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Joint of the ACM Workshops at the International Conference on Intelligent User Interfaces 2025, IUI-WS 2025

作者： Hermann, Julia van Ledden, Sebastian Meske, Christian Löhrke, Enrico Naroska, Edwin Dimitrov, Todor Ölcer, Sabahat Dogangün, Aysegül Institute Positive Computing Ruhr West University of Applied Sciences Lützowstraße 5 Bottrop46236 Germany Institute of Work Science Ruhr University Bochum Wasserstraße 221 Bochum44799 Germany inhaus GmbH Grabenstraße 5 Duisburg47057 Germany Ambient Intelligence Laboratory Niederrhein University of Applied Sciences Reinarzstr. 49 Krefeld47805 Germany Anasoft Technology AG Querenburger Str. 38 Bochum44789 Germany Institute of Computer Science Ruhr West University of Applied Sciences Lützowstraße 5 Bottrop46236 Germany Department of Palliative Medicine University Medical Center Göttingen University of Göttingen Von-Siebold-Straße 3 Göttingen37075 Germany

The increasing demand for nursing care services, coupled with a persistent shortage of skilled caregivers, underscores the urgent need for innovative technological solutions. ExpliCareNEXT addresses this challenge by integrating explainable artificial intelligence (XAI) into caregiving workflows to enhance efficiency, transparency, and trust. The project applies participatory design methods to develop adaptive, human-centered AI systems that support caregivers in their daily tasks while ensuring interpretability and ease of use. Initial findings from field studies in institutional care settings reveal barriers to technology adoption, including high documentation burdens, language barriers, and data privacy concerns. These insights inform the system design, ensuring that AI-driven recommendations align with caregivers' workflows and information needs. Our paper presents the project's objectives and methodological approach, emphasizing interdisciplinary collaboration as key strategies for bridging the gap between technical innovation and practical application. Challenges are explored, highlighting the importance of participatory approaches in fostering trust and usability in safety-critical domains. © 2025 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Ambient intelligence

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“User AR” — Leveraging AI Based Augmented Reality for User Manuals

“User AR” — Leveraging AI Based Augmented Reality for Use...

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International Conference on computing and Networking technology (ICCNT)

作者： Thirumurugan Shanmugam Chirag Chandrashekar Vipin Bhati Maheswari Subburaj Arun Kumar Sivaraman Ajmery Sultana College of Computing and Information Sciences University of Technology and Applied Sciences Suhar Oman School of Computer Science and Engineering Vellore Institute of Technology Chennai India Digital Engineering Solution Center Photon Interactive Inc Toronto Ontario Canada School of Computer Science and Technology Algoma University Brampton Ontario Canada

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project’s aim is to make this AR application, which provides detailed explanations of all the device’s functions, more widely used than traditional user manuals. Augmented reality, which overlays digital information onto the real world, can be employed to develop an AR-based user manual for products or devices. Mobile applications that use a camera to recognize a product or device and display dynamic graphics, such as 3 D models, animations, or text, are one example of AR user manuals. These augmented reality user manuals enable consumers to quickly and easily learn how to operate, assemble, repair, or troubleshoot their purchased products. Experiments show that the proposed algorithm was able to recognize the object accurately with a percentage of 95.2%.

关键词： Three-dimensional displays Biological system modeling Manuals Transforms Animation Software User experience Artificial intelligence Usability Augmented reality

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MOAC: Multi-Objective Adaptive Clustering Optimization Algorithm Based on Cluster Adaptation

MOAC: Multi-Objective Adaptive Clustering Optimization Algor...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Hossam M. J. Mustafa Dheeb Albashish Ahmad Khasawneh Department of Data Science and Artificial Intelligence Faculty of Information Technology University of Petra Amman Jordan Computer Science Department Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Salt Jordan School of Computing Skyline University College Sharjah UAE

