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Effect of Self-efficacy on Open Card Sorts for Websites

Effect of Self-efficacy on Open Card Sorts for Websites

Thematic area Human Interface and the Management of Information, HIMI 2022 Part of the 24th HCI International Conference, HCII 2022

作者： Katsanos, Christos Zafeiriou, Georgia Liapis, Alexandros Department of Informatics Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical and Computer Engineering University of Peloponnese Patras Greece School of Science and Technology Hellenic Open University Patras Greece

ISBN: (纸本)9783031064234

Card sorting is a popular way for creating website information architectures based on users’ mental models. This paper explores the effect of participants’ self-efficacy on card sorting results. A two-phase study was carried out. The first phase involved 40 participants rating their self-efficacy on a standardized scale, followed by an open card sort experiment. The median self-efficacy score was used to split the open card sort data into two groups: one for low and one for high participants’ self-efficacy. These two datasets were analyzed following state-of-the-art techniques for open card sort data analysis, which resulted in two information architectures for the eshop. In the second phase, two functional prototypes were first created for the eshop, one for each information architecture of the first phase. Subsequently, 30 participants interacted with both prototypes in a user testing study. This paper found that users interacting with the information architecture produced by open card sort participants with low self-efficacy made statistically significantly more correct first clicks, significantly less time to find content items, rated the tasks as significantly easier, and provided higher perceived usability ratings compared to when they interacted with the information architecture produced by users with high self-efficacy. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Websites

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Using Free Space Optics for Beamsteering of 5G Metasurface Antenna

Using Free Space Optics for Beamsteering of 5G Metasurface A...

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IEEE Photonics Conference (IPC)

作者： A. Bilal A. Quddious M. A. Antoniades A. Kanno P.T. Dat K. Inagaki T. Kawanishi S. Iezekiel Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus EMPHASIS Research Centre University of Cyprus Nicosia Cyprus Chair for RF and Photonics Engineering Technische Universität Dresden Germany Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada National Institute of Information and Communications Technology Tokyo Japan Graduate School of Engineering Nagoya Institute of Technology Aichi Japan Faculty of Science and Engineering Waseda University Tokyo Japan

Beamsteering of a sub-6 GHz 5G metasurface antenna is achieved via free space optical control. A scanning angle of 66 ° is demonstrated at 3.25 GHz. The antenna consists of varactor-based phase shifters, whose vo...

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A Dynamic Optimization Framework for Computation Rate Maximization in UAV-Assisted Mobile Edge Computing

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IEEE Transactions on Vehicular technology 2025年

作者： Chen, Yang Pi, Dechang Yang, Shengxiang Xu, Yue Wang, Bi Qin, Shuo Wang, Yintong Xi'an University of Science and Technology Artificial Intelligence and Computer Science Xi'an China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing China De Montfort University School of Computer Science and Informatics LeicesterLE1 9BH United Kingdom Jiangxi University of Science and Technology Faculty of Information Engineering Ganzhou China Xi'an University of Posts and Telecommunications School of Computer Science and Technology Xi'an China Nanjing Xiaozhuang University School of Information Engineering Nanjing China

Mobile edge computing (MEC) significantly boosts the computing power and reduces the energy consumption of Internet of Things (IoT) devices, serving as a valuable complement to cloud computing. The application of unmanned aerial vehicle (UAV) for MEC systems can effectively alleviate the issue of insufficient or damaged communication facilities in remote areas, further expanding the scope of MEC applications. In this article, we present a system model for UAV-assisted wireless-powered MEC systems in a dynamic environment with the objective of maximizing the computation rate of user devices. Due to the complexity of the optimization objective in dynamic environments, we propose a swarm intelligence-based optimization framework with a mechanism for responding to environmental changes, which is intended to enhance population diversity in both static and dynamic environments with the aim of overcoming premature convergence. We integrate particle swarm optimization and harmony search into the proposed framework, naming them DOPSO and DOHS, respectively. Simulation results for two offloading modes in UAV-assisted MEC systems indicate that the proposed framework significantly outperforms other dynamic optimization algorithms. © 1967-2012 IEEE.

关键词： Energy utilization

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Concierge: Towards Accuracy-Driven Bandwidth Allocation for Video Analytics Applications in Edge Network

Concierge: Towards Accuracy-Driven Bandwidth Allocation for ...

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IEEE International Conference on Edge Computing (EDGE)

作者： Yuyang Huang Faishal Zharfan Hendrawan Hendrawan Haryadi S. Gunawi Junchen Jiang Department of Computer Science University of Chicago Chicago USA School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia

ISBN: (数字)9798350368499

ISBN: (纸本)9798350368505

When performing inference on sensor data, edge video analytics applications may not always need high-fidelity data, since important information may not appear all the time. Consequently, each edge AI application’s bandwidth demand is highly dynamic. Thus, a shared edge system should dynamically allocate more bandwidth to the applications in need to reach high accuracy at each moment. However, previous bandwidth allocators are ill-suited because they are agnostic to the timevarying impact of bandwidth on each application’s *** short paper explores a new accuracy-driven approach to bandwidth allocation, which periodically re-allocates bandwidth across edge AI applications based on the sensitivity of each application’s accuracy to its bandwidth share. To examine its practical benefit and technical challenges, we present a concrete accuracy-driven bandwidth allocator called ConciERGE, which exposes a simple yet efficient interface to estimate each application’s sensitivity to a small change in its bandwidth *** run CONCIERGE on state-of-the-art video-analytics applications with real video streams and show its early promise in greatly improving the inference accuracy of video analytics.

