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2nd IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks

作者： Feeney, Kevin Lewis, David Argyroudis, Patroklos Nolan, Keith O'Sullivan, Declan Knowledge and Data Engineering Group (KDEG) Department of Computer Science and Statistics TCD Ireland CTVR Ireland Knowledge and Data Engineering Group Department of Computer Science and Statistics Ireland CTVR TCD Ireland

ISBN: (纸本)9781424406623

The management of dynamic spectrum access requires the coordination of administrative functions across multiple organizations, from regulators to secondary market operators and commons cooperatives. Policy-based management approaches to dynamic spectrum access must therefore support a wide range of organizational types and handle the relationships between policies authored separately within those organizations. A policy language for administering dynamic access to spectrum resources has been proposed by the DARPA XG initiative. This defines an expressive and extensible language for specifying policies that determine the correct behavior of RIF transceivers as spectrum usage opportunities occur. However, this language does not yet address the mapping of policies onto the complex organizational settings that are likely to emerge in the dynamic spectrum access domain. This paper defines how the DARPA XG policy language can be integrated with a meta-policy management scheme that provides the powerful organizational modeling and policy authoring authority control needed to manage the evolution of policies in these complex organizational settings. The benefits of this integration are illustrated through a case study based on the potential use of dynamic spectrum access licenses granted in Ireland for the DySpan 2007 conference, and the organizational conflicts that even this simple scenario can yield. The practical issues of administering a multi-organizational policy management system are examined through an analysis of the interactions between existing components that are being assembled to demonstrate sophisticated policy management of a cognitive radio system.

关键词： Abstracting

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A novel augmented reality-based simulator for enhancing orthopedic surgical training

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computers in Biology and Medicine 2025年 185卷 109536-109536页

作者： Wu, Luohong Seibold, Matthias Cavalcanti, Nicola A. Hein, Jonas Gerth, Tatiana Lekar, Roni Hoch, Armando Vlachopoulos, Lazaros Grabner, Helmut Zingg, Patrick Farshad, Mazda Fürnstahl, Philipp Research in Orthopedic Computer Science Balgrist University Hospital University of Zurich Lengghalde 5 Zurich8008 Switzerland Department of Orthopaedics Balgrist University Hospital University of Zurich Forchstrasse 340 Zurich8008 Switzerland Computer Vision and Geometry Group ETH Zurich Ramistrasse 101 Zurich8092 Switzerland Institute of Computer Science School of Engineering Zurich University of Applied Sciences Obere Kirchgasse 2 Winterthur8401 Switzerland Institute of Data Analysis and Process Design School of Engineering Zurich University of Applied Sciences Technikumstrasse 81 Winterthur8400 Switzerland

Background: Total Hip Arthroplasty (THA) is a well-established and common orthopedic surgery. Due to the complexity involved in THA, orthopedic surgeons require rigorous training. However, the current gold standard, the tutor-guided and -evaluated apprenticeship model is time-consuming, costly, and poses risks to patients. There is a pressing need for additional training resources to enhance the efficiency and safety of the training process. In this work, we present a novel Augmented Reality (AR)-based simulator designed for THA that helps enable a new self-paced training and learning paradigm without the need for instructors. Methods: The simulator reduces the need for instructors by integrating an AR guidance module and an automated performance evaluation module. Three types of AR guidance were developed: Overlay, Virtual Twin, and Sectional Views. A feasibility study was conducted with five resident surgeons and two senior surgeons to compare these guidance methods quantitatively and qualitatively. The automated performance evaluation module was assessed against manual performance evaluation using Bland-Altman analysis with limits of agreement (LoA) and Mann–Whitney U tests. Results: The quantitative feasibility results indicate the efficacy of the developed AR guidance, characterized by mean transitional and rotational deviation errors below 3 mm and 3 degrees. Based on the qualitative results, we provide recommendations for efficient AR guidance designs. The Bland-Altman analysis results (0.22±1.32mm with LoA −2.37 to 2.81 mm for distance deviation, 0.94±2.41 degrees with LoA −3.78 to 5.66 degrees for yaw deviation, −0.34±1.30 degrees with LoA −2.90 to 2.22 degrees for pitch deviation) and p-values of Mann–Whitney U tests (0.64 for distance deviation, 0.12 for yaw deviation, 0.11 for pitch deviation) indicate no statistically significant differences between the automated and manual performance evaluation at a significance level of 0.05. Conclusion: This work sh

