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AmplitudeArrow: On-the-Go AR Menu Selection Using Consecutive Simple Head Gestures and Amplitude Visualization

IEEE Transactions on Visualization and computer Graphics 2025年第05期31卷 3408-3417页

作者： Tian, Yang Zhang, Youpeng Yan, Yukang Zhao, Shengdong Ma, Xiaojuan Shi, Yuanchun Guangxi University Department of Computer Science Guangxi Key Laboratory of Multimedia Communications and Network Technology China University of Rochester Department of Computer Science United States City University of Hong Kong School of Creative Media and the Department of Computer Science Hong Kong Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Tsinghua University Department of Computer Science and Technology China

Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger gestures. In this work, we propose and evaluate the AmplitudeArrow (AA) technique designed for on-the-go AR menu selection to demonstrate that consecutive simple head gestures can also be an effective input modality for heads-up computing. Specifically, AA arranges menu icons into one/two row(s). To select a target icon, the user first makes their head yaw to pre-select the target icon or the column containing it and then makes their head pitch to make the arrow in the target icon expand until the arrow covers the target icon completely, i.e., the pitch amplitude surpasses the selection confirmation threshold. User studies indicated that AA demonstrated robust resistance to walking-caused head perturbation and external factors such as other people/obstacles, delivering high accuracy (error rate © 1995-2012 IEEE.

关键词： Augmented reality

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Quality characteristics in IoT systems: learnings from an industry multi case study

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Discover Internet of Things 2025年第1期5卷 1-22页

作者： Alkhabbas, Fahed Munir, Hussan Spalazzese, Romina Davidsson, Paul Department of Computer Science and Media Technology Malmö University Malmö Sweden Sustainable Digitalisation Research Centre Malmö University Malmö Sweden

The Internet of Things (IoT) has transformed our daily life by enabling devices and objects to collect data, communicate, and collaborate to provision novel types of services. Engineering IoT systems is a complex process that should consider a number of quality characteristics to meet the systems’ goals. Towards identifying the key quality characteristics of IoT systems, in this study, we conduct semi-structured interviews with seven companies developing IoT solutions within smart energy, smart healthcare, smart surveillance, and smart buildings application areas. The study used the ISO/IEC 25010 model as a reference and a qualitative research approach, i.e., we conducted semi-structured interviews with ten experts and performed content analysis on the data collected from the interviews. The study findings reveal that the ISO/IEC 25010 model does not include the following key quality characteristics that practitioners consider when engineering IoT systems: trust, privacy, and energy consumption. Additionally, we report about trade-offs between quality characteristics, architectural constraints, and challenges related to the achievement of the identified quality characteristics when engineering IoT systems in practice. © The Author(s) 2025.

关键词： Smart homes

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Sufficient learning: mining denser high-quality pixel-level labels for edge detection

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Neural Computing and Applications 2025年第14期37卷 8245-8260页

作者： Chen, Xiaodiao Yang, Wenya Wu, Wen Tao, Xiuting Mao, Xiaoyang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Media and Design Hangzhou Dianzi University Hangzhou310018 China Department of Computer Science and Engineering University of Yamanashi Kofu400-8511 Japan

The success of deep neural networks can largely be attributed to large-scale datasets with accurate annotations. In many practical applications, labels are annotated by multiple annotators, resulting in ambiguous labels. To mitigate the impact of such labels, existing deep edge detectors tend only to exploit sparsely reliable samples with high-confident labels by simple voting mechanism. However, lacking sufficient pixel-level learnable samples would severely degrade detection performance. To combat this issue, this work designs a sufficient learning framework for edge detection (SLED) to sufficiently explore reliable samples for model learning, which consists of three stages: sample selection, label refinement, and retraining. Firstly, we propose a sample selection scheme founded on history prediction, which aims to identify more reliable samples and locate unreliable regions containing potentially reliable samples. Secondly, we design an efficient label refinement strategy based on a graph convolutional network (GCN) to propagate reliable label information to unreliable nodes. This strategy can generate "new training targets" with denser, reliable labels for all images. Finally, we retrain the network with these new targets, obtaining a more robust model. Experimental results on three widely used benchmarks show that the proposed detector outperforms all compared SOTA methods. Our code will be released at https://***/wenya1994/SLED. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Pixels