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

This paper presents a novel Multi-Objective Adaptive Clustering (MOAC) algorithm that addresses current limitations in existing multi-objective optimization methods. While current algorithms often struggle with premature convergence and diversity preservation, the MOAC algorithm proposes a novel clustering-based approach to search space by partitioning. The algorithm combines cluster evolution with an adaptive parameter control mechanism to achieve significant improvements in balancing exploration-exploitation and diversity maintenance. MOAC employs k-means clustering for population partitioning, combined with cluster-based genetic operators, and an efficient diversity preservation scheme. Extensive experiments across different ZDT, MMF, and UF test suites demonstrate that MOAC's performance is superior, which achieves outstanding hypervolume and IGD values in complex multi-modal problems. The algorithm shows strength in handling complex Pareto fronts, and comprehensive statistical analysis through the Friedman rankings test with a 2.21 rank. Moreover. The results reveal that MOAC is a robust approach for complex multi-objective optimization problems, particularly maintaining solution diversity and achieving consistent convergence across different problems.

关键词： Ranking (statistics) Statistical analysis Metaheuristics Clustering algorithms Optimization methods Evolutionary computation Partitioning algorithms Maintenance Convergence Genetic operators

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Advancing Brain Image Segmentation: A Comprehensive Exploration of Enhanced VGG16 Architecture for Precise Neuroanatomical Mapping

Advancing Brain Image Segmentation: A Comprehensive Explorat...

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computing and Data Analytics (ICCDA), International Conference on

作者： Sheeja Kumari V Wilfred Blessing N. R Suresh Palarimath Hemalatha Gunasekaran Poorana Senthilkumar S Sutherlin Subitha G Department of Computational Intelligence Saveetha School of Engineering SIMATS University Chennai India College of Computing and Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman College of Computing and Information Sciences University of Technology and Applied Sciences Salalah Sultanate of Oman Department of Computer Applications Dr. N. G. P Arts and Science College Coimbatore Tamil Nadu India Department of Computer Science and Engineering Stella Mary's College of Engineering Kanyakumari Dt Tamil Nadu India

ISBN: (数字)9798331542573

ISBN: (纸本)9798331542580

This study introduces a novel approach to brain image segmentation by enhancing the VGG16 architecture. Neuroanatomical mapping is a critical aspect of understanding brain structures, and our research focuses on refining the VGG16 model to achieve more accurate segmentation results. We address the intricacies of brain image analysis, optimizing the architecture for the nuanced structures inherent in neuroimaging data. Our enhanced VGG16 model is thoroughly evaluated across diverse datasets, demonstrating superior performance compared to state-of-the-art segmentation methods. The framework proves robust and efficient in accurately delineating complex brain regions. We also delve into the interpretability of the segmentation results, shedding light on the learned features that contribute to the model's efficacy. The significance of our work extends beyond improved segmentation, offering valuable insights into the interpretability of deep learning models in neuroimaging. This research contributes to the broader field of biomedical image analysis and holds promise for applications in neuroscience and medical diagnostics. The proposed enhanced VGG16 framework emerges as a potent tool for advancing our understanding of brain structures and enhancing the capabilities of neuroimaging technologies.

关键词： Neuroimaging Deep learning Measurement Image segmentation Neuroscience Biological system modeling Refining Computer architecture Brain modeling Medical diagnosis

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Timing Jitter Remover for Optical Non-Gaussian State Preparation

Timing Jitter Remover for Optical Non-Gaussian State Prepara...

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Frontiers in Optics, FiO 2022

作者： Sonoyama, Tatsuki Takahashi, Kazuma Takasu, Sachiko Hattori, Kaori Fukuda, Daiji Asavanant, Warit Takase, Kan Fukui, Kosuke Yoshikawa, Jun-Ichi Endo, Mamoru Furusawa, Akira Department of Applied Physics School of Engineering The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-8656 Japan National Institute of Advanced Industrial Science and Technology Ibaraki Tsukuba305-8563 Japan AIST-UTokyo Advanced Operando-Measurement Technology Open Innovation Laboratory Ibaraki Tsukuba305-8563 Japan Optical Quantum Computing Research Team RIKEN Center for Quantum Computing 2-1 Hirosawa Saitama Wako351-0198 Japan

In this work, we remove the timing jitter of the photon-number-resolving detector which limited non-Gaussian state preparation using a continuous-wave light source, and generate a Schrödinger cat state with Wigne...