关键词： Augmented reality

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Dense Neural Network Based Arrhythmia Classification on Low-cost and Low-compute Micro-controller

arXiv

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arXiv 2025年

作者： Zishan, Md. Abu Obaida Shihab, H.M. Islam, Sabik Sadman Riya, Maliha Alam Rahman, Gazi Mashrur Noor, Jannatun Research Group Department of Computer Science and Engineering School of Data and Sciences BRAC University Dhaka Bangladesh Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

The electrocardiogram (ECG) monitoring device is an expensive albeit essential device for the treatment and diagnosis of cardiovascular diseases (CVD). The cost of this device typically ranges from $2000 to $10000. Several studies have implemented ECG monitoring systems in micro-controller units (MCU) to reduce industrial development costs by up to 20 times. However, to match industry-grade systems and display heartbeats effectively, it is essential to develop an efficient algorithm for detecting arrhythmia (irregular heartbeat). Hence in this study, a dense neural network is developed to detect arrhythmia on the Arduino Nano. The Nano consists of the ATMega328 microcontroller with a 16MHz clock, 2KB of SRAM, and 32KB of program memory. Additionally, the AD8232 SparkFun Single-Lead Heart Rate Monitor is used as the ECG sensor. The implemented neural network model consists of two layers (excluding the input) with 10 and four neurons respectively with sigmoid activation function. However, four approaches are explored to choose the appropriate activation functions. The model has a size of 1.267 KB, achieves an F1 score (macro-average) of 78.3% for classifying four types of arrhythmia, an accuracy rate of 96.38%, and requires 0.001314 MOps of floating-point operations (FLOPs). This study has achieved notable advancements compared to recent studies, particularly in terms of neural network metrics and the efficient utilization of computational resources. © 2025, CC BY-NC-ND.

关键词： Static random access storage

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Agricultural data Privacy: Emerging platforms & strategies

Food and Humanity

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Food and Humanity 2025年 4卷

作者： Gavai, Anand K. Bouzembrak, Yamine Xhani, Donika Sedrakyan, Gayane Meuwissen, Miranda P.M. Souza, Renata Guizzardi-Silva Marvin, Hans J.P. van Hillegersberg, Jos Industrial Engineering & Business Information Systems University of Twente Enschede Netherlands Information Technology Group Wageningen University and Research Wageningen Netherlands Business Economics Group Wageningen University and Research Wageningen Netherlands Research department of HAYAN Group B.V Rhenen Netherlands Jheronimus Academy of Data Science (JADS) s-Hertogenbosch Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Eindhoven 5612 AP Netherlands

Background: In today's world, grappling with the dual challenges of energy scarcity and climate change, the agricultural and food supply chains are at a crucial juncture for transformation. data privacy within these sectors is increasingly significant, as the integration of advanced digital platforms becomes essential for improving efficiency and sustainability. Scope and Approach: This systematic literature review (SLR) explores the development and application of privacy-preserving data platforms specifically tailored to the agricultural and food supply chains. The review focuses on the evolving landscape of data architectures, data sovereignty, and advanced privacy-preserving techniques. Techniques such as anonymization, encryption, differential privacy, and federated learning are examined, along with the legal and ethical considerations surrounding data sharing in the context of global energy and climate-related challenges. Key Findings and Conclusions: The review synthesizes findings from a broad spectrum of studies published over the last decade, uncovering significant advancements in privacy-preserving technologies which may show their benefit for the agricultural and food sectors. It identifies dominant research trends and promising future directions for enhancing data security and sustainability. The key findings underscore the vital importance of safeguarding data privacy in these sectors, highlighting the potential of advanced privacy techniques to protect sensitive agricultural data while balancing the need for transparency and operational effectiveness. The review concludes that adopting these innovative privacy methods is crucial for fostering a more sustainable and secure future for agriculture and food systems, contributing to the global Sustainable Development Goals such as SDG 2 (Zero Hunger) and SDG 12 (Responsible Consumption and Production) by improving precision agriculture, optimizing yields (crop and animal) and reducing food waste. © 2025 The