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Incremental-learning-based graph neural networks on edge-forwarding devices for network intrusion detection

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Alexandria Engineering Journal 2025年 126卷 81-89页

作者： Gao, Qiang Kausar, Samina Zhang, HuaXiong College of Media Engineering Communication University of Zhejiang Hangzhou310018 China Department of Computer Sciences and Information Technology University of Kotli Azad Jammu and Kashmir Kotli11100 Pakistan School of Computer Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China

Graph neural networks have become one of the research hotspots for network intrusion detection due to their natural suitability for representing computer networks. However, most of the related research on training GNNs is centralized, and this approach involves long-distance transmission and dumping of network data, so it is inefficient to perform, has the potential for privacy leakage, and introduces an additional transmission burden to the network. To address these challenges, this paper investigates the feasibility of offloading both graph neural networks' training and inference phases to edge-forwarding devices such as switches. We propose a distributed framework that aggregates residual computational resources from edge-forwarding devices into a micro-computing network. This framework then migrates GNN execution to edge-forwarding devices through a hybrid parallelism paradigm, thus locally detecting network anomalies to reduce network data transmission significantly. Meanwhile, to address the problem of computational and memory constraints of edge-forwarding devices, we propose a novel attention heatmap-driven memoryless incremental learning algorithm that learns network features and detects anomalies with minimal resources while avoiding catastrophic forgetting. Finally, we implement and verify the feasibility of the above framework and algorithm using a general-purpose embedded system and open-source software. The experiments show that although each edge-forwarding device's computational and memory load is light, the framework performs similarly to traditional approaches. To the best of our knowledge, this is the first approach that offloads a graph neural network model to edge-forwarding devices. © 2025 The Authors

关键词： Network intrusion

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A Case Study in Playful Facilitation for Designing Embodied Experiences 9th

A Case Study in Playful Facilitation for Designing Embodie...

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9th EAI International Conference on Design, Learning, and Innovation, DLI 2024

作者： Font, Jose O’Bryan, Mikaela Alvarez, Alberto Robinson, Raquel Game Lab Department of Computer Science and Media Technology Malmö University Malmö Sweden IT University of Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031856624

Embodied design is fostering relevant advancements in novel design methods for learning and innovation. Nevertheless, practice and research show that teaching and facilitating embodied design is a challenging task. This paper presents a playful approach for conducting embodied design sessions, which adapts existing card-game mechanics to improve the accessibility and usability of existing embodied design toolboxes during a design session. Our approach hypothesizes that card-game mechanics can address usual pitfalls common to card-based facilitation toolkits by adding structure, guidelines, and playfulness. We also envision that such toolboxes, that have become popular in the research in participatory and co-design contexts, can seamlessly incorporate card-game mechanics like the ones we propose. We describe the setting of the design session, as well as present and discuss the results obtained from observation and participant questionnaires. Finally, we also outline the next steps for designing an embodied workshop in sustainability based on the results from this study. We strive to analyze our proposed method by its capacity to blend into the structure and materials of a card-based toolbox, eliciting the playfulness embedded in the leveraged card-game mechanics to implicitly provide support and guidance through the session, to both facilitators and participants, without disrupting the flexible and open flow of an movement-based design session. © ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Game design

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Video-Audio Multimodal Fall Detection Method 21st

Video-Audio Multimodal Fall Detection Method

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Jamali, Mahtab Davidsson, Paul Khoshkangini, Reza Mihailescu, Radu-Casian Sexton, Elin Johannesson, Viktor Tillström, Jonas Internet of Things and People Research Center Department of Computer Science and Media Technology Malmö University Malmö Sweden Sigma Technology Gothenburg Sweden

