorithm by comparing it to alternative techniques, considering both theoretical analysis and numerical results from a classification task. Copyright © 2024, The Authors. All rights reserved.

关键词： Federated learning

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SML: A Backdoor Defense for Non-Intrusive Speech Quality Assessment via Semi-Supervised and Multi-Task Learning

SML: A Backdoor Defense for Non-Intrusive Speech Quality Ass...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ying Ren Wenjie Zhang Jiahong Ye Jie Li Diqun Yan Bin Ma Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Digital Technology and Engineering Ningbo University of Finance and Economics Ningbo China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Non-intrusive speech quality assessment (NISQA) is widely used in speech downstream tasks due to its ability to predict the quality of speech without a reference speech. However, few researchers have focused on the backdoor security of NISQA. Despite the backdoor defenses have been extensively studied to mitigate the threat of maliciously modifications in deep neural networks. In particular, semi-supervised based backdoor defenses have excellent defensive performance by depriving backdoor attacks of their most essential need. But these defense methods rely on data-augmentation consistency and thus cannot be applied to NISQA. In this work, we propose a backdoor defense based on semi-supervised and multi-task learning (SML). Semi-supervised learning is based on the simple assumption that the same input should be as consistent as possible in two similar models. Multi-task learning further improves the prediction performance of mean opinion score (MOS) by learning the tasks of perceptual evaluation of speech quality (PESQ), short-time objective intelligibility (STOI) and speech distortion index (SDI). Extensive experiments involving five backdoor defenses against five backdoor attacks on two benchmark datasets demonstrate the superiority of our SML approach.

关键词： Acoustic distortion Artificial neural networks Semisupervised learning Benchmark testing Multitasking Acoustics Quality assessment Security Indexes Speech processing

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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Congress on Evolutionary Computation

作者： Yongfeng Li Yuze Zhang Lijia Ma Junkai Ji Dugang Liu Victor C. M. Leung Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms.

关键词： Measurement Correlation Sociology Evolutionary computation Reinforcement learning Germanium Computational efficiency

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KKL Observer Synthesis for Nonlinear Systems via Physics-Informed Learning

arXiv

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arXiv 2025年

作者： Niazi, M.B. Umar Cao, John Barreau, Matthieu Johansson, Karl Henrik Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology StockholmSE-100 44 Sweden Laboratory for Information and Decision Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical Engineering Stanford University StanfordCA94305 United States

This paper proposes a novel learning approach for designing Kazantzis-Kravaris/Luenberger (KKL) observers for autonomous nonlinear systems. The design of a KKL observer involves finding an injective map that transforms the system state into a higher-dimensional observer state, whose dynamics is linear and stable. The observer’s state is then mapped back to the original system coordinates via the inverse map to obtain the state estimate. However, finding this transformation and its inverse is quite challenging. We propose to sequentially approximate these maps by neural networks that are trained using physics-informed learning. We generate synthetic data for training by numerically solving the system and observer dynamics. Theoretical guarantees for the robustness of state estimation against approximation error and system uncertainties are provided. Additionally, a systematic method for optimizing observer performance through parameter selection is presented. The effectiveness of the proposed approach is demonstrated through numerical simulations on benchmark examples and its application to sensor fault detection and isolation in a network of Kuramoto oscillators using learned KKL observers. © 2025, CC BY.

关键词： State estimation

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