关键词： data Infrastructure data platforms data privacy Food systems Sustainability

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PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation

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Medical Image Analysis 2025年 103卷 103623页

作者： Chatterjee, Soumick Gaidzik, Franziska Sciarra, Alessandro Mattern, Hendrik Janiga, Gábor Speck, Oliver Nürnberger, Andreas Pathiraja, Sahani Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Laboratory of Fluid Dynamics and Technical Flows Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Faculty of Natural Sciences Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany Centre for Behavioural Brain Sciences Magdeburg Germany School of Mathematics & Statistics UNSW Sydney Australia

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets. These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification. This work proposes the PULASki method as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets. This approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems. The proposed method was analysed for two structurally different segmentation tasks (intracranial vessel and multiple sclerosis (MS) lesion) and compare our results to four well-established baselines in terms of quantitative metrics and qualitative output. These experiments involve class-imbalanced datasets characterised by challenging features, including suboptimal signal-to-noise ratios and high ambiguity. Empirical results demonstrate the PULASKi method outperforms all baselines at the 5% significance level. Our experiments are also of the first to present a comparative study of the computationally feasible segmentation of complex geometries using 3D patches and the traditional use of 2D slices. The generated segmentations are shown to be much more anatomically plausible than in the 2D case, particularly for the vessel task. Our method can also be applied to a wide range of multi-label segmentation tasks and is useful for downstream tasks such as hemodynamic modelling (computational fluid dynamics and data assimilation), clinical decision making, and treatment planning. © 2

关键词： Image segmentation

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Motor imagery enhances performance beyond the imagined action

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Proceedings of the National Academy of sciences of the United States of America 2025年第20期122卷 e2423642122页

作者： Gippert, Magdalena Shih, Pei-Cheng Heed, Tobias Howard, Ian S. Jamshidi Idaji, Mina Villringer, Arno Sehm, Bernhard Nikulin, Vadim V. Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig 04103 Germany Sony Computer Science Laboratories 141-0022 Tokyo Japan Department of Psychology and Centre for Cognitive Neuroscience University of Salzburg 5020 Salzburg Austria School of Engineering Computing and Mathematics Faculty of Science and Engineering University of Plymouth Plymouth PL4 8AA United Kingdom Machine Learning Group Berlin Institute for the Foundations of Learning and Data 10587 Berlin Germany Machine Learning Group Institute of Software Engineering and Theoretical Computer Science Electrical Engineering and Computer Science Faculty Technical University Berlin 10587 Berlin Germany Department of Neurology Martin Luther University of Halle-Wittenberg Halle (Saale) 06120 Germany

Motor imagery is frequently utilized to improve the performance of specific target movements in sports and rehabilitation. In this study, we show that motor imagery can facilitate learning of not only the imagined target movements but also sequentially linked overt movements. Hybrid sequences comprising imagined and physically executed segments allowed participants to learn specific movement characteristics of the executed segments when they were consistently associated with specific imagined segments. Electrophysiological recordings revealed that the degree of event-related synchronization in the alpha and beta bands during a basic motor imagery task was correlated with imagery-evoked motor learning. Thus, both behavioral and neural evidence indicate that motor imagery's benefits extend beyond the imagined movements, improving performance in linked overt movements. This provides decisive evidence for the functional equivalence of imagined and overt movements and suggests applications for imagery in sports and rehabilitation.