ISBN: (纸本)9789819601240

Falls frequently present substantial safety hazards to those who are alone, particularly the elderly. Deploying a rapid and proficient method for detecting falls is a highly effective approach to tackle this concealed peril. The majority of existing fall detection methods rely on either visual data or wearable devices, both of which have drawbacks. This research presents a multimodal approach that integrates video and audio modalities to address the issue of fall detection systems and enhances the accuracy of fall detection in challenging environmental conditions. This multimodal approach, which leverages the benefits of attention mechanism in both video and audio streams, utilizes features from both modalities through feature-level fusion to detect falls in unfavorable conditions where visual systems alone are unable to do so. We assessed the performance of our multimodal fall detection model using Le2i and UP-Fall datasets. Additionally, we compared our findings with other fall detection methods. The outstanding results of our multimodal model indicate its superior performance compared to single fall detection models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fall detection

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An Intelligent Diagnostic Framework Based on Digital Twins and Partial Transfer Learning: Methodology and Industrial Application

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 1-13页

作者： Azari, Mehdi Saman Ricci, Liana Santini, Stefania Flammini, Francesco Linnaeus University Department of Computer Science and Media Technology Växjo352 52 Sweden GE Power Sweden Steam Power Unit Norrköping602 23 Sweden University of Naples Federico II Department of Electrical Engineering and Information Technology Naples80125 Italy Mälardalen University School of Innovation Design and Engineering Eskilstuna632 17 Sweden Linnaeus University Department of Computer Science and Media Technology Växjö352 52 Sweden

In Industry 4.0, efficient fault diagnosis is crucial for predictive maintenance but is often hindered by significant domain shifts between training and testing domains and lack of training datasets, limiting the effectiveness of machine learning in practice. Transfer learning has been used to address these challenges by utilizing knowledge from similar source domains. However, the scarcity of faulty data from real machines and the difficulty of obtaining labeled datasets from lab machines as source domain pose significant obstacles. This paper presents a novel diagnostic framework that integrates digital twins and transfer learning to overcome these limitations. Digital twins generate training datasets, while a model update strategy based on parameter sensitivity analysis improves adaptability. The framework also incorporates a partial transfer diagnostic model with a double attention mechanism to handle data distribution discrepancies and label inconsistencies between digital twins and real machines. Validated on an industrial rotating machine case study using real data, the proposed approach improves diagnostic accuracy by over 10% compared to state-of-the-art methods. © 2023 IEEE.

关键词： Predictive maintenance

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ZigzagNetVis: Suggesting temporal resolutions for graph visualization using zigzag persistence

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IEEE Transactions on Visualization and computer Graphics 2025年第5期31卷 3450-3458页

作者： Tinarrage, Raphaël Ponciano, Jean R. Linhares, Claudio D. G. Traina, Agma J. M. Poco, Jorge Fundação Getulio Vargas School of Applied Mathematics Rio de Janeiro Brazil University of São Paulo Institute of Mathematics and Computer Sciences São Carlos Brazil Linnaeus University Department of Computer Science and Media Technology Växjö Sweden

Temporal graphs are commonly used to represent complex systems and track the evolution of their constituents over time. Visualizing these graphs is crucial as it allows one to quickly identify anomalies, trends, patterns, and other properties that facilitate better decision-making. In this context, selecting an appropriate temporal resolution is essential for constructing and visually analyzing the layout. The choice of resolution is particularly important, especially when dealing with temporally sparse graphs. In such cases, changing the temporal resolution by grouping events (i.e., edges) from consecutive timestamps - a technique known as timeslicing - can aid in the analysis and reveal patterns that might not be discernible otherwise. However, selecting an appropriate temporal resolution is a challenging task. In this paper, we propose ZigzagNetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing a given graph, i.e., resolutions that lead to substantial topological changes in the graph structure. ZigzagNetVis achieves this by leveraging zigzag persistent homology, a well-established technique from Topological Data Analysis (TDA). To improve visual graph analysis, ZigzagNetVis incorporates the colored barcode, a novel timeline-based visualization inspired by persistence barcodes commonly used in TDA. We also contribute with a web-based system prototype that implements suggestion methodology and visualization tools. Finally, we demonstrate the usefulness and effectiveness of ZigzagNetVis through a usage scenario, a user study with 27 participants, and a detailed quantitative evaluation. © 1995-2012 IEEE.