ISBN: (纸本)9781557528209

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Simulation vignette generation from answer set specifications 17

Simulation vignette generation from answer set specification...

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17th Annual International Conference on Information Systems for Crisis Response and Management, ISCRAM 2020

作者： Rouwendal van Schijndel, Dashley K. Hannay, Jo E. Stolpe, Audun University of Oslo Department of Technology Systems Norway Norwegian Computing Center Department of Applied Research in Information Technology Norway University of Oslo Department of Technology Systems Norwegian Computing Center Department of Applied Research in Information Technology Norway

ISBN: (纸本)9781949373271

We investigate an approach that allows exercise managers to design simulations with an explicit focus on building skills, rather than having to focus on all the objects and interactions that a simulation must have. Exercise managers may design exercises at various levels of abstraction and always independently of how those sessions are implemented in simulations, while simulation components that implement the design are assembled and to some extent, automatically, behind the scenes. We outline (1) how Answer Set Programming can assist exercise managers in exercise planning and (2) how automated stage and content generation may be used to invoke appropriate simulation components to realize the design. For deliberate and recurrent training of decision-making skills, stages and content must vary to avoid familiarity (testing effects). We conclude by distilling a main research hypothesis that stipulates how (1) and (2) represent two modes of automated reasoning (so-called deductive versus abductive) and how that distinction clarifies the planning task. © 2020 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.

关键词： Mixed reality

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Two-Tier Clustering with Routing Protocol for IoT Assisted WSN

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Computers, Materials & Continua 2022年第5期71卷 3375-3392页

作者： AArokiaraj Jovith Mahantesh Mathapati M.Sundarrajan N.Gnanasankaran Seifedine Kadry Maytham N.Meqdad Shabnam Mohamed Aslam Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Rajarajeswari College of EngineeringBengaluru560074India Department of Computer Science and Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Computer Science Thiagarajar CollegeMadurai625019India Department of Applied Data Science Noroff University CollegeKristiansandNorway Al-Mustaqbal University College HillahIraq Department of Information Technology College of Computing and Information SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia

In recent times,Internet of Things(IoT)has become a hot research topic and it aims at interlinking several sensor-enabled devices mainly for data gathering and tracking *** Sensor Network(WSN)is an important component in IoT paradigm since its inception and has become the most preferred platform to deploy several smart city application areas like home automation,smart buildings,intelligent transportation,disaster management,and other such IoT-based *** methods are widely-employed energy efficient techniques with a primary purpose i.e.,to balance the energy among sensor *** and routing processes are considered as Non-Polynomial(NP)hard problems whereas bio-inspired techniques have been employed for a known time to resolve such *** current research paper designs an Energy Efficient Two-Tier Clustering with Multi-hop Routing Protocol(EETTC-MRP)for IoT *** presented EETTC-MRP technique operates on different stages namely,tentative Cluster Head(CH)selection,final CH selection,and *** first stage of the proposed EETTC-MRP technique,a type II fuzzy logic-based tentative CH(T2FL-TCH)selection is ***,Quantum Group Teaching Optimization Algorithm-based Final CH selection(QGTOA-FCH)technique is deployed to derive an optimum group of CHs in the ***,Political Optimizer based Multihop Routing(PO-MHR)technique is also employed to derive an optimal selection of routes between CHs in the *** order to validate the efficacy of EETTC-MRP method,a series of experiments was conducted and the outcomes were examined under distinct *** experimental analysis infers that the proposed EETTC-MRP technique is superior to other methods under different measures.

关键词： Wireless networks internet of things energy efficiency clustering multi-hop routing metaheuristics

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Attention-Guided Deep Learning Framework For Movement Quality Assessment

Attention-Guided Deep Learning Framework For Movement Qualit...