关键词： adaptation force field motor imagery motor learning motor sequence

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Enhanced Performance and data Privacy in Lung Nodule Classification via Federated Deep Learning Approach 24

Enhanced Performance and Data Privacy in Lung Nodule Classif...

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2024 7th International Conference on Healthcare Service Management, ICHSM 2024

作者： Nguyen, Duc-Khanh Li, Ai-Hsien Adam Lai, Yen-Jun Chiu, Yen-Ling Phan, Dinh-Van Chien, Ting-Ying Chan, Chien-Lung Department of Information Management Yuan Ze University Taoyuan Taiwan Department of Statistics and Informatics University of Economics The University of Danang Danang Viet Nam Division of Cardiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Radiology Far Eastern Memorial Hospital New Taipei Taiwan Graduate Institute of Medicine and Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan Taiwan Department of Medical Research Far Eastern Memorial Hospital New Taipei Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan Taiwan ZDT Group- YZU Joint Research and Development Center for Big Data Taoyuan Taiwan Yuan Ze University Taoyuan Taiwan

ISBN: (纸本)9798400710162

This study explores the feasibility of deep learning for classifying nodule neoplasms, analyzing their performance on two openly available datasets, LUNGx SPIE, and LIDC-IDRI. These datasets offer valuable diversity in image quantity, quality, and malignancy annotation methodologies, posing a real-world challenge for effective model development. To handle the disparities in these diverse datasets, we aim to develop a robust classification model trained using federated learning. This decentralized approach allows model training across datasets without sharing patient data, preserving privacy and overcoming data fragmentation. Our experiments demonstrate that federated learning enables the development of a robust and superior deep learning model compared to individual models trained on limited data. This combination of deep learning, and federated learning holds immense promise for nodule neoplasm classification and can significantly improve patient care and outcomes. © 2024 Copyright held by the owner/author(s).

关键词： Federated learning

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Development, implementation and evaluation of a smartphone application aimed to reduce sedentary time and increase physical activity among Indian sedentary office workers – findings from SMART-STEP trial

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BMC Public Health 2025年第1期25卷 1-16页

作者： Chandrasekaran, Baskaran Arumugam, Ashokan Pesola, Arto J. Kundapur, Poornima Panduranga Rao, Chythra R. Department of Exercise and Sports Sciences Manipal College of Health Professions Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Physiotherapy College of Health Sciences University of Sharjah P.O. Box: 27272 Sharjah United Arab Emirates Neuromusculoskeletal Rehabilitation Research Group RIMHS – Research Institute of Medical and Health Sciences University of Sharjah P.O. Box: 27272 Sharjah United Arab Emirates Sustainable Engineering Asset Management Research Group RISE - Research Institute of Sciences and Engineering University of Sharjah P.O. Box: 27272 Sharjah United Arab Emirates Adjunct Faculty Department of Physiotherapy Manipal College of Health Professions Manipal Academy of Higher Education Karnataka Manipal India Active Life Lab South-Eastern Finland University of Applied Sciences Mikkeli 50100 Finland Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Community Medicine Kasturba Medical College Manipal Manipal Academy of Higher Education Karnataka Manipal 576104 India