关键词： Image color analysis Vibrations Visualization Color Fluctuations Training Metaverse Sensitivity Gaze tracking Brightness

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Teaching Agent-Based Modeling for Simulating Social Systems – A Research-Based Learning Approach 25th

Teaching Agent-Based Modeling for Simulating Social Systems ...

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25th International Workshop on Multi-Agent-Based Simulation, MABS 2024

作者： Lorig, Fabian Belfrage, Michael Johansson, Emil Department of Computer Science and Media Technology Malmö University Malmö Sweden Internet of Things and People Research Center Malmö University Malmö Sweden

ISBN: (纸本)9783031880162

Existing courses on agent-based modeling and simulating (ABMS) are mainly aimed at doctoral students and many modelers have acquired their ABMS skills by teaching themselves. This paper reports and reflects on the development of an undergraduate course on ABMS of social systems. It presents a problem-based approach to teaching ABMS of social systems, the Integrated Learning Outcomes (ILOs), and the course structure. This paper discusses the constructive alignment of the syllabus, presents the results from the course evaluation, and draws conclusions for further editions of the course. Rather than proposing how such courses should be structured, we discuss the feasibility of the pursued research-based learning approach. Our goal is to inspire other researchers and teachers to develop similar courses, to encourage the establishment of a general curriculum, and to promote ABMS in undergraduate education. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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The Role of Artificial Intelligence in Improving Histopathological Diagnosis of Prostate Cancer: A Review

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Journal of Engineering and science in Medical Diagnostics and Therapy 2025年第2期8卷 020801页

作者： Afifi, Shereen Faragallah, M. Hamdy Taha, Radwa Baig, Mirza Ullah, Ehsan Gholam Hosseini, Hamid Hassanein, Sally I. Computer Science and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Biochemistry Department Faculty of Science Helwan University Cairo11795 Egypt Digital Media and Engineering Department Faculty of Media Engineering and Technology German University Cairo11835 Egypt Digital Front Door Orion Health Auckland1010 New Zealand Department of Pathology and Laboratory Medicine Health New Zealand Te Toka Tumai Auckland1061 New Zealand School of Engineering Computer and Mathematical Sciences Auckland University of Technology Private Bag 92006 Auckland1142 New Zealand Biochemistry Department Faculty of Pharmacy and Biotechnology German University Cairo11835 Egypt

This review investigates the effectiveness of exploiting the massive Artificial Intelligence (AI) technology in the diagnosis of prostate cancer histopathological images. It focuses on studying and analyzing the current state and practice for utilizing AI tools, including significant machine learning and deep learning models in the histopathological image analysis process. The preferred reporting items for systematic reviews and meta-Analyses (PRISMA) methodology was adopted for conducting this systematic review to include recent research articles that have been published since 2017. Leveraging novel deep learning models and advanced imaging techniques, AI demonstrates promising capabilities in improving accuracy and efficiency in detecting and classifying prostate cancer. A comprehensive comparison of existing works has been presented with in-depth discussions around current limitations and key challenges while proposing some future advancements. This study aims to pave the way for future research and further integration of AI into the diagnostic processes toward early detection, personalized treatment strategies, and enhanced patient outcomes in the context of a prostate cancer diagnosis. Copyright © 2025 by ASME.

关键词： Contrastive Learning

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