关键词： Accuracy Sensitivity Visual analytics Estimation Bandwidth Edge AI Channel allocation

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XHGA: Expanding the Capabilities of Cross-Modal Wrist-Worn Devices for Multi-Task Hand Gesture Applications

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IEEE Transactions on Mobile Computing 2025年

作者： Guo, Kaiwen Tang, Hui Xu, Tianyi Zhou, Hao Lyu, Mengxia Liu, Zhi Wang, Xiaoyan Li, Xiang-Yang Ocean University of China School of Computer Science and Technology Qingdao266100 China University of Science and Technology of China School of Computer Science and Technology Hefei230027 China Deqing Alpha Innovation Institute China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan

Hand gesture applications (HGA) are essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., requiring multi-task ability to support newly arrived HGA tasks. In this paper, we propose a novel wrist-worn multi-task HGA system named XHGA, which can implement modal-domain combination, data-domain adaptation and label-domain extension to ensure the performance in multi-task scenarios. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gestures through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism, while achieves multi-objective recognition simultaneously by employing an adaptive loss function weighting method. Extensive experiments demonstrate that XHGA can achieve an average accuracy of 92.7% with only using 15 labeled data per gesture under three HGA tasks. Compared with the state-of-the-art multi-modal approach, XHGA reduces 82.7% training time, and 47.7% storage, with about 5% improvements in accuracy. © 2002-2012 IEEE.

关键词： Wireless sensor networks

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A Systematic Literature Review of XAI-based Approaches on Brain Disease Detection using Brain MRI Images

A Systematic Literature Review of XAI-based Approaches on Br...

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Advanced Research in Computing (ICARC), International Conference on

作者： Savin Madapatha Pumudu Fernando Department of Computer Science and Engineering University of Westminster London UK School of Computing Informatics Institute of Technology Colombo Sri Lanka

Brain Magnetic Resonance Imaging (MRI) analysis is a widely used medical procedure for the early diagnosis of various brain diseases. Accurate pathology identification during the brain MRI analysis procedure is crucial as misdiagnoses or missed findings can greatly affect a patient's treatment and long-term prediction. With the recent advancement of Artificial Intelligence (AI) in the medical field, researchers have approached various techniques to detect brain diseases using AI. Although AI models exhibit high accuracy, they suffer from a lack of transparency and interpretability, paving the way for the development of eXplainable Artificial Intelligence (XAI) methods in brain disease diagnosis. Image segmentation, machine learning, deep learning and XAI are important for assisting the diagnostic procedure. In this paper, a comprehensive overview of various existing techniques in brain disease detection using MRI is presented, starting with image segmentation techniques, followed by classification techniques, and finally, XAI techniques. In conclusion, the paper identifies a critical need for further research on XAI integration to advance brain disease detection.

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Composite metamorphic relations for integration testing 22

Composite metamorphic relations for integration testing

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8th International Conference on computer technology Applications, ICCTA 2022

作者： Iakusheva, Sofia Khritankov, Anton Phystech School of Applied Mathematics and Informatics Moscow Institute of Physics and Technology Russia Faculty of Computer Science Dept. of Software Engineering Hse University Russia

ISBN: (纸本)9781450396226

Metamorphic testing is a testing method for problems without test oracles. Integration testing allows for detecting errors in complex systems that may not be found during the testing of their components. In this paper, we propose a novel approach that applies metamorphic testing in integration testing. The main idea is to develop a composite metamorphic relation for the system represented as an acyclic graph. This relation is a logical function of metamorphic relations for the parts of the system (vertices of the graph). It takes into account the features of the parts. Also, it can simplify the search for failure by identifying the subsystem with error. In this paper's theoretical part, we describe an algorithm of relation design. Then, we apply our method to a bioinformatics system for comparative genetic analysis of tissues using production tools. This experiment proves our method can be applied to real-life pipelines and find errors in them. © 2022 ACM.

关键词： Integration testing

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IDeploySmart: A Machine Learning-Based Approach for Predicting the Deployment Type of Identity Servers

IDeploySmart: A Machine Learning-Based Approach for Predicti...