Background: Excessive sedentary time (ST) is linked to an increased risk of cardiometabolic diseases. Although various behavioral interventions have emerged to reduce ST, the potential of smartphone (SmPh)-based interventions remains relatively unexplored in workplace settings. This study aimed to explore the development, implementation and acceptability of a SmPh application among Indian desk-based office workers. Methods: One hundred thirty-six office workers were randomised to one of three interventions for six months: (1) SmPh-based ST and physical activity (PA) reminders (SMART);(2) traditional education (TRADE) and (3) usual work group (CONT). A threshold of 70% adherence (equivalent to responding to at least 580 out of 828 assigned prompts) was used to classify participants as ‘compliant’. Of 44 SMART group participants who were assigned to 24 weeks of intervention, nine participants were purposefully selected based on compliance, Moreover, they were interviewed for potential barriers associated with the SmPh application using semi-deductive approach. Results: The SMART STEP application was developed over eight months, during which three versions were created and pilot tested. The cost of application development was reasonable (≈ $1,860). Of 44 participants who received SmPh application-based reminders, 37 completed the two follow-ups at 3rd and 6th month. Mean prompt engagement rates, defined as ‘the percentage of prompts participants actively responded to’, during the 1st, 2nd, 3rd, 4th, 5th, and 6th months were 77% (n = 107), 59% (n = 82), 54% (n = 74), 45% (n = 63), 43% (n = 59), and 31% (n = 43), respectively. Barriers such as workload, lack of movement sensing, and insufficient organizational and peer support were key factors contributing to the decline in long-term engagement among office workers. Conclusion: The SMART-STEP application appears to be an affordable and promising solution for reducing ST and promoting PA among office workers in low-resour

关键词： Acceptability Productivity Safe work Sedentary time Smartphone application Sustainable industry

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SoAy: A Solution-based LLM API-using Methodology for Academic Information Seeking 25

SoAy: A Solution-based LLM API-using Methodology for Academi...

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Proceedings of the 31st ACM SIGKDD Conference on knowledge Discovery and data Mining V.1

作者： Yuanchun Wang Jifan Yu Zijun Yao Jing Zhang Yuyang Xie Shangqing Tu Yiyang Fu Youhe Feng Jinkai Zhang Jingyao Zhang Bowen Huang Yuanyao Li Huihui Yuan Lei Hou Juanzi Li Jie Tang School of Information Renmin University of China Beijing China & Key Laboratory of Data Engineering and Knowledge Engineering MOE Beijing China Institute of Education Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China School of Information Renmin University of China Beijing China & Engineering Research Center of Database and Business Intelligence MOE Beijing China School of Information Renmin University of China Beijing China Zhipu AI Beijing China

ISBN: (纸本)9798400712456

Applying large language models (LLMs) to academic API usage shows promise in reducing researchers' efforts to seek academic information. However, current LLM methods for using APIs struggle with the complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM methodology for academic information seeking. SoAy enables LLMs to generate code for invoking APIs, guided by a pre-constructed API calling sequence referred to as a solution. This solution simplifies the model's understanding of complex API relationships, while the generated code enhances reasoning efficiency. LLMs are aligned with this solution-oriented, code-based reasoning method by automatically enumerating valid API coupling sequences and transforming them into queries and executable *** evaluate SoAy, we introduce SoAyBench, an evaluation benchmark accompanied by SoAyEval, built upon a cloned environment of APIs from AMiner. Experimental results demonstrate a 34.58-75.99% performance improvement compared to state-of-the-art LLM API-based baselines. All datasets, codes, tuned models, and deployed online services are publicly accessible at https://***/RUCKBReasoning/SoAy.

关键词： academic information seeking

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Benchmarking knowledge-based context delivery systems

Benchmarking knowledge-based context delivery systems

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2006 International Conference on Autonomic and Autonomous Systems, ICAS'06

作者： Keeney, John Lewis, David O'Sullivan, Declan Department of Computer Science Knowledge and Data Engineering Group Trinity College Dublin Dublin Ireland

ISBN: (纸本)0769526535

As context-aware systems become more widespread and mobile there is an increasing need for a common distributed event platform for gathering context information and delivering to context-aware applications. The likely heterogeneity across the body of context information can be addressed using runtime reasoning over ontology-based context models. However, existing knowledge-based reasoning is not typically optimised for real-time operation so its inclusion in any context delivery platform needs to be carefully evaluated from a performance perspective. In this paper we propose a benchmark for knowledge-based context delivery platforms and in particular examine suitable knowledge benchmarks for assessing the ability of platforms to deal with semantic interoperability. © 2006 IEEE.

关键词： Benchmarking

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