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

作者： Aditya Kanade Mansi Sharma Manivannan Muniyandi Department of Electrical Engineering Indian Institute of Technology Madras India Department of Computer Science and Engineering Amrita School of Computing Coimbatore Amrita Vishwa Vidyapeetham India Department of Applied Mechanics Indian Institute of Technology Madras India

Physical rehabilitation programs frequently begin with a brief stay in the hospital and continue with home-based rehabilitation. Lack of feedback on exercise correctness is a significant issue in home-based rehabilitation. Deep learning-based movement quality assessment (MQA) can assist with home-based rehabilitation by providing the necessary quantitative feedback. However, systems developed for home-based settings must be fast and offer interpretability of the generated assessment. In this paper, we explore an attention-guided transformer-based architecture for MQA. A comparative analysis against the current state-of-the-art methods is undertaken to establish the validity of the proposed model. Further, we show that the proposed model offers significant performance improvement in training and inference time, which is pivotal for any real-time system. Finally, we show that analysis of the attention maps of the proposed model can give critical insights into the decision-making process of the deep-learning model, thus improving the overall interpretability of predicted assessment scores.

关键词： Training Deep learning Analytical models Predictive models Signal processing Transformers Feature extraction

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Privacy-Preserving Node Classification in Customer Services With Federated Graph Neural Networks

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 630-642页

作者： Zengyi Huang Min Tang Yuxing Wei Guoqiang Deng School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation and the Center for Applied Mathematics of Guangxi Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning China

As the Internet of Things progresses, the volume of information stored in electronic devices has surged dramatically. Employing Graph Convolutional Networks (GCNs) to model graph data derived from link relationships has led to widespread applications in consumer electronics, such as user preference analysis and personalized recommendations. However, with practical graph data scattered among various owners, traditional centralized GCN methods falter owing to privacy constraints. Thus, creating a distributed GCN framework that upholds model quality and user privacy remains a daunting task. In this paper, we pioneer the use of functional encryption in a federated learning GCN framework, termed FPGCN. This innovative technique achieves performance parity with centralized GCNs, capable of producing equivalent node representations without the need for direct access to raw data. By employing a hierarchical split learning framework coupled with privacy-preserving aggregation mechanisms, it enables secure and collaborative multi-party GCN training, ensuring confidentiality while maintaining model efficacy. Comprehensive experimental results in three real-world datasets demonstrate the capacity of FPGCN to match the predictive accuracy observed in centralized scenarios, while also showcasing its performance efficiency.

关键词： Data privacy Privacy Training Accuracy Consumer electronics Cryptography Data models Protection Federated learning Distributed databases

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Buck Boost Converter Control in an IoT-Based Wind System Using Supervised Learning Technique 1

Buck Boost Converter Control in an IoT-Based Wind System Usi...

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1st International Conference on Innovative Sustainable Technologies for Energy, Mechatronics and Smart Systems, ISTEMS 2024

作者： Anbuselvan, N. Hemalatha, S. Lakshminarayanan, Suresh Sivanantham, E. Mohankumar, N. Thamizhamuthu, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Tagore Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India University of Technology and Applied Sciences - Almusanna Oman Department of Engineering and Electronics South Al Batina Oman Rajalakshmi Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India B.V Raju Institute of Technology Department of Electronics and Communication Engineering Telangana India Srm Institute of Science and Technology Kattankulathur Campus-Chengalpattu Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798350384246

In recent years, distributed energy resources (DERs) have gained significance as a way of introducing a substantial quantity of renewable energy sources (RESs) into power grids. Wind turbines are accessible in windy areas, giving wind energy systems (WES) a realistic alternative for clients. This work attempts to increase the effectiveness and adaptability of WES by presenting a novel way of managing DC-DC buck-boost converters. The suggested method combines an Internet of Things (IoT) system for monitoring with supervised learning algorithms to make energy conversion more stable, efficient, and able to adapt in real time to changing wind conditions. In this research, creating a reliable supervised learning control method is the primary goal. The second goal is to optimize the most important parameters of the converter. Using the new supervised learning algorithm, the study changes variables like duty cycle, switching frequency, and inductance to get the best results based on past performance data and how the system is working now. It investigates the influence of these parameters on overall system efficiency and stability. The incorporation of an IoT monitoring system into the WES is an essential component of the study. Various sensors monitor variables such as wind speed, converter input and output voltages, and currents in real-time and are shown as results. © 2024 IEEE.

关键词： Buck-Boost converter

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