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International Conference on Information technology Research (ICITR)

作者： Mamoru D. L. S. Amjadh Ifthikar Computer Science and Engineering University of Westminster London United Kingdom School of Computing Informatics Institute of Technology Colombo Sri Lanka

ISBN: (数字)9798331511425

ISBN: (纸本)9798331511432

Identity servers are integral to modern Information technology (IT) infrastructures, managing secure access to critical applications through authentication and authorization processes. However, determining the optimal deployment strategy for these servers in complex environments remains challenging, especially when considering factors like performance, scalability, and security. This paper introduces IDeploySmart, a machine learning-based system designed to predict the most efficient deployment type of identity servers. By employing Gradient Boosting Regressor, IDeploySmart achieves a prediction accuracy of 92.5%, surpassing industry benchmarks. The system was tested across multiple deployment scenarios, showing significant improvements in system performance, scalability, and security. This paper details the system's design, data preprocessing techniques, model evaluations, and real-world applications, providing insights into how machine learning can enhance identity server deployment strategies in modern IT infrastructure.

关键词： Industries Scalability System performance Data preprocessing Predictive models Boosting Data models Servers Security Information technology

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Enhanced Self-Deployment in IoT Sensor Networks through Leveraging 3D-Voronoi Diagrams with an Advanced Genetic Algorithm

Enhanced Self-Deployment in IoT Sensor Networks through Leve...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Andreou Andreas Constandinos X. Mavromoustakis Evangelos Markakis Athina Bourdena George Mastorakis Department of Computer Science University of Nicosia Nicosia Cyprus Department of Electrical and Computer Engineering Hellenic Mediterranean University Crete Greece Department of Management Science and Technology Hellenic Mediterranean University Crete Greece

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Smart spaces integrate advanced technologies like the Internet of Things (IoT), Machine Learning, and Artificial Intelligence (AI) to enhance automation and control within various environments. Effective deployment of IoT nodes is crucial for maximizing coverage, minimizing costs, and ensuring network stability in these spaces. This paper presents a novel approach combining 3D Voronoi diagrams with a modified Genetic Algorithm (GA) to optimize IoT node placement in three-dimensional environments. The proposed method starts with node placement using a homogeneous Poisson Point Process (PPP) and partitions the space into Voronoi cells, followed by iterative adjustments using the modified GA. The method achieves a 15% improvement in coverage ratio, a 10% reduction in deployment effort, and a 20% increase in network stability compared to existing algorithms, with results statistically significant at 5%. Moreover, optimising sensor placements indirectly enhances network security by reducing redundant data paths and strengthening network resilience. This study provides a scalable, efficient solution for IoT network deployment in complex environments, addressing key challenges in smart space optimization and paving the way for more secure and robust IoT infrastructures.

关键词： Sensor placement Three-dimensional displays Machine learning Network security Partitioning algorithms Internet of Things Smart spaces Optimization Genetic algorithms Resilience

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INSAFEDARE Project: Innovative Applications of Assessment and Assurance of Data and Synthetic Data for Regulatory Decision Support

INSAFEDARE Project: Innovative Applications of Assessment an...

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34th Medical informatics Europe Conference, MIE 2024

作者： Gallos, Parisis Matragkas, Nicholas Islam, Saif ul Epiphaniou, Gregory Hansen, Scott Harrison, Stuart van Dijk, Bram Haas, Marcel Pappous, Giorgos Brouwer, Simon Torlontano, Francesco Abbasi, Saadullah Farooq Pournik, Omid Churm, James Mantas, John Parra-Calderón, Carlos Luis Petkousis, Dimitrios Weber, Patrick Dzingina, Benjamin Mraidha, Chokri Maple, Carsten Achterberg, Jim Spruit, Marco Saratsioti, Evi Moustaghfir, Younes Arvanitis, Theodoros N. European Federation for Medical Informatics Switzerland CEA List Université Paris-Saclay France University of Warwick United Kingdom The Open Group United Kingdom ETHOS Digital Health United Kingdom Leiden University Medical Center Netherlands Health Technology Certification Limited Cyprus Syntho BV Netherlands Istituto Italiano Per La Privacy Italy Department of Electronic Electrical and Systems Engineering School of Engineering University of Birmingham Birmingham United Kingdom

ISBN: (纸本)9781643685335

Digital health solutions hold promise for enhancing healthcare delivery and patient outcomes, primarily driven by advancements such as machine learning, artificial intelligence, and data science, which enable the development of integrated care systems. Techniques for generating synthetic data from real datasets are highly advanced and continually evolving. This paper aims to present the INSAFEDARE project's ambition regarding medical devices' regulation and how real and synthetic data can be used to check if devices are safe and effective. The project will consist of three pillars: a) assurance of new state-of-the-art technologies and approaches (such as synthetic data), which will support the validation methods as part of regulatory decision-making;b) technical and scientific, focusing on data-based safety assurance, as well as discovery, integration and use of datasets, and use of machine learning approaches;and c) delivery to practice, through co-production involving relevant stakeholders, dissemination and sustainability of the project's outputs. Finally, INSAFEDARE will develop an open syllabus and training certification for health professionals focused on quality assurance. © 2024 The Authors.

关键词： Electronic health record

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